Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer.

ABSTRACT: The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model pe

SUBMITTER: Gyorffy B 

PROVIDER: S-EPMC4354298 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature