Improving T-cell access to difficult-to-treat tumors using advanced imaging and machine learning
Official title: Defining therapeutic strategies for boosting T-cell infiltration into cold tumors with spatial proteomics and machine learning
Funded at California Institute of Technology · NIH-10743501 · Data updated 2026-11-12
This study is looking at ways to make cancer treatments work better for patients with cold tumors, which are tough because they don't have many immune cells fighting the cancer; by using advanced technology to understand the tumor environment, the researchers hope to find new ways to help the immune system do its job.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | California Institute of Technology NIH-funded |
| Lab location | 1 site (Pasadena, United States) |
| Project start | September 5, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $423,078 |
| NIH project number | 1R21CA284221-01 |
| Project ID | NIH-10743501 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with cold tumors, such as certain types of breast, liver, prostate, or colon cancers, who have not responded to standard immunotherapy treatments.
✕Less likely to help
Patients with hot tumors, where T-cell infiltration is already effective, may not benefit from this specific research.
What this research studies
This research focuses on enhancing the effectiveness of immunotherapies for patients with cold tumors, which are characterized by low T-cell infiltration due to suppressive environments created by cancer cells. By utilizing advanced spatial proteomic techniques, the study aims to analyze the tumor microenvironment at a molecular level to identify factors that inhibit T-cell function. The project employs machine learning algorithms to process large datasets, ultimately seeking to develop targeted therapeutic strategies that can be tested in clinical settings to improve T-cell access and activity in these challenging tumors.
Why it matters
Potential benefit: If successful, this research could lead to new treatment options that enhance the effectiveness of immunotherapies for patients with cold tumors, potentially improving outcomes for those with difficult-to-treat cancers.
How similar studies have gone: While the approach of using spatial proteomics and machine learning is innovative, similar strategies in other contexts have shown promise in enhancing immunotherapy effectiveness, indicating potential for success.
Where this research is happening
-
California Institute of Technology
Pasadena, United States Active
Researchers
- Principal investigator: Thomson, Matthew W. — California Institute of Technology
- Study coordinator: Thomson, Matthew W.
Related research
Other NIH-funded projects on the same topics.
-
Genetic and Neural Drivers of Sleep in Zebrafish
Researchers are studying how genes and brain signals control sleep using zebrafish to understand why sleep disorders happen and how to treat them.
-
Wireless, skin-friendly neck wearable to support voice/airway care
A neck-worn, wireless sensor system combines two measurements to help people with voice and airway problems manage symptoms and stay adherent to treatment.
-
Bacterial gene activity in human tissues to link microbes and host health
A research project to map which genes are active in both human tissues and their resident bacteria to better understand conditions like Crohn’s disease.
-
In Situ Amplification for Biological Imaging using Hybridization Chain Reaction
A research grant supporting a new imaging approach that helps scientists visualize where RNA and proteins are located inside tissues and embryos.
-
WormBase: a life science data hub for C. elegans and other nematodes
Aimed at continuously improving WormBase, a key online data resource used by researchers studying C. elegans and related nematodes, to help scientists access genome and gene information more efficiently.
-
Alliance Central: A platform to build smarter genome knowledge bases
A NIH-funded project to create a next-generation software platform and data framework that helps scientists integrate and harmonize genome and model-organism data to better understand human health and disease.
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.