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乳腺癌的临床蛋白质组学揭示了一种新的乳腺癌分类层次。

Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification.

机构信息

Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Oncology Division, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.

出版信息

Cancer Res. 2018 Oct 15;78(20):6001-6010. doi: 10.1158/0008-5472.CAN-18-1079. Epub 2018 Aug 28.

Abstract

Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the molecular basis of breast cancer subtypes and aiming to associate them with clinical outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry-based proteomic analysis on more than 130 clinical breast samples to demonstrate intertumor heterogeneity across three breast cancer subtypes and healthy tissue. Unsupervised analysis identified four proteomic clusters, among them, one that represents a novel luminal subtype characterized by increased PI3K signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. These results demonstrate the importance of deep proteomic analysis, which may affect cancer treatment decision making. These findings utilize extensive proteomics to identify a novel luminal breast cancer subtype, highlighting the added value of clinical proteomics in breast cancer to identify unique features not observable by genomic approaches. .

摘要

乳腺癌分类一直是全球众多努力的焦点,分析乳腺癌亚型的分子基础,并旨在将其与临床结果相关联,从而改进当前的诊断常规。乳腺癌的基因组和转录组谱已经得到很好的建立,然而,蛋白质组学对这些谱的贡献尚未阐明。在这项工作中,我们利用基于质谱的蛋白质组学分析方法对超过 130 个临床乳腺癌样本进行了分析,以证明三种乳腺癌亚型和健康组织之间的肿瘤间异质性。无监督分析确定了四个蛋白质组学簇,其中之一代表了一种新的腔型亚型,其特征是 PI3K 信号增加。该亚型使用独立的基于蛋白质的数据集进一步验证,但在两个独立的转录组队列中未得到验证。这些结果表明了深入的蛋白质组学分析的重要性,这可能会影响癌症治疗决策。这些发现利用广泛的蛋白质组学来识别一种新的腔型乳腺癌亚型,突出了临床蛋白质组学在乳腺癌中的附加价值,以识别通过基因组方法无法观察到的独特特征。

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