Biomarker Discovery for Heterogeneous Diseases

Abstract

Modern genomic and proteomic studies reveal that many diseases are heterogeneous, comprising multiple different subtypes. The common notion that one biomarker can be predictive for all patients may need to be replaced by an understanding that each subtype has its own set of unique biomarkers, affecting how discovery studies are designed and analyzed. We used Monte Carlo simulation to measure and compare the performance of eight selection methods with homogeneous and heterogeneous diseases using both single-stage and two-stage designs. We also applied the selection methods in an actual proteomic biomarker screening study of heterogeneous breast cancer cases. Different selection methods were optimal, and more than two-fold larger sample sizes were needed for heterogeneous diseases compared with homogeneous diseases. We also found that for larger studies, two-stage designs can achieve nearly the same statistical power as single-stage designs at significantly reduced cost. We found that disease heterogeneity profoundly affected biomarker performance. We report sample size requirements and provide guidance on the design and analysis of biomarker discovery studies for both homogeneous and heterogeneous diseases. We have shown that studies to identify biomarkers for the early detection of heterogeneous disease require different statistical selection methods and larger sample sizes than if the disease were homogeneous. These findings provide a methodologic platform for biomarker discovery of heterogeneous diseases.