In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities
Open accessAbstract
Large efforts dedicated to detect somatic alterations across tumor genomes/exomes are expected to produce significant improvements in precision cancer medicine. However, high inter-tumor heterogeneity is a major obstacle to developing and applying therapeutic targeted agents to treat most cancer patients. Here, we offer a comprehensive assessment of the scope of targeted therapeutic agents in a large pan-cancer cohort. We developed an in silico prescription strategy based on identification of the driver alterations in each tumor and their druggability options. Although relatively few tumors are tractable by approved agents following clinical guidelines (5.9%), up to 40.2% could benefit from different repurposing options, and up to 73.3% considering treatments currently under clinical investigation. We also identified 80 therapeutically targetable cancer genes.
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The modeled mutations cover 7 of the 10 most frequent driver mutations reported by Rubio-Perez et al. (2015) and account for 22.1% of driver mutations observed in patient samples.
“The modeled mutations cover 7 of the 10 most frequent driver mutations reported by Rubio-Perez et al. (2015) and account for 22.1% of driver mutations observed in patient samples.”
From Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model (2018) · cites this paper as Rubio-Perez et al., 2015
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- Authors
- Rubio-Perez, C., Tamborero, D., Schroeder, M. P., Antolín, A. A., Deu-Pons, J., Perez-Llamas, C., Mestres, J., Gonzalez-Perez, A., & Lopez-Bigas, N.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2015-03
- Volume
- 27
- Issue
- 3
- Pages
- 382-396
- ISSN
- 1535-6108 (ISSN-L)
- OpenAlex
- W2082426444
- Cited by
- 296
- References
- 41
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 9 · 2023: 12 · 2022: 16 · 2021: 18 · 2020: 26 · 2019: 44 · 2018: 44 · 2017: 43 · 2016: 48 · 2015: 20 · 2014: 1 · 2012: 1
- FWCI
- 17.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cancer Cell (publishedVersion)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, other-oa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- targeted therapy, pan-cancer cohort, driver alterations, druggability, drug repurposing, precision oncology, intertumoral heterogeneity, clinical trial drugs
- MeSH terms
- Antineoplastic Agents, Clinical Protocols, Clinical Trials as Topic, Decision Making, Computer-Assisted, DNA Mutational Analysis, Humans, Neoplasms, Cohort Studies, Computational Biology, Precision Medicine, Drug Repositioning, Carcinogenesis
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