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

> **NIH NIH K99** · STANFORD UNIVERSITY · 2023 · $157,788

## Abstract

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 b...

## Key facts

- **NIH application ID:** 10684199
- **Project number:** 5K99CA255586-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Gina Bouchard
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $157,788
- **Award type:** 5
- **Project period:** 2022-09-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10684199

## Citation

> US National Institutes of Health, RePORTER application 10684199, Exploring O-glycoproteomics to prevent metabolic radioresistance in the tumor microenvironment (5K99CA255586-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10684199. Licensed CC0.

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