Spotting and targeting leftover AML cells with AI and single-cell tools
Official title: Targeting minimal residual disease in AML by using single-cell morphological and biophysical analysis with deep learning
Funded at Massachusetts Institute of Technology · NIH-11189593 · Data updated 2026-11-12
This project uses AI-powered imaging and single-cell measurements to find treatments that could stop relapse in people with AML who are in remission but still have tiny amounts of disease.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts Institute of Technology NIH-funded |
| Lab location | 1 site (Cambridge, United States) |
| Project start | September 9, 2024 |
| Project end | August 31, 2029 |
| Funding year | 2025 |
| Award (this year) | $655,219 |
| NIH project number | 5U01CA282163-02 |
| Project ID | NIH-11189593 on NIH RePORTER |
Who could benefit from this research
✓Could help
People with AML who are in complete remission but test positive for MRD and who can provide blood or bone marrow samples at participating centers are the best fit.
✕Less likely to help
Patients without detectable MRD, those with other cancer types, or anyone unable to provide required samples or attend participating hospitals are unlikely to benefit from this project.
What this research studies
If you have AML and are in remission but still test positive for measurable residual disease (MRD), we will use small blood or bone marrow samples to look at individual leftover leukemia cells. We combine high-resolution, label-free imaging, AI-driven cell sorting, and precise biophysical measurements to reveal differences between cells that might hide drug sensitivities. Our team will link these lab findings to clinical information from participating hospitals to identify which therapies might best target those MRD cells before relapse. The goal is a real-time platform that helps clinicians pick treatments aimed at preventing your leukemia from coming back.
Why it matters
Potential benefit: If successful, this could help doctors choose treatments that prevent relapse by targeting tiny leftover leukemia cells before disease returns.
How similar studies have gone: Related single-cell and functional testing approaches have shown promise for revealing drug sensitivities, but combining label-free AI sorting with precise biophysical measurements for MRD is a novel strategy.
Where this research is happening
-
Massachusetts Institute of Technology
Cambridge, United States Active
Researchers
- Principal investigator: Manalis, Scott R — Massachusetts Institute of Technology
- Study coordinator: Manalis, Scott R
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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.