Spatial features of specific CD103⁺CD8⁺ tissue-resident memory T cell subsets define the prognosis in patients with...

Tissue-resident memory T (T_RM) 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_RM cells but we know little about it.

Through multiplex immunofluorescence (mIF) (CD8, CD103, PD-1, Tim-3, GZMB, CK), the quantity and spatial location of T_RM 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_RM-based spatial immune signature (T_RM-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).

The density of T_RM cells was significantly correlated with the expression of PD-1, Tim-3 and GZMB. Four types of T_RM cell subsets was defined, including T_RM1 (PD-1^-Tim-3^-T_RM), T_RM2 (PD-1⁺Tim-3^-T_RM), T_RM3 (PD-1^-Tim-3⁺T_RM) and T_RM4 (PD-1⁺Tim-3⁺T_RM). The cytotoxicity of T_RM2 was the strongest while that of T_RM4 was the weakest. Compare with T_RM1 and T_RM2, T_RM3 and T_RM4 had better infiltration and stronger interaction with cancer cells. The T_RM-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_RM cells.

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