Proteomic maps of breast cancer subtypes

Systems-wide profiling of breast cancer has so far built on RNA and DNA analysis by microarray and sequencing techniques. Dramatic developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analyzed 40 estrogen receptor positive (luminal), Her2 positive and triple negative breast tumors and reached a quantitative depth of more than 10,000 proteins. Comparison to mRNA classifiers revealed multiple discrepancies between proteins and mRNA markers of breast cancer subtypes. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a 19-protein predictive signature, which discriminates between the breast cancer subtypes, through Support Vector Machine (SVM)-based classification and feature selection. The deep proteome profiles also revealed novel features of breast cancer subtypes, which may be the basis for future development of subtype specific therapeutics.迄今为止，乳腺癌的系统级组学分析均依托基于微阵列与测序技术的RNA和DNA分析展开。蛋白质组学技术的飞速发展如今已可实现临床样本的深度谱分析，且具备极高的蛋白鉴定与定量准确性。本研究共分析了40例雌激素受体阳性（luminal，管腔型）、Her2阳性以及三阴性乳腺肿瘤，实现了超过10000种蛋白质的定量深度覆盖。与mRNA分类器的比对分析显示，乳腺癌亚型的蛋白标志物与mRNA标志物之间存在多处差异。该蛋白质组谱分析揭示了乳腺癌亚型间的功能差异，这些差异与能量代谢、细胞增殖、mRNA翻译及细胞间通讯密切相关。此外，本研究通过基于支持向量机（Support Vector Machine, SVM）的分类及特征选择方法，构建了可区分乳腺癌亚型的19蛋白预测特征集。该深度蛋白质组谱分析还揭示了乳腺癌亚型的全新特征，这些特征有望成为未来开发亚型特异性治疗手段的基础。