Immune landscape in lung cancer: Lymphocyte subsets as potential predictors of treatment outcomes.

e20575 Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of advanced lung cancer, but the lack of reliable biomarkers to predict treatment responses remains a major challenge. This study investigates the predictive value of various lymphocyte subsets in different subtypes of lung cancer, aiming to identify potential biomarkers for improving ICI treatment stratification and clinical outcomes. Methods: We conducted a retrospective analysis of 146 patients with stage III or IV lung cancer who received ICI therapy. The study focused on evaluating the relationship between various lymphocyte subsets and ICI treatment efficacy, with the goal of determining their predictive value for post-treatment outcomes. Lymphocyte subsets, including CD3+CD4+ and CD3+CD8+ T lymphocytes, were assessed and correlated with patient responses to ICI treatment. Results: Subgroup analysis revealed a significant negative correlation (P=0.01) between lower levels of CD3+CD8+ T lymphocytes and improved treatment response in patients with squamous cell carcinoma, whereas no such correlation was observed in patients with lung adenocarcinoma. Furthermore, the predictive ability of lymphocyte subsets varied depending on the type of immunotherapy drug used. In patients treated with anti-programmed cell death ligand 1 (PD-L1) inhibitors, lower levels of CD3+CD8+ T lymphocytes were significantly associated with better treatment outcomes (P=0.002), while no such association was found for programmed death 1 (PD-1) inhibitors. Among patients under 60 years of age, a higher expression of CD3+CD4+ T lymphocytes (P=0.03), combined with lower levels of CD3+CD8+ T lymphocytes (P=0.006), was significantly associated with a positive treatment response. However, in patients over 60, no significant correlation was found between lymphocyte subsets and treatment response. Prognostic analysis identified two key lymphocyte subsets influencing progression-free survival (PFS) after ICI treatment: CD3+CD4+ T lymphocytes (hazard ratio [HR] = 0.50, P=0.006) and CD3+CD8+ T lymphocytes (HR = 1.78, P=0.02). Conclusions: Our findings highlight the significant heterogeneity in the predictive value of different lymphocyte subsets for lung cancer patients undergoing ICI treatment. These results underscore the importance of considering pathological type, immunotherapeutic agent, and patient age when evaluating the role of immune cell subsets in treatment stratification. Our study suggests that lymphocyte subset profiling could serve as a promising tool for predicting ICI treatment outcomes and improving personalized treatment strategies for advanced lung cancer.