Abstract 860: Pan-tissue master regulator inference reveals mechanisms of MHC alterations in cancers

Abstract Introduction: Major histocompatibility complex (MHC) genes are an integral part of the adaptive immune system. The T-cells can detect and kill cancer cells through the recognition of mutant peptides on the cell surface via the MHC. As a result, many cancers evade the immune system by altering their MHC regulation which can also lead to resistance to immune checkpoint blockade (ICB). Therefore, understanding MHC regulation in both baseline (normal state) and cancer is critical for uncovering mechanisms behind MHC alterations. Here, we have undertaken a pan-tissue evaluation to comprehensively decode MHC transcriptional regulation in healthy tissues and various cancers using a large compendium of single-cell RNA-seq (scRNA-seq) datasets. Methods: To study MHC class I (MHC-I) and MHC class II (MHC-II) transcriptional regulation at baseline we leveraged a large-scale scRNA-seq dataset encompassing 18 tissues and 124 epithelial cell types. Upon re-clustering and annotation, we investigated MHC expression across cell types and tissues and quantified its heterogeneity using the Gini coefficient. Next, we sought to identify transcription factors (TF) predictive of MHC-I and MHC-II expression, for that, we employed a master regulator inference that utilizes uni- and multi- regression models. Further, to investigate MHC and nominated TFs dysregulation in cancer, we curated a compendium of scRNA-seq datasets comprising 43 datasets, 20 cancer types, across 538 patients. Results: In normal epithelial cell types, we observed a large range (~8 fold) in MHC-I expression. We also detected MHC-II expression in unexpected cell types such as club cells in the prostate. We found that lung epithelial cells showed elevated MHC-II expression. Immunohistochemistry was performed to validate these findings. Using master regulator inference, we nominated 104 and 107 TFs which predict MHC-I and MHC-II expression, respectively. The nominated TFs showed significant enrichment (p < 0.001) for known TFs, alongside putative novel transcriptional regulators such as YBX1 (MHC-I p < 2e-16, MHC-II p = 8e-07) and XBP1 (MHC-I p < 2e-16, MHC-II p = 7e-04). Using our single-cell compendium of 20 cancer types we also observed a pan-cancer repression in MHC genes (p < 0.001). Next, we utilized an ICB cohort to identify specific TFs that could predict therapeutic benefits. Interestingly, XBP1 (p < 1e-16) and SP100 (p < 2.2e-08) were strong predictors of patients' survival. We also observed a large heterogeneity in MHC-II expression among colon cancer patients. Upon investigation, we found that patients with high MHC-II expression were mismatch repair deficient, this implicates MHC-II upregulation as an immune evasion mechanism. Conclusion: In conclusion, we have comprehensively evaluated MHC expression in 124 epithelial cell types and across 20 cancers identifying TFs predictive of MHC-I and MHC-II expression and their effects on patient outcomes. Citation Format: Mahnoor N. Gondal, Rahul Mannan, Yi Bao, Jing Hu, Marcin Cieslik, Arul M. Chinnaiyan. Pan-tissue master regulator inference reveals mechanisms of MHC alterations in cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 860.