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Deciphering the response and resistance to immunecheckpoint inhibitors in lung cancer with artificial intelligence-based analysis: the pioneer and quantic joint-projects

hal.structure.identifierSimulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]
dc.contributor.authorCICCOLINI, Joseph
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBENZEKRY, Sébastien
hal.structure.identifierCentre de Recherche en Cancérologie de Marseille [CRCM]
hal.structure.identifierInstitut Gustave Roussy [IGR]
hal.structure.identifierDirection de la recherche clinique [Gustave Roussy]
dc.contributor.authorBARLESI, Fabrice
dc.date.accessioned2024-04-04T02:47:12Z
dc.date.available2024-04-04T02:47:12Z
dc.date.issued2020-08
dc.identifier.issn0007-0920
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191621
dc.description.abstractEnDespite striking results, clinical outcome with immune checkpoint inhibitors remains too often uncertain. This joint-project aims at generating dense longitudinal data in lung cancer patients undergoing anti-PD1 or anti-PDL1 therapy, alone or in combination with other anticancer agents. Mathematical modelling with mechanistic learning algorithms will be used next to decipher the mechanisms underlying response or resistance to immunotherapy. Ultimately, this project should help to better understand the mechanisms underlying resistance to immune checkpoint inhibitors and identify a serial of actionable items to increase the efficacy of immunotherapy.
dc.description.sponsorshipPrecision Immuno-Oncology for advanced Non small cell lung cancer patients with PD-1 ICI Resistance - ANR-17-RHUS-0007
dc.language.isoen
dc.publisherCancer Research UK
dc.title.enDeciphering the response and resistance to immunecheckpoint inhibitors in lung cancer with artificial intelligence-based analysis: the pioneer and quantic joint-projects
dc.typeArticle de revue
dc.identifier.doi10.1038/s41416-020-0918-3
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halSciences du Vivant [q-bio]/Cancer
dc.subject.halPhysique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an]
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologie
dc.subject.halSciences du Vivant [q-bio]/Sciences pharmaceutiques/Pharmacologie
dc.subject.halStatistiques [stat]/Applications [stat.AP]
bordeaux.journalBritish Journal of Cancer
bordeaux.page337-338
bordeaux.volume123
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue3
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03147110
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03147110v1
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