Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies.

BackgroundThe advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).MethodsWe evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.ResultsBoth biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.ConclusionsBiomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness

Prognostic and predictive value of circulating tumor cells and CXCR4 expression as biomarkers for a CXCR4 peptide antagonist in combination with carboplatin-etoposide in small cell lung cancer: exploratory analysis of a phase II study.

Background Circulating tumor cells (CTCs) and chemokine (C-X-C motif) receptor 4 (CXCR4) expression in CTCs and tumor tissue were evaluated as prognostic or predictive markers of CXCR4 peptide antagonist LY2510924 plus carboplatin-etoposide (CE) versus CE in extensive-stage disease small cell lung cancer (ED-SCLC). Methods This exploratory analysis of a phase II study evaluated CXCR4 expression in baseline tumor tissue and peripheral blood CTCs and in post-treatment CTCs. Optimum cutoff values were determined for CTC counts and CXCR4 expression in tumors and CTCs as predictors of survival outcome. Kaplan-Meier estimates and hazard ratios were used to determine biomarker prognostic and predictive values. Results There was weak positive correlation at baseline between CXCR4 expression in tumor tissue and CTCs. Optimum cutoff values were H-score ≥ 210 for CXCR4+ tumor, ≥7% CTCs with CXCR4 expression (CXCR4+ CTCs), and ≥6 CTCs/7.5 mL blood. Baseline H-score for CXCR4+ tumor was not prognostic of progression-free survival (PFS) or overall survival (OS). Baseline CXCR4+ CTCs ≥7% was prognostic of shorter PFS. CTCs ≥6 at baseline and cycle 2, day 1 were prognostic of shorter PFS and OS. None of the biomarkers at their respective optimum cutoffs was predictive of treatment response of LY2510924 plus CE versus CE. Conclusions In patients with ED-SCLC, baseline CXCR4 expression in tumor tissue was not prognostic of survival or predictive of LY2510924 treatment response. Baseline CXCR4+ CTCs ≥7% was prognostic of shorter PFS. CTC count ≥6 at baseline and after 1 cycle of treatment were prognostic of shorter PFS and OS

A Bayesian adaptive marker‐stratified design for molecularly targeted agents with customized hierarchical modeling

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker‐stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta‐binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a “customized” equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs

Prognostic importance of emerging cardiac, inflammatory, and renal biomarkers in chronic heart failure patients with reduced ejection fraction and anaemia: RED-HF study

Aims: To test the prognostic value of emerging biomarkers in the Reduction of Events by Darbepoetin Alfa in Heart Failure (RED-HF) trial. Methods and results: Circulating cardiac [N-terminal pro-B-type natriuretic peptide (NT-proBNP), and high-sensitivity troponin T (hsTnT)], neurohumoral [mid-regional pro-adrenomedullin (MR-proADM) and copeptin], renal (cystatin C), and inflammatory [high-sensitivity C-reactive protein (hsCRP)] biomarkers were measured at randomization in 1853 participants with complete data. The relationship between these biomarkers and the primary composite endpoint of heart failure hospitalization or cardiovascular death over 28 months of follow-up (n = 834) was evaluated using Cox proportional hazards regression, the c-statistic and the net reclassification index (NRI). After adjustment, the hazard ratio (HR) for the composite outcome in the top tertile of the distribution compared to the lowest tertile for each biomarker was: NT-proBNP 3.96 (95% CI 3.16–4.98), hsTnT 3.09 (95% CI 2.47–3.88), MR-proADM 2.28 (95% CI 1.83–2.84), copeptin 1.66 (95% CI 1.35–2.04), cystatin C 1.92 (95% CI 1.55–2.37), and hsCRP 1.51 (95% CI 1.27–1.80). A basic clinical prediction model was improved on addition of each biomarker individually, most strongly by NT-proBNP (NRI +62.3%, P < 0.001), but thereafter was only improved marginally by addition of hsTnT (NRI +33.1%, P = 0.004). Further addition of biomarkers did not improve discrimination further. Findings were similar for all-cause mortality. Conclusion: Once NT-proBNP is included, only hsTnT moderately further improved risk stratification in this group of chronic heart failure with reduced ejection fraction patients with moderate anaemia. NT-proBNP and hsTnT far outperform other emerging biomarkers in prediction of adverse outcome

Biomarker analyses of clinical outcomes in patients with advanced hepatocellular carcinoma treated with Sorafenib with or without Erlotinib in the SEARCH Trial

Purpose: Sorafenib is the current standard therapy for advanced HCC, but validated biomarkers predicting clinical outcomes are lacking. This study aimed to identify biomarkers predicting prognosis and/or response to sorafenib, with or without erlotinib, in HCC patients from the phase 3 SEARCH trial. Experimental Design: 720 patients were randomized to receive oral sorafenib 400 mg BID plus erlotinib 150 mg QD or placebo. Fifteen growth factors relevant to the treatment regimen and/or to HCC were measured in baseline plasma samples. Results: Baseline plasma biomarkers were measured in 494 (69%) patients (sorafenib plus erlotinib, n=243; sorafenib plus placebo, n=251). Treatment arm–independent analyses showed that elevated HGF (HR, 1.687 [high vs low expression]; endpoint multiplicity adjusted [e-adj] P=0.0001) and elevated plasma VEGF-A (HR, 1.386; e-adj P=0..0377) were significantly associated with poor OS in multivariate analyses, and low plasma KIT (HR, 0.75 [high vs low]; P=0.0233; e-adj P=0.2793) tended to correlate with poorer OS. High plasma VEGF-C independently correlated with longer TTP (HR, 0.633; e-adj P=0.0010) and trended toward associating with improved disease control rate (univariate:OR, 2.047; P=0.030; e-adj P=0.420). In 67% of evaluable patients (339/494), a multimarker signature of HGF, VEGF-A, KIT, epigen, and VEGF-C correlated with improved median OS in multivariate analysis (HR, 0.150; P<0.00001). No biomarker predicted efficacy from erlotinib. Conclusions: Baseline plasma HGF, VEGF-A, KIT, and VEGF-C correlated with clinical outcomes in HCC patients treated with sorafenib with or without erlotinib. These biomarkers plus epigen constituted a multimarker signature for improved OS

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer.

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan-Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment

Integrative analysis of the colorectal cancer proteome : potential clinical impact

Peer reviewedPostprin

Neutrophil-to-lymphocyte ratio as a bladder cancer biomarker: assessing prognostic and predictive value in SWOG 8710

No abstract available

A biomarker guided approach in heart failure

Heart failure is one of the commonest diagnoses presenting to physicians in the community or hospital care. Symptoms are often subjective, with clinicians having to rely on clinical assessment and radiological imaging to manage these patients. Treatment is often symptomatic with no clear therapeutic goals as yet identified. To date, there are no objective measures to diagnose, predict, prognosticate or guide therapy in compensated and decompensated heart failure, which is why a novel biomarker guided management approach is gaining so much momentum in the clinical community. This review encompasses recent data on this new approach and details on the potential clinical benefits of the most widely studied cardiac biomarkers currently available.peer-reviewe