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

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  • Dimitrios Mathios, Johns Hopkins University
  • ,
  • Jakob Sidenius Johansen, Københavns Universitet
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  • Stephen Cristiano, Johns Hopkins University
  • ,
  • Jamie E. Medina, Johns Hopkins University
  • ,
  • Jillian Phallen, Johns Hopkins University
  • ,
  • Klaus R. Larsen, Københavns Universitet
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  • Daniel C. Bruhm, Johns Hopkins University
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  • Noushin Niknafs, Johns Hopkins University
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  • Leonardo Ferreira, Johns Hopkins University
  • ,
  • Vilmos Adleff, Johns Hopkins University
  • ,
  • Jia Yuee Chiao, Johns Hopkins University
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  • Alessandro Leal, Johns Hopkins University
  • ,
  • Michael Noe, Johns Hopkins University
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  • James R. White, Johns Hopkins University
  • ,
  • Adith S. Arun, Johns Hopkins University
  • ,
  • Carolyn Hruban, Johns Hopkins University
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  • Akshaya V. Annapragada, Johns Hopkins University
  • ,
  • Sarah Østrup Jensen
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  • Mai Britt Worm Ørntoft
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  • Anders Husted Madsen
  • Beatriz Carvalho, Netherlands Cancer Institute
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  • Meike de Wit, Netherlands Cancer Institute
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  • Jacob Carey, Delfi Diagnostics
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  • Nicholas C. Dracopoli, Delfi Diagnostics
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  • Tara Maddala, Delfi Diagnostics
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  • Kenneth C. Fang, Delfi Diagnostics
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  • Anne Renee Hartman, Delfi Diagnostics
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  • Patrick M. Forde, Johns Hopkins University
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  • Valsamo Anagnostou, Johns Hopkins University
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  • Julie R. Brahmer, Johns Hopkins University
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  • Remond J.A. Fijneman, Netherlands Cancer Institute
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  • Hans Jørgen Nielsen, Department of Surgical Gastroenterology 360, Københavns Universitet
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  • Gerrit A. Meijer, Netherlands Cancer Institute
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  • Claus Lindbjerg Andersen
  • Anders Mellemgaard, Københavns Universitet
  • ,
  • Stig E. Bojesen, Københavns Universitet
  • ,
  • Robert B. Scharpf, Johns Hopkins University
  • ,
  • Victor E. Velculescu, Johns Hopkins University

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.

OriginalsprogEngelsk
Artikelnummer5060
TidsskriftNature Communications
Vol/bind12
ISSN2041-1723
DOI
StatusUdgivet - dec. 2021

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