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Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial
Joseph Ciccolini1,2, Sébastien Benzekry3, Fabrice Barlesi4,5
1Centre de Recherche en Cancérologie de Marseille, SMARTc, Aix-Marseille Université, Inserm U1068, CNRS UMR 7258, Institut Paoli Calmettes, 13009, Marseille, France. joseph.ciccolini@ap-hm.fr.
Abstract:
This project aims to generate dense longitudinal data in lung cancer patients undergoing anti-PD1/PDL1 therapy. Mathematical modelling with mechanistic learning algorithms will help decipher the mechanisms underlying the response or resistance to immunotherapy. A better understanding of these mechanisms should help identifying actionable items to increase the efficacy of immune-checkpoint inhibitors.
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