Mukherjee A, Russell R, Chin Suet-Feung, Liu B, Rueda O M, Ali H R, Turashvili G, Mahler-Araujo B, Ellis I O, Aparicio S, Caldas C, Provenzano E
1Department of Histopathology, Division of Cancer and Stem cells, School of Medicine, University of Nottingham, Nottingham, UK.
2Nottingham University Hospitals NHS Trust, Nottingham, UK.
NPJ Breast Cancer. 2018 Mar 7;4:5. doi: 10.1038/s41523-018-0056-8. eCollection 2018.
The integration of genomic and transcriptomic profiles of 2000 breast tumours from the METABRIC [Molecular Taxonomy of Breast Cancer International Consortium] cohort revealed ten subtypes, termed integrative clusters (IntClust/s), characterised by distinct genomic drivers. Central histopathology ( = 1643) review was undertaken to explore the relationship between these ten molecular subtypes and traditional clinicopathological features. IntClust subtypes were significantly associated with histological type, tumour grade, receptor status, and lymphocytic infiltration ( < 0.0001). Lymph node status and Nottingham Prognostic Index [NPI] categories were also significantly associated with IntClust subtype. IntClust 3 was enriched for tubular and lobular carcinomas, the latter largely accounting for the association with mutations in this cluster. Mucinous carcinomas were not present in IntClusts 5 or 10, but did not show an association with any of the remaining IntClusts. In contrast, medullary-like cancers were associated with IntClust 10 (15/26). Hormone receptor-positive tumours were scattered across all IntClusts. IntClust 5 was dominated by HER2 positivity (127/151), including both hormone receptor-positive (60/72) and hormone receptor-negative tumours (67/77). Triple-negative tumours comprised the majority of IntClust 10 (132/159) and around a quarter of IntClust 4 (52/217). Whilst the ten IntClust subtypes of breast cancer show characteristic patterns of association with traditional clinicopathological variables, no IntClust can be adequately identified by these variables alone. Hence, the addition of genomic stratification has the potential to enhance the biological relevance of the current clinical evaluation and facilitate genome-guided therapeutic strategies.
对来自METABRIC(乳腺癌国际分子分类联盟)队列的2000例乳腺肿瘤的基因组和转录组图谱进行整合,揭示了10种亚型,称为整合簇(IntClust/s),其特征为具有不同的基因组驱动因素。进行了中心组织病理学(n = 1643)审查,以探讨这10种分子亚型与传统临床病理特征之间的关系。IntClust亚型与组织学类型、肿瘤分级、受体状态和淋巴细胞浸润显著相关(P < 0.0001)。淋巴结状态和诺丁汉预后指数(NPI)类别也与IntClust亚型显著相关。IntClust 3富含管状和小叶癌,后者在很大程度上解释了该簇与 突变的关联。黏液癌在IntClust 5或10中不存在,但与其余任何IntClust均无关联。相比之下,髓样癌与IntClust 10相关(15/26)。激素受体阳性肿瘤分散在所有IntClust中。IntClust 5以HER2阳性为主(127/151),包括激素受体阳性(60/72)和激素受体阴性肿瘤(67/77)。三阴性肿瘤占IntClust 10的大多数(132/159),约占IntClust 4的四分之一(52/217)。虽然乳腺癌的10种IntClust亚型显示出与传统临床病理变量相关的特征模式,但仅通过这些变量无法充分识别任何IntClust。因此,增加基因组分层有可能增强当前临床评估的生物学相关性,并促进基因组指导的治疗策略。