An integrative analysis of cancer gene expression studies using Bayesian latent factor modeling

TL;DR

This study integrates laboratory and observational breast cancer data using Bayesian latent factor models to identify gene expression biomarkers linked to tumor microenvironment responses and patient survival.

Contribution

It introduces a Bayesian sparse factor modeling approach to connect experimental biomarkers with clinical outcomes across different data contexts.

Findings

Identified microenvironment-related prognostic factors predicting survival.

Linked experimental gene expression signatures to clinical outcomes.

Demonstrated the potential for targeted therapeutic strategies.

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.