Biomarker discovery for heterogeneous diseases.

ABSTRACT:

Background

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.

Methods

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.

Results

Different selection methods were optimal, and more than two-fold larger sample sizes

SUBMITTER: Wallstrom G 

PROVIDER: S-EPMC3842033 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature