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

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...

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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379179/
https://www.ncbi.nlm.nih.gov/pubmed/34417454
http://dx.doi.org/10.1038/s41467-021-24994-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379179/
https://www.ncbi.nlm.nih.gov/pubmed/34417454
http://dx.doi.org/10.1038/s41467-021-24994-w

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