Bibliographic Details
| Title: |
Detection and characterization of lung cancer using cell-free DNA fragmentomes. |
| Authors: |
Mathios, Dimitrios, Johansen, Jakob Sidenius, Cristiano, Stephen, Medina, Jamie E., Phallen, Jillian, Larsen, Klaus R., Bruhm, Daniel C., Niknafs, Noushin, Ferreira, Leonardo, Adleff, Vilmos, Chiao, Jia Yuee, Leal, Alessandro, Noe, Michael, White, James R., Arun, Adith S., Hruban, Carolyn, Annapragada, Akshaya V., Jensen, Sarah Østrup, Ørntoft, Mai-Britt Worm, Madsen, Anders Husted |
| Source: |
Nature Communications; 8/20/2021, Vol. 12 Issue 1, p1-14, 14p |
| Subject Terms: |
LUNG cancer, CELL-free DNA, SMALL cell lung cancer, NON-small-cell lung carcinoma, COMPUTED tomography, CIRCULATING tumor DNA |
| Abstract: |
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. DNA from tumour cells can be detected in the blood of cancer patients. Here, the authors show that cell free DNA fragmentation patterns can identify lung cancer patients and when this information is further interrogated it can be used to predict lung cancer histological subtype. [ABSTRACT FROM AUTHOR] |
|
Copyright of Nature Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |
