Automated Computational Flow Cytometry Correlates Decreasing Neutrophil-to-Lymphocyte Ratio to Improved Survival in NSCLC after Immune Checkpoint Blockade.

Immune checkpoint blockade (ICB) therapy has transformed non-small cell lung cancer (NSCLC) treatment and improved overall survival (OS). However, not all patients are responsive. Using computational cytometry analysis to identify immune cell subsets and early dynamic changes, we aimed to unravel the mechanisms underlying diverse responses to ICB in NSCLC. Peripheral blood from 34 patients with NSCLC treated with nivolumab monotherapy was collected at three time points (baseline, week 2, and week 4, referred to as TP1, TP2 and TP3, respectively). Six flow cytometry panels provided comprehensive immune cell profiling, and an R pipeline was designed for data analysis. Differences in abundances, ratios, and functional marker expression were explored in relation to survival. Two additional cohorts were collected and processed similarly. The computational pipeline provided reliable results and is generalizable to new patient cohorts. A decrease in the neutrophil-to-lymphocyte ratio (NLR) between TP2 and TP3 correlated with longer OS. Additionally, patients with an increase in CD8+ T cells between TP2 and TP3 had a higher survival probability. Lastly, we identified a CD11c+ eosinophil subset that increased in patients with a longer OS. Overall, the automated computational approach could be used to analyze clinical multicenter cytometry data in an objective and a reproducible way. Moreover, potential dynamic biomarkers to assess prognosis during ICB therapy in NSCLC were identified, including changes in NLR, CD8+ T cells, and CD11c+ eosinophils. This provides a foundation for further research, emphasizing validation of the pipeline and biomarkers in larger, diverse cohorts and independent datasets to assess their robustness and generalizability.