Using AI to find tissue and genetic markers to guide immune therapy for stomach cancer
Using artificial intelligence to discover spatial, genomic, and pathologic biomarkers to guide and augment immune checkpoint inhibitor therapy for gastric cancer
This project uses artificial intelligence to find tissue and genomic signs that help doctors choose and improve immune checkpoint drugs for people with gastric (stomach) cancer.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Ut Southwestern Medical Center NIH-funded |
| Lab location | 1 site (Dallas, United States) |
| Project ID | NIH-11294323 on NIH RePORTER |
What this research studies
Researchers will combine tumor gene activity data, high-resolution pathology images, and spatial information about where cells sit in the tumor to train AI models. The team will search for markers that predict who benefits from immune checkpoint inhibitors and for targets that could make these drugs work better. Work will use patient tumor samples and clinical information, with computational scientists and surgeon-scientists collaborating to validate findings. The plan includes developing models from existing data and testing them on independent patient tissue sets to confirm real-world usefulness.
Who could benefit from this research
Good fit: People with diagnosed gastric (stomach) cancer, especially those with available tumor tissue or who are being considered for immune checkpoint inhibitor therapy, would be the ideal candidates to contribute samples or data.
Not a fit: Patients without available tumor tissue, those with cancers unrelated to the stomach, or those not eligible for immune checkpoint therapy may not directly benefit from or participate in this work.
Why it matters
Potential benefit: If successful, this could help doctors pick patients who are likely to benefit from immune checkpoint drugs and suggest new treatments that boost response rates.
How similar studies have performed: Previous studies have identified markers like PD-L1 and MSI and early AI approaches show promise, but combining spatial, genomic, and digital pathology data for gastric cancer is a relatively new and evolving approach.
Where this research is happening
Dallas, United States
- Ut Southwestern Medical Center — Dallas, United States (Active)
Researchers
- Principal investigator: Wang, Sam C. — Ut Southwestern Medical Center
- Study coordinator: Wang, Sam C.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.