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        <datestamp>2026-10-02T05:37:36Z</datestamp>
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          <dc:title>Table 1_Model-based integration of chimeric antigen receptor T-cell kinetics and tumor features to predict therapeutic outcomes in patients with B-cell malignancies.xlsx</dc:title>
          <dc:creator>Jonas Schadt (25162659)</dc:creator>
          <dc:creator>Maximilian Alexander Röhnert (25162662)</dc:creator>
          <dc:creator>Paul Matthiensen (25162665)</dc:creator>
          <dc:creator>Uta Oelschlägel (11800880)</dc:creator>
          <dc:creator>Martin Bornhäuser (219605)</dc:creator>
          <dc:creator>Frank Kroschinsky (10067536)</dc:creator>
          <dc:creator>Raphael Teipel (13948128)</dc:creator>
          <dc:creator>Ingmar Glauche (261610)</dc:creator>
          <dc:creator>Malte von Bonin (400911)</dc:creator>
          <dc:creator>Artur César Fassoni (7252418)</dc:creator>
          <dc:subject>Genetic Immunology</dc:subject>
          <dc:subject>antigen density</dc:subject>
          <dc:subject>B-cell lymphoma</dc:subject>
          <dc:subject>CAR T-cell kinetics</dc:subject>
          <dc:subject>mathematical model</dc:subject>
          <dc:subject>ordinary differential equations</dc:subject>
          <dc:subject>predictive biomarkers</dc:subject>
          <dc:subject>treatment outcome</dc:subject>
          <dc:subject>tumor burden</dc:subject>
          <dc:description>Introduction&lt;p&gt;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.&lt;/p&gt;Methods&lt;p&gt;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.&lt;/p&gt;Results&lt;p&gt;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.&lt;/p&gt;Conclusion&lt;p&gt;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.&lt;/p&gt;</dc:description>
          <dc:date>2026-10-02T05:37:36Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.3389/fimmu.2026.1927857.s001</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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