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Cancer immunotherapy: it’s time to better predict patients’ response

Abstract

In less than a decade, half a dozen immune checkpoint inhibitors have been approved and are currently revolutionising the treatment of many cancer (sub)types. With the clinical evaluation of novel delivery approaches (e.g. oncolytic viruses, cancer vaccines, natural killer cell-mediated cytotoxicity) and combination therapies (e.g. chemo/radio-immunotherapy) as well as the emergence of novel promising targets (e.g. TIGIT, LAG-3, TIM-3), the ‘immunotherapy tsunami’ is not about to end anytime soon. However, this enthusiasm in the field is somewhat tempered by both the relatively low percentage (<15%) of patients who display an effective anti-cancer immune response and the inability to accurately identify them. Recently, several existing or acquired features/parameters have been shown to impact the efficacy of immune checkpoint inhibitors. In the present review, we critically discuss current knowledge regarding predictive biomarkers for checkpoint inhibitor-based immunotherapy, highlight the missing/unclear links and emphasise the importance of characterising each neoplasm and its microenvironment in order to better guide the course of treatment.

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Fig. 1: Descriptive overview of current and emerging immune checkpoint receptor-ligand complexes.
Fig. 2: Overview of the different parameters (potential biomarkers) that might influence the patient’s response to immune checkpoint inhibitors, and their potential associations.
Fig. 3: Representation of individual bacterial species (composing gut flora) that might influence checkpoint inhibitor-based immunotherapy (non-exhaustive list).
Fig. 4: Overview of the main peripheral blood-based predictive biomarkers for immune checkpoint inhibitor-based cancer immunotherapy.
Fig. 5: Proposed workflow for deciding an optimal treatment course.

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Acknowledgements

The authors thank their colleagues at the University of Liege for their helpful discussions. C.P. and M.A. are Televie fellows.

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C.P., M.A. and M.H. reviewed the literature and wrote the manuscript. C.P., M.A. and M.H. generated the figures. G.J., P.D. and P.H. provided edits and approved the final version.

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Correspondence to Michael Herfs.

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C.P., M.A., P.D., P.H. and M.H. have no conflict of interest to declare. G.J. reports grants, personal fees and/or non-financial support from Novartis, Roche, Pfizer, Lilly, Amgen, Bristol-Myers Squibb, Astra-Zeneca, Daiichi Sankyo, Abbvie, Medimmune and MerckKGaA (not directly related to the submitted work).

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M.H. is a Research Associate at the Belgian Fund for Scientific Research (FNRS). This work was supported in part by the FNRS (MIS F.4520.20; CDR/OL J.0111.20), the University of Liege (Crédits Sectoriels de Recherche en Sciences de la Santé 2018-2020), the Télévie (PDR Televie 7.8507.19) and the Leon Fredericq Foundation.

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Pilard, C., Ancion, M., Delvenne, P. et al. Cancer immunotherapy: it’s time to better predict patients’ response. Br J Cancer (2021). https://doi.org/10.1038/s41416-021-01413-x

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