Detecting survival-associated biomarkers from heterogeneous populations
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AbstractDetection of prognostic factors associated with patients' survival outcome helps gain insights into a disease and guide treatment decisions. The rapid advancement of high-throughput technologies has yielded plentiful genomic biomarkers as candidate prognostic factors, but most are of limited use in clinical application. As the price of the technology drops over time, many genomic studies are conducted to explore a common scientific question in different cohorts to identify more reproducible and credible biomarkers. However, new challenges arise from heterogeneity in study populations and designs when jointly analyzing the multiple studies. For example, patients from different cohorts show different demographic characteristics and risk profiles. Existing high-dimensional variable selection methods for survival analysis, however, are restricted to single study analysis. We propose a novel Cox model based two-stage variable selection method called "Cox-TOTEM" to detect survival-associated biomarkers common in multiple genomic studies. Simulations showed our method greatly improved the sensitivity of variable selection as compared to the separate applications of existing methods to each study, especially when the signals are weak or when the studies are heterogeneous. An application of our method to TCGA transcriptomic data identified essential survival associated genes related to the common disease mechanism of five Pan-Gynecologic cancers. Copyright 2021, The Author(s).
SponsorsResearch reported in this publication was supported by the National Institute on Drug Abuse (NIDA) of National Institute of Health under the award number 1DP1DA048968-01 to S.C. and T.M., and the National Science Foundation under the award number DMS 2014971 to T.S. This project was also supported by the University of Maryland Graduate School Faculty-Student Research Award.
Identifier to cite or link to this itemhttp://hdl.handle.net/10713/15171
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