A predictive model for immunotherapy efficacy in cancer based on dynamic changes of CD8+ T cells: a pan-cancer retrospective study

Objective Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but reliable peripheral blood biomarkers for monitoring treatment response and predicting prognosis remain limited. This study aimed to analyze the dynamic changes of lymphocyte subsets in patients receiving ICI therapy, evaluate their role in treatment response monitoring and prognosis assessment, and develop a practical clinical risk stratification tool. Methods A total of 121 patients with malignancies who received ICI therapy and had available lymphocyte subset data were retrospectively enrolled. Peripheral blood lymphocyte subsets and routine blood test data were collected before and after treatment. The associations between changes in these parameters and treatment response as well as progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. A risk score model and a simplified clinical scoring system were constructed and validated using time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier analysis. Results Pan-cancer analysis showed that a decrease in CD8+ T cell count after treatment was significantly associated with progressive disease (PD) and inversely correlated with PFS (HR = 0.2308, 95% CI: 0.0875-0.5636). A non-immunotherapy validation cohort further confirmed the immunotherapy-specific nature of CD8+ T cell dynamics. Multivariate Cox analysis identified decreased CD8+ T cell count, elevated neutrophil-to-lymphocyte ratio (NLR), multiple lines of therapy, and specific cancer types (hepatopancreatobiliary malignancies) as independent unfavorable prognostic factors. Time-dependent area under the curve (AUC) values at 2.5, 3.5, and 5.7 months were 0.727, 0.827, and 0.853, respectively, indicating good predictive performance. The risk score based on these variables stratified patients into low-, medium-, and high-risk groups (median PFS: not reached, not reached, and 4.5 months, respectively; p<0.001). A simplified clinical scoring system also effectively distinguished different prognostic groups (median PFS: not reached, 6.2 months, and 4.3 months, respectively; p<0.001). Conclusions The dynamic change in CD8+ T cell count before and after treatment is an independent predictor of PFS in patients receiving ICI therapy and exhibits immunotherapy specificity. The proposed risk stratification tool, incorporating CD8+ T cell dynamics, NLR change, and key clinical variables, provides a simple and effective approach for prognostic assessment and may facilitate individualized treatment decision-making in clinical practice.