Johansson HJ, Socciarelli F, Vacanti NM, Haugen MH, Zhu Y, Siavelis I, Fernandez-Woodbridge A, Aure MR, Sennblad B, Vesterlund M, Branca RM, Orre LM, Huss M, Fredlund E, Beraki E, Garred Ø, Boekel J, Sauer T, Zhao W, Nord S, Höglander EK, Jans DC, Brismar H, Haukaas TH, Bathen TF, Schlichting E, Naume B, Consortia Oslo Breast Cancer Research Consortium (OSBREAC) , Luders T, Borgen E, Kristensen VN, Russnes HG, Lingjærde OC, Mills GB, Sahlberg KK, Børresen-Dale AL, Lehtiö J
Nat Commun 10 (1) 1600 [2019-04-08; online 2019-04-08]
In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets.
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