Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker.

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Iasonos, Alexia; Chapman, Paul B; Satagopan, Jaya M

2016-05-01

An increased interest has been expressed in finding predictive biomarkers that can guide treatment options for both mutation carriers and noncarriers. The statistical assessment of variation in treatment benefit (TB) according to the biomarker carrier status plays an important role in evaluating predictive biomarkers. For time-to-event endpoints, the hazard ratio (HR) for interaction between treatment and a biomarker from a proportional hazards regression model is commonly used as a measure of variation in TB. Although this can be easily obtained using available statistical software packages, the interpretation of HR is not straightforward. In this article, we propose different summary measures of variation in TB on the scale of survival probabilities for evaluating a predictive biomarker. The proposed summary measures can be easily interpreted as quantifying differential in TB in terms of relative risk or excess absolute risk due to treatment in carriers versus noncarriers. We illustrate the use and interpretation of the proposed measures with data from completed clinical trials. We encourage clinical practitioners to interpret variation in TB in terms of measures based on survival probabilities, particularly in terms of excess absolute risk, as opposed to HR. Clin Cancer Res; 22(9); 2114-20. ©2016 AACR. ©2016 American Association for Cancer Research.

Serum biomarkers predictive of depressive episodes in panic disorder.

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Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

2016-02-01

Panic disorder with or without comorbid agoraphobia (PD/PDA) has been linked to an increased risk to develop subsequent depressive episodes, yet the underlying pathophysiology of these disorders remains poorly understood. We aimed to identify a biomarker panel predictive for the development of a depressive disorder (major depressive disorder and/or dysthymia) within a 2-year-follow-up period. Blood serum concentrations of 165 analytes were evaluated in 120 PD/PDA patients without depressive disorder baseline diagnosis (6-month-recency) in the Netherlands Study of Depression and Anxiety (NESDA). We assessed the predictive performance of serum biomarkers, clinical, and self-report variables using receiver operating characteristics curves (ROC) and the area under the ROC curve (AUC). False-discovery-rate corrected logistic regression model selection of serum analytes and covariates identified an optimal predictive panel comprised of tetranectin and creatine kinase MB along with patient gender and scores from the Inventory of Depressive Symptomatology (IDS) rating scale. Combined, an AUC of 0.87 was reached for identifying the PD/PDA patients who developed a depressive disorder within 2 years (n = 44). The addition of biomarkers represented a significant (p = 0.010) improvement over using gender and IDS alone as predictors (AUC = 0.78). For the first time, we report on a combination of biological serum markers, clinical variables and self-report inventories that can detect PD/PDA patients at increased risk of developing subsequent depressive disorders with good predictive performance in a naturalistic cohort design. After an independent validation our proposed biomarkers could prove useful in the detection of at-risk PD/PDA patients, allowing for early therapeutic interventions and improving clinical outcome. Copyright © 2015 Elsevier Ltd. All rights reserved.

Clinical studies of biomarkers in suicide prediction

OpenAIRE

Jokinen, Jussi

2007-01-01

Suicide is a major clinical problem in psychiatry and suicidal behaviours can be seen as a nosological entity per se. Predicting suicide is difficult due to its low base-rate and the limited specificity of clinical predictors. Prospective biological studies suggest that dysfunctions in the hypothalamo pituitary adrenal (HPA) axis and the serotonergic system have predictive power for suicide in mood disorders. Suicide attempt is the most robust clinical predictor making suici...

Human cervicovaginal fluid biomarkers to predict term and preterm labor

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Heng, Yujing J.; Liong, Stella; Permezel, Michael; Rice, Gregory E.; Di Quinzio, Megan K. W.; Georgiou, Harry M.

2015-01-01

Preterm birth (PTB; birth before 37 completed weeks of gestation) remains the major cause of neonatal morbidity and mortality. The current generation of biomarkers predictive of PTB have limited utility. In pregnancy, the human cervicovaginal fluid (CVF) proteome is a reflection of the local biochemical milieu and is influenced by the physical changes occurring in the vagina, cervix and adjacent overlying fetal membranes. Term and preterm labor (PTL) share common pathways of cervical ripening, myometrial activation and fetal membranes rupture leading to birth. We therefore hypothesize that CVF biomarkers predictive of labor may be similar in both the term and preterm labor setting. In this review, we summarize some of the existing published literature as well as our team's breadth of work utilizing the CVF for the discovery and validation of putative CVF biomarkers predictive of human labor. Our team established an efficient method for collecting serial CVF samples for optimal 2-dimensional gel electrophoresis resolution and analysis. We first embarked on CVF biomarker discovery for the prediction of spontaneous onset of term labor using 2D-electrophoresis and solution array multiple analyte profiling. 2D-electrophoretic analyses were subsequently performed on CVF samples associated with PTB. Several proteins have been successfully validated and demonstrate that these biomarkers are associated with term and PTL and may be predictive of both term and PTL. In addition, the measurement of these putative biomarkers was found to be robust to the influences of vaginal microflora and/or semen. The future development of a multiple biomarker bed-side test would help improve the prediction of PTB and the clinical management of patients. PMID:26029118

Biomarkers in prostate cancer - Current clinical utility and future perspectives.

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Kretschmer, Alexander; Tilki, Derya

2017-12-01

Current tendencies in the treatment course of prostate cancer patients increase the need for reliable biomarkers that help in decision-making in a challenging clinical setting. Within the last decade, several novel biomarkers have been introduced. In the following comprehensive review article, we focus on diagnostic (PHI ® , 4K score, SelectMDx ® , ConfirmMDx ® , PCA3, MiPS, ExoDX ® , mpMRI) and prognostic (OncotypeDX GPS ® , Prolaris ® , ProMark ® , DNA-ploidy, Decipher ® ) biomarkers that are in widespread clinical use and are supported by evidence. Hereby, we focus on multiple clinical situations in which innovative biomarkers may guide decision-making in prostate cancer therapy. In addition, we describe novel liquid biopsy approaches (circulating tumor cells, cell-free DNA) that have been described as predictive biomarkers in metastatic castration-resistant prostate cancer and might support an individual patient-centred oncological approach in the nearer future. Copyright © 2017 Elsevier B.V. All rights reserved.

Clinical Risk Assessment in the Antiphospholipid Syndrome: Current Landscape and Emerging Biomarkers.

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Chaturvedi, Shruti; McCrae, Keith R

2017-07-01

Laboratory criteria for the classification of antiphospholipid syndrome include the detection of a lupus anticoagulant and/or anticardiolipin and anti-β2-glycoprotein I antibodies. However, the majority of patients who test positive in these assays do not have thrombosis. Current risk-stratification tools are largely limited to the antiphospholipid antibody profile and traditional thrombotic risk factors. Novel biomarkers that correlate with disease activity and potentially provide insight into future clinical events include domain 1 specific anti-β 2 GPI antibodies, antibodies to other phospholipids or phospholipid/protein antigens (such as anti-PS/PT), and functional/biological assays such as thrombin generation, complement activation, levels of circulating microparticles, and annexin A5 resistance. Clinical risk scores may also have value in predicting clinical events. Biomarkers that predict thrombosis risk in patients with antiphospholipid antibodies have been long sought, and several biomarkers have been proposed. Ultimately, integration of biomarkers with established assays and clinical characteristics may offer the best chance of identifying patients at highest risk of APS-related complications.

Correlation between Circulating Fungal Biomarkers and Clinical Outcome in Invasive Aspergillosis.

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Dionysios Neofytos

Full Text Available Objective means are needed to predict and assess clinical response in patients treated for invasive aspergillosis (IA. We examined whether early changes in serum galactomannan (GM and/or β-D-glucan (BDG can predict clinical outcomes. Patients with proven or probable IA were prospectively enrolled, and serial GM and BDG levels and GM optical density indices (GMI were calculated twice weekly for 6 weeks following initiation of standard-of-care antifungal therapy. Changes in these biomarkers during the first 2 and 6 weeks of treatment were analyzed for associations with clinical response and survival at weeks 6 and 12. Among 47 patients with IA, 53.2% (25/47 and 65.9% (27/41 had clinical response by weeks 6 and 12, respectively. Changes in biomarkers during the first 2 weeks were associated with clinical response at 6 weeks (GMI, P = 0.03 and 12 weeks (GM+BDG composite, P = 0.05; GM, P = 0.04; GMI, P = 0.02. Changes in biomarkers during the first 6 weeks were also associated with clinical response at 6 weeks (GM, P = 0.05; GMI, P = 0.03 and 12 weeks (BDG+GM, P = 0.02; GM, P = 0.02; GMI, P = 0.01. Overall survival rates at 6 weeks and 12 weeks were 87.2% (41/47 and 79.1% (34/43, respectively. Decreasing biomarkers in the first 2 weeks were associated with survival at 6 weeks (BDG+GM, P = 0.03; BDG, P = 0.01; GM, P = 0.03 and at 12 weeks (BDG+GM, P = 0.01; BDG, P = 0.03; GM, P = 0.01; GMI, P = 0.007. Similar correlations occurred for biomarkers measured over 6 weeks. Patients with negative baseline GMI and/or persistently negative GMI during the first 2 weeks were more likely to have CR and survival. These results suggest that changes of biomarkers may be informative to predict and/or assess response to therapy and survival in patients treated for IA.

Biomarkers of Host Response Predict Primary End-Point Radiological Pneumonia in Tanzanian Children with Clinical Pneumonia: A Prospective Cohort Study.

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Laura K Erdman

Full Text Available Diagnosing pediatric pneumonia is challenging in low-resource settings. The World Health Organization (WHO has defined primary end-point radiological pneumonia for use in epidemiological and vaccine studies. However, radiography requires expertise and is often inaccessible. We hypothesized that plasma biomarkers of inflammation and endothelial activation may be useful surrogates for end-point pneumonia, and may provide insight into its biological significance.We studied children with WHO-defined clinical pneumonia (n = 155 within a prospective cohort of 1,005 consecutive febrile children presenting to Tanzanian outpatient clinics. Based on x-ray findings, participants were categorized as primary end-point pneumonia (n = 30, other infiltrates (n = 31, or normal chest x-ray (n = 94. Plasma levels of 7 host response biomarkers at presentation were measured by ELISA. Associations between biomarker levels and radiological findings were assessed by Kruskal-Wallis test and multivariable logistic regression. Biomarker ability to predict radiological findings was evaluated using receiver operating characteristic curve analysis and Classification and Regression Tree analysis.Compared to children with normal x-ray, children with end-point pneumonia had significantly higher C-reactive protein, procalcitonin and Chitinase 3-like-1, while those with other infiltrates had elevated procalcitonin and von Willebrand Factor and decreased soluble Tie-2 and endoglin. Clinical variables were not predictive of radiological findings. Classification and Regression Tree analysis generated multi-marker models with improved performance over single markers for discriminating between groups. A model based on C-reactive protein and Chitinase 3-like-1 discriminated between end-point pneumonia and non-end-point pneumonia with 93.3% sensitivity (95% confidence interval 76.5-98.8, 80.8% specificity (72.6-87.1, positive likelihood ratio 4.9 (3.4-7.1, negative likelihood ratio 0

Biomarkers of Host Response Predict Primary End-Point Radiological Pneumonia in Tanzanian Children with Clinical Pneumonia: A Prospective Cohort Study

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Erdman, Laura K.; D’Acremont, Valérie; Hayford, Kyla; Kilowoko, Mary; Kyungu, Esther; Hongoa, Philipina; Alamo, Leonor; Streiner, David L.; Genton, Blaise; Kain, Kevin C.

2015-01-01

Background Diagnosing pediatric pneumonia is challenging in low-resource settings. The World Health Organization (WHO) has defined primary end-point radiological pneumonia for use in epidemiological and vaccine studies. However, radiography requires expertise and is often inaccessible. We hypothesized that plasma biomarkers of inflammation and endothelial activation may be useful surrogates for end-point pneumonia, and may provide insight into its biological significance. Methods We studied children with WHO-defined clinical pneumonia (n = 155) within a prospective cohort of 1,005 consecutive febrile children presenting to Tanzanian outpatient clinics. Based on x-ray findings, participants were categorized as primary end-point pneumonia (n = 30), other infiltrates (n = 31), or normal chest x-ray (n = 94). Plasma levels of 7 host response biomarkers at presentation were measured by ELISA. Associations between biomarker levels and radiological findings were assessed by Kruskal-Wallis test and multivariable logistic regression. Biomarker ability to predict radiological findings was evaluated using receiver operating characteristic curve analysis and Classification and Regression Tree analysis. Results Compared to children with normal x-ray, children with end-point pneumonia had significantly higher C-reactive protein, procalcitonin and Chitinase 3-like-1, while those with other infiltrates had elevated procalcitonin and von Willebrand Factor and decreased soluble Tie-2 and endoglin. Clinical variables were not predictive of radiological findings. Classification and Regression Tree analysis generated multi-marker models with improved performance over single markers for discriminating between groups. A model based on C-reactive protein and Chitinase 3-like-1 discriminated between end-point pneumonia and non-end-point pneumonia with 93.3% sensitivity (95% confidence interval 76.5–98.8), 80.8% specificity (72.6–87.1), positive likelihood ratio 4.9 (3.4–7

The role of novel biomarkers in predicting diabetic nephropathy: a review

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Uwaezuoke SN

2017-08-01

Full Text Available Samuel N Uwaezuoke Pediatric Nephrology Firm, Department of Pediatrics, University of Nigeria Teaching Hospital, Ituku-Ozalla, Enugu, Nigeria Abstract: Diabetic nephropathy (DN is one of the microvascular complications of the kidney arising commonly from type 1 diabetes mellitus (T1DM, and occasionally from type 2 diabetes mellitus (T2DM. Microalbuminuria serves as an early indicator of DN risk and a predictor of its progression as well as cardiovascular disease risk in both T1DM and T2DM. Although microalbuminuria remains the gold standard for early detection of DN, it is not a sufficiently accurate predictor of DN risk due to some limitations. Thus, there is a paradigm shift to novel biomarkers which would help to predict DN risk early enough and possibly prevent the occurrence of end-stage kidney disease. These new biomarkers have been broadly classified into glomerular biomarkers, tubular biomarkers, biomarkers of inflammation, biomarkers of oxidative stress, and miscellaneous biomarkers which also include podocyte biomarkers, some of which are also considered as tubular and glomerular biomarkers. Although they are potentially useful for the evaluation of DN, current data still preclude the routine clinical use of majority of them. However, their validation using high-quality and large longitudinal studies is of paramount importance, as well as the subsequent development of a biomarker panel which can reliably predict and evaluate this renal microvascular disease. This paper aims to review the predictive role of these biomarkers in the evaluation of DN. Keywords: type 1 diabetes mellitus, renal microvascular complication, microalbuminuria, end-stage kidney disease, biomarker panel

Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes

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Pena, Michelle J; de Zeeuw, Dick; Mischak, Harald

2015-01-01

biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple......Diabetic kidney disease occurs in ∼ 25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification...... and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple...

Biomarkers for AAA: Encouraging steps but clinical relevance still to be delivered.

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Htun, Nay Min; Peter, Karlheinz

2014-10-01

Potential biomarkers have been investigated using proteomic studies in a variety of diseases. Some biomarkers have central roles in both diagnosis and monitoring of various disorders in clinical medicine, such as troponins, brain natriuretic peptide, and C-reactive protein. Although several biomarkers have been suggested in human abdominal aortic aneurysm (AAA), reliable markers have been lacking. In this issue, Moxon et al. [Proteomics Clin Appl. 2014, 8, 762-772] undertook a broad and systematic proteomic approach toward identification of biomarkers in a commonly used AAA mouse model (AAA created by angiotensin-II infusion). In this mouse model, apolipoprotein C1 and matrix metalloproteinase-9 were identified as novel biomarkers of stable AAA. This finding represents an important step forward, toward a clinically relevant role of biomarkers in AAA. This also encourages for further studies toward the identification of biomarkers (or their combinations) that can predict AAA progression and rupture, which would represent a major progress in AAA management and would establish an AAA biomarker as a much anticipated clinical tool. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

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Simon-Shlomo ePoil

2013-10-01

Full Text Available Alzheimer's disease (AD is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a two-year period. We followed 86 patients initially diagnosed with MCI for two years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13–30 Hz can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/. We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention.

MTR-18 Predictive Biomarkers Of Bevacizumab Response In Recurrent Glioblastoma Patients

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Urup, Thomas; Michaelsen, Signe Regner; Olsen, Lars Rønn

2015-01-01

Bevacizumab (BEV) plus chemotherapy has shown activity in recurrent glioblastoma (GBM). However, the prognosis varies and only one third of patients have a durable clinical response to BEV combination therapy. Recent findings from a randomized phase-3 study (AVAglio) indicate that patients...... with the proneural GBM subtype have a survival benefit when treated with BEV in combination with standard treatment. However, no validated biomarkers able to predict BEV response have been identified and the biology reflecting a clinical BEV response is poorly understood. The primary objective of this study...... was to evaluate the predictive and prognostic value of GBM subtypes in recurrent GBM patients treated with BEV therapy. The secondary objective was to identify biomarkers able to predict response to BEV therapy in recurrent GBM patients. METHODS: A total of 90 recurrent GBM patients treated with BEV combination...

Predictive Biomarkers for Bevacizumab in Anti-tumor Therapy

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Qingqing PAN

2011-07-01

Full Text Available Bevacizumab, the monoclonal antibody of vascular endothelial growth factor (VEGF has been applied to the therapy of several neoplasms, but an appropriate biomarker to predict the efficacy has not been found. Those markers can originate from peripheral circulation, tumor tissue and genes. Some researches have found that low level of vascular cell adhesion molecule-1 (VCAM-1, E-selectin, angiopoietin 2 (Ang-2 in circulation or carbonic anhydrase 9 (CA9, CD31-microvessel density (CD31-MVD in tumor tissue can predict better activity of bevacizumab. Moreover, high level of soluble VEGFR2 (sVEGFR2 in circulation or the ratio of phosphorylated-VEGFR2 (p-VEGFR2 and VEGFR2 in tumor tissue increasing has the same predictive function. As to the gene, VEGF-634 CC, VEGF-1498 TT and VEGFR2 H472Q are only related to the side effct. Thus more clinical tirals and basic researches should be performed to find out effective biomarkers in bevacizumab’s therapy.

Phosphoproteomic biomarkers predicting histologic nonalcoholic steatohepatitis and fibrosis.

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Younossi, Zobair M; Baranova, Ancha; Stepanova, Maria; Page, Sandra; Calvert, Valerie S; Afendy, Arian; Goodman, Zachary; Chandhoke, Vikas; Liotta, Lance; Petricoin, Emanuel

2010-06-04

The progression of nonalcoholic fatty liver disease (NAFLD) has been linked to deregulated exchange of the endocrine signaling between adipose and liver tissue. Proteomic assays for the phosphorylation events that characterize the activated or deactivated state of the kinase-driven signaling cascades in visceral adipose tissue (VAT) could shed light on the pathogenesis of nonalcoholic steatohepatitis (NASH) and related fibrosis. Reverse-phase protein microarrays (RPMA) were used to develop biomarkers for NASH and fibrosis using VAT collected from 167 NAFLD patients (training cohort, N = 117; testing cohort, N = 50). Three types of models were developed for NASH and advanced fibrosis: clinical models, proteomics models, and combination models. NASH was predicted by a model that included measurements of two components of the insulin signaling pathway: AKT kinase and insulin receptor substrate 1 (IRS1). The models for fibrosis were less reliable when predictions were based on phosphoproteomic, clinical, or the combination data. The best performing model relied on levels of the phosphorylation of GSK3 as well as on two subunits of cyclic AMP regulated protein kinase A (PKA). Phosphoproteomics technology could potentially be used to provide pathogenic information about NASH and NASH-related fibrosis. This information can lead to a clinically relevant diagnostic/prognostic biomarker for NASH.

Elevation in inflammatory serum biomarkers predicts response to trastuzumab-containing therapy.

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Ahmed A Alkhateeb

Full Text Available Approximately half of all HER2/neu-overexpressing breast cancer patients do not respond to trastuzumab-containing therapy. Therefore, there remains an urgent and unmet clinical need for the development of predictive biomarkers for trastuzumab response. Recently, several lines of evidence have demonstrated that the inflammatory tumor microenvironment is a major contributor to therapy resistance in breast cancer. In order to explore the predictive value of inflammation in breast cancer patients, we measured the inflammatory biomarkers serum ferritin and C-reactive protein (CRP in 66 patients immediately before undergoing trastuzumab-containing therapy and evaluated their progression-free and overall survival. The elevation in pre-treatment serum ferritin (>250 ng/ml or CRP (>7.25 mg/l was a significant predictor of reduced progression-free survival and shorter overall survival. When patients were stratified based on their serum ferritin and CRP levels, patients with elevation in both inflammatory biomarkers had a markedly poorer response to trastuzumab-containing therapy. Therefore, the elevation in inflammatory serum biomarkers may reflect a pathological state that decreases the clinical efficacy of this therapy. Anti-inflammatory drugs and life-style changes to decrease inflammation in cancer patients should be explored as possible strategies to sensitize patients to anti-cancer therapeutics.

MAGIC biomarkers predict long term outcomes for steroid-resistant acute GVHD.

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Major-Monfried, Hannah; Renteria, Anne S; Pawarode, Attaphol; Reddy, Pavan; Ayuk, Francis; Holler, Ernst; Efebera, Yvonne A; Hogan, William J; Wölfl, Matthias; Qayed, Muna; Hexner, Elizabeth O; Wudhikarn, Kitsada; Ordemann, Rainer; Young, Rachel; Shah, Jay; Hartwell, Matthew J; Chaudhry, Mohammed; Aziz, Mina; Etra, Aaron; Yanik, Gregory A; Kröger, Nicolaus; Weber, Daniela; Chen, Yi-Bin; Nakamura, Ryotaro; Rösler, Wolf; Kitko, Carrie L; Harris, Andrew C; Pulsipher, Michael; Reshef, Ran; Kowalyk, Steven; Morales, George; Torres, Ivan; Özbek, Umut; Ferrara, James L M; Levine, John E

2018-03-15

Acute graft versus host disease (GVHD) is treated with systemic corticosteroid immunosuppression. Clinical response after one week of therapy often guides further treatment decisions, but long term outcomes vary widely between centers and more accurate predictive tests are urgently needed. We analyzed clinical data and blood samples taken after one week of systemic treatment for GVHD from 507 patients from 17 centers of the Mount Sinai Acute GVHD International Consortium (MAGIC), dividing them into test (n=236) and two validation cohorts separated in time (n = 142 and 129, respectively). Initial response to systemic steroids correlated with response at four weeks, one-year non-relapse mortality (NRM) and overall survival (OS). A previously validated algorithm of two MAGIC biomarkers (ST2 and REG3α) consistently separated steroid resistant patients into two groups with dramatically different NRM and OS (p<0.001 for all three cohorts). High biomarker probability, resistance to steroids and GVHD severity (Minnesota risk) were all significant predictors of NRM in multivariate analysis. A direct comparison of receiver operating curves showed the area under the curve for biomarker probability (0.82) was significantly greater than that for steroid response (0.68, p=0.004) and for Minnesota risk (0.72, p=0.005). In conclusion, MAGIC biomarker probabilities generated after one week of systemic treatment for GVHD predict long term outcomes in steroid resistant GVHD better than clinical criteria and should prove useful in developing better treatment strategies. Copyright © 2018 American Society of Hematology.

Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes

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Mayer, Gert; Heerspink, Hiddo J L; Aschauer, Constantin

2017-01-01

hormone 1, hepatocyte growth factor, matrix metalloproteinase (MMP) 2, MMP7, MMP8, MMP13, tyrosine kinase, and tumor necrosis factor receptor-1. These biomarkers were measured in baseline serum samples from 1,765 patients recruited into two large clinical trials. eGFR decline was predicted based...... on molecular markers, clinical risk factors (including baseline eGFR and albuminuria), and both combined, and these predictions were evaluated using mixed linear regression models for longitudinal data. RESULTS: The variability of annual eGFR loss explained by the biomarkers, indicated by the adjusted R2 value......, combined with clinical variables, enhances the prediction of renal function loss over a wide range of baseline eGFR values in patients with type 2 diabetes and CKD....

BRAIN NATRIURETIC PEPTIDE (BNP: BIOMARKER FOR RISK STRATIFICATION AND FUNCTIONAL RECOVERY PREDICTION IN ISCHEMIC STROKE

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STANESCU Ioana

2015-02-01

Full Text Available Functional outcome after cardiovascular and cerebrovascular events is traditionally predicted using demographic and clinical variables like age, gender, blood pressure, cholesterol levels, diabetes status, smoking habits or pre-existing morbidity. Identification of new variables will improve the risk stratification of specific categories of patients. Numerous blood-based biomarkers associated with increased cardiovascular risk have been identified; some of them even predict cardiovascular events. Investigators have tried to produce prediction models by incorporating traditional risk factors and biomarkers. (1. Widely-available, rapidly processed and less expensive biomarkers could be used in the future to guide management of complex cerebrovascular patients in order to maximize their recovery (2 Recently, studies have demonstrated that biomarkers can predict not only the risk for a specific clinical event, but also the risk of death of vascular cause and the functional outcome after cardiovascular or cerebrovascular events. Early prediction of fatal outcome after stroke may improve therapeutic strategies (such as the use of more aggressive treatments or inclusion of patients in clinical trials and guide decision-making processes in order to maximize patient’s chances for survival and recovery. (3 Long term functional outcome after stroke is one of the most difficult variables to predict. Elevated serum levels of brain natriuretic peptide (BNP are powerful predictor of outcomes in patients with cardiovascular disease (heart failure, atrial fibrillation. Potential role of BNP in predicting atrial fibrillation occurrence, cardio-embolic stroke and post-stroke mortality have been proved in many studies. However, data concerning the potential role of BNP in predicting short term and long term functional outcomes after stroke remain controversial.

Plasma Biomarkers Discriminate Clinical Forms of Multiple Sclerosis

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Tejera-Alhambra, Marta; Casrouge, Armanda; de Andrés, Clara; Seyfferth, Ansgar; Ramos-Medina, Rocío; Alonso, Bárbara; Vega, Janet; Fernández-Paredes, Lidia; Albert, Matthew L.; Sánchez-Ramón, Silvia

2015-01-01

Multiple sclerosis, the most common cause of neurological disability in young population after trauma, represents a significant public health burden. Current challenges associated with management of multiple sclerosis (MS) patients stem from the lack of biomarkers that might enable stratification of the different clinical forms of MS and thus prompt treatment for those patients with progressive MS, for whom there is currently no therapy available. In the present work we analyzed a set of thirty different plasma cytokines, chemokines and growth factors present in circulation of 129 MS patients with different clinical forms (relapsing remitting, secondary progressive and primary progressive MS) and 53 healthy controls, across two independent cohorts. The set of plasma analytes was quantified with Luminex xMAP technology and their predictive power regarding clinical outcome was evaluated both individually using ROC curves and in combination using logistic regression analysis. Our results from two independent cohorts of MS patients demonstrate that the divergent clinical and histology-based MS forms are associated with distinct profiles of circulating plasma protein biomarkers, with distinct signatures being composed of chemokines and growth/angiogenic factors. With this work, we propose that an evaluation of a set of 4 circulating biomarkers (HGF, Eotaxin/CCL11, EGF and MIP-1β/CCL4) in MS patients might serve as an effective tool in the diagnosis and more personalized therapeutic targeting of MS patients. PMID:26039252

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

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Borlawsky Tara B

2010-10-01

Full Text Available Abstract Background Chronic lymphocytic leukemia (CLL is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. Results In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network. Conclusions We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.

OARSI Clinical Trials Recommendations: Soluble biomarker assessments in clinical trials in osteoarthritis.

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Kraus, V B; Blanco, F J; Englund, M; Henrotin, Y; Lohmander, L S; Losina, E; Önnerfjord, P; Persiani, S

2015-05-01

The objective of this work was to describe requirements for inclusion of soluble biomarkers in osteoarthritis (OA) clinical trials and progress toward OA-related biomarker qualification. The Guidelines for Biomarkers Working Group, representing experts in the field of OA biomarker research from both academia and industry, convened to discuss issues related to soluble biomarkers and to make recommendations for their use in OA clinical trials based on current knowledge and anticipated benefits. This document summarizes current guidance on use of biomarkers in OA clinical trials and their utility at five stages, including preclinical development and phase I to phase IV trials. As demonstrated by this summary, biomarkers can provide value at all stages of therapeutics development. When resources permit, we recommend collection of biospecimens in all OA clinical trials for a wide variety of reasons but in particular, to determine whether biomarkers are useful in identifying those individuals most likely to receive clinically important benefits from an intervention; and to determine whether biomarkers are useful for identifying individuals at earlier stages of OA in order to institute treatment at a time more amenable to disease modification. Copyright © 2015 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Biomarkers for predicting complete debulking in ovarian cancer

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Fagö-Olsen, Carsten Lindberg; Ottesen, Bent; Christensen, Ib Jarle

2014-01-01

AIM: We aimed to construct and validate a model based on biomarkers to predict complete primary debulking surgery for ovarian cancer patients. PATIENTS AND METHODS: The study consisted of three parts: Part I: Biomarker data obtained from mass spectrometry, baseline data and, surgical outcome were...... used to construct predictive indices for complete tumour resection; Part II: sera from randomly selected patients from part I were analyzed using enzyme-linked immunosorbent assay (ELISA) to investigate the correlation to mass spectrometry; Part III: the indices from part I were validated in a new.......64. CONCLUSION: Our validated model based on biomarkers was unable to predict surgical outcome for patients with ovarian cancer....

Some aspects of cancer biomarkers and their clinical application in solid tumors – revisited

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Isaac D

2017-07-01

Full Text Available Cancer biomarkers can be used for a variety of purposes related to screening, prediction, stratification, detection, diagnosis, prognosis, treatment design, and monitoring of a therapeutic response. One of the most important characteristics of a given biomarker includes ease of collection allowing for a non-invasive approach and frequent sampling. Such samples may be obtained from serum or plasma, sputum, bronchoalveolar lavage, saliva, nipple discharge, pleural, or peritoneal effusions. Validation of different biomarkers is considered a mandatory method for useful evaluation. In this review, we highlight the clinical applicability of some cancer biomarkers, as well as future approaches for their development and collection, which may help guide clinicians and researchers. The role of liquid biopsies will also be summarized. Further studies using liquid biopsies are needed to elucidate the significance of various sources of biomarkers suitable for clinical application.

Predicting the disease of Alzheimer with SNP biomarkers and clinical data using data mining classification approach: decision tree.

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Erdoğan, Onur; Aydin Son, Yeşim

2014-01-01

Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.

Biomarkers of Renal Function : Towards Clinical Actionability

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Binnenmars, S Heleen; Hijmans, R S; Navis, G; de Borst, M H

This review provides an overview of the clinical value of themost relevant renal biomarkers, focusing on two main clinical conditions: acute kidney injury and chronic kidney disease. We categorize biomarkers according to their actionability, in terms of a documented response to treatment in relation

Evaluating biomarkers for prognostic enrichment of clinical trials.

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Kerr, Kathleen F; Roth, Jeremy; Zhu, Kehao; Thiessen-Philbrook, Heather; Meisner, Allison; Wilson, Francis Perry; Coca, Steven; Parikh, Chirag R

2017-12-01

A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

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Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

2015-08-01

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Biomarkers in T cell therapy clinical trials

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Kalos Michael

2011-08-01

Full Text Available Abstract T cell therapy represents an emerging and promising modality for the treatment of both infectious disease and cancer. Data from recent clinical trials have highlighted the potential for this therapeutic modality to effect potent anti-tumor activity. Biomarkers, operationally defined as biological parameters measured from patients that provide information about treatment impact, play a central role in the development of novel therapeutic agents. In the absence of information about primary clinical endpoints, biomarkers can provide critical insights that allow investigators to guide the clinical development of the candidate product. In the context of cell therapy trials, the definition of biomarkers can be extended to include a description of parameters of the cell product that are important for product bioactivity. This review will focus on biomarker studies as they relate to T cell therapy trials, and more specifically: i. An overview and description of categories and classes of biomarkers that are specifically relevant to T cell therapy trials, and ii. Insights into future directions and challenges for the appropriate development of biomarkers to evaluate both product bioactivity and treatment efficacy of T cell therapy trials.

Improving diagnosis, prognosis and prediction by using biomarkers in CRC patients (Review).

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Nikolouzakis, Taxiarchis Konstantinos; Vassilopoulou, Loukia; Fragkiadaki, Persefoni; Mariolis Sapsakos, Theodoros; Papadakis, Georgios Z; Spandidos, Demetrios A; Tsatsakis, Aristides M; Tsiaoussis, John

2018-06-01

Colorectal cancer (CRC) is among the most common cancers. In fact, it is placed in the third place among the most diagnosed cancer in men, after lung and prostate cancer, and in the second one for the most diagnosed cancer in women, following breast cancer. Moreover, its high mortality rates classifies it among the leading causes of cancer‑related death worldwide. Thus, in order to help clinicians to optimize their practice, it is crucial to introduce more effective tools that will improve not only early diagnosis, but also prediction of the most likely progression of the disease and response to chemotherapy. In that way, they will be able to decrease both morbidity and mortality of their patients. In accordance with that, colon cancer research has described numerous biomarkers for diagnostic, prognostic and predictive purposes that either alone or as part of a panel would help improve patient's clinical management. This review aims to describe the most accepted biomarkers among those proposed for use in CRC divided based on the clinical specimen that is examined (tissue, faeces or blood) along with their restrictions. Lastly, new insight in CRC monitoring will be discussed presenting promising emerging biomarkers (telomerase activity, telomere length and micronuclei frequency).

DNA Methylation Biomarkers: Cancer and Beyond

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Thomas Mikeska

2014-09-01

Full Text Available Biomarkers are naturally-occurring characteristics by which a particular pathological process or disease can be identified or monitored. They can reflect past environmental exposures, predict disease onset or course, or determine a patient’s response to therapy. Epigenetic changes are such characteristics, with most epigenetic biomarkers discovered to date based on the epigenetic mark of DNA methylation. Many tissue types are suitable for the discovery of DNA methylation biomarkers including cell-based samples such as blood and tumor material and cell-free DNA samples such as plasma. DNA methylation biomarkers with diagnostic, prognostic and predictive power are already in clinical trials or in a clinical setting for cancer. Outside cancer, strong evidence that complex disease originates in early life is opening up exciting new avenues for the detection of DNA methylation biomarkers for adverse early life environment and for estimation of future disease risk. However, there are a number of limitations to overcome before such biomarkers reach the clinic. Nevertheless, DNA methylation biomarkers have great potential to contribute to personalized medicine throughout life. We review the current state of play for DNA methylation biomarkers, discuss the barriers that must be crossed on the way to implementation in a clinical setting, and predict their future use for human disease.

CSF biomarkers of Alzheimer's disease concord with amyloid-β PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts.

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Hansson, Oskar; Seibyl, John; Stomrud, Erik; Zetterberg, Henrik; Trojanowski, John Q; Bittner, Tobias; Lifke, Valeria; Corradini, Veronika; Eichenlaub, Udo; Batrla, Richard; Buck, Katharina; Zink, Katharina; Rabe, Christina; Blennow, Kaj; Shaw, Leslie M

2018-03-01

We studied whether fully automated Elecsys cerebrospinal fluid (CSF) immunoassay results were concordant with positron emission tomography (PET) and predicted clinical progression, even with cutoffs established in an independent cohort. Cutoffs for Elecsys amyloid-β 1-42 (Aβ), total tau/Aβ(1-42), and phosphorylated tau/Aβ(1-42) were defined against [ 18 F]flutemetamol PET in Swedish BioFINDER (n = 277) and validated against [ 18 F]florbetapir PET in Alzheimer's Disease Neuroimaging Initiative (n = 646). Clinical progression in patients with mild cognitive impairment (n = 619) was studied. CSF total tau/Aβ(1-42) and phosphorylated tau/Aβ(1-42) ratios were highly concordant with PET classification in BioFINDER (overall percent agreement: 90%; area under the curve: 94%). The CSF biomarker statuses established by predefined cutoffs were highly concordant with PET classification in Alzheimer's Disease Neuroimaging Initiative (overall percent agreement: 89%-90%; area under the curves: 96%) and predicted greater 2-year clinical decline in patients with mild cognitive impairment. Strikingly, tau/Aβ ratios were as accurate as semiquantitative PET image assessment in predicting visual read-based outcomes. Elecsys CSF biomarker assays may provide reliable alternatives to PET in Alzheimer's disease diagnosis. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Robust prediction of anti-cancer drug sensitivity and sensitivity-specific biomarker.

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Heewon Park

Full Text Available The personal genomics era has attracted a large amount of attention for anti-cancer therapy by patient-specific analysis. Patient-specific analysis enables discovery of individual genomic characteristics for each patient, and thus we can effectively predict individual genetic risk of disease and perform personalized anti-cancer therapy. Although the existing methods for patient-specific analysis have successfully uncovered crucial biomarkers, their performance takes a sudden turn for the worst in the presence of outliers, since the methods are based on non-robust manners. In practice, clinical and genomic alterations datasets usually contain outliers from various sources (e.g., experiment error, coding error, etc. and the outliers may significantly affect the result of patient-specific analysis. We propose a robust methodology for patient-specific analysis in line with the NetwrokProfiler. In the proposed method, outliers in high dimensional gene expression levels and drug response datasets are simultaneously controlled by robust Mahalanobis distance in robust principal component space. Thus, we can effectively perform for predicting anti-cancer drug sensitivity and identifying sensitivity-specific biomarkers for individual patients. We observe through Monte Carlo simulations that the proposed robust method produces outstanding performances for predicting response variable in the presence of outliers. We also apply the proposed methodology to the Sanger dataset in order to uncover cancer biomarkers and predict anti-cancer drug sensitivity, and show the effectiveness of our method.

Circulating predictive and diagnostic biomarkers for hepatitis B virus-associated hepatocellular carcinoma

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Van Hees, Stijn; Michielsen, Peter; Vanwolleghem, Thomas

2016-01-01

Chronic hepatitis B virus (HBV) infected patients have an almost 100-fold increased risk to develop hepatocellular carcinoma (HCC). HCC is the fifth most common and third most deadly cancer worldwide. Up to 50% of newly diagnosed HCC cases are attributed to HBV infection. Early detection improves survival and can be achieved through regular screening. Six-monthly abdominal ultrasound, either alone or in combination with alpha-fetoprotein serum levels, has been widely endorsed for this purpose. Both techniques however yield limited diagnostic accuracy, which is not improved when they are combined. Alternative circulating or histological markers to predict or diagnose HCC are therefore urgently needed. Recent advances in systems biology technologies have enabled the identification of several new putative circulating biomarkers. Although results from studies assessing combinations of these biomarkers are promising, evidence for their clinical utility remains low. In addition, most of the studies conducted so far show limitations in design. Attention must be paid for instance to different ethnicities and different etiologies when studying biomarkers for hepatocellular carcinoma. This review provides an overview on the current understandings and recent progress in the field of diagnostic and predictive circulating biomarkers for hepatocellular carcinoma in chronically infected HBV patients and discusses the future prospects. PMID:27729734

Prospective evaluation of biomarkers for prediction of quality of life in community-acquired pneumonia.

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Nickler, Manuela; Schaffner, Daniela; Christ-Crain, Mirjam; Ottiger, Manuel; Thomann, Robert; Hoess, Claus; Henzen, Christoph; Mueller, Beat; Schuetz, Philipp

2016-11-01

Most clinical research investigated prognostic biomarkers for their ability to predict cardiovascular events or mortality. It is unknown whether biomarkers allow prediction of quality of life (QoL) after survival of the acute event. Herein, we investigated the prognostic potential of well-established inflammatory/cardiovascular blood biomarkers including white blood cells (WBC), C-reactive protein (CRP), procalcitonin (PCT), pro-adrenomedullin (proADM) and pro-atrial natriuretic peptide (proANP) in regard to a decline in QoL in a well-defined cohort of patients with community-acquired pneumonia (CAP). Within this secondary analysis including 753 patients with a final inpatient diagnosis of CAP from a multicenter trial, we investigated associations between admission biomarker levels and decline in QoL assessed by the EQ-5D health questionnaire from admission to day 30 and after 6 years. Admission proADM and proANP levels significantly predicted decline of the weighted EQ-5D index after 30 days (n=753) with adjusted odds ratios (ORs) of 2.0 ([95% CI 1.1-3.8]; p=0.027) and 3.7 ([95% CI 2.2-6.0]; pscale (VAS). Initial WBC, PCT and CRP values did not well predict QoL at any time point. ProADM and proANP accurately predict short- and long-term decline in QoL across most dimensions in CAP patients. It will be interesting to reveal underlying physiopathology in future studies.

Prominent deep medullary veins: a predictive biomarker for stroke risk from transient ischemic attack?

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Duan, Yang; Xu, Zhihua; Li, Hongyi; Cai, Xiaonan; Chang, Cancan; Yang, Benqiang

2018-05-01

Background Deep medullary veins (DMVs) are a biomarker of severity and prognosis in patients with acute cerebral infarction. However, their clinical significance remains unclear in patients with transient ischemic attack (TIA). Purpose To determine whether prominent deep medullary veins (PDMVs) are a predictive biomarker for stroke risk after TIA. Material and Methods Clinical and imaging data of 49 patients with TIA and 49 sex- and age-matched controls were studied. PDMVs were defined as DMVs with a score of 3 (TDMVs) or asymmetric DMVs (ADMVs), and the relationship between PDMVs and clinical features was analyzed. The DMV score based on susceptibility weighted imaging (SWI) ranged from 0 (not visible) to 3 (very prominent) and was calculated for both hemispheres separately. A different score in each hemisphere was defined as ADMVs and an equal score was defined as symmetric DMVs. The asymmetry and score of DMVs were compared between the two groups and with respect to the time from TIA onset to imaging analysis. Results Agreement between neuroradiologists for the DMV asymmetry/score on SWI was excellent. The frequency of ADMVs and TDMVs was significantly higher in patients with TIA than controls ( P  0.05); PDMVs were not correlated with age, blood pressure, or diabetes. However, PDMVs were associated with the ABCD2 score (≥4), clinical symptoms, and duration of TIA (≥10 min). Conclusion Prominent deep medullary veins is a predictive biomarker for the risk of stroke in many patients having suffered from TIA.

Biomarkers in the clinical development of asthma therapies.

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Staton, Tracy L; Choy, David F; Arron, Joseph R

2016-01-01

Here we review how biomarkers have been used in the design, execution and interpretation of recent clinical studies of therapeutic candidates targeting cytokine-mediated inflammatory pathways in asthma. This review focuses on type 2 inflammation, as there are multiple therapeutics and/or clinical studies that can be compared within that specific pathway. Comparative analyses of data from these clinical studies illustrate the utility of biomarkers to quantify pharmacodynamic effects, clarify mechanism of action and stratify patients, which may facilitate the interpretation of outcomes in the development of molecularly targeted therapies. These case examples provide a basis for biomarker considerations in the design of future studies in the asthma setting.

Biomarkers in Airway Diseases

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Janice M Leung

2013-01-01

Full Text Available The inherent limitations of spirometry and clinical history have prompted clinicians and scientists to search for surrogate markers of airway diseases. Although few biomarkers have been widely accepted into the clinical armamentarium, the authors explore three sources of biomarkers that have shown promise as indicators of disease severity and treatment response. In asthma, exhaled nitric oxide measurements can predict steroid responsiveness and sputum eosinophil counts have been used to titrate anti-inflammatory therapies. In chronic obstructive pulmonary disease, inflammatory plasma biomarkers, such as fibrinogen, club cell secretory protein-16 and surfactant protein D, can denote greater severity and predict the risk of exacerbations. While the multitude of disease phenotypes in respiratory medicine make biomarker development especially challenging, these three may soon play key roles in the diagnosis and management of airway diseases.

DR6 as a diagnostic and predictive biomarker in adult sarcoma.

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Kun Yang

Full Text Available The Death Receptor 6 (DR6 protein is elevated in the serum of ovarian cancer patients. We tested DR6 serum protein levels as a diagnostic/predictive biomarker in several epithelial tumors and sarcomas.DR6 gene expression profiles were screened in publically available arrays of solid tumors. A quantitative immunofluorescent western blot analysis was developed to test the serum of healthy controls and patients with sarcoma, uterine carcinosarcoma, bladder, liver, and pancreatic carcinomas. Change in DR6 serum levels was used to assay the ability of DR6 to predict the response to therapy of sarcoma patients.DR6 mRNA is highly expressed in all tumor types assayed. Western blot analysis of serum DR6 protein demonstrated high reproducibility (r = 0.97. Compared to healthy donor controls, DR6 serum levels were not elevated in patients with uterine carcinosarcoma, bladder, liver, or pancreatic cancers. Serum DR6 protein levels from adult sarcoma patients were significantly elevated (p<0.001. This was most evident for patients with synovial sarcoma. Change in serum DR6 levels during therapy correlated with clinical benefit from therapy (sensitivity 75%, and positive predictive value 87%.DR6 may be a clinically useful diagnostic and predictive serum biomarker for some adult sarcoma subtypes.Diagnosis of sarcoma can be difficult and can lead to improper management of these cancers. DR6 serum protein may be a tool to aid in the diagnosis of some sarcomatous tumors to improve treatment planning. For patients with advanced disease, rising DR6 levels predict non-response to therapy and may expedite therapeutic decision making and reduce reliance on radiologic imaging.

Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error.

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Shipitsin, M; Small, C; Choudhury, S; Giladi, E; Friedlander, S; Nardone, J; Hussain, S; Hurley, A D; Ernst, C; Huang, Y E; Chang, H; Nifong, T P; Rimm, D L; Dunyak, J; Loda, M; Berman, D M; Blume-Jensen, P

2014-09-09

Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a 'high' and a 'low' tumour microarray, respectively. Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error. Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.

Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects.

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Hinton, David J; Vázquez, Marely Santiago; Geske, Jennifer R; Hitschfeld, Mario J; Ho, Ada M C; Karpyak, Victor M; Biernacka, Joanna M; Choi, Doo-Sup

2017-05-31

Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.

Utility of Urinary Biomarkers in Predicting Loss of Residual Renal Function: The balANZ Trial

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Cho, Yeoungjee; Johnson, David W.; Vesey, David A.; Hawley, Carmel M.; Clarke, Margaret; Topley, Nicholas

2015-01-01

♦ Background: The ability of urinary biomarkers to predict residual renal function (RRF) decline in peritoneal dialysis (PD) patients has not been defined. The present study aimed to explore the utility of established biomarkers from kidney injury models for predicting loss of RRF in incident PD patients, and to evaluate the impact on RRF of using neutral-pH PD solution low in glucose degradation products. ♦ Methods: The study included 50 randomly selected participants from the balANZ trial who had completed 24 months of follow-up. A change in glomerular filtration rate (GFR) was used as the primary clinical outcome measure. In a mixed-effects general linear model, baseline measurements of 18 novel urinary biomarkers and albumin were used to predict GFR change. The model was further used to evaluate the impact of biocompatible PD solution on RRF, adjusted for each biomarker. ♦ Results: Baseline albuminuria was not a useful predictor of change in RRF in PD patients (p = 0.84). Only clusterin was a significant predictor of GFR decline in the whole population (p = 0.04, adjusted for baseline GFR and albuminuria). However, the relationship was no longer apparent when albuminuria was removed from the model (p = 0.31). When the effect of the administered PD solutions was examined using a model adjusted for PD solution type, baseline albuminuria, and GFR, higher baseline urinary concentrations of trefoil factor 3 (TFF3, p = 0.02), kidney injury molecule 1 (KIM-1, p = 0.04), and interferon γ-induced protein 10 (IP-10, p = 0.03) were associated with more rapid decline of RRF in patients receiving conventional PD solution compared with biocompatible PD solution. ♦ Conclusions: Higher urinary levels of kidney injury biomarkers (TFF3, KIM-1, IP-10) at baseline predicted significantly slower RRF decline in patients receiving biocompatible PD solutions. Findings from the present investigation should help to guide future studies to validate the utility of urinary

Plasma sCD14 as a biomarker to predict pulmonary exacerbations in cystic fibrosis.

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Bradley S Quon

Full Text Available BACKGROUND: One in four cystic fibrosis (CF patients diagnosed with a pulmonary exacerbation will not recover their baseline lung function despite standard treatment. This highlights the importance of preventing such events. Clinical decision-making can be improved through a simple blood test that predicts individuals at elevated short-term risk of an exacerbation. METHODS: We obtained plasma samples from 30 stable CF patients from the St. Paul's Hospital Adult CF Clinic (Vancouver, Canada. For 15 patients, an additional plasma sample was obtained during an exacerbation. Soluble CD14 (sCD14 and C-reactive protein (CRP were quantified using ELISA kits. Myeloperoxidase (MPO, interleukin(IL-6, IL-1β, monocyte chemoattractant protein-1 (MCP-1, vascular endothelial growth factor (VEGF, and granulocyte colony-stimulating factor (G-CSF were quantified using Luminex™ immunoassays. Stable state biomarker levels were examined in their ability to predict individuals that would experience a pulmonary exacerbation requiring intravenous (IV antibiotics within 4 months. Paired stable and exacerbation plasma biomarker levels were also compared. RESULTS: sCD14 levels were significantly higher in patients that experienced a pulmonary exacerbation requiring IV antibiotics within 4 months (p = 0.001. sCD14 cut-off value of 1450 ng/mL was associated with an area under the curve of 0.91 (95% CI 0.83-0.99 for predicting an exacerbation within 4 months of a stable visit, with a sensitivity of 100% and specificity of 82%. Plasma sCD14 levels were significantly higher during exacerbations than during periods of clinical stability (p = 0.03. CONCLUSIONS: Plasma sCD14 is a promising biomarker for identifying CF patients who will exacerbate within 4 months of a stable visit but requires further study in larger, independent cohorts.

The current status of biomarkers for predicting toxicity

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Campion, Sarah; Aubrecht, Jiri; Boekelheide, Kim; Brewster, David W; Vaidya, Vishal S; Anderson, Linnea; Burt, Deborah; Dere, Edward; Hwang, Kathleen; Pacheco, Sara; Saikumar, Janani; Schomaker, Shelli; Sigman, Mark; Goodsaid, Federico

2013-01-01

Introduction There are significant rates of attrition in drug development. A number of compounds fail to progress past preclinical development due to limited tools that accurately monitor toxicity in preclinical studies and in the clinic. Research has focused on improving tools for the detection of organ-specific toxicity through the identification and characterization of biomarkers of toxicity. Areas covered This article reviews what we know about emerging biomarkers in toxicology, with a focus on the 2012 Northeast Society of Toxicology meeting titled ‘Translational Biomarkers in Toxicology.’ The areas covered in this meeting are summarized and include biomarkers of testicular injury and dysfunction, emerging biomarkers of kidney injury and translation of emerging biomarkers from preclinical species to human populations. The authors also provide a discussion about the biomarker qualification process and possible improvements to this process. Expert opinion There is currently a gap between the scientific work in the development and qualification of novel biomarkers for nonclinical drug safety assessment and how these biomarkers are actually used in drug safety assessment. A clear and efficient path to regulatory acceptance is needed so that breakthroughs in the biomarker toolkit for nonclinical drug safety assessment can be utilized to aid in the drug development process. PMID:23961847

An etiologic prediction model incorporating biomarkers to predict the bladder cancer risk associated with occupational exposure to aromatic amines: a pilot study

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Mastrangelo, Giuseppe; Carta, Angela; Arici, Cecilia; Pavanello, Sofia; Porru, Stefano

2017-01-01

Background No etiological prediction model incorporating biomarkers is available to predict bladder cancer risk associated with occupational exposure to aromatic amines. Methods Cases were 199 bladder cancer patients. Clinical, laboratory and genetic data were predictors in logistic regression models (full and short) in which the dependent variable was 1 for 15 patients with aromatic amines related bladder cancer and 0 otherwise. The receiver operating characteristics approach was adopted; th...

IGFBP3 methylation is a novel diagnostic and predictive biomarker in colorectal cancer.

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Lucia Perez-Carbonell

Full Text Available Aberrant hypermethylation of cancer-related genes has emerged as a promising strategy for the development of diagnostic, prognostic and predictive biomarkers in human cancer, including colorectal cancer (CRC. The aim of this study was to perform a systematic and comprehensive analysis of a panel of CRC-specific genes as potential diagnostic, prognostic and predictive biomarkers in a large, population-based CRC cohort.Methylation status of the SEPT9, TWIST1, IGFBP3, GAS7, ALX4 and miR137 genes was studied by quantitative bisulfite pyrosequencing in a population-based cohort of 425 CRC patients.Methylation levels of all genes analyzed were significantly higher in tumor tissues compared to normal mucosa (p<0.0001; however, cancer-associated hypermethylation was most frequently observed for miR137 (86.7% and IGFBP3 (83% in CRC patients. Methylation analysis using the combination of these two genes demonstrated greatest accuracy for the identification of colonic tumors (sensitivity 95.5%; specificity 90.5%. Low levels of IGFBP3 promoter methylation emerged as an independent risk factor for predicting poor disease free survival in stage II and III CRC patients (HR = 0.49, 95% CI: 0.28-0.85, p = 0.01. Our results also suggest that stage II & III CRC patients with high levels of IGFBP3 methylation do not benefit from adjuvant 5FU-based chemotherapy.By analyzing a large, population-based CRC cohort, we demonstrate the potential clinical significance of miR137 and IGFBP3 hypermethylation as promising diagnostic biomarkers in CRC. Our data also revealed that IGFBP3 hypermethylation may serve as an independent prognostic and predictive biomarker in stage II and III CRC patients.

Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials

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Nils Ternès

2017-05-01

Full Text Available Abstract Background Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions. Methods Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso, we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation; estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap; and visualize them graphically (pointwise or smoothed with spline. We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured. Results In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4

Integration of gene expression, clinical, and demographic information in relation to asthma status to identify biomarkers associated with subtypes of childhood asthma

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Advances in biomarker development have improved our ability to detect early changes at the molecular, cellular, and pre-clinical level that are often predictive of adverse health outcomes. Biomarkers for monitoring the underlying molecular mechanisms of disease are of increasing...

A Proteomic Approach Identifies Candidate Early Biomarkers to Predict Severe Dengue in Children.

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Dang My Nhi

2016-02-01

Full Text Available Severe dengue with severe plasma leakage (SD-SPL is the most frequent of dengue severe form. Plasma biomarkers for early predictive diagnosis of SD-SPL are required in the primary clinics for the prevention of dengue death.Among 63 confirmed dengue pediatric patients recruited, hospital based longitudinal study detected six SD-SPL and ten dengue with warning sign (DWS. To identify the specific proteins increased or decreased in the SD-SPL plasma obtained 6-48 hours before the shock compared with the DWS, the isobaric tags for relative and absolute quantification (iTRAQ technology was performed using four patients each group. Validation was undertaken in 6 SD-SPL and 10 DWS patients.Nineteen plasma proteins exhibited significantly different relative concentrations (p<0.05, with five over-expressed and fourteen under-expressed in SD-SPL compared with DWS. The individual protein was classified to either blood coagulation, vascular regulation, cellular transport-related processes or immune response. The immunoblot quantification showed angiotensinogen and antithrombin III significantly increased in SD-SPL whole plasma of early stage compared with DWS subjects. Even using this small number of samples, antithrombin III predicted SD-SPL before shock occurrence with accuracy.Proteins identified here may serve as candidate predictive markers to diagnose SD-SPL for timely clinical management. Since the number of subjects are small, so further studies are needed to confirm all these biomarkers.

Time-dependent efficacy of longitudinal biomarker for clinical endpoint.

Science.gov (United States)

Kolamunnage-Dona, Ruwanthi; Williamson, Paula R

2018-06-01

Joint modelling of longitudinal biomarker and event-time processes has gained its popularity in recent years as they yield more accurate and precise estimates. Considering this modelling framework, a new methodology for evaluating the time-dependent efficacy of a longitudinal biomarker for clinical endpoint is proposed in this article. In particular, the proposed model assesses how well longitudinally repeated measurements of a biomarker over various time periods (0,t) distinguish between individuals who developed the disease by time t and individuals who remain disease-free beyond time t. The receiver operating characteristic curve is used to provide the corresponding efficacy summaries at various t based on the association between longitudinal biomarker trajectory and risk of clinical endpoint prior to each time point. The model also allows detecting the time period over which a biomarker should be monitored for its best discriminatory value. The proposed approach is evaluated through simulation and illustrated on the motivating dataset from a prospective observational study of biomarkers to diagnose the onset of sepsis.

Risk Factors and Biomarkers of Age-Related Macular Degeneration

Science.gov (United States)

Lambert, Nathan G.; Singh, Malkit K.; ElShelmani, Hanan; Mansergh, Fiona C.; Wride, Michael A.; Padilla, Maximilian; Keegan, David; Hogg, Ruth E.; Ambati, Balamurali K.

2016-01-01

A biomarker can be a substance or structure measured in body parts, fluids or products that can affect or predict disease incidence. As age-related macular degeneration (AMD) is the leading cause of blindness in the developed world, much research and effort has been invested in the identification of different biomarkers to predict disease incidence, identify at risk individuals, elucidate causative pathophysiological etiologies, guide screening, monitoring and treatment parameters, and predict disease outcomes. To date, a host of genetic, environmental, proteomic, and cellular targets have been identified as both risk factors and potential biomarkers for AMD. Despite this, their use has been confined to research settings and has not yet crossed into the clinical arena. A greater understanding of these factors and their use as potential biomarkers for AMD can guide future research and clinical practice. This article will discuss known risk factors and novel, potential biomarkers of AMD in addition to their application in both academic and clinical settings. PMID:27156982

Clinical librarian support for rapid review of clinical utility of cancer molecular biomarkers.

Science.gov (United States)

Geng, Yimin; Fowler, Clara S; Fulton, Stephanie

2015-01-01

The clinical librarian used a restricted literature searching and quality-filtering approach to provide relevant clinical evidence for the use of cancer molecular biomarkers by institutional policy makers and clinicians in the rapid review process. The librarian-provided evidence was compared with the cited references in the institutional molecular biomarker algorithm. The overall incorporation rate of the librarian-provided references into the algorithm was above 80%. This study suggests the usefulness of clinical librarian expertise for clinical practice. The searching and filtering methods for high-level evidence can be adopted by information professionals who are involved in the rapid literature review.

Clinical Translation and Validation of a Predictive Biomarker for Patritumab, an Anti-human Epidermal Growth Factor Receptor 3 (HER3) Monoclonal Antibody, in Patients With Advanced Non-small Cell Lung Cancer.

Science.gov (United States)

Mendell, Jeanne; Freeman, Daniel J; Feng, Wenqin; Hettmann, Thore; Schneider, Matthias; Blum, Sabine; Ruhe, Jens; Bange, Johannes; Nakamaru, Kenji; Chen, Shuquan; Tsuchihashi, Zenta; von Pawel, Joachim; Copigneaux, Catherine; Beckman, Robert A

2015-03-01

During early clinical development, prospective identification of a predictive biomarker and validation of an assay method may not always be feasible. Dichotomizing a continuous biomarker measure to classify responders also leads to challenges. We present a case study of a prospective-retrospective approach for a continuous biomarker identified after patient enrollment but defined prospectively before the unblinding of data. An analysis of the strengths and weaknesses of this approach and the challenges encountered in its practical application are also provided. HERALD (NCT02134015) was a double-blind, phase 2 study in patients with non-small cell lung cancer (NSCLC) randomized to erlotinib with placebo or with high or low doses of patritumab, a monoclonal antibody targeted against human epidermal growth factor receptor 3 (HER3). While the primary objective was to assess safety and progression-free survival (PFS), a secondary objective was to determine a single predictive biomarker hypothesis to identify subjects most likely to benefit from the addition of patritumab. Although not identified as the primary biomarker in the study protocol, on the basis of preclinical results from 2 independent laboratories, expression levels of the HER3 ligand heregulin (HRG) were prospectively declared the predictive biomarker before data unblinding but after subject enrollment. An assay to measure HRG mRNA was developed and validated. Other biomarkers, such as epidermal growth factor receptor (EGFR) mutation status, were also evaluated in an exploratory fashion. The cutoff value for high vs. low HRG mRNA levels was set at the median delta threshold cycle. A maximum likelihood analysis was performed to evaluate the provisional cutoff. The relationship of HRG values to PFS hazard ratios (HRs) was assessed as a measure of internal validation. Additional NSCLC samples were analyzed to characterize HRG mRNA distribution. The subgroup of patients with high HRG mRNA levels ("HRG

Biomarker kinetics in the prediction of VAP diagnosis: results from the BioVAP study

NARCIS (Netherlands)

Póvoa, Pedro; Martin-Loeches, Ignacio; Ramirez, Paula; Bos, Lieuwe D.; Esperatti, Mariano; Silvestre, Joana; Gili, Gisela; Goma, Gema; Berlanga, Eugenio; Espasa, Mateu; Gonçalves, Elsa; Torres, Antoni; Artigas, Antonio

2016-01-01

Prediction of diagnosis of ventilator-associated pneumonia (VAP) remains difficult. Our aim was to assess the value of biomarker kinetics in VAP prediction. We performed a prospective, multicenter, observational study to evaluate predictive accuracy of biomarker kinetics, namely C-reactive protein

Inflammatory and Other Biomarkers: Role in Pathophysiology and Prediction of Gestational Diabetes Mellitus

Science.gov (United States)

Abell, Sally K.; De Courten, Barbora; Boyle, Jacqueline A.; Teede, Helena J.

2015-01-01

Understanding pathophysiology and identifying mothers at risk of major pregnancy complications is vital to effective prevention and optimal management. However, in current antenatal care, understanding of pathophysiology of complications is limited. In gestational diabetes mellitus (GDM), risk prediction is mostly based on maternal history and clinical risk factors and may not optimally identify high risk pregnancies. Hence, universal screening is widely recommended. Here, we will explore the literature on GDM and biomarkers including inflammatory markers, adipokines, endothelial function and lipids to advance understanding of pathophysiology and explore risk prediction, with a goal to guide prevention and treatment of GDM. PMID:26110385

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort.

Science.gov (United States)

Lam, Raymond W; Milev, Roumen; Rotzinger, Susan; Andreazza, Ana C; Blier, Pierre; Brenner, Colleen; Daskalakis, Zafiris J; Dharsee, Moyez; Downar, Jonathan; Evans, Kenneth R; Farzan, Faranak; Foster, Jane A; Frey, Benicio N; Geraci, Joseph; Giacobbe, Peter; Feilotter, Harriet E; Hall, Geoffrey B; Harkness, Kate L; Hassel, Stefanie; Ismail, Zahinoor; Leri, Francesco; Liotti, Mario; MacQueen, Glenda M; McAndrews, Mary Pat; Minuzzi, Luciano; Müller, Daniel J; Parikh, Sagar V; Placenza, Franca M; Quilty, Lena C; Ravindran, Arun V; Salomons, Tim V; Soares, Claudio N; Strother, Stephen C; Turecki, Gustavo; Vaccarino, Anthony L; Vila-Rodriguez, Fidel; Kennedy, Sidney H

2016-04-16

Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants

Clinical utility of asthma biomarkers: from bench to bedside

Directory of Open Access Journals (Sweden)

Vijverberg SJH

2013-08-01

Full Text Available Susanne JH Vijverberg,1,2,* Bart Hilvering,2,* Jan AM Raaijmakers,1 Jan-Willem J Lammers,2 Anke-Hilse Maitland-van der Zee,1,* Leo Koenderman2,* 1Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands; 2Department of Respiratory Medicine, University Medical Centre Utrecht, Utrecht, The Netherlands *These authors contributed equally to this work Abstract: Asthma is a chronic disease characterized by airway inflammation, bronchial hyperresponsiveness, and recurrent episodes of reversible airway obstruction. The disease is very heterogeneous in onset, course, and response to treatment, and seems to encompass a broad collection of heterogeneous disease subtypes with different underlying pathophysiological mechanisms. There is a strong need for easily interpreted clinical biomarkers to assess the nature and severity of the disease. Currently available biomarkers for clinical practice – for example markers in bronchial lavage, bronchial biopsies, sputum, or fraction of exhaled nitric oxide (FeNO – are limited due to invasiveness or lack of specificity. The assessment of markers in peripheral blood might be a good alternative to study airway inflammation more specifically, compared to FeNO, and in a less invasive manner, compared to bronchoalveolar lavage, biopsies, or sputum induction. In addition, promising novel biomarkers are discovered in the field of breath metabolomics (eg, volatile organic compounds and (pharmacogenomics. Biomarker research in asthma is increasingly shifting from the assessment of the value of single biomarkers to multidimensional approaches in which the clinical value of a combination of various markers is studied. This could eventually lead to the development of a clinically applicable algorithm composed of various markers and clinical features to phenotype asthma and improve diagnosis and asthma management

Predictive Biomarkers of Radiation Sensitivity in Rectal Cancer

Science.gov (United States)

Tut, Thein Ga

repair (MMR) proteins, the insufficiency of which is characteristic of CRCs with microsatellite instability (MSI). MSI may enable unlimited replicative potential of malignant cell that leads to radiation injury resistance. Therefore, these proteins were characterized in both CRC cell lines (MMR proteins) and different (core and invasive front) rectal cancer tissues (Plk1, gammaH2AX and MMR proteins) exposed to radiation. Histopathological grading of tumour regression was performed following radiotherapy in rectal cancer as a marker of radiotherapy response and a surrogate indicator of patient prognosis. Though MMR protein expressions correlated with improved in vitro cell survival following radiation, these findings could only be partially replicated in patient tissue samples. This may not be entirely unexpected, given intratumoural heterogeneity in genetic profiles and oxygenation between individual cancer cells, their interaction with stromal environment and a multitude of other factors that cannot be adequately replicated in cell line experiments. In our rectal cancer patient cohort, histopathological regression following radiotherapy did appear to correlate with better clinical outcome, but certainly no replacement for the routine pTNM staging with which it was compared. Overexpression of Plk1 in the primary rectal cancer also correlates with poor tumour regression and reduced overall survival. High level of gammaH2AX correlates with higher tumour stage, perineural invasion and vascular invasion. However, interpretation of the results is limited by the small number of positivity amongst the cohort, with respect to gammaH2AX and MMR proteins. The combined analysis of all the proteins examined in this thesis revealed no interactions, possibly suggesting these biomarkers act individually within the DDR pathway, rather than in a demonstrably interdependent manner. Though our results are mixed, finding biomarkers predictive of radiation response is nonetheless critical

Myopodin methylation is a prognostic biomarker and predicts antiangiogenic response in advanced kidney cancer.

Science.gov (United States)

Pompas-Veganzones, N; Sandonis, V; Perez-Lanzac, Alberto; Beltran, M; Beardo, P; Juárez, A; Vazquez, F; Cozar, J M; Alvarez-Ossorio, J L; Sanchez-Carbayo, Marta

2016-10-01

Myopodin is a cytoskeleton protein that shuttles to the nucleus depending on the cellular differentiation and stress. It has shown tumor suppressor functions. Myopodin methylation status was useful for staging bladder and colon tumors and predicting clinical outcome. To our knowledge, myopodin has not been tested in kidney cancer to date. The purpose of this study was to evaluate whether myopodin methylation status could be clinically useful in renal cancer (1) as a prognostic biomarker and 2) as a predictive factor of response to antiangiogenic therapy in patients with metastatic disease. Methylation-specific polymerase chain reactions (MS-PCR) were used to evaluate myopodin methylation in 88 kidney tumors. These belonged to patients with localized disease and no evidence of disease during follow-up (n = 25) (group 1), and 63 patients under antiangiogenic therapy (sunitinib, sorafenib, pazopanib, and temsirolimus), from which group 2 had non-metastatic disease at diagnosis (n = 32), and group 3 showed metastatic disease at diagnosis (n = 31). Univariate and multivariate Cox analyses were utilized to assess outcome and response to antiangiogenic agents taking progression, disease-specific survival, and overall survival as clinical endpoints. Myopodin was methylated in 50 out of the 88 kidney tumors (56.8 %). Among the 88 cases analyzed, 10 of them recurred (11.4 %), 51 progressed (57.9 %), and 40 died of disease (45.4 %). Myopodin methylation status correlated to MSKCC Risk score (p = 0.050) and the presence of distant metastasis (p = 0.039). Taking all patients, an unmethylated myopodin identified patients with shorter progression-free survival, disease-specific survival, and overall survival. Using also in univariate and multivariate models, an unmethylated myopodin predicted response to antiangiogenic therapy (groups 2 and 3) using progression-free survival, disease-specific, and overall survival as clinical endpoints. Myopodin was revealed

Clinical risk stratification optimizes value of biomarkers to predict new-onset heart failure in a community-based cohort.

Science.gov (United States)

Brouwers, Frank P; van Gilst, Wiek H; Damman, Kevin; van den Berg, Maarten P; Gansevoort, Ron T; Bakker, Stephan J L; Hillege, Hans L; van Veldhuisen, Dirk J; van der Harst, Pim; de Boer, Rudolf A

2014-09-01

We aim to identify and quantify the value of biomarkers for incident new-onset heart failure (HF) in a community-based cohort and subgroups based on cardiovascular risk and evaluate the prognostic value of 13 biomarkers for HF with reduced and preserved ejection fraction. Thirteen biomarkers reflecting diverse pathophysiologic domains were examined in 8569 HF-free participants in Prevention of Vascular and Renal Endstage Disease (mean age, 49 years; 50% men). Subjects were categorized in 2 risk groups based on cardiovascular history. Incremental value per biomarker was assessed using Harrell C-indices. One hundred sixty-eight subjects (2.4%) were diagnosed with new-onset HF in the low-risk group (n=6915; Framingham Risk Score, 5.9%) and 206 (12.2%) subjects in the high-risk group (n=1654; Framingham Risk Score, 18.6%). The association of natriuretic peptides, adrenomedullin, endothelin, and galectin-3 with new-onset HF was stronger in the high-risk group (all Prisk for new-onset HF between both risk groups. The best model for new-onset HF included the combination of N-terminal pro-B-type natriuretic peptide, troponin-T, and urinary albumin excretion, increasing model accuracy to 0.81 (9.5%, Prisk group. Except for a modest effect of cystatin-C, no biomarker was associated with increased risk for HF with preserved ejection fraction. Risk stratification increases the incremental value per biomarker to predict new-onset HF, especially HF with reduced ejection fraction. We suggest that routine biomarker testing should be limited to the use of natriuretic peptides and troponin-T in patients with increased cardiovascular risk. © 2014 American Heart Association, Inc.

Biomarkers of PTSD: military applications and considerations.

Science.gov (United States)

Lehrner, Amy; Yehuda, Rachel

2014-01-01

Although there are no established biomarkers for posttraumatic stress disorder (PTSD) as yet, biological investigations of PTSD have made progress identifying the pathophysiology of PTSD. Given the biological and clinical complexity of PTSD, it is increasingly unlikely that a single biomarker of disease will be identified. Rather, investigations will more likely identify different biomarkers that indicate the presence of clinically significant PTSD symptoms, associate with risk for PTSD following trauma exposure, and predict or identify recovery. While there has been much interest in PTSD biomarkers, there has been less discussion of their potential clinical applications, and of the social, legal, and ethical implications of such biomarkers. This article will discuss possible applications of PTSD biomarkers, including the social, legal, and ethical implications of such biomarkers, with an emphasis on military applications. Literature on applications of PTSD biomarkers and on potential ethical and legal implications will be reviewed. Biologically informed research findings hold promise for prevention, assessment, treatment planning, and the development of prophylactic and treatment interventions. As with any biological indicator of disorder, there are potentially positive and negative clinical, social, legal, and ethical consequences of using such biomarkers. Potential clinical applications of PTSD biomarkers hold promise for clinicians, patients, and employers. The search for biomarkers of PTSD should occur in tandem with an interdisciplinary discussion regarding the potential implications of applying biological findings in clinical and employment settings.

Predictive biomarkers for death and rehospitalization in comorbid frail elderly heart failure patients.

Science.gov (United States)

Pacho, Cristina; Domingo, Mar; Núñez, Raquel; Lupón, Josep; Núñez, Julio; Barallat, Jaume; Moliner, Pedro; de Antonio, Marta; Santesmases, Javier; Cediel, Germán; Roura, Santiago; Pastor, M Cruz; Tor, Jordi; Bayes-Genis, Antoni

2018-05-09

Heart failure (HF) is associated with a high rate of readmissions within 30 days post-discharge and in the following year, especially in frail elderly patients. Biomarker data are scarce in this high-risk population. This study assessed the value of early post-discharge circulating levels of ST2, NT-proBNP, CA125, and hs-TnI for predicting 30-day and 1-year outcomes in comorbid frail elderly patients with HF with mainly preserved ejection fraction (HFpEF). Blood samples were obtained at the first visit shortly after discharge (4.9 ± 2 days). The primary endpoint was the composite of all-cause mortality or HF-related rehospitalization at 30 days and at 1 year. All-cause mortality alone at one year was also a major endpoint. HF-related rehospitalizations alone were secondary end-points. From February 2014 to November 2016, 522 consecutive patients attending the STOP-HF Clinic were included (57.1% women, age 82 ± 8.7 years, mean Barthel index 70 ± 25, mean Charlson comorbidity index 5.6 ± 2.2). The composite endpoint occurred in 8.6% patients at 30 days and in 38.5% at 1 year. In multivariable analysis, ST2 [hazard ratio (HR) 1.53; 95% CI 1.19-1.97; p = 0.001] was the only predictive biomarker at 30 days; at 1 year, both ST2 (HR 1.34; 95% CI 1.15-1.56; p < 0.001) and NT-proBNP (HR 1.19; 95% CI 1.02-1.40; p = 0.03) remained significant. The addition of ST2 and NT-proBNP into a clinical predictive model increased the AUC from 0.70 to 0.75 at 30 days (p = 0.02) and from 0.71 to 0.74 at 1 year (p < 0.05). For all-cause death at 1 year, ST2 (HR 1.50; 95% CI 1.26-1.80; p < 0.001), and CA125 (HR 1.41; 95% CI 1.21-1.63; p < 0.001) remained independent predictors in multivariable analysis. The addition of ST2 and CA125 into a clinical predictive model increased the AUC from 0.74 to 0.78 (p = 0.03). For HF-related hospitalizations, ST2 was the only predictive biomarker in multivariable analyses, both at 30�

Identification of predictive biomarkers of disease state in transition dairy cows.

Science.gov (United States)

Hailemariam, D; Mandal, R; Saleem, F; Dunn, S M; Wishart, D S; Ametaj, B N

2014-05-01

phosphatidylcholine diacyl C42:6, could be used to discriminate healthy controls from diseased cows 1 wk before parturition. A 3-metabolite plasma biomarker profile was developed that could predict which cows would develop periparturient diseases, up to 4 wk before clinical symptoms appearing, with a sensitivity of 87% and a specificity of 85%. This is the first report showing that periparturient diseases can be predicted in dairy cattle before their development using a multimetabolite biomarker model. Further research is warranted to validate these potential predictive biomarkers. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

APRIL is a novel clinical chemo-resistance biomarker in colorectal adenocarcinoma identified by gene expression profiling

International Nuclear Information System (INIS)

Petty, Russell D; Wang, Weiguang; Gilbert, Fiona; Semple, Scot; Collie-Duguid, Elaina SR; Samuel, Leslie M; Murray, Graeme I; MacDonald, Graham; O'Kelly, Terrence; Loudon, Malcolm; Binnie, Norman; Aly, Emad; McKinlay, Aileen

2009-01-01

5-Fluorouracil(5FU) and oral analogues, such as capecitabine, remain one of the most useful agents for the treatment of colorectal adenocarcinoma. Low toxicity and convenience of administration facilitate use, however clinical resistance is a major limitation. Investigation has failed to fully explain the molecular mechanisms of resistance and no clinically useful predictive biomarkers for 5FU resistance have been identified. We investigated the molecular mechanisms of clinical 5FU resistance in colorectal adenocarcinoma patients in a prospective biomarker discovery project utilising gene expression profiling. The aim was to identify novel 5FU resistance mechanisms and qualify these as candidate biomarkers and therapeutic targets. Putative treatment specific gene expression changes were identified in a transcriptomics study of rectal adenocarcinomas, biopsied and profiled before and after pre-operative short-course radiotherapy or 5FU based chemo-radiotherapy, using microarrays. Tumour from untreated controls at diagnosis and resection identified treatment-independent gene expression changes. Candidate 5FU chemo-resistant genes were identified by comparison of gene expression data sets from these clinical specimens with gene expression signatures from our previous studies of colorectal cancer cell lines, where parental and daughter lines resistant to 5FU were compared. A colorectal adenocarcinoma tissue microarray (n = 234, resected tumours) was used as an independent set to qualify candidates thus identified. APRIL/TNFSF13 mRNA was significantly upregulated following 5FU based concurrent chemo-radiotherapy and in 5FU resistant colorectal adenocarcinoma cell lines but not in radiotherapy alone treated colorectal adenocarcinomas. Consistent withAPRIL's known function as an autocrine or paracrine secreted molecule, stromal but not tumour cell protein expression by immunohistochemistry was correlated with poor prognosis (p = 0.019) in the independent set

A biomarker profile for predicting efficacy of cisplatin-vinorelbine therapy in malignant pleural mesothelioma

DEFF Research Database (Denmark)

Zimling, Zarah Glad; Sørensen, Jens Benn; Gerds, Thomas Alexander

2012-01-01

Malignant pleural mesothelioma (MPM) has a dismal prognosis. Treatment results may be improved by biomarker-directed therapy. We investigated the baseline expression and impact on outcome of predictive biomarkers ERCC1, BRCA1, and class III β-tubulin in a cohort of MPM patients treated with cispl......Malignant pleural mesothelioma (MPM) has a dismal prognosis. Treatment results may be improved by biomarker-directed therapy. We investigated the baseline expression and impact on outcome of predictive biomarkers ERCC1, BRCA1, and class III β-tubulin in a cohort of MPM patients treated...

Association between biomarkers and clinical characteristics in chronic subdural hematoma patients assessed with lasso regression.

Directory of Open Access Journals (Sweden)

Are Hugo Pripp

Full Text Available Chronic subdural hematoma (CSDH is characterized by an "old" encapsulated collection of blood and blood breakdown products between the brain and its outermost covering (the dura. Recognized risk factors for development of CSDH are head injury, old age and using anticoagulation medication, but its underlying pathophysiological processes are still unclear. It is assumed that a complex local process of interrelated mechanisms including inflammation, neomembrane formation, angiogenesis and fibrinolysis could be related to its development and propagation. However, the association between the biomarkers of inflammation and angiogenesis, and the clinical and radiological characteristics of CSDH patients, need further investigation. The high number of biomarkers compared to the number of observations, the correlation between biomarkers, missing data and skewed distributions may limit the usefulness of classical statistical methods. We therefore explored lasso regression to assess the association between 30 biomarkers of inflammation and angiogenesis at the site of lesions, and selected clinical and radiological characteristics in a cohort of 93 patients. Lasso regression performs both variable selection and regularization to improve the predictive accuracy and interpretability of the statistical model. The results from the lasso regression showed analysis exhibited lack of robust statistical association between the biomarkers in hematoma fluid with age, gender, brain infarct, neurological deficiencies and volume of hematoma. However, there were associations between several of the biomarkers with postoperative recurrence requiring reoperation. The statistical analysis with lasso regression supported previous findings that the immunological characteristics of CSDH are local. The relationship between biomarkers, the radiological appearance of lesions and recurrence requiring reoperation have been inclusive using classical statistical methods on these data

How Nanotechnology and Biomedical Engineering Are Supporting the Identification of Predictive Biomarkers in Neuro-Oncology.

Science.gov (United States)

Ganau, Mario; Paris, Marco; Syrmos, Nikolaos; Ganau, Laura; Ligarotti, Gianfranco K I; Moghaddamjou, Ali; Prisco, Lara; Ambu, Rossano; Chibbaro, Salvatore

2018-02-26

The field of neuro-oncology is rapidly progressing and internalizing many of the recent discoveries coming from research conducted in basic science laboratories worldwide. This systematic review aims to summarize the impact of nanotechnology and biomedical engineering in defining clinically meaningful predictive biomarkers with a potential application in the management of patients with brain tumors. Data were collected through a review of the existing English literature performed on Scopus, MEDLINE, MEDLINE in Process, EMBASE, and/or Cochrane Central Register of Controlled Trials: all available basic science and clinical papers relevant to address the above-stated research question were included and analyzed in this study. Based on the results of this systematic review we can conclude that: (1) the advances in nanotechnology and bioengineering are supporting tremendous efforts in optimizing the methods for genomic, epigenomic and proteomic profiling; (2) a successful translational approach is attempting to identify a growing number of biomarkers, some of which appear to be promising candidates in many areas of neuro-oncology; (3) the designing of Randomized Controlled Trials will be warranted to better define the prognostic value of those biomarkers and biosignatures.

How Nanotechnology and Biomedical Engineering Are Supporting the Identification of Predictive Biomarkers in Neuro-Oncology

Directory of Open Access Journals (Sweden)

Mario Ganau

2018-02-01

Full Text Available The field of neuro-oncology is rapidly progressing and internalizing many of the recent discoveries coming from research conducted in basic science laboratories worldwide. This systematic review aims to summarize the impact of nanotechnology and biomedical engineering in defining clinically meaningful predictive biomarkers with a potential application in the management of patients with brain tumors. Data were collected through a review of the existing English literature performed on Scopus, MEDLINE, MEDLINE in Process, EMBASE, and/or Cochrane Central Register of Controlled Trials: all available basic science and clinical papers relevant to address the above-stated research question were included and analyzed in this study. Based on the results of this systematic review we can conclude that: (1 the advances in nanotechnology and bioengineering are supporting tremendous efforts in optimizing the methods for genomic, epigenomic and proteomic profiling; (2 a successful translational approach is attempting to identify a growing number of biomarkers, some of which appear to be promising candidates in many areas of neuro-oncology; (3 the designing of Randomized Controlled Trials will be warranted to better define the prognostic value of those biomarkers and biosignatures.

The BioFIND study: Characteristics of a clinically typical Parkinson's disease biomarker cohort

Science.gov (United States)

Goldman, Jennifer G.; Alcalay, Roy N.; Xie, Tao; Tuite, Paul; Henchcliffe, Claire; Hogarth, Penelope; Amara, Amy W.; Frank, Samuel; Rudolph, Alice; Casaceli, Cynthia; Andrews, Howard; Gwinn, Katrina; Sutherland, Margaret; Kopil, Catherine; Vincent, Lona; Frasier, Mark

2016-01-01

ABSTRACT Background Identifying PD‐specific biomarkers in biofluids will greatly aid in diagnosis, monitoring progression, and therapeutic interventions. PD biomarkers have been limited by poor discriminatory power, partly driven by heterogeneity of the disease, variability of collection protocols, and focus on de novo, unmedicated patients. Thus, a platform for biomarker discovery and validation in well‐characterized, clinically typical, moderate to advanced PD cohorts is critically needed. Methods BioFIND (Fox Investigation for New Discovery of Biomarkers in Parkinson's Disease) is a cross‐sectional, multicenter biomarker study that established a repository of clinical data, blood, DNA, RNA, CSF, saliva, and urine samples from 118 moderate to advanced PD and 88 healthy control subjects. Inclusion criteria were designed to maximize diagnostic specificity by selecting participants with clinically typical PD symptoms, and clinical data and biospecimen collection utilized standardized procedures to minimize variability across sites. Results We present the study methodology and data on the cohort's clinical characteristics. Motor scores and biospecimen samples including plasma are available for practically defined off and on states and thus enable testing the effects of PD medications on biomarkers. Other biospecimens are available from off state PD assessments and from controls. Conclusion Our cohort provides a valuable resource for biomarker discovery and validation in PD. Clinical data and biospecimens, available through The Michael J. Fox Foundation for Parkinson's Research and the National Institute of Neurological Disorders and Stroke, can serve as a platform for discovering biomarkers in clinically typical PD and comparisons across PD's broad and heterogeneous spectrum. © 2016 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society PMID:27113479

Biomarkers of PTSD: military applications and considerations

Directory of Open Access Journals (Sweden)

Amy Lehrner

2014-08-01

Full Text Available Background: Although there are no established biomarkers for posttraumatic stress disorder (PTSD as yet, biological investigations of PTSD have made progress identifying the pathophysiology of PTSD. Given the biological and clinical complexity of PTSD, it is increasingly unlikely that a single biomarker of disease will be identified. Rather, investigations will more likely identify different biomarkers that indicate the presence of clinically significant PTSD symptoms, associate with risk for PTSD following trauma exposure, and predict or identify recovery. While there has been much interest in PTSD biomarkers, there has been less discussion of their potential clinical applications, and of the social, legal, and ethical implications of such biomarkers. Objective: This article will discuss possible applications of PTSD biomarkers, including the social, legal, and ethical implications of such biomarkers, with an emphasis on military applications. Method: Literature on applications of PTSD biomarkers and on potential ethical and legal implications will be reviewed. Results: Biologically informed research findings hold promise for prevention, assessment, treatment planning, and the development of prophylactic and treatment interventions. As with any biological indicator of disorder, there are potentially positive and negative clinical, social, legal, and ethical consequences of using such biomarkers. Conclusions: Potential clinical applications of PTSD biomarkers hold promise for clinicians, patients, and employers. The search for biomarkers of PTSD should occur in tandem with an interdisciplinary discussion regarding the potential implications of applying biological findings in clinical and employment settings.

Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data.

Science.gov (United States)

Richardson, Alice; Signor, Ben M; Lidbury, Brett A; Badrick, Tony

2016-11-01

Big Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia. Copyright © 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Diagnostic and prognostic epigenetic biomarkers in cancer.

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Costa-Pinheiro, Pedro; Montezuma, Diana; Henrique, Rui; Jerónimo, Carmen

2015-01-01

Growing cancer incidence and mortality worldwide demands development of accurate biomarkers to perfect detection, diagnosis, prognostication and monitoring. Urologic (prostate, bladder, kidney), lung, breast and colorectal cancers are the most common and despite major advances in their characterization, this has seldom translated into biomarkers amenable for clinical practice. Epigenetic alterations are innovative cancer biomarkers owing to stability, frequency, reversibility and accessibility in body fluids, entailing great potential of assay development to assist in patient management. Several studies identified putative epigenetic cancer biomarkers, some of which have been commercialized. However, large multicenter validation studies are required to foster translation to the clinics. Herein we review the most promising epigenetic detection, diagnostic, prognostic and predictive biomarkers for the most common cancers.

Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers

Directory of Open Access Journals (Sweden)

Xiao Da

2014-01-01

Full Text Available This study evaluates the individual, as well as relative and joint value of indices obtained from magnetic resonance imaging (MRI patterns of brain atrophy (quantified by the SPARE-AD index, cerebrospinal fluid (CSF biomarkers, APOE genotype, and cognitive performance (ADAS-Cog in progression from mild cognitive impairment (MCI to Alzheimer's disease (AD within a variable follow-up period up to 6 years, using data from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1. SPARE-AD was first established as a highly sensitive and specific MRI-marker of AD vs. cognitively normal (CN subjects (AUC = 0.98. Baseline predictive values of all aforementioned indices were then compared using survival analysis on 381 MCI subjects. SPARE-AD and ADAS-Cog were found to have similar predictive value, and their combination was significantly better than their individual performance. APOE genotype did not significantly improve prediction, although the combination of SPARE-AD, ADAS-Cog and APOE ε4 provided the highest hazard ratio estimates of 17.8 (last vs. first quartile. In a subset of 192 MCI patients who also had CSF biomarkers, the addition of Aβ1–42, t-tau, and p-tau181p to the previous model did not improve predictive value significantly over SPARE-AD and ADAS-Cog combined. Importantly, in amyloid-negative patients with MCI, SPARE-AD had high predictive power of clinical progression. Our findings suggest that SPARE-AD and ADAS-Cog in combination offer the highest predictive power of conversion from MCI to AD, which is improved, albeit not significantly, by APOE genotype. The finding that SPARE-AD in amyloid-negative MCI patients was predictive of clinical progression is not expected under the amyloid hypothesis and merits further investigation.

WONOEP appraisal: Biomarkers of epilepsy-associated comorbidities.

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Ravizza, Teresa; Onat, Filiz Y; Brooks-Kayal, Amy R; Depaulis, Antoine; Galanopoulou, Aristea S; Mazarati, Andrey; Numis, Adam L; Sankar, Raman; Friedman, Alon

2017-03-01

Neurologic and psychiatric comorbidities are common in patients with epilepsy. Diagnostic, predictive, and pharmacodynamic biomarkers of such comorbidities do not exist. They may share pathogenetic mechanisms with epileptogenesis/ictogenesis, and as such are an unmet clinical need. The objectives of the subgroup on biomarkers of comorbidities at the XIII Workshop on the Neurobiology of Epilepsy (WONOEP) were to present the state-of-the-art recent research findings in the field that highlighting potential biomarkers for comorbidities in epilepsy. We review recent progress in the field, including molecular, imaging, and genetic biomarkers of comorbidities as discussed during the WONOEP meeting on August 31-September 4, 2015, in Heybeliada Island (Istanbul, Turkey). We further highlight new directions and concepts from studies on comorbidities and potential new biomarkers for the prediction, diagnosis, and treatment of epilepsy-associated comorbidities. The activation of various molecular signaling pathways such as the "Janus Kinase/Signal Transducer and Activator of Transcription," "mammalian Target of Rapamycin," and oxidative stress have been shown to correlate with the presence and severity of subsequent cognitive abnormalities. Furthermore, dysfunction in serotonergic transmission, hyperactivity of the hypothalamic-pituitary-adrenocortical axis, the role of the inflammatory cytokines, and the contributions of genetic factors have all recently been regarded as relevant for understanding epilepsy-associated depression and cognitive deficits. Recent evidence supports the utility of imaging studies as potential biomarkers. The role of such biomarker may be far beyond the diagnosis of comorbidities, as accumulating clinical data indicate that comorbidities can predict epilepsy outcomes. Future research is required to reveal whether molecular changes in specific signaling pathways or advanced imaging techniques could be detected in the clinical settings and correlate

Targeted genomic biomarkers for diagnosis and therapy: from basic research to clinical perspective

International Nuclear Information System (INIS)

Thakur, Mathew L.

2014-01-01

In 2010, more than 30,000 men succumbed to prostate cancer (PC) and more than 240,000 new PC cases were identified in the USA. Digital rectal examination, MRI, and a blood test for prostate specific antigen (PSA) determination play a significant role in detecting advanced PC. However, they are not considered reliable tools for early warning of PC, to detect recurrent cancer or to determine metastatic status of the disease. Unreliable diagnosis results in undertreatment or overtreatment of patients with minimal benefit, enormous morbidity, incontinence, and/or impotence. Histology remains the mainstay of PC confirmation. However, out of >750,000 biopsies performed each year in the USA, >65% show benign pathology, causing patient morbidity and costing hundreds of millions of healthcare dollars. Biological fluids, including urine, represent a promising source of biomarkers for detection and prediction of PC prognosis. Because urine is available non-invasively and readily, numerous studies targeting DNA, RNA, protein and metabolite based biomarkers have been performed. However, none have yet reached the clinic. Even FDA approved PCA3 test has low sensitivity and limitations in predicting aggressive PC

Biomarkers of the Dementia

Directory of Open Access Journals (Sweden)

Mikio Shoji

2011-01-01

Full Text Available Recent advances in biomarker studies on dementia are summarized here. CSF Aβ40, Aβ42, total tau, and phosphorylated tau are the most sensitive biomarkers for diagnosis of Alzheimer's disease (AD and prediction of onset of AD from mild cognitive impairment (MCI. Based on this progress, new diagnostic criteria for AD, MCI, and preclinical AD were proposed by National Institute of Aging (NIA and Alzheimer's Association in August 2010. In these new criteria, progress in biomarker identification and amyloid imaging studies in the past 10 years have added critical information. Huge contributions of basic and clinical studies have established clinical evidence supporting these markers. Based on this progress, essential therapy for cure of AD is urgently expected.

Harnessing Cerebrospinal Fluid Biomarkers in Clinical Trials for Treating Alzheimer's and Parkinson's Diseases: Potential and Challenges.

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Kim, Dana; Kim, Young Sam; Shin, Dong Wun; Park, Chang Shin; Kang, Ju Hee

2016-10-01

No disease-modifying therapies (DMT) for neurodegenerative diseases (NDs) have been established, particularly for Alzheimer's disease (AD) and Parkinson's disease (PD). It is unclear why candidate drugs that successfully demonstrate therapeutic effects in animal models fail to show disease-modifying effects in clinical trials. To overcome this hurdle, patients with homogeneous pathologies should be detected as early as possible. The early detection of AD patients using sufficiently tested biomarkers could demonstrate the potential usefulness of combining biomarkers with clinical measures as a diagnostic tool. Cerebrospinal fluid (CSF) biomarkers for NDs are being incorporated in clinical trials designed with the aim of detecting patients earlier, evaluating target engagement, collecting homogeneous patients, facilitating prevention trials, and testing the potential of surrogate markers relative to clinical measures. In this review we summarize the latest information on CSF biomarkers in NDs, particularly AD and PD, and their use in clinical trials. The large number of issues related to CSF biomarker measurements and applications has resulted in relatively few clinical trials on CSF biomarkers being conducted. However, the available CSF biomarker data obtained in clinical trials support the advantages of incorporating CSF biomarkers in clinical trials, even though the data have mostly been obtained in AD trials. We describe the current issues with and ongoing efforts for the use of CSF biomarkers in clinical trials and the plans to harness CSF biomarkers for the development of DMT and clinical routines. This effort requires nationwide, global, and multidisciplinary efforts in academia, industry, and regulatory agencies to facilitate a new era.

Biomarkers of World Trade Center Particulate Matter Exposure: Physiology of distal airway and blood biomarkers that predict FEV1 decline

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Weiden, Michael D.; Kwon, Sophia; Caraher, Erin; Berger, Kenneth I.; Reibman, Joan; Rom, William N.; Prezant, David J.; Nolan, Anna

2016-01-01

Biomarkers can be important predictors of disease severity and progression. The intense exposure to particulates and other toxins from the destruction of the World Trade Center (WTC) overwhelmed the lung’s normal protective barriers. The Fire Department of New York (FDNY) cohort not only had baseline pre-exposure lung function measures but also had serum samples banked soon after their WTC exposure. This well phenotyped group of highly exposed first responders is an ideal cohort for biomarker discovery and eventual validation. Disease progression was heterogeneous in this group in that some individuals subsequently developed abnormal lung function while others recovered. Airflow obstruction predominated in WTC exposed patients who were symptomatic. Multiple independent disease pathways may cause this abnormal FEV1 after irritant exposure. WTC exposure activates one or more of these pathways causing abnormal FEV1 in an individual. Our hypothesis was that serum biomarkers expressed within 6 months after World Trade Center (WTC) exposure reflect active disease pathways and predict subsequent development or protection from abnormal FEV1predictive biomarkers of WTC-LI. We have identified biomarkers of Inflammation, metabolic derangement, protease/antiprotease balance and vascular injury expressed in serum within 6 months of WTC exposure that were predictive of their FEV1 up to 7 years after their WTC exposure. Predicting future risk of airway injury after particulate exposures can focus monitoring and early treatment on a subset of patients in greatest need of these services. PMID:26024341

DNA Repair Biomarkers Predict Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer

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Alexander, Brian M.; Wang Xiaozhe; Niemierko, Andrzej; Weaver, David T.; Mak, Raymond H.; Roof, Kevin S.; Fidias, Panagiotis; Wain, John; Choi, Noah C.

2012-01-01

Purpose: The addition of neoadjuvant chemoradiotherapy prior to surgical resection for esophageal cancer has improved clinical outcomes in some trials. Pathologic complete response (pCR) following neoadjuvant therapy is associated with better clinical outcome in these patients, but only 22% to 40% of patients achieve pCR. Because both chemotherapy and radiotherapy act by inducing DNA damage, we analyzed proteins selected from multiple DNA repair pathways, using quantitative immunohistochemistry coupled with a digital pathology platform, as possible biomarkers of treatment response and clinical outcome. Methods and Materials: We identified 79 patients diagnosed with esophageal cancer between October 1994 and September 2002, with biopsy tissue available, who underwent neoadjuvant chemoradiotherapy prior to surgery at the Massachusetts General Hospital and used their archived, formalin-fixed, paraffin-embedded biopsy samples to create tissue microarrays (TMA). TMA sections were stained using antibodies against proteins in various DNA repair pathways including XPF, FANCD2, PAR, MLH1, PARP1, and phosphorylated MAPKAP kinase 2 (pMK2). Stained TMA slides were evaluated using machine-based image analysis, and scoring incorporated both the intensity and the quantity of positive tumor nuclei. Biomarker scores and clinical data were assessed for correlations with clinical outcome. Results: Higher scores for MLH1 (p = 0.018) and lower scores for FANCD2 (p = 0.037) were associated with pathologic response to neoadjuvant chemoradiation on multivariable analysis. Staining of MLH1, PARP1, XPF, and PAR was associated with recurrence-free survival, and staining of PARP1 and FANCD2 was associated with overall survival on multivariable analysis. Conclusions: DNA repair proteins analyzed by immunohistochemistry may be useful as predictive markers for response to neoadjuvant chemoradiotherapy in patients with esophageal cancer. These results are hypothesis generating and need

Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma

DEFF Research Database (Denmark)

Lauss, Martin; Donia, Marco; Harbst, Katja

2017-01-01

Adoptive T-cell therapy (ACT) is a highly intensive immunotherapy regime that has yielded remarkable response rates and many durable responses in clinical trials in melanoma; however, 50-60% of the patients have no clinical benefit. Here, we searched for predictive biomarkers to ACT in melanoma. ...

The Growing Need for Validated Biomarkers and Endpoints for Dry Eye Clinical Research.

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Roy, Neeta S; Wei, Yi; Kuklinski, Eric; Asbell, Penny A

2017-05-01

Biomarkers with minimally invasive and reproducible objective metrics provide the key to future paradigm shifts in understanding of the underlying causes of dry eye disease (DED) and approaches to treatment of DED. We review biomarkers and their validity in providing objective metrics for DED clinical research and patient care. The English-language literature in PubMed primarily over the last decade was surveyed for studies related to identification of biomarkers of DED: (1) inflammation, (2) point-of-care, (3) ocular imaging, and (4) genetics. Relevant studies in each group were individually evaluated for (1) methodological and analytical details, (2) data and concordance with other similar studies, and (3) potential to serve as validated biomarkers with objective metrics. Significant work has been done to identify biomarkers for DED clinical trials and for patient care. Interstudy variation among studies dealing with the same biomarker type was high. This could be attributed to biologic variations and/or differences in processing, and data analysis. Correlation with other signs and symptoms of DED was not always clear or present. Many of the biomarkers reviewed show the potential to serve as validated and objective metrics for clinical research and patient care in DED. Interstudy variation for a given biomarker emphasizes the need for detailed reporting of study methodology, including information on subject characteristics, quality control, processing, and analysis methods to optimize development of nonsubjective metrics. Biomarker development offers a rich opportunity to significantly move forward clinical research and patient care in DED. DED is an unmet medical need - a chronic pain syndrome associated with variable vision that affects quality of life, is common with advancing age, interferes with the comfortable use of contact lenses, and can diminish results of eye surgeries, such as cataract extraction, LASIK, and glaucoma procedures. It is a worldwide

Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers.

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Kerr, Kathleen F; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

2014-08-07

The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients' risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. Copyright © 2014 by the American Society of Nephrology.

Alzheimer Disease Biomarkers as Outcome Measures for Clinical Trials in MCI.

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Caroli, Anna; Prestia, Annapaola; Wade, Sara; Chen, Kewei; Ayutyanont, Napatkamon; Landau, Susan M; Madison, Cindee M; Haense, Cathleen; Herholz, Karl; Reiman, Eric M; Jagust, William J; Frisoni, Giovanni B

2015-01-01

The aim of this study was to compare the performance and power of the best-established diagnostic biological markers as outcome measures for clinical trials in patients with mild cognitive impairment (MCI). Magnetic resonance imaging, F-18 fluorodeoxyglucose positron emission tomography markers, and Alzheimer's Disease Assessment Scale-cognitive subscale were compared in terms of effect size and statistical power over different follow-up periods in 2 MCI groups, selected from Alzheimer's Disease Neuroimaging Initiative data set based on cerebrospinal fluid (abnormal cerebrospinal fluid Aβ1-42 concentration-ABETA+) or magnetic resonance imaging evidence of Alzheimer disease (positivity to hippocampal atrophy-HIPPO+). Biomarkers progression was modeled through mixed effect models. Scaled slope was chosen as measure of effect size. Biomarkers power was estimated using simulation algorithms. Seventy-four ABETA+ and 51 HIPPO+ MCI patients were included in the study. Imaging biomarkers of neurodegeneration, especially MR measurements, showed highest performance. For all biomarkers and both MCI groups, power increased with increasing follow-up time, irrespective of biomarker assessment frequency. These findings provide information about biomarker enrichment and outcome measurements that could be employed to reduce MCI patient samples and treatment duration in future clinical trials.

Blood biomarkers are helpful in the prediction of response to chemoradiation in rectal cancer: A prospective, hypothesis driven study on patients with locally advanced rectal cancer

International Nuclear Information System (INIS)

Buijsen, Jeroen; Stiphout, Ruud G. van; Menheere, Paul P.C.A.; Lammering, Guido; Lambin, Philippe

2014-01-01

Purpose/objective: Chemoradiation (CRT) has been shown to lead to downsizing of an important portion of rectal cancers. In order to tailor treatment at an earlier stage during treatment, predictive models are being developed. Adding blood biomarkers may be attractive for prediction, as they can be collected very easily and determined with excellent reproducibility in clinical practice. The hypothesis of this study was that blood biomarkers related to tumor load, hypoxia and inflammation can help to predict response to CRT in rectal cancer. Material/methods: 295 patients with locally advanced rectal cancer who were planned to undergo CRT were prospectively entered into a biobank protocol ( (NCT01067872)). Blood samples were drawn before start of CRT. Nine biomarkers were selected, based on a previously defined hypothesis, and measured in a standardized way by a certified lab: CEA, CA19-9, LDH, CRP, IL-6, IL-8, CA IX, osteopontin and 25-OH-vitamin D. Outcome was analyzed in two ways: pCR vs. non-pCR and responders (defined as ypT0-2N0) vs. non-responders (all other ypTN stages). Results: 276 patients could be analyzed. 20.7% developed a pCR and 47.1% were classified as responders. In univariate analysis CEA (p = 0.001) and osteopontin (p = 0.012) were significant predictors for pCR. Taking response as outcome CEA (p < 0.001), IL-8 (p < 0.001) and osteopontin (p = 0.004) were significant predictors. In multivariate analysis CEA was the strongest predictor for pCR (OR 0.92, p = 0.019) and CEA and IL-8 predicted for response (OR 0.97, p = 0.029 and OR 0.94, p = 0.036). The model based on biomarkers only had an AUC of 0.65 for pCR and 0.68 for response; the strongest model included clinical data, PET-data and biomarkers and had an AUC of 0.81 for pCR and 0.78 for response. Conclusion: CEA and IL-8 were identified as predictive biomarkers for tumor response and PCR after CRT in rectal cancer. Incorporation of these blood biomarkers leads to an additional accuracy of

New and emerging prognostic and predictive genetic biomarkers in B-cell precursor acute lymphoblastic leukemia

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Moorman, Anthony V.

2016-01-01

Acute lymphoblastic leukemia (ALL) is a heterogeneous disease at the genetic level. Chromosomal abnormalities are used as diagnostic, prognostic and predictive biomarkers to provide subtype, outcome and drug response information. t(12;21)/ETV6-RUNX1 and high hyper-diploidy are good-risk prognostic biomarkers whereas KMT2A (MLL) translocations, t(17;19)/TCF3-HLF, haploidy or low hypodiploidy are high-risk biomarkers. t(9;22)/BCR-ABL1 patients require targeted treatment (imatinib/dasatinib), whereas iAMP21 patients achieve better outcomes when treated intensively. High-risk genetic biomarkers are four times more prevalent in adults compared to children. The application of genomic technologies to cases without an established abnormality (B-other) reveals copy number alterations which can be used either individually or in combination as prognostic biomarkers. Transcriptome sequencing studies have identified a network of fusion genes involving kinase genes - ABL1, ABL2, PDGFRB, CSF1R, CRLF2, JAK2 and EPOR. In vitro and in vivo studies along with emerging clinical observations indicate that patients with a kinase-activating aberration may respond to treatment with small molecular inhibitors like imatinib/dasatinib and ruxolitinib. Further work is required to determine the true frequency of these abnormalities across the age spectrum and the optimal way to incorporate such inhibitors into protocols. In conclusion, genetic biomarkers are playing an increasingly important role in the management of patients with ALL. PMID:27033238

Predictive biomarkers for type 2 of diabetes mellitus: Bridging the gap between systems research and personalized medicine.

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Kraniotou, Christina; Karadima, Vasiliki; Bellos, George; Tsangaris, George Th

2018-03-05

The global incidence of metabolic disorders like type 2 diabetes mellitus (DM2) has assumed epidemic proportions, leading to adverse health and socio-economic impacts. It is therefore of critical importance the early diagnosis of DM2 patients and the detection of those at increased risk of disease. In this respect, Precision Medicine (PM) is an emerging approach that includes practices, tests, decisions and treatments adapted to the characteristics of each patient. With regard to DM2, PM manages a wealth of "omics" data (genomic, metabolic, proteomic, environmental, clinical and paraclinical) to increase the number of clinically validated biomarkers in order to identify patients in early stage even before the prediabetic phase. In this paper, we discuss the epidemic dimension of metabolic disorders like type 2 diabetes mellitus (DM2) and the urgent demand for novel biomarkers to reduce the incidence or even delay the onset of DM2. Recent research data produced by "multi-omics" technologies (genomics/epigenomics, transcriptomics, proteomics and metabolomics), suggest that many potential biomarkers might be helpful in the prediction and early diagnosis of DM2. Predictive, Preventive and Personalized Medicine (PPPM) manages and integrates these data to apply personalized, preventive, and therapeutic approaches. This is significant because there is an emerging need for establishing channels for communication and personalized consultation between systems research and precision medicine, as the medicine of the future. Copyright © 2018 Elsevier B.V. All rights reserved.

The potential of predictive analytics to provide clinical decision support in depression treatment planning.

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Kessler, Ronald C

2018-01-01

To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD). Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem. Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

A Contemporary Review of the Treatment Landscape and the Role of Predictive and Prognostic Biomarkers in Pancreatic Adenocarcinoma

Directory of Open Access Journals (Sweden)

Irene S. Yu

2018-01-01

Full Text Available Pancreatic cancer continues to represent one of the leading causes of cancer-related morbidity and mortality in the developed world. Over the past decade, novel systemic therapy combination regimens have contributed to clinically meaningful and statistically significant improvements in overall survival as compared to conventional monotherapy. However, the prognosis for most patients remains guarded secondary to the advanced stages of disease at presentation. There is growing consensus that outcomes can be further optimized with the use of predictive and prognostic biomarkers whereby the former can be enriching for patients who would benefit from therapies and the latter can inform decision-making regarding the need and timing of advanced care planning. One of the challenges of current biomarkers is the lack of standardization across clinical practices such that comparability between jurisdictions can be difficult or even impossible. This inconsistency can impede widespread implementation of their use. In this review article, we provide a comprehensive overview of the contemporary treatment options for pancreatic cancer and we offer some insights into the existing landscape and future directions of biomarker development for this disease.

NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.

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Zou, Meng; Liu, Zhaoqi; Zhang, Xiang-Sun; Wang, Yong

2015-10-15

In prognosis and survival studies, an important goal is to identify multi-biomarker panels with predictive power using molecular characteristics or clinical observations. Such analysis is often challenged by censored, small-sample-size, but high-dimensional genomic profiles or clinical data. Therefore, sophisticated models and algorithms are in pressing need. In this study, we propose a novel Area Under Curve (AUC) optimization method for multi-biomarker panel identification named Nearest Centroid Classifier for AUC optimization (NCC-AUC). Our method is motived by the connection between AUC score for classification accuracy evaluation and Harrell's concordance index in survival analysis. This connection allows us to convert the survival time regression problem to a binary classification problem. Then an optimization model is formulated to directly maximize AUC and meanwhile minimize the number of selected features to construct a predictor in the nearest centroid classifier framework. NCC-AUC shows its great performance by validating both in genomic data of breast cancer and clinical data of stage IB Non-Small-Cell Lung Cancer (NSCLC). For the genomic data, NCC-AUC outperforms Support Vector Machine (SVM) and Support Vector Machine-based Recursive Feature Elimination (SVM-RFE) in classification accuracy. It tends to select a multi-biomarker panel with low average redundancy and enriched biological meanings. Also NCC-AUC is more significant in separation of low and high risk cohorts than widely used Cox model (Cox proportional-hazards regression model) and L1-Cox model (L1 penalized in Cox model). These performance gains of NCC-AUC are quite robust across 5 subtypes of breast cancer. Further in an independent clinical data, NCC-AUC outperforms SVM and SVM-RFE in predictive accuracy and is consistently better than Cox model and L1-Cox model in grouping patients into high and low risk categories. In summary, NCC-AUC provides a rigorous optimization framework to

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

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Phan, John H.; Moffitt, Richard A.; Stokes, Todd H.; Liu, Jian; Young, Andrew N.; Nie, Shuming; Wang, May D.

2013-01-01

Recent advances in biomarker discovery, biocomputing, and nanotechnology have raised new opportunities for the emerging field of personalized medicine in which disease detection, diagnosis, and therapy are tailored to each individual’s molecular profile, and also for predictive medicine that uses genetic/molecular information to predict disease development, progression, and clinical outcome. Here we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, together with prospects and challenges in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases. PMID:19409634

Clinical Relevance of Prognostic and Predictive Molecular Markers in Gliomas.

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Siegal, Tali

2016-01-01

Sorting and grading of glial tumors by the WHO classification provide clinicians with guidance as to the predicted course of the disease and choice of treatment. Nonetheless, histologically identical tumors may have very different outcome and response to treatment. Molecular markers that carry both diagnostic and prognostic information add useful tools to traditional classification by redefining tumor subtypes within each WHO category. Therefore, molecular markers have become an integral part of tumor assessment in modern neuro-oncology and biomarker status now guides clinical decisions in some subtypes of gliomas. The routine assessment of IDH status improves histological diagnostic accuracy by differentiating diffuse glioma from reactive gliosis. It carries a favorable prognostic implication for all glial tumors and it is predictive for chemotherapeutic response in anaplastic oligodendrogliomas with codeletion of 1p/19q chromosomes. Glial tumors that contain chromosomal codeletion of 1p/19q are defined as tumors of oligodendroglial lineage and have favorable prognosis. MGMT promoter methylation is a favorable prognostic marker in astrocytic high-grade gliomas and it is predictive for chemotherapeutic response in anaplastic gliomas with wild-type IDH1/2 and in glioblastoma of the elderly. The clinical implication of other molecular markers of gliomas like mutations of EGFR and ATRX genes and BRAF fusion or point mutation is highlighted. The potential of molecular biomarker-based classification to guide future therapeutic approach is discussed and accentuated.

Predictive values of semi-quantitative procalcitonin test and common biomarkers for the clinical outcomes of community-acquired pneumonia.

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Ugajin, Motoi; Yamaki, Kenichi; Hirasawa, Natsuko; Yagi, Takeo

2014-04-01

The semi-quantitative serum procalcitonin test (Brahms PCT-Q) is available conveniently in clinical practice. However, there are few data on the relationship between results for this semi-quantitative procalcitonin test and clinical outcomes of community-acquired pneumonia (CAP). We investigated the usefulness of this procalcitonin test for predicting the clinical outcomes of CAP in comparison with severity scoring systems and the blood urea nitrogen/serum albumin (B/A) ratio, which has been reported to be a simple but reliable prognostic indicator in our prior CAP study. This retrospective study included data from subjects who were hospitalized for CAP from August 2010 through October 2012 and who were administered the semi-quantitative serum procalcitonin test on admission. The demographic characteristics; laboratory biomarkers; microbiological test results; Pneumonia Severity Index scores; confusion, urea nitrogen, breathing frequency, blood pressure, ≥ 65 years of age (CURB-65) scale scores; and age, dehydration, respiratory failure, orientation disturbance, pressure (A-DROP) scale scores on hospital admission were retrieved from their medical charts. The outcomes were mortality within 28 days of hospital admission and the need for intensive care. Of the 213 subjects with CAP who were enrolled in the study, 20 died within 28 days of hospital admission, and 32 required intensive care. Mortality did not differ significantly among subjects with different semi-quantitative serum procalcitonin levels; however, subjects with serum procalcitonin levels ≥ 10.0 ng/mL were more likely to require intensive care than those with lower levels (P pneumonia. Using the receiver operating characteristic curves for mortality, the area under the curve was 0.86 for Pneumonia Severity Index class, 0.81 for B/A ratio, 0.81 for A-DROP, 0.80 for CURB-65, and 0.57 for semi-quantitative procalcitonin test. The semi-quantitative serum procalcitonin level on hospital admission was less

Current Role for Biomarkers in Clinical Diagnosis of Alzheimer Disease and Frontotemporal Dementia.

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Sheikh-Bahaei, Nasim; Sajjadi, Seyed Ahmad; Pierce, Aimee L

2017-11-14

Purpose of review Alzheimer's disease (AD) and frontotemporal dementia can often be diagnosed accurately with careful clinical history, cognitive testing, neurological examination, and structural brain MRI. However, there are certain circumstances wherein detection of specific biomarkers of neurodegeneration or underlying AD pathology will impact the clinical diagnosis or treatment plan. We will review the currently available biomarkers for AD and frontotemporal dementia (FTD) and discuss their clinical importance. Recent findings With the advent of 18 F-labeled tracers that bind amyloid plaques, amyloid PET is now clinically available for the detection of amyloid pathology and to aid in a biomarker-supported diagnosis of AD or mild cognitive impairment (MCI) due to AD. It is not yet possible to test for the specific FTD pathologies (tau or TDP-43); however, a diagnosis of FTD may be "imaging supported" based upon specific MRI or FDG-PET findings. Cerebrospinal fluid measures of amyloid-beta, total-tau, and phospho-tau are clinically available and allow detection of both of the cardinal pathologies of AD: amyloid and tau pathology. Summary It is appropriate to pursue biomarker testing in cases of MCI and dementia when there remains diagnostic uncertainty and the result will impact diagnosis or treatment. Practically speaking, due to the rising prevalence of amyloid positivity with advancing age, measurement of biomarkers in cases of MCI and dementia is most helpful in early-onset patients, patients with atypical clinical presentations, or when considering referral for AD clinical trials.

Reinventing clinical trials: a review of innovative biomarker trial designs in cancer therapies.

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Lin, Ja-An; He, Pei

2015-06-01

Recently, new clinical trial designs involving biomarkers have been studied and proposed in cancer clinical research, in the hope of incorporating the rapid growing basic research into clinical practices. Journal articles related to various biomarkers and their role in cancer clinical trial, articles and books about statistical issues in trial design, and regulatory website, documents, and guidance for submission of targeted cancer therapies. The drug development process involves four phases. The confirmatory Phase III is essential in regulatory approval of a special treatment. Regulatory agency has restrictions on confirmatory trials 'using adaptive designs'. No rule of thumb to pick the most appropriate design for biomarker-related trials. Statistical issues to solve in new designs. Regulatory acceptance of the 'newly proposed trial designs'. Biomarker-related trial designs that can resolve the statistical issues and satisfy the regulatory requirement. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Enhancing the 'real world' prediction of cardiovascular events and major bleeding with the CHA2DS2-VASc and HAS-BLED scores using multiple biomarkers.

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Roldán, Vanessa; Rivera-Caravaca, José Miguel; Shantsila, Alena; García-Fernández, Amaya; Esteve-Pastor, María Asunción; Vilchez, Juan Antonio; Romera, Marta; Valdés, Mariano; Vicente, Vicente; Marín, Francisco; Lip, Gregory Y H

2018-02-01

Atrial fibrillation (AF)-European guidelines suggest the use of biomarkers to stratify patients for stroke and bleeding risks. We investigated if a multibiomarker strategy improved the predictive performance of CHA 2 DS 2 -VASc and HAS-BLED in anticoagulated AF patients. We included consecutive patients stabilized for six months on vitamin K antagonists (INRs 2.0-3.0). High sensitivity troponin T, NT-proBNP, interleukin-6, von Willebrand factor concentrations and glomerular filtration rate (eGFR; using MDRD-4 formula) were quantified at baseline. Time in therapeutic range (TTR) was recorded at six months after inclusion. Patients were follow-up during a median of 2375 (IQR 1564-2887) days and all adverse events were recorded. In 1361 patients, adding four blood biomarkers, TTR and MDRD-eGFR, the predictive value of CHA 2 DS 2 -VASc increased significantly by c-index (0.63 vs. 0.65; p = .030) and IDI (0.85%; p originals scores. Addition of biomarkers enhanced the predictive value of CHA 2 DS 2 -VASc and HAS-BLED, although the overall improvement was modest and the added predictive advantage over original scores was marginal. Key Messages Recent atrial fibrillation (AF)-European guidelines for the first time suggest the use of biomarkers to stratify patients for stroke and bleeding risks, but their usefulness in real world for risk stratification is still questionable. In this cohort study involving 1361 AF patients optimally anticoagulated with vitamin K antagonists, adding high sensitivity troponin T, N-terminal pro-B-type natriuretic peptide, interleukin 6, von Willebrand factor, glomerular filtration rate (by the MDRD-4 formula) and time in therapeutic range, increased the predictive value of CHA 2 DS 2 -VASc for cardiovascular events, but not the predictive value of HAS-BLED for major bleeding. Reclassification analyses did not show improvement adding multiple biomarkers. Despite the improvement observed, the added predictive advantage is marginal and

Identification of potential prognostic microRNA biomarkers for predicting survival in patients with hepatocellular carcinoma

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Liao X

2018-04-01

Full Text Available Xiwen Liao,1 Guangzhi Zhu,1 Rui Huang,2 Chengkun Yang,1 Xiangkun Wang,1 Ketuan Huang,1 Tingdong Yu,1 Chuangye Han,1 Hao Su,1 Tao Peng1 1Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China Background: The aim of the present study was to identify potential prognostic microRNA (miRNA biomarkers for hepatocellular carcinoma (HCC prognosis prediction based on a dataset from The Cancer Genome Atlas (TCGA. Materials and methods: A miRNA sequencing dataset and corresponding clinical parameters of HCC were obtained from TCGA. Genome-wide univariate Cox regression analysis was used to screen prognostic differentially expressed miRNAs (DEMs, and multivariable Cox regression analysis was used for prognostic signature construction. Comprehensive survival analysis was performed to evaluate the prognostic value of the prognostic signature. Results: Five miRNAs were regarded as prognostic DEMs and used for prognostic signature construction. The five-DEM prognostic signature performed well in prognosis prediction (adjusted P < 0.0001, adjusted hazard ratio = 2.249, 95% confidence interval =1.491–3.394, and time-dependent receiver–operating characteristic (ROC analysis showed an area under the curve (AUC of 0.765, 0.745, 0.725, and 0.687 for 1-, 2-, 3-, and 5-year HCC overall survival (OS prediction, respectively. Comprehensive survival analysis of the prognostic signature suggests that the risk score model could serve as an independent factor of HCC and perform better in prognosis prediction than other traditional clinical indicators. Functional assessment of the target genes of hsa-mir-139 and hsa-mir-5003 indicates that they were significantly enriched in multiple biological processes and pathways, including cell proliferation and cell migration

Clinical Significance of Cerebrovascular Biomarkers and White Matter Tract Integrity in Alzheimer Disease: Clinical correlations With Neurobehavioral Data in Cross-Sectional and After 18 Months Follow-ups.

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Wu, Ming-Kung; Lu, Yan-Ting; Huang, Chi-Wei; Lin, Pin-Hsuan; Chen, Nai-Ching; Lui, Chun-Chung; Chang, Wen-Neng; Lee, Chen-Chang; Chang, Ya-Ting; Chen, Sz-Fan; Chang, Chiung-Chih

2015-07-01

Cerebrovascular risk factors and white matter (WM) damage lead to worse cognitive performance in Alzheimer dementia (AD). This study investigated WM microstructure using diffusion tensor imaging in patients with mild to moderate AD and investigated specific fiber tract involvement with respect to predefined cerebrovascular risk factors and neurobehavioral data prediction cross-sectionally and after 18 months. To identify the primary pathoanatomic relationships of risk biomarkers to fiber tract integrity, we predefined 11 major association tracts and calculated tract specific fractional anisotropy (FA) values. Eighty-five patients with AD underwent neurobehavioral assessments including the minimental state examination (MMSE) and 12-item neuropsychiatric inventory twice with a 1.5-year interval to represent major outcome factors. In the cross-sectional data, total cholesterol, low-density lipoprotein, vitamin B12, and homocysteine levels correlated variably with WM FA values. After entering the biomarkers and WM FA into a regression model to predict neurobehavioral outcomes, only fiber tract FA or homocysteine level predicted the MMSE score, and fiber tract FA or age predicted the neuropsychiatric inventory total scores and subdomains of apathy, disinhibition, and aberrant motor behavior. In the follow-up neurobehavioral data, the mean global FA value predicted the MMSE and aberrant motor behavior subdomain, while age predicted the anxiety and elation subdomains. Cerebrovascular risk biomarkers may modify WM microstructural organization, while the association with fiber integrity showed greater clinical significance to the prediction of neurobehavioral outcomes both cross-sectionally and longitudinally.

Molecular Biomarkers in the Clinical Management of Prostate Cancer.

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Udager, Aaron M; Tomlins, Scott A

2018-01-08

Prostate cancer, one of the most common noncutaneous malignancies in men, is a heterogeneous disease with variable clinical outcome. Although the majority of patients harbor indolent tumors that are essentially cured by local therapy, subsets of patients present with aggressive disease or recur/progress after primary treatment. With this in mind, modern clinical approaches to prostate cancer emphasize the need to reduce overdiagnosis and overtreatment via personalized medicine. Advances in our understanding of prostate cancer pathogenesis, coupled with recent technologic innovations, have facilitated the development and validation of numerous molecular biomarkers, representing a range of macromolecules assayed from a variety of patient sample types, to help guide the clinical management of prostate cancer, including early detection, diagnosis, prognostication, and targeted therapeutic selection. Herein, we review the current state of the art regarding prostate cancer molecular biomarkers, emphasizing those with demonstrated utility in clinical practice. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

Automated interpretable computational biology in the clinic: a framework to predict disease severity and stratify patients from clinical data

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Soumya Banerjee

2017-10-01

Full Text Available We outline an automated computational and machine learning framework that predicts disease severity and stratifies patients. We apply our framework to available clinical data. Our algorithm automatically generates insights and predicts disease severity with minimal operator intervention. The computational framework presented here can be used to stratify patients, predict disease severity and propose novel biomarkers for disease. Insights from machine learning algorithms coupled with clinical data may help guide therapy, personalize treatment and help clinicians understand the change in disease over time. Computational techniques like these can be used in translational medicine in close collaboration with clinicians and healthcare providers. Our models are also interpretable, allowing clinicians with minimal machine learning experience to engage in model building. This work is a step towards automated machine learning in the clinic.

Identification of airway mucosal type 2 inflammation by using clinical biomarkers in asthmatic patients.

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Silkoff, Philip E; Laviolette, Michel; Singh, Dave; FitzGerald, J Mark; Kelsen, Steven; Backer, Vibeke; Porsbjerg, Celeste M; Girodet, Pierre-Olivier; Berger, Patrick; Kline, Joel N; Chupp, Geoffrey; Susulic, Vedrana S; Barnathan, Elliot S; Baribaud, Frédéric; Loza, Matthew J

2017-09-01

The Airways Disease Endotyping for Personalized Therapeutics (ADEPT) study profiled patients with mild, moderate, and severe asthma and nonatopic healthy control subjects. We explored this data set to define type 2 inflammation based on airway mucosal IL-13-driven gene expression and how this related to clinically accessible biomarkers. IL-13-driven gene expression was evaluated in several human cell lines. We then defined type 2 status in 25 healthy subjects, 28 patients with mild asthma, 29 patients with moderate asthma, and 26 patients with severe asthma based on airway mucosal expression of (1) CCL26 (the most differentially expressed gene), (2) periostin, or (3) a multigene IL-13 in vitro signature (IVS). Clinically accessible biomarkers included fraction of exhaled nitric oxide (Feno) values, blood eosinophil (bEOS) counts, serum CCL26 expression, and serum CCL17 expression. Expression of airway mucosal CCL26, periostin, and IL-13-IVS all facilitated segregation of subjects into type 2-high and type 2-low asthmatic groups, but in the ADEPT study population CCL26 expression was optimal. All subjects with high airway mucosal CCL26 expression and moderate-to-severe asthma had Feno values (≥35 ppb) and/or high bEOS counts (≥300 cells/mm 3 ) compared with a minority (36%) of subjects with low airway mucosal CCL26 expression. A combination of Feno values, bEOS counts, and serum CCL17 and CCL26 expression had 100% positive predictive value and 87% negative predictive value for airway mucosal CCL26-high status. Clinical variables did not differ between subjects with type 2-high and type 2-low status. Eosinophilic inflammation was associated with but not limited to airway mucosal type 2 gene expression. A panel of clinical biomarkers accurately classified type 2 status based on airway mucosal CCL26, periostin, or IL-13-IVS gene expression. Use of Feno values, bEOS counts, and serum marker levels (eg, CCL26 and CCL17) in combination might allow patient

Biomarkers in differentiating clinical dengue cases: A prospective cohort study

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Gary Kim Kuan Low

2015-12-01

Full Text Available Objective: To evaluate five biomarkers (neopterin, vascular endothelial growth factor-A, thrombomodulin, soluble vascular cell adhesion molecule 1 and pentraxin 3 in differentiating clinical dengue cases. Methods: A prospective cohort study was conducted whereby the blood samples were obtained at day of presentation and the final diagnosis were obtained at the end of patients’ follow-up. All patients included in the study were 15 years old or older, not pregnant, not infected by dengue previously and did not have cancer, autoimmune or haematological disorder. Median test was performed to compare the biomarker levels. A subgroup Mann-Whitney U test was analysed between severe dengue and non-severe dengue cases. Monte Carlo method was used to estimate the 2-tailed probability (P value for independent variables with unequal number of patients. Results: All biomarkers except thrombomodulin has P value < 0.001 in differentiating among the healthy subjects, non-dengue fever, dengue without warning signs and dengue with warning signs/severe dengue. Subgroup analysis for all the biomarkers between severe dengue and non-severe dengue cases was not statistically significant except vascular endothelial growth factor-A (P < 0.05. Conclusions: Certain biomarkers were able to differentiate the clinical dengue cases. This could be potentially useful in classifying and determining the severity of dengue infected patients in the hospital.

Biomarkers of adverse drug reactions.

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Carr, Daniel F; Pirmohamed, Munir

2018-02-01

Adverse drug reactions can be caused by a wide range of therapeutics. Adverse drug reactions affect many bodily organ systems and vary widely in severity. Milder adverse drug reactions often resolve quickly following withdrawal of the casual drug or sometimes after dose reduction. Some adverse drug reactions are severe and lead to significant organ/tissue injury which can be fatal. Adverse drug reactions also represent a financial burden to both healthcare providers and the pharmaceutical industry. Thus, a number of stakeholders would benefit from development of new, robust biomarkers for the prediction, diagnosis, and prognostication of adverse drug reactions. There has been significant recent progress in identifying predictive genomic biomarkers with the potential to be used in clinical settings to reduce the burden of adverse drug reactions. These have included biomarkers that can be used to alter drug dose (for example, Thiopurine methyltransferase (TPMT) and azathioprine dose) and drug choice. The latter have in particular included human leukocyte antigen (HLA) biomarkers which identify susceptibility to immune-mediated injuries to major organs such as skin, liver, and bone marrow from a variety of drugs. This review covers both the current state of the art with regard to genomic adverse drug reaction biomarkers. We also review circulating biomarkers that have the potential to be used for both diagnosis and prognosis, and have the added advantage of providing mechanistic information. In the future, we will not be relying on single biomarkers (genomic/non-genomic), but on multiple biomarker panels, integrated through the application of different omics technologies, which will provide information on predisposition, early diagnosis, prognosis, and mechanisms. Impact statement • Genetic and circulating biomarkers present significant opportunities to personalize patient therapy to minimize the risk of adverse drug reactions. ADRs are a significant heath issue

Biomarkers of acute lung injury: worth their salt?

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Proudfoot Alastair G

2011-12-01

Full Text Available Abstract The validation of biomarkers has become a key goal of translational biomedical research. The purpose of this article is to discuss the role of biomarkers in the management of acute lung injury (ALI and related research. Biomarkers should be sensitive and specific indicators of clinically important processes and should change in a relevant timeframe to affect recruitment to trials or clinical management. We do not believe that they necessarily need to reflect pathogenic processes. We critically examined current strategies used to identify biomarkers and which, owing to expedience, have been dominated by reanalysis of blood derived markers from large multicenter Phase 3 studies. Combining new and existing validated biomarkers with physiological and other data may add predictive power and facilitate the development of important aids to research and therapy.

Urinary aminopeptidase activities as early and predictive biomarkers of renal dysfunction in cisplatin-treated rats.

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Andrés Quesada

Full Text Available This study analyzes the fluorimetric determination of alanyl- (Ala, glutamyl- (Glu, leucyl-cystinyl- (Cys and aspartyl-aminopeptidase (AspAp urinary enzymatic activities as early and predictive biomarkers of renal dysfunction in cisplatin-treated rats. Male Wistar rats (n = 8 each group received a single subcutaneous injection of either saline or cisplatin 3.5 or 7 mg/kg, and urine samples were taken at 0, 1, 2, 3 and 14 days after treatment. In urine samples we determined Ala, Glu, Cys and AspAp activities, proteinuria, N-acetyl-β-D-glucosaminidase (NAG, albumin, and neutrophil gelatinase-associated lipocalin (NGAL. Plasma creatinine, creatinine clearance and renal morphological variables were measured at the end of the experiment. CysAp, NAG and albumin were increased 48 hours after treatment in the cisplatin 3.5 mg/kg treated group. At 24 hours, all urinary aminopeptidase activities and albuminuria were significantly increased in the cisplatin 7 mg/kg treated group. Aminopeptidase urinary activities correlated (p0.259 with plasma creatinine, creatinine clearance and/or kidney weight/body weight ratio at the end of the experiment and they could be considered as predictive biomarkers of renal injury severity. ROC-AUC analysis was made to study their sensitivity and specificity to distinguish between treated and untreated rats at day 1. All aminopeptidase activities showed an AUC>0.633. We conclude that Ala, Cys, Glu and AspAp enzymatic activities are early and predictive urinary biomarkers of the renal dysfunction induced by cisplatin. These determinations can be very useful in the prognostic and diagnostic of renal dysfunction in preclinical research and clinical practice.

Biomarkers of evasive resistance predict disease progression in cancer patients treated with antiangiogenic therapies

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Pircher, Andreas; Jöhrer, Karin; Kocher, Florian; Steiner, Normann; Graziadei, Ivo; Heidegger, Isabel; Pichler, Renate; Leonhartsberger, Nicolai; Kremser, Christian; Kern, Johann; Untergasser, Gerold; Gunsilius, Eberhard; Hilbe, Wolfgang

2016-01-01

Numerous antiangiogenic agents are approved for the treatment of oncological diseases. However, almost all patients develop evasive resistance mechanisms against antiangiogenic therapies. Currently no predictive biomarker for therapy resistance or response has been established. Therefore, the aim of our study was to identify biomarkers predicting the development of therapy resistance in patients with hepatocellular cancer (n = 11), renal cell cancer (n = 7) and non-small cell lung cancer (n = 2). Thereby we measured levels of angiogenic growth factors, tumor perfusion, circulating endothelial cells (CEC), circulating endothelial progenitor cells (CEP) and tumor endothelial markers (TEM) in patients during the course of therapy with antiangiogenic agents, and correlated them with the time to antiangiogenic progression (aTTP). Importantly, at disease progression, we observed an increase of proangiogenic factors, upregulation of CEC/CEP levels and downregulation of TEMs, such as Robo4 and endothelial cell-specific chemotaxis regulator (ECSCR), reflecting the formation of torturous tumor vessels. Increased TEM expression levels tended to correlate with prolonged aTTP (ECSCR high = 275 days vs. ECSCR low = 92.5 days; p = 0.07 and for Robo4 high = 387 days vs. Robo4 low = 90.0 days; p = 0.08). This indicates that loss of vascular stabilization factors aggravates the development of antiangiogenic resistance. Thus, our observations confirm that CEP/CEC populations, proangiogenic cytokines and TEMs contribute to evasive resistance in antiangiogenic treated patients. Higher TEM expression during disease progression may have clinical and pathophysiological implications, however, validation of our results is warranted for further biomarker development. PMID:26956051

Alzheimer biomarkers and clinical Alzheimer disease were not associated with increased cerebrovascular disease in a memory clinic population.

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Spies, Petra E; Verbeek, Marcel M; Sjogren, Magnus J C; de Leeuw, Frank-Erik; Claassen, Jurgen A H R

2014-01-01

Preclinical and post-mortem studies suggest that Alzheimer disease (AD) causes cerebrovascular dysfunction, and therefore may enhance susceptibility to cerebrovascular disease (CVD). The objective of this study was to investigate this association in a memory clinic population. The AD biomarkers CSF amyloid β42, amyloid β40 and APOE-ε4 status have all been linked to increased CVD risk in AD, and therefore the first aim of this study was to analyze the association between these biomarkers and CVD. In 92 memory clinic patients the cross-sectional association between AD biomarkersand the severity of CVD was investigated with linear regression analysis. Additionally, we studied whether AD biomarkers modified the relation between vascular risk factors and CVD. CVD was assessed on MRI through a visual rating scale.Analyses were adjusted for age. The second aim of this study was to investigate the association between clinical AD and CVD, where 'clinical AD' was defined as follows: impairment in episodic memory, hippocampal atrophy and an aberrant concentration of cerebrospinal fluid (CSF) biomarkers. 47 of the 92 patients had AD. No association between CSF amyloid β42, amyloid β40 or APOE-ε4 status and CVD severity was found, nor did these AD biomarkers modify the relation between vascular risk factors and CVD. Clinical AD was not associated with CVD severity (p=0.83). Patients with more vascular risk factors had more CVD, but this relationship was not convincingly modified by AD (p=0.06). In this memory clinic population, CVD in patients with AD was related to vascular risk factors and age, comparable to patients without AD. Therefore, in our study, the preclinical and post-mortem evidence that AD would predispose to CVD could not be translated clinically. Further work, including replication of this work in a different and larger sample, is warranted.

UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry.

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Zhao, Ying-Yong; Cheng, Xian-Long; Vaziri, Nosratola D; Liu, Shuman; Lin, Rui-Chao

2014-10-01

Metabonomics is a powerful and promising analytic tool that allows assessment of global low-molecular-weight metabolites in biological systems. It has a great potential for identifying useful biomarkers for early diagnosis, prognosis and assessment of therapeutic interventions in clinical practice. The aim of this review is to provide a brief summary of the recent advances in UPLC-based metabonomic approach for biomarker discovery in a variety of diseases, and to discuss their significance in clinical chemistry. All the available information on UPLC-based metabonomic applications for discovering biomarkers of diseases were collected via a library and electronic search (using Web of Science, Pubmed, ScienceDirect, Springer, Google Scholar, etc.). Metabonomics has been used in clinical chemistry to identify and evaluate potential biomarkers and therapeutic targets in various diseases affecting the liver (hepatocarcinoma and liver cirrhosis), lung (lung cancer and pneumonia), gastrointestinal tract (colorectal cancer) and urogenital tract (prostate cancer, ovarian cancer and chronic kidney disease), as well as metabolic diseases (diabetes) and neuropsychiatric disorders (Alzheimer's disease and schizophrenia), etc. The information provided highlights the potential value of determination of endogenous low-molecular-weight metabolites and the advantages and potential drawbacks of the application of UPLC-based metabonomics in clinical setting. Copyright © 2014 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Biomarkers and insulin sensitivity in women with Polycystic Ovary Syndrome: Characteristics and predictive capacity.

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Cassar, Samantha; Teede, Helena J; Harrison, Cheryce L; Joham, Anju E; Moran, Lisa J; Stepto, Nigel K

2015-07-01

Polycystic ovary syndrome (PCOS) is a common endocrine disorder associated with metabolic complications. Metabolic biomarkers with roles in obesity, glycaemic control and lipid metabolism are potentially relevant in PCOS. The aim was to investigate metabolic biomarkers in lean and overweight women with and without PCOS and to determine whether any biomarker was able to predict insulin resistance in PCOS. Cross-sectional study. Eighty-four women (22 overweight and 22 lean women with PCOS, 18 overweight and 22 lean women without PCOS) were recruited from the community and categorized based on PCOS and BMI status. Primary outcomes were metabolic biomarkers [ghrelin, resistin, visfatin, glucagon-like peptide-1 (GLP-1), leptin, plasminogen activator inhibitor -1 (PAI-1), glucose-dependent insulinotropic polypeptide (GIP) and C-Peptide] measured using the Bio-Plex Pro Diabetes assay and insulin sensitivity as assessed by glucose infusion rate on euglycaemic-hyperinsulinaemic clamp. The biomarkers C-peptide, leptin, ghrelin and visfatin were different between overweight and lean women, irrespective of PCOS status. The concentration of circulating biomarkers did not differ between women with PCOS diagnosed by the Rotterdam criteria or National Institute of Health criteria. PAI-1 was the only biomarker that significantly predicted insulin resistance in both control women (P = 0.04) and women with PCOS (P = 0.01). Biomarkers associated with metabolic diseases appear more strongly associated with obesity rather than PCOS status. PAI-1 may also be a novel independent biomarker and predictor of insulin resistance in women with and without PCOS. © 2014 John Wiley & Sons Ltd.

Biomarker assessment and molecular testing for prognostication in breast cancer.

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Kos, Zuzana; Dabbs, David J

2016-01-01

Current treatment of breast cancer incorporates clinical, pathological and molecular data. Oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) define prognosis and identify tumours for targeted therapy, and remain the sole established single-molecule biomarkers defining the minimum breast cancer pathology data set. Ki67 remains one of the most promising yet controversial biomarkers in breast cancer, implemented routinely in some, but not all, pathology departments. Beyond the single-molecule biomarkers, a host of multigene expression tests have been developed to interrogate the driver pathways and biology of individual breast cancers to predict clinical outcome more accurately. A minority of these assays have entered into clinical practice. This review focuses on the established biomarkers of ER, PR and HER2, the controversial but clinically implemented biomarker Ki67 and the currently marketed gene expression signatures. © 2015 John Wiley & Sons Ltd.

Variability of CSF Alzheimer's disease biomarkers: implications for clinical practice.

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Stephanie J B Vos

Full Text Available BACKGROUND: Cerebrospinal fluid (CSF biomarkers are increasingly being used for diagnosis of Alzheimer's disease (AD. OBJECTIVE: We investigated the influence of CSF intralaboratory and interlaboratory variability on diagnostic CSF-based AD classification of subjects and identified causes of this variation. METHODS: We measured CSF amyloid-β (Aβ 1-42, total tau (t-tau, and phosphorylated tau (p-tau by INNOTEST enzyme-linked-immunosorbent assays (ELISA in a memory clinic population (n = 126. Samples were measured twice in a single or two laboratories that served as reference labs for CSF analyses in the Netherlands. Predefined cut-offs were used to classify CSF biomarkers as normal or abnormal/AD pattern. RESULTS: CSF intralaboratory variability was higher for Aβ1-42 than for t-tau and p-tau. Reanalysis led to a change in biomarker classification (normal vs. abnormal of 26% of the subjects based on Aβ1-42, 10% based on t-tau, and 29% based on p-tau. The changes in absolute biomarker concentrations were paralleled by a similar change in levels of internal control samples between different assay lots. CSF interlaboratory variability was higher for p-tau than for Aβ1-42 and t-tau, and reanalysis led to a change in biomarker classification of 12% of the subjects based on Aβ1-42, 1% based on t-tau, and 22% based on p-tau. CONCLUSIONS: Intralaboratory and interlaboratory CSF variability frequently led to change in diagnostic CSF-based AD classification for Aβ1-42 and p-tau. Lot-to-lot variation was a major cause of intralaboratory variability. This will have implications for the use of these biomarkers in clinical practice.

Recommendations for cerebrospinal fluid Alzheimer's disease biomarkers in the diagnostic evaluation of mild cognitive impairment

DEFF Research Database (Denmark)

Herukka, Sanna-Kaisa; Simonsen, Anja Hviid; Andreasen, Niels

2017-01-01

) patient counseling. The working group recommended using CSF AD biomarkers in the diagnostic workup of MCI patients, after prebiomarker counseling, as an add-on to clinical evaluation to predict functional decline or conversion to AD dementia and to guide disease management. Because of insufficient...... evidence, it was uncertain whether CSF AD biomarkers outperform imaging biomarkers. Furthermore, the working group provided recommendations for interpretation of ambiguous CSF biomarker results and for pre- and post-biomarker counseling....... impairment (MCI). The recommendations were developed by a multidisciplinary working group and based on the available evidence and consensus from focused group discussions for 1) prediction of clinical progression to Alzheimer's disease (AD) dementia, 2) cost-effectiveness, 3) interpretation of results, and 4...

A prospective phase II trial exploring the association between tumor microenvironment biomarkers and clinical activity of ipilimumab in advanced melanoma

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Hamid Omid

2011-11-01

ipilimumab clinical activity. The observed pharmacodynamic changes in gene expression warrant further analysis to determine whether treatment-emergent changes in gene expression may be associated with clinical efficacy. Further studies are required to determine the predictive value of these and other potential biomarkers associated with clinical response to ipilimumab.

Predictive Biomarkers of Gastroesophageal Reflux Disease and Barrett's Esophagus in World Trade Center Exposed Firefighters: a 15 Year Longitudinal Study.

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Haider, Syed H; Kwon, Sophia; Lam, Rachel; Lee, Audrey K; Caraher, Erin J; Crowley, George; Zhang, Liqun; Schwartz, Theresa M; Zeig-Owens, Rachel; Liu, Mengling; Prezant, David J; Nolan, Anna

2018-02-15

Gastroesophageal reflux disease (GERD) and Barrett's Esophagus (BE), which are prevalent in the World Trade Center (WTC) exposed and general populations, negatively impact quality of life and cost of healthcare. GERD, a risk factor of BE, is linked to obstructive airways disease (OAD). We aim to identify serum biomarkers of GERD/BE, and assess the respiratory and clinical phenotype of a longitudinal cohort of never-smoking, male, WTC-exposed rescue workers presenting with pulmonary symptoms. Biomarkers collected soon after WTC-exposure were evaluated in optimized predictive models of GERD/BE. In the WTC-exposed cohort, the prevalence of BE is at least 6 times higher than in the general population. GERD/BE cases had similar lung function, D LCO , bronchodilator response and long-acting β-agonist use compared to controls. In confounder-adjusted regression models, TNF-α ≥ 6 pg/mL predicted both GERD and BE. GERD was also predicted by C-peptide ≥ 360 pg/mL, while BE was predicted by fractalkine ≥ 250 pg/mL and IP-10 ≥ 290 pg/mL. Finally, participants with GERD had significantly increased use of short-acting β-agonist compared to controls. Overall, biomarkers sampled prior to GERD/BE presentation showed strong predictive abilities of disease development. This study frames future investigations to further our understanding of aerodigestive pathology due to particulate matter exposure.

Emerging biomarkers for cancer immunotherapy in melanoma.

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Axelrod, Margaret L; Johnson, Douglas B; Balko, Justin M

2017-09-14

The treatment and prognosis of metastatic melanoma has changed substantially since the advent of novel immune checkpoint inhibitors (ICI), agents that enhance the anti-tumor immune response. Despite the success of these agents, clinically actionable biomarkers to aid patient and regimen selection are lacking. Herein, we summarize and review the evidence for candidate biomarkers of response to ICIs in melanoma. Many of these candidates can be examined as parts of a known molecular pathway of immune response, while others are clinical in nature. Due to the ability of ICIs to illicit dramatic and durable responses, well-validated biomarkers that can be effectively implemented in the clinic will require strong negative predictive values that do not limit patients with who may benefit from ICI therapy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers.

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Puchades-Carrasco, Leonor; Palomino-Schätzlein, Martina; Pérez-Rambla, Clara; Pineda-Lucena, Antonio

2016-05-01

Metabolomics, a systems biology approach focused on the global study of the metabolome, offers a tremendous potential in the analysis of clinical samples. Among other applications, metabolomics enables mapping of biochemical alterations involved in the pathogenesis of diseases, and offers the opportunity to noninvasively identify diagnostic, prognostic and predictive biomarkers that could translate into early therapeutic interventions. Particularly, metabolomics by Nuclear Magnetic Resonance (NMR) has the ability to simultaneously detect and structurally characterize an abundance of metabolic components, even when their identities are unknown. Analysis of the data generated using this experimental approach requires the application of statistical and bioinformatics tools for the correct interpretation of the results. This review focuses on the different steps involved in the metabolomics characterization of biofluids for clinical applications, ranging from the design of the study to the biological interpretation of the results. Particular emphasis is devoted to the specific procedures required for the processing and interpretation of NMR data with a focus on the identification of clinically relevant biomarkers. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Biomarker-driven phenotyping in Parkinson disease: a translational missing link in disease-modifying clinical trials

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Espay, Alberto J.; Schwarzschild, Michael A.; Tanner, Caroline M.; Fernandez, Hubert H; Simon, David K.; Leverenz, James B.; Merola, Aristide; Chen-Plotkin, Alice; Brundin, Patrik; Kauffman, Marcelo A.; Erro, Roberto; Kieburtz, Karl; Woo, Daniel; Macklin, Eric A.; Standaert, David G.; Lang, Anthony E.

2016-01-01

Past clinical trials of putative neuroprotective therapies have targeted Parkinson disease (PD) as a single pathogenic disease entity. From an Oslerian clinico-pathologic perspective, the wide complexity of PD converges into Lewy bodies and justifies a reductionist approach to PD: a single-mechanism therapy can affect most of those sharing the classic pathologic hallmark. From a systems-biology perspective, PD is a group of disorders that, while related by sharing the feature of nigral dopamine-neuron degeneration, exhibit unique genetic, biological and molecular abnormalities, which probably respond differentially to a given therapeutic approach, particularly for strategies aimed at neuroprotection. Under this model, only biomarker-defined, homogenous subtypes of PD are likely to respond optimally to therapies proven to affect the biological processes within each subtype. Therefore, we suggest that precision medicine applied to PD requires a reevaluation of the biomarker-discovery effort. This effort is currently centered on correlating biological measures to clinical features of PD and on identifying factors that predict whether various prodromal states will convert into the classical movement disorder. We suggest, instead, that subtyping of PD requires the reverse view, where abnormal biological signals (i.e., biomarkers) rather than clinical definitions are used to define disease phenotypes. Successful development of disease-modifying strategies will depend on how relevant the specific biological processes addressed by an intervention are to the pathogenetic mechanisms in the subgroup of targeted patients. This precision-medicine approach will likely yield smaller but well-defined subsets of PD amenable to successful neuroprotection. PMID:28233927

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

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Kim, Hwi Young; Lee, Dong Hyeon; Lee, Jeong-Hoon; Cho, Young Youn; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-03-20

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS). This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted. A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values). This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

A statistical approach to evaluate the performance of cardiac biomarkers in predicting death due to acute myocardial infarction: time-dependent ROC curve

Science.gov (United States)

Karaismailoğlu, Eda; Dikmen, Zeliha Günnur; Akbıyık, Filiz; Karaağaoğlu, Ahmet Ergun

2018-04-30

Background/aim: Myoglobin, cardiac troponin T, B-type natriuretic peptide (BNP), and creatine kinase isoenzyme MB (CK-MB) are frequently used biomarkers for evaluating risk of patients admitted to an emergency department with chest pain. Recently, time- dependent receiver operating characteristic (ROC) analysis has been used to evaluate the predictive power of biomarkers where disease status can change over time. We aimed to determine the best set of biomarkers that estimate cardiac death during follow-up time. We also obtained optimal cut-off values of these biomarkers, which differentiates between patients with and without risk of death. A web tool was developed to estimate time intervals in risk. Materials and methods: A total of 410 patients admitted to the emergency department with chest pain and shortness of breath were included. Cox regression analysis was used to determine an optimal set of biomarkers that can be used for estimating cardiac death and to combine the significant biomarkers. Time-dependent ROC analysis was performed for evaluating performances of significant biomarkers and a combined biomarker during 240 h. The bootstrap method was used to compare statistical significance and the Youden index was used to determine optimal cut-off values. Results : Myoglobin and BNP were significant by multivariate Cox regression analysis. Areas under the time-dependent ROC curves of myoglobin and BNP were about 0.80 during 240 h, and that of the combined biomarker (myoglobin + BNP) increased to 0.90 during the first 180 h. Conclusion: Although myoglobin is not clinically specific to a cardiac event, in our study both myoglobin and BNP were found to be statistically significant for estimating cardiac death. Using this combined biomarker may increase the power of prediction. Our web tool can be useful for evaluating the risk status of new patients and helping clinicians in making decisions.

Searching for Clinically Relevant Biomarkers in Geriatric Oncology.

Science.gov (United States)

Katsila, Theodora; Patrinos, George P; Kardamakis, Dimitrios

2018-01-01

Ageing, which is associated with a progressive decline and functional deterioration in multiple organ systems, is highly heterogeneous, both inter- and intraindividually. For this, tailored-made theranostics and optimum patient stratification become fundamental, when decision-making in elderly patients is considered. In particular, when cancer incidence and cancer-related mortality and morbidity are taken into account, elderly patient care is a public health concern. In this review, we focus on oncogeriatrics and highlight current opportunities and challenges with an emphasis on the unmet need of clinically relevant biomarkers in elderly cancer patients. We performed a literature search on PubMed and Scopus databases for articles published in English between 2000 and 2017 coupled to text mining and analysis. Considering the top insights, we derived from our literature analysis that information knowledge needs to turn into knowledge growth in oncogeriatrics towards clinically relevant biomarkers, cost-effective practices, updated educational schemes for health professionals (in particular, geriatricians and oncologists), and awareness of ethical issues. We conclude with an interdisciplinary call to omics, geriatricians, oncologists, informatics, and policy-makers communities that Big Data should be translated into decision-making in the clinic.

Methylated genes as new cancer biomarkers.

LENUS (Irish Health Repository)

Duffy, M J

2012-02-01

Aberrant hypermethylation of promoter regions in specific genes is a key event in the formation and progression of cancer. In at least some situations, these aberrant alterations occur early in the formation of malignancy and appear to be tumour specific. Multiple reports have suggested that measurement of the methylation status of the promoter regions of specific genes can aid early detection of cancer, determine prognosis and predict therapy responses. Promising DNA methylation biomarkers include the use of methylated GSTP1 for aiding the early diagnosis of prostate cancer, methylated PITX2 for predicting outcome in lymph node-negative breast cancer patients and methylated MGMT in predicting benefit from alkylating agents in patients with glioblastomas. However, prior to clinical utilisation, these findings require validation in prospective clinical studies. Furthermore, assays for measuring gene methylation need to be standardised, simplified and evaluated in external quality assurance programmes. It is concluded that methylated genes have the potential to provide a new generation of cancer biomarkers.

Nephron segment specific microRNA biomarkers of pre-clinical drug-induced renal toxicity: Opportunities and challenges

Energy Technology Data Exchange (ETDEWEB)

Nassirpour, Rounak, E-mail: Rounak.nassirpour@pfizer.com [Drug Safety, Pfizer Worldwide Research and Development, 1 Burtt Rd, Andover, MA 01810 (United States); Ramaiah, Shashi K. [Drug Safety, Pfizer Worldwide Research and Development, 610 Main Street, Cambridge, MA 02139 (United States); Whiteley, Laurence O. [Drug Safety, Pfizer Worldwide Research and Development, 1 Burtt Rd, Andover, MA 01810 (United States)

2016-12-01

Drug-induced nephrotoxicity is a common drug development complication for pharmaceutical companies. Sensitive, specific, translatable and non-invasive biomarkers of renal toxicity are urgently needed to diagnose nephron segment specific injury. The currently available gold standard biomarkers for nephrotoxicity are not kidney-specific, lack sensitivity for early detection, and are not suitable for renal damage localization (glomerular vs tubulointerstitial injury). MicroRNAs (miRNAs) are increasingly gaining momentum as promising biomarkers of various organ toxicities, including drug induced renal injury. This is mostly due to their stability in easily accessible biofluids, ease of developing nucleic acids detection compared to protein detection assays, as well as their interspecies translatability. Increasing concordance of miRNA findings by standardizing methodology most suitable for their detection and quantitation, as well as characterization of their expression pattern in a cell type specific manner, will accelerate progress toward validation of these miRNAs as biomarkers in pre-clinical, and clinical settings. This review aims to highlight the current pre-clinical findings surrounding miRNAs as biomarkers in two important segments of the nephron, the glomerulus and tubules. - Highlights: • miRNAs are promising biomarkers of drug-induced kidney injury. • Summarized pre-clinical miRNA biomarkers of drug-induced nephrotoxicity. • Described the strengths and challenges associated with miRNAs as biomarkers.

What is a biomarker? Research investments and lack of clinical integration necessitate a review of biomarker terminology and validation schema.

Science.gov (United States)

Ptolemy, Adam S; Rifai, Nader

2010-01-01

A continual trend of annual growth can be seen within research devoted to the discovery and validation of disease biomarkers within both the natural and clinical sciences. This expansion of intellectual endeavours was quantified through database searches of (a) research grant awards provided by the various branches of the National Institutes of Health (NIH) and (b) academic publications. A search of awards presented between 1986 and 2009 revealed a total of 28,856 grants awarded by the NIH containing the term "biomarker". The total funds for these awards in 2008 and 2009 alone were over $2.5 billion. During the same respective time frames, searches of "biomarker" and either "discovery", "genomics", "proteomics" or "metabolomics" yielded a total of 4,928 NIH grants whose combined funding exceeded $1.2 billion. The derived trend in NIH awards paralleled the annual expansion in "biomarker" literature. A PubMed search for the term, between 1990 and 2009, revealed a total of 441,510 published articles, with 38,457 published in 2008. These enormous investments and academic outputs however have not translated into the expected integration of new biomarkers for patient care. For example no proteomics derived biomarkers are currently being utilized in routine clinical management. This translational chasm necessitates a review of the previously proposed biomarker definitions and evaluation schema. A subsequent discussion of both the analytical and pre-analytical considerations for such research is also presented within. This required knowledge should aid scientists in their pursuit and validation of new biological markers of disease.

Prognostic and predictive biomarkers in colorectal cancer. Towards precision medicine

NARCIS (Netherlands)

Reimers, Marlies Suzanne

2015-01-01

The aim of this thesis was to define prognostic and predictive biomarkers in colorectal cancer for improved risk stratification and treatment benefit in the individual patient, with the introduction of precision medicine in the near future as the ultimate goal. By definition, precision medicine is

Fluid biomarkers in clinical trials of Alzheimer’s disease therapeutics

Directory of Open Access Journals (Sweden)

Aaron eRitter

2015-08-01

Full Text Available With the demographic shift of the global population towards longer life expectancy, the number of people living with Alzheimer’s disease (AD has rapidly expanded and is projected to triple by the year 2050. Current treatments provide symptomatic relief but do not affect the underlying pathology of the disease. Therapies that prevent or slow the progression of the disease are urgently needed to avoid this growing public health emergency. Insights gained from decades of research have begun to unlock the pathophysiology of this complex disease and have provided targets for disease modifying therapies. In the last decade, few therapeutic agents designed to modify the underlying disease process have progressed to clinical trials and none have been brought to market. With the focus on disease modification, biomarkers promise to play an increasingly important role in clinical trials. Six biomarkers have now been included in diagnostic criteria for AD and are regularly incorporated into clinical trials. Three biomarkers are neuroimaging measures—hippocampal atrophy measured by magnetic resonance imaging (MRI, amyloid uptake as measured by Pittsburg compound B positron emission tomography (PiB PET, and decreased fluorodeoxyglucose (18F uptake as measured by positron emission tomography (FDG PET—and three are sampled from fluid sources—cerebrospinal fluid (CSF levels

An etiologic prediction model incorporating biomarkers to predict the bladder cancer risk associated with occupational exposure to aromatic amines: a pilot study.

Science.gov (United States)

Mastrangelo, Giuseppe; Carta, Angela; Arici, Cecilia; Pavanello, Sofia; Porru, Stefano

2017-01-01

No etiological prediction model incorporating biomarkers is available to predict bladder cancer risk associated with occupational exposure to aromatic amines. Cases were 199 bladder cancer patients. Clinical, laboratory and genetic data were predictors in logistic regression models (full and short) in which the dependent variable was 1 for 15 patients with aromatic amines related bladder cancer and 0 otherwise. The receiver operating characteristics approach was adopted; the area under the curve was used to evaluate discriminatory ability of models. Area under the curve was 0.93 for the full model (including age, smoking and coffee habits, DNA adducts, 12 genotypes) and 0.86 for the short model (including smoking, DNA adducts, 3 genotypes). Using the "best cut-off" of predicted probability of a positive outcome, percentage of cases correctly classified was 92% (full model) against 75% (short model). Cancers classified as "positive outcome" are those to be referred for evaluation by an occupational physician for etiological diagnosis; these patients were 28 (full model) or 60 (short model). Using 3 genotypes instead of 12 can double the number of patients with suspect of aromatic amine related cancer, thus increasing costs of etiologic appraisal. Integrating clinical, laboratory and genetic factors, we developed the first etiologic prediction model for aromatic amine related bladder cancer. Discriminatory ability was excellent, particularly for the full model, allowing individualized predictions. Validation of our model in external populations is essential for practical use in the clinical setting.

Biomarker pattern of ARIA-E participants in phase 3 randomized clinical trials with bapineuzumab.

Science.gov (United States)

Liu, Enchi; Wang, Dai; Sperling, Reisa; Salloway, Stephen; Fox, Nick C; Blennow, Kaj; Scheltens, Philip; Schmidt, Mark E; Streffer, Johannes; Novak, Gerald; Einstein, Steve; Booth, Kevin; Ketter, Nzeera; Brashear, H Robert

2018-03-06

To evaluate whether amyloid-related imaging abnormalities with edema/effusion (ARIA-E) observed in bapineuzumab clinical trials was associated with specific biomarker patterns. Bapineuzumab, an anti-β-amyloid monoclonal antibody, was evaluated in patients with mild to moderate Alzheimer disease. Amyloid PET imaging, CSF biomarkers, or volumetric MRI (vMRI) were assessed. A total of 1,512 participants underwent one or more biomarker assessments; 154 developed incident ARIA-E. No differences were observed at baseline between ARIA-E and non-ARIA-E participants in brain amyloid burden by PET, the majority of vMRI measures, or CSF biomarkers, with the exception of lower baseline CSF Aβ 42 in APOE ε4 noncarrier ARIA-E vs non-ARIA-E groups (bapineuzumab non-ARIA-E p = 0.027; placebo non-ARIA-E p = 0.012). At week 71, bapineuzumab-treated participants with ARIA-E vs non-ARIA-E showed greater reduction in brain amyloid PET, greater reductions in CSF phosphorylated tau (p-tau) (all comparisons p < 0.01), and total tau (t-tau) (all comparisons p < 0.025), and greater hippocampal volume reduction and ventricular enlargement (all p < 0.05). Greater reduction in CSF Aβ 40 concentrations was observed for ARIA-E versus both non-ARIA-E groups (bapineuzumab/placebo non-ARIA-E p = 0.015/0.049). No group differences were observed at week 71 for changes in whole brain volume or CSF Aβ 42 . Baseline biomarkers largely do not predict risk for developing ARIA-E. ARIA-E was associated with significant longitudinal changes in several biomarkers, with larger reductions in amyloid PET and CSF p-tau and t-tau concentrations, and paradoxically greater hippocampal volume reduction and ventricular enlargement, suggesting that ARIA-E in bapineuzumab-treated cases may be related to increased Aβ efflux from the brain and affecting downstream pathogenic processes. © 2018 American Academy of Neurology.

An Evidence-Based Approach to the Use of Predictive Biomarkers in the Treatment of Non- Small Cell Lung Cancer (NSCLC)

International Nuclear Information System (INIS)

Quinton, Cindy; Ellis, Peter M.

2011-01-01

Recent advances in the treatment of non-small cell lung cancer (NSCLC) have led to improvements in patient survival and quality of life. It is unclear whether molecular abnormalities associated with NSCLC cell survival, growth and proliferation are useful in predicting treatment benefit. We conducted a systematic review to establish which biomarkers contribute meaningfully to the management of NSCLC. A team of researchers searched PubMed and conference proceedings (ASCO, ESMO, IASLC, USCAP) using MESH terms for NSCLC and randomized trials (RCT), plus keywords for variables of interest. Evidence from multiple RCTs confirmed that histologic subtype is prognostic for survival and predictive of treatment efficacy and/or toxicity in NSCLC. Likewise, activating mutations of the epidermal growth factor receptor (EGFR) are associated with benefit from EGFR tyrosine kinase inhibitors in patients with advanced non-squamous NSCLC and should be assessed routinely. No biomarkers to date reliably predict response to anti-Vascular Endothelial Growth Factor (VEGF) therapies. There are inconsistent data on the role of ERCC1, BRCA, Beta tubulin III, RRM1, K-RAS, or TP-53 in treatment decisions. These tests should not be routinely used in selecting treatment at this time, whereas EML4/ALK translocations predict responses to specific targeted agents, the optimal assessment of this molecular abnormality has yet to be established. Personalized care of patients with NSCLC based on biomarkers is increasingly important to both clinical practice and research

Perceived age as a biomarker of ageing: a clinical methodology

DEFF Research Database (Denmark)

Gunn, David A; Murray, Peter G; Tomlin, Cyrena C

2008-01-01

In a previous field-based study, how old one looks for one's age (perceived age) was found to be predictive of mortality in elderly individuals. In conjunction, perceived age is of relevance and interest to the layperson. Here, a clinical methodology for generating perceived age as a biomarker...... of facial ageing is detailed. The methodology utilises facial photographs of subjects to present images to large numbers of age assessors who are primarily nationals of the country of study origin. In five observational studies in five different countries involving 874 female subjects it was found...... that subject age and assessor gender, nationality, age and ageing expertise had little effect on the perceived age data generated. However, increasing the numbers of age assessors up to 50 substantially increased the reproducibility of the mean perceived age for an image and a minimum of 10 assessors were...

The NINDS Parkinson's disease biomarkers program: The Ninds Parkinson's Disease Biomarkers Program

Energy Technology Data Exchange (ETDEWEB)

Rosenthal, Liana S. [Department of Neurology, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Drake, Daniel [Department of Biostatistics, Columbia University, New York New York USA; Alcalay, Roy N. [Department of Neurology, Columbia University, New York New York USA; Babcock, Debra [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Bowman, F. DuBois [Department of Biostatistics, Columbia University, New York New York USA; Chen-Plotkin, Alice [Department of Neurology, University of Pennsylvania, Philadelphia Pennsylvania USA; Dawson, Ted M. [Department of Neurology, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Solomon H. Snyder Department of Neuroscience, Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Dewey, Richard B. [Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas USA; German, Dwight C. [Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas USA; Huang, Xuemei [Department of Neurology, Penn State Hershey Medical Center, Hershey Pennsylvania USA; Landin, Barry [Center for Information Technology, National Institutes of Health, Bethesda Maryland USA; McAuliffe, Matthew [Center for Information Technology, National Institutes of Health, Bethesda Maryland USA; Petyuk, Vladislav A. [Biological Sciences Division, Pacific Northwest National Laboratory, Richland Washington USA; Scherzer, Clemens R. [Department of Neurology, Brigham & Women' s Hospital, Harvard Medical School, Cambridge Massachusetts USA; Hillaire-Clarke, Coryse St. [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Sieber, Beth-Anne [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Sutherland, Margaret [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Tarn, Chi [Coriell Institute for Medical Research, Camden New Jersey USA; West, Andrew [Department of Neurology, University of Alabama at Birmingham, Birmingham USA; Vaillancourt, David [Department of Applied Physiology and Kinesiology, University of Florida, Gainesville Florida USA; Zhang, Jing [Department of Pathology, University of Washington, Seattle Washington USA; Gwinn, Katrina [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA

2015-10-07

Background: Neuroprotection for Parkinson Disease (PD) remains elusive. Biomarkers hold the promise of removing roadblocks to therapy development. The National Institute of Neurological Disorders and Stroke (NINDS) has therefore established the Parkinson’s Disease Biomarkers Program (PDBP) to promote discovery of biomarkers for use in phase II-III clinical trials in PD. Methods: The PDBP facilitates biomarker development to improve neuroprotective clinical trial design, essential for advancing therapeutics for PD. To date, eleven consortium projects in the PDBP are focused on the development of clinical and laboratory-based PD biomarkers for diagnosis, progression tracking, and/or the prediction of prognosis. Seven of these projects also provide detailed longitudinal data and biospecimens from PD patients and controls, as a resource for all PD researchers. Standardized operating procedures and pooled reference samples have been created in order to allow cross-project comparisons and assessment of batch effects. A web-based Data Management Resource facilitates rapid sharing of data and biosamples across the entire PD research community for additional biomarker projects. Results: Here we describe the PDBP, highlight standard operating procedures for the collection of biospecimens and data, and provide an interim report with quality control analysis on the first 1082 participants and 1033 samples with quality control analysis collected as of October 2014. Conclusions: By making samples and data available to academics and industry, encouraging the adoption of existing standards, and providing a resource which complements existing programs, the PDBP will accelerate the pace of PD biomarker research, with the goal of improving diagnostic methods and treatment.

Validation of New Cancer Biomarkers

DEFF Research Database (Denmark)

Duffy, Michael J; Sturgeon, Catherine M; Söletormos, Georg

2015-01-01

BACKGROUND: Biomarkers are playing increasingly important roles in the detection and management of patients with cancer. Despite an enormous number of publications on cancer biomarkers, few of these biomarkers are in widespread clinical use. CONTENT: In this review, we discuss the key steps...... in advancing a newly discovered cancer candidate biomarker from pilot studies to clinical application. Four main steps are necessary for a biomarker to reach the clinic: analytical validation of the biomarker assay, clinical validation of the biomarker test, demonstration of clinical value from performance...... of the biomarker test, and regulatory approval. In addition to these 4 steps, all biomarker studies should be reported in a detailed and transparent manner, using previously published checklists and guidelines. Finally, all biomarker studies relating to demonstration of clinical value should be registered before...

LABORATORY BIOMARKERS FOR ANKYLOSING SPONDYLITIS

Directory of Open Access Journals (Sweden)

E. N. Aleksandrova

2017-01-01

Full Text Available Ankylosing spondylitis (AS is a chronic inflammatory disease from a group of spondyloarthritis (SpA, which is characterized by lesions of the sacroiliac joints and spine with the common involvement of entheses and peripheral joints in the pathological process. Advances in modern laboratory medicine have contributed to a substantial expansion of the range of pathogenetic, diagnostic, and prognostic biomarkers of AS. As of now, there are key pathogenetic biomarkers of AS (therapeutic targets, which include tumor necrosis factor-α (TNF-α, interleukin 17 (IL-17, and IL-23. Among the laboratory diagnostic and prognostic biomarkers, HLA-B27 and C-reactive protein are of the greatest value in clinical practice; the former for the early diagnosis of the disease and the latter for the assessment of disease activity, the risk of radiographic progression and the efficiency of therapy. Anti-CD74 antibodies are a new biomarker that has high sensitivity and specificity values in diagnosing axial SpA at an early stage. A number of laboratory biomarkers, including calprotectin, matrix metalloproteinase-3 (MMP-3, vascular endothelial growth factor, Dickkopf-1 (Dkk-1, and C-terminal telopeptide of type II collagen (CTX II do not well reflect disease activity, but may predict progressive structural changes in the spine and sacroiliac joints in AS. Blood calprotectin level monitoring allows the effective prediction of a response to therapy with TNF inhibitors and anti-IL-17А monoclonal antibodies. The prospects for the laboratory diagnosis of AS are associated with the clinical validation of candidate biomarkers during large-scale prospective cohort studies and with a search for new proteomic, transcriptomic and genomic markers, by using innovative molecular and cellular technologies.

Predicting Anthracycline Benefit

DEFF Research Database (Denmark)

Bartlett, John M S; McConkey, Christopher C; Munro, Alison F

2015-01-01

PURPOSE: Evidence supporting the clinical utility of predictive biomarkers of anthracycline activity is weak, with a recent meta-analysis failing to provide strong evidence for either HER2 or TOP2A. Having previously shown that duplication of chromosome 17 pericentromeric alpha satellite as measu......PURPOSE: Evidence supporting the clinical utility of predictive biomarkers of anthracycline activity is weak, with a recent meta-analysis failing to provide strong evidence for either HER2 or TOP2A. Having previously shown that duplication of chromosome 17 pericentromeric alpha satellite...

Potential Immune Biomarkers in Diagnosis and Clinical Management for Systemic Lupus Erythematosus

Directory of Open Access Journals (Sweden)

Zecevic Lamija

2018-04-01

Full Text Available Background: There is still no reliable, specific biomarker for precision diagnosis and clinical monitoring of systemic lupus erythematosus. The aim of this study was to investigate the importance of the determination of immunofenotypic profiles (T, B lymphocytes and NK cells and serum cytokine concentrations (IL-17 and IFN-alpha as potential biomarkers for this disease.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

Science.gov (United States)

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Pretreatment data is highly predictive of liver chemistry signals in clinical trials.

Science.gov (United States)

Cai, Zhaohui; Bresell, Anders; Steinberg, Mark H; Silberg, Debra G; Furlong, Stephen T

2012-01-01

The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy's law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.

Cross-validation of biomarkers for the early differential diagnosis and prognosis of dementia in a clinical setting

International Nuclear Information System (INIS)

Perani, Daniela; Cerami, Chiara; Caminiti, Silvia Paola; Santangelo, Roberto; Coppi, Elisabetta; Ferrari, Laura; Magnani, Giuseppe; Pinto, Patrizia; Passerini, Gabriella; Falini, Andrea; Iannaccone, Sandro; Cappa, Stefano Francesco; Comi, Giancarlo; Gianolli, Luigi

2016-01-01

The aim of this study was to evaluate the supportive role of molecular and structural biomarkers (CSF protein levels, FDG PET and MRI) in the early differential diagnosis of dementia in a large sample of patients with neurodegenerative dementia, and in determining the risk of disease progression in subjects with mild cognitive impairment (MCI). We evaluated the supportive role of CSF Aβ 42 , t-Tau, p-Tau levels, conventional brain MRI and visual assessment of FDG PET SPM t-maps in the early diagnosis of dementia and the evaluation of MCI progression. Diagnosis based on molecular biomarkers showed the best fit with the final diagnosis at a long follow-up. FDG PET SPM t-maps had the highest diagnostic accuracy in Alzheimer's disease and in the differential diagnosis of non-Alzheimer's disease dementias. The p-tau/Aβ 42 ratio was the only CSF biomarker providing a significant classification rate for Alzheimer's disease. An Alzheimer's disease-positive metabolic pattern as shown by FDG PET SPM in MCI was the best predictor of conversion to Alzheimer's disease. In this clinical setting, FDG PET SPM t-maps and the p-tau/Aβ 42 ratio improved clinical diagnostic accuracy, supporting the importance of these biomarkers in the emerging diagnostic criteria for Alzheimer's disease dementia. FDG PET using SPM t-maps had the highest predictive value by identifying hypometabolic patterns in different neurodegenerative dementias and normal brain metabolism in MCI, confirming its additional crucial exclusionary role. (orig.)

Cross-validation of biomarkers for the early differential diagnosis and prognosis of dementia in a clinical setting

Energy Technology Data Exchange (ETDEWEB)

Perani, Daniela [Vita-Salute San Raffaele University, Milan (Italy); San Raffaele Scientific Institute, Division of Neuroscience, Milan (Italy); San Raffaele Hospital, Nuclear Medicine Unit, Milan (Italy); Cerami, Chiara [Vita-Salute San Raffaele University, Milan (Italy); San Raffaele Scientific Institute, Division of Neuroscience, Milan (Italy); San Raffaele Hospital, Clinical Neuroscience Department, Milan (Italy); Caminiti, Silvia Paola [Vita-Salute San Raffaele University, Milan (Italy); San Raffaele Scientific Institute, Division of Neuroscience, Milan (Italy); Santangelo, Roberto; Coppi, Elisabetta; Ferrari, Laura; Magnani, Giuseppe [San Raffaele Hospital, Department of Neurology, Milan (Italy); Pinto, Patrizia [Papa Giovanni XXIII Hospital, Department of Neurology, Bergamo (Italy); Passerini, Gabriella [Servizio di Medicina di Laboratorio OSR, Milan (Italy); Falini, Andrea [Vita-Salute San Raffaele University, Milan (Italy); San Raffaele Scientific Institute, Division of Neuroscience, Milan (Italy); San Raffaele Hospital, CERMAC - Department of Neuroradiology, Milan (Italy); Iannaccone, Sandro [San Raffaele Hospital, Clinical Neuroscience Department, Milan (Italy); Cappa, Stefano Francesco [San Raffaele Scientific Institute, Division of Neuroscience, Milan (Italy); IUSS Pavia, Pavia (Italy); Comi, Giancarlo [Vita-Salute San Raffaele University, Milan (Italy); San Raffaele Hospital, Department of Neurology, Milan (Italy); Gianolli, Luigi [San Raffaele Hospital, Nuclear Medicine Unit, Milan (Italy)

2016-03-15

The aim of this study was to evaluate the supportive role of molecular and structural biomarkers (CSF protein levels, FDG PET and MRI) in the early differential diagnosis of dementia in a large sample of patients with neurodegenerative dementia, and in determining the risk of disease progression in subjects with mild cognitive impairment (MCI). We evaluated the supportive role of CSF Aβ{sub 42}, t-Tau, p-Tau levels, conventional brain MRI and visual assessment of FDG PET SPM t-maps in the early diagnosis of dementia and the evaluation of MCI progression. Diagnosis based on molecular biomarkers showed the best fit with the final diagnosis at a long follow-up. FDG PET SPM t-maps had the highest diagnostic accuracy in Alzheimer's disease and in the differential diagnosis of non-Alzheimer's disease dementias. The p-tau/Aβ{sub 42} ratio was the only CSF biomarker providing a significant classification rate for Alzheimer's disease. An Alzheimer's disease-positive metabolic pattern as shown by FDG PET SPM in MCI was the best predictor of conversion to Alzheimer's disease. In this clinical setting, FDG PET SPM t-maps and the p-tau/Aβ{sub 42} ratio improved clinical diagnostic accuracy, supporting the importance of these biomarkers in the emerging diagnostic criteria for Alzheimer's disease dementia. FDG PET using SPM t-maps had the highest predictive value by identifying hypometabolic patterns in different neurodegenerative dementias and normal brain metabolism in MCI, confirming its additional crucial exclusionary role. (orig.)

Identification of a 5-protein biomarker molecular signature for predicting Alzheimer's disease.

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Martín Gómez Ravetti

Full Text Available BACKGROUND: Alzheimer's disease (AD is a progressive brain disease with a huge cost to human lives. The impact of the disease is also a growing concern for the governments of developing countries, in particular due to the increasingly high number of elderly citizens at risk. Alzheimer's is the most common form of dementia, a common term for memory loss and other cognitive impairments. There is no current cure for AD, but there are drug and non-drug based approaches for its treatment. In general the drug-treatments are directed at slowing the progression of symptoms. They have proved to be effective in a large group of patients but success is directly correlated with identifying the disease carriers at its early stages. This justifies the need for timely and accurate forms of diagnosis via molecular means. We report here a 5-protein biomarker molecular signature that achieves, on average, a 96% total accuracy in predicting clinical AD. The signature is composed of the abundances of IL-1alpha, IL-3, EGF, TNF-alpha and G-CSF. METHODOLOGY/PRINCIPAL FINDINGS: Our results are based on a recent molecular dataset that has attracted worldwide attention. Our paper illustrates that improved results can be obtained with the abundance of only five proteins. Our methodology consisted of the application of an integrative data analysis method. This four step process included: a abundance quantization, b feature selection, c literature analysis, d selection of a classifier algorithm which is independent of the feature selection process. These steps were performed without using any sample of the test datasets. For the first two steps, we used the application of Fayyad and Irani's discretization algorithm for selection and quantization, which in turn creates an instance of the (alpha-beta-k-Feature Set problem; a numerical solution of this problem led to the selection of only 10 proteins. CONCLUSIONS/SIGNIFICANCE: the previous study has provided an extremely

Prognostic biomarkers in osteoarthritis

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Attur, Mukundan; Krasnokutsky-Samuels, Svetlana; Samuels, Jonathan; Abramson, Steven B.

2013-01-01

Purpose of review Identification of patients at risk for incident disease or disease progression in osteoarthritis remains challenging, as radiography is an insensitive reflection of molecular changes that presage cartilage and bone abnormalities. Thus there is a widely appreciated need for biochemical and imaging biomarkers. We describe recent developments with such biomarkers to identify osteoarthritis patients who are at risk for disease progression. Recent findings The biochemical markers currently under evaluation include anabolic, catabolic, and inflammatory molecules representing diverse biological pathways. A few promising cartilage and bone degradation and synthesis biomarkers are in various stages of development, awaiting further validation in larger populations. A number of studies have shown elevated expression levels of inflammatory biomarkers, both locally (synovial fluid) and systemically (serum and plasma). These chemical biomarkers are under evaluation in combination with imaging biomarkers to predict early onset and the burden of disease. Summary Prognostic biomarkers may be used in clinical knee osteoarthritis to identify subgroups in whom the disease progresses at different rates. This could facilitate our understanding of the pathogenesis and allow us to differentiate phenotypes within a heterogeneous knee osteoarthritis population. Ultimately, such findings may help facilitate the development of disease-modifying osteoarthritis drugs (DMOADs). PMID:23169101

Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

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Wang, Jiaxin; Liang, Yanchun; Wang, Yan; Cui, Juan; Liu, Ming; Du, Wei; Xu, Ying

2013-01-01

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer. PMID:24324552

Biomarkers in Scleroderma: Progressing from Association to Clinical Utility.

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Ligon, Colin; Hummers, Laura K

2016-03-01

Scleroderma is a heterogenous disease characterized by autoimmunity, a characteristic vasculopathy, and often widely varying extents of deep organ fibrosis. Recent advances in the understanding of scleroderma's evolution have improved the ability to identify subgroups of patients with similar prognosis in order to improve risk stratification, enrich clinical trials for patients likely to benefit from specific therapies, and identify promising therapeutic targets for intervention. High-throughput technologies have recently identified fibrotic and inflammatory effectors in scleroderma that exhibit strong prognostic ability and may be tied to disease evolution. Increasingly, the use of collections of assayed circulating proteins and patterns of gene expression in tissue has replaced single-marker investigations in understanding the evolution of scleroderma and in objectively characterizing disease extent. Lastly, identification of shared patterns of disease evolution has allowed classification of patients into latent disease subtypes, which may allow rapid clinical prognostication and targeted management in both clinical and research settings. The concept of biomarkers in scleroderma is expanding to include nontraditional measures of aggregate protein signatures and disease evolution. This review examines the recent advances in biomarkers with a focus on those approaches poised to guide prospective management or themselves serve as quantitative surrogate disease outcomes.

Innovative biomarkers in psychiatric disorders: a major clinical challenge in psychiatry.

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Lozupone, Madia; Seripa, Davide; Stella, Eleonora; La Montagna, Maddalena; Solfrizzi, Vincenzo; Quaranta, Nicola; Veneziani, Federica; Cester, Alberto; Sardone, Rodolfo; Bonfiglio, Caterina; Giannelli, Gianluigi; Bisceglia, Paola; Bringiotti, Roberto; Daniele, Antonio; Greco, Antonio; Bellomo, Antonello; Logroscino, Giancarlo; Panza, Francesco

2017-09-01

Currently, the diagnosis of psychiatric illnesses is based upon DSM-5 criteria. Although endophenotype-specificity for a particular disorder is discussed, the identification of objective biomarkers is ongoing for aiding diagnosis, prognosis, or clinical response to treatment. We need to improve the understanding of the biological abnormalities in psychiatric illnesses across conventional diagnostic boundaries. The present review investigates the innovative post-genomic knowledge used for psychiatric illness diagnostics and treatment response, with a particular focus on proteomics. Areas covered: This review underlines the contribution that psychiatric innovative biomarkers have reached in relation to diagnosis and theragnosis of psychiatric illnesses. Furthermore, it encompasses a reliable representation of their involvement in disease through proteomics, metabolomics/pharmacometabolomics and lipidomics techniques, including the possible role that gut microbiota and CYP2D6 polimorphisms may play in psychiatric illnesses. Expert opinion: Etiologic heterogeneity, variable expressivity, and epigenetics may impact clinical manifestations, making it difficult for a single measurement to be pathognomonic for multifaceted psychiatric disorders. Academic, industry, or government's partnerships may successfully identify and validate new biomarkers so that unfailing clinical tests can be developed. Proteomics, metabolomics, and lipidomics techniques are considered to be helpful tools beyond neuroimaging and neuropsychology for the phenotypic characterization of brain diseases.

Biomarkers of fibrosis and impaired liver function in chronic hepatitis C: how well do they predict clinical outcomes?

DEFF Research Database (Denmark)

Peters, L.; Rockstroh, J.K.

2010-01-01

PURPOSE OF REVIEW: To review the recent literature on the prognostic value of biomarkers of liver fibrosis and impaired liver function in patients with chronic hepatitis C with or without HIV coinfection. RECENT FINDINGS: A combination of standard blood tests seems to be useful in identifying...... levels of the fibrosis marker hyaluronic acid are a strong predictor of clinical complications. A smaller study found hyaluronic acid and two other fibrosis tests, aspartate aminotransferase-to-platelet ratio index (APRI) and Fib-4, to be independent predictors of mortality when included in models...

The prognostic blood biomarker proadrenomedullin for outcome prediction in patients with chronic obstructive pulmonary disease (COPD): a qualitative clinical review.

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Schuetz, Philipp; Marlowe, Robert J; Mueller, Beat

2015-03-01

Plasma proadrenomedullin (ProADM) is a blood biomarker that may aid in multidimensional risk assessment of patients with chronic obstructive pulmonary disease (COPD). Co-secreted 1:1 with adrenomedullin (ADM), ProADM is a less biologically active, more chemically stable surrogate for this pluripotent regulatory peptide, which due to biological and ex vivo physical characteristics is difficult to reliably directly quantify. Upregulated by hypoxia, inflammatory cytokines, bacterial products, and shear stress and expressed widely in pulmonary cells and ubiquitously throughout the body, ADM exerts or mediates vasodilatory, natriuretic, diuretic, antioxidative, anti-inflammatory, antimicrobial, and metabolic effects. Observational data from four separate studies totaling 1366 patients suggest that as a single factor, ProADM is a significant independent, and accurate, long-term all-cause mortality predictor in COPD. This body of work also suggests that combined with different groups of demographic/clinical variables, ProADM provides significant incremental long-term mortality prediction power relative to the groups of variables alone. Additionally, the literature contains indications that ProADM may be a global cardiopulmonary stress marker, potentially supplying prognostic information when cardiopulmonary exercise testing results such as 6-min walk distance are unavailable due to time or other resource constraints or to a patient's advanced disease. Prospective, randomized, controlled interventional studies are needed to demonstrate whether ProADM use in risk-based guidance of site-of-care, monitoring, and treatment decisions improves clinical, quality-of-life, or pharmacoeconomic outcomes in patients with COPD.

Biomarkers of atherosclerotic plaque vulnerability and their clinical significance

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Ran LIU

2016-09-01

Full Text Available Inflammatory reaction plays a crucial role in the occurence and development of atherosclerosis. Both basic and clinical trials have provided evidence that the expression of inflammatory biomarkers are closely related with the degree of atherosclerosis. Treatment towards inflammatory factors would bring benefit to atherosclerotic patients. This review highlighted the mechanistic rationale and specific therapies targeting traditional and novel inflammatory biomarkers, including C-reactive protein (CRP, interleukin-17 (IL-17, secretory phospholipase A2 (sPLA2, lipoprotein-associated phospholipase A2 (Lp-PLA2, endoglin, chemokine receptor and 5-lipoxygenase (5-LO, so as to review its mechanism of action and treatment prospect. DOI: 10.3969/j.issn.1672-6731.2016.09.004

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

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Wang, Hui; Shi, Tujin; Qian, Wei-Jun; Liu, Tao; Kagan, Jacob; Srivastava, Sudhir; Smith, Richard D.; Rodland, Karin D.; Camp, David G.

2015-12-04

Mass spectrometry-based proteomics has become an indispensable tool in biomedical research with broad applications ranging from fundamental biology, systems biology, and biomarker discovery. Recent advances in LC-MS have made it become a major technology in clinical applications, especially in cancer biomarker discovery and verification. To overcome the challenges associated with the analysis of clinical samples, such as extremely wide dynamic range of protein concentrations in biofluids and the need to perform high throughput and accurate quantification, significant efforts have been devoted to improve the overall performance of LC-MS bases clinical proteomics. In this review, we summarize the recent advances in LC-MS in the aspect of cancer biomarker discovery and quantification, and discuss its potentials, limitations, and future perspectives.

Evaluation of biomarkers for the prediction of pre-eclampsia in women with type 1 diabetes mellitus: A systematic review.

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Wotherspoon, Amy C; Young, Ian S; McCance, David R; Holmes, Valerie A

2016-07-01

Pre-eclampsia is a leading cause of maternal and perinatal morbidity and mortality. Women with type 1 diabetes are considered a high-risk group for developing pre-eclampsia. Much research has focused on biomarkers as a means of screening for pre-eclampsia in the general maternal population; however, there is a lack of evidence for women with type 1 diabetes. To undertake a systematic review to identify potential biomarkers for the prediction of pre-eclampsia in women with type 1 diabetes. We searched Medline, EMBASE, Maternity and Infant Care, Scopus, Web of Science and CINAHL SELECTION CRITERIA: Studies were included if they measured biomarkers in blood or urine of women who developed pre-eclampsia and had pre-gestational type 1 diabetes mellitus Data collection and analysis A narrative synthesis was adopted as a meta-analysis could not be performed, due to high study heterogeneity. A total of 72 records were screened, with 21 eligible studies being included in the review. A wide range of biomarkers was investigated and study size varied from 34 to 1258 participants. No single biomarker appeared to be effective in predicting pre-eclampsia; however, glycaemic control was associated with an increased risk while a combination of angiogenic and anti-angiogenic factors seemed to be potentially useful. Limited evidence suggests that combinations of biomarkers may be more effective in predicting pre-eclampsia than single biomarkers. Further research is needed to verify the predictive potential of biomarkers that have been measured in the general maternal population, as many studies exclude women with diabetes preceding pregnancy. Copyright © 2016 Elsevier Inc. All rights reserved.

Thalamic functional connectivity predicts seizure laterality in individual TLE patients: application of a biomarker development strategy.

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Barron, Daniel S; Fox, Peter T; Pardoe, Heath; Lancaster, Jack; Price, Larry R; Blackmon, Karen; Berry, Kristen; Cavazos, Jose E; Kuzniecky, Ruben; Devinsky, Orrin; Thesen, Thomas

2015-01-01

Noninvasive markers of brain function could yield biomarkers in many neurological disorders. Disease models constrained by coordinate-based meta-analysis are likely to increase this yield. Here, we evaluate a thalamic model of temporal lobe epilepsy that we proposed in a coordinate-based meta-analysis and extended in a diffusion tractography study of an independent patient population. Specifically, we evaluated whether thalamic functional connectivity (resting-state fMRI-BOLD) with temporal lobe areas can predict seizure onset laterality, as established with intracranial EEG. Twenty-four lesional and non-lesional temporal lobe epilepsy patients were studied. No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons). Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength) successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional) predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional) achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

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Otto Savolainen

Full Text Available The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D risk that would improve prediction of T2D over current risk markers.Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629. Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D.Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA, smoking, serum adiponectin alone, and in combination with metabolomics had the largest areas under the curve (AUC (0.794 (95% confidence interval [0.738-0.850] and 0.808 [0.749-0.867] respectively, with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]. Prediction based on non-blood based measures was 0.638 [0.565-0.711].Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.

Prognostic Biomarkers Used for Localised Prostate Cancer Management: A Systematic Review.

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Lamy, Pierre-Jean; Allory, Yves; Gauchez, Anne-Sophie; Asselain, Bernard; Beuzeboc, Philippe; de Cremoux, Patricia; Fontugne, Jacqueline; Georges, Agnès; Hennequin, Christophe; Lehmann-Che, Jacqueline; Massard, Christophe; Millet, Ingrid; Murez, Thibaut; Schlageter, Marie-Hélène; Rouvière, Olivier; Kassab-Chahmi, Diana; Rozet, François; Descotes, Jean-Luc; Rébillard, Xavier

2017-03-07

Prostate cancer stratification is based on tumour size, pretreatment PSA level, and Gleason score, but it remains imperfect. Current research focuses on the discovery and validation of novel prognostic biomarkers to improve the identification of patients at risk of aggressive cancer or of tumour relapse. This systematic review by the Intergroupe Coopérateur Francophone de Recherche en Onco-urologie (ICFuro) analysed new evidence on the analytical validity and clinical validity and utility of six prognostic biomarkers (PHI, 4Kscore, MiPS, GPS, Prolaris, Decipher). All available data for the six biomarkers published between January 2002 and April 2015 were systematically searched and reviewed. The main endpoints were aggressive prostate cancer prediction, additional value compared to classical prognostic parameters, and clinical benefit for patients with localised prostate cancer. The preanalytical and analytical validations were heterogeneous for all tests and often not adequate for the molecular signatures. Each biomarker was studied for specific indications (candidates for a first or second biopsy, and potential candidates for active surveillance, radical prostatectomy, or adjuvant treatment) for which the level of evidence (LOE) was variable. PHI and 4Kscore were the biomarkers with the highest LOE for discriminating aggressive and indolent tumours in different indications. Blood biomarkers (PHI and 4Kscore) have the highest LOE for the prediction of more aggressive prostate cancer and could help clinicians to manage patients with localised prostate cancer. The other biomarkers show a potential prognostic value; however, they should be evaluated in additional studies to confirm their clinical validity. We reviewed studies assessing the value of six prognostic biomarkers for prostate cancer. On the basis of the available evidence, some biomarkers could help in discriminating between aggressive and non-aggressive tumours with an additional value compared to the

Pharmacogenomic Biomarkers

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Sandra C. Kirkwood

2002-01-01

Full Text Available Pharmacogenomic biomarkers hold great promise for the future of medicine and have been touted as a means to personalize prescriptions. Genetic biomarkers for disease susceptibility including both Mendelian and complex disease promise to result in improved understanding of the pathophysiology of disease, identification of new potential therapeutic targets, and improved molecular classification of disease. However essential to fulfilling the promise of individualized therapeutic intervention is the identification of drug activity biomarkers that stratify individuals based on likely response to a particular therapeutic, both positive response, efficacy, and negative response, development of side effect or toxicity. Prior to the widespread clinical application of a genetic biomarker multiple scientific studies must be completed to identify the genetic variants and delineate their functional significance in the pathophysiology of a carefully defined phenotype. The applicability of the genetic biomarker in the human population must then be verified through both retrospective studies utilizing stored or clinical trial samples, and through clinical trials prospectively stratifying patients based on the biomarker. The risk conferred by the polymorphism and the applicability in the general population must be clearly understood. Thus, the development and widespread application of a pharmacogenomic biomarker is an involved process and for most disease states we are just at the beginning of the journey towards individualized therapy and improved clinical outcome.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

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Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J., E-mail: bje@mayo.edu [Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Coufalova, Lucie [Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Department of Neurosurgery of First Faculty of Medicine, Charles University in Prague, Military University Hospital, Prague 128 21 (Czech Republic); International Clinical Research Center, St. Anne’s University Hospital Brno, Brno 656 91 (Czech Republic); Lachance, Daniel H. [Department of Neurology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Parney, Ian F. [Department of Neurologic Surgery, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Carter, Rickey E. [Department of Health Sciences Research, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States); Buckner, Jan C. [Department of Medical Oncology, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905 (United States)

2016-06-15

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O{sup 6}-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

International Nuclear Information System (INIS)

Korfiatis, Panagiotis; Kline, Timothy L.; Erickson, Bradley J.; Coufalova, Lucie; Lachance, Daniel H.; Parney, Ian F.; Carter, Rickey E.; Buckner, Jan C.

2016-01-01

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O 6 -methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

Exploring the Limitations of Peripheral Blood Transcriptional Biomarkers in Predicting Influenza Vaccine Responsiveness

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Luca Marchetti

2017-01-01

Full Text Available Systems biology has been recently applied to vaccinology to better understand immunological responses to the influenza vaccine. Particular attention has been paid to the identification of early signatures capable of predicting vaccine immunogenicity. Building from previous studies, we employed a recently established algorithm for signature-based clustering of expression profiles, SCUDO, to provide new insights into why blood-derived transcriptome biomarkers often fail to predict the seroresponse to the influenza virus vaccination. Specifically, preexisting immunity against one or more vaccine antigens, which was found to negatively affect the seroresponse, was identified as a confounding factor able to decouple early transcriptome from later antibody responses, resulting in the degradation of a biomarker predictive power. Finally, the broadly accepted definition of seroresponse to influenza virus vaccine, represented by the maximum response across the vaccine-targeted strains, was compared to a composite measure integrating the responses against all strains. This analysis revealed that composite measures provide a more accurate assessment of the seroresponse to multicomponent influenza vaccines.

Early biomarkers of joint damage in rheumatoid and psoriatic arthritis.

LENUS (Irish Health Repository)

Mc Ardle, Angela

2015-01-01

Joint destruction, as evidenced by radiographic findings, is a significant problem for patients suffering from rheumatoid arthritis and psoriatic arthritis. Inherently irreversible and frequently progressive, the process of joint damage begins at and even before the clinical onset of disease. However, rheumatoid and psoriatic arthropathies are heterogeneous in nature and not all patients progress to joint damage. It is therefore important to identify patients susceptible to joint destruction in order to initiate more aggressive treatment as soon as possible and thereby potentially prevent irreversible joint damage. At the same time, the high cost and potential side effects associated with aggressive treatment mean it is also important not to over treat patients and especially those who, even if left untreated, would not progress to joint destruction. It is therefore clear that a protein biomarker signature that could predict joint damage at an early stage would support more informed clinical decisions on the most appropriate treatment regimens for individual patients. Although many candidate biomarkers for rheumatoid and psoriatic arthritis have been reported in the literature, relatively few have reached clinical use and as a consequence the number of prognostic biomarkers used in rheumatology has remained relatively static for several years. It has become evident that a significant challenge in the transition of biomarker candidates to clinical diagnostic assays lies in the development of suitably robust biomarker assays, especially multiplexed assays, and their clinical validation in appropriate patient sample cohorts. Recent developments in mass spectrometry-based targeted quantitative protein measurements have transformed our ability to rapidly develop multiplexed protein biomarker assays. These advances are likely to have a significant impact on the validation of biomarkers in the future. In this review, we have comprehensively compiled a list of candidate

Biomarkers in psoriasis and psoriatic arthritis.

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Villanova, Federica; Di Meglio, Paola; Nestle, Frank O

2013-04-01

Psoriasis is a common immune-mediated disease of the skin, which associates in 20-30% of patients with psoriatic arthritis (PsA). The immunopathogenesis of both conditions is not fully understood as it is the result of a complex interaction between genetic, environmental and immunological factors. At present there is no cure for psoriasis and there are no specific markers that can accurately predict disease progression and therapeutic response. Therefore, biomarkers for disease prognosis and response to treatment are urgently needed to help clinicians with objective indications to improve patient management and outcomes. Although many efforts have been made to identify psoriasis/PsA biomarkers none of them has yet been translated into routine clinical practice. In this review we summarise the different classes of possible biomarkers explored in psoriasis and PsA so far and discuss novel strategies for biomarker discovery.

Other biomarkers for detecting prostate cancer.

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Nogueira, Lucas; Corradi, Renato; Eastham, James A

2010-01-01

Prostate-specific antigen (PSA) has been used for detecting prostate cancer since 1994. Although it is the best cancer biomarker available, PSA is not perfect. It lacks both the sensitivity and specificity to accurately detect the presence of prostate cancer. None of the PSA thresholds currently in use consistently identify patients with prostate cancer and exclude patients without cancer. Novel approaches to improve our ability to detect prostate cancer and predict the course of the disease are needed. Additional methods for detecting prostate cancer have been evaluated. Despite the discovery of many new biomarkers, only a few have shown some clinical value. These markers include human kallikrein 2, urokinase-type plasminogen activator receptor, prostate-specific membrane antigen, early prostate cancer antigen, PCA3, alpha-methylacyl-CoA racemase and glutathione S-transferase pi hypermethylation. We review the reports on biomarkers for prostate cancer detection, and their possible role in the clinical practice.

[Mixed depressions: clinical and neurophysiological biomarkers].

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Micoulaud Franchi, J-A; Geoffroy, P-A; Vion-Dury, J; Balzani, C; Belzeaux, R; Maurel, M; Cermolacce, M; Fakra, E; Azorin, J-M

2013-12-01

Epidemiological studies of major depressive episodes (MDE) highlighted the frequent association of symptoms or signs of mania or hypomania with depressive syndrome. Beyond the strict definition of DSM-IV, epidemiological recognition of a subset of MDE characterized by the presence of symptoms or signs of the opposite polarity is clinically important because it is associated with pejorative prognosis and therapeutic response compared to the subgroup of "typical MDE". The development of DSM-5 took into account the epidemiological data. DSM-5 opted for a more dimensional perspective in implementing the concept of "mixed features" from an "episode" to a "specification" of mood disorder. As outlined in the DSM-5: "Mixed features associated with a major depressive episode have been found to be a significant risk factor for the development of bipolar I and II disorder. As a result, it is clinically useful to note the presence of this specifier for treatment planning and monitoring of response to therapeutic". However, the mixed features are sometimes difficult to identify, and neurophysiological biomarkers would be useful to make a more specific diagnosis. Two neurophysiological models make it possible to better understand MDE with mixed features : i) the emotional regulation model that highlights a tendency to hyper-reactive and unstable emotion response, and ii) the vigilance regulation model that highlights, through EEG recording, a tendency to unstable vigilance. Further research is required to better understand relationships between these two models. These models provide the opportunity of a neurophysiological framework to better understand the mixed features associated with MDE and to identify potential neurophysiological biomarkers to guide therapeutic strategies. Copyright © 2013 L’Encéphale. Published by Elsevier Masson SAS.. All rights reserved.

Adiponectin as a routine clinical biomarker.

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Kishida, Ken; Funahashi, Tohru; Shimomura, Iichiro

2014-01-01

Adiponectin is a protein synthesized and secreted predominantly by adipocytes into the peripheral blood. However, circulating adiponectin level is inversely related with body weight, especially visceral fat accumulation. The mechanism of this paradoxical relation remains obscure. Low circulating adiponectin concentrations (hypoadiponectinemia; osteoporosis, and cancer (endometrial cancer, postmenopausal breast cancer, leukemia, colon cancer, gastric cancer, prostate cancer). On the other hand, hyperadiponectinemia is associated with cardiac, renal and pulmonary diseases. This review article focuses on the significance of adiponectin as a clinical biomarker of obesity-related diseases. Routine measurement of adiponectin in patients with lifestyle-related diseases is highly recommended. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cardiovascular Disease Biomarkers Predict Susceptibility or Resistance to Lung Injury in World Trade Center Dust Exposed Firefighters

Science.gov (United States)

Weiden, Michael D.; Naveed, Bushra; Kwon, Sophia; Cho, Soo Jung; Comfort, Ashley L.; Prezant, David J.; Rom, William N.; Nolan, Anna

2013-01-01

Pulmonary vascular loss is an early feature of chronic obstructive pulmonary disease. Biomarkers of inflammation and of metabolic syndrome, predicts loss of lung function in World Trade Center Lung Injury (WTC-LI). We investigated if other cardiovascular disease (CVD) biomarkers also predicted WTC-LI. This nested case-cohort study used 801 never smoker, WTC exposed firefighters with normal pre-9/11 lung function presenting for subspecialty pulmonary evaluation (SPE) before March, 2008. A representative sub-cohort of 124/801 with serum drawn within six months of 9/11 defined CVD biomarker distribution. Post-9/11/01 FEV1 at subspecialty exam defined cases: susceptible WTC-LI cases with FEV1≤77% predicted (66/801) and resistant WTC-LI cases with FEV1≥107% (68/801). All models were adjusted for WTC exposure intensity, BMI at SPE, age at 9/11, and pre-9/11 FEV1. Susceptible WTC-LI cases had higher levels of Apo-AII, CRP, and MIP-4 with significant RRs of 3.85, 3.93, and 0.26 respectively with an area under the curve (AUC) of 0.858. Resistant WTC-LI cases had significantly higher sVCAM and lower MPO with RRs of 2.24, and 2.89 respectively; AUC 0.830. Biomarkers of CVD in serum six-month post-9/11 predicted either susceptibility or resistance to WTC-LI. These biomarkers may define pathways producing or protecting subjects from pulmonary vascular disease and associated loss of lung function after an irritant exposure. PMID:22903969

Long-term Prognosis in COPD Exacerbation: Role of Biomarkers, Clinical Variables and Exacerbation Type.

Science.gov (United States)

Grolimund, Eva; Kutz, Alexander; Marlowe, Robert J; Vögeli, Alaadin; Alan, Murat; Christ-Crain, Mirjam; Thomann, Robert; Falconnier, Claudine; Hoess, Claus; Henzen, Christoph; Zimmerli, Werner; Mueller, Beat; Schuetz, Philipp

2015-06-01

Long-term outcome prediction in COPD is challenging. We conducted a prospective 5-7-year follow-up study in patients with COPD to determine the association of exacerbation type, discharge levels of inflammatory biomarkers including procalctionin (PCT), C-reactive protein (CRP), white blood cell count (WBC) and plasma proadrenomedullin (ProADM), alone or combined with demographic/clinical characteristics, with long-term all-cause mortality in the COPD setting. The analyzed cohort comprised 469 patients with index hospitalization for pneumonic (n = 252) or non-pneumonic (n = 217) COPD exacerbation. Five-to-seven-year vital status was ascertained via structured phone interviews with patients or their household members/primary care physicians. We investigated predictive accuracy using univariate and multivariate Cox regression models and area under the receiver operating characteristic curve (AUC). After a median [25th-75th percentile] 6.1 [5.6-6.5] years, mortality was 55% (95%CI 50%-59%). Discharge ProADM concentration was strongly associated with 5-7-year non-survival: adjusted hazard ratio (HR)/10-fold increase (95%CI) 10.4 (6.2-17.7). Weaker associations were found for PCT and no significant associations were found for CRP or WBC. Combining ProADM with demographic/clinical variables including age, smoking status, BMI, New York Heart Association dyspnea class, exacerbation type, and comorbidities significantly improved long-term predictive accuracy over that of the demographic/clinical model alone: AUC (95%CI) 0.745 (0.701-0.789) versus 0.727 (0.681-0.772), (p) = .043. In patients hospitalized for COPD exacerbation, discharge ProADM levels appeared to accurately predict 5-7-year all-cause mortality and to improve long-term prognostic accuracy of multidimensional demographic/clinical mortality risk assessment.

Biomarkers for Response to Neoadjuvant Chemoradiation for Rectal Cancer

International Nuclear Information System (INIS)

Kuremsky, Jeffrey G.; Tepper, Joel E.; McLeod, Howard L. Phar

2009-01-01

Locally advanced rectal cancer (LARC) is currently treated with neoadjuvant chemoradiation. Although approximately 45% of patients respond to neoadjuvant therapy with T-level downstaging, there is no effective method of predicting which patients will respond. Molecular biomarkers have been investigated for their ability to predict outcome in LARC treated with neoadjuvant chemotherapy and radiation. A literature search using PubMed resulted in the initial assessment of 1,204 articles. Articles addressing the ability of a biomarker to predict outcome for LARC treated with neoadjuvant chemotherapy and radiation were included. Six biomarkers met the criteria for review: p53, epidermal growth factor receptor (EGFR), thymidylate synthase, Ki-67, p21, and bcl-2/bax. On the basis of composite data, p53 is unlikely to have utility as a predictor of response. Epidermal growth factor receptor has shown promise as a predictor when quantitatively evaluated in pretreatment biopsies or when EGFR polymorphisms are evaluated in germline DNA. Thymidylate synthase, when evaluated for polymorphisms in germline DNA, is promising as a predictive biomarker. Ki-67 and bcl-2 are not useful in predicting outcome. p21 needs to be further evaluated to determine its usefulness in predicting outcome. Bax requires more investigation to determine its usefulness. Epidermal growth factor receptor, thymidylate synthase, and p21 should be evaluated in larger prospective clinical trials for their ability to guide preoperative therapy choices in LARC.

Feasibility of Conducting Autism Biomarker Research in the Clinical Setting.

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Sices, Laura; Pawlowski, Katherine; Farfel, Laura; Phillips, Deirdre; Howe, Yamini; Cochran, David M; Choueiri, Roula; Forbes, Peter W; Brewster, Stephanie J; Frazier, Jean A; Neumeyer, Ann; Bridgemohan, Carolyn

2017-09-01

Recruitment and completion of research activities during regular clinical care has the potential to increase research participation in complex neurodevelopmental disorders. We evaluated the feasibility, and effect on clinical care, of conducting biomarker research within a subspecialty clinical visit for autism spectrum disorder (ASD). Children, aged 5 to 10 years, were recruited by providers in ASD clinics at 5 institutions. Biomarkers collected were growth measurements, head circumference, neurologic and dysmorphology examinations, digit ratio (2D:4D) measurement, and platelet serotonin and urinary melatonin sulfate excretion levels. Parents completed the Aberrant Behavior Checklist-Community and a medical/demographic questionnaire. Cognitive level was abstracted from the medical record. Parents and clinicians completed surveys on the effect of the study on the clinical visit. Eighty-three children and their caregivers participated. Factors limiting participation included difficulty reaching families by phone and parent concern about the study blood draw requirement. All children completed at least 4 of 7 planned research activities. Demographic factors, educational placement, and child behavior were not associated with completion of study activities. Lower nonverbal cognitive function was weakly associated with fewer activities completed. Forty-four percent of clinicians reported an effect of the research study on the clinical visit. However, neither parent-reported nor clinician-reported effect was associated with the degree of study activity completion. Recruiting study participants in the context of scheduled ASD clinical visits required significant effort. However, once recruited, participants completed most study activities, regardless of behavioral symptom severity. Research activities did not adversely affect the clinical visit.

Blood eosinophil levels as a biomarker in COPD.

Science.gov (United States)

Brusselle, Guy; Pavord, Ian D; Landis, Sarah; Pascoe, Steven; Lettis, Sally; Morjaria, Nikhil; Barnes, Neil; Hilton, Emma

2018-05-01

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disorder and patients respond differently to treatment. Blood eosinophils are a potential biomarker to stratify patient subsets for COPD therapy. We reviewed the value of blood eosinophils in predicting exacerbation risk and response to corticosteroid treatment in the available literature (PubMed articles in English; keywords: "COPD" and "eosinophil"; published prior to May 2017). Overall, clinical data suggest that in patients with a history of COPD exacerbations, a higher blood eosinophil count predicts an increased risk of future exacerbations and is associated with improved response to treatment with inhaled corticosteroids (in combination with long-acting bronchodilator[s]). Blood eosinophils are therefore a promising biomarker for phenotyping patients with COPD, although prospective studies are needed to assess blood eosinophils as a biomarker of corticosteroid response for this. Copyright © 2018 Elsevier Ltd. All rights reserved.

Circulating biomarkers for predicting cardiovascular disease risk; a systematic review and comprehensive overview of meta-analyses.

Directory of Open Access Journals (Sweden)

Thijs C van Holten

Full Text Available BACKGROUND: Cardiovascular disease is one of the major causes of death worldwide. Assessing the risk for cardiovascular disease is an important aspect in clinical decision making and setting a therapeutic strategy, and the use of serological biomarkers may improve this. Despite an overwhelming number of studies and meta-analyses on biomarkers and cardiovascular disease, there are no comprehensive studies comparing the relevance of each biomarker. We performed a systematic review of meta-analyses on levels of serological biomarkers for atherothrombosis to compare the relevance of the most commonly studied biomarkers. METHODS AND FINDINGS: Medline and Embase were screened on search terms that were related to "arterial ischemic events" and "meta-analyses". The meta-analyses were sorted by patient groups without pre-existing cardiovascular disease, with cardiovascular disease and heterogeneous groups concerning general populations, groups with and without cardiovascular disease, or miscellaneous. These were subsequently sorted by end-point for cardiovascular disease or stroke and summarized in tables. We have identified 85 relevant full text articles, with 214 meta-analyses. Markers for primary cardiovascular events include, from high to low result: C-reactive protein, fibrinogen, cholesterol, apolipoprotein B, the apolipoprotein A/apolipoprotein B ratio, high density lipoprotein, and vitamin D. Markers for secondary cardiovascular events include, from high to low result: cardiac troponins I and T, C-reactive protein, serum creatinine, and cystatin C. For primary stroke, fibrinogen and serum uric acid are strong risk markers. Limitations reside in that there is no acknowledged search strategy for prognostic studies or meta-analyses. CONCLUSIONS: For primary cardiovascular events, markers with strong predictive potential are mainly associated with lipids. For secondary cardiovascular events, markers are more associated with ischemia. Fibrinogen is a

Biomarkers of drug-induced vascular injury

International Nuclear Information System (INIS)

Brott, D.; Gould, S.; Jones, H.; Schofield, J.; Prior, H.; Valentin, J.P; Bjurstrom, S.; Kenne, K.; Schuppe-Koistinen, I.; Katein, A.; Foster-Brown, L.; Betton, G.; Richardson, R.; Evans, G.; Louden, C.

2005-01-01

In pre-clinical safety studies, drug-induced vascular injury is an issue of concern because there are no obvious diagnostic markers for pre-clinical or clinical monitoring and there is an intellectual gap in our understanding of the pathogenesis of this lesion. While vasodilatation and increased shear stress appear to play a role, the exact mechanism(s) of injury to the primary targets, smooth muscle and endothelial cells are unknown. However, evaluation of novel markers for potential clinical monitoring with a mechanistic underpinning would add value in risk assessment and management. This mini review focuses on the progress to identify diagnostic markers of drug-induced vascular injury. Von Willebrand factor (vWF), released upon perturbation of endothelial cells, is transiently increased in plasma prior to morphological evidence of damage in dogs or rats treated with vascular toxicants. Therefore, vWF might be a predictive biomarker of vascular injury. However, vWF is not an appropriate biomarker of lesion progression or severity since levels return to baseline values when there is morphological evidence of injury. A potential mechanistically linked biomarker of vascular injury is caveolin-1. Expression of this protein, localized primarily to smooth muscle and endothelial cells, decreases with the onset of vascular damage. Since vascular injury involves multiple mediators and cell types, evaluation of a panel rather than a single biomarker may be more useful in monitoring early and severe progressive vascular injury

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers.

Science.gov (United States)

Yamada, Takashi; Hashimoto, Ryu-Ichiro; Yahata, Noriaki; Ichikawa, Naho; Yoshihara, Yujiro; Okamoto, Yasumasa; Kato, Nobumasa; Takahashi, Hidehiko; Kawato, Mitsuo

2017-10-01

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use. © The Author 2017. Published by Oxford University Press on behalf of CINP.

Models of Hepatocellular Carcinoma and Biomarker Strategy

Energy Technology Data Exchange (ETDEWEB)

Bagi, Cedo M., E-mail: cedo.bagi@pfizer.com; Andresen, Catharine J. [Global Science & Technology, PGRD, Pfizer Inc, Groton, CT 06340 (United States)

2010-07-07

The overwhelming need to improve preclinical models in oncology has stimulated research efforts to refine and validate robust orthotopic models that closely mimic the disease population and therefore have the potential to better predict clinical outcome with novel therapies. Sophisticated technologies including bioluminescence, contrast enhanced ultrasound imaging, positron emission tomography, computed tomography and magnetic resonance imaging have been added to existing serum- and histology-based biomarkers to assist with patient selection and the design of clinical trials. The rationale for the use of human hepatocellular carcinoma (HCC) cell lines, implementation of xenograft and orthotopic animal models and utilization of available biomarkers have been discussed, providing guidelines to facilitate preclinical research for the development of treatments for HCC patients.

Taking a new biomarker into routine use – A perspective from the routine clinical biochemistry laboratory

Science.gov (United States)

Sturgeon, Catharine; Hill, Robert; Hortin, Glen L; Thompson, Douglas

2010-01-01

There is increasing pressure to provide cost-effective healthcare based on “best practice.” Consequently, new biomarkers are only likely to be introduced into routine clinical biochemistry departments if they are supported by a strong evidence base and if the results will improve patient management and outcome. This requires convincing evidence of the benefits of introducing the new test, ideally reflected in fewer hospital admissions, fewer additional investigations and/or fewer clinic visits. Carefully designed audit and cost-benefit studies in relevant patient groups must demonstrate that introducing the biomarker delivers an improved and more effective clinical pathway. From the laboratory perspective, pre-analytical requirements must be thoroughly investigated at an early stage. Good stability of the biomarker in relevant physiological matrices is essential to avoid the need for special processing. Absence of specific timing requirements for sampling and knowledge of the effect of medications that might be used to treat the patients in whom the biomarker will be measured is also highly desirable. Analytically, automation is essential in modern high-throughput clinical laboratories. Assays must therefore be robust, fulfilling standard requirements for linearity on dilution, precision and reproducibility, both within- and between-run. Provision of measurements by a limited number of specialized reference laboratories may be most appropriate, especially when a new biomarker is first introduced into routine practice. PMID:21137030

Heterogeneity as Biomarker in Tumour Imaging (abstract only)

NARCIS (Netherlands)

Alić, L.; Veenland, J.F.

2013-01-01

PURPOSE: Tumour heterogeneity could be a valuable biomarker for differentiation, grading, response monitoring and outcome prediction. Many quantification techniques have been described, however in clinical practice these methods are scarcely used. The aim of this study is to evaluate the performance

Biomarkers and correlative endpoints for immunotherapy trials.

Science.gov (United States)

Morse, Michael A; Osada, Takuya; Hobeika, Amy; Patel, Sandip; Lyerly, H Kim

2013-01-01

Immunotherapies for lung cancer are reaching phase III clinical trial, but the ultimate success likely will depend on developing biomarkers to guide development and choosing patient populations most likely to benefit. Because the immune response to cancer involves multiple cell types and cytokines, some spatially and temporally separated, it is likely that multiple biomarkers will be required to fully characterize efficacy of the vaccine and predict eventual benefit. Peripheral blood markers of response, such as the ELISPOT assay and cytokine flow cytometry analyses of peripheral blood mononuclear cells following immunotherapy, remain the standard approach, but it is increasingly important to obtain tissue to study the immune response at the site of the tumor. Earlier clinical endpoints such as response rate and progression-free survival do not correlate with overall survival demonstrated for some immunotherapies, suggesting the need to develop other intermediary clinical endpoints. Insofar as all these biomarkers and surrogate endpoints are relevant in multiple malignancies, it may be possible to extrapolate findings to immunotherapy of lung cancer.

Taxane resistance in breast cancer: mechanisms, predictive biomarkers and circumvention strategies.

Science.gov (United States)

Murray, S; Briasoulis, E; Linardou, H; Bafaloukos, D; Papadimitriou, C

2012-11-01

prominent finding is that pharmaceutical down-regulation of HER-2 appears to reverse the taxane resistance. Currently no valid practical biomarkers exist that can predict resistance to the taxanes in breast cancer supporting the principle of individualized cancer therapy. The incorporation of several biomarker analyses into prospectively designed studies in this setting are needed. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Delphic consensus assessment: imaging and biomarkers in gastroenteropancreatic neuroendocrine tumor disease management

Directory of Open Access Journals (Sweden)

Kjell Oberg

2016-09-01

Full Text Available The complexity of the clinical management of neuroendocrine neoplasia (NEN is exacerbated by limitations in imaging modalities and a paucity of clinically useful biomarkers. Limitations in currently available imaging modalities reflect difficulties in measuring an intrinsically indolent disease, resolution inadequacies and inter-/intra-facility device variability and that RECIST (Response Evaluation Criteria in Solid Tumors criteria are not optimal for NEN. Limitations of currently used biomarkers are that they are secretory biomarkers (chromogranin A, serotonin, neuron-specific enolase and pancreastatin; monoanalyte measurements; and lack sensitivity, specificity and predictive capacity. None of them meet the NIH metrics for clinical usage. A multinational, multidisciplinary Delphi consensus meeting of NEN experts (n = 33 assessed current imaging strategies and biomarkers in NEN management. Consensus (>75% was achieved for 78% of the 142 questions. The panel concluded that morphological imaging has a diagnostic value. However, both imaging and current single-analyte biomarkers exhibit substantial limitations in measuring the disease status and predicting the therapeutic efficacy. RECIST remains suboptimal as a metric. A critical unmet need is the development of a clinico-biological tool to provide enhanced information regarding precise disease status and treatment response. The group considered that circulating RNA was better than current general NEN biomarkers and preliminary clinical data were considered promising. It was resolved that circulating multianalyte mRNA (NETest had clinical utility in both diagnosis and monitoring disease status and therapeutic efficacy. Overall, it was concluded that a combination of tumor spatial and functional imaging with circulating transcripts (mRNA would represent the future strategy for real-time monitoring of disease progress and therapeutic efficacy.

Biomarkers for equine joint injury and osteoarthritis.

Science.gov (United States)

McIlwraith, C Wayne; Kawcak, Christopher E; Frisbie, David D; Little, Christopher B; Clegg, Peter D; Peffers, Mandy J; Karsdal, Morten A; Ekman, Stina; Laverty, Sheila; Slayden, Richard A; Sandell, Linda J; Lohmander, L S; Kraus, Virginia B

2018-03-01

We report the results of a symposium aimed at identifying validated biomarkers that can be used to complement clinical observations for diagnosis and prognosis of joint injury leading to equine osteoarthritis (OA). Biomarkers might also predict pre-fracture change that could lead to catastrophic bone failure in equine athletes. The workshop was attended by leading scientists in the fields of equine and human musculoskeletal biomarkers to enable cross-disciplinary exchange and improve knowledge in both. Detailed proceedings with strategic planning was written, added to, edited and referenced to develop this manuscript. The most recent information from work in equine and human osteoarthritic biomarkers was accumulated, including the use of personalized healthcare to stratify OA phenotypes, transcriptome analysis of anterior cruciate ligament (ACL) and meniscal injuries in the human knee. The spectrum of "wet" biomarker assays that are antibody based that have achieved usefulness in both humans and horses, imaging biomarkers and the role they can play in equine and human OA was discussed. Prediction of musculoskeletal injury in the horse remains a challenge, and the potential usefulness of spectroscopy, metabolomics, proteomics, and development of biobanks to classify biomarkers in different stages of equine and human OA were reviewed. The participants concluded that new information and studies in equine musculoskeletal biomarkers have potential translational value for humans and vice versa. OA is equally important in humans and horses, and the welfare issues associated with catastrophic musculoskeletal injury in horses add further emphasis to the need for good validated biomarkers in the horse. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:823-831, 2018. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Potential biomarkers for bipolar disorder: Where do we stand?

Directory of Open Access Journals (Sweden)

Rajesh Sagar

2017-01-01

Full Text Available Bipolar disorder (BD is a severe, recurrent mood disorder, associated with a significant morbidity and mortality, with high rates of suicides and medical comorbidities. There is a high risk of mood disorders among the first-degree relatives of patients with BD. In the current clinical practice, the diagnosis of BD is made by history taking, interview and behavioural observations, thereby lacking an objective, biological validation. This approach may result in underdiagnosis, misdiagnosis and eventually poorer outcomes. Due to the heterogeneity of BD, the possibility of developing a single, specific biomarker is still remote; however, there is a set of promising biomarkers which may serve as predictive, prognostic or treatment markers in the future. The review presents a critical appraisal and update on some of the most promising candidates for biomarkers, namely, neuroimaging markers, peripheral biomarkers and genetic markers, including a brief discussion on cognitive endophenotypes as indicative of genetic risk. The lessons learnt from other fields and specialties in medicine need to be applied to psychiatry to translate the knowledge from 'bench to bedside' by means of clinically useful biomarkers. Overall, the biomarkers may help in pushing the shift towards personalized medicine for psychiatric patients.

Biomarkers for personalized oncology: recent advances and future challenges.

Science.gov (United States)

Kalia, Madhu

2015-03-01

Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells and oncology is a branch of medicine that deals with tumors. The last decade has seen significant advances in the development of biomarkers in oncology that play a critical role in understanding molecular and cellular mechanisms which drive tumor initiation, maintenance and progression. Clinical molecular diagnostics and biomarker discoveries in oncology are advancing rapidly as we begin to understand the complex mechanisms that transform a normal cell into an abnormal one. These discoveries have fueled the development of novel drug targets and new treatment strategies. The standard of care for patients with advanced-stage cancers has shifted away from an empirical treatment strategy based on the clinical-pathological profile to one where a biomarker driven treatment algorithm based on the molecular profile of the tumor is used. Recent advances in multiplex genotyping technologies and high-throughput genomic profiling by next-generation sequencing make possible the rapid and comprehensive analysis of the cancer genome of individual patients even from very little tumor biopsy material. Predictive (diagnostic) biomarkers are helpful in matching targeted therapies with patients and in preventing toxicity of standard (systemic) therapies. Prognostic biomarkers identify somatic germ line mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncotherapy for the treatment of five diseases: chronic myeloid leukemia, colon, breast, lung cancer and melanoma and these biomarkers are being used successfully to evaluate benefits that can be achieved through targeted therapy. Examples of these molecularly targeted biomarker therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia and

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research.

Science.gov (United States)

W Adams, Zachary; McClure, Erin A; Gray, Kevin M; Danielson, Carla Kmett; Treiber, Frank A; Ruggiero, Kenneth J

2017-02-01

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders. Copyright © 2016 Elsevier Ltd. All rights reserved.

Molecular alterations and biomarkers in colorectal cancer

Science.gov (United States)

Grady, William M.; Pritchard, Colin C.

2013-01-01

The promise of precision medicine is now a clinical reality. Advances in our understanding of the molecular genetics of colorectal cancer genetics is leading to the development of a variety of biomarkers that are being used as early detection markers, prognostic markers, and markers for predicting treatment responses. This is no more evident than in the recent advances in testing colorectal cancers for specific molecular alterations in order to guide treatment with the monoclonal antibody therapies cetuximab and panitumumab, which target the epidermal growth factor receptor (EGFR). In this review, we update a prior review published in 2010 and describe our current understanding of the molecular pathogenesis of colorectal cancer and how these alterations relate to emerging biomarkers for early detection and risk stratification (diagnostic markers), prognosis (prognostic markers), and the prediction of treatment responses (predictive markers). PMID:24178577

Prediction of Chronic Kidney Disease Stage 3 by CKD273, a Urinary Proteomic Biomarker

DEFF Research Database (Denmark)

Pontillo, Claudia; Zhang, Zhen-Yu; Schanstra, Joost P

2017-01-01

Introduction: CKD273 is a urinary biomarker, which in advanced chronic kidney disease predicts further deterioration. We investigated whether CKD273 can also predict a decline of estimated glomerular filtration rate (eGFR) to ... threshold (P = 0.086). Discussion: In conclusion, while accounting for baseline eGFR, albuminuria, and covariables, CKD273 adds to the prediction of stage 3 chronic kidney disease, at which point intervention remains an achievable therapeutic target....

Association of oxidative stress biomarkers with adiposity and clinical staging in women with breast cancer.

Science.gov (United States)

Carioca, A A F; Verde, S M M L; Luzia, L A; Rondó, P H C; Latorre, M R D O; Ellery, T H P; Damasceno, N R T

2015-11-01

Breast cancer is a disease characterised by both oxidative reactions and inflammation. However, few studies have focused on the oxidative and inflammatory biomarkers. The aim of the present study was to evaluate the association between oxidative stress markers and adiposity and clinical staging, as well as the association between the oxidative and the antioxidant biomarkers of women with breast cancer. A total of 135 cases of breast cancer occurring in 2011 and 2012 were assessed. After exclusions, 101 pre- and post-menopausal women with clinical staging I to IV were eligible to participate in the study. The anthropometric evaluation was performed by collecting data on waist circumference, body mass index and body composition. The socioeconomic and clinical profiles were determined using a standard questionnaire. For the oxidative biomarkers, thiobarbituric acid reactive substances (TBARS), oxidative DNA damage (8-hydroxy-2-deoxyguanosine (8-OHdG)), low-density lipoprotein(-) (LDL(-)), autoantibody anti-LDL(-) and liposoluble antioxidants (α-tocopherol, retinol and β-carotene) were analysed. The data were analysed using differences in the mean values, correlation tests and multiple linear regression. The antioxidant levels were higher in postmenopausal women with clinical staging I and II and negative lymph nodes. The TBARS level was associated with clinical staging. Adiposity was associated with levels of retinol and 8-OHdG, whereas LDL(-), 8-OHdG and TBARS were correlated with liposoluble antioxidants after adjusting for the confounders. The adiposity and clinical staging of patients were associated with oxidative stress. The oxidative and antioxidant biomarkers showed a negative correlation in patients with breast cancer.

Biomarkers, Trauma, and Sepsis in Pediatrics: A Review

Directory of Open Access Journals (Sweden)

Marianne Frieri

2016-01-01

Full Text Available Context: There is a logical connection with biomarkers, trauma, and sepsis. This review paper provides new information and clinical practice implications. Biomarkers are very important especially in pediatrics. Procalcitonin and other biomarkers are helpful in identifying neonatal sepsis, defense mechanisms of the immune system. Pediatric trauma and sepsis is very important both in infants and in children. Stress management both in trauma is based upon the notion that stress causes an immune imbalance in susceptible individuals. Evidence Acquisition: Data sources included studies indexed in PubMed, a meta- analysis, predictive values, research strategies, and quality assessments. A recent paper by one of the authors stated marked increase in serum procalcitonin during the course of a septic process often indicates an exacerbation of the illness, and a decreasing level is a sign of improvement. A review of epidemiologic studies on pediatric soccer patients was also addressed. Keywords for searching included biomarkers, immunity, trauma, and sepsis. Results: Of 50 reviewed articles, 34 eligible articles were selected including biomarkers, predictive values for procalcitonin, identifying children at risk for intra-abdominal injuries, blunt trauma, and epidemiology, a meta-analysis. Of neonatal associated sepsis, the NF-kappa B pathway by inflammatory stimuli in human neutrophils, predictive value of gelsolin for the outcomes of preterm neonates, a meta-analysis interleukin-8 for neonatal sepsis diagnosis. Conclusions: Biomarkers are very important especially in pediatrics. Procalcitonin and other biomarkers are helpful in identifying neonatal sepsis, defense mechanisms, and physiological functions of the immune system. Pediatric trauma and sepsis is very important both in infants and in children. Various topics were covered such as biomarkers, trauma, sepsis, inflammation, innate immunity, role of neutrophils and IL-8, reactive oxygen species

[Alzheimer's disease cerebro-spinal fluid biomarkers: A clinical research tool sometimes useful in daily clinical practice of memory clinics for the diagnosis of complex cases].

Science.gov (United States)

Magnin, E; Dumurgier, J; Bouaziz-Amar, E; Bombois, S; Wallon, D; Gabelle, A; Lehmann, S; Blanc, F; Bousiges, O; Hannequin, D; Jung, B; Miguet-Alfonsi, C; Quillard, M; Pasquier, F; Peoc'h, K; Laplanche, J-L; Hugon, J; Paquet, C

2017-04-01

The role of biomarkers in clinical research was recently highlighted in the new criteria for the diagnosis of Alzheimer's disease. Cerebro-spinal fluid (CSF) biomarkers (total Tau protein, threonine 181 phosphorylated Tau protein and amyloid Aβ1-42 peptide) are associated with cerebral neuropathological lesions observed in Alzheimer's disease (neuronal death, neurofibrillary tangle with abnormal Tau deposits and amyloid plaque). Aβ1-40 amyloid peptide dosage helps to interpret Aβ1-42 results. As suggested in the latest international criteria and the French HAS (Haute Autorité de santé) recommendations, using theses CSF biomarkers should not be systematic but sometimes could be performed to improve confidence about the diagnostic of Alzheimer's disease in young subjects or in complex clinical situations. Future biomarkers actually in development will additionally help in diagnostic process (differential diagnosis) and in prognostic evaluation of neurodegenerative diseases. Copyright © 2016 Société Nationale Française de Médecine Interne (SNFMI). Published by Elsevier SAS. All rights reserved.

Development and validation of a Luminex assay for detection of a predictive biomarker for PROSTVAC-VF therapy

Science.gov (United States)

Lucas, Julie L.; Tacheny, Erin A.; Ferris, Allison; Galusha, Michelle; Srivastava, Apurva K.; Ganguly, Aniruddha; Williams, P. Mickey; Sachs, Michael C.; Thurin, Magdalena; Tricoli, James V.; Ricker, Winnie; Gildersleeve, Jeffrey C.

2017-01-01

Cancer therapies can provide substantially improved survival in some patients while other seemingly similar patients receive little or no benefit. Strategies to identify patients likely to respond well to a given therapy could significantly improve health care outcomes by maximizing clinical benefits while reducing toxicities and adverse effects. Using a glycan microarray assay, we recently reported that pretreatment serum levels of IgM specific to blood group A trisaccharide (BG-Atri) correlate positively with overall survival of cancer patients on PROSTVAC-VF therapy. The results suggested anti-BG-Atri IgM measured prior to treatment could serve as a biomarker for identifying patients likely to benefit from PROSTVAC-VF. For continued development and clinical application of serum IgM specific to BG-Atri as a predictive biomarker, a clinical assay was needed. In this study, we developed and validated a Luminex-based clinical assay for measuring serum IgM specific to BG-Atri. IgM levels were measured with the Luminex assay and compared to levels measured using the microarray for 126 healthy individuals and 77 prostate cancer patients. This assay provided reproducible and consistent results with low %CVs, and tolerance ranges were established for the assay. IgM levels measured using the Luminex assay were found to be highly correlated to the microarray results with R values of 0.93–0.95. This assay is a Laboratory Developed Test (LDT) and is suitable for evaluating thousands of serum samples in CLIA certified laboratories that have validated the assay. In addition, the study demonstrates that discoveries made using neoglycoprotein-based microarrays can be readily migrated to a clinical assay. PMID:28771597

Analysis of a urinary biomarker panel for obstructive nephropathy and clinical outcomes.

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Yuanyuan Xie

Full Text Available To follow up renal function changes in patients with obstructive nephropathy and to evaluate the predictive value of biomarker panel in renal prognosis.A total of 108 patients with obstructive nephropathy were enrolled in the study; 90 patients completed the follow-up. At multiple time points before and after obstruction resolution, urinary samples were prospectively collected in patients with obstructive nephropathy; the levels of urinary kidney injury molecule-1 (uKIM-1, liver-type fatty acid-binding protein (uL-FABP, and neutrophil gelatinase associated lipocalin (uNGAL were determined by enzyme-linked immunosorbent assay (ELISA. After 1 year of follow-up, the predictive values of biomarker panel for determining the prognosis of obstructive nephropathy were evaluated.uKIM-1 (r = 0.823, uL-FABP (r = 0.670, and uNGAL (r = 0.720 levels were positively correlated with the serum creatinine level (all P96.69 pg/mg creatinine (Cr, a preoperative uL-FABP>154.62 ng/mg Cr, and a 72-h postoperative uL-FABP>99.86 ng/mg Cr were all positively correlated with poor prognosis (all P<0.01.Biomarker panel may be used as a marker for early screening of patients with obstructive nephropathy and for determining poor prognosis.

Evaluation of a web based informatics system with data mining tools for predicting outcomes with quantitative imaging features in stroke rehabilitation clinical trials

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Wang, Ximing; Kim, Bokkyu; Park, Ji Hoon; Wang, Erik; Forsyth, Sydney; Lim, Cody; Ravi, Ragini; Karibyan, Sarkis; Sanchez, Alexander; Liu, Brent

2017-03-01

Quantitative imaging biomarkers are used widely in clinical trials for tracking and evaluation of medical interventions. Previously, we have presented a web based informatics system utilizing quantitative imaging features for predicting outcomes in stroke rehabilitation clinical trials. The system integrates imaging features extraction tools and a web-based statistical analysis tool. The tools include a generalized linear mixed model(GLMM) that can investigate potential significance and correlation based on features extracted from clinical data and quantitative biomarkers. The imaging features extraction tools allow the user to collect imaging features and the GLMM module allows the user to select clinical data and imaging features such as stroke lesion characteristics from the database as regressors and regressands. This paper discusses the application scenario and evaluation results of the system in a stroke rehabilitation clinical trial. The system was utilized to manage clinical data and extract imaging biomarkers including stroke lesion volume, location and ventricle/brain ratio. The GLMM module was validated and the efficiency of data analysis was also evaluated.

Intracranial pressure-induced optic nerve sheath response as a predictive biomarker for optic disc edema in astronauts.

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Wostyn, Peter; De Deyn, Peter Paul

2017-11-01

A significant proportion of the astronauts who spend extended periods in microgravity develop ophthalmic abnormalities. Understanding this syndrome, called visual impairment and intracranial pressure (VIIP), has become a high priority for National Aeronautics and Space Administration, especially in view of future long-duration missions (e.g., Mars missions). Moreover, to ensure selection of astronaut candidates who will be able to complete long-duration missions with low risk of the VIIP syndrome, it is imperative to identify biomarkers for VIIP risk prediction. Here, we hypothesize that the optic nerve sheath response to alterations in intracranial pressure may be a potential predictive biomarker for optic disc edema in astronauts. If confirmed, this biomarker could be used for preflight identification of astronauts at risk for developing VIIP-associated optic disc edema.

Biomarkers as Potential Treatment Targets in Inflammatory Bowel Disease: A Systematic Review

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Travis B Murdoch

2015-01-01

Full Text Available There is increasing interest in the concept of ‘treat-to-target’ in inflammatory bowel disease as a mechanism to standardize management and prevent complications. While clinical, radiographic and endoscopic treatment end points will figure prominently in this promising management paradigm, the role that noninvasive biomarkers will play is currently undefined. The goal of the present systematic review was to investigate the potential value of biomarkers as treatment targets in inflammatory bowel disease, with particular focus on those best studied: serum C-reactive protein (CRP and fecal calprotectin. In Crohn disease, elevated CRP levels at baseline predict response to anti-tumour necrosis factor agents, and normalization is usually associated with clinical and endoscopic remission. CRP and hemoglobin levels can be used to help predict clinical relapse in the context of withdrawal of therapy. Ultimately, the authors conclude that currently available biomarkers should not be used as treatment targets in inflammatory bowel disease because they have inadequate operational characteristics to make them safe surrogates for clinical, endoscopic and radiographic evaluation. However, CRP and fecal calprotectin are important adjunctive measures that help alert the clinician to pursue further investigation.

Regulatory T cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to intravenous immunoglobulin therapy.

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Maddur, Mohan S; Stephen-Victor, Emmanuel; Das, Mrinmoy; Prakhar, Praveen; Sharma, Varun K; Singh, Vikas; Rabin, Magalie; Trinath, Jamma; Balaji, Kithiganahalli N; Bolgert, Francis; Vallat, Jean-Michel; Magy, Laurent; Kaveri, Srini V; Bayry, Jagadeesh

2017-03-20

Intravenous immunoglobulin (IVIG) is a polyspecific pooled immunoglobulin G preparation and one of the commonly used therapeutics for autoimmune diseases including those of neurological origin. A recent report in murine model proposed that IVIG expands regulatory T (T reg ) cells via induction of interleukin 33 (IL-33). However, translational insight on these observations is lacking. Ten newly diagnosed Guillain-Barré syndrome (GBS) patients were treated with IVIG at the rate of 0.4 g/kg for three to five consecutive days. Clinical evaluation for muscular weakness was performed by Medical Research Council (MRC) and modified Rankin scoring (MRS) system. Heparinized blood samples were collected before and 1, 2, and 4-5 weeks post-IVIG therapy. Peripheral blood mononuclear cells were stained for surface CD4 and intracellular Foxp3, IFN-γ, and tumor necrosis factor alpha (TNF-α) and were analyzed by flow cytometry. IL-33 and prostaglandin E2 in the plasma were measured by ELISA. The fold changes in plasma IL-33 at week 1 showed no correlation with the MRC and MRS scores at weeks 1, 2, and ≥4 post-IVIG therapy. Clinical recovery following IVIG therapy appears to be associated with T reg cell response. Contrary to murine study, there was no association between the fold changes in IL-33 at week 1 and T reg cell frequency at weeks 1, 2, and ≥4 post-IVIG therapy. T reg cell-mediated clinical response to IVIG therapy in GBS patients was associated with reciprocal regulation of effector T cells-expressing TNF-α. T reg cell expansion by IVIG in patients with autoimmune diseases lack correlation with IL-33. T reg cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to IVIG therapy.

Innovative biomarkers for predicting type 2 diabetes mellitus: relevance to dietary management of frailty in older adults.

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Ikwuobe, John; Bellary, Srikanth; Griffiths, Helen R

2016-06-01

Type 2 diabetes mellitus (T2DM) increases in prevalence in the elderly. There is evidence for significant muscle loss and accelerated cognitive impairment in older adults with T2DM; these comorbidities are critical features of frailty. In the early stages of T2DM, insulin sensitivity can be improved by a "healthy" diet. Management of insulin resistance by diet in people over 65 years of age should be carefully re-evaluated because of the risk for falling due to hypoglycaemia. To date, an optimal dietary programme for older adults with insulin resistance and T2DM has not been described. The use of biomarkers to identify those at risk for T2DM will enable clinicians to offer early dietary advice that will delay onset of disease and of frailty. Here we have used an in silico literature search for putative novel biomarkers of T2DM risk and frailty. We suggest that plasma bilirubin, plasma, urinary DPP4-positive microparticles and plasma pigment epithelium-derived factor merit further investigation as predictive biomarkers for T2DM and frailty risk in older adults. Bilirubin is screened routinely in clinical practice. Measurement of specific microparticle frequency in urine is less invasive than a blood sample so is a good choice for biomonitoring. Future studies should investigate whether early dietary changes, such as increased intake of whey protein and micronutrients that improve muscle function and insulin sensitivity, affect biomarkers and can reduce the longer term complication of frailty in people at risk for T2DM.

Thalamic functional connectivity predicts seizure laterality in individual TLE patients: Application of a biomarker development strategy

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Daniel S. Barron

2015-01-01

No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons. Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

The discovery and development of proteomic safety biomarkers for the detection of drug-induced liver toxicity

International Nuclear Information System (INIS)

Amacher, David E.

2010-01-01

Biomarkers are biometric measurements that provide critical quantitative information about the biological condition of the animal or individual being tested. In drug safety studies, established toxicity biomarkers are used along with other conventional study data to determine dose-limiting organ toxicity, and to define species sensitivity for new chemical entities intended for possible use as human medicines. A continuing goal of drug safety scientists in the pharmaceutical industry is to discover and develop better trans-species biomarkers that can be used to determine target organ toxicities for preclinical species in short-term studies at dose levels that are some multiple of the intended human dose and again later in full development for monitoring clinical trials at lower therapeutic doses. Of particular value are early, predictive, noninvasive biomarkers that have in vitro, in vivo, and clinical transferability. Such translational biomarkers bridge animal testing used in preclinical science and human studies that are part of subsequent clinical testing. Although suitable for in vivo preclinical regulatory studies, conventional hepatic safety biomarkers are basically confirmatory markers because they signal organ toxicity after some pathological damage has occurred, and are therefore not well-suited for short-term, predictive screening assays early in the discovery-to-development progression of new chemical entities (NCEs) available in limited quantities. Efforts between regulatory agencies and the pharmaceutical industry are underway for the coordinated discovery, qualification, verification and validation of early predictive toxicity biomarkers. Early predictive safety biomarkers are those that are detectable and quantifiable prior to the onset of irreversible tissue injury and which are associated with a mechanism of action relevant to a specific type of potential hepatic injury. Potential drug toxicity biomarkers are typically endogenous macromolecules in

[Inflammatory biomarkers in ischemic acute coronary syndrome].

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Domínguez-Rodríguez, Alberto; Abreu-González, Pedro

2015-10-01

Diagnosing acute coronary syndrome (ACS) in the emergency department is often a complex process. Inflammatory markers might be useful for the rapid assessment of a patient's overall risk and might also help predict future episodes. The clinical use of these biomarkers could potentially lower the number of emergency visits and help in the prevention of future adverse events. The aim of this review was to evaluate the clinical utility of markers of cardiovascular inflammation in emergency patients with ACS. Based on a critical analysis of a selection of the literature, we concluded that none of the biomarkers of cardiovascular inflammation would at present be useful for stratifying risk in emergency situations, aiding prognosis, or guiding therapy for patients with ACS.

The utility of P300 as a schizophrenia endophenotype and predictive biomarker: clinical and socio-demographic modulators in COGS-2.

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Turetsky, Bruce I; Dress, Erich M; Braff, David L; Calkins, Monica E; Green, Michael F; Greenwood, Tiffany A; Gur, Raquel E; Gur, Ruben C; Lazzeroni, Laura C; Nuechterlein, Keith H; Radant, Allen D; Seidman, Larry J; Siever, Larry J; Silverman, Jeremy M; Sprock, Joyce; Stone, William S; Sugar, Catherine A; Swerdlow, Neal R; Tsuang, Debby W; Tsuang, Ming T; Light, Gregory

2015-04-01

Reduced auditory P300 amplitude is a robust schizophrenia deficit exhibiting the qualities of a viable genetic endophenotype. These include heritability, test-retest reliability, and trait-like stability. Recent evidence suggests that P300 may also serve as a predictive biomarker for transition to psychosis during the schizophrenia prodrome. Historically, the utility of the P300 has been limited by its clinical nonspecificity, cross-site measurement variability, and required EEG expertise. The Consortium on the Genetics of Schizophrenia (COGS-2) study provided an opportunity to examine the consistency of the measure across multiple sites with varying degrees of EEG experience, and to identify important modulating factors that contribute to measurement variability. Auditory P300 was acquired from 649 controls and 587 patients at 5 sites. An overall patient deficit was observed with effect size 0.62. Each site independently observed a significant patient deficit, but site differences also existed. In patients, site differences reflected clinical differences in positive symptomatology and functional capacity. In controls, site differences reflected differences in racial stratification, smoking and substance use history. These factors differentially suppressed the P300 response, but only in control subjects. This led to an attenuated patient-control difference among smokers and among African Americans with history of substance use. These findings indicate that the P300 can be adequately assessed quantitatively, across sites, without substantial EEG expertise. Measurements are suitable for both genetic endophenotype analyses and studies of psychosis risk and conversion. However, careful attention must be given to selection of appropriate comparison samples to avoid misleading false negative results. Copyright © 2014 Elsevier B.V. All rights reserved.

Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making.

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Prescott, Jeffrey William

2013-02-01

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.

Neuropathological biomarker candidates in brain tumors: key issues for translational efficiency.

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Hainfellner, J A; Heinzl, H

2010-01-01

Brain tumors comprise a large spectrum of rare malignancies in children and adults that are often associated with severe neurological symptoms and fatal outcome. Neuropathological tumor typing provides both prognostic and predictive tissue information which is the basis for optimal postoperative patient management and therapy. Molecular biomarkers may extend and refine prognostic and predictive information in a brain tumor case, providing more individualized and optimized treatment options. In the recent past a few neuropathological brain tumor biomarkers have translated smoothly into clinical use whereas many candidates show protracted translation. We investigated the causes of protracted translation of candidate brain tumor biomarkers. Considering the research environment from personal, social and systemic perspectives we identified eight determinants of translational success: methodology, funding, statistics, organization, phases of research, cooperation, self-reflection, and scientific progeny. Smoothly translating biomarkers are associated with low degrees of translational complexity whereas biomarkers with protracted translation are associated with high degrees. Key issues for translational efficiency of neuropathological brain tumor biomarker research seem to be related to (i) the strict orientation to the mission of medical research, that is the improval of medical practice as primordial purpose of research, (ii) definition of research priorities according to clinical needs, and (iii) absorption of translational complexities by means of operatively beneficial standards. To this end, concrete actions should comprise adequate scientific education of young investigators, and shaping of integrative diagnostics and therapy research both on the local level and the level of influential international brain tumor research platforms.

Predictive Potential of Twenty-Two Biochemical Biomarkers for Coronary Artery Disease in Type 2 Diabetes Mellitus

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Edimar Cristiano Pereira

2015-01-01

Full Text Available We investigated the potential of a panel of 22 biomarkers to predict the presence of coronary artery disease (CAD in type 2 diabetes mellitus (DM2 patients. The study enrolled 96 DM2 patients with (n = 75 and without (n = 21 evidence of CAD. We assessed a biochemical profile that included 22 biomarkers: total cholesterol, LDL, HDL, LDL/HDL, triglycerides, glucose, glycated hemoglobin, fructosamine, homocysteine, cysteine, methionine, reduced glutathione, oxidized glutathione, reduced glutathione/oxidized glutathione, L-arginine, asymmetric dimethyl-L-arginine, symmetric dimethyl-L-arginine, asymmetric dimethyl-L-arginine/L-arginine, nitrate plus nitrite, S-nitrosothiols, nitrotyrosine, and n-acetyl-β-glucosaminidase. Prediction models were built using logistic regression models. We found that eight biomarkers (methionine, nitratate plus nitrite, n-acetyl-β-glucosaminidase, BMI, LDL, HDL, reduced glutathione, and L-arginine/asymmetric dimethyl-L-arginine along with gender and BMI were significantly associated with the odds of CAD in DM2. These preliminary findings support the notion that emerging biochemical markers might be used for CAD prediction in patients with DM2. Our findings warrant further investigation with large, well-designed studies.

Potential of Mass Spectrometry in Developing Clinical Laboratory Biomarkers of Nonvolatiles in Exhaled Breath.

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Beck, Olof; Olin, Anna-Carin; Mirgorodskaya, Ekaterina

2016-01-01

Exhaled breath contains nonvolatile substances that are part of aerosol particles of submicrometer size. These particles are formed and exhaled as a result of normal breathing and contain material from distal airways of the respiratory system. Exhaled breath can be used to monitor biomarkers of both endogenous and exogenous origin and constitutes an attractive specimen for medical investigations. This review summarizes the present status regarding potential biomarkers of nonvolatile compounds in exhaled breath. The field of exhaled breath condensate is briefly reviewed, together with more recent work on more selective collection procedures for exhaled particles. The relation of these particles to the surfactant in the terminal parts of the respiratory system is described. The literature on potential endogenous low molecular weight compounds as well as protein biomarkers is reviewed. The possibility to measure exposure to therapeutic and abused drugs is demonstrated. Finally, the potential future role and importance of mass spectrometry is discussed. Nonvolatile compounds exit the lung as aerosol particles that can be sampled easily and selectively. The clinical applications of potential biomarkers in exhaled breath comprise diagnosis of disease, monitoring of disease progress, monitoring of drug therapy, and toxicological investigations. © 2015 American Association for Clinical Chemistry.

From differences in means between cases and controls to risk stratification: a business plan for biomarker development.

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Wentzensen, Nicolas; Wacholder, Sholom

2013-02-01

Researchers developing biomarkers for early detection can determine the potential for clinical benefit at early stages of development. We provide the theoretical background showing the quantitative connection between biomarker levels in cases and controls and clinically meaningful risk measures, as well as a spreadsheet for researchers to use in their own research. We provide researchers with tools to decide whether a test is useful, whether it needs technical improvement, whether it may work only in specific populations, or whether any further development is futile. The methods described here apply to any method that aims to estimate risk of disease based on biomarkers, clinical tests, genetics, environment, or behavior. Many efforts go into futile biomarker development and premature clinical testing. In many instances, predictions for translational success or failure can be made early, simply based on critical analysis of case–control data. Our article presents well-established theory in a form that can be appreciated by biomarker researchers. Furthermore, we provide an interactive spreadsheet that links biomarker performance with specific disease characteristics to evaluate the promise of biomarker candidates at an early stage.

Biomarkers in Vasculitis

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Monach, Paul A.

2014-01-01

Purpose of review Better biomarkers are needed for guiding management of patients with vasculitis. Large cohorts and technological advances had led to an increase in pre-clinical studies of potential biomarkers. Recent findings The most interesting markers described recently include a gene expression signature in CD8+ T cells that predicts tendency to relapse or remain relapse-free in ANCA-associated vasculitis, and a pair of urinary proteins that are elevated in Kawasaki disease but not other febrile illnesses. Both of these studies used “omics” technologies to generate and then test hypotheses. More conventional hypothesis-based studies have indicated that the following circulating proteins have potential to improve upon clinically available tests: pentraxin-3 in giant cell arteritis and Takayasu’s arteritis; von Willebrand factor antigen in childhood central nervous system vasculitis; eotaxin-3 and other markers related to eosinophils or Th2 immune responses in eosinophilic granulomatosis with polyangiitis (Churg-Strauss syndrome); and MMP-3, TIMP-1, and CXCL13 in ANCA-associated vasculitis. Summary New markers testable in blood and urine have the potential to assist with diagnosis, staging, assessment of current disease activity, and prognosis. However, the standards for clinical usefulness, in particular the demonstration of either very high sensitivity or very high specificity, have yet to be met for clinically relevant outcomes. PMID:24257367

Angiogenesis-Related Biomarkers (sFlt-1/PLGF in the Prediction and Diagnosis of Placental Dysfunction: An Approach for Clinical Integration

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Ignacio Herraiz

2015-08-01

Full Text Available Placental dysfunction is involved in a group of obstetrical conditions including preeclampsia, intrauterine growth restriction, and placental abruption. Their timely and accurate recognition is often a challenge since diagnostic criteria are still based on nonspecific signs and symptoms. The discovering of the role of angiogenic-related factors (sFlt-1/PlGF in the underlying pathophysiology of placental dysfunction, taking into account that angiogenesis-related biomarkers are not specific to any particular placental insufficiency-related disease, has marked an important step for improving their early diagnosis and prognosis assessment. However, sFlt-1/PlGF has not been yet established as a part of most guidelines. We will review the current evidence on the clinical utility of sFlt-1/PlGF and propose a new protocol for its clinical integration.

Cardiovascular biomarkers in clinical studies of type 2 diabetes

DEFF Research Database (Denmark)

Baldassarre, M P A; Andersen, A; Consoli, A

2018-01-01

biomarkers and 3) novel biomarkers (oxidative stress and endothelial dysfunction biomarkers). Within each category we present the currently best validated biomarkers with special focus on the population of interest (type 2 diabetes). For each individual biomarker, the physiological role, the validation...

New developments and concepts related to biomarker application to vaccines

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Ahmed, S. Sohail; Black, Steve; Ulmer, Jeffrey

2012-01-01

Summary This minireview will provide a perspective on new developments and concepts related to biomarker applications for vaccines. In the context of preventive vaccines, biomarkers have the potential to predict adverse events in select subjects due to differences in genetic make‐up/underlying medical conditions or to predict effectiveness (good versus poor response). When expanding them to therapeutic vaccines, their utility in identification of patients most likely to respond favourably (or avoid potentially negative effects of treatment) becomes self‐explanatory. Despite the progress made so far on dissection of various pathways of biological significance in humans, there is still plenty to unravel about the mysteries related to the quantitative and qualitative aspects of the human host response. This review will provide a focused overview of new concepts and developments in the field of vaccine biomarkers including (i) vaccine‐dependent signatures predicting subject response and safety, (ii) predicting therapeutic vaccine efficacy in chronic diseases, (iii) exploring the genetic make‐up of the host that may modulate subject‐specific adverse events or affect the quality of immune responses, and (iv) the topic of volunteer stratification as a result of biomarker screening (e.g. for therapeutic vaccines but also potentially for preventive vaccines) or as a reflection of an effort to compare select groups (e.g. vaccinated subjects versus patients recovering from infection) to enable the discovery of clinically relevant biomarkers for preventive vaccines. PMID:21895991

Precision Oncology Medicine: The Clinical Relevance of Patient-Specific Biomarkers Used to Optimize Cancer Treatment.

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Schmidt, Keith T; Chau, Cindy H; Price, Douglas K; Figg, William D

2016-12-01

Precision medicine in oncology is the result of an increasing awareness of patient-specific clinical features coupled with the development of genomic-based diagnostics and targeted therapeutics. Companion diagnostics designed for specific drug-target pairs were the first to widely utilize clinically applicable tumor biomarkers (eg, HER2, EGFR), directing treatment for patients whose tumors exhibit a mutation susceptible to an FDA-approved targeted therapy (eg, trastuzumab, erlotinib). Clinically relevant germline mutations in drug-metabolizing enzymes and transporters (eg, TPMT, DPYD) have been shown to impact drug response, providing a rationale for individualized dosing to optimize treatment. The use of multigene expression-based assays to analyze an array of prognostic biomarkers has been shown to help direct treatment decisions, especially in breast cancer (eg, Oncotype DX). More recently, the use of next-generation sequencing to detect many potential "actionable" cancer molecular alterations is further shifting the 1 gene-1 drug paradigm toward a more comprehensive, multigene approach. Currently, many clinical trials (eg, NCI-MATCH, NCI-MPACT) are assessing novel diagnostic tools with a combination of different targeted therapeutics while also examining tumor biomarkers that were previously unexplored in a variety of cancer histologies. Results from ongoing trials such as the NCI-MATCH will help determine the clinical utility and future development of the precision-medicine approach. © 2016, The American College of Clinical Pharmacology.

Clinical Risk Stratification Optimizes Value of Biomarkers to Predict New-Onset Heart Failure in a Community-Based Cohort

NARCIS (Netherlands)

Brouwers, Frank P.; van Gilst, Wiek H.; Damman, Kevin; van den Berg, Maarten P.; Gansevoort, Ron T.; Bakker, Stephan J. L.; Hillege, Hans L.; van Veldhuisen, Dirk J.; van der Harst, Pim; de Boer, Rudolf A.

Background-We aim to identify and quantify the value of biomarkers for incident new-onset heart failure (HF) in a community-based cohort and subgroups based on cardiovascular risk and evaluate the prognostic value of 13 biomarkers for HF with reduced and preserved ejection fraction. Methods and

Biomarkers in acute heart failure.

Science.gov (United States)

Mallick, Aditi; Januzzi, James L

2015-06-01

The care of patients with acutely decompensated heart failure is being reshaped by the availability and understanding of several novel and emerging heart failure biomarkers. The gold standard biomarkers in heart failure are B-type natriuretic peptide and N-terminal pro-B-type natriuretic peptide, which play an important role in the diagnosis, prognosis, and management of acute decompensated heart failure. Novel biomarkers that are increasingly involved in the processes of myocardial injury, neurohormonal activation, and ventricular remodeling are showing promise in improving diagnosis and prognosis among patients with acute decompensated heart failure. These include midregional proatrial natriuretic peptide, soluble ST2, galectin-3, highly-sensitive troponin, and midregional proadrenomedullin. There has also been an emergence of biomarkers for evaluation of acute decompensated heart failure that assist in the differential diagnosis of dyspnea, such as procalcitonin (for identification of acute pneumonia), as well as markers that predict complications of acute decompensated heart failure, such as renal injury markers. In this article, we will review the pathophysiology and usefulness of established and emerging biomarkers for the clinical diagnosis, prognosis, and management of acute decompensated heart failure. Copyright © 2015 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

The Wide and Complex Field of NAFLD Biomarker Research: Trends.

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Wichro, Erika; Macheiner, Tanja; Schmid, Jasmin; Kavsek, Barbara; Sargsyan, Karine

2014-01-01

Background. Nonalcoholic fatty liver disease is now acknowledged as a complex public health issue linked to sedentary lifestyle, obesity, and related disorders like type 2 diabetes and metabolic syndrome. Aims. We aimed to retrieve its trends out of the huge amount of published data. Therefore, we conducted an extensive literature search to identify possible biomarker and/or biomarker combinations by retrospectively assessing and evaluating common and novel biomarkers to predict progression and prognosis of obesity related liver diseases. Methodology. We analyzed finally 62 articles accounting for 157 cohorts and 45,288 subjects. Results. Despite the various approaches, most cohorts were considerably small and rarely comparable. Also, we found that the same standard parameters were measured rather than novel biomarkers. Diagnostics approaches appeared incomparable. Conclusions. Further collaborative investigations on harmonizing ways of data acquisition and identifying such biomarkers for clinical use are necessary to yield sufficient significant results of potential biomarkers.

WONOEP appraisal: Development of epilepsy biomarkers-What we can learn from our patients?

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Jozwiak, Sergiusz; Becker, Albert; Cepeda, Carlos; Engel, Jerome; Gnatkovsky, Vadym; Huberfeld, Gilles; Kaya, Mehmet; Kobow, Katja; Simonato, Michele; Loeb, Jeffrey A

2017-06-01

Current medications for patients with epilepsy work in only two of three patients. For those medications that do work, they only suppress seizures. They treat the symptoms, but do not modify the underlying disease, forcing patients to take these drugs with significant side effects, often for the rest of their lives. A major limitation in our ability to advance new therapeutics that permanently prevent, reduce the frequency of, or cure epilepsy comes from a lack of understanding of the disease coupled with a lack of reliable biomarkers that can predict who has or who will get epilepsy. The main goal of this report is to present a number of approaches for identifying reliable biomarkers from observing patients with brain disorders that have a high probability of producing epilepsy. A given biomarker, or more likely a profile of biomarkers, will have both a quantity and a time course during epileptogenesis that can be used to predict who will get the disease, to confirm epilepsy as a diagnosis, to identify coexisting pathologies, and to monitor the course of treatments. Additional studies in patients and animal models could identify common and clinically valuable biomarkers to successfully translate animal studies into new and effective clinical trials. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Relevance of Follow-Up in Patients with Core Clinical Criteria for Alzheimer Disease and Normal CSF biomarkers.

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Vercruysse, Olivier; Paquet, Claire; Gabelle, Audrey; Delbeuck, Xavier; Blanc, Frederic; Wallon, David; Dumurgier, Julien; Magnin, Eloi; Martinaud, Olivier; Jung, Barbara; Bousiges, Olivier; Lehmann, Sylvain; Delaby, Constance; Quillard-Murain, Muriel; Peoc'h, Katell; Laplanche, Jean-Louis; Bouaziz-Amar, Elodie; Hannequin, Didier; Sablonniere, Bernard; Buee, Luc; Hugon, Jacques; Schraen, Susanna; Pasquier, Florence; Bombois, Stephanie

2018-01-09

Few patients with a normal cerebrospinal fluid (CSF) biomarker profile fulfill the clinical criteria for Alzheimer disease (AD). The aim of this study was to test the hypothesis of misdiagnoses for these patients. Patients from the e-PLM centers fulfilling the core clinical criteria for probable AD dementia or mild cognitive impairment due to AD (AD-MCI), with normal CSF A1-42, T-tau and P-tau biomarkers and clinical follow-up, were included. Clinical and imaging data were reviewed by an independent board, from baseline (visit with clinical evaluation and CSF analysis) to the end of the follow-up, for a final diagnosis. In the e-PLM cohort of 1098 AD patients with CSF analysis, 37 (3.3%) patients (20 with AD dementia core clinical criteria and 17 with AD-MCI core clinical criteria) had normal CSF biomarker profile and a clinical follow-up. All patients presented with episodic memory impairment and 27 (73%) had medial temporal lobe atrophy on MRI-scan. After a median follow-up of 36 months (range 7-74), the final diagnosis was AD MCI or dementia for 9 (24%) patients, and unlikely due to AD for 28 (76%) patients. A misdiagnosis was corrected in 18 (49%) patients (mood disorders, non-AD degenerative dementia, vascular cognitive impairment, alcohol cognitive disorders, temporal epilepsy and hippocampal sclerosis), and 10 (27%) patients had cognitive disorders of undetermined etiology. AD diagnosis (MCI or dementia) with normal CSF biomarkers is a rare condition. A clinical follow-up is particularly recommended to consider an alternative diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Association of definition of acute kidney injury by cystatin C rise with biomarkers and clinical outcomes in children undergoing cardiac surgery.

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Zappitelli, Michael; Greenberg, Jason H; Coca, Steven G; Krawczeski, Catherine D; Li, Simon; Thiessen-Philbrook, Heather R; Bennett, Michael R; Devarajan, Prasad; Parikh, Chirag R

2015-06-01

Research has identified improved biomarkers of acute kidney injury (AKI). Cystatin C (CysC) is a better glomerular filtration rate marker than serum creatinine (SCr) and may improve AKI definition. To determine if defining clinical AKI by increases in CysC vs SCr alters associations with biomarkers and clinical outcomes. Three-center prospective cohort study of intensive care units in New Haven, Connecticut, Cincinnati, Ohio, and Montreal, Quebec, Canada. Participants were 287 patients 18 years or younger without preoperative AKI or end-stage renal disease who were undergoing cardiac surgery. The study dates were July 1, 2007, through December 31, 2009. For biomarker vs clinical AKI associations, the exposures were first postoperative (0-6 hours after surgery) urine interleukin 18, neutrophil gelatinase-associated lipocalin, kidney injury molecule 1, and liver fatty acid-binding protein. For clinical AKI outcome associations, the exposure was Kidney Disease: Improving Global Outcomes AKI definition (based on SCr or CysC). Clinical AKI, length of stay, and length of mechanical ventilation. We determined areas under the receiver operating characteristic curve and odds ratios for first postoperative biomarkers to predict AKI. The SCr-defined vs CysC-defined AKI incidence differed substantially (43.6% vs 20.6%). Percentage agreement was 71% (κ = 0.38); stage 2 or worse AKI percentage agreement was 95%. Interleukin 18 and kidney injury molecule 1 discriminated for CysC-defined AKI better than for SCr-defined AKI. For interleukin 18 and kidney injury molecule 1, the areas under the receiver operating characteristic curve were 0.74 and 0.65, respectively, for CysC-defined AKI, and 0.66 and 0.58, respectively, for SCr-defined AKI. Fifth (vs first) quintile concentrations of both biomarkers were more strongly associated with CysC-defined AKI. For interleukin 18 and kidney injury molecule 1, the odds ratios were 16.19 (95% CI, 3.55-73.93) and 6.93 (95% CI, 1

Curated compendium of human transcriptional biomarker data.

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Golightly, Nathan P; Bell, Avery; Bischoff, Anna I; Hollingsworth, Parker D; Piccolo, Stephen R

2018-04-17

One important use of genome-wide transcriptional profiles is to identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for clinical application. Data from thousands of translational-biomarker studies have been deposited in public repositories, enabling reuse. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways. To address this problem, we curated 45 publicly available, translational-biomarker datasets from a variety of human diseases. To increase the data's utility, we reprocessed the raw expression data using a uniform computational pipeline, addressed quality-control problems, mapped the clinical annotations to a controlled vocabulary, and prepared consistently structured, analysis-ready data files. These data, along with scripts we used to prepare the data, are available in a public repository. We believe these data will be particularly useful to researchers seeking to perform benchmarking studies-for example, to compare and optimize machine-learning algorithms' ability to predict biomedical outcomes.

Omalizumab for severe asthma: toward personalized treatment based on biomarker profile and clinical history

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Tabatabaian F

2018-04-01

Full Text Available Farnaz Tabatabaian, Dennis K Ledford Division of Allergy and Immunology, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA Abstract: Asthma is a heterogeneous syndrome with numerous underlining molecular and inflammatory mechanisms contributing to the wide spectrum of clinical phenotypes. Multiple therapies targeting severe asthma with type 2 (T2 high inflammation are or soon will be available. T2 high inflammation is defined as inflammation associated with atopy or eosinophilia or an increase in cytokines associated with T-helper 2 lymphocytes. Omalizumab is a humanized anti-IgE monoclonal antibody and the first biologic therapy approved for moderate–severe allergic asthma. Despite the specificity of biologic therapies like omalizumab, clinical response is variable, with approximately 50% of treated patients achieving the primary outcome. A prior identification of the ideal candidate for therapy would improve patient outcomes and optimize the use of health care resources. As the number of biologic therapies for asthma increases, the goal is identification of biomarkers or clinical phenotypes likely to respond to a specific therapy. This review focuses on potential biomarkers and clinical history that may identify responders to omalizumab therapy for asthma. Keywords: severe persistent asthma, asthma phenotype and endotype, T2 high inflammation, omalizumab, asthma biomarkers, eosinophils, fractional exhaled nitric oxide, IgE

Knowledge-based identification of soluble biomarkers: hepatic fibrosis in NAFLD as an example.

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Page, Sandra; Birerdinc, Aybike; Estep, Michael; Stepanova, Maria; Afendy, Arian; Petricoin, Emanuel; Younossi, Zobair; Chandhoke, Vikas; Baranova, Ancha

2013-01-01

The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD) as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1) NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2) biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases.

Knowledge-based identification of soluble biomarkers: hepatic fibrosis in NAFLD as an example.

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Sandra Page

Full Text Available The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1 NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2 biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases.

Proteomic Approaches in Biomarker Discovery: New Perspectives in Cancer Diagnostics

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Kocevar, Nina; Komel, Radovan

2014-01-01

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies. PMID:24550697

Pretreatment data is highly predictive of liver chemistry signals in clinical trials

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Cai Z

2012-11-01

Full Text Available Zhaohui Cai,1,* Anders Bresell,2,* Mark H Steinberg,1 Debra G Silberg,1 Stephen T Furlong11AstraZeneca Pharmaceuticals, Wilmington, DE, USA; 2AstraZeneca Pharmaceuticals, Södertälje, Sweden*These authors contributed equally to this workPurpose: The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline information.Patients and methods: Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results.Results: Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests.Conclusion: It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.Keywords: bilirubin, Hy’s Law, ALT, GGT, baseline, prediction

Serum Metabolite Biomarkers Discriminate Healthy Smokers from COPD Smokers

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Chen, Qiuying; Deeb, Ruba S.; Ma, Yuliang; Staudt, Michelle R.; Crystal, Ronald G.; Gross, Steven S.

2015-01-01

COPD (chronic obstructive pulmonary disease) is defined by a fixed expiratory airflow obstruction associated with disordered airways and alveolar destruction. COPD is caused by cigarette smoking and is the third greatest cause of mortality in the US. Forced expiratory volume in 1 second (FEV1) is the only validated clinical marker of COPD, but it correlates poorly with clinical features and is not sensitive enough to predict the early onset of disease. Using LC/MS global untargeted metabolite profiling of serum samples from a well-defined cohort of healthy smokers (n = 37), COPD smokers (n = 41) and non-smokers (n = 37), we sought to discover serum metabolic markers with known and/or unknown molecular identities that are associated with early-onset COPD. A total of 1,181 distinct molecular ions were detected in 95% of sera from all study subjects and 23 were found to be differentially-expressed in COPD-smokers vs. healthy-smokers. These 23 putative biomarkers were differentially-correlated with lung function parameters and used to generate a COPD prediction model possessing 87.8% sensitivity and 86.5% specificity. In an independent validation set, this model correctly predicted COPD in 8/10 individuals. These serum biomarkers included myoinositol, glycerophopshoinositol, fumarate, cysteinesulfonic acid, a modified version of fibrinogen peptide B (mFBP), and three doubly-charged peptides with undefined sequence that significantly and positively correlate with mFBP levels. Together, elevated levels of serum mFBP and additional disease-associated biomarkers point to a role for chronic inflammation, thrombosis, and oxidative stress in remodeling of the COPD airways. Serum metabolite biomarkers offer a promising and accessible window for recognition of early-stage COPD. PMID:26674646

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

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Cole, James H.; Poudel, Rudra P. K.; Tsagkrasoulis, Dimosthenis; Caan, Matthan W. A.; Steves, Claire; Spector, Tim D.; Montana, Giovanni

2017-01-01

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of

DI/LC-MS/MS-Based Metabolic Profiling for Identification of Early Predictive Serum Biomarkers of Metritis in Transition Dairy Cows.

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Zhang, Guanshi; Deng, Qilan; Mandal, Rupasri; Wishart, David S; Ametaj, Burim N

2017-09-27

The objectives of this study were to evaluate alterations of metabolites in the blood of dairy cows before, during, and after diagnosis of metritis and identify predictive serum metabolite biomarkers for metritis. DI/LC-MS/MS was used to analyze serum samples collected from both healthy and metritic cows during -8, -4, disease diagnosis, +4, and +8 wks relative to parturition. Results indicated that cows with metritis experienced altered concentrations of serum amino acids, glycerophospholipids, sphingolipids, acylcarnitines, and biogenic amines during the entire experimental period. Moreover, two sets of predictive biomarker models and one set of diagnostic biomarker models for metritis were developed, and all of them showed high sensitivity and specificity (e.g., high AUC values by the ROC curve evaluation), which indicate that serum metabolites identified have pretty accurate predictive, diagnostic, and prognostic abilities for metritis in transition dairy cows.

Data from a targeted proteomics approach to discover biomarkers in saliva for the clinical diagnosis of periodontitis

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V. Orti

2018-06-01

Full Text Available This study focused on the search for new biomarkers based on liquid chromatography-multiple reaction monitoring (LC-MRM proteomics profiling of whole saliva from patients with periodontitis compared to healthy subjects. The LC-MRM profiling approach is a new and innovative method that has already been validated for the absolute and multiplexed quantification of biomarkers in several diseases. The dataset for this study was produced using LC-MRM to monitor protein levels in a multiplex assay, it provides clinical information on salivary biomarkers of periodontitis. The data presented here is an extension of our recently published research article (Mertens et al., 2017 [1]. Keywords: Clinical chemistry, Mass spectrometry, Proteomics, Saliva biochemistry, Oral disease, Periodontitis

Microparticles provide a novel biomarker to predict severe clinical outcomes of dengue virus infection.

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Punyadee, Nuntaya; Mairiang, Dumrong; Thiemmeca, Somchai; Komoltri, Chulaluk; Pan-Ngum, Wirichada; Chomanee, Nusara; Charngkaew, Komgrid; Tangthawornchaikul, Nattaya; Limpitikul, Wannee; Vasanawathana, Sirijitt; Malasit, Prida; Avirutnan, Panisadee

2015-02-01

Shedding of microparticles (MPs) is a consequence of apoptotic cell death and cellular activation. Low levels of circulating MPs in blood help maintain homeostasis, whereas increased MP generation is linked to many pathological conditions. Herein, we investigated the role of MPs in dengue virus (DENV) infection. Infection of various susceptible cells by DENV led to apoptotic death and MP release. These MPs harbored a viral envelope protein and a nonstructural protein 1 (NS1) on their surfaces. Ex vivo analysis of clinical specimens from patients with infections of different degrees of severity at multiple time points revealed that MPs generated from erythrocytes and platelets are two major MP populations in the circulation of DENV-infected patients. Elevated levels of red blood cell-derived MPs (RMPs) directly correlated with DENV disease severity, whereas a significant decrease in platelet-derived MPs was associated with a bleeding tendency. Removal by mononuclear cells of complement-opsonized NS1-anti-NS1 immune complexes bound to erythrocytes via complement receptor type 1 triggered MP shedding in vitro, a process that could explain the increased levels of RMPs in severe dengue. These findings point to the multiple roles of MPs in dengue pathogenesis. They offer a potential novel biomarker candidate capable of differentiating dengue fever from the more serious dengue hemorrhagic fever. Dengue is the most important mosquito-transmitted viral disease in the world. No vaccines or specific treatments are available. Rapid diagnosis and immediate treatment are the keys to achieve a positive outcome. Dengue virus (DENV) infection, like some other medical conditions, changes the level and composition of microparticles (MPs), tiny bag-like structures which are normally present at low levels in the blood of healthy individuals. This study investigated how MPs in culture and patients' blood are changed in response to DENV infection. Infection of cells led to programmed

Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance

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Gonzalez de Castro, D; Clarke, P A; Al-Lazikani, B; Workman, P

2013-01-01

The progressive elucidation of the molecular pathogenesis of cancer has fueled the rational development of targeted drugs for patient populations stratified by genetic characteristics. Here we discuss general challenges relating to molecular diagnostics and describe predictive biomarkers for personalized cancer medicine. We also highlight resistance mechanisms for epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer. We envisage a future requiring the use of longitudinal genome sequencing and other omics technologies alongside combinatorial treatment to overcome cellular and molecular heterogeneity and prevent resistance caused by clonal evolution. PMID:23361103

Evaluation of different biomarkers to predict individual radiosensitivity in an inter-laboratory comparison--lessons for future studies.

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Burkhard Greve

Full Text Available Radiotherapy is a powerful cure for several types of solid tumours, but its application is often limited because of severe side effects in individual patients. With the aim to find biomarkers capable of predicting normal tissue side reactions we analysed the radiation responses of cells from individual head and neck tumour and breast cancer patients of different clinical radiosensitivity in a multicentric study. Multiple parameters of cellular radiosensitivity were analysed in coded samples of peripheral blood lymphocytes (PBLs and derived lymphoblastoid cell lines (LCLs from 15 clinical radio-hypersensitive tumour patients and compared to age- and sex-matched non-radiosensitive patient controls and 15 lymphoblastoid cell lines from age- and sex- matched healthy controls of the KORA study. Experimental parameters included ionizing radiation (IR-induced cell death (AnnexinV, induction and repair of DNA strand breaks (Comet assay, induction of yH2AX foci (as a result of DNA double strand breaks, and whole genome expression analyses. Considerable inter-individual differences in IR-induced DNA strand breaks and their repair and/or cell death could be detected in primary and immortalised cells with the applied assays. The group of clinically radiosensitive patients was not unequivocally distinguishable from normal responding patients nor were individual overreacting patients in the test system unambiguously identified by two different laboratories. Thus, the in vitro test systems investigated here seem not to be appropriate for a general prediction of clinical reactions during or after radiotherapy due to the experimental variability compared to the small effect of radiation sensitivity. Genome-wide expression analysis however revealed a set of 67 marker genes which were differentially induced 6 h after in vitro-irradiation in lymphocytes from radio-hypersensitive and non-radiosensitive patients. These results warrant future validation in larger

Screening Preoperative Peptide Biomarkers for Predicting Postoperative Myocardial Infarction after Coronary Artery Bypass Grafting

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Jiang, Zhibin; Hu, Ping; Liu, Jianxin; Wang, Dianjun; Jin, Longyu; Hong, Chao

2014-01-01

Postoperative myocardial infarction (PMI) is one of the most serious complications of cardiac surgeries. No preoperative biomarker is currently available for predicting PMI after cardiac surgeries. In the present study, we used a phage display peptide library to screen potential preoperative peptide biomarkers for predicting PMI after coronary artery bypass grafting (CABG) surgery. Twenty patients who developed PMI after CABG and 20 age-, sex-, and body mass index-matched patients without PMI after CABG were enrolled as a discovery cohort. Another 50 patients who developed PMI after CABG and 50 randomly selected patients without PMI after CABG were enrolled as a validation cohort to validate the potential peptide biomarkers identified in the discovery cohort. Fifty randomly selected healthy volunteers were also enrolled in the validation phase as a healthy control group. In the discovery/screening phase, 17 out of 20 randomly selected phage clones exhibited specific reaction with purified sera IgG from the PMI group, among which 11 came from the same phage clone with inserted peptide sequence GVIMVIAVSCVF (named PMI-1). In the validation phase, phage ELISA showed that serum IgG from 90% of patients in the PMI group had a positive reaction with PMI-1; in contrast, only 14% and 6% of patients in the non-PMI group and the healthy control group had a positive reaction with PMI-1, respectively. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the PMI-1 phage clone to preoperatively identify patients who would develop PMI after CABG were 90.0%, 86.0%, 86.5, 89.5% and 88.0%, respectively. The absorbance value of the PMI-1 phage clone showed statistically significant correlation with the peak postoperative serum cardiac troponin I level (r = 0.349, p = 0.012) in the PMI group. In conclusion, we for the first time identified a mimic peptide (PMI-1) with high validity in preoperative prediction of PMI after CABG. PMID

Prediction of fruit and vegetable intake from biomarkers using individual participant data of diet-controlled intervention studies

DEFF Research Database (Denmark)

Souverein, Olga W; de Vries, Jeanne H M; Freese, Riitta

2015-01-01

concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable...

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

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Kirouac, Daniel C; Schaefer, Gabriele; Chan, Jocelyn; Merchant, Mark; Orr, Christine; Huang, Shih-Min A; Moffat, John; Liu, Lichuan; Gadkar, Kapil; Ramanujan, Saroja

2017-01-01

Approximately 10% of colorectal cancers harbor BRAF V600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

Searching for neurodegeneration in multiple sclerosis at clinical onset: Diagnostic value of biomarkers.

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Novakova, Lenka; Axelsson, Markus; Malmeström, Clas; Imberg, Henrik; Elias, Olle; Zetterberg, Henrik; Nerman, Olle; Lycke, Jan

2018-01-01

Neurodegeneration occurs during the early stages of multiple sclerosis. It is an essential, devastating part of the pathophysiology. Tools for measuring the degree of neurodegeneration could improve diagnostics and patient characterization. This study aimed to determine the diagnostic value of biomarkers of degeneration in patients with recent clinical onset of suspected multiple sclerosis, and to evaluate these biomarkers for characterizing disease course. This cross-sectional study included 271 patients with clinical features of suspected multiple sclerosis onset and was the baseline of a prospective study. After diagnostic investigations, the patients were classified into the following disease groups: patients with clinically isolated syndrome (n = 4) or early relapsing remitting multiple sclerosis (early RRMS; n = 93); patients with relapsing remitting multiple sclerosis with disease durations ≥2 years (established RRMS; n = 39); patients without multiple sclerosis, but showing symptoms (symptomatic controls; n = 89); and patients diagnosed with other diseases (n = 46). In addition, we included healthy controls (n = 51) and patients with progressive multiple sclerosis (n = 23). We analyzed six biomarkers of neurodegeneration: cerebrospinal fluid neurofilament light chain levels; cerebral spinal fluid glial fibrillary acidic protein; cerebral spinal fluid tau; retinal nerve fiber layer thickness; macula volume; and the brain parenchymal fraction. Except for increased cerebral spinal fluid neurofilament light chain levels, median 670 ng/L (IQR 400-2110), we could not find signs of early degeneration in the early disease group with recent clinical onset. However, the intrathecal immunoglobin G production and cerebral spinal fluid neurofilament light chain levels showed diagnostic value. Moreover, elevated levels of cerebral spinal fluid glial fibrillary acidic protein, thin retinal nerve fiber layers, and low brain parenchymal fractions were associated with

The Emerging Role of Biomarkers in the Diagnosis of Gestational Diabetes Mellitus

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Natassia Rodrigo

2018-05-01

Full Text Available Gestational diabetes mellitus (GDM is a common complication of pregnancy; its rising incidence is a result of increased maternal obesity and older maternal age together with altered diagnostic criteria identifying a greater proportion of pregnant women with GDM. Its consequences are far-reaching, associated with poorer maternal and neonatal outcomes compared to non-GDM pregnancies, and GDM has implications for metabolic health in both mother and offspring. Objective markers to identify women at high risk for the development of GDM are useful to target therapy and potentially prevent its development. Established clinical risk factors for GDM include overweight/obesity, age, ethnicity, and family history of diabetes, though they lack specificity for its development. The addition of biomarkers to predictive models of GDM may improve the ability to identify women at risk of GDM prior to its development. These biomarkers reflect the pathophysiologic mechanisms of GDM involving insulin resistance, chronic inflammation, and altered placental function. In addition, the role of epigenetic changes in GDM pathogenesis highlights the complex interplay between genetic and environmental factors, potentially offering further refinement of the prediction of GDM risk. In this review, we will discuss the clinical challenges associated with the diagnosis of GDM and its current pathophysiologic basis, giving rise to potential biomarkers that may aid in its identification. While not yet validated for clinical use, we explore the possible clinical role of biomarkers in the future. We also explore novel diagnostic tools, including high throughput methodologies, that may have potential future application in the identification of women with GDM.

The potential biomarkers in predicting pathologic response of breast cancer to three different chemotherapy regimens: a case control study

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Xu Chaoyang

2009-07-01

Full Text Available Abstract Background Preoperative chemotherapy (PCT has become the standard of care in locally advanced breast cancer. The identification of patient-specific tumor characteristics that can improve the ability to predict response to therapy would help optimize treatment, improve treatment outcomes, and avoid unnecessary exposure to potential toxicities. This study is to determine whether selected biomarkers could predict pathologic response (PR of breast tumors to three different PCT regimens, and to identify a subset of patients who would benefit from a given type of treatment. Methods 118 patients with primary breast tumor were identified and three PCT regimens including DEC (docetaxel+epirubicin+cyclophosphamide, VFC (vinorelbine/vincristine+5-fluorouracil+cyclophosphamide and EFC (epirubicin+5-fluorouracil+cyclophosphamide were investigated. Expression of steroid receptors, HER2, P-gp, MRP, GST-pi and Topo-II was evaluated by immunohistochemical scoring on tumor tissues obtained before and after PCT. The PR of breast carcinoma was graded according to Sataloff's classification. Chi square test, logistic regression and Cochran-Mantel-Haenszel assay were performed to determine the association between biomarkers and PR, as well as the effectiveness of each regimen on induction of PR. Results There was a clear-cut correlation between the expression of ER and decreased PR to PCT in all three different regimens (p p Conclusion ER is an independent predictive factor for PR to PCT regimens including DEC, VFC and EFC in primary breast tumors, while HER2 is only predictive for DEC regimen. Expression of PgR, Topo-II, P-gp, MRP and GST-pi are not predictive for PR to any PCT regimens investigated. Results obtained in this clinical study may be helpful for the selection of appropriate treatments for breast cancer patients.

Imaging biomarkers to predict response to anti-HER2 (ErbB2) therapy in preclinical models of breast cancer

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Shah, Chirayu; Miller, Todd W.; Wyatt, Shelby K.; McKinley, Eliot T.; Olivares, Maria Graciela; Sanchez, Violeta; Nolting, Donald D.; Buck, Jason R.; Zhao, Ping; Ansari, M. Sib; Baldwin, Ronald M.; Gore, John C.; Schiff, Rachel; Arteaga, Carlos L.; Manning, H. Charles

2010-01-01

Purpose To evaluate non-invasive imaging methods as predictive biomarkers of response to trastuzumab in mouse models of HER2-overexpressing breast cancer. The correlation between tumor regression and molecular imaging of apoptosis, glucose metabolism, and cellular proliferation was evaluated longitudinally in responding and non-responding tumor-bearing cohorts. Experimental Design Mammary tumors from MMTV/HER2 transgenic female mice were transplanted into syngeneic female mice. BT474 human breast carcinoma cell line xenografts were grown in athymic nude mice. Tumor cell apoptosis (NIR700-Annexin-V accumulation), glucose metabolism ([18F]FDG-PET), and proliferation ([18F]FLT-PET) were evaluated throughout a bi-weekly trastuzumab regimen. Imaging metrics were validated by direct measurement of tumor size and immunohistochemical (IHC) analysis of cleaved caspase-3, phosphorylated AKT (p-AKT) and Ki67. Results NIR700-Annexin-V accumulated significantly in trastuzumab-treated MMTV/HER2 and BT474 tumors that ultimately regressed, but not in non-responding or vehicle-treated tumors. Uptake of [18F]FDG was not affected by trastuzumab treatment in MMTV/HER2 or BT474 tumors. [18F]FLT PET imaging predicted trastuzumab response in BT474 tumors but not in MMTV/HER2 tumors, which exhibited modest uptake of [18F]FLT. Close agreement was observed between imaging metrics and IHC analysis. Conclusions Molecular imaging of apoptosis accurately predicts trastuzumab-induced regression of HER2(+) tumors and may warrant clinical exploration to predict early response to neoadjuvant trastuzumab. Trastuzumab does not appear to alter glucose metabolism substantially enough to afford [18F]FDG-PET significant predictive value in this setting. Although promising in one preclinical model, further studies are required to determine the overall value of [18F]FLT-PET as a biomarker of response to trastuzumab in HER2+ breast cancer. PMID:19584166

Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers.

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Rantalainen, Mattias; Klevebring, Daniel; Lindberg, Johan; Ivansson, Emma; Rosin, Gustaf; Kis, Lorand; Celebioglu, Fuat; Fredriksson, Irma; Czene, Kamila; Frisell, Jan; Hartman, Johan; Bergh, Jonas; Grönberg, Henrik

2016-11-30

Sequencing-based breast cancer diagnostics have the potential to replace routine biomarkers and provide molecular characterization that enable personalized precision medicine. Here we investigate the concordance between sequencing-based and routine diagnostic biomarkers and to what extent tumor sequencing contributes clinically actionable information. We applied DNA- and RNA-sequencing to characterize tumors from 307 breast cancer patients with replication in up to 739 patients. We developed models to predict status of routine biomarkers (ER, HER2,Ki-67, histological grade) from sequencing data. Non-routine biomarkers, including mutations in BRCA1, BRCA2 and ERBB2(HER2), and additional clinically actionable somatic alterations were also investigated. Concordance with routine diagnostic biomarkers was high for ER status (AUC = 0.95;AUC(replication) = 0.97) and HER2 status (AUC = 0.97;AUC(replication) = 0.92). The transcriptomic grade model enabled classification of histological grade 1 and histological grade 3 tumors with high accuracy (AUC = 0.98;AUC(replication) = 0.94). Clinically actionable mutations in BRCA1, BRCA2 and ERBB2(HER2) were detected in 5.5% of patients, while 53% had genomic alterations matching ongoing or concluded breast cancer studies. Sequencing-based molecular profiling can be applied as an alternative to histopathology to determine ER and HER2 status, in addition to providing improved tumor grading and clinically actionable mutations and molecular subtypes. Our results suggest that sequencing-based breast cancer diagnostics in a near future can replace routine biomarkers.

Impact of biomarker development on drug safety assessment

International Nuclear Information System (INIS)

Marrer, Estelle; Dieterle, Frank

2010-01-01

Drug safety has always been a key aspect of drug development. Recently, the Vioxx case and several cases of serious adverse events being linked to high-profile products have increased the importance of drug safety, especially in the eyes of drug development companies and global regulatory agencies. Safety biomarkers are increasingly being seen as helping to provide the clarity, predictability, and certainty needed to gain confidence in decision making: early-stage projects can be stopped quicker, late-stage projects become less risky. Public and private organizations are investing heavily in terms of time, money and manpower on safety biomarker development. An illustrative and 'door opening' safety biomarker success story is the recent recognition of kidney safety biomarkers for pre-clinical and limited translational contexts by FDA and EMEA. This milestone achieved for kidney biomarkers and the 'know how' acquired is being transferred to other organ toxicities, namely liver, heart, vascular system. New technologies and molecular-based approaches, i.e., molecular pathology as a complement to the classical toolbox, allow promising discoveries in the safety biomarker field. This review will focus on the utility and use of safety biomarkers all along drug development, highlighting the present gaps and opportunities identified in organ toxicity monitoring. A last part will be dedicated to safety biomarker development in general, from identification to diagnostic tests, using the kidney safety biomarkers success as an illustrative example.

Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers.

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Cole, James H; Franke, Katja

2017-12-01

The brain changes as we age and these changes are associated with functional deterioration and neurodegenerative disease. It is vital that we better understand individual differences in the brain ageing process; hence, techniques for making individualised predictions of brain ageing have been developed. We present evidence supporting the use of neuroimaging-based 'brain age' as a biomarker of an individual's brain health. Increasingly, research is showing how brain disease or poor physical health negatively impacts brain age. Importantly, recent evidence shows that having an 'older'-appearing brain relates to advanced physiological and cognitive ageing and the risk of mortality. We discuss controversies surrounding brain age and highlight emerging trends such as the use of multimodality neuroimaging and the employment of 'deep learning' methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Profiling biomarkers of traumatic axonal injury: From mouse to man.

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Manivannan, Susruta; Makwana, Milan; Ahmed, Aminul Islam; Zaben, Malik

2018-05-18

Traumatic brain injury (TBI) poses a major public health problem on a global scale. Its burden results from high mortality and significant morbidity in survivors. This stems, in part, from an ongoing inadequacy in diagnostic and prognostic indicators despite significant technological advances. Traumatic axonal injury (TAI) is a key driver of the ongoing pathological process following TBI, causing chronic neurological deficits and disability. The science underpinning biomarkers of TAI has been a subject of many reviews in recent literature. However, in this review we provide a comprehensive account of biomarkers from animal models to clinical studies, bridging the gap between experimental science and clinical medicine. We have discussed pathogenesis, temporal kinetics, relationships to neuro-imaging, and, most importantly, clinical applicability in order to provide a holistic perspective of how this could improve TBI diagnosis and predict clinical outcome in a real-life setting. We conclude that early and reliable identification of axonal injury post-TBI with the help of body fluid biomarkers could enhance current care of TBI patients by (i) increasing speed and accuracy of diagnosis, (ii) providing invaluable prognostic information, (iii) allow efficient allocation of rehabilitation services, and (iv) provide potential therapeutic targets. The optimal model for assessing TAI is likely to involve multiple components, including several blood biomarkers and neuro-imaging modalities, at different time points. Copyright © 2018. Published by Elsevier B.V.

The Functional Diffusion Map: An Imaging Biomarker for the Early Prediction of Cancer Treatment Outcome

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Bradford A. Moffat

2006-04-01

Full Text Available Functional diffusion map (fDM has been recently reported as an early and quantitative biomarker of clinical brain tumor treatment outcome. This MRI approach spatially maps and quantifies treatment-induced changes in tumor water diffusion values resulting from alterations in cell density/cell membrane function and microenvironment. This current study was designed to evaluate the capability of fDM for preclinical evaluation of dose escalation studies and to determine if these changes were correlated with outcome measures (cell kill and overall survival. Serial T2-weighted and diffusion MRI were carried out on rodents with orthotopically implanted 9L brain tumors receiving three doses of 1,3-bis(2-chloroethyl-1-nitrosourea (6.65, 13.3, and 26.6 mg/kg, i.p.. All images were coregistered to baseline T2-weighted images for fDM analysis. Analysis of tumor fDM data on day 4 posttreatment detected dosedependent changes in tumor diffusion values, which were also found to be spatially dependent. Histologic analysis of treated tumors confirmed spatial changes in cellularity as observed by fDM. Early changes in tumor diffusion values were found to be highly correlative with drug dose and independent biologic outcome measures (cell kill and survival. Therefore, the fDM imaging biomarker for early prediction of treatment efficacy can be used in the drug development process.

Increasing the predictive accuracy of amyloid-β blood-borne biomarkers in Alzheimer's disease.

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Watt, Andrew D; Perez, Keyla A; Faux, Noel G; Pike, Kerryn E; Rowe, Christopher C; Bourgeat, Pierrick; Salvado, Olivier; Masters, Colin L; Villemagne, Victor L; Barnham, Kevin J

2011-01-01

Diagnostic measures for Alzheimer's disease (AD) commonly rely on evaluating the levels of amyloid-β (Aβ) peptides within the cerebrospinal fluid (CSF) of affected individuals. These levels are often combined with levels of an additional non-Aβ marker to increase predictive accuracy. Recent efforts to overcome the invasive nature of CSF collection led to the observation of Aβ species within the blood cellular fraction, however, little is known of what additional biomarkers may be found in this membranous fraction. The current study aimed to undertake a discovery-based proteomic investigation of the blood cellular fraction from AD patients (n = 18) and healthy controls (HC; n = 15) using copper immobilized metal affinity capture and Surface Enhanced Laser Desorption/Ionisation Time-Of-Flight Mass Spectrometry. Three candidate biomarkers were observed which could differentiate AD patients from HC (ROC AUC > 0.8). Bivariate pairwise comparisons revealed significant correlations between these markers and measures of AD severity including; MMSE, composite memory, brain amyloid burden, and hippocampal volume. A partial least squares regression model was generated using the three candidate markers along with blood levels of Aβ. This model was able to distinguish AD from HC with high specificity (90%) and sensitivity (77%) and was able to separate individuals with mild cognitive impairment (MCI) who converted to AD from MCI non-converters. While requiring further characterization, these candidate biomarkers reaffirm the potential efficacy of blood-based investigations into neurodegenerative conditions. Furthermore, the findings indicate that the incorporation of non-amyloid markers into predictive models, function to increase the accuracy of the diagnostic potential of Aβ.

Sphingosine-1-Phosphate (S1P) Is a Feasible Biomarker in Predicting the Efficacy of Polymyxin B-Immobilized Fiber Direct Hemoperfusion (PMX-DHP) in Patients with Septic Shock.

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Inoue, Satoshi; Sakamoto, Yuichiro; Koami, Hiroyuki; Yamada C, Kosuke; Nagashima, Futoshi; Miike, Toru; Iwamura, Takashi; Obata, Toru

2018-01-01

The aim of this study was to identify a useful biomarker to predict the efficacy of polymyxin B-immobilized fiber direct hemoperfusion (PMX-DHP) in patients with septic shock. The 44 patients included in this study were divided into two groups. Group A had an increase in systolic blood pressure (SBP) over 30 mmHg after PMX-DHP treatment. Group B had an increase in SBP less than 30 mmHg after PMX-DHP treatment. We evaluated the clinical characteristics and demographics of both groups. We also assessed whether the cause of sepsis affected the efficacy of PMX-DHP and compared the prognosis of both groups. Finally, we investigated whether there were any significant differences in the levels of sepsis-related biomarkers, including sphingosine-1-phosphate (S1P), between both groups before PMX-DHP in an effort to identify a biomarker that could predict the efficacy of PMX-DHP. PMX-DHP significantly increased SBP regardless of the cause of sepsis. Although there was some tendency, PMX-DHP did not significantly improve the prognosis of effective cases in comparison with non-effective cases, probably because of the limited number of patients included. Among the sepsis-related biomarkers, only S1P values were significantly different between the two groups before PMX-DHP, and S1P levels were significantly increased after treatment in the effective cases. S1P levels prior to PMX-DHP can be used to predict its efficacy. In addition, continuous monitoring of S1P levels can indicate the effectiveness of PMX-DHP in patients with septic shock.

Plasma biomarker analysis in pediatric ARDS: generating future framework from a pilot randomized control trial of methylprednisoloneA framework for identifying plasma biomarkers related to clinical outcomes in pediatric ARDS

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Dai eKimura

2016-03-01

Full Text Available Objective: Lung injury activates multiple pro-inflammatory pathways, including neutrophils, epithelial and endothelial injury, and coagulation factors leading to acute respiratory distress syndrome (ARDS. Low-dose methylprednisolone therapy (MPT improved oxygenation and ventilation in early pediatric ARDS without altering duration of mechanical ventilation or mortality. We evaluated the effects of MPT on biomarkers of endothelial (Ang-2, sICAM-1 or epithelial (sRAGE injury, neutrophil activation (MMP-8, and coagulation (PAI-1. Design: Double-blind, placebo-controlled randomized trialSetting: Tertiary-care Pediatric Intensive Care Unit Patients: Mechanically ventilated children (0-18 years with early ARDS.Interventions: Blood samples were collected on Days 0 (before MPT, 7, and 14 during low-dose MPT (n=17 vs. placebo (n=18 therapy. The MPT group received a 2mg/kg loading dose followed by 1mg/kg/day continuous infusions from days 1-7, tapered off over 7 days; placebo group received equivalent amounts of 0.9% saline. We analyzed plasma samples using a multiplex assay for 5 biomarkers of ARDS. Multiple regression models were constructed to predict associations between changes in biomarkers and the clinical outcomes reported earlier including: P/F ratio on days 8&9, plateau pressure on days 1&2, PaCO2 on days 2&3, racemic epinephrine following extubation, and supplemental oxygen at ICU discharge.Results: No differences occurred in biomarker concentrations between the groups on Day 0. On Day 7, reduction in MMP-8 levels (p=0.0016 occurred in the MPT group, whereas increases in sICAM-1 levels (p=0.0005 occurred in the placebo group (no increases in sICAM-1 in the MPT group. sRAGE levels decreased in both MPT and placebo groups (p<0.0001 from Day 0 to Day 7. On Day 7, sRAGE levels were positively correlated with MPT group PaO2/FiO2 ratios on Day 8 (r=0.93, p=0.024. O2 requirements at ICU transfer positively correlated with Day 7 MMP-8 (r=0.85, p=0

Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer.

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Borrebaeck, Carl A K

2017-03-01

Interest in precision diagnostics has been fuelled by the concept that early detection of cancer would benefit patients; that is, if detected early, more tumours should be resectable and treatment more efficacious. Serum contains massive amounts of potentially diagnostic information, and affinity proteomics has risen as an accurate approach to decipher this, to generate actionable information that should result in more precise and evidence-based options to manage cancer. To achieve this, we need to move from single to multiplex biomarkers, a so-called signature, that can provide significantly increased diagnostic accuracy. This Opinion article focuses on the progress being made in identifying protein biomarker signatures of clinical utility, using blood-based proteomics.

Network-based identification of biomarkers coexpressed with multiple pathways.

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Guo, Nancy Lan; Wan, Ying-Wooi

2014-01-01

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

Prostate Cancer Imaging and Biomarkers Guiding Safe Selection of Active Surveillance

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Zachary A. Glaser

2017-10-01

Full Text Available BackgroundActive surveillance (AS is a widely adopted strategy to monitor men with low-risk, localized prostate cancer (PCa. Current AS inclusion criteria may misclassify as many as one in four patients. The advent of multiparametric magnetic resonance imaging (mpMRI and novel PCa biomarkers may offer improved risk stratification. We performed a review of recently published literature to characterize emerging evidence in support of these novel modalities.MethodsAn English literature search was conducted on PubMed for available original investigations on localized PCa, AS, imaging, and biomarkers published within the past 3 years. Our Boolean criteria included the following terms: PCa, AS, imaging, biomarker, genetic, genomic, prospective, retrospective, and comparative. The bibliographies and diagnostic modalities of the identified studies were used to expand our search.ResultsOur review identified 222 original studies. Our expanded search yielded 244 studies. Among these, 70 met our inclusion criteria. Evidence suggests mpMRI offers improved detection of clinically significant PCa, and MRI-fusion technology enhances the sensitivity of surveillance biopsies. Multiple studies demonstrate the promise of commercially available screening assays for prediction of AS failure, and several novel biomarkers show promise in this setting.ConclusionIn the era of AS for men with low-risk PCa, improved strategies for proper stratification are needed. mpMRI has dramatically enhanced the detection of clinically significant PCa. The advent of novel biomarkers for prediction of aggressive disease and AS failure has shown some initial promise, but further validation is warranted.

Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes

NARCIS (Netherlands)

Mayer, Gert; Heerspink, Hiddo J. L.; Aschauer, Constantin; Heinzel, Andreas; Heinze, Georg; Kainz, Alexander; Sunzenauer, Judith; Perco, Paul; de Zeeuw, Dick; Rossing, Peter; Pena, Michelle; Oberbauer, Rainer

OBJECTIVE: Chronic kidney disease (CKD) in diabetes has a complex molecular and likely multifaceted pathophysiology. We aimed to validate a panel of biomarkers identified using a systems biology approach to predict the individual decline of estimated glomerular filtration rate (eGFR) in a large

Predictive Power Estimation Algorithm (PPEA--a new algorithm to reduce overfitting for genomic biomarker discovery.

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Jiangang Liu

Full Text Available Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA, which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1 PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2 the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3 using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4 more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.

Evaluating Chagas disease progression and cure through blood-derived biomarkers: a systematic review.

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Requena-Méndez, Ana; López, Manuel Carlos; Angheben, Andrea; Izquierdo, Luis; Ribeiro, Isabela; Pinazo, Maria-Jesús; Gascon, Joaquim; Muñoz, José

2013-09-01

This article reviews the usefulness of various types of blood-derived biomarkers that are currently being studied to predict the progression of Chagas disease in patients with the indeterminate form, to assess the efficacy of antiparasitic drugs and to identify early cardiac and gastrointestinal damage. The authors used a search strategy based on MEDLINE, Cochrane Library Register for systematic review, EmBase, Global Health and LILACS databases. Out of 1716 screened articles, only 166 articles were eligible for final inclusion. The authors classified the biomarkers according to their biochemical structure and primary biological activity in four groups: i) markers of inflammation and cellular injury, ii) metabolic biomakers, iii) prothrombotic biomarkers and iv) markers derived from specific antigens of the parasite. Several potential biomarkers might have clinical potential for the detection of early cardiopathy. Such capacity is imperative in order to detect high-risk patients who require intensive monitoring and earlier therapy. Prospective studies with longer follow-ups are needed for the appraisal of biomarkers assessing clinical or microbiological cure after therapy. At the same time, studies evaluating more than one biomarker are useful to compare the efficacy among them given the lack of a recognized gold standard.

Can the Growth/Differentiation Factor-15 Be a Surrogate Target in Chronic Heart Failure Biomarker-Guided Therapy?

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Alexander E. Berezin

2017-03-01

Full Text Available Heart failure (HF biomarker-guided therapy is a promising method, which directs to the improvement of clinical status, attenuation of admission/readmission to the hospital and reduction in mortality rate. Many biological markers, like inflammatory cytokines, are under consideration as a surrogate target for HF treatment, while there are known biomarkers with established predictive value, such as natriuretic peptides. However, discovery of new biomarkers reflecting various underlying mechanisms of HF and appearing to be surrogate targets for biomarker-guided therapy is fairly promising. Nowadays, growth/differentiation factor 15 (GDF-15 is suggested a target biomarker for HF treatment. Although elevated level of GDF-15 is associated with HF development, progression, and prognosis, there is no represented evidence regarding the direct comparison of this biomarker with other clinical risk predictors and biomarkers. Moreover, GDF-15 might serve as a contributor to endothelial progenitor cells (EPC dysfunction by inducing EPC death/autophagy and limiting their response to angiopoetic and reparative effects. The short communication was discussed whether GDF-15 is good molecular target for HF biomarker-guided therapy.

Expression profiling of blood samples from an SU5416 Phase III metastatic colorectal cancer clinical trial: a novel strategy for biomarker identification

International Nuclear Information System (INIS)

DePrimo, Samuel E; Wong, Lily M; Khatry, Deepak B; Nicholas, Susan L; Manning, William C; Smolich, Beverly D; O'Farrell, Anne-Marie; Cherrington, Julie M

2003-01-01

Microarray-based gene expression profiling is a powerful approach for the identification of molecular biomarkers of disease, particularly in human cancers. Utility of this approach to measure responses to therapy is less well established, in part due to challenges in obtaining serial biopsies. Identification of suitable surrogate tissues will help minimize limitations imposed by those challenges. This study describes an approach used to identify gene expression changes that might serve as surrogate biomarkers of drug activity. Expression profiling using microarrays was applied to peripheral blood mononuclear cell (PBMC) samples obtained from patients with advanced colorectal cancer participating in a Phase III clinical trial. The PBMC samples were harvested pre-treatment and at the end of the first 6-week cycle from patients receiving standard of care chemotherapy or standard of care plus SU5416, a vascular endothelial growth factor (VEGF) receptor tyrosine kinase (RTK) inhibitor. Results from matched pairs of PBMC samples from 23 patients were queried for expression changes that consistently correlated with SU5416 administration. Thirteen transcripts met this selection criterion; six were further tested by quantitative RT-PCR analysis of 62 additional samples from this trial and a second SU5416 Phase III trial of similar design. This method confirmed four of these transcripts (CD24, lactoferrin, lipocalin 2, and MMP-9) as potential biomarkers of drug treatment. Discriminant analysis showed that expression profiles of these 4 transcripts could be used to classify patients by treatment arm in a predictive fashion. These results establish a foundation for the further exploration of peripheral blood cells as a surrogate system for biomarker analyses in clinical oncology studies

Expression profiling of blood samples from an SU5416 Phase III metastatic colorectal cancer clinical trial: a novel strategy for biomarker identification

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Smolich Beverly D

2003-02-01

Full Text Available Abstract Background Microarray-based gene expression profiling is a powerful approach for the identification of molecular biomarkers of disease, particularly in human cancers. Utility of this approach to measure responses to therapy is less well established, in part due to challenges in obtaining serial biopsies. Identification of suitable surrogate tissues will help minimize limitations imposed by those challenges. This study describes an approach used to identify gene expression changes that might serve as surrogate biomarkers of drug activity. Methods Expression profiling using microarrays was applied to peripheral blood mononuclear cell (PBMC samples obtained from patients with advanced colorectal cancer participating in a Phase III clinical trial. The PBMC samples were harvested pre-treatment and at the end of the first 6-week cycle from patients receiving standard of care chemotherapy or standard of care plus SU5416, a vascular endothelial growth factor (VEGF receptor tyrosine kinase (RTK inhibitor. Results from matched pairs of PBMC samples from 23 patients were queried for expression changes that consistently correlated with SU5416 administration. Results Thirteen transcripts met this selection criterion; six were further tested by quantitative RT-PCR analysis of 62 additional samples from this trial and a second SU5416 Phase III trial of similar design. This method confirmed four of these transcripts (CD24, lactoferrin, lipocalin 2, and MMP-9 as potential biomarkers of drug treatment. Discriminant analysis showed that expression profiles of these 4 transcripts could be used to classify patients by treatment arm in a predictive fashion. Conclusions These results establish a foundation for the further exploration of peripheral blood cells as a surrogate system for biomarker analyses in clinical oncology studies.

Discriminative capacity of biomarkers for acute stroke in the emergency department.

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Glickman, Seth W; Phillips, Samantha; Anstrom, Kevin J; Laskowitz, Daniel T; Cairns, Charles B

2011-09-01

Acute ischemic stroke remains largely a clinical diagnosis. To assess the potential of several biomarkers to distinguish acute ischemic stroke from mimics in the emergency department (ED). In this prospective study, 63 patients with suspected acute stroke were enrolled. Blood samples were collected at ED presentation and assayed for B-type natriuretic peptide, C-reactive protein (CRP), matrix metalloproteinase 9 (MMP-9), D-dimer, and protein S100B. Final diagnosis of stroke was rendered by blinded independent stroke experts after review of all clinical, imaging, and conventional laboratory data during admission. Logistic regression and bootstrapping models were used to evaluate the association between biomarker values and acute stroke. Thirty-four patients had a final diagnosis of stroke and 29 with mimics. The initial ED values of CRP, MMP-9, and S100B (C-indices of 0.808, 0.811, and 0.719, respectively) and the National Institutes of Health Stroke Scale (NIHSS) (C-index 0.887) predicted acute cerebral ischemia. CRP levels added discriminative value over clinical variables alone in the diagnosis of stroke. When the levels of CRP were added to the NIHSS, the combination was highly predictive of stroke (bootstrap mean C-index 0.951, 90% Confidence Interval 0.903-0.991, likelihood test p = 0.004). Biomarker testing with CRP and potentially MMP-9 and S100B, may add valuable and time-sensitive diagnostic information in the early evaluation of patients with suspected stroke in the ED. Future prospective evaluations are necessary to validate the diagnostic capability of these biomarkers for acute ischemic stroke in the ED before they should be considered for use in clinical practice. Copyright © 2011 Elsevier Inc. All rights reserved.

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

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Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

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Elisa Passini

2017-09-01

Full Text Available Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC50/Hill coefficient. Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca2+-transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs. Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca2+/late Na+ currents and Na+/Ca2+-exchanger, reduced Na+/K+-pump are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density

Predictive inference for best linear combination of biomarkers subject to limits of detection.

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Coolen-Maturi, Tahani

2017-08-15

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric predictive inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Candidate immune biomarkers for radioimmunotherapy.

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Levy, Antonin; Nigro, Giulia; Sansonetti, Philippe J; Deutsch, Eric

2017-08-01

Newly available immune checkpoint blockers (ICBs), capable to revert tumor immune tolerance, are revolutionizing the anticancer armamentarium. Recent evidence also established that ionizing radiation (IR) could produce antitumor immune responses, and may as well synergize with ICBs. Multiple radioimmunotherapy combinations are thenceforth currently assessed in early clinical trials. Past examples have highlighted the need for treatment personalization, and there is an unmet need to decipher immunological biomarkers that could allow selecting patients who could benefit from these promising but expensive associations. Recent studies have identified potential predictive and prognostic immune assays at the cellular (tumor microenvironment composition), genomic (mutational/neoantigen load), and peripheral blood levels. Within this review, we collected the available evidence regarding potential personalized immune biomarker-directed radiation therapy strategies that might be used for patient selection in the era of radioimmunotherapy. Copyright © 2017. Published by Elsevier B.V.

Improving acute kidney injury diagnostics using predictive analytics.

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Basu, Rajit K; Gist, Katja; Wheeler, Derek S

2015-12-01

Acute kidney injury (AKI) is a multifactorial syndrome affecting an alarming proportion of hospitalized patients. Although early recognition may expedite management, the ability to identify patients at-risk and those suffering real-time injury is inconsistent. The review will summarize the recent reports describing advancements in the area of AKI epidemiology, specifically focusing on risk scoring and predictive analytics. In the critical care population, the primary underlying factors limiting prediction models include an inability to properly account for patient heterogeneity and underperforming metrics used to assess kidney function. Severity of illness scores demonstrate limited AKI predictive performance. Recent evidence suggests traditional methods for detecting AKI may be leveraged and ultimately replaced by newer, more sophisticated analytical tools capable of prediction and identification: risk stratification, novel AKI biomarkers, and clinical information systems. Additionally, the utility of novel biomarkers may be optimized through targeting using patient context, and may provide more granular information about the injury phenotype. Finally, manipulation of the electronic health record allows for real-time recognition of injury. Integrating a high-functioning clinical information system with risk stratification methodology and novel biomarker yields a predictive analytic model for AKI diagnostics.

Hemostatic biomarkers in dogs with chronic congestive heart failure

DEFF Research Database (Denmark)

Tarnow, Inge; Falk, Torkel; Tidholm, Anna

2007-01-01

Background: Chronic congestive heart failure (CHF) in humans is associated with abnormal hemostasis, and abnormalities in hemostatic biomarkers carry a poor prognosis. Alterations in hemostatic pathways can be involved in the pathogenesis of CHF in dogs, and microthrombosis in the myocardium could...... contribute to increased mortality. Hypothesis: That plasma concentration or activity of hemostatic biomarkers is altered in dogs with CHF and that these factors predict mortality. Animals: Thirty-four dogs with CHF caused by either dilated cardiomyopathy (DCM, n = 14) or degenerative valvular disease (CDVD......, n = 20) compared with 23 healthy age-matched control dogs were included in this study. Dogs with CHF were recruited from 2 referral cardiology clinics, and control dogs were owned by friends or colleagues of the investigators. Methods: Clinical examination and echocardiography were performed in all...

Biomarkers, lactate, and clinical scores as outcome predictors in systemic poisons exposures.

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Lionte, C; Sorodoc, V; Tuchilus, C; Cimpoiesu, D; Jaba, E

2017-07-01

Acute exposure to systemic poisons represents an important challenge in clinical toxicology. We aimed to analyze the potential role of cardiac biomarkers, routine laboratory tests, and clinical scores as morbidity and in-hospital mortality predictors in patients intoxicated with various systemic poisons. We conducted a prospective study on adults acutely exposed to systemic poisons. We determined the PSS, Glasgow Coma Scale (GCS), and we performed electrocardiogram, laboratory tests, lactate and cardiac biomarkers (which were reassessed 4 h, respectively 6 h later). Of 120 patients included, 45% developed complications, 19.2% had a poor outcome, and 5% died. Multivariate logistic regression sustained lactate (odds ratio (OR) 1.58; confidence interval (CI) 95%: 0.97-2.59; p 0.066), MB isoenzyme of creatine kinase (6h-CKMB; OR 1.08; CI 95%: 1.02-1.16; p 0.018) as predictors for a poor outcome. A GCS poisons exposure. Receiver operating characteristic analysis showed that brain natriuretic peptide (area under the curve (AUC), 0.96; CI 95%: 0.92-0.99; p poisons exposure.

Diagnostic and Prognostic Stratification in the Emergency Department Using Urinary Biomarkers of Nephron Damage

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Nickolas, Thomas L.; Schmidt-Ott, Kai M.; Canetta, Pietro; Forster, Catherine; Singer, Eugenia; Sise, Meghan; Elger, Antje; Maarouf, Omar; Sola-Del Valle, David Antonio; O'Rourke, Matthew; Sherman, Evan; Lee, Peter; Geara, Abdallah; Imus, Philip; Guddati, Achuta; Polland, Allison; Rahman, Wasiq; Elitok, Saban; Malik, Nasir; Giglio, James; El-Sayegh, Suzanne; Devarajan, Prasad; Hebbar, Sudarshan; Saggi, Subodh J.; Hahn, Barry; Kettritz, Ralph; Luft, Friedrich C.; Barasch, Jonathan

2012-01-01

Objectives This study aimed to determine the diagnostic and prognostic value of urinary biomarkers of intrinsic acute kidney injury (AKI) when patients were triaged in the emergency department. Background Intrinsic AKI is associated with nephron injury and results in poor clinical outcomes. Several urinary biomarkers have been proposed to detect and measure intrinsic AKI. Methods In a multicenter prospective cohort study, 5 urinary biomarkers (urinary neutrophil gelatinase–associated lipocalin, kidney injury molecule-1, urinary liver-type fatty acid binding protein, urinary interleukin-18, and cystatin C) were measured in 1,635 unselected emergency department patients at the time of hospital admission. We determined whether the biomarkers diagnosed intrinsic AKI and predicted adverse outcomes during hospitalization. Results All biomarkers were elevated in intrinsic AKI, but urinary neutrophil gelatinase-associated lipocalin was most useful (81% specificity, 68% sensitivity at a 104-ng/ml cutoff) and predictive of the severity and duration of AKI. Intrinsic AKI was strongly associated with adverse in-hospital outcomes. Urinary neutrophil gelatinase-associated lipocalin and urinary kidney injury molecule 1 predicted a composite outcome of dialysis initiation or death during hospitalization, and both improved the net risk classification compared with conventional assessments. These biomarkers also identified a substantial subpopulation with low serum creatinine at hospital admission, but who were at risk of adverse events. Conclusion Urinary biomarkers of nephron damage enable prospective diagnostic and prognostic stratification in the emergency department. PMID:22240130

Clinical, functional, behavioural and epigenomic biomarkers of obesity.

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Lafortuna, Claudio L; Tovar, Armando R; Rastelli, Fabio; Tabozzi, Sarah A; Caramenti, Martina; Orozco-Ruiz, Ximena; Aguilar-Lopez, Miriam; Guevara-Cruz, Martha; Avila-Nava, Azalia; Torres, Nimbe; Bertoli, Gloria

2017-06-01

Overweight and obesity are highly prevalent conditions worldwide, linked to an increased risk for death, disability and disease due to metabolic and biochemical abnormalities affecting the biological human system throughout different domains. Biomarkers, defined as indicators of biological processes in health and disease, relevant for body mass excess management have been identified according to different criteria, including anthropometric and molecular indexes, as well as physiological and behavioural aspects. Analysing these different biomarkers, we identified their potential role in diagnosis, prognosis and treatment. Epigenetic biomarkers, cellular mediators of inflammation and factors related to microbiota-host interactions may be considered to have a theranostic value. Though, the molecular processes responsible for the biological phenomenology detected by the other analysed markers, is not clear yet. Nevertheless, these biomarkers possess valuable diagnostic and prognostic power. A new frontier for theranostic biomarkers can be foreseen in the exploitation of parameters defining behaviours and lifestyles linked to the risk of obesity, capable to describe the effects of interventions for obesity prevention and treatment which include also behaviour change strategies.

Candidate proteins, metabolites and transcripts in the Biomarkers for Spinal Muscular Atrophy (BforSMA clinical study.

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Richard S Finkel

Full Text Available Spinal Muscular Atrophy (SMA is a neurodegenerative motor neuron disorder resulting from a homozygous mutation of the survival of motor neuron 1 (SMN1 gene. The gene product, SMN protein, functions in RNA biosynthesis in all tissues. In humans, a nearly identical gene, SMN2, rescues an otherwise lethal phenotype by producing a small amount of full-length SMN protein. SMN2 copy number inversely correlates with disease severity. Identifying other novel biomarkers could inform clinical trial design and identify novel therapeutic targets.To identify novel candidate biomarkers associated with disease severity in SMA using unbiased proteomic, metabolomic and transcriptomic approaches.A cross-sectional single evaluation was performed in 108 children with genetically confirmed SMA, aged 2-12 years, manifesting a broad range of disease severity and selected to distinguish factors associated with SMA type and present functional ability independent of age. Blood and urine specimens from these and 22 age-matched healthy controls were interrogated using proteomic, metabolomic and transcriptomic discovery platforms. Analyte associations were evaluated against a primary measure of disease severity, the Modified Hammersmith Functional Motor Scale (MHFMS and to a number of secondary clinical measures.A total of 200 candidate biomarkers correlate with MHFMS scores: 97 plasma proteins, 59 plasma metabolites (9 amino acids, 10 free fatty acids, 12 lipids and 28 GC/MS metabolites and 44 urine metabolites. No transcripts correlated with MHFMS.In this cross-sectional study, "BforSMA" (Biomarkers for SMA, candidate protein and metabolite markers were identified. No transcript biomarker candidates were identified. Additional mining of this rich dataset may yield important insights into relevant SMA-related pathophysiology and biological network associations. Additional prospective studies are needed to confirm these findings, demonstrate sensitivity to change with

A biomarker panel for non-alcoholic steatohepatitis (NASH) and NASH-related fibrosis.

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Younossi, Zobair M; Page, Sandra; Rafiq, Nila; Birerdinc, Aybike; Stepanova, Maria; Hossain, Noreen; Afendy, Arian; Younoszai, Zahra; Goodman, Zachary; Baranova, Ancha

2011-04-01

Patients with biopsy-proven NASH and especially those with fibrosis are at risk for progressive liver disease, emphasizing the clinical importance of developing non-invasive biomarkers for NASH and NASH-related fibrosis. This study examines the performance of a new biomarker panel for NASH and NASH-related fibrosis with a combination of clinical and laboratory variables. Enrolled patients had biopsy-proven NAFLD. Clinical data, laboratory data, and serum samples were collected at the time of biopsy. Fasting serum was assayed for adiponectin, resistin, glucose, M30, M65, Tissue inhibitor of metalloproteinases-1 (Timp-1), ProCollagen 3 N-terminal peptide (PIIINP), and hyaluronic acid (HA). Regression models predictive of NASH, NASH-related fibrosis, and NASH-related advanced fibrosis were designed and cross-validated. Of the 79 enrolled NAFLD patients, 40 had biopsy-proven NASH and 39 had non-NASH NAFLD. Clinical and laboratory data were from this cohort were used to develop a NAFLD Diagnostic Panel that includes three models (models for NASH, NASH-related fibrosis, and NASH-related advanced fibrosis). The model for predicting NASH includes diabetes, gender, BMI, triglycerides, M30 (apoptosis), and M65-M30 (necrosis) [AUC: 0.81, 95% CI, 0.70-0.89, 300 p value <9E 301 (-06)]. The NASH-related fibrosis prediction model includes the same predictors [AUC: 0.80, 95% CI 0.68-0.88, 307 p value <0.00014]. Finally, the NASH-related advanced fibrosis model includes type 2 diabetes, serum triglycerides, Timp-1, and AST [AUC: 0.81, 95% CI, 0.70-0.89; p value, 0.000062]. This NAFLD Diagnostic Panel based on a clinical and laboratory data has good performance characteristics and is easy to use. This biomarker panel could become useful in the management of patients with NAFLD.

Current and Future Applications of Biomedical Engineering for Proteomic Profiling: Predictive Biomarkers in Neuro-Traumatology

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Mario Ganau

2018-02-01

Full Text Available This systematic review aims to summarize the impact of nanotechnology and biomedical engineering in defining clinically meaningful predictive biomarkers in patients with traumatic brain injury (TBI, a critical worldwide health problem with an estimated 10 billion people affected annually worldwide. Data were collected through a review of the existing English literature performed on Scopus, MEDLINE, MEDLINE in Process, EMBASE, and/or Cochrane Central Register of Controlled Trials. Only experimental articles revolving around the management of TBI, in which the role of new devices based on innovative discoveries coming from the field of nanotechnology and biomedical engineering were highlighted, have been included and analyzed in this study. Based on theresults gathered from this research on innovative methods for genomics, epigenomics, and proteomics, their future application in this field seems promising. Despite the outstanding technical challenges of identifying reliable biosignatures for TBI and the mixed nature of studies herein described (single cells proteomics, biofilms, sensors, etc., the clinical implementation of those discoveries will allow us to gain confidence in the use of advanced neuromonitoring modalities with a potential dramatic improvement in the management of those patients.

[Neuroimaging and Blood Biomarkers in Functional Prognosis after Stroke].

Science.gov (United States)

Branco, João Paulo; Costa, Joana Santos; Sargento-Freitas, João; Oliveira, Sandra; Mendes, Bruno; Laíns, Jorge; Pinheiro, João

2016-11-01

Stroke remains one of the leading causes of morbidity and mortality around the world and it is associated with an important long-term functional disability. Some neuroimaging resources and certain peripheral blood or cerebrospinal fluid proteins can give important information about etiology, therapeutic approach, follow-up and functional prognosis in acute ischemic stroke patients. However, among the scientific community, there is currently more interest in the stroke vital prognosis over the functional prognosis. Predicting the functional prognosis during acute phase would allow more objective rehabilitation programs and better management of the available resources. The aim of this work is to review the potential role of acute phase neuroimaging and blood biomarkers as functional recovery predictors after ischemic stroke. Review of the literature published between 2005 and 2015, in English, using the terms "ischemic stroke", "neuroimaging" e "blood biomarkers". We included nine studies, based on abstract reading. Computerized tomography, transcranial doppler ultrasound and diffuse magnetic resonance imaging show potential predictive value, based on the blood flow study and the evaluation of stroke's volume and localization, especially when combined with the National Institutes of Health Stroke Scale. Several biomarkers have been studied as diagnostic, risk stratification and prognostic tools, namely the S100 calcium binding protein B, C-reactive protein, matrix metalloproteinases and cerebral natriuretic peptide. Although some biomarkers and neuroimaging techniques have potential predictive value, none of the studies were able to support its use, alone or in association, as a clinically useful functionality predictor model. All the evaluated markers were considered insufficient to predict functional prognosis at three months, when applied in the first hours after stroke. Additional studies are necessary to identify reliable predictive markers for functional

Biomarker discovery for colon cancer using a 761 gene RT-PCR assay

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Hackett James R

2007-08-01

Full Text Available Abstract Background Reverse transcription PCR (RT-PCR is widely recognized to be the gold standard method for quantifying gene expression. Studies using RT-PCR technology as a discovery tool have historically been limited to relatively small gene sets compared to other gene expression platforms such as microarrays. We have recently shown that TaqMan® RT-PCR can be scaled up to profile expression for 192 genes in fixed paraffin-embedded (FPE clinical study tumor specimens. This technology has also been used to develop and commercialize a widely used clinical test for breast cancer prognosis and prediction, the Onco typeDX™ assay. A similar need exists in colon cancer for a test that provides information on the likelihood of disease recurrence in colon cancer (prognosis and the likelihood of tumor response to standard chemotherapy regimens (prediction. We have now scaled our RT-PCR assay to efficiently screen 761 biomarkers across hundreds of patient samples and applied this process to biomarker discovery in colon cancer. This screening strategy remains attractive due to the inherent advantages of maintaining platform consistency from discovery through clinical application. Results RNA was extracted from formalin fixed paraffin embedded (FPE tissue, as old as 28 years, from 354 patients enrolled in NSABP C-01 and C-02 colon cancer studies. Multiplexed reverse transcription reactions were performed using a gene specific primer pool containing 761 unique primers. PCR was performed as independent TaqMan® reactions for each candidate gene. Hierarchal clustering demonstrates that genes expected to co-express form obvious, distinct and in certain cases very tightly correlated clusters, validating the reliability of this technical approach to biomarker discovery. Conclusion We have developed a high throughput, quantitatively precise multi-analyte gene expression platform for biomarker discovery that approaches low density DNA arrays in numbers of

Evaluation of candidate biomarkers to predict cancer cell sensitivity or resistance to PARP-1 inhibitor treatment

DEFF Research Database (Denmark)

Oplustilova, L.; Wolanin, K.; Bartkova, J.

2012-01-01

combinations with camptothecin or ionizing radiation. Furthermore, monitoring pARsylation and Rad51 foci formation as surrogate markers for PARP activity and HR, respectively, supported their candidacy for biomarkers of PARP-1i responses. As to resistance mechanisms, we confrmed the role of the multidrug......(ADp-ribose) polymerase-1 (PARP-1), an enzyme critical for repair pathways alternative to HR. While promising, treatment with PARP-1 inhibitors (PARP-1i) faces some hurdles, including (1) acquired resistance, (2) search for other sensitizing, non-BRCA1/2 cancer defects and (3) lack of biomarkers to predict response...

A proteomic analysis identifies candidate early biomarkers to predict ovarian hyperstimulation syndrome in polycystic ovarian syndrome patients.

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Wu, Lan; Sun, Yazhou; Wan, Jun; Luan, Ting; Cheng, Qing; Tan, Yong

2017-07-01

Ovarian hyperstimulation syndrome (OHSS) is a potentially life‑threatening, iatrogenic complication that occurs during assisted reproduction. Polycystic ovarian syndrome (PCOS) significantly increases the risk of OHSS during controlled ovarian stimulation. Therefore, a more effective early prediction technique is required in PCOS patients. Quantitative proteomic analysis of serum proteins indicates the potential diagnostic value for disease. In the present study, the authors revealed the differentially expressed proteins in OHSS patients with PCOS as new diagnostic biomarkers. The promising proteins obtained from liquid chromatography‑mass spectrometry were subjected to ELISA and western blotting assay for further confirmation. A total of 57 proteins were identified with significant difference, of which 29 proteins were upregulated and 28 proteins were downregulated in OHSS patients. Haptoglobin, fibrinogen and lipoprotein lipase were selected as candidate biomarkers. Receiver operating characteristic curve analysis demonstrated all three proteins may have potential as biomarkers to discriminate OHSS in PCOS patients. Haptoglobin, fibrinogen and lipoprotein lipase have never been reported as a predictive marker of OHSS in PCOS patients, and their potential roles in OHSS occurrence deserve further studies. The proteomic results reported in the present study may gain deeper insights into the pathophysiology of OHSS.

Measurement and Clinical Significance of Biomarkers of Oxidative Stress in Humans

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Ilaria Marrocco

2017-01-01

Full Text Available Oxidative stress is the result of the imbalance between reactive oxygen species (ROS formation and enzymatic and nonenzymatic antioxidants. Biomarkers of oxidative stress are relevant in the evaluation of the disease status and of the health-enhancing effects of antioxidants. We aim to discuss the major methodological bias of methods used for the evaluation of oxidative stress in humans. There is a lack of consensus concerning the validation, standardization, and reproducibility of methods for the measurement of the following: (1 ROS in leukocytes and platelets by flow cytometry, (2 markers based on ROS-induced modifications of lipids, DNA, and proteins, (3 enzymatic players of redox status, and (4 total antioxidant capacity of human body fluids. It has been suggested that the bias of each method could be overcome by using indexes of oxidative stress that include more than one marker. However, the choice of the markers considered in the global index should be dictated by the aim of the study and its design, as well as by the clinical relevance in the selected subjects. In conclusion, the clinical significance of biomarkers of oxidative stress in humans must come from a critical analysis of the markers that should give an overall index of redox status in particular conditions.

Biomarkers of traumatic injury are transported from brain to blood via the glymphatic system.

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Plog, Benjamin A; Dashnaw, Matthew L; Hitomi, Emi; Peng, Weiguo; Liao, Yonghong; Lou, Nanhong; Deane, Rashid; Nedergaard, Maiken

2015-01-14

The nonspecific and variable presentation of traumatic brain injury (TBI) has motivated an intense search for blood-based biomarkers that can objectively predict the severity of injury. However, it is not known how cytosolic proteins released from traumatized brain tissue reach the peripheral blood. Here we show in a murine TBI model that CSF movement through the recently characterized glymphatic pathway transports biomarkers to blood via the cervical lymphatics. Clinically relevant manipulation of glymphatic activity, including sleep deprivation and cisternotomy, suppressed or eliminated TBI-induced increases in serum S100β, GFAP, and neuron specific enolase. We conclude that routine TBI patient management may limit the clinical utility of blood-based biomarkers because their brain-to-blood transport depends on glymphatic activity. Copyright © 2015 the authors 0270-6474/15/350518-09$15.00/0.

Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error

OpenAIRE

Shipitsin, M; Small, C; Choudhury, S; Giladi, E; Friedlander, S; Nardone, J; Hussain, S; Hurley, A D; Ernst, C; Huang, Y E; Chang, H; Nifong, T P; Rimm, D L; Dunyak, J; Loda, M

2014-01-01

Background: Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. Methods: Prostatectom...

Improving the quality of biomarker discovery research: the right samples and enough of them.

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Pepe, Margaret S; Li, Christopher I; Feng, Ziding

2015-06-01

Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs. The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportion of useful markers the study should identify (Discovery Power); and (iii) the tolerable number of useless markers amongst those identified (False Leads Expected, FLE). We apply the methodology to a study of 9,000 candidate biomarkers for risk of colon cancer recurrence where a useful biomarker has positive predictive value ≥ 30%. We find that 40 patients with recurrence and 160 without recurrence suffice to filter out 98% of useless markers (2% FLE) while identifying 95% of useful biomarkers (95% Discovery Power). Alternative methods for sample size calculation required more assumptions. Biomarker discovery research should utilize quality biospecimen repositories and include sample sizes that enable markers meeting prespecified performance characteristics for well-defined clinical applications to be identified. The scientific rigor of discovery research should be improved. ©2015 American Association for Cancer Research.

Clinical utility of pretreatment prediction of chemoradiotherapy response in rectal cancer: a review.

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Yoo, Byong Chul; Yeo, Seung-Gu

2017-03-01

Approximately 20% of all patients with locally advanced rectal cancer experience pathologically complete responses following neoadjuvant chemoradiotherapy (CRT) and standard surgery. The utility of radical surgery for patients exhibiting good CRT responses has been challenged. Organ-sparing strategies for selected patients exhibiting complete clinical responses include local excision or no immediate surgery. The subjects of this tailored management are patients whose presenting disease corresponds to current indications of neoadjuvant CRT, and their post-CRT tumor response is assessed by clinical and radiological examinations. However, a model predictive of the CRT response, applied before any treatment commenced, would be valuable to facilitate such a personalized approach. This would increase organ preservation, particularly in patients for whom upfront CRT is not generally prescribed. Molecular biomarkers hold the greatest promise for development of a pretreatment predictive model of CRT response. A combination of clinicopathological, radiological, and molecular markers will be necessary to render the model robust. Molecular research will also contribute to the development of drugs that can overcome the radioresistance of rectal tumors. Current treatments for rectal cancer are based on the expected prognosis given the presenting disease extent. In the future, treatment schemes may be modified by including the predicted CRT response evaluated at presentation.

Six-SOMAmer Index Relating to Immune, Protease and Angiogenic Functions Predicts Progression in IPF.

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Shanna L Ashley

Full Text Available Biomarkers in easily accessible compartments like peripheral blood that can predict disease progression in idiopathic pulmonary fibrosis (IPF would be clinically useful regarding clinical trial participation or treatment decisions for patients. In this study, we used unbiased proteomics to identify relevant disease progression biomarkers in IPF.Plasma from IPF patients was measured using an 1129 analyte slow off-rate modified aptamer (SOMAmer array, and patient outcomes were followed over the next 80 weeks. Receiver operating characteristic (ROC curves evaluated sensitivity and specificity for levels of each biomarker and estimated area under the curve (AUC when prognostic biomarker thresholds were used to predict disease progression. Both logistic and Cox regression models advised biomarker selection for a composite disease progression index; index biomarkers were weighted via expected progression-free days lost during follow-up with a biomarker on the unfavorable side of the threshold.A six-analyte index, scaled 0 to 11, composed of markers of immune function, proteolysis and angiogenesis [high levels of ficolin-2 (FCN2, cathepsin-S (Cath-S, legumain (LGMN and soluble vascular endothelial growth factor receptor 2 (VEGFsR2, but low levels of inducible T cell costimulator (ICOS or trypsin 3 (TRY3] predicted better progression-free survival in IPF with a ROC AUC of 0.91. An index score ≥ 3 (group ≥ 2 was strongly associated with IPF progression after adjustment for age, gender, smoking status, immunomodulation, forced vital capacity % predicted and diffusing capacity for carbon monoxide % predicted (HR 16.8, 95% CI 2.2-126.7, P = 0.006.This index, derived from the largest proteomic analysis of IPF plasma samples to date, could be useful for clinical decision making in IPF, and the identified analytes suggest biological processes that may promote disease progression.

A utilitarian comparison of two alcohol use biomarkers with self-reported drinking history collected in antenatal clinics.

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May, Philip A; Hasken, Julie M; De Vries, Marlene M; Marais, Anna-Susan; Stegall, Julie M; Marsden, Daniel; Parry, Charles D H; Seedat, Soraya; Tabachnick, Barbara

2018-04-01

Alcohol use is reported accurately among pregnant women in some populations. Self-reported alcohol use via the AUDIT and 90-day recall for 193 women from antenatal clinics was compared to biomarker results: phosphatidylethanol (PEth) from bloodspots and ethyl glucuronide (EtG) in fingernails. AUDIT was positive for 67.9% of respondents, and 65.3% directly reported drinking. Individual biomarkers detected less drinking (PEth = 57.0%, EtG = 38.9%) than self-report. But 64.8% had drinking-positive values (>8 ng) on one or both biomarkers, which was not significantly different from self-report. Biomarkers indicated that 3.1% -6.8% of drinkers denied drinking. Combined biomarker sensitivity was 95% -80% and specificity 49% -76% for drinking in the previous 7-90 days. Combined biomarker results have their best yield (89.6%) and accuracy (78.8%) when measuring 90 day drinking. Women reported their alcohol use accurately, and the combined use of PEth and EtG is supported. Copyright © 2018 Elsevier Inc. All rights reserved.

RNA Biomarkers: Frontier of Precision Medicine for Cancer

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Xiaochen Xi

2017-02-01

Full Text Available As an essential part of central dogma, RNA delivers genetic and regulatory information and reflects cellular states. Based on high‐throughput sequencing technologies, cumulating data show that various RNA molecules are able to serve as biomarkers for the diagnosis and prognosis of various diseases, for instance, cancer. In particular, detectable in various bio‐fluids, such as serum, saliva and urine, extracellular RNAs (exRNAs are emerging as non‐invasive biomarkers for earlier cancer diagnosis, tumor progression monitor, and prediction of therapy response. In this review, we summarize the latest studies on various types of RNA biomarkers, especially extracellular RNAs, in cancer diagnosis and prognosis, and illustrate several well‐known RNA biomarkers of clinical utility. In addition, we describe and discuss general procedures and issues in investigating exRNA biomarkers, and perspectives on utility of exRNAs in precision medicine.

Clinical proteomics: Applications for prostate cancer biomarker discovery and detection.

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Petricoin, Emanuel F; Ornstein, David K; Liotta, Lance A

2004-01-01

The science of proteomics comprises much more than simply generating lists of proteins that change in expression as a cause of or consequence of pathophysiology. The goal of proteomics should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. Serum proteomic pattern diagnostics is a new type of proteomic concept in which patterns of ion signatures generated from high dimensional mass spectrometry data are used as diagnostic classifiers. This recent approach has exciting potential for clinical utility of diagnostic patterns because low molecular weight metabolites, peptides, and protein fragments may have higher accuracy than traditional biomarkers of cancer detection. Intriguingly, we now have discovered that this diagnostic information exists in a bound state, complexed with circulating highly abundant carrier proteins. These diagnostic fragments may one day be harvested by circulating nanoparticles, designed to absorb, enrich, and amplify the repertoire of diagnostic biomarkers generated-even at the critical, initial stages of carcinogenesis. Copyright 2004 Elsevier Inc.

Altered metabolomic-genomic signature: A potential noninvasive biomarker of epilepsy.

Science.gov (United States)

Wu, Helen C; Dachet, Fabien; Ghoddoussi, Farhad; Bagla, Shruti; Fuerst, Darren; Stanley, Jeffrey A; Galloway, Matthew P; Loeb, Jeffrey A

2017-09-01

This study aimed to identify noninvasive biomarkers of human epilepsy that can reliably detect and localize epileptic brain regions. Having noninvasive biomarkers would greatly enhance patient diagnosis, patient monitoring, and novel therapy development. At the present time, only surgically invasive, direct brain recordings are capable of detecting these regions with precision, which severely limits the pace and scope of both clinical management and research progress in epilepsy. We compared high versus low or nonspiking regions in nine medically intractable epilepsy surgery patients by performing integrated metabolomic-genomic-histological analyses of electrically mapped human cortical regions using high-resolution magic angle spinning proton magnetic resonance spectroscopy, cDNA microarrays, and histological analysis. We found a highly consistent and predictive metabolite logistic regression model with reduced lactate and increased creatine plus phosphocreatine and choline, suggestive of a chronically altered metabolic state in epileptic brain regions. Linking gene expression, cellular, and histological differences to these key metabolites using a hierarchical clustering approach predicted altered metabolic vascular coupling in the affected regions. Consistently, these predictions were validated histologically, showing both neovascularization and newly discovered, millimeter-sized microlesions. Using a systems biology approach on electrically mapped human cortex provides new evidence for spatially segregated, metabolic derangements in both neurovascular and synaptic architecture in human epileptic brain regions that could be a noninvasively detectable biomarker of epilepsy. These findings both highlight the immense power of a systems biology approach and identify a potentially important role that magnetic resonance spectroscopy can play in the research and clinical management of epilepsy. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Predictive cytogenetic biomarkers for colorectal neoplasia in medium risk patients.

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Ionescu, E M; Nicolaie, T; Ionescu, M A; Becheanu, G; Andrei, F; Diculescu, M; Ciocirlan, M

2015-01-01

DNA damage and chromosomal alterations in peripheral lymphocytes parallels DNA mutations in tumor tissues. The aim of our study was to predict the presence of neoplastic colorectal lesions by specific biomarkers in "medium risk" individuals (age 50 to 75, with no personal or family of any colorectal neoplasia). We designed a prospective cohort observational study including patients undergoing diagnostic or opportunistic screening colonoscopy. Specific biomarkers were analyzed for each patient in peripheral lymphocytes - presence of micronuclei (MN), nucleoplasmic bridges (NPB) and the Nuclear Division Index (NDI) by the cytokinesis-blocked micronucleus assay (CBMN). Of 98 patients included, 57 were "medium risk" individuals. MN frequency and NPB presence were not significantly different in patients with neoplastic lesions compared to controls. In "medium risk" individuals, mean NDI was significantly lower for patients with any neoplastic lesions (adenomas and adenocarcinomas, AUROC 0.668, p 00.5), for patients with advanced neoplasia (advanced adenoma and adenocarcinoma, AUROC 0.636 p 0.029) as well as for patients with adenocarcinoma (AUROC 0.650, p 0.048), for each comparison with the rest of the population. For a cut-off of 1.8, in "medium risk" individuals, an NDI inferior to that value may predict any neoplastic lesion with a sensitivity of 97.7%, an advanced neoplastic lesion with a sensitivity of 97% and adenocarcinoma with a sensitivity of 94.4%. NDI score may have a role as a colorectal cancer-screening test in "medium risk" individuals. DNA = deoxyribonucleic acid; CRC = colorectal cancer; EU = European Union; WHO = World Health Organization; FOBT = fecal occult blood test; CBMN = cytokinesis-blocked micronucleus assay; MN = micronuclei; NPB = nucleoplasmic bridges; NDI = Nuclear Division Index; FAP = familial adenomatous polyposis; HNPCC = hereditary non-polypoid colorectal cancer; IBD = inflammatory bowel diseases; ROC = receiver operating

HumanMethylation450K Array–Identified Biomarkers Predict Tumour Recurrence/Progression at Initial Diagnosis of High-risk Non-muscle Invasive Bladder Cancer

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Mark O Kitchen

2018-01-01

Full Text Available Background: High-risk non-muscle invasive bladder cancer (HR-NMIBC is a clinically unpredictable disease. Despite clinical risk estimation tools, many patients are undertreated with intra-vesical therapies alone, whereas others may be over-treated with early radical surgery. Molecular biomarkers, particularly DNA methylation, have been reported as predictive of tumour/patient outcomes in numerous solid organ and haematologic malignancies; however, there are few reports in HR-NMIBC and none using genome-wide array assessment. We therefore sought to identify novel DNA methylation markers of HR-NMIBC clinical outcomes that might predict tumour behaviour at initial diagnosis and help guide patient management. Patients and methods: A total of 21 primary initial diagnosis HR-NMIBC tumours were analysed by Illumina HumanMethylation450 BeadChip arrays and subsequently bisulphite Pyrosequencing. In all, 7 had not recurred at 1 year after resection and 14 had recurred and/or progressed despite intra-vesical BCG. A further independent cohort of 32 HR-NMIBC tumours (17 no recurrence and 15 recurrence and/or progression despite BCG were also assessed by bisulphite Pyrosequencing. Results: Array analyses identified 206 CpG loci that segregated non-recurrent HR-NMIBC tumours from clinically more aggressive recurrence/progression tumours. Hypermethylation of CpG cg11850659 and hypomethylation of CpG cg01149192 in combination predicted HR-NMIBC recurrence and/or progression within 1 year of diagnosis with 83% sensitivity, 79% specificity, and 83% positive and 79% negative predictive values. Conclusions: This is the first genome-wide DNA methylation analysis of a unique HR-NMIBC tumour cohort encompassing known 1-year clinical outcomes. Our analyses identified potential novel epigenetic markers that could help guide individual patient management in this clinically unpredictable disease.

Inflammation-regulating factors in ascites as predictive biomarkers of drug resistance and progression-free survival in serous epithelial ovarian cancers

International Nuclear Information System (INIS)

Lane, Denis; Matte, Isabelle; Garde-Granger, Perrine; Laplante, Claude; Carignan, Alex; Rancourt, Claudine; Piché, Alain

2015-01-01

Platinum-based combination therapy is the standard first-line treatment for women with advanced serous epithelial ovarian carcinoma (EOC). However, about 20 % will not respond and are considered clinically resistant. The availability of biomarkers to predict responses to the initial therapy would provide a practical approach to identify women who would benefit from a more appropriate first-line treatment. Ascites is an attractive inflammatory fluid for biomarker discovery as it is easy and minimally invasive to obtain. The aim of this study was to evaluate whether six selected inflammation-regulating factors in ascites could serve as diagnostic or drug resistance biomarkers in patients with advanced serous EOC. A total of 53 women with stage III/IV serous EOC and 10 women with benign conditions were enrolled in this study. Eleven of the 53 women with serous EOC were considered clinically resistant to treatment with progression-free survival < 6 months. Ascites were collected at the time of the debulking surgery and the levels of cytokines were measured by ELISA. The six selected cytokines were evaluated for their ability to discriminate serous EOC from benign controls, and to discriminate platinum resistant from platinum sensitive patients. Median ascites levels of IL-6, IL-10 and osteoprotegerin (OPG) were significantly higher in women with advanced serous EOC than in controls (P ≤ 0.012). There were no significant difference in the median ascites levels of leptin, soluble urokinase plasminogen activator receptor (suPAR) and CCL18 among serous EOC women and controls. In Receiver Operator curve (ROC) analysis, IL-6, IL-10 and OPG had a high area under the curve value of 0.905, 0.832 and 0.825 respectively for distinguishing EOC from benign controls. ROC analysis of individual cytokines revealed low discriminating potential to stratify patients according to their sensitivity to first-line treatment. The combination of biomarkers with the highest discriminating

Biomarkers of myocardial stress and fibrosis as predictors of mode of death in patients with chronic heart failure.

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Ahmad, Tariq; Fiuzat, Mona; Neely, Benjamin; Neely, Megan L; Pencina, Michael J; Kraus, William E; Zannad, Faiez; Whellan, David J; Donahue, Mark P; Piña, Ileana L; Adams, Kirkwood F; Kitzman, Dalane W; O'Connor, Christopher M; Felker, G Michael

2014-06-01

The aim of this study was to determine whether biomarkers of myocardial stress and fibrosis improve prediction of the mode of death in patients with chronic heart failure. The 2 most common modes of death in patients with chronic heart failure are pump failure and sudden cardiac death. Prediction of the mode of death may facilitate treatment decisions. The relationship between amino-terminal pro-brain natriuretic peptide (NT-proBNP), galectin-3, and ST2, biomarkers that reflect different pathogenic pathways in heart failure (myocardial stress and fibrosis), and mode of death is unknown. HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) was a randomized controlled trial of exercise training versus usual care in patients with chronic heart failure due to left ventricular systolic dysfunction (left ventricular ejection fraction ≤35%). An independent clinical events committee prospectively adjudicated mode of death. NT-proBNP, galectin-3, and ST2 levels were assessed at baseline in 813 subjects. Associations between biomarkers and mode of death were assessed using cause-specific Cox proportional hazards modeling, and interaction testing was used to measure differential associations between biomarkers and pump failure versus sudden cardiac death. Discrimination and risk reclassification metrics were used to assess the added value of galectin-3 and ST2 in predicting mode of death risk beyond a clinical model that included NT-proBNP. After a median follow-up period of 2.5 years, there were 155 deaths: 49 from pump failure, 42 from sudden cardiac death, and 64 from other causes. Elevations in all biomarkers were associated with increased risk for both pump failure and sudden cardiac death in both adjusted and unadjusted analyses. In each case, increases in the biomarker had a stronger association with pump failure than sudden cardiac death, but this relationship was attenuated after adjustment for clinical risk factors. Clinical

Four-hour quantitative real-time polymerase chain reaction-based comprehensive chromosome screening and accumulating evidence of accuracy, safety, predictive value, and clinical efficacy.

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Treff, Nathan R; Scott, Richard T

2013-03-15

Embryonic comprehensive chromosomal euploidy may represent a powerful biomarker to improve the success of IVF. However, there are a number of aneuploidy screening strategies to consider, including different technologic platforms with which to interrogate the embryonic DNA, and different embryonic developmental stages from which DNA can be analyzed. Although there are advantages and disadvantages associated with each strategy, a series of experiments producing evidence of accuracy, safety, clinical predictive value, and clinical efficacy indicate that trophectoderm biopsy and quantitative real-time polymerase chain reaction (qPCR)-based comprehensive chromosome screening (CCS) may represent a useful strategy to improve the success of IVF. This Biomarkers in Reproductive Medicine special issue review summarizes the accumulated experience with the development and clinical application of a 4-hour blastocyst qPCR-based CCS technology. Copyright © 2013 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

[Diagnosis of acute heart failure and relevance of biomarkers in elderly patients].

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Ruiz Ortega, Raúl Antonio; Manzano, Luis; Montero-Pérez-Barquero, Manuel

2014-03-01

Diagnosis of acute heart failure (HF) is difficult in elderly patients with multiple comorbidities. Risk scales and classification criteria based exclusively on clinical manifestations, such as the Framingham scales, lack sufficient specificity. In addition to clinical manifestations, diagnosis should be based on two key factors: natriuretic peptides and echocardiographic study. When there is clinical suspicion of acute HF, a normal natriuretic peptide level will rule out this process. When a consistent clinical suspicion is present, an echocardiographic study should also be performed. Diagnosis of HF with preserved ejection fraction (HF/pEF) requires detection of an enlarged left atrium or the presence of parameters of diastolic dysfunction. Elevation of cardiac biomarkers seems to be due to myocardial injury and the compensatory mechanisms of the body against this injury (hormone and inflammatory response and repair mechanisms). Elevation of markers of cardiac damage (troponins and natriuretic peptides) have been shown to be useful both in the diagnosis of acute HF and in prediction of outcome. MMP-2 could be useful in the diagnosis of HF/pEF. In addition to biomarkers with diagnostic value, other biomarkers are helpful in prognosis in the acute phase of HF, such as biomarkers of renal failure (eGFR, cystatin and urea), inflammation (cytokines and CRP), and the cell regeneration marker, galectin-3. A promising idea that is under investigation is the use of panels of biomarkers, which could allow more accurate diagnosis and prognosis of acute HF. Copyright © 2014 Elsevier España, S.L. All rights reserved.

Development and validation of a prognostic model using blood biomarker information for prediction of survival of non-small-cell lung cancer patients treated with combined chemotherapy and radiation or radiotherapy alone (NCT00181519, NCT00573040, and NCT00572325).

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Dehing-Oberije, Cary; Aerts, Hugo; Yu, Shipeng; De Ruysscher, Dirk; Menheere, Paul; Hilvo, Mika; van der Weide, Hiska; Rao, Bharat; Lambin, Philippe

2011-10-01

Currently, prediction of survival for non-small-cell lung cancer patients treated with (chemo)radiotherapy is mainly based on clinical factors. The hypothesis of this prospective study was that blood biomarkers related to hypoxia, inflammation, and tumor load would have an added prognostic value for predicting survival. Clinical data and blood samples were collected prospectively (NCT00181519, NCT00573040, and NCT00572325) from 106 inoperable non-small-cell lung cancer patients (Stages I-IIIB), treated with curative intent with radiotherapy alone or combined with chemotherapy. Blood biomarkers, including lactate dehydrogenase, C-reactive protein, osteopontin, carbonic anhydrase IX, interleukin (IL) 6, IL-8, carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1, were measured. A multivariate model, built on a large patient population (N = 322) and externally validated, was used as a baseline model. An extended model was created by selecting additional biomarkers. The model's performance was expressed as the area under the curve (AUC) of the receiver operating characteristic and assessed by use of leave-one-out cross validation as well as a validation cohort (n = 52). The baseline model consisted of gender, World Health Organization performance status, forced expiratory volume, number of positive lymph node stations, and gross tumor volume and yielded an AUC of 0.72. The extended model included two additional blood biomarkers (CEA and IL-6) and resulted in a leave-one-out AUC of 0.81. The performance of the extended model was significantly better than the clinical model (p = 0.004). The AUC on the validation cohort was 0.66 and 0.76, respectively. The performance of the prognostic model for survival improved markedly by adding two blood biomarkers: CEA and IL-6. Copyright © 2011 Elsevier Inc. All rights reserved.

Biomarkers of cytokine release syndrome and neurotoxicity related to CAR-T cell therapy.

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Wang, Zhenguang; Han, Weidong

2018-01-01

Severe cytokine release syndrome (CRS) and neurotoxicity following chimeric antigen receptor T cell (CAR-T) therapy can be life-threatening in some cases, and management of those toxicities is still a great challenge for physicians. Researchers hope to understand the pathophysiology of CRS and neurotoxicity, and identify predictive biomarkers that can forecast those toxicities in advance. Some risk factors for severe CRS and/or neurotoxicity including patient and treatment characteristics have been identified in multiple clinical trials of CAR-T cell therapy. Moreover, several groups have identified some predictive biomarkers that are able to determine beforehand which patients may suffer severe CRS and/or neurotoxicity during CAR-T cell therapy, facilitating testing of early intervention strategies for those toxicities. However, further studies are needed to better understand the biology and related risk factors for CRS and/or neurotoxicity, and determine if those identified predictors can be extrapolated to other series. Herein, we review the pathophysiology of CRS and neurotoxicity, and summarize the progress of predictive biomarkers to improve CAR-T cell therapy in cancer.

Biomarkers in Autism

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Robert eHendren

2014-08-01

Full Text Available Autism spectrum disorders (ASD are complex, heterogeneous disorders caused by an interaction between genetic vulnerability and environmental factors. In an effort to better target the underlying roots of ASD for diagnosis and treatment, efforts to identify reliable biomarkers in genetics, neuroimaging, gene expression and measures of the body’s metabolism are growing. For this article, we review the published studies of potential biomarkers in autism and conclude that while there is increasing promise of finding biomarkers that can help us target treatment, there are none with enough evidence to support routine clinical use unless medical illness is suspected. Promising biomarkers include those for mitochondrial function, oxidative stress, and immune function. Genetic clusters are also suggesting the potential for useful biomarkers.

Predicting progression to dementia in persons with mild cognitive impairment using cerebrospinal fluid markers.

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Handels, Ron L H; Vos, Stephanie J B; Kramberger, Milica G; Jelic, Vesna; Blennow, Kaj; van Buchem, Mark; van der Flier, Wiesje; Freund-Levi, Yvonne; Hampel, Harald; Olde Rikkert, Marcel; Oleksik, Ania; Pirtosek, Zvezdan; Scheltens, Philip; Soininen, Hilkka; Teunissen, Charlotte; Tsolaki, Magda; Wallin, Asa K; Winblad, Bengt; Verhey, Frans R J; Visser, Pieter Jelle

2017-08-01

We aimed to determine the added value of cerebrospinal fluid (CSF) to clinical and imaging tests to predict progression from mild cognitive impairment (MCI) to any type of dementia. The risk of progression to dementia was estimated using two logistic regression models based on 250 MCI participants: the first included standard clinical measures (demographic, clinical, and imaging test information) without CSF biomarkers, and the second included standard clinical measures with CSF biomarkers. Adding CSF improved predictive accuracy with 0.11 (scale from 0-1). Of all participants, 136 (54%) had a change in risk score of 0.10 or higher (which was considered clinically relevant), of whom in 101, it was in agreement with their dementia status at follow-up. An individual person's risk of progression from MCI to dementia can be improved by relying on CSF biomarkers in addition to recommended clinical and imaging tests for usual care. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

[Autoantibodies as biomarkers].

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Tron, François

2014-01-01

Activation and differentiation of autoreactive B-lymphocytes lead to the production of autoantibodies, which are thus the direct consequence of the autoimmune process. They often constitute biomarkers of autoimmune diseases and are measured by tests displaying various diagnosis sensitivity and specificity. Autoantibody titers can be correlated to the disease activity and certain autoantibody populations associated with particular clinical manifestations or tissue lesions. The demonstration that autoantibodies appear years before the onset of autoimmune diseases indicates that their presence in healthy individuals may be a predictive marker of the occurrence of disease. Certain autoantibodies could also be predictive markers of a therapeutic response to biologics and of the occurrence of side effects as well. Thus, autoantibodies are useful tools in the diagnosis and the management of patients with organ specific or non-organ specific autoimmune diseases at different steps of the autoimmune process. Copyright © 2013. Published by Elsevier Masson SAS.

An epigenetic biomarker of aging for lifespan and healthspan

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Levine, Morgan E.; Lu, Ake T.; Quach, Austin; Chen, Brian H.; Assimes, Themistocles L.; Bandinelli, Stefania; Hou, Lifang; Baccarelli, Andrea A.; Stewart, James D.; Li, Yun; Whitsel, Eric A.; Wilson, James G; Reiner, Alex P; Aviv, Abraham; Lohman, Kurt; Liu, Yongmei; Ferrucci, Luigi

2018-01-01

Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging. PMID:29676998

Comparing and combining biomarkers as principle surrogates for time-to-event clinical endpoints.

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Gabriel, Erin E; Sachs, Michael C; Gilbert, Peter B

2015-02-10

Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial. Copyright © 2014 John Wiley & Sons, Ltd.

Tissue- and Serum-Associated Biomarkers of Hepatocellular Carcinoma

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Chauhan, Ranjit; Lahiri, Nivedita

2016-01-01

Hepatocellular carcinoma (HCC), one of the leading causes of cancer deaths in the world, is offering a challenge to human beings, with the current modes of treatment being a palliative approach. Lack of proper curative or preventive treatment methods encouraged extensive research around the world with an aim to detect a vaccine or therapeutic target biomolecule that could lead to development of a drug or vaccine against HCC. Biomarkers or biological disease markers have emerged as a potential tool as drug/vaccine targets, as they can accurately diagnose, predict, and even prevent the diseases. Biomarker expression in tissue, serum, plasma, or urine can detect tumor in very early stages of its development and monitor the cancer progression and also the effect of therapeutic interventions. Biomarker discoveries are driven by advanced techniques, such as proteomics, transcriptomics, whole genome sequencing, micro- and micro-RNA arrays, and translational clinics. In this review, an overview of the potential of tissue- and serum-associated HCC biomarkers as diagnostic, prognostic, and therapeutic targets for drug development is presented. In addition, we highlight recently developed micro-RNA, long noncoding RNA biomarkers, and single-nucleotide changes, which may be used independently or as complementary biomarkers. These active investigations going on around the world aimed at conquering HCC might show a bright light in the near future. PMID:27398029

Tissue- and Serum-Associated Biomarkers of Hepatocellular Carcinoma

Directory of Open Access Journals (Sweden)

Ranjit Chauhan

2016-01-01

Full Text Available Hepatocellular carcinoma (HCC, one of the leading causes of cancer deaths in the world, is offering a challenge to human beings, with the current modes of treatment being a palliative approach. Lack of proper curative or preventive treatment methods encouraged extensive research around the world with an aim to detect a vaccine or therapeutic target biomolecule that could lead to development of a drug or vaccine against HCC. Biomarkers or biological disease markers have emerged as a potential tool as drug/vaccine targets, as they can accurately diagnose, predict, and even prevent the diseases. Biomarker expression in tissue, serum, plasma, or urine can detect tumor in very early stages of its development and monitor the cancer progression and also the effect of therapeutic interventions. Biomarker discoveries are driven by advanced techniques, such as proteomics, transcriptomics, whole genome sequencing, micro- and micro-RNA arrays, and translational clinics. In this review, an overview of the potential of tissue- and serum-associated HCC biomarkers as diagnostic, prognostic, and therapeutic targets for drug development is presented. In addition, we highlight recently developed micro-RNA, long noncoding RNA biomarkers, and single-nucleotide changes, which may be used independently or as complementary biomarkers. These active investigations going on around the world aimed at conquering HCC might show a bright light in the near future.

Can Biomarker Assessment on Circulating Tumor Cells Help Direct Therapy in Metastatic Breast Cancer?

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Natalie Turner

2014-03-01

Full Text Available Circulating tumor cell (CTC count has prognostic significance in metastatic breast cancer, but the predictive utility of CTCs is uncertain. Molecular studies on CTCs have often been limited by a low number of CTCs isolated from a high background of leukocytes. Improved enrichment techniques are now allowing molecular characterisation of single CTCs, whereby molecular markers on single CTCs may provide a real-time assessment of tumor biomarker status from a blood test or “liquid biopsy”, potentially negating the need for a more invasive tissue biopsy. The predictive ability of CTC biomarker analysis has predominantly been assessed in relation to HER2, with variable and inconclusive results. Limited data exist for other biomarkers, such as the estrogen receptor. In addition to the need to define and validate the most accurate and reproducible method for CTC molecular analysis, the clinical relevance of biomarkers, including gain of HER2 on CTC after HER2 negative primary breast cancer, remains uncertain. This review summarises the currently available data relating to biomarker evaluation on CTCs and its role in directing management in metastatic breast cancer, discusses limitations, and outlines measures that may enable future development of this approach.

Immunohistochemical and molecular imaging biomarker signature for the prediction of failure site after chemoradiation for head and neck squamous cell carcinoma

DEFF Research Database (Denmark)

Rasmussen, Gregers Brünnich; Håkansson, Katrin E; Vogelius, Ivan R

2017-01-01

.23; p: .025), Bcl-2 (HR: 2.6; p: .08), SUVmax (HR: 3.5; p: .095) and GTV (HR: 1.7; p: .063). CONCLUSIONS: The models successfully distinguished between risk of locoregional failure and risk of distant metastasis, which is important information for clinical decision-making. High p53 expression has......OBJECTIVE: To identify a failure site-specific prognostic model by combining immunohistochemistry (IHC) and molecular imaging information to predict long-term failure type in squamous cell carcinoma of the head and neck. PATIENT AND METHODS: Tissue microarray blocks of 196 head and neck squamous...... cell carcinoma cases were stained for a panel of biomarkers using IHC. Gross tumor volume (GTV) from the PET/CT radiation treatment planning CT scan, maximal Standard Uptake Value (SUVmax) of fludeoxyglucose (FDG) and clinical information were included in the model building using Cox proportional...

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

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Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Biomarkers for acute kidney injury in decompensated cirrhosis: A Prospective Study.

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Jaques, David A; Spahr, Laurent; Berra, Gregory; Poffet, Vincent; Lescuyer, Pierre; Gerstel, Eric; Garin, Nicolas; Martin, Pierre-Yves; Ponte, Belen

2018-01-25

Acute kidney injury (AKI) is a frequent complication in cirrhotic patients. As serum creatinine is a poor marker of renal function in this population, we aimed to study the utility of several biomarkers in this context. A prospective study was conducted in hospitalized patients with decompensated cirrhosis. Serum creatinine (SCr), Cystatin C (CystC), NGAL and urinary NGAL, KIM-1, protein, albumin and sodium were measured on three separate occasions. Renal resistive index (RRI) was obtained. We analyzed the value of these biomarkers to determine the presence of AKI, its etiology [prerenal, acute tubular necrosis (ATN), or hepatorenal (HRS)], its severity and a composite clinical outcome at 30 days (death, dialysis and intensive care admission). We included 105 patients, of which 55 had AKI. SCr, CystC, NGAL (plasma and urinary), urinary sodium and RRI at inclusion were independently associated with the presence of AKI. SCr, CystC and plasma NGAL were able to predict the subsequent development of AKI. Pre-renal state showed lower levels of SCr, NGAL (plasma and urinary) and RRI. ATN patients had high levels of NGAL (plasma and urinary) as well as urinary protein and sodium. HRS patients presented an intermediate pattern. All biomarkers paralleled the severity of AKI. SCr, CystC and plasma NGAL predicted the development of the composite clinical outcome with the same performance as the MELD score. In patients with decompensated cirrhosis, early measurement of renal biomarkers provides valuable information on AKI etiology. It could also improve AKI diagnosis and prognosis. This article is protected by copyright. All rights reserved.

Canine babesiosis: a perspective on clinical complications, biomarkers, and treatment

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Köster LS

2015-04-01

Full Text Available Liza S Köster,1 Remo G Lobetti,2 Patrick Kelly1 1Department of Clinical Sciences, One Health Center for Zoonoses and Tropical Veterinary Medicine, Ross University School of Veterinary Medicine, St Kitts, West Indies; 2Bryanston Veterinary Hospital, Bryanston, South Africa Abstract: Canine babesiosis is a common tick transmitted disease of dogs worldwide. A number of Babesia sp. can infect dogs and the spectrum is increasing as molecular methods are developed to differentiate organisms. Clinical signs are generally attributed to hemolysis caused by the organisms in the erythrocytes but in some animals with some Babesia spp. there can be an immune mediated component to the anemia and/or a severe inflammatory reaction associated. This complicated form of canine babesiosis is associated with high morbidity and mortality. A variety of clinical markers has been investigated to enable clinicians to provide more accurate prognoses and adapt their treatments which vary according to the infecting species. In this review, we discuss the taxonomy, clinical signs, diagnostic imaging, clinical biomarkers, treatment, and prophylaxis of one of the most common and important diseases of dogs worldwide. Keywords: babesiosis, vector-borne disease, dog

EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

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Chun-Chuan Chen

Full Text Available Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation. In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

Clinical-Radiological Parameters Improve the Prediction of the Thrombolysis Time Window by Both MRI Signal Intensities and DWI-FLAIR Mismatch.

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Madai, Vince Istvan; Wood, Carla N; Galinovic, Ivana; Grittner, Ulrike; Piper, Sophie K; Revankar, Gajanan S; Martin, Steve Z; Zaro-Weber, Olivier; Moeller-Hartmann, Walter; von Samson-Himmelstjerna, Federico C; Heiss, Wolf-Dieter; Ebinger, Martin; Fiebach, Jochen B; Sobesky, Jan

2016-01-01

With regard to acute stroke, patients with unknown time from stroke onset are not eligible for thrombolysis. Quantitative diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR) MRI relative signal intensity (rSI) biomarkers have been introduced to predict eligibility for thrombolysis, but have shown heterogeneous results in the past. In the present work, we investigated whether the inclusion of easily obtainable clinical-radiological parameters would improve the prediction of the thrombolysis time window by rSIs and compared their performance to the visual DWI-FLAIR mismatch. In a retrospective study, patients from 2 centers with proven stroke with onset value/mean value of the unaffected hemisphere). Additionally, the visual DWI-FLAIR mismatch was evaluated. Prediction of the thrombolysis time window was evaluated by the area-under-the-curve (AUC) derived from receiver operating characteristic (ROC) curve analysis. Factors such as the association of age, National Institutes of Health Stroke Scale, MRI field strength, lesion size, vessel occlusion and Wahlund-Score with rSI were investigated and the models were adjusted and stratified accordingly. In 82 patients, the unadjusted rSI measures DWI-mean and -SD showed the highest AUCs (AUC 0.86-0.87). Adjustment for clinical-radiological covariates significantly improved the performance of FLAIR-mean (0.91) and DWI-SD (0.91). The best prediction results based on the AUC were found for the final stratified and adjusted models of DWI-SD (0.94) and FLAIR-mean (0.96) and a multivariable DWI-FLAIR model (0.95). The adjusted visual DWI-FLAIR mismatch did not perform in a significantly worse manner (0.89). ADC-rSIs showed fair performance in all models. Quantitative DWI and FLAIR MRI biomarkers as well as the visual DWI-FLAIR mismatch provide excellent prediction of eligibility for thrombolysis in acute stroke, when easily obtainable clinical-radiological parameters are included in the prediction

Thymus and activation-regulated chemokine as a clinical biomarker in atopic dermatitis.

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Kataoka, Yoko

2014-03-01

Thymus and activation-regulated chemokine (TARC/CCL17) is a member of the T-helper 2 chemokine family. In Japan, serum TARC level has been commercially measured since 2008. After years of experience, we realized that TARC is an extremely useful clinical biomarker for atopic dermatitis (AD) treatment. Usually, physicians conduct a visual examination to determine whether their treatment has been successful; however, the visual examination results may not always be accurate; in such cases, serum TARC levels should be measured to eliminate any ambiguity regarding the treatment outcome. When the waning and waxing of eczema and fluctuations in the serum TARC levels were considered, we frequently found that AD does not follow a natural course but follows non-regulated inflammatory floating caused by insufficient intermittent topical treatment. Serum TARC is a promising biomarker for remission and can be used for accurately monitoring proactive treatment for long-term control. Abnormally high serum TARC levels indicate accelerated pathogenesis of cutaneous inflammation. Rapid normalization and maintaining normal serum TARC levels using appropriate topical treatment is a reasonable strategy for alleviating inflammation without upregulating cytokine expression. Observing serum TARC levels during early intervention for severe infantile AD is worthwhile to determine initial disease activity and evaluate treatment efficacy. Appropriate control of severe early-onset infantile AD is important for improving prognosis of eczema and for preventing food allergies. Additionally, this biomarker is useful for improving patient adherence. Dermatologists will be able to make great progress in treating AD by adopting biomarkers such as TARC for accurately assessing non-visible subclinical disorders. © 2014 Japanese Dermatological Association.

PODCAST: From Lost in Translation to Paradise Found: Enabling Protein Biomarker Method Transfer by Mass Spectrometry | Office of Cancer Clinical Proteomics Research

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Translation of novel biomarkers into clinical care for the evaluation of therapeutic safety and efficacy has been slow, partly attributable to the cost and complexity of immunoassay development.  The potential for liquid chromatography-tandem mass spectrometry (LC-MS/MS) to streamline the translation of novel protein biomarkers is profound.  Drs. Henry Rodriguez and Andrew Hoofnagle discuss what the future may be for clinical proteomics. This is an American Association for Clinical Chemistry (AACC) podcast.

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets

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Karacali Bilge

2007-10-01

Full Text Available Abstract Background Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a all genes on the microarray platform and b a list of known disease-related genes (a priori selection. We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms. Results Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform. Conclusion Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine

Effects of dietary restriction on adipose mass and biomarkers of healthy aging in human.

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Lettieri-Barbato, Daniele; Giovannetti, Esmeralda; Aquilano, Katia

2016-11-29

In developing countries the rise of obesity and obesity-related metabolic disorders, such as cardiovascular diseases and type 2 diabetes, reflects the changes in lifestyle habits and wrong dietary choices. Dietary restriction (DR) regimens have been shown to extend health span and lifespan in many animal models including primates. Identifying biomarkers predictive of clinical benefits of treatment is one of the primary goals of precision medicine. To monitor the clinical outcomes of DR interventions in humans, several biomarkers are commonly adopted. However, a validated link between the behaviors of such biomarkers and DR effects is lacking at present time. Through a systematic analysis of human intervention studies, we evaluated the effect size of DR (i.e. calorie restriction, very low calorie diet, intermittent fasting, alternate day fasting) on health-related biomarkers. We found that DR is effective in reducing total and visceral adipose mass and improving inflammatory cytokines profile and adiponectin/leptin ratio. By analysing the levels of canonical biomarkers of healthy aging, we also validated the changes of insulin, IGF-1 and IGFBP-1,2 to monitor DR effects. Collectively, we developed a useful platform to evaluate the human responses to dietary regimens low in calories.

Enlarged pulmonary artery is predicted by vascular injury biomarkers and is associated with WTC-Lung Injury in exposed fire fighters: a case–control study

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Schenck, Edward J; Echevarria, Ghislaine C; Girvin, Francis G; Kwon, Sophia; Comfort, Ashley L; Rom, William N; Prezant, David J; Weiden, Michael D; Nolan, Anna

2014-01-01

Objectives We hypothesise that there is an association between an elevated pulmonary artery/aorta (PA/A) and World Trade Center-Lung Injury (WTC-LI). We assessed if serum vascular disease biomarkers were predictive of an elevated PA/A. Design Retrospective case-cohort analysis of thoracic CT scans of WTC-exposed firefighters who were symptomatic between 9/12/2001 and 3/10/2008. Quantification of vascular-associated biomarkers from serum collected within 200 days of exposure. Setting Urban tertiary care centre and occupational healthcare centre. Participants Male never-smoking firefighters with accurate pre-9/11 forced expiratory volume in 1 s (FEV1) ≥75%, serum sampled ≤200 days of exposure was the baseline cohort (n=801). A subcohort (n=97) with available CT scans and serum biomarkers was identified. WTC-LI was defined as FEV1≤77% at the subspecialty pulmonary evaluation (n=34) and compared with controls (n=63) to determine the associated PA/A ratio. The subcohort was restratified based on PA/A≥0.92 (n=38) and PA/A<0.92(n=59) to determine serum vascular biomarkers that were predictive of this vasculopathy. Outcome measures The primary outcome of this study was to identify a PA/A ratio in a cohort of individuals exposed to WTC dust that was associated with WTC-LI. The secondary outcome was to identify serum biomarkers predictive of the PA/A ratio using logistic regression. Results PA/A≥0.92 was associated with WTC-LI, OR of 4.02 (95% CI 1.21 to 13.41; p=0.023) when adjusted for exposure, body mass index and age at CT. Elevated macrophage derived chemokine and soluble endothelial selectin were predictive of PA/A≥0.92, (OR, 95% CI 2.08, 1.05 to 4.11, p=0.036; 1.33, 1.06 to 1.68, p=0.016, respectively), while the increased total plasminogen activator inhibitor 1 was predictive of not having PA/A≥0.92 (OR 0.88, 0.79 to 0.98; p=0.024). Conclusions Elevated PA/A was associated with WTC-LI. Development of an elevated PA/A was predicted by biomarkers of

Cardiac biomarkers in Neonatology

OpenAIRE

Vijlbrief, D.C.

2015-01-01

In this thesis, the role for cardiac biomarkers in neonatology was investigated. Several clinically relevant results were reported. In term and preterm infants, hypoxia and subsequent adaptation play an important role in cardiac biomarker elevation. The elevated natriuretic peptides are indicative of abnormal function; elevated troponins are suggestive for cardiomyocyte damage. This methodology makes these biomarkers of additional value in the treatment of newborn infants, separate or as a co...

Toxicogenomic multigene biomarker for predicting the future onset of proximal tubular injury in rats

International Nuclear Information System (INIS)

Minowa, Yohsuke; Kondo, Chiaki; Uehara, Takeki; Morikawa, Yuji; Okuno, Yasushi; Nakatsu, Noriyuki; Ono, Atsushi; Maruyama, Toshiyuki; Kato, Ikuo; Yamate, Jyoji; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro

2012-01-01

Drug-induced renal tubular injury is a major concern in the preclinical safety evaluation of drug candidates. Toxicogenomics is now a generally accepted tool for identifying chemicals with potential safety problems. The specific aim of the present study was to develop a model for use in predicting the future onset of drug-induced proximal tubular injury following repeated dosing with various nephrotoxicants. In total, 41 nephrotoxic and nonnephrotoxic compounds were used for the present analysis. Male Sprague-Dawley rats were dosed orally or intravenously once daily. Animals were exposed to three different doses (low, middle, and high) of each compound, and kidney tissue was collected at 3, 6, 9, and 24 h after single dosing, and on days 4, 8, 15, and 29 after repeated dosing. Gene expression profiles were generated from kidney total RNA using Affymetrix DNA microarrays. Filter-type gene selection and linear classification algorithms were employed to discriminate future onset of proximal tubular injury. We identified genomic biomarkers for use in future onset prediction using the gene expression profiles determined on day 1, when most of the nephrotoxicants had yet to produce detectable histopathological changes. The model was evaluated using a five-fold cross validation, and achieved a sensitivity of 93% and selectivity of 90% with 19 probes. We also found that the prediction accuracy of the optimized model was substantially higher than that produced by any of the single genomic biomarkers or histopathology. The genes included in our model were primarily involved in DNA replication, cell cycle control, apoptosis, and responses to oxidative stress and chemical stimuli. In summary, our toxicogenomic model is particularly useful for predicting the future onset of proximal tubular injury.

Data Mining Approaches for Genomic Biomarker Development: Applications Using Drug Screening Data from the Cancer Genome Project and the Cancer Cell Line Encyclopedia.

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David G Covell

Full Text Available Developing reliable biomarkers of tumor cell drug sensitivity and resistance can guide hypothesis-driven basic science research and influence pre-therapy clinical decisions. A popular strategy for developing biomarkers uses characterizations of human tumor samples against a range of cancer drug responses that correlate with genomic change; developed largely from the efforts of the Cancer Cell Line Encyclopedia (CCLE and Sanger Cancer Genome Project (CGP. The purpose of this study is to provide an independent analysis of this data that aims to vet existing and add novel perspectives to biomarker discoveries and applications. Existing and alternative data mining and statistical methods will be used to a evaluate drug responses of compounds with similar mechanism of action (MOA, b examine measures of gene expression (GE, copy number (CN and mutation status (MUT biomarkers, combined with gene set enrichment analysis (GSEA, for hypothesizing biological processes important for drug response, c conduct global comparisons of GE, CN and MUT as biomarkers across all drugs screened in the CGP dataset, and d assess the positive predictive power of CGP-derived GE biomarkers as predictors of drug response in CCLE tumor cells. The perspectives derived from individual and global examinations of GEs, MUTs and CNs confirm existing and reveal unique and shared roles for these biomarkers in tumor cell drug sensitivity and resistance. Applications of CGP-derived genomic biomarkers to predict the drug response of CCLE tumor cells finds a highly significant ROC, with a positive predictive power of 0.78. The results of this study expand the available data mining and analysis methods for genomic biomarker development and provide additional support for using biomarkers to guide hypothesis-driven basic science research and pre-therapy clinical decisions.

Methylated genes as new cancer biomarkers

DEFF Research Database (Denmark)

Brunner, Nils; Duffy, M.J; Napieralski, R.

2009-01-01

Aberrant hypermethylation of promoter regions in specific genes is a key event in the formation and progression of cancer. In at least some situations, these aberrant alterations occur early in the formation of malignancy and appear to be tumour specific. Multiple reports have suggested that meas......Aberrant hypermethylation of promoter regions in specific genes is a key event in the formation and progression of cancer. In at least some situations, these aberrant alterations occur early in the formation of malignancy and appear to be tumour specific. Multiple reports have suggested...... that measurement of the methylation status of the promoter regions of specific genes can aid early detection of cancer, determine prognosis and predict therapy responses. Promising DNA methylation biomarkers include the use of methylated GSTP1 for aiding the early diagnosis of prostate cancer, methylated PITX2...... for predicting outcome in lymph node-negative breast cancer patients and methylated MGMT in predicting benefit from alkylating agents in patients with glioblastomas. However, prior to clinical utilisation, these findings require validation in prospective clinical studies. Furthermore, assays for measuring gene...

PD-L1 expression as a predictive biomarker in advanced non-small-cell lung cancer: updated survival data.

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Aguiar, Pedro N; De Mello, Ramon Andrade; Hall, Peter; Tadokoro, Hakaru; Lima Lopes, Gilberto de

2017-05-01

The treatment of non-small-cell lung cancer has changed after the development of the immune checkpoint inhibitors. Although the most studied biomarker is PD-L1 expression, its clinical significance is still debatable. In this article, we show the updated survival analysis of all published data. We searched in network and conference data sources for relevant clinical studies of immunotherapy for non-small-cell lung cancer that assessed the PD-L1 expression even as an exploratory analysis. The updated survival hazard ratios (HR) were included in the analysis. 14 studies with 2857 patients were included (2019 treated with immunotherapy). The response rate was as higher among PD-L1-positive patients (RR: 2.19, 95% CI: 1.63-2.94). PD-L1 expression was also related to better progression-free survival (HR: 0.69, 95% CI: 0.57-0.85) and better overall survival (HR: 0.77, 95% CI: 0.67-0.89). PD-L1 overexpression predicts activity as well as better survival for patients treated with immune checkpoint inhibitors.

Circulating biomarkers for predicting cardiovascular disease risk : a systematic review and comprehensive overview of meta-analyses

NARCIS (Netherlands)

Holten, van T.C.; Waanders, L.F.; Groot, de P.G.; Vissers, J.; Hoefer, I.E.; Pasterkamp, G.; Prins, M.W.J.; Roest, M.

2013-01-01

Background : Cardiovascular disease is one of the major causes of death worldwide. Assessing the risk for cardiovascular disease is an important aspect in clinical decision making and setting a therapeutic strategy, and the use of serological biomarkers may improve this. Despite an overwhelming

Clinical and Biomarkers Difference in Prepartum and Postpartum Eclampsia.

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Berhan, Yifru; Endeshaw, Gezahegn

2015-07-01

There is a large body of literature which assessed the incidence and risk factors of eclampsia, but little was done in assessing the association of clinical features and biological markers with prepartum and postpartum eclampsia. A total of 361 eclamptic women admitted to three teaching hospitals between 2008 and 2013 were included in this analysis. A comparative analysis was done for several clinical and biological variables to assess their association with prepartum and postpartum eclampsia. The overall incidence of eclampsia was 1.2% (prepartum 71% and postpartum 29%). The majority of women with prepartum eclampsia were young, primigravida, more hypertensive, symptomatic and proteinuric. Conversely, the majorities of the women with post-partum eclampsia were adult, multiparous, carrying pregnancy to term, anemic, thrombocytopenic, and with hepatic dysfunction. The commonest severity symptom (headache) was less common in postpartum eclamptic women. The incidence of eclampsia was among the highest in the world. And, the analysis has shown that the clinical and biochemical spectrum of prepartum and postpartum eclampsia were apparently different. The majority of the women who developed postpartum eclampsia were multiparous and adult. Derangement of biomarkers was also more common in women with postpartum eclampsia.

A diagnostic scale for Alzheimer's disease based on cerebrospinal fluid biomarker profiles.

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Lehmann, Sylvain; Dumurgier, Julien; Schraen, Susanna; Wallon, David; Blanc, Frédéric; Magnin, Eloi; Bombois, Stéphanie; Bousiges, Olivier; Campion, Dominique; Cretin, Benjamin; Delaby, Constance; Hannequin, Didier; Jung, Barbara; Hugon, Jacques; Laplanche, Jean-Louis; Miguet-Alfonsi, Carole; Peoc'h, Katell; Philippi, Nathalie; Quillard-Muraine, Muriel; Sablonnière, Bernard; Touchon, Jacques; Vercruysse, Olivier; Paquet, Claire; Pasquier, Florence; Gabelle, Audrey

2014-01-01

The relevance of the cerebrospinal fluid (CSF) biomarkers for the diagnosis of Alzheimer's disease (AD) and related disorders is clearly established. However, the question remains on how to use these data, which are often heterogeneous (not all biomarkers being pathologic). The objective of this study is to propose to physicians in memory clinics a biologic scale of probabilities that the patient with cognitive impairments has an Alzheimer's disease (AD) pathologic process. For that purpose, we took advantage of the multicenter data of our Paris-North, Lille, and Montpellier (PLM) study, which has emerged through the initial sharing of information from these memory centers. Different models combining the CSF levels of amyloid-β 42, tau, and p-tau(181) were tested to generate categories of patients with very low (75%), and very high predictive values (>90%) for positive AD. In total, 1,273 patients (646 AD and 627 non-AD) from six independent memory-clinic cohorts were included. A prediction model based on logistic regressions achieved a very good stratification of the population but had the disadvantages of needing mathematical optimization and being difficult to use in daily clinical practice. Remarkably, a simple and intuitive model based on the number (from zero to three) of three pathologic CSF biomarkers resulted in a very efficient predictive scale for AD in patients seen in memory clinics. The scale's overall predictive value for AD for the different categories were as follows: class 0, 9.6% (95% confidence interval (CI), 6.0% to 13.2%); class 1, 24.7% (95% CI, 18.0% to 31.3%); class 2, 77.2% (95% CI, 67.8% to 86.5%); and class 3, 94.2% (95% CI, 90.7% to 97.7%). In addition, with this scale, significantly more patients were correctly classified than with the logistic regression. Its superiority in model performance was validated by the computation of the net reclassification index (NRI). The model was also validated in an independent multicenter dataset of

Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

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Choi, Jonghwan; Park, Sanghyun; Yoon, Youngmi; Ahn, Jaegyoon

2017-11-15

Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. https://github.com/mathcom/CPR. jgahn@inu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Blood-based biomarkers of aggressive prostate cancer.

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Men Long Liong

Full Text Available PURPOSE: Prostate cancer is a bimodal disease with aggressive and indolent forms. Current prostate-specific-antigen testing and digital rectal examination screening provide ambiguous results leading to both under-and over-treatment. Accurate, consistent diagnosis is crucial to risk-stratify patients and facilitate clinical decision making as to treatment versus active surveillance. Diagnosis is currently achieved by needle biopsy, a painful procedure. Thus, there is a clinical need for a minimally-invasive test to determine prostate cancer aggressiveness. A blood sample to predict Gleason score, which is known to reflect aggressiveness of the cancer, could serve as such a test. MATERIALS AND METHODS: Blood mRNA was isolated from North American and Malaysian prostate cancer patients/controls. Microarray analysis was conducted utilizing the Affymetrix U133 plus 2·0 platform. Expression profiles from 255 patients/controls generated 85 candidate biomarkers. Following quantitative real-time PCR (qRT-PCR analysis, ten disease-associated biomarkers remained for paired statistical analysis and normalization. RESULTS: Microarray analysis was conducted to identify 85 genes differentially expressed between aggressive prostate cancer (Gleason score ≥8 and controls. Expression of these genes was qRT-PCR verified. Statistical analysis yielded a final seven-gene panel evaluated as six gene-ratio duplexes. This molecular signature predicted as aggressive (ie, Gleason score ≥8 55% of G6 samples, 49% of G7(3+4, 79% of G7(4+3 and 83% of G8-10, while rejecting 98% of controls. CONCLUSION: In this study, we have developed a novel, blood-based biomarker panel which can be used as the basis of a simple blood test to identify men with aggressive prostate cancer and thereby reduce the overdiagnosis and overtreatment that currently results from diagnosis using PSA alone. We discuss possible clinical uses of the panel to identify men more likely to benefit from

Early-Phase Studies of Biomarkers

DEFF Research Database (Denmark)

Pepe, Margaret S.; Janes, Holly; Li, Christopher I.

2016-01-01

of a positive biomarker test in cases (true positive) to cost associated with a positive biomarker test in controls (false positive). Guidance is offered on soliciting the cost/benefit ratio. The calculations are based on the longstanding decision theory concept of providing a net benefit on average...... impact on patient outcomes of using the biomarker to make clinical decisions....

Biomarker Guided Therapy in Chronic Heart Failure

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Bektas, Sema

2015-01-01

This review article addresses the question of whether biomarker-guided therapy is ready for clinical implementation in chronic heart failure. The most well-known biomarkers in heart failure are natriuretic peptides, namely B-type natriuretic peptide (BNP) and N-terminal pro-BNP. They are well-established in the diagnostic process of acute heart failure and prediction of disease prognosis. They may also be helpful in screening patients at risk of developing heart failure. Although studied by 11 small- to medium-scale trials resulting in several positive meta-analyses, it is less well-established whether natriuretic peptides are also helpful for guiding chronic heart failure therapy. This uncertainty is expressed by differences in European and American guideline recommendations. In addition to reviewing the evidence surrounding the use of natriuretic peptides to guide chronic heart failure therapy, this article gives an overview of the shortcomings of the trials, how the results may be interpreted and the future directions necessary to fill the current gaps in knowledge. Therapy guidance in chronic heart failure using other biomarkers has not been prospectively tested to date. Emerging biomarkers, such as galectin-3 and soluble ST2, might be useful in this regard, as suggested by several post-hoc analyses. PMID:28785440

Admission biomarkers of trauma-induced secondary cardiac injury predict adverse cardiac events and are associated with plasma catecholamine levels.

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Naganathar, Sriveena; De'Ath, Henry D; Wall, Johanna; Brohi, Karim

2015-07-01

Secondary cardiac injury and dysfunction may be important contributors to poor outcomes in trauma patients, but the pathophysiology and clinical impact remain unclear. Early elevations in cardiac injury markers have been associated with the development of adverse cardiac events (ACEs), prolonged intensive care unit stays, and increased mortality. Studies of preinjury β-blocker use suggest a potential protective effect in critically ill trauma patients. This study aimed to prospectively examine the association of early biomarker evidence of trauma-induced secondary cardiac injury (TISCI) and ACEs and to examine the potential contribution of circulating catecholamines to its pathophysiology. Injured patients who met the study criteria were recruited at a single major trauma center. A blood sample was collected immediately on arrival. Serum epinephrine (E), norepinephrine (NE), and cardiac biomarkers including heart-related fatty acid binding protein (H-FABP) were assayed. Data were prospectively collected on ACEs. Of 300 patients recruited, 38 (13%) developed an ACE and had increased mortality (19% vs. 9%, p = 0.01) and longer intensive care unit stays (13 days, p < 0.001). H-FABP was elevated on admission in 56% of the patients, predicted the development of ACE, and was associated with higher mortality (14% vs. 5%, p = 0.01). Admission E and NE levels were strongly associated with elevations in H-FABP and ACEs (E, 274.0 pg/mL vs. 622.5 pg/mL, p < 0.001; NE, 1,063.2 pg/mL vs. 2,032.6 pg/mL, p < 0.001). Catecholamine effect on the development of TISCI or ACEs was not statistically independent of injury severity or depth of shock. Admission levels of H-FABP predict the development of ACEs and may be useful for prognosis and stratification of trauma patients. The development of TISCI and ACEs was associated with high admission levels of catecholamines, but their role in pathogenesis remains unclear. Clinical trials of adrenergic blockade may have the potential to

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

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Korolev, Igor O.; Symonds, Laura L.; Bozoki, Andrea C.

2016-01-01

Background Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. Methods Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. Results Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. Conclusions We developed an accurate prognostic model for predicting

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification.

Directory of Open Access Journals (Sweden)

Igor O Korolev

Full Text Available Individuals with mild cognitive impairment (MCI have a substantially increased risk of developing dementia due to Alzheimer's disease (AD. In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level.Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139 and those who did not (n = 120 during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework.Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87. Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex. Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions.We developed an accurate prognostic model for predicting MCI-to-dementia progression

Molecular Characterization of Gastric Carcinoma: Therapeutic Implications for Biomarkers and Targets

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Lionel Kankeu Fonkoua

2018-03-01

Full Text Available Palliative chemotherapy is the mainstay of treatment of advanced gastric carcinoma (GC. Monoclonal antibodies including trastuzumab, ramucirumab, and pembrolizumab have been shown to provide additional benefits. However, the clinical outcomes are often unpredictable and they can vary widely among patients. Currently, no biomarker is available for predicting treatment response in the individual patient except human epidermal growth factor receptor 2 (HER2 amplification and programmed death-ligand 1 (PD-L1 expression for effectiveness of trastuzumab and pembrolizumab, respectively. Multi-platform molecular analysis of cancer, including GC, may help identify predictive biomarkers to guide selection of therapeutic agents. Molecular classification of GC by The Cancer Genome Atlas Research Network and the Asian Cancer Research Group is expected to identify therapeutic targets and predictive biomarkers. Complementary to molecular characterization of GC is molecular profiling by expression analysis and genomic sequencing of tumor DNA. Initial analysis of patients with gastroesophageal carcinoma demonstrates that the ratio of progression-free survival (PFS on molecular profile (MP-based treatment to PFS on treatment prior to molecular profiling exceeds 1.3, suggesting the potential value of MP in guiding selection of individualized therapy. Future strategies aiming to integrate molecular classification and profiling of tumors with therapeutic agents for achieving the goal of personalized treatment of GC are indicated.

Molecular Characterization of Gastric Carcinoma: Therapeutic Implications for Biomarkers and Targets.

Science.gov (United States)

Kankeu Fonkoua, Lionel; Yee, Nelson S

2018-03-09

Palliative chemotherapy is the mainstay of treatment of advanced gastric carcinoma (GC). Monoclonal antibodies including trastuzumab, ramucirumab, and pembrolizumab have been shown to provide additional benefits. However, the clinical outcomes are often unpredictable and they can vary widely among patients. Currently, no biomarker is available for predicting treatment response in the individual patient except human epidermal growth factor receptor 2 (HER2) amplification and programmed death-ligand 1 (PD-L1) expression for effectiveness of trastuzumab and pembrolizumab, respectively. Multi-platform molecular analysis of cancer, including GC, may help identify predictive biomarkers to guide selection of therapeutic agents. Molecular classification of GC by The Cancer Genome Atlas Research Network and the Asian Cancer Research Group is expected to identify therapeutic targets and predictive biomarkers. Complementary to molecular characterization of GC is molecular profiling by expression analysis and genomic sequencing of tumor DNA. Initial analysis of patients with gastroesophageal carcinoma demonstrates that the ratio of progression-free survival (PFS) on molecular profile (MP)-based treatment to PFS on treatment prior to molecular profiling exceeds 1.3, suggesting the potential value of MP in guiding selection of individualized therapy. Future strategies aiming to integrate molecular classification and profiling of tumors with therapeutic agents for achieving the goal of personalized treatment of GC are indicated.

Clinical drug development using dynamic biomarkers to enable personalized health care in Chronic Obstructive Pulmonary Disease

DEFF Research Database (Denmark)

Bihlet, Asger R; Karsdal, Morten A; Bay-Jensen, Anne-Christine

2015-01-01

Despite massive investments in development of novel treatments for heterogeneous diseases such as Chronic Obstructive Pulmonary Disease (COPD), the resources spent have only benefitted a fraction of the population treated. Personalized Health Care to guide selection of a suitable patient population...... at higher risk of progression. We review the role of extra-cellular matrix proteins found to be upregulated in COPD. Novel biomarkers of connective tissue remodeling which may provide added value for a personalized approach by detecting subgroups of patients with active disease suitable for pharmacological...... already in the clinical development of new compounds could offer a solution. In this review, we discuss past successes and failures in drug development and biomarker research in COPD. We describe research in COPD phenotypes, and the required characteristics of a suitable biomarker for identifying patients...

Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best

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Nauck Matthias

2011-07-01

Full Text Available Abstract Background Associations between measures of subjective health and mortality risk have previously been shown. We assessed the impact and comparative predictive performance of a multi-biomarker panel on this association. Methods Data from 4,261 individuals aged 20-79 years recruited for the population-based Study of Health in Pomerania was used. During an average 9.7 year follow-up, 456 deaths (10.7% occurred. Subjective health was assessed by SF-12 derived physical (PCS-12 and mental component summaries (MCS-12, and a single-item self-rated health (SRH question. We implemented Cox proportional-hazards regression models to investigate the association of subjective health with mortality and to assess the impact of a combination of 10 biomarkers on this association. Variable selection procedures were used to identify a parsimonious set of subjective health measures and biomarkers, whose predictive ability was compared using receiver operating characteristic (ROC curves, C-statistics, and reclassification methods. Results In age- and gender-adjusted Cox models, poor SRH (hazard ratio (HR, 2.07; 95% CI, 1.34-3.20 and low PCS-12 scores (lowest vs. highest quartile: HR, 1.75; 95% CI, 1.31-2.33 were significantly associated with increased risk of all-cause mortality; an association independent of various covariates and biomarkers. Furthermore, selected subjective health measures yielded a significantly higher C-statistic (0.883 compared to the selected biomarker panel (0.872, whereas a combined assessment showed the highest C-statistic (0.887 with a highly significant integrated discrimination improvement of 1.5% (p Conclusion Adding biomarker information did not affect the association of subjective health measures with mortality, but significantly improved risk stratification. Thus, a combined assessment of self-reported subjective health and measured biomarkers may be useful to identify high-risk individuals for intensified monitoring.

Clinical significance of the serum biomarker index detection in children with Henoch-Schonlein purpura.

Science.gov (United States)

Purevdorj, Narangerel; Mu, Yun; Gu, Yajun; Zheng, Fang; Wang, Ran; Yu, Jinwei; Sun, Xuguo

2018-02-01

To explore a panel of serum biomarkers for laboratory diagnosis of pediatric Henoch-Schönlein purpura (HSP). The blood white blood cells (WBC) and serum levels of serum amyloid A (SAA), interleukin 6 (IL-6), immunoglobulin A (IgA), immunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin E (IgE), C-reactive protein (CRP), complement component 3 (C3), complement component 4 (C4), and ASO (anti-streptolysin O) were detected in 127 patients with Henoch-Schonlein purpura (HSP), 110 cases of septicemia patients, and 121 healthy volunteers. The diagnostic ability of biomarkers selected from HSP and septicemia patients was analyzed by ROC curve. By designing the calculation model, the biomarker index was calculated for laboratory diagnosis of HSP and differential diagnosis between HSP and septicemia. The levels of serum WBC, CRP, IL-6 and SAA in the septicemia patients were significantly higher than those in the control group (p<0.05). Compared with the healthy individuals, serum levels of WBC, CRP, IL-6, SAA, IgA and IgM were significantly increased in patients with HSP (p<0.05). The area under the curve (AUC) of SAA, IgA, IgM, WBC, IL-6, and CRP in the patients with HSP was 0.964, 0.855, 0.849, 0.787, 0.765, and 0.622, respectively. The values of SAA, IgA, IgM, WBC, IL-6, and CRP in septicemia patients were 0.700, 0.428, 0.689, 0.682, 0.891, and 0.853, respectively. Biomarker index=SAA+IgA/4000+IgM/4000×0.4CRPmean valueCRPi . The biomarker index in HSP patients was significantly higher than that of the healthy controls. However, the biomarker index in septicemia patients was significantly lower than the control. The biomarker index of HSP patients is higher than that of the control group. While in the infectious disease represented by septicemia, it is decreased. The detection of biomarker index could exclude the interference of infection as the auxiliary examination to HSP patients. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by

Biomarkers in DILI: one more step forward

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Mercedes Robles-Díaz

2016-08-01

Full Text Available Despite being relatively rare, drug-induced liver injury (DILI is a serious condition, both for the individual patient due to the risk of acute liver failure, and for the drug development industry and regulatory agencies due to associations with drug development attritions, black box warnings and postmarketing withdrawals. A major limitation in DILI diagnosis and prediction is the current lack of specific biomarkers. Despite refined usage of traditional liver biomarkers in DILI, reliable disease outcome predictions are still difficult to make. These limitations have driven the growing interest in developing new more sensitive and specific DILI biomarkers, which can improve early DILI prediction, diagnosis and course of action. Several promising DILI biomarker candidates have been discovered to date, including mechanistic-based biomarker candidates such as glutamate dehydrogenase, high-mobility group box 1 protein and keratin-18, which can also provide information on the injury mechanism of different causative agents. Furthermore, microRNAs have received much attention lately as potential non-invasive DILI biomarker candidates, in particular miR-122. Advances in omics technologies offer a new approach for biomarker exploration studies. The ability to screen a large number of molecules (for example metabolites, proteins or DNA simultaneously enables the identification of ‘toxicity signatures’, which may be used to enhance preclinical safety assessments and disease diagnostics. Omics-based studies can also provide information on the underlying mechanisms of distinct forms of DILI that may further facilitate the identification of early diagnostic biomarkers and safer implementation of personalized medicine. In this review we summarize recent advances in the area of DILI biomarker studies.

Biomarkers of PTSD: military applications and considerations

OpenAIRE

Amy Lehrner; Rachel Yehuda

2014-01-01

Background: Although there are no established biomarkers for posttraumatic stress disorder (PTSD) as yet, biological investigations of PTSD have made progress identifying the pathophysiology of PTSD. Given the biological and clinical complexity of PTSD, it is increasingly unlikely that a single biomarker of disease will be identified. Rather, investigations will more likely identify different biomarkers that indicate the presence of clinically significant PTSD symptoms, associate with risk fo...

Target biomarker profile for the clinical management of paracetamol overdose

Science.gov (United States)

Vliegenthart, A D Bastiaan; Antoine, Daniel J; Dear, James W

2015-01-01

Paracetamol (acetaminophen) overdose is one of the most common causes of acute liver injury in the Western world. To improve patient care and reduce pressure on already stretched health care providers new biomarkers are needed that identify or exclude liver injury soon after an overdose of paracetamol is ingested. This review highlights the current state of paracetamol poisoning management and how novel biomarkers could improve patient care and save healthcare providers money. Based on the widely used concept of defining a target product profile, a target biomarker profile is proposed that identifies desirable and acceptable key properties for a biomarker in development to enable the improved treatment of this patient population. The current biomarker candidates, with improved hepatic specificity and based on the fundamental mechanistic basis of paracetamol-induced liver injury, are reviewed and their performance compared with our target profile. PMID:26076366

Multiple Sclerosis Cerebrospinal Fluid Biomarkers

Directory of Open Access Journals (Sweden)

Gavin Giovannoni

2006-01-01

Full Text Available Cerebrospinal fluid (CSF is the body fluid closest to the pathology of multiple sclerosis (MS. For many candidate biomarkers CSF is the only fluid that can be investigated. Several factors need to be standardized when sampling CSF for biomarker research: time/volume of CSF collection, sample processing/storage, and the temporal relationship of sampling to clinical or MRI markers of disease activity. Assays used for biomarker detection must be validated so as to optimize the power of the studies. A formal method for establishing whether or not a particular biomarker can be used as a surrogate end-point needs to be adopted. This process is similar to that used in clinical trials, where the reporting of studies has to be done in a standardized way with sufficient detail to permit a critical review of the study and to enable others to reproduce the study design. A commitment must be made to report negative studies so as to prevent publication bias. Pre-defined consensus criteria need to be developed for MS-related prognostic biomarkers. Currently no candidate biomarker is suitable as a surrogate end-point. Bulk biomarkers of the neurodegenerative process such as glial fibrillary acidic protein (GFAP and neurofilaments (NF have advantages over intermittent inflammatory markers.

Adjustment of Serum HE4 to reduced Glomerular filtration and its use in Biomarker-based prediction of deep Myometrial invasion in endometrial cancer

DEFF Research Database (Denmark)

Chovanec, Josef; Selingerova, Iveta; Greplova, Kristina

2017-01-01

Background: We investigated the efficacy of circulating biomarkers together with histological grade and age to predict deep myometrial invasion (dMI) in endometrial cancer patients. Methods: HE4ren was developed adjusting HE4 serum levels towards decreased glomerular filtration rate as quantified...... levels to reduced eGFR that enables quantification of time-dependent changes in HE4 production and elimination irrespective of age and renal function in women. Utilizing HE4ren improves performance of biomarker-based models for prediction of dMI in endometrial cancer patients....

Biomarker Qualification: Toward a Multiple Stakeholder Framework for Biomarker Development, Regulatory Acceptance, and Utilization.

Science.gov (United States)

Amur, S; LaVange, L; Zineh, I; Buckman-Garner, S; Woodcock, J

2015-07-01

The discovery, development, and use of biomarkers for a variety of drug development purposes are areas of tremendous interest and need. Biomarkers can become accepted for use through submission of biomarker data during the drug approval process. Another emerging pathway for acceptance of biomarkers is via the biomarker qualification program developed by the Center for Drug Evaluation and Research (CDER, US Food and Drug Administration). Evidentiary standards are needed to develop and evaluate various types of biomarkers for their intended use and multiple stakeholders, including academia, industry, government, and consortia must work together to help develop this evidence. The article describes various types of biomarkers that can be useful in drug development and evidentiary considerations that are important for qualification. A path forward for coordinating efforts to identify and explore needed biomarkers is proposed for consideration. © 2015 American Society for Clinical Pharmacology and Therapeutics.

Trends in qualifying biomarkers in drug safety. Consensus of the 2011 meeting of the spanish society of clinical pharmacology.

Science.gov (United States)

Agúndez, José A G; Del Barrio, Jaime; Padró, Teresa; Stephens, Camilla; Farré, Magí; Andrade, Raúl J; Badimon, Lina; García-Martín, Elena; Vilahur, Gemma; Lucena, M Isabel

2012-01-01

In this paper we discuss the consensus view on the use of qualifying biomarkers in drug safety, raised within the frame of the XXIV meeting of the Spanish Society of Clinical Pharmacology held in Málaga (Spain) in October, 2011. The widespread use of biomarkers as surrogate endpoints is a goal that scientists have long been pursuing. Thirty years ago, when molecular pharmacogenomics evolved, we anticipated that these genetic biomarkers would soon obviate the routine use of drug therapies in a way that patients should adapt to the therapy rather than the opposite. This expected revolution in routine clinical practice never took place as quickly nor with the intensity as initially expected. The concerted action of operating multicenter networks holds great promise for future studies to identify biomarkers related to drug toxicity and to provide better insight into the underlying pathogenesis. Today some pharmacogenomic advances are already widely accepted, but pharmacogenomics still needs further development to elaborate more precise algorithms and many barriers to implementing individualized medicine exist. We briefly discuss our view about these barriers and we provide suggestions and areas of focus to advance in the field.

AUC-based biomarker ensemble with an application on gene scores predicting low bone mineral density.

Science.gov (United States)

Zhao, X G; Dai, W; Li, Y; Tian, L

2011-11-01

The area under the receiver operating characteristic (ROC) curve (AUC), long regarded as a 'golden' measure for the predictiveness of a continuous score, has propelled the need to develop AUC-based predictors. However, the AUC-based ensemble methods are rather scant, largely due to the fact that the associated objective function is neither continuous nor concave. Indeed, there is no reliable numerical algorithm identifying optimal combination of a set of biomarkers to maximize the AUC, especially when the number of biomarkers is large. We have proposed a novel AUC-based statistical ensemble methods for combining multiple biomarkers to differentiate a binary response of interest. Specifically, we propose to replace the non-continuous and non-convex AUC objective function by a convex surrogate loss function, whose minimizer can be efficiently identified. With the established framework, the lasso and other regularization techniques enable feature selections. Extensive simulations have demonstrated the superiority of the new methods to the existing methods. The proposal has been applied to a gene expression dataset to construct gene expression scores to differentiate elderly women with low bone mineral density (BMD) and those with normal BMD. The AUCs of the resulting scores in the independent test dataset has been satisfactory. Aiming for directly maximizing AUC, the proposed AUC-based ensemble method provides an efficient means of generating a stable combination of multiple biomarkers, which is especially useful under the high-dimensional settings. lutian@stanford.edu. Supplementary data are available at Bioinformatics online.

Biomarkers in Prodromal Parkinson Disease: a Qualitative Review.

Science.gov (United States)

Cooper, Christine A; Chahine, Lama M

2016-11-01

Over the past several years, the concept of prodromal Parkinson disease (PD) has been increasingly recognized. This term refers to individuals who do not fulfill motor diagnostic criteria for PD, but who have clinical, genetic, or biomarker characteristics suggesting risk of developing PD in the future. Clinical diagnosis of prodromal PD has low specificity, prompting the need for objective biomarkers with higher specificity. In this qualitative review, we discuss objectively defined putative biomarkers for PD and prodromal PD. We searched Pubmed and Embase for articles pertaining to objective biomarkers for PD and their application in prodromal cohorts. Articles were selected based on relevance and methodology. Objective biomarkers of demonstrated utility in prodromal PD include ligand-based imaging and transcranial sonography. Development of serum, cerebrospinal fluid, and tissue-based biomarkers is underway, but their application in prodromal PD has yet to meaningfully occur. Combining objective biomarkers with clinical or genetic prodromal features increases the sensitivity and specificity for identifying prodromal PD. Several objective biomarkers for prodromal PD show promise but require further study, including their application to and validation in prodromal cohorts followed longitudinally. Accurate identification of prodromal PD will likely require a multimodal approach. (JINS, 2016, 22, 956-967).

Di-22:6-bis(monoacylglycerol)phosphate: A clinical biomarker of drug-induced phospholipidosis for drug development and safety assessment

International Nuclear Information System (INIS)

Liu, Nanjun; Tengstrand, Elizabeth A.; Chourb, Lisa; Hsieh, Frank Y.

2014-01-01

The inability to routinely monitor drug-induced phospholipidosis (DIPL) presents a challenge in pharmaceutical drug development and in the clinic. Several nonclinical studies have shown di-docosahexaenoyl (22:6) bis(monoacylglycerol) phosphate (di-22:6-BMP) to be a reliable biomarker of tissue DIPL that can be monitored in the plasma/serum and urine. The aim of this study was to show the relevance of di-22:6-BMP as a DIPL biomarker for drug development and safety assessment in humans. DIPL shares many similarities with the inherited lysosomal storage disorder Niemann–Pick type C (NPC) disease. DIPL and NPC result in similar changes in lysosomal function and cholesterol status that lead to the accumulation of multi-lamellar bodies (myeloid bodies) in cells and tissues. To validate di-22:6-BMP as a biomarker of DIPL for clinical studies, NPC patients and healthy donors were classified by receiver operator curve analysis based on urinary di-22:6-BMP concentrations. By showing 96.7-specificity and 100-sensitivity to identify NPC disease, di-22:6-BMP can be used to assess DIPL in human studies. The mean concentration of di-22:6-BMP in the urine of NPC patients was 51.4-fold (p ≤ 0.05) above the healthy baseline range. Additionally, baseline levels of di-22:6-BMP were assessed in healthy non-medicated laboratory animals (rats, mice, dogs, and monkeys) and human subjects to define normal reference ranges for nonclinical/clinical studies. The baseline ranges of di-22:6-BMP in the plasma, serum, and urine of humans and laboratory animals were species dependent. The results of this study support the role of di-22:6-BMP as a biomarker of DIPL for pharmaceutical drug development and health care settings. - Highlights: • A reliable biomarker of drug-induced phospholipidosis (DIPL) is needed for humans. • Di-22:6-BMP is specific/sensitive for DIPL in animals as published in literatures. • The di-22:6-BMP biomarker can be validated for humans via NPC patients. • DIPL

Di-22:6-bis(monoacylglycerol)phosphate: A clinical biomarker of drug-induced phospholipidosis for drug development and safety assessment

Energy Technology Data Exchange (ETDEWEB)

Liu, Nanjun; Tengstrand, Elizabeth A.; Chourb, Lisa; Hsieh, Frank Y., E-mail: frank.hsieh@nextcea.com

2014-09-15

The inability to routinely monitor drug-induced phospholipidosis (DIPL) presents a challenge in pharmaceutical drug development and in the clinic. Several nonclinical studies have shown di-docosahexaenoyl (22:6) bis(monoacylglycerol) phosphate (di-22:6-BMP) to be a reliable biomarker of tissue DIPL that can be monitored in the plasma/serum and urine. The aim of this study was to show the relevance of di-22:6-BMP as a DIPL biomarker for drug development and safety assessment in humans. DIPL shares many similarities with the inherited lysosomal storage disorder Niemann–Pick type C (NPC) disease. DIPL and NPC result in similar changes in lysosomal function and cholesterol status that lead to the accumulation of multi-lamellar bodies (myeloid bodies) in cells and tissues. To validate di-22:6-BMP as a biomarker of DIPL for clinical studies, NPC patients and healthy donors were classified by receiver operator curve analysis based on urinary di-22:6-BMP concentrations. By showing 96.7-specificity and 100-sensitivity to identify NPC disease, di-22:6-BMP can be used to assess DIPL in human studies. The mean concentration of di-22:6-BMP in the urine of NPC patients was 51.4-fold (p ≤ 0.05) above the healthy baseline range. Additionally, baseline levels of di-22:6-BMP were assessed in healthy non-medicated laboratory animals (rats, mice, dogs, and monkeys) and human subjects to define normal reference ranges for nonclinical/clinical studies. The baseline ranges of di-22:6-BMP in the plasma, serum, and urine of humans and laboratory animals were species dependent. The results of this study support the role of di-22:6-BMP as a biomarker of DIPL for pharmaceutical drug development and health care settings. - Highlights: • A reliable biomarker of drug-induced phospholipidosis (DIPL) is needed for humans. • Di-22:6-BMP is specific/sensitive for DIPL in animals as published in literatures. • The di-22:6-BMP biomarker can be validated for humans via NPC patients. • DIPL

Mass spectrometry in biomarker applications: from untargeted discovery to targeted verification, and implications for platform convergence and clinical application

Energy Technology Data Exchange (ETDEWEB)

Smith, Richard D.

2012-03-01

It is really only in the last ten years that mass spectrometry (MS) has had a truly significant (but still small) impact on biomedical research. Much of this impact can be attributed to proteomics and its more basic applications. Early biomedical applications have included a number of efforts aimed at developing new biomarkers; however, the success of these endeavors to date have been quite modest - essentially confined to preclinical applications - and have often suffered from combinations of immature technology and hubris. Now that MS-based proteomics is reaching adolescence, it is appropriate to ask if and when biomarker-related applications will extend to the clinical realm, and what developments will be essential for this transition. Biomarker development can be described as a multistage process consisting of discovery, qualification, verification, research assay optimization, validation, and commercialization (1). From a MS perspective, it is possible to 'bin' measurements into 1 of 2 categories - those aimed at discovering potential protein biomarkers and those seeking to verify and validate biomarkers. Approaches in both categories generally involve digesting proteins (e.g., with trypsin) as a first step to yield peptides that can be effectively detected and identified with MS. Discovery-based approaches use broad 'unbiased' or 'undirected' measurements that attempt to cover as many proteins as possible in the hope of revealing promising biomarker candidates. A key challenge with this approach stems from the extremely large dynamic range (i.e., relative stoichiometry) of proteins of potential interest in biofluids such as plasma and the expectation that biomarker proteins of the greatest clinical value for many diseases may very well be present at low relative abundances (2). Protein concentrations in plasma extend from approximately 10{sup 10} pg/mL for albumin to approximately 10 pg/mL and below for interleukins and other

A comparison of early diagnostic utility of Alzheimer disease biomarkers in brain magnetic resonance and cerebrospinal fluid.

Science.gov (United States)

Monge Argilés, J A; Blanco Cantó, M A; Leiva Salinas, C; Flors, L; Muñoz Ruiz, C; Sánchez Payá, J; Gasparini Berenguer, R; Leiva Santana, C

2014-09-01

The goals of this study were to compare the early diagnostic utility of Alzheimer disease biomarkers in the CSF with those in brain MRI in conditions found in our clinical practice, and to ascertain the diagnostic accuracy of both techniques used together. Between 2008 and 2009, we included 30 patients with mild cognitive impairment (MCI) who were examined using 1.5 Tesla brain MRI and AD biomarker analysis in CSF. MRI studies were evaluated by 2 radiologists according to the Korf́s visual scale. CSF biomarkers were analysed using INNOTEST reagents for Aβ1-42, total-tau and phospho-tau181p. We evaluated clinical changes 2 years after inclusion. By 2 years after inclusion, 15 of the original 30 patients (50%) had developed AD (NINCDS-ADRA criteria). The predictive utility of AD biomarkers in CSF (RR 2.7; 95% CI, 1.1-6.7; Pde Neurología. Published by Elsevier Espana. All rights reserved.

Biomarkers in spinal cord compression Ethics and perspectives

Directory of Open Access Journals (Sweden)

Iencean A.St.

2016-09-01

Full Text Available The phosphorylated form of the high-molecular-weight neurofilament subunit NF-H (pNF-H in serum or in cerebro-spinal fluid (CSF is a specific lesional biomarker for spinal cord injury. The lesional biomarkers and the reaction biomarkers are both presented after several hours post-injury. The specific predictive patterns of lesional biomarkers could be used to aid clinicians with making a diagnosis and establishing a prognosis, and evaluating therapeutic interventions. Diagnosis, prognosis, and treatment guidance based on biomarker used as a predictive indicator can determine ethical difficulties by differentiated therapies in patients with spinal cord compression. At this point based on studies until today we cannot take a decision based on biomarker limiting the treatment of neurological recovery in patients with complete spinal cord injury because we do not know the complexity of the biological response to spinal cord compression.

The Indian Consensus Document on cardiac biomarker

Directory of Open Access Journals (Sweden)

I. Satyamurthy

2014-01-01

Full Text Available Despite recent advances, the diagnosis and management of heart failure evades the clinicians. The etiology of congestive heart failure (CHF in the Indian scenario comprises of coronary artery disease, diabetes mellitus and hypertension. With better insights into the pathophysiology of CHF, biomarkers have evolved rapidly and received diagnostic and prognostic value. In CHF biomarkers prove as measures of the extent of pathophysiological derangement; examples include biomarkers of myocyte necrosis, myocardial remodeling, neurohormonal activation, etc. In CHF biomarkers act as indicators for the presence, degree of severity and prognosis of the disease, they may be employed in combination with the present conventional clinical assessments. These make the biomarkers feasible options against the present expensive measurements and may provide clinical benefits.

A novel diagnostic biomarker panel for obesity-related nonalcoholic steatohepatitis (NASH).

Science.gov (United States)

Younossi, Zobair M; Jarrar, Mohammed; Nugent, Clare; Randhawa, Manpreet; Afendy, Mariam; Stepanova, Maria; Rafiq, Nila; Goodman, Zachary; Chandhoke, Vikas; Baranova, Ancha

2008-11-01

Within the spectrum of nonalcoholic fatty liver disease (NAFLD), only patients with nonalcoholic steatohepatitis (NASH) show convincing evidence for progression. To date, liver biopsy remains the gold standard for the diagnosis of NASH; however, liver biopsy is expensive and associated with a small risk, emphasizing the urgent need for noninvasive diagnostic biomarkers. Recent findings suggest a role for apoptosis and adipocytokines in the pathogenesis of NASH. The aim of this study was to develop a noninvasive diagnostic biomarker for NASH. The study included 101 patients with liver biopsies who were tested with enzyme-linked immunosorbent assay (ELISA)-based assays. Of these, 69 were included in the biomarker development set and 32 were included in the biomarker validation set. Clinical data and serum samples were collected at the time of biopsy. Fasting serum samples were assayed for adiponectin, resistin, insulin, glucose, TNF-alpha, IL-6, IL-8, cytokeratin CK-18 (M65 antigen), and caspase-cleaved CK-18 (M30 antigen). Data analysis revealed that the levels of M30 antigen (cleaved CK-18) predicted histological NASH with 70% sensitivity and 83.7% specificity and area under the curve (AUC) = 0.711, p < 10(-4), whereas the predictive value of the levels of intact CK-18 (M65) was higher (63.6% sensitivity and 89.4% specificity and AUC = 0.814, p < 10(-4)). Histological NASH could be predicted by a combination of Cleaved CK-18, a product of the subtraction of Cleaved CK-18 level from intact CK-18 level, serum adiponectin, and serum resistin with a sensitivity of 95.45% sensitivity, specificity of 70.21%, and AUC of 0.908 (p < 10(-4)). Blinded validation of this model confirmed its reliability for separating NASH from simple steatosis. Four ELISA-based tests were combined to form a simple diagnostic biomarker for NASH.

Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

Directory of Open Access Journals (Sweden)

Sharma Animesh

2007-01-01

Full Text Available Abstract Background The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. Results Our method discerned just seven biomarkers that precisely categorized the four subgroups of cancer both in training and blind samples. For the same problem, others suggested 19–94 genes. These seven biomarkers include three novel genes (NAB2, LSP1 and EHD1 – not identified by others with distinct class-specific signatures and important role in cancer biology, including cellular proliferation, transendothelial migration and trafficking of MHC class antigens. Interestingly, NAB2 is downregulated in other tumors including Non-Hodgkin lymphoma and Neuroblastoma but we observed moderate to high upregulation in a few cases of Ewing sarcoma and Rabhdomyosarcoma, suggesting that NAB2 might be mutated in these tumors. These genes can discover the subgroups correctly with unsupervised learning, can differentiate non-SRBCT samples and they perform equally well with other machine learning tools including support vector machines. These biomarkers lead to four simple human interpretable

Effects of triclosan on host response and microbial biomarkers during experimental gingivitis.

Science.gov (United States)

Pancer, Brooke A; Kott, Diana; Sugai, James V; Panagakos, Fotinos S; Braun, Thomas M; Teles, Ricardo P; Giannobile, William V; Kinney, Janet S

2016-05-01

This exploratory randomized, controlled clinical trial sought to evaluate anti-inflammatory and -microbial effects of triclosan during experimental gingivitis as assessed by host response biomarkers and biofilm microbial pathogens. Thirty participants were randomized to triclosan or control dentifrice groups who ceased homecare for 21 days in an experimental gingivitis (EG) protocol. Plaque and gingival indices and saliva, plaque, and gingival crevicular fluid (GCF) were assessed/collected at days 0, 14, 21 and 35. Levels and proportions of 40 bacterial species from plaque samples were determined using checkerboard DNA-DNA hybridization. Ten biomarkers associated with inflammation, matrix degradation, and host protection were measured from GCF and saliva and analysed using a multiplex array. Participants were stratified as "high" or "low" responders based on gingival index and GCF biomarkers and bacterial biofilm were combined to generate receiver operating characteristic curves and predict gingivitis susceptibility. No differences in mean PI and GI values were observed between groups and non-significant trends of reduction of host response biomarkers with triclosan treatment. Triclosan significantly reduced levels of A. actinomycetemcomitans and P. gingivalis during induction of gingivitis. Triclosan reduced microbial levels during gingivitis development (ClinicalTrials.gov NCT01799226). © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

PET imaging-based phenotyping as a predictive biomarker of response to tyrosine kinase inhibitor therapy in non-small cell lung cancer: Are we there yet?

Energy Technology Data Exchange (ETDEWEB)

Gerbaudo, Victor H.; Kim, Chun K. [Div. of Nuclear Medicine and Molecular Imaging, Dept. of Radiology,Brigham and Women' s Hospital and Harvard Medical School, Boston (United States)

2017-03-15

The increased understanding of the molecular pathology of different malignancies, especially lung cancer, has directed investigational efforts to center on the identification of different molecular targets and on the development of targeted therapies against these targets. A good representative is the epidermal growth factor receptor (EGFR); a major driver of non-small cell lung cancer tumorigenesis. Today, tumor growth inhibition is possible after treating lung tumors expressing somatic mutations of the EGFR gene with tyrosine kinase inhibitors (TKI). This opened the doors to biomarker-directed precision or personalized treatments for lung cancer patients. The success of these targeted anticancer therapies depends in part on being able to identify biomarkers and their patho-molecular make-up in order to select patients that could respond to specific therapeutic agents. While the identification of reliable biomarkers is crucial to predict response to treatment before it begins, it is also essential to be able to monitor treatment early during therapy to avoid the toxicity and morbidity of futile treatment in non-responding patients. In this context, we share our perspective on the role of PET imaging-based phenotyping in the personalized care of lung cancer patients to non-invasively direct and monitor the treatment efficacy of TKIs in clinical practice.

Novel Biomarkers of Physical Activity Maintenance in Midlife Women: Preliminary Investigation

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Kelly A. Bosak

2018-04-01

Full Text Available The precision health initiative is leading the discovery of novel biomarkers as important indicators of biological processes or responses to behavior, such as physical activity. Neural biomarkers identified by magnetic resonance imaging (MRI hold promise to inform future research, and ultimately, for transfer to the clinical setting to optimize health outcomes. This study investigated resting-state and functional brain biomarkers between midlife women who were maintaining physical activity in accordance with the current national guidelines and previously acquired age-matched sedentary controls. Approval was obtained from the Human Subjects Committee. Participants included nondiabetic, healthy weight to overweight (body mass index 19–29.9 kg/m2 women (n = 12 aged 40–64 years. Control group data were used from participants enrolled in our previous functional MRI study and baseline resting-state MRI data from a subset of sedentary (<500 kcal of physical activity per week midlife women who were enrolled in a 9-month exercise intervention conducted in our imaging center. Differential activation of the inferior frontal gyrus (IFG and greater connectivity with the dorsolateral prefrontal cortex (dlPFC was identified between physically active women and sedentary controls. After correcting for multiple comparisons, these differences in biomarkers of physical activity maintenance did not reach statistical significance. Preliminary evidence in this small sample suggests that neural biomarkers of physical activity maintenance involve activations in the brain region associated with areas involved in implementing goal-directed behavior. Specifically, activation of the IFG and connectivity with the dlPFC is identified as a neural biomarker to explain and predict long-term physical activity maintenance for healthy aging. Future studies should evaluate these biomarker links with relevant clinical correlations.

Diagnostic and prognostic stratification in the emergency department using urinary biomarkers of nephron damage: a multicenter prospective cohort study.

Science.gov (United States)

Nickolas, Thomas L; Schmidt-Ott, Kai M; Canetta, Pietro; Forster, Catherine; Singer, Eugenia; Sise, Meghan; Elger, Antje; Maarouf, Omar; Sola-Del Valle, David Antonio; O'Rourke, Matthew; Sherman, Evan; Lee, Peter; Geara, Abdallah; Imus, Philip; Guddati, Achuta; Polland, Allison; Rahman, Wasiq; Elitok, Saban; Malik, Nasir; Giglio, James; El-Sayegh, Suzanne; Devarajan, Prasad; Hebbar, Sudarshan; Saggi, Subodh J; Hahn, Barry; Kettritz, Ralph; Luft, Friedrich C; Barasch, Jonathan

2012-01-17

This study aimed to determine the diagnostic and prognostic value of urinary biomarkers of intrinsic acute kidney injury (AKI) when patients were triaged in the emergency department. Intrinsic AKI is associated with nephron injury and results in poor clinical outcomes. Several urinary biomarkers have been proposed to detect and measure intrinsic AKI. In a multicenter prospective cohort study, 5 urinary biomarkers (urinary neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, urinary liver-type fatty acid binding protein, urinary interleukin-18, and cystatin C) were measured in 1,635 unselected emergency department patients at the time of hospital admission. We determined whether the biomarkers diagnosed intrinsic AKI and predicted adverse outcomes during hospitalization. All biomarkers were elevated in intrinsic AKI, but urinary neutrophil gelatinase-associated lipocalin was most useful (81% specificity, 68% sensitivity at a 104-ng/ml cutoff) and predictive of the severity and duration of AKI. Intrinsic AKI was strongly associated with adverse in-hospital outcomes. Urinary neutrophil gelatinase-associated lipocalin and urinary kidney injury molecule 1 predicted a composite outcome of dialysis initiation or death during hospitalization, and both improved the net risk classification compared with conventional assessments. These biomarkers also identified a substantial subpopulation with low serum creatinine at hospital admission, but who were at risk of adverse events. Urinary biomarkers of nephron damage enable prospective diagnostic and prognostic stratification in the emergency department. Copyright © 2012 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

MicroRNA biomarkers in glioblastoma

DEFF Research Database (Denmark)

Hermansen, Simon Kjær; Kristensen, Bjarne Winther

2013-01-01

tissues. Understanding these alterations is key to developing new biomarkers and intelligent treatment strategies. This review presents an overview of current knowledge about miRNA alterations in glioblastoma while focusing on the clinical future of miRNAs as biomarkers and discussing the strengths...

Imaging biomarkers as surrogate endpoints for drug development

International Nuclear Information System (INIS)

Richter, Wolf S.

2006-01-01

The employment of biomarkers (including imaging biomarkers, especially PET) in drug development has gained increasing attention during recent years. This has been partly stimulated by the hope that the integration of biomarkers into drug development programmes may be a means to increase the efficiency and effectiveness of the drug development process by early identification of promising drug candidates - thereby counteracting the rising costs of drug development. More importantly, however, the interest in biomarkers for drug development is the logical consequence of recent advances in biosciences and medicine which are leading to target-specific treatments in the framework of ''personalised medicine''. A considerable proportion of target-specific drugs will show effects in subgroups of patients only. Biomarkers are a means to identify potential responders, or patient subgroups at risk for specific side-effects. Biomarkers are used in early drug development in the context of translational medicine to gain information about the drug's potential in different patient groups and disease states. The information obtained at this stage is mainly important for designing subsequent clinical trials and to identify promising drug candidates. Biomarkers in later phases of clinical development may - if properly validated - serve as surrogate endpoints for clinical outcomes. Regulatory agencies in the EU and the USA have facilitated the use of biomarkers early in the development process. The validation of biomarkers as surrogate endpoints is part of FDA's ''critical path initiative''. (orig.)

MMP-7 is a predictive biomarker of disease progression in patients with idiopathic pulmonary fibrosis

Directory of Open Access Journals (Sweden)

Yasmina Bauer

2017-03-01

Full Text Available Idiopathic pulmonary fibrosis (IPF is a progressive interstitial lung disease with poor prognosis, which is characterised by destruction of normal lung architecture and excessive deposition of lung extracellular matrix. The heterogeneity of disease progression in patients with IPF poses significant obstacles to patient care and prevents efficient development of novel therapeutic interventions. Blood biomarkers, reflecting pathobiological processes in the lung, could provide objective evidence of the underlying disease. Longitudinally collected serum samples from the Bosentan Use in Interstitial Lung Disease (BUILD-3 trial were used to measure four biomarkers (metalloproteinase-7 (MMP-7, Fas death receptor ligand, osteopontin and procollagen type I C-peptide, to assess their potential prognostic capabilities and to follow changes during disease progression in patients with IPF. In baseline BUILD-3 samples, only MMP-7 showed clearly elevated protein levels compared with samples from healthy controls, and further investigations demonstrated that MMP-7 levels also increased over time. Baseline levels of MMP-7 were able to predict patients who had higher risk of worsening and, notably, baseline levels of MMP-7 could predict changes in FVC as early as month 4. MMP-7 shows potential to be a reliable predictor of lung function decline and disease progression.

Clinical significance of circulating miR-25-3p as a novel diagnostic and prognostic biomarker in osteosarcoma.

Science.gov (United States)

Fujiwara, Tomohiro; Uotani, Koji; Yoshida, Aki; Morita, Takuya; Nezu, Yutaka; Kobayashi, Eisuke; Yoshida, Akihiko; Uehara, Takenori; Omori, Toshinori; Sugiu, Kazuhisa; Komatsubara, Tadashi; Takeda, Ken; Kunisada, Toshiyuki; Kawamura, Machiko; Kawai, Akira; Ochiya, Takahiro; Ozaki, Toshifumi

2017-05-16

Emerging evidence has suggested that circulating microRNAs (miRNAs) in body fluids have novel diagnostic and prognostic significance for patients with malignant diseases. The lack of useful biomarkers is a crucial problem of bone and soft tissue sarcomas; therefore, we investigated the circulating miRNA signature and its clinical relevance in osteosarcoma. Global miRNA profiling was performed using patient serum collected from a discovery cohort of osteosarcoma patients and controls and cell culture media. The secretion of the detected miRNAs from osteosarcoma cells and clinical relevance of serum miRNA levels were evaluated using in vitro and in vivo models and a validation patient cohort. Discovery screening identified 236 serum miRNAs that were highly expressed in osteosarcoma patients compared with controls, and eight among these were also identified in the cell culture media. Upregulated expression levels of miR-17-5p and miR-25-3p were identified in osteosarcoma cells, and these were abundantly secreted into the culture media in tumor-derived exosomes. Serum miR-25-3p levels were significantly higher in osteosarcoma patients than in control individuals in the validation cohort, with favorable sensitivity and specificity compared with serum alkaline phosphatase. Furthermore, serum miR-25-3p levels at diagnosis were correlated with patient prognosis and reflected tumor burden in both in vivo models and patients; these associations were more sensitive than those of serum alkaline phosphatase. Serum-based circulating miR-25-3p may serve as a non-invasive blood-based biomarker for tumor monitoring and prognostic prediction in osteosarcoma patients.

Nanomaterial-Based Electrochemical Immunosensors for Clinically Significant Biomarkers

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Niina J. Ronkainen

2014-06-01

Full Text Available Nanotechnology has played a crucial role in the development of biosensors over the past decade. The development, testing, optimization, and validation of new biosensors has become a highly interdisciplinary effort involving experts in chemistry, biology, physics, engineering, and medicine. The sensitivity, the specificity and the reproducibility of biosensors have improved tremendously as a result of incorporating nanomaterials in their design. In general, nanomaterials-based electrochemical immunosensors amplify the sensitivity by facilitating greater loading of the larger sensing surface with biorecognition molecules as well as improving the electrochemical properties of the transducer. The most common types of nanomaterials and their properties will be described. In addition, the utilization of nanomaterials in immunosensors for biomarker detection will be discussed since these biosensors have enormous potential for a myriad of clinical uses. Electrochemical immunosensors provide a specific and simple analytical alternative as evidenced by their brief analysis times, inexpensive instrumentation, lower assay cost as well as good portability and amenability to miniaturization. The role nanomaterials play in biosensors, their ability to improve detection capabilities in low concentration analytes yielding clinically useful data and their impact on other biosensor performance properties will be discussed. Finally, the most common types of electroanalytical detection methods will be briefly touched upon.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer.

LENUS (Irish Health Repository)

Tonry, Claire L

2016-07-18

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and\\/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Directory of Open Access Journals (Sweden)

Claire L. Tonry

2016-07-01

Full Text Available Prostate Cancer (PCa is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i might best receive no treatment (active surveillance of the disease; (ii would benefit from existing treatments; or (iii those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Prediction of non-recovery from ventilator-demanding acute respiratory failure, ARDS and death using lung damage biomarkers

DEFF Research Database (Denmark)

Jensen, Jens Ulrik Stæhr; Itenov, Theis Skovsgaard; Thormar, Katrin M

2016-01-01

BACKGROUND: It is unclear whether biomarkers of alveolar damage (surfactant protein D, SPD) or conductive airway damage (club cell secretory protein 16, CC16) measured early after intensive care admittance are associated with one-month clinical respiratory prognosis. If patients who do not recove...

Volume-based predictive biomarkers of sequential FDG-PET/CT for sunitinib in cancer of unknown primary: identification of the best benefited patients

International Nuclear Information System (INIS)

Ma, Yifei; Xu, Wei; Xiao, Jianru; Bai, Ruojing; Li, Yiming; Yu, Hongyu; Yang, Chunshan; Shi, Huazheng; Zhang, Jian; Li, Jidong; Wang, Chenguang

2017-01-01

To test the performance of sequential "1"8F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) in predicting survival after sunitinib therapies in patients with cancer of unknown primary (CUP). CUP patients were enrolled for sequential PET/CT scanning for sunitinib and a control group. Univariate and multivariate analysis were applied to test the efficacy of sunitinib therapy in CUP patients. Next, sequential analyses involving PET/CT parameters were performed to identify and validate sensitive PET/CT biomarkers for sunitinib therapy. Finally, time-dependent receiver operating characteristic (TDROC) analyses were performed to compare the predictive accuracy. Multivariate analysis proved that sunitinib group had significantly improved survival (p < 0.01) as compared to control group. After cycle 2 of therapy, multivariate analysis identified volume-based PET/CT parameters as sensitive biomarkers for sunitinib (p < 0.01). TDROC curves demonstrated whole-body total lesion glycolysis reduction (Δ WTLG) and follow-up WTLG to have good accuracy for efficacy prediction. This evidence was validated after cycle 4 of therapy with the same method. Sunitinib therapy proved effective in treatment of CUP. PET/CT volume-based parameters may help predict outcome of sunitinib therapy, in which Δ WTLG and follow-up WTLG seem to be sensitive biomarkers for sunitinib efficacy. Patients with greater reduction and lower WTLG at follow-up seem to have better survival outcome. (orig.)

Volume-based predictive biomarkers of sequential FDG-PET/CT for sunitinib in cancer of unknown primary: identification of the best benefited patients

Energy Technology Data Exchange (ETDEWEB)

Ma, Yifei [Second Military Medical University, Department of Orthorpedic Oncology, Changzheng Hospital, Shanghai (China); Second Military Medical University, Department of Pathology, Changzheng Hospital, Shanghai (China); Xu, Wei; Xiao, Jianru [Second Military Medical University, Department of Orthorpedic Oncology, Changzheng Hospital, Shanghai (China); Bai, Ruojing [Geriatrics Institute, Department of Geriatrics, Tianjin Medical University General Hospital, Laboratory of Neuro-Trauma and Neurodegenerative Disorder, Tianjin (China); Li, Yiming [Neurosurgery Institute, Department of Neuro-oncology, Beijing (China); Yu, Hongyu [Second Military Medical University, Department of Pathology, Changzheng Hospital, Shanghai (China); Yang, Chunshan [Panorama Medical Imaging Center, Department of PET/CT Radiology, Shanghai (China); Department of PET/CT Radiology Center, Shanghai (China); Shi, Huazheng; Zhang, Jian [Department of PET/CT Radiology Center, Shanghai (China); Li, Jidong [The First People' s Hospital of Shangqiu, Department of Stomatology, Shangqiu, Henan Province (China); Wang, Chenguang [Second Military Medical University, Department of Radiology, Changzheng Hospital, Shanghai (China)

2017-02-15

To test the performance of sequential {sup 18}F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) in predicting survival after sunitinib therapies in patients with cancer of unknown primary (CUP). CUP patients were enrolled for sequential PET/CT scanning for sunitinib and a control group. Univariate and multivariate analysis were applied to test the efficacy of sunitinib therapy in CUP patients. Next, sequential analyses involving PET/CT parameters were performed to identify and validate sensitive PET/CT biomarkers for sunitinib therapy. Finally, time-dependent receiver operating characteristic (TDROC) analyses were performed to compare the predictive accuracy. Multivariate analysis proved that sunitinib group had significantly improved survival (p < 0.01) as compared to control group. After cycle 2 of therapy, multivariate analysis identified volume-based PET/CT parameters as sensitive biomarkers for sunitinib (p < 0.01). TDROC curves demonstrated whole-body total lesion glycolysis reduction (Δ WTLG) and follow-up WTLG to have good accuracy for efficacy prediction. This evidence was validated after cycle 4 of therapy with the same method. Sunitinib therapy proved effective in treatment of CUP. PET/CT volume-based parameters may help predict outcome of sunitinib therapy, in which Δ WTLG and follow-up WTLG seem to be sensitive biomarkers for sunitinib efficacy. Patients with greater reduction and lower WTLG at follow-up seem to have better survival outcome. (orig.)

FDG-PET as a predictive biomarker for therapy with everolimus in metastatic renal cell cancer

International Nuclear Information System (INIS)

Chen, James L; Appelbaum, Daniel E; Kocherginsky, Masha; Cowey, Charles L; Kimryn Rathmell, Wendy; McDermott, David F; Stadler, Walter M

2013-01-01

The mTOR (mammalian target of rapamycin) inhibitor, everolimus, affects tumor growth by targeting cellular metabolic proliferation pathways and delays renal cell carcinoma (RCC) progression. Preclinical evidence suggests that baseline elevated tumor glucose metabolism as quantified by FDG-PET ([ 18 F] fluorodeoxy-glucose positron emission tomography) may predict antitumor activity. Metastatic RCC (mRCC) patients refractory to vascular endothelial growth factor (VEGF) pathway inhibition were treated with standard dose everolimus. FDG-PET scans were obtained at baseline and 2 weeks; serial computed tomography (CT) scans were obtained at baseline and every 8 weeks. Maximum standardized uptake value (SUVmax) of the most FDG avid lesion, average SUVmax of all measured lesions and their corresponding 2-week relative changes were examined for association with 8-week change in tumor size. A total of 63 patients were enrolled; 50 were evaluable for the primary endpoint of which 48 had both PET scans. Patient characteristics included the following: 36 (72%) clear cell histology and median age 59 (range: 37–80). Median pre- and 2-week treatment average SUVmax were 6.6 (1–17.9) and 4.2 (1–13.9), respectively. Response evaluation criteria in solid tumors (RECIST)-based measurements demonstrated an average change in tumor burden of 0.2% (−32.7% to 35.9%) at 8 weeks. Relative change in average SUVmax was the best predictor of change in tumor burden (all evaluable P = 0.01; clear cell subtype P = 0.02), with modest correlation. Baseline average SUVmax was correlated with overall survival and progression-free survival (PFS) (P = 0.023; 0.020), but not with change in tumor burden. Everolimus therapy decreased SUVs on follow-up PET scans in mRCC patients, but changes were only modestly correlated with changes in tumor size. Thus, clinical use of FDG-PET-based biomarkers is challenged by high variability. In this phase II trial, FDG-PET was explored as a predictive biomarker

Do non-glycaemic markers add value to plasma glucose and hemoglobin a1c in predicting diabetes? Yuport health checkup center study.

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Saori Kashima

Full Text Available Many markers have been indicated as predictors of type 2 diabetes. However, the question of whether or not non-glycaemic (blood biomarkers and non-blood biomarkers have a predictive additive utility when combined with glycaemic (blood biomarkers is unknown. The study aim is to assess this additive utility in a large Japanese population.We used data from a retrospective cohort study conducted from 1998 to 2002 for the baseline and 2002 to 2006 for follow-up, inclusive of 5,142 men (mean age of 51.9 years and 4,847 women (54.1 years at baseline. The cumulative incidence of diabetes [defined either as a fasting plasma glucose (FPG ≥7.00 mmol/l or as clinically diagnosed diabetes] was measured. In addition to glycaemic biomarkers [FPG and hemoglobin A1c (HbA1c], we examined the clinical usefulness of adding non-glycaemic biomarkers and non-blood biomarkers, using sensitivity and specificity, and the area under the curve (AUC of the receiver operating characteristics.The AUCs to predict diabetes were 0.874 and 0.924 for FPG, 0.793 and 0.822 for HbA1c, in men and women, respectively. Glycaemic biomarkers were the best and second-best for diabetes prediction among the markers. All non-glycaemic markers (except uric acid in men and creatinine in both sexes predicted diabetes. Among these biomarkers, the highest AUC in the single-marker analysis was 0.656 for alanine aminotransferase (ALT in men and 0.740 for body mass index in women. The AUC of the combined markers of FPG and HbA1c was 0.895 in men and 0.938 in women, which were marginally increased to 0.904 and 0.940 when adding ALT, respectively.AUC increments were marginal when adding non-glycaemic biomarkers and non-blood biomarkers to the classic model based on FPG and HbA1c. For the prediction of diabetes, FPG and HbA1c are sufficient and the other markers may not be needed in clinical practice.

Performance of diagnostic biomarkers in predicting liver fibrosis among hepatitis C virus-infected Egyptian children

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Yasser E Nassef

2013-11-01

Full Text Available The aim of the present study was to identify specific markers that mirror liver fibrosis progression as an alternative to biopsy when biopsy is contraindicated, especially in children. After liver biopsies were performed, serum samples from 30 hepatitis C virus (HCV paediatric patients (8-14 years were analysed and compared with samples from 30 healthy subjects. All subjects were tested for the presence of serum anti-HCV antibodies. Direct biomarkers for liver fibrosis, including transforming growth factor-β1, tissue inhibitor of matrix metalloproteinase-1 (TIMP-1, hyaluronic acid (HA, procollagen type III amino-terminal peptide (PIIINP and osteopontin (OPN, were measured. The indirect biomarkers aspartate and alanine aminotransferases, albumin and bilirubin were also tested. The results revealed a significant increase in the serum marker levels in HCV-infected children compared with the healthy group, whereas albumin levels exhibited a significant decrease. Significantly higher levels of PIIINP, TIMP-1, OPN and HA were detected in HCV-infected children with moderate to severe fibrosis compared with children with mild fibrosis (p < 0.05. The diagnostic accuracy of these direct biomarkers, represented by sensitivity, specificity and positive predictive value, emphasises the utility of PIIINP, TIMP-1, OPN and HA as indicators of liver fibrosis among HCV-infected children.

Imaging blood-brain barrier dysfunction as a biomarker for epileptogenesis.

Science.gov (United States)

Bar-Klein, Guy; Lublinsky, Svetlana; Kamintsky, Lyn; Noyman, Iris; Veksler, Ronel; Dalipaj, Hotjensa; Senatorov, Vladimir V; Swissa, Evyatar; Rosenbach, Dror; Elazary, Netta; Milikovsky, Dan Z; Milk, Nadav; Kassirer, Michael; Rosman, Yossi; Serlin, Yonatan; Eisenkraft, Arik; Chassidim, Yoash; Parmet, Yisrael; Kaufer, Daniela; Friedman, Alon

2017-06-01

A biomarker that will enable the identification of patients at high-risk for developing post-injury epilepsy is critically required. Microvascular pathology and related blood-brain barrier dysfunction and neuroinflammation were shown to be associated with epileptogenesis after injury. Here we used prospective, longitudinal magnetic resonance imaging to quantitatively follow blood-brain barrier pathology in rats following status epilepticus, late electrocorticography to identify epileptic animals and post-mortem immunohistochemistry to confirm blood-brain barrier dysfunction and neuroinflammation. Finally, to test the pharmacodynamic relevance of the proposed biomarker, two anti-epileptogenic interventions were used; isoflurane anaesthesia and losartan. Our results show that early blood-brain barrier pathology in the piriform network is a sensitive and specific predictor (area under the curve of 0.96, P brain barrier pathology as a clinically relevant predictive, diagnostic and pharmaco!dynamics biomarker for acquired epilepsy. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Biomarkers for Detecting Mitochondrial Disorders

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Josef Finsterer

2018-01-01

Full Text Available (1 Objectives: Mitochondrial disorders (MIDs are a genetically and phenotypically heterogeneous group of slowly or rapidly progressive disorders with onset from birth to senescence. Because of their variegated clinical presentation, MIDs are difficult to diagnose and are frequently missed in their early and late stages. This is why there is a need to provide biomarkers, which can be easily obtained in the case of suspecting a MID to initiate the further diagnostic work-up. (2 Methods: Literature review. (3 Results: Biomarkers for diagnostic purposes are used to confirm a suspected diagnosis and to facilitate and speed up the diagnostic work-up. For diagnosing MIDs, a number of dry and wet biomarkers have been proposed. Dry biomarkers for MIDs include the history and clinical neurological exam and structural and functional imaging studies of the brain, muscle, or myocardium by ultrasound, computed tomography (CT, magnetic resonance imaging (MRI, MR-spectroscopy (MRS, positron emission tomography (PET, or functional MRI. Wet biomarkers from blood, urine, saliva, or cerebrospinal fluid (CSF for diagnosing MIDs include lactate, creatine-kinase, pyruvate, organic acids, amino acids, carnitines, oxidative stress markers, and circulating cytokines. The role of microRNAs, cutaneous respirometry, biopsy, exercise tests, and small molecule reporters as possible biomarkers is unsolved. (4 Conclusions: The disadvantages of most putative biomarkers for MIDs are that they hardly meet the criteria for being acceptable as a biomarker (missing longitudinal studies, not validated, not easily feasible, not cheap, not ubiquitously available and that not all MIDs manifest in the brain, muscle, or myocardium. There is currently a lack of validated biomarkers for diagnosing MIDs.

The future: biomarkers, biosensors, neuroinformatics, and e-neuropsychiatry.

Science.gov (United States)

Lowe, Christopher R

2011-01-01

The emergence of molecular biomarkers for psychological, psychiatric, and neurodegenerative disorders is beginning to change current diagnostic paradigms for this debilitating family of mental illnesses. The development of new genomic, proteomic, and metabolomic tools has created the prospect of sensitive and specific biochemical tests to replace traditional pen-and-paper questionnaires. In the future, the realization of biosensor technologies, point-of-care testing, and the fusion of clinical biomarker data, electroencephalogram, and MRI data with the patient's past medical history, biopatterns, and prognosis may create personalized bioprofiles or fingerprints for brain disorders. Further, the application of mobile communications technology and grid computing to support data-, computation- and knowledge-based tasks will assist disease prediction, diagnosis, prognosis, and compliance monitoring. It is anticipated that, ultimately, mobile devices could become the next generation of personalized pharmacies. Copyright © 2011 Elsevier Inc. All rights reserved.

Pre-analytical and analytical factors influencing Alzheimer's disease cerebrospinal fluid biomarker variability.

Science.gov (United States)

Fourier, Anthony; Portelius, Erik; Zetterberg, Henrik; Blennow, Kaj; Quadrio, Isabelle; Perret-Liaudet, Armand

2015-09-20

A panel of cerebrospinal fluid (CSF) biomarkers including total Tau (t-Tau), phosphorylated Tau protein at residue 181 (p-Tau) and β-amyloid peptides (Aβ42 and Aβ40), is frequently used as an aid in Alzheimer's disease (AD) diagnosis for young patients with cognitive impairment, for predicting prodromal AD in mild cognitive impairment (MCI) subjects, for AD discrimination in atypical clinical phenotypes and for inclusion/exclusion and stratification of patients in clinical trials. Due to variability in absolute levels between laboratories, there is no consensus on medical cut-off value for the CSF AD signature. Thus, for full implementation of this core AD biomarker panel in clinical routine, this issue has to be solved. Variability can be explained both by pre-analytical and analytical factors. For example, the plastic tubes used for CSF collection and storage, the lack of reference material and the variability of the analytical protocols were identified as important sources of variability. The aim of this review is to highlight these pre-analytical and analytical factors and describe efforts done to counteract them in order to establish cut-off values for core CSF AD biomarkers. This review will give the current state of recommendations. Copyright © 2015. Published by Elsevier B.V.

Clinical value of renal injury biomarkers in diagnosis of chronic kidney disease

Directory of Open Access Journals (Sweden)

Cheng-lu ZHANG

2011-12-01

Full Text Available Objective To investigate the levels of renal injury biomarkers in patients with chronic kidney disease(CKD and evaluate their clinical significances in diagnosis of CKD.Methods A total of 66 subjects(37 patients with CKD and 29 healthy individuals were involved in this study.Serum blood urea nitrogen(SBUN was determined by Glutamate dehydrogenase method;serum creatinine(SCr and urinary creatinine(UCr were detected by sarcosine oxidase method;serum uric acid(SUA was measured by uricase colorimetry;serum cystatin C(Cys C and urinary microalbumin(UmAlbwere analyzed by immunological transmission turbidimetry;urinary protein(U-PROwas measured by Coomassies Brilliant Blue(CBB assay.The UmAlb and U-PRO levels were expressed in units of mg/mmolUCr.Results The results of independent samples t test indicated that significant differences were found in SBUN,SCr,SUA,Cys C,UmAlb and U-PRO(P < 0.05 between patient group and healthy control group.The evaluation of diagnostic effects showed that the areas under the curve at ROC plot for SBUN,SCr,SUA,Cys C,UmAlb and U-PRO were 0.907,0.912,0.742,0.982,0.984 and 0.991,respectively.Conclusions U-PRO,UmAlb and Cys C are ideal biomarkers,SCr and SBUN come next,SUA is the weakest when the above biomarkers are applied to evaluate the renal injury and its severity of the patients with CKD.

Novel biomarkers in primary breast core biopsies to predict poor response to neoadjuvant chemotherapy and appearance of metastases.

Science.gov (United States)

Novell, Anna; Morales, Serafin; Valls, Joan; Panadés, Maria José; Salud, Antonieta; Iglesias, Edelmiro; Vilardell, Felip; Matias-Guiu, Xavier; Llombart-Cussac, Antonio

2017-09-01

Drug resistance has been one of the major obstacles limiting the success of cancer chemotherapy. In two thirds of breast cancer patients, large (>1cm) residual tumors are present after neoadjuvant chemotherapy (NCT). The residual tumor and involved nodes have been indicators of relapse and survival very important in breast cancer. The goal of this preliminary study was to assess the predictive significance of a panel of molecular biomarkers, related with the response to treatment or drug resistance to NCT, as determined on the diagnostic tumor. The expression of 22 proteins was examined using immunohistochemistry in tissue microarrays (TMA) from 115 patients of stage II-III breast cancer, treated with NCT. Among studied proteins, there are some that are anti-apoptotic, pro-proliferative, cancer stem cell markers and the Vitamin D Receptor. Other proteins are involved in the identification of molecular subtype, cell cycle regulation or DNA repair. Next, a predictive signature of poor response was generated from independent markers of predictive value. Tumors that expressed four or five conditions (biomarkers of chemoresistance with a determinated cutoff) were associated with a 9-fold increase in the chances of these patients of having a poor response to NCT. Additionally, we also found a worse prognostic signature, generated from independent markers of prognostic value. Tumors which expressed two or three conditions of worst prognostic, were associated with a 6-fold reduction in Distant Disease Free Survival. In conclusion, finding biomarkers of chemoresitance (ypTNM II-III) and metastases can become a stepping stone for future studies that will need to be assessed in a bigger scale.

Protein Biomarkers of Periodontitis in Saliva

Science.gov (United States)

Taylor, John J.

2014-01-01

Periodontitis is a chronic inflammatory condition of the tissues that surround and support the teeth and is initiated by inappropriate and excessive immune responses to bacteria in subgingival dental plaque leading to loss of the integrity of the periodontium, compromised tooth function, and eventually tooth loss. Periodontitis is an economically important disease as it is time-consuming and expensive to treat. Periodontitis has a worldwide prevalence of 5–15% and the prevalence of severe disease in western populations has increased in recent decades. Furthermore, periodontitis is more common in smokers, in obesity, in people with diabetes, and in heart disease patients although the pathogenic processes underpinning these links are, as yet, poorly understood. Diagnosis and monitoring of periodontitis rely on traditional clinical examinations which are inadequate to predict patient susceptibility, disease activity, and response to treatment. Studies of the immunopathogenesis of periodontitis and analysis of mediators in saliva have allowed the identification of many potentially useful biomarkers. Convenient measurement of these biomarkers using chairside analytical devices could form the basis for diagnostic tests which will aid the clinician and the patient in periodontitis management; this review will summarise this field and will identify the experimental, technical, and clinical issues that remain to be addressed before such tests can be implemented. PMID:24944840

Recent developments in biomarkers in Parkinson disease

Science.gov (United States)

Schapira, Anthony H.V.

2013-01-01

Purpose of review Parkinson disease is the second most common neurodegenerative disease after Alzheimer disease, and current demographic trends indicate a life-time risk approaching 4% and predict a doubling of prevalence by 2030. Strategies are being developed to apply recent advances in our understanding of the cause of Parkinson disease to the development of biomarkers that will enable the identification of at-risk individuals, enable early diagnosis and reflect the progression of disease. The latter will be particularly important for the testing of disease-modifying therapies. This review summarizes recent advances in Parkinson disease biomarker development. Recent findings Recent reports continue to reflect the application of a variety of clinical, imaging or biochemical measurements, alone or in combination, to general Parkinson disease populations. Probably the most promising is the assay of alpha-synuclein in the diagnosis and evolution of Parkinson disease. At present, detection techniques are still being refined, but once accurate and reproducible assays are available, it will be important to define the relationship of these to early diagnosis and progression. Alpha-synuclein concentrations may also be modulated by certain disease-modifying agents in development and so may represent a measure of their efficacy. It has to be accepted that no single measure currently fulfils all the necessary criteria for a biomarker in Parkinson disease, but combinations of measures are more likely to deliver benefit. Summary The Parkinson disease biomarker field is approaching a stage when certain combinations of clinical, imaging and biochemical measures may identify a proportion of individuals at risk for developing the disease. However, their general applicability may be limited. Attention is now turning to stratification of Parkinson disease into certain at-risk groups defined by genotype. The application of multimodal screening to these populations may be more

Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer's disease: a systematic review.

Science.gov (United States)

Ruan, Qingwei; D'Onofrio, Grazia; Sancarlo, Daniele; Bao, Zhijun; Greco, Antonio; Yu, Zhuowei

2016-05-16

Neuroimaging-biomarkers of Mild Cognitive Impairment (MCI) allow an early diagnosis in preclinical stages of Alzheimer's disease (AD). The goal in this paper was to review of biomarkers for Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD), with emphasis on neuroimaging biomarkers. A systematic review was conducted from existing literature that draws on markers and evidence for new measurement techniques of neuroimaging in AD, MCI and non-demented subjects. Selection criteria included: 1) age ≥ 60 years; 2) diagnosis of AD according to NIAAA criteria, 3) diagnosis of MCI according to NIAAA criteria with a confirmed progression to AD assessed by clinical follow-up, and 4) acceptable clinical measures of cognitive impairment, disability, quality of life, and global clinical assessments. Seventy-two articles were included in the review. With the development of new radioligands of neuroimaging, today it is possible to measure different aspects of AD neuropathology, early diagnosis of MCI and AD become probable from preclinical stage of AD to AD dementia and non-AD dementia. The panel of noninvasive neuroimaging-biomarkers reviewed provides a set methods to measure brain structural and functional pathophysiological changes in vivo, which are closely associated with preclinical AD, MCI and non-AD dementia. The dynamic measures of these imaging biomarkers are used to predict the disease progression in the early stages and improve the assessment of therapeutic efficacy in these diseases in future clinical trials.