Exploring O-glycoproteomics to prevent metabolic radioresistance in the tumor microenvironment

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PROJECT SUMMARY Radiotherapy (RT) is often the only curative option for patients with inoperable tumors. However, radiation is also known to impair tumor metabolism, leading to radioresistance, the main reason for RT failure. Metabolic reprogramming (MR) in cancer is defined as the ability of the tumor to rewire its energy to fulfill the needs for tumorigenesis and progression. Our group observed for the first time that MR toward the Hexosamine Biosynthesis Pathway (HBP), an understudied glucose pathway leading to protein glycosylation, is associated with poor survival in the lung adenocarcinoma. Precisely, we showed that this metabolic switch happens primarily in Cancer-Associated Fibroblasts (CAFs). This suggests that CAFs redirect their glucose toward HBP, which increases O-glycosylation, a Post-Translational Modification (PTM) known to modulate radioresistance. However, very little is known about 1) which proteins are O-glycosylated after MR toward HBP and 2) how these PTMs affect the cellular behavior and modulate radioresistance. My preliminary results show that metabolic cooperation between cancer and stromal cells alters protein O-glycosylation in both cell types. Therefore, I hypothesize that tumor-stroma crosstalk in the Tumor Microenvironment (TME), leading to changes in the O-glycoproteome, plays a role in radioresistance. To validate this hypothesis, we developed a novel approach that precisely measures the outcome of MR towards HBP (e.g., O-glycoproteome) in the context of tumor-stroma crosstalk. We propose to apply this technique to tumor-stroma organoids designated here as “assembloids” that recapitulate metabolically heterogeneous cell neighborhoods and characterize their O-glycoproteome before and after RT. First, to visualize HBP metabolic heterogeneity in the TME, I will construct an in-situ map of the primary tumor compartments (endothelial, malignant, fibroblast, and immune) enriched for HBP metabolic markers and glycoform structures, using CODEX. CODEX is a cutting- edge multiplexed imaging method that allows for single-cell quantification of up to 50 markers in situ (aim 1). Then, I will deconvolute cell neighborhoods using machine learning and clustering biocomputational approaches to quantify and inform which neighborhoods are active regions of protein O-glycosylation. In aim 2, I will recapitulate HBP-enriched cell neighborhoods using a 3D assembloid model, irradiate them, then characterize metabolic radioresistance patterns using CODEX. Lastly, in aim 3, I will analyze the O-glycoproteome and spatial information of radioresistant assembloids. The O-glycoproteins or upstream drivers to O-glycosylation involved in critical tumor-stroma interactions will be inhibited in an attempt to restore radiosensitivity. The resulting data will generate the first hypothesis synthesis tool exploring an understudied dimension of cell signaling, the O- glycoproteome. They will lead to the discovery of new molecular targets involved in both tumor metabolism and stromal interactions with the primary goal of improving RT response in cancer patients with inoperable tumors.