title: 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 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.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  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.
authors: Center for Personalized Diagnostics, Biodesign Institute, ASU, 1001 S. McAllister Ave, Tempe, AZ 85287, USA. garrick.wallstrom@asu.edu
authors: Garrick
authors: G
authors: Wallstrom
authors: Karen S
authors: KS
authors: Anderson
authors: Joshua
authors: J
authors: LaBaer
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism* / Q000473:pathology*; D016022:Case-Control Studies; D005260:Female; D023281:Genomics / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method; D040901:Proteomics / Q000379:methods*; D012107:Research Design
chemical_list: D014408:Biomarkers, Tumor