Faculty of Medicine, The University of Queensland, Centre for Clinical Research, Royal Brisbane and Women's Hospital, Herston, QLD, Australia.
Pathology Queensland, Royal Brisbane and Women's Hospital, Herston, QLD, Australia.
Adv Exp Med Biol. 2019;1152:75-104. doi: 10.1007/978-3-030-20301-6_6.
Breast cancer encompasses a heterogeneous collection of neoplasms with diverse morphologies, molecular phenotypes, responses to therapy, probabilities of relapse and overall survival. Traditional histopathological classification aims to categorise tumours into subgroups to inform clinical management decisions, but the diversity within these subgroups remains considerable. Application of massively parallel sequencing technologies in breast cancer research has revealed the true depth of variability in terms of the genetic, phenotypic, cellular and microenvironmental constitution of individual tumours, with the realisation that each tumour is exquisitely unique. This poses great challenges in predicting the development of drug resistance, and treating metastatic disease. Central to achieving fully personalised clinical management is translating new insights on breast cancer heterogeneity into the clinical setting, to evolve the taxonomy of breast cancer and improve risk stratification.
乳腺癌包含了一组具有不同形态、分子表型、治疗反应、复发概率和总体生存的异质性肿瘤。传统的组织病理学分类旨在将肿瘤分为亚组，为临床管理决策提供信息，但这些亚组内的异质性仍然很大。大规模平行测序技术在乳腺癌研究中的应用揭示了个体肿瘤在遗传、表型、细胞和微环境组成方面的真正多样性，也使人们认识到每个肿瘤都是极其独特的。这给预测耐药性的发展和治疗转移性疾病带来了巨大的挑战。实现完全个性化临床管理的关键是将乳腺癌异质性的新见解转化到临床实践中，以发展乳腺癌的分类法并改善风险分层。
