Using advanced biomarkers to better predict lung cancer outcomes
Official title: Automatic integrated biomarkers to improve prediction of lung cancer outcomes
Funded at Sloan-Kettering Inst Can Research · NIH-10985665 · Data updated 2026-11-12
This study is looking to help people with non-small cell lung cancer by using new tests and real-life data to better predict how their cancer might behave and how well different treatments could work for them, so they can get more personalized care.
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 | August 13, 2024 |
| Project end | July 31, 2029 |
| Funding year | 2024 |
| Award (this year) | $256,487 |
| NIH project number | 1K08CA286842-01A1 |
| Project ID | NIH-10985665 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with non-small cell lung cancer who have undergone definitive treatment and are at risk for recurrence or metastasis.
✕Less likely to help
Patients with other types of lung cancer or those who are not diagnosed with lung cancer may not receive any benefit from this research.
What this research studies
This research focuses on improving the prediction of outcomes for patients with non-small cell lung cancer (NSCLC) by utilizing advanced biomarkers and real-world data. It aims to answer critical questions about recurrence likelihood, treatment benefits, and the risk of brain metastasis in NSCLC patients. By integrating genomic profiling, imaging, and clinician notes with innovative assays like circulating tumor DNA sequencing, the study seeks to create more personalized treatment plans. The approach leverages advanced data processing techniques to streamline the analysis of complex patient information.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of lung cancer outcomes, allowing for tailored treatment strategies that improve patient survival and quality of life.
How similar studies have gone: Previous research has shown promise in using similar approaches to integrate real-world data and biomarkers for cancer prediction, indicating a potential for success in this novel application.
Where this research is happening
-
Sloan-Kettering Inst Can Research
New York, United States Active
Researchers
- Principal investigator: Jee, Justin — Sloan-Kettering Inst Can Research
- Study coordinator: Jee, Justin
Related research
Other NIH-funded projects on the same topics.
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Blood-based markers to identify lung cancer risk in never-smokers
A researchers team at NYU School of Medicine is testing blood tests that might flag higher lung cancer risk in people who have never smoked, helping decide who might need screening.
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Integrative Analysis of Lung Cancer Risk and Its Causes
A large, multi-part study at the University of New Mexico is exploring why lung cancer happens and how to better prevent and detect it.
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Integrative Analysis of Lung Cancer Risk and Causes
A large NIH project is exploring how tobacco use, behavior, biology, and screening practices influence lung cancer risk and outcomes to help prevent and detect it earlier.
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Digital health strategies to expand lung cancer screening in safety-net clinics
A funded project aims to help low-resource clinics increase the use of lung cancer screening for patients who smoke or are at risk.
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Proteomics-guided matching of lung cancer tumors to preclinical models
This grant aims to improve how we match a patient’s lung cancer tumor to the right preclinical models using protein data.
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Optimizing lung cancer care with smart tools for prevention, early detection, treatment, and survivorship
A U01 grant using AI and biomarkers to improve lung cancer prevention, screening, treatment, and survivorship care for people at risk or living with lung cancer.
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.