Background: Tumor infiltrating lymphocytes (TILs) reflect adaptive antitumor immune responses in cancer and are generally associated with favorable prognosis. However, the relationships between TILs subsets and their spatial arrangement with clinical benefit from immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC) remains less explored.
Methods: We used multiplexed quantitative immunofluorescence panels to determine the association of major TILs subpopulations, CD8+ cytotoxic T cells, CD4+ helper T cells and CD20+ B cells, and T cell exhaustion markers, programmed cell death protein-1 (PD-1),lymphocyte-activation gene 3 (LAG-3) and T cell immunoglobulin mucin-3 (TIM-3) with outcomes in a multi-institutional cohort of baseline tumor samples from 179 patients with NSCLC treated with ICI. The analysis of full-face tumor biopsies including numerous fields of view allowed a detailed spatial analysis and assessment of tumor immune heterogeneity using a multiparametric quadratic entropy metric (Rao's Q Index (RQI)).
Results: TILs were preferentially located in the stromal tissue areas surrounding tumor-cell nests and CD8+ T cells were the most abundant subset. Higher density of stromal CD8+ cytotoxic T cells was significantly associated with longer survival, and this effect was more prominent in programmed death ligand-1 (PD-L1) positive cases. The role of baseline T cell infiltration to stratify PD-L1 expressing cases was confirmed measuring the T cell receptor-burden in an independent NSCLC cohort studied with whole-exome DNA sequencing. High levels of LAG-3 on T cells or elevated RQI heterogeneity index were associated with worse survival in the cohort.
Conclusion: Baseline T cell density and T cell exhaustion marker expression can stratify outcomes in PD-L1 positive patients with NSCLC treated with ICI. Spatial immune heterogeneity can be measured using the RQI and is associated with survival in NSCLC.
Keywords: immunotherapy; lung neoplasms; lymphocytes, tumor-infiltrating; programmed cell death 1 receptor; tumor biomarkers.
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