An integrative pathway-based clinical-genomic model for cancer survival prediction

Xi Chen, Lily Wang and Hemant Ishwaran

Statistics & Probability Letters, 2010, vol. 80, issue 17-18, 1313-1319

Abstract: Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-genomic approach that combines both genomic pathway and clinical information. First, we summarize information from genes in each pathway using Supervised Principal Components (SPCA) to obtain pathway-based genomic predictors. Next, we build a prediction model based on clinical variables and pathway-based genomic predictors using Random Survival Forests (RSF). Our rationale for this two-stage procedure is that the underlying disease process may be influenced by environmental exposure (measured by clinical variables) and perturbations in different pathways (measured by pathway-based genomic variables), as well as their interactions. Using two cancer microarray datasets, we show that the pathway-based clinical-genomic model outperforms gene-based clinical-genomic models, with improved prediction accuracy and interpretability.

Keywords: Microarrays; Gene; expression; Pathway; analysis; Survival; prediction; Random; survival; forests (search for similar items in EconPapers)
Date: 2010
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