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Fragmentation patterns and personalized sequencing of cell-free DNA in urine and plasma of glioma patients

  • Florent Mouliere*
  • , Christopher G. Smith
  • , Katrin Heider
  • , Jing Su
  • , Ymke van der Pol
  • , Mareike Thompson
  • , James Morris
  • , Jonathan C. M. Wan
  • , Dineika Chandrananda
  • , James Hadfield
  • , Marta Grzelak
  • , Irena Hudecova
  • , Dominique-Laurent Couturier
  • , Wendy Cooper
  • , Hui Zhao
  • , Davina Gale
  • , Matthew Eldridge
  • , Colin Watts
  • , Kevin Brindle*
  • , Nitzan Rosenfeld*
  • Richard Mair*
*Corresponding author for this work
  • University of Cambridge
  • Cancer Research UK Cambridge Institute
  • Peter Maccallum Cancer Centre
  • University of Melbourne
  • AstraZeneca
  • Cambridge University Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation Trust
  • University of Birmingham

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Glioma-derived cell-free DNA (cfDNA) is challenging to detect using liquid biopsy because quantities in body fluids are low. We determined the glioma-derived DNA fraction in cerebrospinal fluid (CSF), plasma, and urine samples from patients using sequencing of personalized capture panels guided by analysis of matched tumor biopsies. By sequencing cfDNA across thousands of mutations, identified individually in each patient’s tumor, we detected tumor-derived DNA in the majority of CSF (7/8), plasma (10/12), and urine samples (10/16), with a median tumor fraction of 6.4 × 10−3, 3.1 × 10−5, and 4.7 × 10−5, respectively. We identified a shift in the size distribution of tumor-derived cfDNA fragments in these body fluids. We further analyzed cfDNA fragment sizes using whole-genome sequencing, in urine samples from 35 glioma patients, 27 individuals with non-malignant brain disorders, and 26 healthy individuals. cfDNA in urine of glioma patients was significantly more fragmented compared to urine from patients with non-malignant brain disorders (P = 1.7 × 10−2) and healthy individuals (P = 5.2 × 10−9). Machine learning models integrating fragment length could differentiate urine samples from glioma patients (AUC = 0.80–0.91) suggesting possibilities for truly non-invasive cancer detection.
Original languageEnglish
Article numbere12881
JournalEMBO molecular medicine
Volume13
Issue number8
Early online date2021
DOIs
Publication statusPublished - 9 Aug 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • cell-free DNA
  • circulating tumor DNA
  • fragmentomics
  • gliomas
  • liquid biopsy

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