Plasma proteomics identifies leukemia inhibitory factor (LIF) as a novel predictive biomarker of immune-checkpoint blockade resistance.

BACKGROUND

Immune checkpoint blockers (ICBs) are now widely used in oncology. Most patients, however, do not derive benefit from these agents. Therefore, there is a crucial need to identify novel and reliable biomarkers of resistance to such treatments in order to prescribe potentially toxic and costly treatments only to patients with expected therapeutic benefits. In the wake of genomics, the study of proteins is now emerging as the new frontier for understanding real-time human biology.

PATIENTS AND METHODS

We analyzed the proteome of plasma samples, collected before treatment onset, from two independent prospective cohorts of cancer patients treated with ICB (discovery cohort n = 95, validation cohort n = 292). We then investigated the correlation between protein plasma levels, clinical benefit rate, progression-free survival and overall survival by Cox proportional hazards models.

RESULTS

By using an unbiased proteomics approach, we show that, in both discovery and validation cohorts, elevated baseline serum level of leukemia inhibitory factor (LIF) is associated with a poor clinical outcome in cancer patients treated with ICB, independently of other prognostic factors. We also demonstrated that the circulating level of LIF is inversely correlated with the presence of tertiary lymphoid structures in the tumor microenvironment.

CONCLUSION

This novel clinical dataset brings strong evidence for the role of LIF as a potential suppressor of antitumor immunity and suggests that targeting LIF or its pathway may represent a promising approach to improve efficacy of cancer immunotherapy in combination with ICB.

Informations de copyright

Copyright © 2021 European Society for Medical Oncology. Published by Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Disclosure AB, JPG: Employees of Explicyte. AI: Received research grants from Astra Zeneca, Bayer, BMS, Chugai, Merck, MSD, Pharmamar, Novartis, Roche, and received personal fees from Epizyme, Bayer, Lilly, Roche, and Springworks. BB: Received grants from AstraZeneca, Pfizer, Eli Lilly, Onxeo, Bristol Myers Squibb, Inivata, Abbvie, Amgen, Blueprint Medicines, Celgene, GlaxoSmithKline, Ignyta, Ipsen, Merck KGaA, MSD Oncology, Nektar, PharmaMar, Sanofi, Spectrum Pharmaceuticals, Takeda, Tiziana Therapeutics, Cristal Therapeutics, Daiichi Sankyo, Janssen Oncology, OSE Immunotherapeutics, BeiGene, Boehringer Ingelheim, Genentech, SERVIER, Tolero Pharmaceuticals. YL: Received grants and personal fees from Janssen, during the conduct of the study; personal fees and non-financial support from Astellas, grants and personal fees from Sanofi, personal fees and non-financial support from Roche, personal fees and non-financial support from AstraZeneca, grants, personal fees and non-financial support from MSD, personal fees and non-financial support from BMS, personal fees from Clovis, personal fees and non-financial support from Seattle Genetics, personal fees from Incyte, personal fees from Pfizer. AM: Received research grants from Mersu, Bristol-Myers Squibb, Boehringer Ingelheim, Transgene, MSD and received personal fees from Bristol-Myers Squibb, AstraZeneca, MedImmune, Oncovir, Merieux. JCS: Has received consultancy fees from AstraZeneca, Astex, Clovis, GSK, GamaMabs, Lilly, MSD, Mission Therapeutics, Merus, Pfizer, Pharma Mar, Pierre Fabre, Roche/Genentech, Sanofi, Servier, Symphogen, and Takeda. All other authors have declared no conflicts of interest. Data sharing Individual participant data that underlie the results reported in this article will be available after deidentification beginning 24 months and ending 48 months following article publication to researchers who provide a methodologically sound proposal. Requests should be sent to the corresponding author.