title: Predictive biomarker validation in practice: lessons from real trials.
abstract: BACKGROUND
  With the advent of targeted therapies, biomarkers provide a promising means of individualizing therapy through an integrated approach to prediction using the genetic makeup of the disease and the genotype of the patient. Biomarker validation has therefore become a central topic of discussion in the field of medicine, primarily due to the changing landscape of therapies for treatment of a disease and these therapies purported mechanism(s) of action.
  
  
  OBJECTIVE
  In this report, we discuss the merits and limitations of some of the clinical trial designs for predictive biomarker validation using examples from ongoing or completed clinical trials.
  
  
  METHODS
  The designs are broadly classified as retrospective (i.e., using data from previously well-conducted randomized controlled trials (RCT)) versus prospective (enrichment or targeted, unselected or all-comers, hybrid, and adaptive analysis). We discuss some of these designs in the context of real trials.
  
  
  RESULTS
  Well-designed retrospective analysis of prospective RCT can bring forward effective treatments to marker defined subgroup of patients in a timely manner. An example is the KRAS gene status in colorectal cancer - the benefit from cetuximab and panitumumab was demonstrated to be restricted to patients with wild type status based on prospectively specified analyses using data from previously conducted RCTs. Prospective enrichment designs are appropriate when compelling preliminary evidence suggests that not all patients will benefit from the study treatment under consideration; however, this may sometimes leave questions unanswered. An example is the established benefit of trastuzumab as adjuvant therapy for breast cancer; a clear definition of HER2-positivity and the assay reproducibility have, however, remained unanswered. An all-comers design is optimal where preliminary evidence regarding treatment benefit and assay reproducibility is uncertain (e.g., EGFR expression and tyrosine kinase inhibitors in lung cancer), or to identify the most effective therapy from a panel of regimens (e.g., chemotherapy options in breast cancer).
  
  
  CONCLUSIONS
  The designs discussed here rest on the assumption that the technical feasibility, assay performance metrics, and the logistics of specimen collection are well established and that initial results demonstrate promise with regard to the predictive ability of the marker(s).
  
  
  CONCLUSIONS
  The choice of a clinical trial design is driven by a combination of scientific, clinical, statistical, and ethical considerations. There is no one size fits all solution to predictive biomarker validation.
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA. mandrekar.sumithra@mayo.edu
authors.forename: Sumithra J
authors.initials: SJ
authors.lastname: Mandrekar
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Sargent
mesh_terms: D014408:Biomarkers, Tumor; D006801:Humans; D058990:Molecular Targeted Therapy; D011237:Predictive Value of Tests; D011446:Prospective Studies; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design; D012189:Retrospective Studies
chemical_list: D014408:Biomarkers, Tumor
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