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Early Survival Prediction Framework in CD19-Specific CAR-T Cell Immunotherapy Using a Quantitative Systems Pharmacology Model

Authors :
Michael R. Green
Beth Chasen
Annette Künkele
Charlotte Kloft
Wilhelm Huisinga
Paolo Strati
Nahum Puebla-Osorio
Anna Mueller-Schoell
Cassian Yee
Robin Michelet
Sattva S. Neelapu
Source :
Cancers, Volume 13, Issue 11, Cancers, Vol 13, Iss 2782, p 2782 (2021)
Publication Year :
2021
Publisher :
Freie Universität Berlin, 2021.

Abstract

Chimeric antigen receptor (CAR)-T cell therapy has revolutionized treatment of relapsed/refractory non-Hodgkin lymphoma (NHL). However, since 36–60% of patients relapse, early response prediction is crucial. We present a novel population quantitative systems pharmacology model, integrating literature knowledge on physiology, immunology, and adoptive cell therapy together with 133 CAR-T cell phenotype, 1943 cytokine, and 48 metabolic tumor measurements. The model well described post-infusion concentrations of four CAR-T cell phenotypes and CD19+ metabolic tumor volume over 3 months after CAR-T cell infusion. Leveraging the model, we identified a low expansion subpopulation with significantly lower CAR-T cell expansion capacities amongst 19 NHL patients. Together with two patient-/therapy-related factors (autologous stem cell transplantation, CD4+/CD8+ T cells), the low expansion subpopulation explained 2/3 of the interindividual variability in the CAR-T cell expansion capacities. Moreover, the low expansion subpopulation had poor prognosis as only 1/4 of the low expansion subpopulation compared to 2/3 of the reference population were still alive after 24 months. We translated the expansion capacities into a clinical composite score (CCS) of ‘Maximum naïve CAR-T cell concentrations/Baseline tumor burden’ ratio and propose a CCSTN-value &gt<br />0.00136 (cellsµL−1mL−1 as predictor for survival. Once validated in a larger cohort, the model will foster refining survival prediction and solutions to enhance NHL CAR-T cell therapy response.

Details

Language :
English
Database :
OpenAIRE
Journal :
Cancers, Volume 13, Issue 11, Cancers, Vol 13, Iss 2782, p 2782 (2021)
Accession number :
edsair.doi.dedup.....b67bfd2fea1cc7b00e81a98744a5a7ed