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Shujun Huang

University of Manitoba, Canada

Title: Gene functional analysis of breast cancer multigene signatures

Biography

Biography: Shujun Huang

Abstract

Background: Breast cancer is a heterogeneous disease and the personalized medicine is the hope to improve the clinical outcome. Multi-gene signatures have been extensively studied for breast cancer stratification in the past decades and more than 30 different signatures have been reported. One argument about these signatures is that there are little overlapped genes among the reported signatures. We investigated the breast cancer signature genes to proof our hypothesis that the genes of different signature may share the common functions, and we further tried to use these previously reported signature genes to build better prognostic models.

Methods: A total of 33 signatures and the corresponding gene lists were investigated. We first examined the gene frequency in these signatures and the gene overlapped. Each signature gene list was analyzed by the KEGG pathways and gene ontology (GO) terms with a p-value of 0.1 cutoff and the functional groups were compared among all signatures. The common genes were tested for breast cancer subtype classifier using the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) data. The common genes were also tested for building the Yin Yang gene expression ratio (YMR) signature using public datasets (GSE1456 and GSE2034).

Results: There were only 238 of the total 2239 genes that were overlapped in at least two signatures while 429 of the total 1979 function terms were common in at least two signatures. As we expected, most of these common function terms involve in cell cycle process. Interestingly, though have almost no overlap genes, the signatures used for ER-positive (e.g. OncotypeDx) and the signatures used for ER-negative (e.g. basal signatures) have the common function terms of cell death, regulation of cell proliferation, response to organic substance, intracellular signaling cascade, response to hormone stimulus, response to oxygen levels, bone development, DNA packaging, response to hypoxia, ossification and skeletal system development etc. We used the 62 genes that were common in at least three signatures as classifier and subtyped the 1141 METABRIC data including 144 normal samples into 9 subgroups. These 9 subgroups showed different clinical outcome. Among the 238 common genes, not all were differently expressed between tumor and normal tissues. We selected those genes that are higher expressed in normal then in tumors (21 Yang genes) and those higher in tumors than in normal (72 Yin genes) and built the YMR model signature. This YMR showed significance in risk stratification in the two datasets (GSE1456 and GSE2034).

Conclusions: The debate that no overlap genes among most breast cancer signatures can be partially explained by our discovery that these signature genes represented the similar functions though they are different genes. The genes extracted from these previously reported signature is a good resource for new model development. The subtype classifier and YMR signature built from the common genes showed promising result.