Institut Català de la Salut
[Farina-Morillas M, Ollé-Monràs L, Maas SC, Seoane JA] Cancer Computational Biology Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [de Rojas-P I, Segura MF] Grup de Recerca de Càncer i Malalties Hematològiques Infantils, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Universitat Autònoma de Barcelona, Barcelona, Spain
Vall d'Hebron Barcelona Hospital Campus
2025-11-03T13:40:00Z
2025-11-03T13:40:00Z
2025
Synthetic lethality; Chromatin remodeling; Epidrugs
Letalidad sintética; Remodelación de la cromatina; Fármacos epidemiológicos
Letalitat sintètica; Remodelació de la cromatina; Fàrmacs epidemiològics
Cancer treatment is an ongoing challenge, as directly targeting oncogenic drivers is often unfeasible in many patients due to the lack of druggable targets. This has led to the exploration of alternative strategies, such as exploiting synthetic lethality (SL) relationships between genes. SL facilitates the indirect targeting of oncogenic drivers, as exemplified by the clinical success of PARP inhibitors against BRCA-mutated tumors. Advances in high-throughput perturbation screens and multi-omics technologies have deepened our understanding of SL relationships, while computational models enhance SL predictions to better reflect biological complexity. However, while numerous experimental and computational methods have been developed to identify SL interactions, difficulties remain in translating these findings into clinical applications.This review combines recent progress on SL relationships in cancer with emerging insights into epigenetic regulation, highlighting how epigenetic drugs (epidrugs) can provide new opportunities for targeted interventions, offering a way to minimize off-target effects and enhance therapeutic precision. To advance SL-based therapies, efforts must focus not only on identifying new SL interactions but also on consolidating existing knowledge and integrating experimental and computational approaches to characterize the vulnerabilities of cancer cells. Strengthening this foundation will be critical for the effective development of SL-based cancer treatments.
Article
Versió publicada
Anglès
Càncer - Tractament; Aprenentatge automàtic; Regulació genètica; Medicaments antineoplàstics - Ús terapèutic; DISEASES::Neoplasms; Other subheadings::Other subheadings::Other subheadings::/drug therapy; PHENOMENA AND PROCESSES::Genetic Phenomena::Gene Expression Regulation::Epigenesis, Genetic; CHEMICALS AND DRUGS::Chemical Actions and Uses::Pharmacologic Actions::Therapeutic Uses::Antineoplastic Agents; PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning; ENFERMEDADES::neoplasias; Otros calificadores::Otros calificadores::Otros calificadores::/farmacoterapia; FENÓMENOS Y PROCESOS::fenómenos genéticos::regulación de la expresión génica::epigénesis genética; COMPUESTOS QUÍMICOS Y DROGAS::acciones y usos químicos::acciones farmacológicas::usos terapéuticos::antineoplásicos; FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático
Taylor & Francis
Epigenomics;17(15)
https://doi.org/10.1080/17501911.2025.2548756
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/