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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number5060
JournalNature communications
Issue number1
StatePublished - Dec 1 2021

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)


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