Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC

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 mechan...

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Autores principales: Ciccolini, Joseph, Benzekry, Sébastien, Barlesi, Fabrice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403333/
https://www.ncbi.nlm.nih.gov/pubmed/32541872
http://dx.doi.org/10.1038/s41416-020-0918-3
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author Ciccolini, Joseph
Benzekry, Sébastien
Barlesi, Fabrice
author_facet Ciccolini, Joseph
Benzekry, Sébastien
Barlesi, Fabrice
author_sort Ciccolini, Joseph
collection PubMed
description 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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spelling pubmed-74033332021-06-16 Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC Ciccolini, Joseph Benzekry, Sébastien Barlesi, Fabrice Br J Cancer Comment 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. Nature Publishing Group UK 2020-06-16 2020-08-04 /pmc/articles/PMC7403333/ /pubmed/32541872 http://dx.doi.org/10.1038/s41416-020-0918-3 Text en © Cancer Research UK 2020 https://creativecommons.org/licenses/by/4.0/Note This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution 4.0 International (CC BY 4.0).
spellingShingle Comment
Ciccolini, Joseph
Benzekry, Sébastien
Barlesi, Fabrice
Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC
title Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC
title_full Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC
title_fullStr Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC
title_full_unstemmed Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC
title_short Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC
title_sort deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when pioneer meets quantic
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403333/
https://www.ncbi.nlm.nih.gov/pubmed/32541872
http://dx.doi.org/10.1038/s41416-020-0918-3
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