Capitalizing on Synthetic Lethality of MYC to Treat Cancer in the Digital Age. (March 2021)

• Record Type: Journal Article
• Authors: Thng, Dexter Kai Hao; Toh, Tan Boon; Chow, Edward Kai-Hua
• Is Part Of: Trends in pharmacological sciences. Volume 42:Number 3(2021)
• Page Start: 166
• Page End: 182
• Publication Date: 2021-03
• Subjects: MYC -- synthetic lethality -- cancer -- drug targets -- computational; Pharmacology -- Periodicals; Pharmacology -- trends -- Periodicals; Pharmacologie -- Périodiques; Pharmacology; Electronic journals; Periodicals; 615.1
• DOI: 10.1016/j.tips.2020.11.014
• Languages: English
• ISSNs: 0165-6147

Abstract

Abstract : Deregulation of MYC is among the most frequent oncogenic drivers of cancer. Developing targeted therapies against MYC is, therefore, one of the most critical unmet needs of cancer therapy. Unfortunately, MYC has been labelled as undruggable due to the lack of success in developing clinically relevant MYC-targeted therapies. Synthetic lethality is a promising approach that targets MYC-dependent vulnerabilities in cancer. However, translating the synthetic lethality targets to the clinics is still challenging due to the complex nature of cancers. This review highlights the most promising mechanisms of MYC synthetic lethality and how these discoveries are currently translated into the clinic. Finally, we discuss how in silico computational platforms can improve clinical success of synthetic lethality-based therapy. Highlights: Synthetic lethality can be capitalised on to indirectly target the undruggable MYC oncoprotein and impede tumourigenesis in MYC-driven tumours. Synthetic lethal targets of MYC include regulators of MYC expression, regulators of MYC turnover, and functional pathways downstream of MYC, such as cellular metabolism and proliferation. Development of inhibitors against synthetic lethal targets of MYC have successfully entered the clinical phase, albeit in the early stages with foreseeable challenges in the near future. In silico platforms can be used to streamline and automate the drug development pipeline by leveraging on publicly available databases and computational algorithms.