A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

• Main Authors: 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
• Format: Article
• Language: English
• Published: Nature Publishing Group 2021-06-01
• Series: npj Precision Oncology
• Online Access: https://doi.org/10.1038/s41698-021-00191-2

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.