Abstract 3000: A data-driven and AI-empowered systems biology model of MHC class I antigen presentation pathway
Authors: Wang Jia , Wang Xiao , Dang Pengtao , Zhu Haiqi , Zhou Xinyu , So Kaman , Cao Sha , Zhang Chi 
Published: April 2023

Abstract
Immunotherapy is a new frontline therapeutic strategy for multiple cancer types. However, non-response mechanisms of immune checkpoint inhibitors generally exist in patients of solid cancer such breast and colorectal cancer. Multiple factors that parallelly regulate T cell functions may cause the non-response mechanism, such as regulators of T-cell’s recognition of cancer cells and cytotoxicity activities. Our recent study revealed that the presentation and salvage of MHC class I antigen presentation on cancer cells is a key deterministic factor of T-cell recognition in multiple cancer types.
In this study, we established a systems biology model of the antigen processing and presentation pathways to identify cancer cell-expressing genes that are associated with immune response of breast cancer and effector T cell-mediated cytotoxicity. To the best of our knowledge, there is no established systems biology model of MHC I class antigen presentation pathway. We first manually collected and curated the biological processes that are involved in the MHC class I antigen presentation pathway via an extensive literature review. MHC I class antigen presentation involves a chain of biological processes, namely ubiquitination, proteosome, transporter associated with antigen processing, chaperone-based formation of MHC I class one complex, ER to Golgi, and Golgi to cell membrane exocytosis. We also include branches such as de-ubiquitination, MHC class I complex checking, and endocytosis-based salvage.
We further estimated sample-wise activity level of the MHC class I antigen presentation pathway using transcriptomics data. By treating antigen as a “mass carrying” signal, we developed a systems biology model over the established pathway. The whole biological process was treated as a network model, in which the flux represents antigen presentation activity level. Based on this computational method, we further build a machine-learning pipeline that utilizes omics datasets (cancer genomics, scRNA-seq, spatial transcriptomics) collected from tumor tissue samples to correlate the MHC class I antigen presentation activities with immune response in cancer tumor microenvironment. We identified genes expressed by cancer cells that have high impacts on antigen presentation and are associated with T cell recognition and cytotoxicity. The identified genes are involved in (a) the antigen presentation machinery, (b) the immunoproteasome in antigen processing, (c) the dynamics of antigen presentation on the cell surface, and (d) cytokine and chemokine secretion.
Citation Format: Jia Wang, Xiao Wang, Pengtao Dang, Haiqi Zhu, Xinyu Zhou, Kaman So, Sha Cao, Chi Zhang. A data-driven and AI-empowered systems biology model of MHC class I antigen presentation pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3000.