title: Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile.
abstract: BACKGROUND
  Microarray data have been used for gene signature selection to predict clinical outcomes. Many studies have attempted to identify factors that affect models' performance with only little success. Fine-tuning of model parameters and optimizing each step of the modeling process often results in over-fitting problems without improving performance.
  
  
  RESULTS
  We propose a quantitative measurement, termed consistency degree, to detect the correlation between disease endpoint and gene expression profile. Different endpoints were shown to have different consistency degrees to gene expression profiles. The validity of this measurement to estimate the consistency was tested with significance at a p-value less than 2.2e-16 for all of the studied endpoints. According to the consistency degree score, overall survival milestone outcome of multiple myeloma was proposed to extend from 730 days to 1561 days, which is more consistent with gene expression profile.
  
  
  CONCLUSIONS
  For various clinical endpoints, the maximum predictive powers of different microarray-based models are limited by the correlation between endpoint and gene expression profile of disease samples as indicated by the consistency degree score. In addition, previous defined clinical outcomes can also be reassessed and refined more coherent according to related disease gene expression profile. Our findings point to an entirely new direction for assessing the microarray-based predictive models and provide important information to gene signature based clinical applications.
authors.affiliation: The Center for Bioinformatics and The institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, 200241, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Zhao
authors.forename: Leming
authors.initials: L
authors.lastname: Shi
authors.forename: Weida
authors.initials: W
authors.lastname: Tong
authors.forename: John D
authors.initials: JD
authors.lastname: Shaughnessy
authors.forename: André
authors.initials: A
authors.lastname: Oberthuer
authors.forename: Lajos
authors.initials: L
authors.lastname: Pusztai
authors.forename: Youping
authors.initials: Y
authors.lastname: Deng
authors.forename: W Fraser
authors.initials: WF
authors.lastname: Symmans
authors.forename: Tieliu
authors.initials: T
authors.lastname: Shi
mesh_terms: D020869:Gene Expression Profiling; D006801:Humans; D053208:Kaplan-Meier Estimate; D008957:Models, Genetic; D009101:Multiple Myeloma / Q000235:genetics / Q000401:mortality / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D025341:Principal Component Analysis