Designing T-cell receptors to better target cancer
Official title: MATCHMAKERS: Solving T-cell receptor recognition and design via integrated high-throughput screening and structural, functional and computational approaches
Funded at Brigham and Women's Hospital · NIH-11514454 · Data updated 2026-11-12
Using AI plus large-scale lab data to learn how immune T cells recognize tumor markers so future therapies can target cancer more precisely.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Brigham and Women's Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | May 1, 2024 |
| Project end | April 30, 2027 |
| Funding year | 2026 |
| Award (this year) | $609,766 |
| NIH project number | 3OT2CA297464-01S2 |
| Project ID | NIH-11514454 on NIH RePORTER |
Who could benefit from this research
✓Could help
People with cancer who can donate blood or tumor samples or participate in immune-sampling protocols would be the most likely candidates to contribute data or be considered for related future therapies.
✕Less likely to help
Patients without cancer, those unable to provide samples, or those needing immediate treatment are unlikely to receive direct benefit from this discovery-focused work in the short term.
What this research studies
This project uses lab work and advanced AI to map how your immune T cells spot tumor markers. Researchers will collect large sets of matched T-cell receptors and the tumor pieces they bind from people and mouse models, then run high-throughput structural and functional tests. They will train machine-learning models on these matched pairs and create engineered (synthetic) TCR–antigen combinations to broaden the data. The goal is to build tools that can predict which TCRs recognize which tumor antigens and to design better T-cell therapies for cancer patients.
Why it matters
Potential benefit: If successful, this could enable more precise T-cell–based immunotherapies that better target tumors and reduce off-target effects.
How similar studies have gone: Previous work combining structural data and machine learning has shown promise, but a general, reliable predictor of TCR specificity remains largely unsolved and this project is ambitious and relatively novel.
Where this research is happening
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Brigham and Women's Hospital
Boston, United States Active
Researchers
- Principal investigator: Elledge, Stephen J — Brigham and Women's Hospital
- Study coordinator: Elledge, Stephen J
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.