iTRAQ-based quantitative proteomic analysis strengthens transcriptomic subtyping of triple-negative breast cancer tumors.

Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC). Therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study was to define robust TNBC subtypes with clinical relevance by means of proteomics and transcriptomics. As a first step, unsupervised analyses were conducted in parallel on proteomics and transcriptomics data of 83 TNBC tumors. Proteomics data unsupervised analysis did not permit separation of TNBC into different subtypes, whereas transcriptomics data was able to clearly and robustly identify three subtypes: molecular apocrine (C1), basal-like immune-suppressed (C2) and basal-like immune response (C3). Supervised analysis of proteomics data was then conducted based on transcriptomics subtyping. Thirty out of 62 proteins differentially expressed between C1, C2 and C3 belonged to biological categories which characterized these TNBC clusters: luminal and androgen-regulated proteins (C1), basal, invasion and extracellular matrix (C2) and basal and immune response (interferon pathway and immunoglobulins) (C3). Although proteomics unsupervised analysis of TNBC tumors was unsuccessful at identifying clusters, our integrated approach is promising. Identification and measurement of 30 proteins strengthen subtyping of TNBC based on robust transcriptomics unsupervised analysis. This article is protected by copyright. All rights reserved.