Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer
Abstract Tumors harbor multiple genetic alterations, yet treatment decisions are commonly based on single biomarkers, leading to underutilization of genomic information by comprehensive molecular tests, uncertainty in clinical practice, and frequent treatment failures. Although molecular tumor board...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00943-4 |
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| author | Anna Dirner Dóra Kormos Dóra Lakatos Márton Bolyácz Mária Kocsis-Steinbach Gábor György Kalmár Dóra Tihanyi Ákos Takács Ákos Boldizsár Viktor Kardos Réka Szalkai-Dénes Barbara Vodicska Edit Várkondi Júlia Déri Gábor Pajkos Dóra Mathiász István Vályi-Nagy Richárd Schwáb Maud Kamal Christian Rolfo Arkadiusz Z. Dudek Christophe Le Tourneau Róbert Dóczi László Urbán István Peták |
| author_facet | Anna Dirner Dóra Kormos Dóra Lakatos Márton Bolyácz Mária Kocsis-Steinbach Gábor György Kalmár Dóra Tihanyi Ákos Takács Ákos Boldizsár Viktor Kardos Réka Szalkai-Dénes Barbara Vodicska Edit Várkondi Júlia Déri Gábor Pajkos Dóra Mathiász István Vályi-Nagy Richárd Schwáb Maud Kamal Christian Rolfo Arkadiusz Z. Dudek Christophe Le Tourneau Róbert Dóczi László Urbán István Peták |
| author_sort | Anna Dirner |
| collection | DOAJ |
| description | Abstract Tumors harbor multiple genetic alterations, yet treatment decisions are commonly based on single biomarkers, leading to underutilization of genomic information by comprehensive molecular tests, uncertainty in clinical practice, and frequent treatment failures. Although molecular tumor boards can assist personalized treatments, this process is not scalable or standardized, resulting in highly discordant recommendations. Validated digital solutions for personalized decision support are highly needed. The Digital Drug Assignment (DDA) system is a computational reasoning model that scores treatment options based on the full tumor genomic data. We retrospectively analyzed data of 111 lung cancer patients and found that high-score MTAs (1000≦DDA score) provided significant clinical benefit over other treatments, in terms of ORR, PFS, and OS. These results demonstrate that the DDA system is predictive of relative benefit of the various agents used in lung cancer care. Digital drug assignment can potentially address challenges with complex molecular profiles in routine clinical settings. |
| format | Article |
| id | doaj-art-c799fd8cf4e44a478aef754e169db4ff |
| institution | DOAJ |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-c799fd8cf4e44a478aef754e169db4ff2025-08-20T03:16:33ZengNature Portfolionpj Precision Oncology2397-768X2025-05-019111010.1038/s41698-025-00943-4Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancerAnna Dirner0Dóra Kormos1Dóra Lakatos2Márton Bolyácz3Mária Kocsis-Steinbach4Gábor György Kalmár5Dóra Tihanyi6Ákos Takács7Ákos Boldizsár8Viktor Kardos9Réka Szalkai-Dénes10Barbara Vodicska11Edit Várkondi12Júlia Déri13Gábor Pajkos14Dóra Mathiász15István Vályi-Nagy16Richárd Schwáb17Maud Kamal18Christian Rolfo19Arkadiusz Z. Dudek20Christophe Le Tourneau21Róbert Dóczi22László Urbán23István Peták24Genomate Health IncDepartment of Pulmonology, Mátraháza University and Teaching HospitalGenomate Health IncGenomate Health IncGenomate Health IncGenomate Health IncGenomate Health IncGenomate Health IncOncompass Medicine Hungary LtdDepartment of Pulmonology, Mátraháza University and Teaching HospitalGenomate Health IncGenomate Health IncOncompass Medicine Hungary LtdOncompass Medicine Hungary LtdOncompass Medicine Hungary LtdGenomate Health IncNational Hematology and Infectology Institute, Centrum Hospital of Southern PestMIND ClinicDepartment of Drug Development and Innovation (D3i), Institute CurieDivision of Medical Oncology, The James Comprehensive Cancer Center, Ohio State University, School of Medicine, ColumbusDivision of Oncology, Mayo ClinicDepartment of Drug Development and Innovation (D3i), Institute CurieGenomate Health IncDepartment of Pulmonology, Mátraháza University and Teaching HospitalGenomate Health IncAbstract Tumors harbor multiple genetic alterations, yet treatment decisions are commonly based on single biomarkers, leading to underutilization of genomic information by comprehensive molecular tests, uncertainty in clinical practice, and frequent treatment failures. Although molecular tumor boards can assist personalized treatments, this process is not scalable or standardized, resulting in highly discordant recommendations. Validated digital solutions for personalized decision support are highly needed. The Digital Drug Assignment (DDA) system is a computational reasoning model that scores treatment options based on the full tumor genomic data. We retrospectively analyzed data of 111 lung cancer patients and found that high-score MTAs (1000≦DDA score) provided significant clinical benefit over other treatments, in terms of ORR, PFS, and OS. These results demonstrate that the DDA system is predictive of relative benefit of the various agents used in lung cancer care. Digital drug assignment can potentially address challenges with complex molecular profiles in routine clinical settings.https://doi.org/10.1038/s41698-025-00943-4 |
| spellingShingle | Anna Dirner Dóra Kormos Dóra Lakatos Márton Bolyácz Mária Kocsis-Steinbach Gábor György Kalmár Dóra Tihanyi Ákos Takács Ákos Boldizsár Viktor Kardos Réka Szalkai-Dénes Barbara Vodicska Edit Várkondi Júlia Déri Gábor Pajkos Dóra Mathiász István Vályi-Nagy Richárd Schwáb Maud Kamal Christian Rolfo Arkadiusz Z. Dudek Christophe Le Tourneau Róbert Dóczi László Urbán István Peták Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer npj Precision Oncology |
| title | Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer |
| title_full | Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer |
| title_fullStr | Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer |
| title_full_unstemmed | Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer |
| title_short | Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer |
| title_sort | real world performance analysis of a universal computational reasoning model for precision oncology in lung cancer |
| url | https://doi.org/10.1038/s41698-025-00943-4 |
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