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Permanent URI for this collectionhttps://hdl.handle.net/11443/932

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Author: search.filters.author.Sezerman, O. UgurSubject: search.filters.subject.Cancer

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    A framework for validating AI in precision medicine: considerations from the European ITFoC consortium
    (BMC, 2021-01-01) Tsopra, Rosy; Fernandez, Xose; Luchinat, Claudio; Alberghina, Lilia; Lehrach, Hans; Vanoni, Marco; Dreher, Felix; Sezerman, O. Ugur; Cuggia, Marc; de Tayrac, Marie; Miklasevics, Edvins; Itu, Lucian Mihai; Geanta, Marius; Ogilvie, Lesley; Godey, Florence; Boldisor, Cristian Nicolae; Campillo-Gimenez, Boris; Cioroboiu, Cosmina; Ciusdel, Costin Florian; Coman, Simona; Cubelos, Oliver Hijano; Itu, Alina; Lange, Bodo; Le Gallo, Matthieu; Lespagnol, Alexandra; Mauri, Giancarlo; Soykam, H. Okan; Rance, Bastien; Turano, Paola; Tenori, Leonardo; Vignoli, Alessia; Wierling, Christoph; Benhabiles, Nora; Burgun, Anita
    Background Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European ``ITFoC (Information Technology for the Future Of Cancer){''} consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the ``ITFoC Challenge{''}. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.
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    driveR: a novel method for prioritizing cancer driver genes using somatic genomics data
    (BMC, 2021-01-01) Ulgen, Ege; Sezerman, O. Ugur
    Background: Cancer develops due to ``driver{''} alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results: Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651-0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0-1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions: This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.