A network approach reveals driver genes associated with survival of patients with triple-negative breast cancer

• Type of Material: Article
• Authors: Courtney D Dill, Morehouse School of Medicine; Eric Dammer, Emory University; Ti'ara L Griffen, Morehouse School of Medicine; Nicholas Seyfried, Emory University; James W Lillard, Morehouse School of Medicine
• Language: English
• Date: 2021-05-21
• Publisher: CELL PRESS
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Authors
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.isci.2021.102451
• Title of Journal or Parent Work: ISCIENCE
• Volume: 24
• Issue: 5
• Start Page: 102451
• End Page: 102451
• Grant/Funding Information: This study is funded in part by the National Cancer Institute of the National Institutes of Health U54CA118638 and P30CA138292, the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R25GM058268, the Gates Millennium Scholarship Program, the Winship Cancer Institute, and the Morehouse School of Medicine.
• Keywords: SUBTYPES; MICROARRAY; DEHYDROGENASE; ESTROGEN; PATHWAY; Multidisciplinary Sciences; EXPRESSION; CELLS; IDENTIFICATION; METAANALYSIS; SELECTION; Science & Technology - Other Topics; Science & Technology

Abstract

We aimed to identify triple-negative breast cancer (TNBC) drivers that regulate survival time as predictive signatures that improve TNBC prognostication. Breast cancer (BrCa) transcriptomic tumor biopsies were analyzed, identifying network communities enriched with TNBC-specific differentially expressed genes (DEGs) and correlated strongly to TNBC status. Two anticorrelated modules correlated strongly to TNBC subtype and survival. Querying module-specific hubs and DEGs revealed transcriptional changes associated with high survival. Transcripts were nominated as biomarkers and tested as combinatoric ratios using receiver operator characteristic (ROC) analysis to assess survival prediction. ROC test rounds integrated genes with established interactions to hubs and DEGs of key modules, improving prediction. Finally, we tested whether integration of literature-derived genes for implicated hallmark cancer processes could improve prediction of survival. Complementary coexpression, differential expression, genetic interaction, and survival stratification integrated by ROC optimization uncovered a panel of “linchpin survival genes” predictive of patient survival, representing gene interactions in hallmark cancer processes.