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

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  • 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
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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
Article number5060
JournalNature Communications
Volume12
Issue number1
Pages (from-to)1-14
Number of pages14
ISSN2041-1723
DOIs
Publication statusPublished - 20 Aug 2021

    Research areas

  • Adolescent, Adult, Aged, Aged, 80 and over, Apoptosis, Carcinoma, Non-Small-Cell Lung/diagnosis, Cell Line, Tumor, Circulating Tumor DNA/metabolism, DNA Fragmentation, Diagnosis, Differential, Early Detection of Cancer, Female, Genome, Human, Humans, Lung Neoplasms/diagnosis, Male, Middle Aged, Models, Biological, Neoplasm Metastasis, Neoplasm Staging, Small Cell Lung Carcinoma/diagnosis, Young Adult

ID: 67246868