Spatial colocalization and combined survival benefit of natural killer and CD8 T cells despite profound MHC class I loss in non-small cell lung cancer

Abstract

Background: MHC class I (MHC-I) loss is frequent in non-small cell lung cancer (NSCLC) rendering tumor cells resistant to T cell lysis. NK cells kill MHC-I-deficient tumor cells, and although previous work indicated their presence at NSCLC margins, they were functionally impaired. Within, we evaluated whether NK cell and CD8 T cell infiltration and activation vary with MHC-I expression.

Methods: We used single-stain immunohistochemistry (IHC) and Kaplan-Meier analysis to test the effect of NK cell and CD8 T cell infiltration on overall and disease-free survival. To delineate immune covariates of MHC-I-disparate lung cancers, we used multiplexed immunofluorescence (mIF) imaging followed by multivariate statistical modeling. To identify differences in infiltration and intercellular communication between IFNγ-activated and non-activated lymphocytes, we developed a computational pipeline to enumerate single cell neighborhoods from mIF images followed by multivariate discriminant analysis.

Results: Spatial quantitation of tumor cell MHC-I expression revealed intra- and inter-tumoral heterogeneity, which was associated with the local lymphocyte landscape. IHC analysis revealed that high CD56⁺ cell numbers in patient tumors were positively associated with disease-free survival (DFS) (HR=0.58, p=0.064) and overall survival (OS) (HR=0.496, p=0.041). The OS association strengthened with high counts of both CD56⁺ and CD8⁺ cells (HR=0.199, p<1×10^-3). mIF imaging and multivariate discriminant analysis revealed enrichment of both CD3⁺CD8⁺ T cells and CD3^-CD56⁺ NK cells in MHC-I-bearing tumors (p<0.05). To infer associations of functional cell states and local cell-cell communication, we analyzed spatial single cell neighborhood profiles to delineate the cellular environments of IFNγ^+/- NK cells and T cells. We discovered that both IFNγ⁺ NK and CD8 T cells were more frequently associated with other IFNγ⁺ lymphocytes in comparison to IFNγ^- NK cells and CD8 T cells (p<1×10^-30). Moreover, IFNγ⁺ lymphocytes were most often found clustered near MHC-I⁺ tumor cells.

Conclusions: Tumor-infiltrating NK cells and CD8 T cells jointly affected control of NSCLC tumor progression. Co-association of NK and CD8 T cells was most evident in MHC-I-bearing tumors, especially in the presence of IFNγ. Frequent co-localization of IFNγ⁺ NK cells with other IFNγ⁺ lymphocytes in near-neighbor analysis suggests NSCLC lymphocyte activation is coordinately regulated.

Keywords: CD8 T cells; MHC class I; cell co-localization; interferon-gamma; intratumor heterogeneity; multiplex immunofluorescence; natural killer cells; patient survival; spatial analysis; systems immunology; tissue micro-array; tumor-infiltrating lymphocytes.

Figure 1.. MHC-I expression exhibits intra- and inter-tumor heterogeneity and correlates with lymphocyte occupancy.

(A&D) MHC-I is variably expressed in tumors and across AdenoCA (A) and SCC (D) patients. (B&E) β2M expression correlates positively with MHC-I expression in AdenoCA (B) and SCC (E). (C&F) The heatmaps show Spearman correlations (R) among IHC features in AdneoCA (C) and SCC (F). Insignificant correlations (p>0.05) are shaded white; identity correlations are shaded grey.

Figure 2.. Survival probability increases with increasing CD8⁺ and CD56⁺ lymphocyte infiltrates.

Kaplan-Meier analysis predicts DFS or OS with respect to tumor cell MHC-I HC expression (A&B), CD56⁺ cell counts (C&D), CD8⁺ cell counts (E&F), or coincident CD56⁺ and CD8⁺ counts (G&H) averaged between CT/PT. P-value, log-rank test.

Figure 3.. mIF imaging reveals extent of MHC-I HC expression variation in resected patient tumor tissues.

(A) Representative images exemplify MHC-I loss in AdenoCA and SCC. (B) Histograms of (%) MHC-I⁺ tumor cells in each region of AdenoCA and SCC. (C) The (%) MHC-I⁺ tumor cells in each region per patient for AdenoCA and SCC.

Figure 4.. Tumor infiltrated lymphocytes and IFNγ expression are associated with high tumor cell MHC-I expression.

OPLSDA models discriminate between tumor regions with MHC-I⁺ tumor cell densities (cells/mm²) above or below the median in AdenoCA (A-D) and SCC (E-H). CV, cross-validation accuracy. Significance was determined by a permutation test (p<0.001). (A&E) X scores plot, where each point represents one region projected onto latent variables 1 and 2 (LV1&LV2). (B&F) VIP scores are shown artificially oriented in the direction of loadings on LV1. |VIP|>1 indicates variables with greater than average influence on the separation between groups. (C&G) Spearman correlations among immunologic features in tumor (T) and stroma (S). Insignificant correlations (p>0.05) are shaded white, identity correlations are shaded grey. (D&H) Univariate comparisons between model features. Wilcoxon rank sum test with Benjamini-Hochberg correction (*p<0.05; **p<0.01; ***p<0.0001). Only features with p_adj<0.05 are shown. (I) Representative image shows variation in lymphocyte occupancy and IFNγ staining in MHC-I⁻ (top) or MHC-I⁺ (bottom) regions of the same tumor.

Figure 5.. NK cells and CD8 T cells colocalize in areas with IFNγ staining.

Representative images show NK cells and CD8 T cells clustered near IFNγ in 3 patient tumors.

Figure 6.. IFNγ⁺ NK cells and IFNγ⁺ CD8 T cells associate with other activated lymphocytes and MHC-I⁺ tumor cells.

OPLSDA models discriminate IFNγ^+/− NK cells (A-C) and IFNγ^+/− CD8 T cells (E-G). CV, cross-validation accuracy. Significance was determined by a permutation test (p<0.001). (A&E) X scores plot, where each point represents one region projected onto latent variables 1 and 2 (LV1&LV2). (B&F) VIP scores are shown artificially oriented in the direction of loadings on LV1. |VIP|>1 indicates a variable with greater than average influence on the separation between groups. (C&G) Univariate comparisons between model features. Wilcoxon rank sum test with Benjamini-Hochberg correction (*p<1×10⁻²⁷; **p<1×10⁻³⁰; ***p<1×10⁻⁵⁰). (D&H) The average counts of MHC-I⁺ tumor cell neighbors for IFNγ^+/− NK cells (D) or IFNγ^+/− CD8 T cells (H) in tumor (T) or stroma (S) regions. (I) Representative mIF image showing a cluster of lymphocytes colocalized with IFNγ in an MHC-I⁻ tumor nest. (J) The K function plotted against increasing radii for IFNγ⁺ NK cells and IFNγ⁺ CD8 T cells as the target cell and either MHC-I⁺ (red solid line) or MHC I⁻ (red dashed line) center cells. Grey shading, 95% confidence intervals. Black line, Poisson (null) distribution.