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

Summary 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 thes...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	British journal of cancer 2020-08, Vol.123 (3), p.337-338
Hauptverfasser:	Ciccolini, Joseph, Benzekry, Sébastien, Barlesi, Fabrice
Format:	Artikel
Sprache:	eng
Schlagworte:	631/114/2397 631/67/1059/2325 Algorithms Artificial Intelligence Biomedical and Life Sciences Biomedicine Cancer Research Carcinoma, Non-Small-Cell Lung - drug therapy Carcinoma, Non-Small-Cell Lung - genetics Comment Drug Resistance Drug Resistance, Neoplasm Epidemiology Humans Immune checkpoint inhibitors Immune Checkpoint Inhibitors - therapeutic use Immunotherapy Learning algorithms Life Sciences & Biomedicine Lung cancer Lung Neoplasms - drug therapy Lung Neoplasms - genetics Mathematical models Models, Theoretical Molecular Medicine Oncology PD-1 protein Precision Medicine Randomized Controlled Trials as Topic Science & Technology Treatment Outcome
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	Summary 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.
ISSN:	0007-0920 1532-1827
DOI:	10.1038/s41416-020-0918-3