Breast cancer quantitative proteome and proteogenomic landscape
Johansson, Henrik J.; Socciarelli, Fabio; Vacanti, Nathaniel M.; Haugen, Mads Haugland; Zhu, Yafeng; Siavelis, Ioannis; Fernandez-Woodbridge, Alejandro; Aure, Miriam Ragle; Sennblad, Bengt; Vesterlund, Mattias; Branca, Rui M.; Orre, Lukas M.; Huss, Mikael; Fredlund, Erik; Beraki, Else; Garred, Øystein; Boekel, Jorrit; Sauer, Torill; Zhao, Wei; Nord, Silje; Höglander, Elen K.; Jans, Daniel C.; Brismar, Hjalmar; Haukaas, Tonje Husby; Bathen, Tone Frost; Schlichting, Ellen; Naume, Bjørn; Luders, Torben; Borgen, Elin; Kristensen, Vessela N.; Russnes, Hege Elisabeth Giercksky; Lingjærde, Ole Christian; Mills, Gordon B.; Sahlberg, Kristine Kleivi; Børresen-Dale, Anne-Lise; Lehtiö, Janne
Journal article, Peer reviewed
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OriginalversjonNature Research 10.1038/s41467-019-09018-y
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.