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Abstract: We present an applied study in cancer genomics for integrating data andinferences from laboratory experiments on cancer cell lines with observationaldata obtained from human breast cancer studies. The biological focus is onimproving understanding of transcriptional responses of tumors to changes inthe pH level of the cellular microenvironment. The statistical focus is onconnecting experimentally defined biomarkers of such responses to clinicaloutcome in observational studies of breast cancer patients. Our analysisexemplifies a general strategy for accomplishing this kind of integrationacross contexts. The statistical methodologies employed here draw heavily onBayesian sparse factor models for identifying, modularizing and correlatingwith clinical outcome these signatures of aggregate changes in gene expression.By projecting patterns of biological response linked to specific experimentalinterventions into observational studies where such responses may be evidencedvia variation in gene expression across samples, we are able to definebiomarkers of clinically relevant physiological states and outcomes that arerooted in the biology of the original experiment. Through this approach weidentify microenvironment-related prognostic factors capable of predicting longterm survival in two independent breast cancer datasets. These results suggestpossible directions for future laboratory studies, as well as indicate thepotential for therapeutic advances though targeted disruption of specificpathway components.



Autor: Daniel Merl, Julia Ling-Yu Chen, Jen-Tsan Chi, Mike West

Fuente: https://arxiv.org/







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