Spatial features of specific CD103CD8 tissue-resident memory T cell subsets define the prognosis in patients with non-small cell lung cancer.

BACKGROUND

Tissue-resident memory T (T) cells can reside in the tumor microenvironment and are considered the primary response cells to immunotherapy. Heterogeneity in functional status and spatial distribution may contribute to the controversial role of T cells but we know little about it.

METHODS

Through multiplex immunofluorescence (mIF) (CD8, CD103, PD-1, Tim-3, GZMB, CK), the quantity and spatial location of T cell subsets were recognized in the tissue from 274 patients with NSCLC after radical surgery. By integrating multiple machine learning methods, we constructed a T-based spatial immune signature (T-SIS) to predict the prognosis. Furthermore, we conducted a CD103-related gene set enrichment analysis (GSEA) and verified its finding by another mIF panel (CD8, CD103, CK, CD31, Hif-1α).

RESULTS

The density of T cells was significantly correlated with the expression of PD-1, Tim-3 and GZMB. Four types of T cell subsets was defined, including T (PD-1Tim-3T), T (PD-1Tim-3T), T (PD-1Tim-3T) and T (PD-1Tim-3T). The cytotoxicity of T was the strongest while that of T was the weakest. Compare with T and T, T and T had better infiltration and stronger interaction with cancer cells. The T-SIS was an independent prognostic factor for disease-free survival [HR = 2.43, 95%CI (1.63-3.60), P < 0.001] and showed a better performance than the TNM staging system for recurrence prediction. Furthermore, by CD103-related GSEA and mIF validation, we found a negative association between tumor angiogenesis and infiltration of T cells.

CONCLUSIONS

These findings reveal a significant heterogeneity in the functional status and spatial distribution of T cells, and support it as a biomarker for the prognosis of NSCLC patients. Regulating T cells by targeting tumor angiogenesis may be a potential strategy to improve current immunotherapy.