Addressing Reproducibility in Genomic Signatures by Characterizing Variance and Estimation Stability

• Activity Number: 343
• Type: Topic Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Hector Bravo*
• Companies: University of Maryland
• Keywords: Genomics ; Gene Expression ; Stability ; Biomarkers

Abstract

Gene expression signatures are commonly used to define cancer prognosis methods, yet only a small number of them are deployed in the clinic since many fail subsequent validation. A primary reason for this lack of reproducibility is these signatures' attempt to model the highly variable and unstable genomic behavior of cancer. Our group introduced anti-profiles as a methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a specific cancer type relative to normal tissue, their degree of deviation from normal tissue expression in specific genes involved in tissue differentiation is a stable tumor mark that is reproducible across experiments and cancer types.

Here we show that constructing gene expression signatures based on variability using the anti-profile approach yields classifiers capable of successfully distinguishing benign growths from cancerous growths based on deviation from normal expression. We then show that this same approach generates stable and reproducible signatures that predict probability of relapse and survival based on tumor gene expression.