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

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    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).

    Article  Google Scholar 

  2. 2.

    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).

    CAS  Article  Google Scholar 

  3. 3.

    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).

    Article  Google Scholar 

  4. 4.

    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).

    CAS  Article  Google Scholar 

  5. 5.

    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).

    CAS  Article  Google Scholar 

  6. 6.

    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).

    CAS  Article  Google Scholar 

  7. 7.

    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).

  8. 8.

    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).

    Article  Google Scholar 

Download references

Author information




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

Corresponding author

Correspondence to Joseph Ciccolini.

Ethics declarations

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).

Additional information

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).

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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 (2020).

Download citation