A systematic analysis of the landscape of synthetic lethality-driven precision oncology.

Autor:	Schäffer AA; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Chung Y; Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea., Kammula AV; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA., Ruppin E; Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. Electronic address: eytan.ruppin@nih.gov., Lee JS; Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea; Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; Department of Digital Health & Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Samsung Medical Center, Sungkyunkwan University, Seoul 06351, Republic of Korea. Electronic address: joosang.lee@skku.edu.
Jazyk:	angličtina
Zdroj:	Med (New York, N.Y.) [Med] 2024 Jan 12; Vol. 5 (1), pp. 73-89.e9.
DOI:	10.1016/j.medj.2023.12.009
Abstrakt:	Background: Synthetic lethality (SL) denotes a genetic interaction between two genes whose co-inactivation is detrimental to cells. Because more than 25 years have passed since SL was proposed as a promising way to selectively target cancer vulnerabilities, it is timely to comprehensively assess its impact so far and discuss its future. Methods: We systematically analyzed the literature and clinical trial data from the PubMed and Trialtrove databases to portray the preclinical and clinical landscape of SL oncology. Findings: We identified 235 preclinically validated SL pairs and found 1,207 pertinent clinical trials, and the number keeps increasing over time. About one-third of these SL clinical trials go beyond the typically studied DNA damage response (DDR) pathway, testifying to the recently broadening scope of SL applications in clinical oncology. We find that SL oncology trials have a greater success rate than non-SL-based trials. However, about 75% of the preclinically validated SL interactions have not yet been tested in clinical trials. Conclusions: Dissecting the recent efforts harnessing SL to identify predictive biomarkers, novel therapeutic targets, and effective combination therapy, our systematic analysis reinforces the hope that SL may serve as a key driver of precision oncology going forward. Funding: Funded by the Samsung Research Funding & Incubation Center of Samsung Electronics, the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Republic of Korea government (MSIT), the Kwanjeong Educational Foundation, the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), and Center for Cancer Research (CCR). Competing Interests: Declaration of interests E.R. is a co-founder of Medaware, Ltd.; Metabomed, Ltd.; and Pangea Biomed, Ltd. (divested from the latter). E.R. serves as a non-paid scientific consultant to Pangea Biomed, Ltd., a company developing a precision oncology SL-based multiomics approach. J.S.L. is a scientific consultant at Pangea Biomed, Ltd., and a founder of NGen Biointelligence, Inc. (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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