Objective Analysis and Clinical Significance of the Spatial Tumor-Infiltrating Lymphocyte Patterns in Non–Small Cell Lung Cancer

Abstract Purpose: The spatial arrangement of lymphocytes in the tumor bed (e.g., immune infiltrated, immune excluded, immune desert) is expected to reflect distinct immune evasion mechanisms and to associate with immunotherapy outcomes. However, data supporting these associations are scant and limited by the lack of a clear definition for lymphocyte infiltration patterns and the subjective nature of pathology-based approaches. Experimental Design: We used multiplexed immunofluorescence to study major tumor-infiltrating lymphocyte (TIL) subsets with single-cell resolution in baseline whole-tissue section samples from NSCLC patients treated with immune checkpoint inhibitors (ICI). The spatial TIL patterns were analyzed using a qualitative pathologist-based approach, and an objective analysis of TIL density ratios in tumor/stromal tissues. The association of spatial patterns with outcomes was studied for different TIL markers. Results: The analysis of CD8+ TIL patterns using qualitative assessment identified prominent limitations including the presence of a broad spectrum of phenotypes within most tumors and limited association with outcomes. The utilization of an objective method to classify NSCLCs showed the existence of at least three subgroups with partial overlap with those defined using visual patterns. Using this strategy, a subset of cases with “immune excluded-like” tumors showed prominently worse outcomes, suggesting reduced sensitivity to ICI; however, these results need to be validated. The analysis for other TIL subsets showed different results, underscoring the relevance of the marker selected for spatial TIL pattern evaluation and opportunities for market integration. Conclusions: Our results identified major challenges associated with the qualitative spatial TIL pattern evaluation. We devised a novel objective strategy to overcome some of these limitations that has strong biomarker potential.