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

Translation to population health biomarkers clinical trials drug combination precision oncology synthetic lethality

Journal

Med (New York, N.Y.)
ISSN: 2666-6340
Titre abrégé: Med
Pays: United States
ID NLM: 101769215

Informations de publication

Date de publication:
12 Jan 2024
Historique:
received: 07 07 2023
revised: 10 09 2023
accepted: 13 12 2023
medline: 14 1 2024
pubmed: 14 1 2024
entrez: 13 1 2024
Statut: ppublish

Résumé

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. 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. 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. 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. 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).

Sections du résumé

BACKGROUND 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 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 RESULTS
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 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 BACKGROUND
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).

Identifiants

DOI: 10.1016/j.medj.2023.12.009 PMID: 38218178
pubmed: 38218178
pii: S2666-6340(23)00407-5
doi: 10.1016/j.medj.2023.12.009
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

73-89.e9

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

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.

Auteurs

Alejandro A Schäffer (AA)

Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.

Youngmin Chung (Y)

Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Ashwin V Kammula (AV)

Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.

Eytan 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.

Joo Sang 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.

Classifications MeSH