Detection and characterization of lung cancer using cell-free DNA fragmentomes
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- s41467-021-24994-w
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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 ~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.
Original language | English |
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Article number | 5060 |
Journal | Nature Communications |
Volume | 12 |
Issue number | 1 |
Number of pages | 14 |
ISSN | 2041-1723 |
DOIs | |
Publication status | Published - Dec 2021 |
Bibliographical note
Publisher Copyright:
© 2021, The Author(s).
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