title: Better cancer biomarker discovery through better study design.
abstract: BACKGROUND
  High-throughput laboratory technologies coupled with sophisticated bioinformatics algorithms have tremendous potential for discovering novel biomarkers, or profiles of biomarkers, that could serve as predictors of disease risk, response to treatment or prognosis. We discuss methodological issues in wedding high-throughput approaches for biomarker discovery with the case-control study designs typically used in biomarker discovery studies, especially focusing on nested case-control designs.
  
  
  METHODS
  We review principles for nested case-control study design in relation to biomarker discovery studies and describe how the efficiency of biomarker discovery can be effected by study design choices. We develop a simulated prostate cancer cohort data set and a series of biomarker discovery case-control studies nested within the cohort to illustrate how study design choices can influence biomarker discovery process.
  
  
  RESULTS
  Common elements of nested case-control design, incidence density sampling and matching of controls to cases are not typically factored correctly into biomarker discovery analyses, inducing bias in the discovery process. We illustrate how incidence density sampling and matching of controls to cases reduce the apparent specificity of truly valid biomarkers 'discovered' in a nested case-control study. We also propose and demonstrate a new case-control matching protocol, we call 'antimatching', that improves the efficiency of biomarker discovery studies.
  
  
  CONCLUSIONS
  For a valid, but as yet undiscovered, biomarker(s) disjunctions between correctly designed epidemiologic studies and the practice of biomarker discovery reduce the likelihood that true biomarker(s) will be discovered and increases the false-positive discovery rate.
authors.affiliation: Department of Epidemiology, Mailman School of Public Health, and Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, USA. agr3@columbia.edu
authors.forename: Andrew
authors.initials: A
authors.lastname: Rundle
authors.forename: Habibul
authors.initials: H
authors.lastname: Ahsan
authors.forename: Paolo
authors.initials: P
authors.lastname: Vineis
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism; D016022:Case-Control Studies; D006801:Humans; D016015:Logistic Models; D009369:Neoplasms / Q000453:epidemiology / Q000378:metabolism*; D012107:Research Design / Q000592:standards*
chemical_list: D014408:Biomarkers, Tumor