Model-based integration of chimeric antigen receptor T-cell kinetics and tumor features to predict therapeutic outcomes in patients with B-cell malignancies
- 1Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology , ,
- 2Institute for Medical Informatics and Biometry (IMB), Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology , ,
Abstract
Introduction: Chimeric antigen receptor (CAR) T-cell therapy is a standard treatment for several hematological malignancies, yet patient-specific responses and toxicities remain hard to predict. Although multiple covariates and risk scores exist, their prognostic accuracy remains limited.
Methods: We prospectively measured CAR T-cell kinetics in peripheral blood of 92 patients with B-cell malignancies using multicolor flow cytometry. To explore underlying mechanisms, we developed a mechanistic model of CAR T-cell and tumor cell interactions.
Results: We captured four phases of CAR T-cell kinetics in peripheral blood: distribution, expansion, contraction, and persistence. Patients with weak expansion or deep contraction are more likely to relapse. When fitted to the 46 patients with suDicient data points, the model reproduces individual CAR T-cell kinetics but fails to predict outcomes using uniform tumor parameters. We demonstrate that identical CAR T-cell dynamics may result from diDerent tumor features, including growth rate, initial burden, or antigen density. Accurately estimating these tumor-specific factors may improve relapse prediction, while common surrogates like blood lactate dehydrogenase, International Prognostic Index score, or Ann-Arbor stage do not.
Conclusion: We conclude that CAR T-cell kinetics within the first month can serve as early prognostic markers. Integrating tumor burden estimates is required to reconcile kinetics with individual outcomes and may, upon prospective validation, further improve risk stratification in patients with B-cell malignancies receiving CAR T-cell therapy.
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BibTeX
@article{schadt2026modelbased,
title = {{Model-based integration of chimeric antigen receptor T-cell kinetics and tumor features to predict therapeutic outcomes in patients with B-cell malignancies}},
author = {Jonas Schadt and Maximilian Alexander Röhnert and Paul Matthiensen and Uta Oelschlägel and Martin Bornhäuser and Frank Kroschinsky and Raphael Teipel and Ingmar Glauche and Malte von Bonin and Artur César Fassoni},
journal = {Frontiers in immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1927857},
url = {https://europepmc.org/article/PMC/PMC13672575},
note = {CC BY},
}
RIS
TY - JOUR
TI - Model-based integration of chimeric antigen receptor T-cell kinetics and tumor features to predict therapeutic outcomes in patients with B-cell malignancies
AU - Jonas Schadt
AU - Maximilian Alexander Röhnert
AU - Paul Matthiensen
AU - Uta Oelschlägel
AU - Martin Bornhäuser
AU - Frank Kroschinsky
AU - Raphael Teipel
AU - Ingmar Glauche
AU - Malte von Bonin
AU - Artur César Fassoni
PY - 2026
DA - 2026/10/02
JO - Frontiers in immunology
DO - 10.3389/fimmu.2026.1927857
UR - https://europepmc.org/article/PMC/PMC13672575
AB - Introduction: Chimeric antigen receptor (CAR) T-cell therapy is a standard treatment for several hematological malignancies, yet patient-specific responses and toxicities remain hard to predict. Although multiple covariates and risk scores exist, their prognostic accuracy remains limited. Methods: We prospectively measured CAR T-cell kinetics in peripheral blood of 92 patients with B-cell malignancies using multicolor flow cytometry. To explore underlying mechanisms, we developed a mechanistic model of CAR T-cell and tumor cell interactions. Results: We captured four phases of CAR T-cell kinetics in peripheral blood: distribution, expansion, contraction, and persistence. Patients with weak expansion or deep contraction are more likely to relapse. When fitted to the 46 patients with suDicient data points, the model reproduces individual CAR T-cell kinetics but fails to predict outcomes using uniform tumor parameters. We demonstrate that identical CAR T-cell dynamics may result from diDerent tumor features, including growth rate, initial burden, or antigen density. Accurately estimating these tumor-specific factors may improve relapse prediction, while common surrogates like blood lactate dehydrogenase, International Prognostic Index score, or Ann-Arbor stage do not. Conclusion: We conclude that CAR T-cell kinetics within the first month can serve as early prognostic markers. Integrating tumor burden estimates is required to reconcile kinetics with individual outcomes and may, upon prospective validation, further improve risk stratification in patients with B-cell malignancies receiving CAR T-cell therapy.
SN - 1664-3224
ER -