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

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.