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Immunotherapy and Immune Responses · Journal article
Frontiers in Immunology · August 10, 2026
Encouraging direction, but not yet definitive.
This retrospective study identifies decreased CD8+ T cell count after immunotherapy initiation as an independent prognostic marker for worse progression-free survival (HR=0.23) and develops a risk stratification tool with moderate discriminatory power (AUC 0.73–0.85 at various timepoints). The model requires external prospective validation before clinical implementation.
Retrospective cohort study with internal model validation. 121 patients with malignancies receiving immune checkpoint inhibitor therapy, with available peripheral blood lymphocyte subset data before and after treatment.. Intervention: Immune checkpoint inhibitor therapy with monitoring of peripheral blood lymphocyte subsets before and after treatment.. Compared with: Non-immunotherapy validation cohort to confirm immunotherapy-specific CD8+ T cell dynamics.. n = 121.
Decreased CD8+ T cell count significantly associated with progressive disease and inversely correlated with PFS (HR = 0.2308, 95% CI: 0.0875–0.5636) Time-dependent AUC values at 2.5, 3.5, and 5.7 months were 0.727, 0.827, and 0.853, respectively Risk score stratified patients into low-, medium-, and high-risk groups with median PFS: not reached, not reached, and 4.5 months (p<0.001)
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The CD8+ T cell count change may help clinicians identify patients at high risk of progression during immunotherapy, potentially guiding treatment modifications. However, prospective external validation and comparison with existing biomarkers are necessary before adoption in routine clinical practice.
Retrospective single-cohort study with internal validation showing CD8+ T cell dynamics predict immunotherapy outcomes via a novel risk model, but lacks external validation and prospective design needed to change practice.
As stated by the source record.
Quoted from the source exactly as published.
The CD8+ T cell count change may help clinicians identify patients at high risk of progression during immunotherapy, potentially guiding treatment modifications. However, prospective external validation and comparison with existing biomarkers are necessary before adoption in routine clinical practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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.
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