Abstract：
:Molecular heterogeneity in human breast cancer has challenged diagnosis, prognosis, and clinical treatment. It is well known that molecular subtypes of breast tumors are associated with significant differences in prognosis and survival. Assuming that the differences are attributed to subtype-specific pathways, we then suspect that there might be gene regulatory mechanisms that modulate the behavior of the pathways and their interactions. In this study, we proposed an integrated methodology, including machine learning and information theory, to explore the mechanisms. Using existing data from three large cohorts of human breast cancer populations, we have identified an ensemble of 16 master regulator genes (or MR16) that can discriminate breast tumor samples into four major subtypes. Evidence from gene expression across the three cohorts has consistently indicated that the MR16 can be divided into two groups that demonstrate subtype-specific gene expression patterns. For example, group 1 MRs, including ESR1, FOXA1, and GATA3, are overexpressed in luminal A and luminal B subtypes, but lowly expressed in HER2-enriched and basal-like subtypes. In contrast, group 2 MRs, including FOXM1, EZH2, MYBL2, and ZNF695, display an opposite pattern. Furthermore, evidence from mutual information modeling has congruently indicated that the two groups of MRs either up- or down-regulate cancer driver-related genes in opposite directions. Furthermore, integration of somatic mutations with pathway changes leads to identification of canonical genomic alternations in a subtype-specific fashion. Taken together, these studies have implicated a gene regulatory program for breast tumor progression.
journal_name
Geneticsjournal_title
Geneticsauthors
Li R,Campos J,Iida Jdoi
10.1534/genetics.115.180125subject
Has Abstractpub_date
2015-12-01 00:00:00pages
1341-8issue
4eissn
0016-6731issn
1943-2631pii
genetics.115.180125journal_volume
201pub_type
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