Mariano Natasha C, Marotti Jonathan D, Chen Youdinghuan, Karakyriakou Barbara, Salgado Roberto, Christensen Brock C, Miller Todd W, Kettenbach Arminja N
Department of Biochemistry and Cell Biology, Hanover, NH, USA.
Department of Pathology and Laboratory Medicine, Lebanon, NH, USA.
NPJ Precis Oncol. 2025 Apr 24;9(1):117. doi: 10.1038/s41698-025-00907-8.
Triple-negative breast cancer (TNBC) accounts for approximately 15% of all Breast Cancer (BC) cases with poorer prognosis and clinical outcomes compared to other BC subtypes due to greater tumor heterogeneity and few therapeutically targetable oncogenic drivers. To reveal actionable pathways for anti-cancer treatment, we use a proteomic approach to quantitatively compare the abundances of 6306 proteins across 55 formalin-fixed and paraffin-embedded (FFPE) TNBC tumors. We identified four major TNBC clusters by unsupervised clustering analysis of protein abundances. Analyses of clinicopathological characteristics revealed associations between the proteomic profiles and clinical phenotypes exhibited by each subtype. We validate the findings by inferring immune and stromal cell type composition from genome-wide DNA methylation profiles. Finally, quantitative proteomics on TNBC cell lines was conducted to identify in vitro models for each subtype. Collectively, our data provide subtype-specific insights into molecular drivers, clinicopathological phenotypes, tumor microenvironment (TME) compositions, and potential pharmacologic vulnerabilities for further investigations.
三阴性乳腺癌（TNBC）约占所有乳腺癌（BC）病例的15%，与其他BC亚型相比，其预后和临床结果较差，这是由于肿瘤异质性更高且可治疗的致癌驱动因素较少。为了揭示抗癌治疗的可行途径，我们采用蛋白质组学方法，对55个福尔马林固定石蜡包埋（FFPE）的TNBC肿瘤中的6306种蛋白质丰度进行定量比较。通过对蛋白质丰度进行无监督聚类分析，我们确定了四个主要的TNBC簇。临床病理特征分析揭示了蛋白质组学特征与各亚型所表现出的临床表型之间的关联。我们通过从全基因组DNA甲基化谱推断免疫和基质细胞类型组成来验证这些发现。最后，对TNBC细胞系进行了定量蛋白质组学分析，以确定每种亚型的体外模型。总体而言，我们的数据为分子驱动因素、临床病理表型、肿瘤微环境（TME）组成以及潜在的药物易感性提供了亚型特异性见解，以供进一步研究。
