Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic; Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic.
Department of Biology, Institute of Molecular Systems Biology, Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
Cell Rep. 2019 Jul 16;28(3):832-843.e7. doi: 10.1016/j.celrep.2019.06.046.
Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease-regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification.
准确的乳腺癌分类对于患者管理决策至关重要，并能够实现更精确的癌症治疗。在这里，我们提出了一种基于序贯窗口采集所有理论碎片离子谱（SWATH）质谱的定量蛋白质组学方法，并确定了用于乳腺癌分类的关键蛋白质。该研究基于代表五种传统乳腺癌亚型的 96 个组织样本。SWATH 蛋白质组型模式在很大程度上再现了这些亚型；然而，它们也揭示了传统亚型内的异质性，三阴性肿瘤的异质性最高。对蛋白质组型分类贡献最大的蛋白质包括 INPP4B、CDK1 和 ERBB2，并且与雌激素受体（ER）状态、肿瘤分级状态和 HER2 状态相关。尽管这三个关键蛋白质与转录水平具有很高的相关性（R>0.67），但总体相关性不超过 R=0.29，表明疾病调节基因的蛋白质水平测量具有价值。总的来说，这项研究强调了癌症组织蛋白质组学如何能够实现更准确的患者分层。
