Data integration and mechanistic modelling for breast cancer biology: Current state and future directions. (June 2022)

• Record Type: Journal Article
• Authors: Mo, Hanyi; Breitling, Rainer; Francavilla, Chiara; Schwartz, Jean-Marc
• Is Part Of: Current opinion in endocrine and metabolic research. Volume 24(2022)
• Publication Date: 2022-06
• Subjects: Breast cancer -- Precision oncology -- Multi-omics modelling -- Deep learning -- Network biology; Endocrine glands -- Diseases -- Periodicals; Metabolism -- Disorders -- Periodicals; Endocrine System Diseases; Metabolic Diseases; Endocrine glands -- Diseases; Metabolism -- Disorders; Periodical; Electronic journals; Periodicals; Electronic journals; 616.4
• DOI: 10.1016/j.coemr.2022.100350
• Languages: English
• ISSNs: 2451-9650

Abstract

Abstract: 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.