Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, 171 21, Solna, Sweden.
Cornell University, Division of Nutritional Sciences, Ithaca, NY, 14853, USA.
Nat Commun. 2019 Apr 8;10(1):1600. doi: 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.
在过去的几十年中，分子特征描述已彻底改变了乳腺癌（BC）的研究和治疗方法。在此呈现的、对所有肿瘤中定量的 9995 种蛋白的无偏分析，首次重现了 BC 亚型。此外，预后不良的基底样和管腔 B 型肿瘤可进一步通过免疫成分浸润进行细分，表明目前的分类并不完整。基于蛋白质组的网络可区分乳腺癌肿瘤组的功能蛋白模块，表皮生长因子受体（EGFR）和间质表皮转化因子（MET）的共表达标志着正常样肿瘤的原位导管癌区域，并有助于更准确地分类这一定义不明确的亚型。包含在预后 mRNA 面板中的基因的 mRNA-蛋白相关性显著高于平均值，并且基因拷贝数改变在蛋白水平上受到抑制；这突显了蛋白质组定量在预后和表型分类方面的价值。此外，还鉴定了映射到非编码基因组区域的蛋白质产物；突出了一类新的肿瘤特异性免疫治疗靶标。
