The application of peripheral blood immune profiling in personalized treatment of locally advanced and advanced lung cancer: a nomogram approach - Yin, Tianwen; Li, Yikun; Sun, Qixin; Yuan, Qipeng; Zhu, Shan; Yu, Jinming; Zhang, Tao; Teng, Feifei; Miao, Chuanwang (Frontiers in Oncology, 2025)
PurposeImmunotherapy has revolutionized the treatment of lung cancer, yet many patients experience limited or transient benefits. Identifying those most likely to benefit remains a critical challenge. This study aims to establish a predictive model based on peripheral blood lymphocyte subsets to evaluate treatment responses in locally advanced and advanced lung cancer patients receiving chemotherapy with or without immunotherapy.MethodsWe prospectively enrolled 171 patients, peripheral blood lymphocyte subsets were analyzed pre-treatment, post-treatment, and at disease progression using flow cytometry, focusing on CD3–CD16+CD56+ cells, CD3–CD19+ cells, CD3+CD4+ T cells, CD4+/CD8+ T-cell ratio, and CD3+CD8+ T cells. We assessed correlations between these subsets and treatment efficacy and constructed a nomogram to predict outcomes.ResultsBaseline lymphocyte profiles were closely associated with treatment responses. Elevated CD3–CD16+CD56+ cells, increased CD4+/CD8+ T cell ratio, and higher CD3–CD19+ cells correlated with favorable treatment outcomes, particularly in patients receiving combined therapy. Conversely, higher CD3+ and CD3+CD8+ T cell counts were linked to poorer short-term efficacy. A nomogram integrating five immune parameters achieved an area under the receiver operating characteristic curve (AUC) of 0.778, outperforming individual marker. In the combination therapy subgroup, a four-parameter model achieved an AUC of 0.725. Furthermore, baseline and progression-stage lymphocyte profiles in responder and non-responder cohorts, exhibit no significant differences, indicating stable immune parameters over the disease course.ConclusionPeripheral blood lymphocyte subsets are promising non-invasive biomarkers for predicting treatment responses in locally advanced and advanced lung cancer patients, particularly with immunotherapy. The developed nomogram models enhance predictive accuracy, supporting personalized treatment decisions.