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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Ackermann, C. J., Reck, M., Paz-Ares, L., Barlesi, F. & Califano, R. First-line immune checkpoint blockade for advanced non-small-cell lung cancer: Travelling at the speed of light. Lung Cancer 134, 245–253 (2019).
Hofman, P., Heeke, S., Alix-Panabières, C. & Pantel, K. Liquid biopsy in the era of immuno-oncology: is it ready for prime-time use for cancer patients? Ann. Oncol. 30, 1448–1459 (2019).
Willis, C., Fiander, M., Tran, D., Korytowsky, B., Thomas, J. M., Calderon, F. et al. Tumor mutational burden in lung cancer: a systematic literature review. Oncotarget 10, 6604–6622 (2019).
Derosa, L., Hellmann, M. D., Spaziano, M., Halpenny, D., Fidelle, M., Rizvi, H. et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann. Oncol. 29, 1437–1444 (2018).
Remon, J., Vilariño, N. & Reguart, N. Immune checkpoint inhibitors in non-small cell lung cancer (NSCLC): approaches on special subgroups and unresolved burning questions. Cancer Treat. Rev. 64, 21–29 (2018).
Ciccolini, J., Barbolosi, D., André, N., Benzekry, S. & Barlesi, F. Combinatorial immunotherapy strategies: most gods throw dice, but fate plays chess. Ann. Oncol. 30, 1690–1691 (2019).
Benzekry, S., Barbolosi, D., André, N., Barlesi, F. & Ciccolini, J. Mechanistic learning for combinatorial strategies with immuno-oncology drugs: can model-informed designs help investigators? J. Clin. Oncol. Precis. Oncol. 4, 486–491 (2020).
Barbolosi, D., Ciccolini, J., Lacarelle, B., Barlési, F. & André, N. Computational oncology–mathematical modelling of drug regimens for precision medicine. Nat. Rev. Clin. Oncol. 13, 242–254 (2016).
Ethics approval and consent to participate
Not applicable. The PIONeeR study is registered as NCT03493581.
The authors declare no competing interests.
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