title: An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.
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.
authors: Division of Cancer Biostatistics, Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA.
authors: Xi
authors: X
authors: Chen
authors: Lily
authors: L
authors: Wang
authors: Hemant
authors: H
authors: Ishwaran