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

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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
Issue number17-18
StatePublished - Sep 2010
Externally publishedYes


  • 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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