CICCOLINI, Joseph; BENZEKRY, Sébastien; BARLESI, Fabrice

doi:10.1038/s41416-020-0918-3

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CICCOLINI, Joseph
Simulation and Modeling of Adaptive Response for Therapeutics in Cancer [SMARTc]

BENZEKRY, Sébastien
Modélisation Mathématique pour l'Oncologie [MONC]
Institut de Mathématiques de Bordeaux [IMB]

BARLESI, Fabrice
Centre de Recherche en Cancérologie de Marseille [CRCM]
Institut Gustave Roussy [IGR]
Direction de la recherche clinique [Gustave Roussy]

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Este ítem está publicado en

British Journal of Cancer. 2020-08, vol. 123, n° 3, p. 337-338

Cancer Research UK

Resumen en inglés

Despite 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.< Leer menos