Dynamic surveillance of lymphocyte subsets in patients with non-small cell lung cancer during chemotherapy or combination immunotherapy for early prediction of efficacy

Abstract

Background: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths worldwide. Lymphocytes are the primary executors of the immune system and play essential roles in tumorigenesis and development. We investigated the dynamic changes in peripheral blood lymphocyte subsets to predict the efficacy of chemotherapy or combination immunotherapy in NSCLC.

Methods: This retrospective study collected data from 81 patients with NSCLC who received treatments at the First Affiliated Hospital of Zhengzhou University from May 2021 to May 2023. Patients were divided into response and non-response groups, chemotherapy and combination immunotherapy groups, and first-line and multiline groups. We analyzed the absolute counts of each lymphocyte subset in the peripheral blood at baseline and after each treatment cycle. Within-group and between-group differences were analyzed using paired Wilcoxon signed-rank and Mann-Whitney U tests, respectively. The ability of lymphocyte subsets to predict treatment efficacy was analyzed using receiver operating characteristic curve and logistic regression.

Results: The absolute counts of lymphocyte subsets in the response group significantly increased after the first cycle of chemotherapy or combination immunotherapy, whereas those in the non-response group showed persistent decreases. Ratios of lymphocyte subsets after the first treatment cycle to those at baseline were able to predict treatment efficacy early. Combination immunotherapy could increase lymphocyte counts compared to chemotherapy alone. In addition, patients with NSCLC receiving chemotherapy or combination immunotherapy for the first time mainly presented with elevated lymphocyte levels, whereas multiline patients showed continuous reductions.

Conclusion: Dynamic surveillance of lymphocyte subsets could reflect a more actual immune status and predict efficacy early. Combination immunotherapy protected lymphocyte levels from rapid decrease and patients undergoing multiline treatments were more prone to lymphopenia than those receiving first-line treatment. This study provides a reference for the early prediction of the efficacy of clinical tumor treatment for timely combination of immunotherapy or the improvement of immune status.

Keywords: NSCLC; chemotherapy; efficacy prediction; immunotherapy; lymphocyte subsets.

Figure 1

Dynamic changes in lymphocyte subsets during four treatment cycles in the response and non-response groups. Absolute counts of Lymphocyte (A), T cell (B), CD4⁺ T cell (C), CD8⁺ T cell (D), B cell (E), and NK cell (F) in the response and non-response groups during four consecutive treatment cycles were shown as boxplots. The chemotherapy and combination immunotherapy groups were shown respectively. Within-group and between-group differences were analyzed using the paired Wilcoxon signed-rank test and Mann-Whitney U test, respectively. BL, Baseline; *, P < 0.05; **, P < 0.01; ***, P < 0.001; NS, not significant.

Figure 2

Ratios of absolute counts after each treatment to baseline for each lymphocyte subset. Ratios of absolute counts after each treatment cycle to baseline for Lymphocyte (A), T cell (B), CD4⁺ T cell (C), CD8⁺ T cell (D), B cell (E), and NK cell (F) were shown as boxplots. The chemotherapy and combination immunotherapy groups were shown respectively. Between-group differences were analyzed statistically by Mann-Whitney U test. *, P < 0.05; **, P < 0.01; ***, P < 0.001; NS, not significant.

Figure 3

ROC analysis and Logistic regression of lymphocyte subsets for efficacy prediction. ROC curves were plotted for the ratios of each lymphocyte subset counts after the first treatment cycle to baseline in the response and non-response groups. The chemotherapy (A) and combination immunotherapy (B) groups were shown, respectively. AUC, significance, asymptotic 95% confidence interval, and cut-off values were shown in Table 2 . (C) Logistic regression analysis of combined lymphocyte subsets for efficacy prediction. The two sets of combination indicators with the best predictive power and model evaluation were displayed. P₁ value represented the significance of each lymphocyte subset for efficacy prediction. Model evaluations were generated from logistic regression. F1 score combines the precision and recall to measure accuracy. ROC curves and AUC were used to measure the classification capacity of logistic regression. P₂ value was the likelihood ratio chi-square test to evaluate the validity of predictive models. P < 0.05 was considered statistically significant. OR: odds ratio, 95%CI: 95% confidence interval.

Figure 4

Combination immunotherapy improved absolute counts of lymphocyte subsets compared with chemotherapy alone. Absolute counts of Lymphocyte (A), T cell (B), CD4⁺ T cell (C), CD8⁺ T cell (D), B cell (E), and NK cell (F) in the first-line group and multiline group during four consecutive treatment cycles were shown as boxplots. The ratios of each lymphocyte subset after four treatment cycles to baseline in the first-line (G) and multiline (H) patients. The chemotherapy and combination immunotherapy groups were shown, respectively. Within-group and between-group differences were analyzed using the paired Wilcoxon signed-rank test and Mann-Whitney U test, respectively. BL, Baseline; *, P < 0.05; **, P < 0.01; ***, P < 0.001; NS, not significant.

Figure 5

Dynamic changes in lymphocyte subsets during four treatment cycles in the first-line and multiline groups. Median absolute counts and interquartile ranges of Lymphocyte (A), T cell (B), CD4⁺ T cell (C), CD8⁺ T cell (D), B cell (E), and NK cell (F) in the first-line group and multiline group during four consecutive treatment cycles were shown as folded line charts. The chemotherapy and combination immunotherapy groups were shown, respectively. Within-group differences and between-group differences were analyzed using the paired Wilcoxon signed-rank test and Mann-Whitney U test, respectively. BL, Baseline; *, P < 0.05; **, P < 0.01; ***, P < 0.001; NS, not significant.