A federated digital twin reveals cytomegalovirus reactivation impairs CAR-T cell therapy via IL-15-mediated cytokine competition in B-Cell lymphoma

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• Picked for Hematology (top studies of the week, 18 October 2026): Digital twin predicts CMV impact on CAR‑T efficacy

Abstract

BACKGROUND: Cytomegalovirus (CMV) reactivation occurs in 30 - 40 % of seropositive patients receiving chimeric antigen receptor T-cell (CAR-T) therapy for B-cell lymphoma and is strongly associated with treatment failure. However, the causal immunological mechanism driving this failure whether through direct viral cytopathic effects, T-cell exhaustion, or resource competition remains undefined. Furthermore, existing predictive models lack the mechanistic insight needed to guide intervention. We developed a privacy-preserving, mechanistic digital twin to test the "cytokine sink" hypothesis, wherein CMV-specific CD8+ T cells compete with CAR-T cells for the limiting homeostatic cytokine I L - 15 . METHODS: We constructed a system of ordinary differential equations formalizing this competition for I L - 15 . The model was trained using a Hierarchical Bayesian Federated Averaging algorithm on multi-institutional data from 414 patients across five academic cancer centres (Sites A-E). without centralizing raw patient data. An independent sixth centre (Site F, n = 89 ) served exclusively as the prospective validation site and contributed no data to the training process. In silico counterfactual analyses and simulations of antiviral prophylaxis were performed. RESULTS: The digital twin predicted clinically significant CMV reactivation defined as CMV viral load ≥ 10 , 000   I U / m L by D a y   28 post-infusion (primary outcome; threshold selected based on E C I L - 7 guidance for threshold-triggered pre-emptive antiviral therapy in T-cell-depleted immunotherapy) with an AUROC of 0.91   ( 95 %   C I : 0.88 - 0.94 ) , significantly outperforming existing clinical risk scores. Two pre-specified secondary thresholds were also analysed: (i) any detectable reactivation ( ≥ 1 , 000   I U / m L ) and (ii) severe reactivation ( ≥ 50 , 000   I U / m L ). Under the structural assumptions of the fitted mechanistic model, the data are quantitatively consistent with I L - 15 resource competition as a mechanism linking CMV reactivation to CAR-T impairment. Mechanistically, the model revealed that CMV reactivation was associated with reduced peak CAR-T expansion by 41.8 %   ( p < 0.001 ) in counterfactual analysis. Global sensitivity analysis identified the pre-infusion frequency of CMV-specific T-cell precursors ( ξ ) and the resource competition coefficient ( α ) as the primary drivers of this effect, explaining 38 % and 29 % of output variance, respectively. In silico simulation of a risk-adapted, digital-twin-guided antiviral prophylaxis strategy reduced projected six-month progression by 32 % while reducing aggregate drug exposure by 36 % . In the prospective validation cohort, the model-predicted kinetic impairment independently predicted progression-free survival (hazard ratio [HR] 3.4 ,   95 %   C I : 1.5 - 7.8 ,   p = 0.004 ) . Formal model competition analysis against three alternative mechanistic hypotheses (Results Section 3.3) further supports the cytokine sink hypothesis over exhaustion-based or mediation-only alternatives. CONCLUSION: Under the structural assumptions of the fitted mechanistic model, the data are quantitatively consistent with I L - 15 resource competition as a mechanism linking CMV reactivation to CAR-T impairment. Formal model competition analysis against three alternative mechanistic hypotheses further supports the cytokine sink hypothesis; randomised interventional evidence is required for definitive causal proof. We further demonstrate that a privacy-preserving, mechanistic digital twin can serve as a clinically actionable tool for early risk stratification and personalized intervention, while providing a scalable blueprint for collaborative systems immunology research. Definitive validation of clinical utility requires a randomised controlled trial in which treatment decisions are prospectively guided by the digital twin's predictions.

Abstract as published, via PubMed.