A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial
Abstract Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of t...
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Nature Portfolio
2021
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oai:doaj.org-article:25368c6568c249e3961734d462c716412021-12-02T16:05:49ZA computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial10.1038/s41698-021-00191-22397-768Xhttps://doaj.org/article/25368c6568c249e3961734d462c716412021-06-01T00:00:00Zhttps://doi.org/10.1038/s41698-021-00191-2https://doaj.org/toc/2397-768XAbstract Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.Istvan PetakMaud KamalAnna DirnerIvan BiecheRobert DocziOdette MarianiPeter FilotasAnne SalomonBarbara VodicskaVincent ServoisEdit VarkondiDavid GentienDora TihanyiPatricia TrescaDora LakatosNicolas ServantJulia DeriPauline du RusquecCsilla HegedusDiana Bello RoufaiRichard SchwabCelia DupainIstvan T. Valyi-NagyChristophe Le TourneauNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Precision Oncology, Vol 5, Iss 1, Pp 1-11 (2021) |
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
| spellingShingle |
Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Istvan Petak Maud Kamal Anna Dirner Ivan Bieche Robert Doczi Odette Mariani Peter Filotas Anne Salomon Barbara Vodicska Vincent Servois Edit Varkondi David Gentien Dora Tihanyi Patricia Tresca Dora Lakatos Nicolas Servant Julia Deri Pauline du Rusquec Csilla Hegedus Diana Bello Roufai Richard Schwab Celia Dupain Istvan T. Valyi-Nagy Christophe Le Tourneau A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial |
| description |
Abstract Precision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology. |
| format |
article |
| author |
Istvan Petak Maud Kamal Anna Dirner Ivan Bieche Robert Doczi Odette Mariani Peter Filotas Anne Salomon Barbara Vodicska Vincent Servois Edit Varkondi David Gentien Dora Tihanyi Patricia Tresca Dora Lakatos Nicolas Servant Julia Deri Pauline du Rusquec Csilla Hegedus Diana Bello Roufai Richard Schwab Celia Dupain Istvan T. Valyi-Nagy Christophe Le Tourneau |
| author_facet |
Istvan Petak Maud Kamal Anna Dirner Ivan Bieche Robert Doczi Odette Mariani Peter Filotas Anne Salomon Barbara Vodicska Vincent Servois Edit Varkondi David Gentien Dora Tihanyi Patricia Tresca Dora Lakatos Nicolas Servant Julia Deri Pauline du Rusquec Csilla Hegedus Diana Bello Roufai Richard Schwab Celia Dupain Istvan T. Valyi-Nagy Christophe Le Tourneau |
| author_sort |
Istvan Petak |
| title |
A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial |
| title_short |
A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial |
| title_full |
A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial |
| title_fullStr |
A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial |
| title_full_unstemmed |
A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial |
| title_sort |
computational method for prioritizing targeted therapies in precision oncology: performance analysis in the shiva01 trial |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/25368c6568c249e3961734d462c71641 |
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