Identification of novel prognostic indicators for triple-negative breast cancer patients through integrative analysis of cancer genomics data and protein interactome data.
Triple negative breast cancers (TNBCs) are highly heterogeneous and aggressive without targeted treatment. Here, we aim to systematically dissect TNBCs from a prognosis point of view by building a subnetwork atlas for TNBC prognosis through integrating multi-dimensional cancer genomics data from The Cancer Genome Atlas (TCGA) project and the interactome data from three different interaction networks. The subnetworks are represented as the protein-protein interaction modules perturbed by multiple genetic and epigenetic interacting mechanisms contributing to patient survival. Predictive power of these subnetwork-derived prognostic models is evaluated using Monte Carlo cross-validation and the concordance index (C-index). We uncover subnetwork biomarkers of low oncogenic GTPase activity, low ubiquitin/proteasome degradation, effective protection from oxidative damage, and tightly immune response are linked to better prognosis. Such a systematic approach to integrate massive amount of cancer genomics data into clinical practice for TNBC prognosis can effectively dissect the molecular mechanisms underlying TNBC patient outcomes and provide potential opportunities to optimize treatment and develop therapeutics.
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, PR China.
Fan
F
Zhang
Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Chunyan
C
Ren
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, PR China.
Hengqiang
H
Zhao
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, PR China.
Lei
L
Yang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, PR China.
Fei
F
Su
Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Ming-Ming
MM
Zhou
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, PR China.
Junwei
J
Han
Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Eric A
EA
Sobie
Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Martin J
MJ
Walsh
D014408:Biomarkers, Tumor; D005260:Female; D023281:Genomics; D006801:Humans; D009010:Monte Carlo Method; D011379:Prognosis; D060066:Protein Interaction Maps; D064726:Triple Negative Breast Neoplasms / Q000235:genetics / Q000401:mortality* / Q000628:therapy
D014408:Biomarkers, Tumor
GTPase; landmark for cancer prognosis; oxidative damage; triple-negative breast cancer; ubiquitination
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