ArticleCancer immunology, immunotherapy : CII2026
Dynamic circulating biomarkers for predicting adverse events in PRaG therapy: a pooled analysis and model construction from PRaG series clinical trials.
Article in Cancer immunology, immunotherapy : CII, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPRaG therapy (PD-1/PD-L1 inhibitor combined with Radiotherapy and Granulocyte-macrophage colony-stimulating factor) has emerged as a novel, intensive, and promising combined regimen for advanced solid tumors. It has been adopted as a National Health Commission-promoted technology in China, demonstrating significant survival benefits. This study aims to delineate the adverse events (AEs) profile of PRaG therapy and evaluate the predictive value of dynamic circulating biomarkers for moderate-to-severe AEs.
methods203 patients with advanced solid tumors initiating PRaG therapy were prospectively enrolled and stratified by maximum treatment-related AEs severity (AE ≤ Grade1 vs. AE ≥ Grade2). The predictive value was assessed by comparing circulating indicators at baseline and one cycle prior to AEs onset (pre-AEs cycle). A predictive model was then constructed and subjected to independent internal validation.
resultsPatients experiencing AE ≥ Grade2 exhibited significantly superior survival outcomes. Multivariate analysis of dynamic circulating indicators revealed that pre-AEs cycle levels of Interleukin-2 (IL-2, p = 0.004), CD4 + effector memory T cells (p = 0.001), CD8 + effector memory T cells (p = 0.012), and the CD4 + effector/CD8 + effector memory T-cell ratio (p < 0.001) were independent predictors for AE ≥ Grade2. Integrating these fine typing of circulating T lymphocyte subsets into the prediction model significantly enhanced performance (AUC increased from 0.685 to 0.867), with independent internal validation confirming model reliability (AUC = 0.913).
conclusionThis study successfully delineated the real-world safety profile of PRaG therapy and identified a distinct set of dynamic circulating biomarkers strongly associated with moderate-to-severe AEs. The established non-invasive prediction model offers an effective tool for clinical early warning and proactive management in PRaG patients.
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