(A-1) Tumor MHC class I Expression Levels Correlate with Altered Immune Cell Colocalization in Non-Small Cell Lung Cancer Tumor Microenvironment

Despite significant efforts in prevention and treatment, lung cancer remains the leading cause of cancer related deaths worldwide. Non-small cell lung cancer (NSCLC) represents 85% of these cases and is associated with variable treatment response rates and poor outcomes¹. Our current understanding of the mechanisms which drive response in specific patients remains poor and there is a need to better understand the processes by which therapies fail to prevent disease progression². 

In NSCLC, one common immune evasion mechanism is the loss of the expression of Major Histocompatibility Complex (MHC) class I proteins on the surface of tumor cells³. Normally upregulated in response to Interferon Gamma (IFNg) signaling from immune cells, MHC class I forms a complex with the T cell receptor on cytotoxic CD8⁺ T cells leading to the MHC class I dependent recognition and clearance of tumor cells⁴. The loss of MHC class I renders CD8⁺ T cells unable to recognize and act upon cancer cells. This loss alters the composition and phenotype of immune cells in the tumor microenvironment, presumably leading to worse outcomes. However, variable effects of MHC I loss have been observed across studies and among cohorts^5-7. While the direct effect of this loss on CD8⁺ T cells is understood, there is a need to better understand how it affects the local immune signaling and the subsequent spatial distribution of other immune cells.

In this study we seek to investigate the spatial relationship between MHC class I expression and other markers of immune function in NSCLC.

Multiplex immunofluorescence (mIF) imaging was conducted on a cohort of NSCLC biopsies from the University of Virginia (UVA) patients using the Vectra imaging system and followed by quantitative image analysis using Halo software. Distance metrics were calculated on digitized object data using the SPATSTAT⁸ package in R. Spatial transcriptomics was performed on a separate cohort of NSCLC patient biopsies using the GeoMx platform. Cell type scores were calculated by summing the expression of a set of cell type specific marker genes and pairwise Pearson correlation coefficients were calculated in Python. For population level data, patient data from CBioPortal^9,10 was queried based on the availability of both mRNA expression and matching overall survival data. Z-scored mRNA expression data for relevant immune related genes was used to hierarchically cluster patients into distinct groups using Ward’s minimum variance method¹¹. Kaplan-Meier estimators were constructed for patients in each group and patient outcomes were compared between groups by using log rank tests on the Kaplan Meier curves.

To investigate how the expression of MHC class I on tumor cells colocalized with interferon gamma (IFNg+, used as a proxy for activation) expressing immune cells, we used mIF imaging on NSCLC tumor biopsies from UVA patients. IFNg+CD8+ T cells actively colocalized with MHC class I-expressing tumor cells but were present less than random chance in MHC class I-deficient tumor cells (Fig.1A). However, activated NK cells colocalized to a similar degree but more than random chance, irrespective of tumor MHC class I expression (Fig.1B). Contrary to our expectations, higher MHC class I expression on tumor cells was not associated with better outcomes in our UVA cohort (Fig. 1C), suggesting mechanisms other than infiltrating CD8 T cells are responsible for improving patient survival.

To investigate additional mechanisms that might explain the equivalent survival in predominantly MHC class I deficient patients, we used spatial transcriptomics on a set of regions of interest (ROIs) in an MHC class I-disparate subset of these patients. Interestingly, in some patients we saw significantly enriched expression of MHC class II genes correlating with the expression of MHC class I (Fig 1D). Furthermore, we observed significant positive correlations between MHC class II expression and the expression of macrophage markers. To test whether if this signature played in a role in patient outcomes, we analyzed a large cohort of publicly available data to investigate how MHC class II expression in a tumor is related to patient outcomes and observed that increased MHC class II expression in a tumor biopsy prior to treatment is correlated with better survival outcomes (Fig 1E-F)

Together we interpret these results to indicate that the infiltration of antigen presenting cells into a tumor along with IFNg induced MHC class II expression¹², which might lead to improved tumor clearance and better outcomes. These findings may point to an important role that myeloid cells play in both shaping the tumor environment and inducing tumor clearance, which is complementary to CD8 T cell immune axis. Future studies will investigate the mechanisms of macrophage infiltration and their role in antigen presentation in the MHC class I-deficient tumors.

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11.       Joe H. Ward Jr. (1963) Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, 58:301, 236 244, DOI: 10.1080/01621459.1963.10500845

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