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.

Result: 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.

Figure 1

Schematic illustration of types of biomarkers arising during carcinogenesis amenable to high throughput approaches to biomarker discovery. During carcinogenesis adherent gene expression is thought to occur, giving rise to differences in RNA transcription, protein expression and metabolic pathways that produce targets for biomarker discovery studies. Similarly during carcinogenesis there can be changes in the patterns of CpG methylation in DNA promoter regions that also generate targets for biomarker discovery studies. Inter-individual differences in genetic polymorphisms or mutations are thought to produce differences in genetic susceptibility that interacts with exposures to trigger carcinogenesis. Key elements of this genetic variation can be discovered using genome wide association studies (GWAS).

Figure 2

This diagram illustrates the selection of controls for two cases in a nested case control study. For subject 1, at the time of disease development (D) subjects 2, 4–7 and 9 are eligible to be selected as controls (C). Subject 6 is eligible to be a control for subject 1, despite the fact that subject 6 later develops disease. At the time that subject 6 develops disease subjects 2, 7 and 9 are eligible to be selected as controls, regardless of whether they were selected as controls for subject 1.