Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway; Department of Pathology, Oslo University Hospital Radiumhospitalet, Oslo, Norway.
Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway; Department of Computer Science, University of Oslo, Oslo, Norway.
Am J Pathol. 2017 Oct;187(10):2152-2162. doi: 10.1016/j.ajpath.2017.04.022. Epub 2017 Jul 19.
Breast carcinomas can be stratified into different entities based on clinical behavior, histologic features, and/or by biological properties. A classification of breast cancer should be based on underlying biology, which we know must be determined by the somatic genomic landscape of mutations. Moreover, because the latest generations of anticancer agents are founded on biological mechanisms, a detailed molecular stratification is a requirement for appropriate clinical management. Such stratification, based on genomic drivers, will be important for selecting patients for clinical trials. It will also facilitate the discovery of novel drivers, the study of tumor evolution, and the identification of mechanisms of treatment resistance. Assays for risk stratification have focused mainly on response prediction to existing treatment regimens. Molecular stratification based on gene expression profiling revealed that breast cancers could be classified in so-called intrinsic subtypes (luminal A and B, HER2-enriched, basal-like, and normal-like), which mostly corresponded to hormone receptor and HER2 status, and further stratified luminal tumors based on proliferation. The realization that a significant proportion of the gene expression landscape is determined by the somatic copy number alterations that drive expression in cis led to the newer classification of breast cancers into integrative clusters. This stratification of breast cancers into integrative clusters reveals prototypical patterns of single-nucleotide variants and is associated with distinct clinical courses and response to therapy.
乳腺癌可以根据临床行为、组织学特征和/或生物学特性分为不同的实体。乳腺癌的分类应基于潜在的生物学特性,我们知道这必须由突变的体细胞基因组景观决定。此外,由于最新一代的抗癌药物是基于生物学机制开发的,因此详细的分子分层是进行适当临床管理的要求。这种基于基因组驱动因素的分层对于选择患者进行临床试验非常重要。它还有助于发现新的驱动因素、研究肿瘤进化以及确定治疗耐药的机制。风险分层的检测主要集中在对现有治疗方案的反应预测上。基于基因表达谱的分子分层表明,乳腺癌可以分为所谓的固有亚型(luminal A 和 B、HER2 富集型、基底样和正常样),这些亚型主要与激素受体和 HER2 状态相对应,并进一步根据增殖对 luminal 肿瘤进行分层。认识到基因表达图谱的很大一部分是由顺式驱动表达的体细胞拷贝数改变决定的,这导致了对乳腺癌的新的整合聚类分类。这种乳腺癌的整合聚类分层揭示了典型的单核苷酸变异模式,并与不同的临床病程和对治疗的反应相关。