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

Research output: Contribution to journalArticle

18 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1313-1319
Number of pages7
JournalStatistics and Probability Letters
Volume80
Issue number17-18
DOIs
StatePublished - Sep 1 2010
Externally publishedYes

Keywords

  • Gene expression
  • Microarrays
  • Pathway analysis
  • Random survival forests
  • Survival prediction

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

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