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

Dimitrios Mathios; Jakob Sidenius Johansen; Stephen Cristiano; Jamie E. Medina; Jillian Phallen; Klaus R. Larsen; Daniel C. Bruhm; Noushin Niknafs; Leonardo Ferreira; Vilmos Adleff; Jia Yuee Chiao; Alessandro Leal; Michael Noe; James R. White; Adith S. Arun; Carolyn Hruban; Akshaya V. Annapragada; Sarah Østrup Jensen; Mai Britt Worm Ørntoft; Anders Husted Madsen; Beatriz Carvalho; Meike de Wit; Jacob Carey; Nicholas C. Dracopoli; Tara Maddala; Kenneth C. Fang; Anne Renee Hartman; Patrick M. Forde; Valsamo Anagnostou; Julie R. Brahmer; Remond J.A. Fijneman; Hans Jørgen Nielsen; Gerrit A. Meijer; Claus Lindbjerg Andersen; Anders Mellemgaard; Stig E. Bojesen; Robert B. Scharpf; Victor E. Velculescu

doi:10.1038/s41467-021-24994-w

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

Dimitrios Mathios, Jakob Sidenius Johansen, Stephen Cristiano, Jamie E. Medina, Jillian Phallen, Klaus R. Larsen, Daniel C. Bruhm, Noushin Niknafs, Leonardo Ferreira, Vilmos Adleff, Jia Yuee Chiao, Alessandro Leal, Michael Noe, James R. White, Adith S. Arun, Carolyn Hruban, Akshaya V. Annapragada, Sarah Østrup Jensen, Mai Britt Worm Ørntoft, Anders Husted MadsenBeatriz Carvalho, Meike de Wit, Jacob Carey, Nicholas C. Dracopoli, Tara Maddala, Kenneth C. Fang, Anne Renee Hartman, Patrick M. Forde, Valsamo Anagnostou, Julie R. Brahmer, Remond J.A. Fijneman, Hans Jørgen Nielsen, Gerrit A. Meijer, Claus Lindbjerg Andersen, Anders Mellemgaard, Stig E. Bojesen, Robert B. Scharpf^*, Victor E. Velculescu

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

232 Citationer (Scopus)

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.

Originalsprog	Engelsk
Artikelnummer	5060
Tidsskrift	Nature Communications
Vol/bind	12
ISSN	2041-1723
DOI	https://doi.org/10.1038/s41467-021-24994-w
Status	Udgivet - dec. 2021

Adgang til dokumentet

10.1038/s41467-021-24994-w

Andre filer og links

Link to publication in Scopus

Citationsformater

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, Artikel 5060. https://doi.org/10.1038/s41467-021-24994-w

@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 Research",

}

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, bind 12, 5060. https://doi.org/10.1038/s41467-021-24994-w

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.

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

SN - 2041-1723

VL - 12

JO - Nature Communications

JF - Nature Communications

M1 - 5060

ER -

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

Abstract

Adgang til dokumentet

Andre filer og links

Fingeraftryk

Citationsformater