Clinical trial designs for predictive biomarker validation: one size does not fit all.
Traditionally, anatomic staging systems have been used to provide predictions of individual patient outcome and, to a lesser extent, guide the choice of treatment in cancer patients. With targeted therapies, biomarkers have the potential for providing added value through an integrated approach to prediction using the genetic makeup of the tumor and the genotype of the patient for treatment selection and patient management. Specifically, biomarkers can aid in patient stratification (risk assessment), treatment response identification (surrogate markers), or differential diagnosis (identifying individuals who are likely to respond to specific drugs). In this study, we explore two major topics in relation to the design of clinical trials for predictive marker validation. First, we discuss the appropriateness of an enrichment (i.e., targeted) vs. an unselected design through case studies focusing on the clinical question(s) at hand, the strength of the preliminary evidence, and assay reproducibility. Second, we evaluate the efficiency (total number of events and sample size) of two unselected predictive marker designs for validation of a marker under a wide range of clinically relevant scenarios, exploring the impact of the prevalence of the marker and the hazard ratios for the treatment comparisons. The review and evaluation of these designs represents an essential step toward the goal of personalized medicine because we explicitly seek to explore and evaluate the methodology for the clinical validation of biomarker guided therapy.
Mandrekar
Sumithra J
SJ
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota 55905, USA. mandrekar.sumithra@mayo.edu
Sargent
Daniel J
DJ
D000911:Antibodies, Monoclonal / Q000187:drug effects / Q000494:pharmacology / Q000627:therapeutic use; D061067:Antibodies, Monoclonal, Humanized; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000188:drug therapy / Q000378:metabolism; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy / Q000378:metabolism; D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D015179:Colorectal Neoplasms / Q000188:drug therapy / Q000378:metabolism; D018572:Disease-Free Survival; D023381:Endpoint Determination; D066246:ErbB Receptors / Q000096:biosynthesis; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy / Q000378:metabolism; D000077544:Panitumumab; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011518:Proto-Oncogene Proteins / Q000096:biosynthesis; D016283:Proto-Oncogene Proteins p21(ras); D018719:Erb-b2 Receptor Tyrosine Kinases / Q000096:biosynthesis; D012107:Research Design / Q000706:statistics & numerical data; D000068878:Trastuzumab; D018631:ras Proteins / Q000096:biosynthesis
D000911:Antibodies, Monoclonal; D061067:Antibodies, Monoclonal, Humanized; D014408:Biomarkers, Tumor; D066246:ErbB Receptors; D000077544:Panitumumab; D011518:Proto-Oncogene Proteins; D016283:Proto-Oncogene Proteins p21(ras); D018719:Erb-b2 Receptor Tyrosine Kinases; D000068878:Trastuzumab; D018631:ras Proteins; C117307:KRAS protein, human; C512478:EGFR protein, human; C508053:ERBB2 protein, human