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December 2009 An integrative analysis of cancer gene expression studies using Bayesian latent factor modeling
Daniel Merl, Julia Ling-Yu Chen, Jen-Tsan Chi, Mike West
Ann. Appl. Stat. 3(4): 1675-1694 (December 2009). DOI: 10.1214/09-AOAS261

Abstract

We present an applied study in cancer genomics for integrating data and inferences from laboratory experiments on cancer cell lines with observational data obtained from human breast cancer studies. The biological focus is on improving understanding of transcriptional responses of tumors to changes in the pH level of the cellular microenvironment. The statistical focus is on connecting experimentally defined biomarkers of such responses to clinical outcome in observational studies of breast cancer patients. Our analysis exemplifies a general strategy for accomplishing this kind of integration across contexts. The statistical methodologies employed here draw heavily on Bayesian sparse factor models for identifying, modularizing and correlating with clinical outcome these signatures of aggregate changes in gene expression. By projecting patterns of biological response linked to specific experimental interventions into observational studies where such responses may be evidenced via variation in gene expression across samples, we are able to define biomarkers of clinically relevant physiological states and outcomes that are rooted in the biology of the original experiment. Through this approach we identify microenvironment-related prognostic factors capable of predicting long term survival in two independent breast cancer datasets. These results suggest possible directions for future laboratory studies, as well as indicate the potential for therapeutic advances though targeted disruption of specific pathway components.

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Daniel Merl. Julia Ling-Yu Chen. Jen-Tsan Chi. Mike West. "An integrative analysis of cancer gene expression studies using Bayesian latent factor modeling." Ann. Appl. Stat. 3 (4) 1675 - 1694, December 2009. https://doi.org/10.1214/09-AOAS261

Information

Published: December 2009
First available in Project Euclid: 1 March 2010

zbMATH: 1184.62190
MathSciNet: MR2752153
Digital Object Identifier: 10.1214/09-AOAS261

Keywords: Acidosis and neutralization pathways in cancer , Bayesian latent factor models , breast cancer genomics , gene expression signatures , integrative cancer genomics , micro-environmental parameters in cancer , Weibull survival models

Rights: Copyright © 2009 Institute of Mathematical Statistics

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Vol.3 • No. 4 • December 2009
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