Unité de Bioinfomique, Institut de Cancérologie de l'Ouest, Bd Jacques Monod, 44805, Saint Herblain Cedex, France.
Unité Mixte de Génomique du Cancer, Institut de Cancérologie de l'Ouest - René Gauducheau, Bd Jacques Monod, 44805, Saint Herblain Cedex, France.
Proteomics. 2019 Nov;19(21-22):e1800484. doi: 10.1002/pmic.201800484. Epub 2019 May 7.
Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC). Therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study is to define robust TNBC subtypes with clinical relevance by means of proteomics and transcriptomics. As a first step, unsupervised analyses are conducted in parallel on proteomics and transcriptomics data of 83 TNBC tumors. Proteomics data unsupervised analysis did not permit separation of TNBC into different subtypes, whereas transcriptomics data are able to clearly and robustly identify three subtypes: molecular apocrine (C1), basal-like immune-suppressed (C2), and basal-like immune response (C3). Supervised analysis of proteomics data are then conducted based on transcriptomics subtyping. Thirty out of 62 proteins differentially expressed between C1, C2, and C3 belonged to biological categories which characterized these TNBC clusters: luminal and androgen-regulated proteins (C1), basal, invasion, and extracellular matrix (C2), and basal and immune response (interferon pathway and immunoglobulins) (C3). Although proteomics unsupervised analysis of TNBC tumors is unsuccessful at identifying clusters, the integrated approach is promising. Identification and measurement of 30 proteins strengthen subtyping of TNBC based on robust transcriptomics unsupervised analysis.
异质性和缺乏靶向治疗是三阴性乳腺癌 (TNBC) 精准治疗的两个主要障碍。因此，需要进行分子亚型分类和治疗途径鉴定，以优化医疗护理。本研究的目的是通过蛋白质组学和转录组学定义具有临床相关性的稳健 TNBC 亚型。作为第一步，对 83 例 TNBC 肿瘤的蛋白质组学和转录组学数据进行平行的无监督分析。蛋白质组学数据的无监督分析不能将 TNBC 分为不同的亚型，而转录组学数据能够清晰而稳健地识别出三种亚型：分子大汗腺型 (C1)、基底样免疫抑制型 (C2) 和基底样免疫反应型 (C3)。然后基于转录组学亚型对蛋白质组学数据进行有监督分析。在 C1、C2 和 C3 之间差异表达的 62 种蛋白质中有 30 种属于这些 TNBC 簇的生物学类别：腔面和雄激素调节蛋白 (C1)、基底、侵袭和细胞外基质 (C2) 以及基底和免疫反应 (干扰素途径和免疫球蛋白) (C3)。尽管 TNBC 肿瘤的蛋白质组学无监督分析无法识别聚类，但综合方法具有前景。对 30 种蛋白质的鉴定和测量加强了基于稳健转录组学无监督分析的 TNBC 亚型分类。
