In silico RNA isoform screening to identify potential cancer driver exons with therapeutic applications

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• dc.contributor.author Anglada-Girotto, Miquel
• dc.contributor.author Ciampi, Ludovica
• dc.contributor.author Bonnal, Sophie
• dc.contributor.author Head, Sarah A.
• dc.contributor.author Miravet Verde, Samuel, 1992-
• dc.contributor.author Serrano Pubull, Luis, 1982-
• dc.date.accessioned 2024-12-02T07:17:10Z
• dc.date.available 2024-12-02T07:17:10Z
• dc.date.issued 2024
• dc.description.abstract Alternative splicing is crucial for cancer progression and can be targeted pharmacologically, yet identifying driver exons genome-wide remains challenging. We propose identifying such exons by associating statistically gene-level cancer dependencies from knockdown viability screens with splicing profiles and gene expression. Our models predict the effects of splicing perturbations on cell proliferation from transcriptomic data, enabling in silico RNA screening and prioritizing targets for splicing-based therapies. We identified 1,073 exons impacting cell proliferation, many from genes not previously linked to cancer. Experimental validation confirms their influence on proliferation, especially in highly proliferative cancer cell lines. Integrating pharmacological screens with splicing dependencies highlights the potential driver exons affecting drug sensitivity. Our models also allow predicting treatment outcomes from tumor transcriptomes, suggesting applications in precision oncology. This study presents an approach to identifying cancer driver exon and their therapeutic potential, emphasizing alternative splicing as a cancer target.
• dc.description.sponsorship We thank Carolina Segura-Morales for the support in the experimental validations. We thank Javier Delgado and Damiano Cianferoni for their support in the structural analyzes. Also, we thank Juan Valcárcel, Manuel Irimia, Donate Weghorn, Xavier Hernandez-Alias, Marc Weber, and Reza Sodaei for the insightful discussions and feedback provided throughout the development of this project. We acknowledge the support of the Spanish Ministry of Science and Innovation through the Centro de Excelencia Severo Ochoa (CEX2020-001049-S, MCIN/AEI /10.13039/501100011033), and the Generalitat de Catalunya through the CERCA programme and to the EMBL partnership. We are grateful to the CRG Core Technologies Programme for their support and assistance in this work. The results shown here are in part based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga) and by Helsinki University (EGAD00001006456). Finally, we would like to thank the developer team behind VastDB as they created a resource that unifies and facilitates the splicing event-centric analysis of transcriptomes from the computational to the experimental sides. This project was funded by grants from the Plan Estatal de Investigación Científica y Técnica y de Innovación: PGC2018-101271-B-I00 and PID2021-122341NB-I00, (AEI/FEDER, UE) to L.S., and PID2021-122341NB-I00 project funded MICIU /AEI /10.13039/501100011033 / (FEDER, UE) to L.S.
• dc.format.mimetype application/pdf
• dc.identifier.citation Anglada-Girotto M, Ciampi L, Bonnal S, Head SA, Miravet-Verde S, Serrano L. In silico RNA isoform screening to identify potential cancer driver exons with therapeutic applications. Nat Commun. 2024 Aug 15;15(1):7039. DOI: 10.1038/s41467-024-51380-z
• dc.identifier.issn 2041-1723
• dc.identifier.uri http://hdl.handle.net/10230/68870
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Nat Commun. 2024 Aug 15;15(1):7039
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PGC2018-101271-B-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-122341NB-I00
• dc.rights © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Cancer genomics
• dc.subject.keyword Oncogenes
• dc.subject.keyword Prognostic markers
• dc.subject.keyword Target identification
• dc.title In silico RNA isoform screening to identify potential cancer driver exons with therapeutic applications
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion