Browsing by Author "Lehrach, Hans"

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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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    Genomics and drug profiling of fatal TCF3-HLF-positive acute lymphoblastic leukemia identifies recurrent mutation patterns and therapeutic options
    (NATURE PUBLISHING GROUP, 2015-01-01) Fischer, Ute; Forster, Michael; Rinaldi, Anna; Risch, Thomas; Sungalee, Stephanie; Warnatz, Hans-Joerg; Bornhauser, Beat; Gombert, Michael; Kratsch, Christina; Stuetz, Adrian M.; Sultan, Marc; Tchinda, Joelle; Worth, Catherine L.; Amstislavskiy, Vyacheslav; Badarinarayan, Nandini; Baruchel, Andre; Bartram, Thies; Basso, Giuseppe; Canpolat, Cengiz; Cario, Gunnar; Cave, Helene; Dakaj, Dardane; Delorenzi, Mauro; Dobay, Maria Pamela; Eckert, Cornelia; Ellinghaus, Eva; Eugster, Sabrina; Frismantas, Viktoras; Ginzel, Sebastian; Haas, Oskar A.; Heidenreich, Olaf; Hemmrich-Stanisak, Georg; Hezaveh, Kebria; Hoell, Jessica I.; Hornhardt, Sabine; Husemann, Peter; Kachroo, Priyadarshini; Kratz, Christian P.; te Kronnie, Geertruy; Marovca, Blerim; Niggli, Felix; McHardy, Alice C.; Moorman, Anthony V.; Panzer-Gruemayer, Renate; Petersen, Britt S.; Raeder, Benjamin; Ralser, Meryem; Rosenstiel, Philip; Schaefer, Daniel; Schrappe, Martin; Schreiber, Stefan; Schuette, Moritz; Stade, Bjoern; Thiele, Ralf; von der Weid, Nicolas; Vora, Ajay; Zaliova, Marketa; Zhang, Langhui; Zichner, Thomas; Zimmermann, Martin; Lehrach, Hans; Borkhardt, Arndt; Bourquin, Jean-Pierre; Franke, Andre; Korbel, Jan O.; Stanulla, Martin; Yaspo, Marie-Laure
    TCF3-HLF-positive acute lymphoblastic leukemia (ALL) is currently incurable. Using an integrated approach, we uncovered distinct mutation, gene expression and drug response profiles in TCF3-HLF-positive and treatment-responsive TCF3-PBX1-positive ALL. We identified recurrent intragenic deletions of PAX5 or VPREB1 in constellation with the fusion of TCF3 and HLF. Moreover somatic mutations in the non-translocated allele of TCF3 and a reduction of PAX5 gene dosage in TCF3-HLF ALL suggest cooperation within a restricted genetic context. The enrichment for stem cell and myeloid features in the TCF3-HLF signature may reflect reprogramming by TCF3-HLF of a lymphoid-committed cell of origin toward a hybrid, drug-resistant hematopoietic state. Drug response profiling of matched patient-derived xenografts revealed a distinct profile for TCF3-HLF ALL with resistance to conventional chemotherapeutics but sensitivity to glucocorticoids, anthracyclines and agents in clinical development. Striking on-target sensitivity was achieved with the BCL2-specific inhibitor venetoclax (ABT-199). This integrated approach thus provides alternative treatment options for this deadly disease.