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Detection and characterization of lung cancer using cell-free DNA fragmentomes. / 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.
In:
Nature Communications, Vol. 12, 5060, 12.2021.
Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review
Harvard
Mathios, D, Johansen, JS, Cristiano, S, Medina, JE, Phallen, J, Larsen, KR, Bruhm, DC, Niknafs, N, Ferreira, L, Adleff, V, Chiao, JY, Leal, A, Noe, M, White, JR, Arun, AS, Hruban, C, Annapragada, AV
, Jensen, SØ, Ørntoft, MBW
, Madsen, AH, Carvalho, B, de Wit, M, Carey, J, Dracopoli, NC, Maddala, T, Fang, KC, Hartman, AR, Forde, PM, Anagnostou, V, Brahmer, JR, Fijneman, RJA, Nielsen, HJ, Meijer, GA
, Andersen, CL, Mellemgaard, A, Bojesen, SE, Scharpf, RB & Velculescu, VE 2021, '
Detection and characterization of lung cancer using cell-free DNA fragmentomes',
Nature Communications, vol. 12, 5060.
https://doi.org/10.1038/s41467-021-24994-wAPA
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,
12, [5060].
https://doi.org/10.1038/s41467-021-24994-wCBE
Mathios D, Johansen JS, Cristiano S, Medina JE, Phallen J, Larsen KR, Bruhm DC, Niknafs N, Ferreira L, Adleff V, Chiao JY, Leal A, Noe M, White JR, Arun AS, Hruban C, Annapragada AV
, Jensen SØ, Ørntoft MBW
, Madsen AH, Carvalho B, de Wit M, Carey J, Dracopoli NC, Maddala T, Fang KC, Hartman AR, Forde PM, Anagnostou V, Brahmer JR, Fijneman RJA, Nielsen HJ, Meijer GA
, Andersen CL, Mellemgaard A, Bojesen SE, Scharpf RB, Velculescu VE. 2021.
Detection and characterization of lung cancer using cell-free DNA fragmentomes.
Nature Communications. 12:Article 5060.
https://doi.org/10.1038/s41467-021-24994-wMLA
Vancouver
Author
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. /
Detection and characterization of lung cancer using cell-free DNA fragmentomes. In:
Nature Communications. 2021 ; Vol. 12.
Bibtex
@article{d804c9a3a8f04169906c2a1949baf89e,
title = "Detection and characterization of lung cancer using cell-free DNA fragmentomes",
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.",
author = "Dimitrios Mathios and Johansen, {Jakob Sidenius} and Stephen Cristiano and Medina, {Jamie E.} and Jillian Phallen and Larsen, {Klaus R.} and Bruhm, {Daniel C.} and Noushin Niknafs and Leonardo Ferreira and Vilmos Adleff and Chiao, {Jia Yuee} and Alessandro Leal and Michael Noe and White, {James R.} and Arun, {Adith S.} and Carolyn Hruban and Annapragada, {Akshaya V.} and Jensen, {Sarah {\O}strup} and {\O}rntoft, {Mai Britt Worm} and Madsen, {Anders Husted} and Beatriz Carvalho and {de Wit}, Meike and Jacob Carey and Dracopoli, {Nicholas C.} and Tara Maddala and Fang, {Kenneth C.} and Hartman, {Anne Renee} and Forde, {Patrick M.} and Valsamo Anagnostou and Brahmer, {Julie R.} and Fijneman, {Remond J.A.} and Nielsen, {Hans J{\o}rgen} and Meijer, {Gerrit A.} and Andersen, {Claus Lindbjerg} and Anders Mellemgaard and Bojesen, {Stig E.} and Scharpf, {Robert B.} and Velculescu, {Victor E.}",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s).",
year = "2021",
month = dec,
doi = "10.1038/s41467-021-24994-w",
language = "English",
volume = "12",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
}
RIS
TY - JOUR
T1 - Detection and characterization of lung cancer using cell-free DNA fragmentomes
AU - Mathios, Dimitrios
AU - Johansen, Jakob Sidenius
AU - Cristiano, Stephen
AU - Medina, Jamie E.
AU - Phallen, Jillian
AU - Larsen, Klaus R.
AU - Bruhm, Daniel C.
AU - Niknafs, Noushin
AU - Ferreira, Leonardo
AU - Adleff, Vilmos
AU - Chiao, Jia Yuee
AU - Leal, Alessandro
AU - Noe, Michael
AU - White, James R.
AU - Arun, Adith S.
AU - Hruban, Carolyn
AU - Annapragada, Akshaya V.
AU - Jensen, Sarah Østrup
AU - Ørntoft, Mai Britt Worm
AU - Madsen, Anders Husted
AU - Carvalho, Beatriz
AU - de Wit, Meike
AU - Carey, Jacob
AU - Dracopoli, Nicholas C.
AU - Maddala, Tara
AU - Fang, Kenneth C.
AU - Hartman, Anne Renee
AU - Forde, Patrick M.
AU - Anagnostou, Valsamo
AU - Brahmer, Julie R.
AU - Fijneman, Remond J.A.
AU - Nielsen, Hans Jørgen
AU - Meijer, Gerrit A.
AU - Andersen, Claus Lindbjerg
AU - Mellemgaard, Anders
AU - Bojesen, Stig E.
AU - Scharpf, Robert B.
AU - Velculescu, Victor E.
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85113257362&partnerID=8YFLogxK
U2 - 10.1038/s41467-021-24994-w
DO - 10.1038/s41467-021-24994-w
M3 - Journal article
C2 - 34417454
AN - SCOPUS:85113257362
VL - 12
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
M1 - 5060
ER -