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Biomarkers: hopes and challenges in the path from discovery to clinical practice

Biomarkers are objectively measured indicators of normal or pathological processes that may be helpful in diagnosis, staging, monitoring treatment, or prognostic evaluation of a disease. Although development of genomic, metabolomic and proteomic technologies has contributed to an explosion in identification of candidate analytes, validation remains expensive and challenging, and successful introduction of new biomarkers to clinical practice occurs at a very slow pace. The goal of this introductory overview is to provide the context for a series of review manuscripts published in the special issue on biomarkers. The promises and challenges of biomarker discovery are highlighted. Discovery and implementation of transformative new biomarkers in clinical practice requires close collaborations between scientists, clinicians and industry. High throughput technologies can identify a myriad of promising candidates but cannot predict their clinical value. In addition to rapid effective and systematic approaches for clinical validation, there is a need to study and establish links between the purported biomarker and the pathophysiologic basis of the disease of interest. Biomarkers are most informative when they provide insights into activation of specific pathways, thus serving as windows into the molecular basis of the disease.

Multigene expression-based predictors for sensitivity to Vorinostat and Velcade in non-small cell lung cancer

The ability to predict the efficacy of molecularly targeted therapies for non-small cell lung cancer (NSCLC) for an individual patient remains problematic. The purpose of this study was to identify, using a refined "coexpression extrapolation (COXEN)" algorithm with a continuous spectrum of drug activity, tumor biomarkers that predict drug sensitivity and therapeutic efficacy in NSCLC to Vorinostat, a histone deacetylase inhibitor, and Velcade, a proteasome inhibitor. Using our refined COXEN algorithm, biomarker prediction models were discovered and trained for Vorinostat and Velcade based on the in vitro drug activity profiles of nine NSCLC cell lines (NCI-9). Independently, a panel of 40 NSCLC cell lines (UVA-40) were treated with Vorinostat or Velcade to obtain 50% growth inhibition values. Genome-wide expression profiles for both the NCI-9 and UVA-40 cell lines were determined using the Affymetrix HG-U133A platform. Modeling generated multigene expression signatures for Vorinostat (45-gene; P = 0.002) and Velcade (15-gene; P = 0.0002), with one overlapping gene (CFLAR). Examination of Vorinostat gene ontogeny revealed a predilection for cellular replication and death, whereas that of Velcade suggested involvement in cellular development and carcinogenesis. Multivariate regression modeling of the refined COXEN scores significantly predicted the activity of combination therapy in NSCLC cells (P = 0.007). Through the refinement of the COXEN algorithm, we provide an in silico method to generate biomarkers that predict tumor sensitivity to molecularly targeted therapies. Use of this refined COXEN method has significant implications for the a priori examination of targeted therapies to more effectively streamline subsequent clinical trial design and cost.