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 these mechanisms should help identifying actionable items to increase the efficacy of immune-checkpoint inhibitors.

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Authors

Contributions

J.C., S.B. and F.B. wrote the paper.

Corresponding author

Correspondence to Joseph Ciccolini.

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Ethics approval and consent to participate

Not applicable. The PIONeeR study is registered as NCT03493581.

Data availability

Not applicable.

Competing interests

The authors declare no competing interests.

Funding information

PIONeeR is supported by the French National Research Agency (grant# ANR-17-RHUS-0007), and is a partnership between AMU, APHM, AstraZeneca, Centre Léon Bérard, CNRS, HalioDx, ImCheck Therapeutics, Innate Pharma, Inserm and Institut Paoli Calmettes with an APHM sponsoring. QUANTIC is funded by ITMO Cancer AVIESAN and French Institut National du Cancer (grant #19CM148-00).

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Ciccolini, J., Benzekry, S. & Barlesi, F. Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC. Br J Cancer 123, 337–338 (2020). https://doi.org/10.1038/s41416-020-0918-3

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