Advanced machine learning to improve blood-based cancer detection in melanoma and lung cancer
Official title: Advanced machine learning to empower ultra-sensitive liquid biopsy in melanoma and non-small cell lung cancer
Funded at Sloan-Kettering Inst Can Research · NIH-11370107 · Data updated 2026-11-12
A physician-scientist is using cutting-edge AI to make blood tests for melanoma and non-small cell lung cancer much more sensitive, so doctors can monitor cancer without relying on scans.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Sloan-Kettering Inst Can Research NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | July 4, 2023 |
| Project end | June 30, 2028 |
| Funding year | 2026 |
| Award (this year) | $292,597 |
| NIH project number | 5K08CA263301-04 |
| Project ID | NIH-11370107 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with melanoma or non-small cell lung cancer who are undergoing treatment and willing to provide blood samples for research.
✕Less likely to help
Patients not undergoing treatment with ctDNA-guided monitoring may not directly benefit from this research.
What this research studies
This grant focuses on using whole-genome sequencing of blood plasma and advanced machine learning to detect tiny amounts of cancer DNA in the blood. The approach builds on prior work that already increased sensitivity over traditional targeted sequencing, and it now aims to add epigenetic features to further boost detection. The goal is to track tumor burden and early treatment response in patients receiving immunotherapy, potentially allowing earlier treatment decisions. The research involves analyzing patient blood samples and developing algorithms that can distinguish cancer signals from normal DNA. If successful, this could lead to more precise and timely management of cancer during and after therapy.
Why it matters
Potential benefit: If successful, patients could receive earlier and more accurate information about how well treatment is working from a simple blood test.
How similar studies have gone: Previous work showed that plasma whole-genome sequencing with AI can detect ctDNA earlier than imaging in some cases; this project extends those methods with epigenetic data.
Where this research is happening
-
Sloan-Kettering Inst Can Research
New York, United States Active
Researchers
- Principal investigator: Widman, Adam — Sloan-Kettering Inst Can Research
- Study coordinator: Widman, Adam
Related research
Other NIH-funded projects on the same topics.
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Improving blood tests for early cancer detection and treatment monitoring in melanoma and lung cancer
This work aims to make blood tests much better at finding tiny amounts of cancer DNA to help people with melanoma and non-small cell lung cancer.
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Using advanced machine learning to improve blood tests for detecting cancer
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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.