# Using artificial intelligence to discover spatial, genomic, and pathologic biomarkers to guide and augment immune checkpoint inhibitor therapy for gastric cancer

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2024 · $677,533

## Abstract

PROJECT SUMMARY/ABSTRACT
Outcomes for patients with gastric cancer, which is the fourth-most lethal cancer worldwide, are poor and
improved therapies are needed. While immune checkpoint inhibitors (ICIs) extend survival for patients suffering
from a variety of cancers, most gastric cancer patients do not respond to ICIs. The overall low response of
gastric cancers to ICI therapy is not only disappointing in terms of inability to improve overall survival, but it
also means that a large portion of patients treated with ICIs suffer the toxicities of the treatment without any
clinical benefit. Thus, to improve outcomes for gastric cancer patients, there is a critical need to identify novel
biomarkers that guide ICI use and to find new strategies that augment ICI efficacy. The investigators’ long-
term goal is to gain a deeper understanding of the gastric cancer tumor microenvironment to refine the use of
ICIs. The objective of this proposal is to identify biomarkers that predict ICI response and to discover targets
that are actionable to improve ICI efficacy. Based on the investigative team’s published and unpublished
results, the central hypothesis of the proposal is that artificial intelligence can analyze transcriptomic, digital
pathology, and spatial data to guide ICI use for patients with gastric cancer. To test this hypothesis, a close
collaboration between a computational scientist and surgeon-scientist has been established to pursue 3 aims.
In Aim 1, we will identify novel transcriptomic, digital pathology, and spatial biomarkers that predict ICI
response by comparing tumor samples obtained from gastric cancer patients who did and did not respond to
ICIs. In Aim 2, we will use explainable machine learning approaches to analyze multimodal datasets to predict
ICI response. In Aim 3, we will perform preclinical testing of therapies that target pathways and cell-cell
interactions that are enriched in ICI nonresponders in an effort to increase ICI efficacy. Three candidate targets
have already been identified. The conceptual innovations of this proposal are 1) that artificial intelligence can
analyze multiple streams of data to discover predictive biomarkers, identify ICI response mechanisms, and
predict ICI response, and 2) there are actionable mediators of ICI nonresponse that are identifiable via detailed
analysis of the tumor microenvironment. The proposal is also supported by multiple technical innovations that
include the use of 1) cutting-edge spatial profiling techniques to simultaneously acquire both spatial and
transcriptomic information within the tumor microenvironment, 2) novel artificial intelligence algorithms that
identify image-based predictive biomarkers, 3) novel artificial intelligence algorithms to integrate and process
high-dimensional datasets and provide practical guidance on the probability of ICI response, and 4) a novel
gastric cancer organoid platform to test candidate therapies to improve ICI response. The expected res...

## Key facts

- **NIH application ID:** 10904106
- **Project number:** 1R01CA276690-01A1
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Tae Hyun Hwang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $677,533
- **Award type:** 1
- **Project period:** 2024-04-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10904106

## Citation

> US National Institutes of Health, RePORTER application 10904106, Using artificial intelligence to discover spatial, genomic, and pathologic biomarkers to guide and augment immune checkpoint inhibitor therapy for gastric cancer (1R01CA276690-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10904106. Licensed CC0.

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