Journal
NATURE COMMUNICATIONS
Volume 12, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-021-24994-w
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Funding
- Dr. Miriam and Sheldon G. Adelson Medical Research Foundation
- SU2C in-Time Lung Cancer Interception Dream Team Grant
- Stand Up to Cancer-Dutch Cancer Society International Translational Cancer Research Dream Team Grant [SU2C-AACR-DT1415]
- Gray Foundation
- Commonwealth Foundation
- Mark Foundation for Cancer Research
- Lundbeck Foundation
- Roche Denmark
- US National Institutes of Health [CA121113, CA006973, CA233259, 1T32GM136577]
- Common Fund of the Office of the Director of the National Institutes of Health
- NCI
- NHGRI
- NHLBI
- NIDA
- NIMH
- NINDS
- Delfi Diagnostics
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Non-invasive assessment of cell-free DNA (cfDNA) provides an opportunity for cancer detection and intervention. Using a machine learning model and genome-wide analysis, the study successfully detected tumor-derived cfDNA in a prospective study of 365 individuals at risk for lung cancer. Combining clinical factors and CEA levels, the approach showed high accuracy in distinguishing lung cancer subtypes and the fragmentation score served as an independent prognostic indicator of survival.
Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across similar to 13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.
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