Improving T-cell access to difficult-to-treat tumors using advanced imaging and machine learning
Defining therapeutic strategies for boosting T-cell infiltration into cold tumors with spatial proteomics and machine learning
['FUNDING_R21'] · CALIFORNIA INSTITUTE OF TECHNOLOGY · NIH-10743501
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
| Phase | ['FUNDING_R21'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | CALIFORNIA INSTITUTE OF TECHNOLOGY (nih funded) |
| Locations | 1 site (PASADENA, UNITED STATES) |
| Trial ID | NIH-10743501 on ClinicalTrials.gov |
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.
Who could benefit from this research
Good fit: 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.
Not a fit: Patients with hot tumors, where T-cell infiltration is already effective, may not benefit from this specific research.
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 performed: 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
PASADENA, UNITED STATES
- CALIFORNIA INSTITUTE OF TECHNOLOGY — PASADENA, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: THOMSON, MATTHEW W. — CALIFORNIA INSTITUTE OF TECHNOLOGY
- Study coordinator: THOMSON, MATTHEW W.
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.
Conditions: Breast Cancer