Evidence map›Paper›PMID 42890243›Full record

ArticleFrontiers in immunology2026

Model-based integration of chimeric antigen receptor T-cell kinetics and tumor features to predict therapeutic outcomes in patients with B-cell malignancies.

Jonas Schadt, Maximilian Alexander Röhnert, Paul Matthiensen, Uta Oelschlägel, Martin Bornhäuser, Frank Kroschinsky, Raphael Teipel, Ingmar Glauche, Malte von Bonin, Artur César Fassoni

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jonas SchadtDepartment of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Maximilian Alexander RöhnertDepartment of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Paul MatthiensenDepartment of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Uta OelschlägelDepartment of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Martin BornhäuserDepartment of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Frank KroschinskyDepartment of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Raphael TeipelDepartment of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Ingmar GlaucheInstitute for Medical Informatics and Biometry (IMB), Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Malte von BoninDepartment of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Artur César FassoniInstitute for Medical Informatics and Biometry (IMB), Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Immunotherapy, AdoptiveLymphoma, B-CellReceptors, Chimeric AntigenT-LymphocytesFemaleHumansKineticsMalePrognosisProspective StudiesTreatment OutcomeReceptors, Chimeric Antigenantigen densityB-cell lymphomaCAR T-cell kineticsmathematical modelordinary differential equationspredictive biomarkerstreatment outcometumor burden

Identifiers

PMID42890243
PMCPMC13672575

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.