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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt


  • Dimitrios Mathios
  • Jakob Sidenius Johansen
  • Stephen Cristiano
  • Jamie E. Medina
  • Jillian Phallen
  • 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
  • Robert B. Scharpf
  • Victor E. Velculescu

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.

TidsskriftNature Communications
Udgave nummer1
Antal sider14
StatusUdgivet - dec. 2021

Bibliografisk note

Funding Information:
We would like to note that Hans Jørgen Nielsen, a co-author on this study and a pioneer in the field of early cancer detection, passed away during the course of this study. We thank members of our laboratories for critical review of the manuscript. This work was supported in part by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, SU2C in-Time Lung Cancer Interception Dream Team Grant, Stand Up to Cancer-Dutch Cancer Society International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415), the Gray Foundation, the Commonwealth Foundation, the Mark Foundation for Cancer Research, the Lundbeck Foundation, Roche Denmark unrestricted grant, a research grant from Delfi Diagnostics, and US National Institutes of Health grants CA121113, CA006973, CA233259, and 1T32GM136577. Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. The results published here are in part based upon data generated by the TCGA Research Network: and The Genotype-Tissue Expression (GTEx) Project. GTEX was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.

Publisher Copyright:
© 2021, The Author(s).

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