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Deconvolution of clinical variance in CAR-T cell pharmacology and response.


ABSTRACT: Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement. The model is trained using clinical data from CAR-T products in different hematological malignancies and identifies cell-intrinsic differences in the turnover rate of memory cells and cytotoxic potency of effectors as the primary determinants of clinical response. Using a machine learning workflow, we demonstrate that product-intrinsic differences can accurately predict patient outcomes based on pre-infusion transcriptomes, and additional pharmacological variance arises from cellular interactions with patient tumors. We found that transcriptional signatures outperform T cell immunophenotyping as predictive of clinical response for two CD19-targeted CAR-T products in three indications, enabling a new phase of predictive CAR-T product development.

SUBMITTER: Kirouac DC 

PROVIDER: S-EPMC10635825 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Deconvolution of clinical variance in CAR-T cell pharmacology and response.

Kirouac Daniel C DC   Zmurchok Cole C   Deyati Avisek A   Sicherman Jordan J   Bond Chris C   Zandstra Peter W PW  

Nature biotechnology 20230227 11


Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement. The model is trained using clinical data from CAR-T products in dif  ...[more]

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