4.7 Article

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

Journal

EMBO MOLECULAR MEDICINE
Volume 13, Issue 8, Pages -

Publisher

WILEY
DOI: 10.15252/emmm.202012881

Keywords

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

Funding

  1. Cambridge NIHR Biomedical Research Centre
  2. Mark Foundation of Cancer Research
  3. University of Cambridge
  4. Cancer Research UK [A20240, A29580, A17197, A16465]
  5. European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC [337905]
  6. Dutch Cancer Fund [KWF-12822]
  7. Cambridge Cancer Centre

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The study focused on detecting glioma-derived cell-free DNA (cfDNA) in body fluids and found that tumor-derived DNA could be identified in the majority of CSF, plasma, and urine samples with different tumor fractions. They also discovered a significant difference in cfDNA fragment sizes between glioma patients and individuals with non-malignant brain disorders or healthy individuals, which could potentially aid in non-invasive cancer detection using machine learning models.
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 x 10(-3), 3.1 x 10(-5), and 4.7 x 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 x 10(-2)) and healthy individuals (P = 5.2 x 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.

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