文章：

整合蛋白质基因组学分析揭示三阴性乳腺癌亚型中独特的潜在可操作治疗弱点

Integrated Proteogenomic Analysis Reveals Distinct Potentially Actionable Therapeutic Vulnerabilities in Triple-Negative Breast Cancer Subtypes

原文发布日期：25 January 2024

DOI: 10.3390/cancers16030516

类型: Article

开放获取: 是

英文摘要：

Triple-negative breast cancer (TNBC) is characterized by an aggressive clinical presentation and a paucity of clinically actionable genomic alterations. Here, we utilized the Cancer Genome Atlas (TCGA) to explore the proteogenomic landscape of TNBC subtypes to see whether genomic alterations can be inferred from proteomic data. We found only 4% of the protein level changes are explained by mutations, while 21% of the protein and 35% of the transcriptomics changes were determined by copy number alterations (CNAs). We found tighter coupling between proteome and genome in some genes that are predicted to be the targets of drug inhibitors, including CDKs, PI3K, tyrosine kinase (TKI), and mTOR. The validation of our proteogenomic workflow using mass spectrometry Clinical Proteomic Tumor Analysis Consortium (MS-CPTAC) data also demonstrated the highest correlation between protein–RNA–CNA. The integrated proteogenomic approach helps to prioritize potentially actionable targets and may enable the acceleration of personalized cancer treatment.

摘要翻译： 

三阴性乳腺癌（TNBC）具有侵袭性临床表现和临床可操作基因组改变稀少的特点。本研究利用癌症基因组图谱（TCGA）数据库探索TNBC亚型的蛋白质组-基因组学特征，以验证能否通过蛋白质组数据推断基因组变异。研究发现仅有4%的蛋白质水平变化可由基因突变解释，而21%的蛋白质变化与35%的转录组变化由拷贝数变异（CNAs）决定。在CDKs、PI3K、酪氨酸激酶（TKI）和mTOR等药物抑制剂预测靶点基因中，观察到蛋白质组与基因组之间存在更紧密的耦合关系。采用临床蛋白质组肿瘤分析联盟质谱数据（MS-CPTAC）验证蛋白质组-基因组工作流程，进一步证实蛋白质-RNA-CNA三者间存在高度相关性。这种整合的蛋白质组-基因组学方法有助于优先筛选潜在可操作靶点，可能推动个性化癌症治疗的发展进程。

原文链接：

Integrated Proteogenomic Analysis Reveals Distinct Potentially Actionable Therapeutic Vulnerabilities in Triple-Negative Breast Cancer Subtypes