Mo Hanyi, Breitling Rainer, Francavilla Chiara, Schwartz Jean-Marc
Division of Evolution, Infection and Genomics, School of Biological Sciences, University of Manchester, Manchester, M13 9PT, UK.
Division of Molecular and Cellular Function, School of Biological Sciences, University of Manchester, Manchester, M13 9PT, UK.
Curr Opin Endocr Metab Res. 2022 Jun;24:None. doi: 10.1016/j.coemr.2022.100350.
Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of 'big data' and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.
乳腺癌是全球威胁女性健康的最常见癌症之一。可用的治疗选择有限、频繁复发以及耐药性加剧了乳腺癌患者的预后。因此，迫切需要研究新治疗方案的方法，同时考虑到乳腺癌巨大的分子异质性。分子谱分析技术的最新进展，包括基因组学、表观基因组学、转录组学、蛋白质组学和代谢组学数据，使得能够在多个组学相互作用网络层面研究乳腺癌生物学。系统生物学方法，包括“大数据”的计算推断和特定通路的机制建模，正在兴起，以识别乳腺癌亚型特征的潜在新组合以及更多样化的靶向治疗方法。
