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Cited by 472

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Detection and characterization of lung cancer using cell-free DNA fragmentomes

Open access
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, Carvalho, Beatriz, de Wit, Meike, Carey, Jacob, Dracopoli, Nicholas C., Maddala, Tara, Fang, Kenneth C., Hartman, Anne-Renee, Forde, Patrick M., Anagnostou, Valsamo, Brahmer, Julie R., Fijneman, Remond J. A., Nielsen, Hans Jørgen, Meijer, Gerrit A., Andersen, Claus Lindbjerg, Mellemgaard, Anders, Bojesen, Stig E., Scharpf, Robert B., Velculescu, Victor E.·2021·Nature Communications
DOI 10.1038/s41467-021-24994-w
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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.

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Metadata

Authors
Mathios, D., Johansen, J. S., Cristiano, S., Medina, J. E., Phallen, J., Larsen, K. R., Bruhm, D. C., Niknafs, N., Ferreira, L., Adleff, V., Chiao, J. Y., Leal, A., Noe, M., White, J. R., Arun, A. S., Hruban, C., Annapragada, A. V., Jensen, S. Ø., Ørntoft, M.-B. W., Madsen, A. H., Carvalho, B., de Wit, M., Carey, J., Dracopoli, N. C., Maddala, T., Fang, K. C., Hartman, A.-R., Forde, P. M., Anagnostou, V., Brahmer, J. R., Fijneman, R. J. A., Nielsen, H. J., Meijer, G. A., Andersen, C. L., Mellemgaard, A., Bojesen, S. E., Scharpf, R. B., & Velculescu, V. E.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-08-20
Volume
12
Issue
1
Pages
5060-5060
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-021-24994-w
OpenAlex
W3194069390
Cited by
472
References
15
FWCI
25.49
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Detection and characterization of lung cancer using cell-free DNA fragmentomes. https://app.cochat.ai/papers/10.1038/s41467-021-24994-w
The paper
Mathios, D., Johansen, J. S., Cristiano, S., Medina, J. E., Phallen, J., Larsen, K. R., Bruhm, D. C., Niknafs, N., Ferreira, L., Adleff, V., Chiao, J. Y., Leal, A., Noe, M., White, J. R., Arun, A. S., Hruban, C., Annapragada, A. V., Jensen, S. Ø., Ørntoft, M.-B. W., . . . Velculescu, V. E. (2021). Detection and characterization of lung cancer using cell-free DNA fragmentomes. Nature Communications. https://doi.org/10.1038/s41467-021-24994-w

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.