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

Florent Moulière(University of Cambridge), Christopher G. Smith(University of Cambridge), Katrin Heider(University of Cambridge), Jing Su(University of Cambridge), Ymke van der Pol(Amsterdam University Medical Centers), M Thompson(University of Cambridge), James Morris(University of Cambridge), Jonathan C. M. Wan(University of Cambridge), Dineika Chandrananda(University of Cambridge), James Hadfield(University of Cambridge), Marta Grzelak(University of Cambridge), Irena Hudecova(University of Cambridge), Dominique‐Laurent Couturier(University of Cambridge), Wendy N. Cooper(University of Cambridge), Hui Zhao(University of Cambridge), Davina Gale(University of Cambridge), Matthew Eldridge(University of Cambridge), Colin Watts(University of Cambridge), Kevin M. Brindle(University of Cambridge), Nitzan Rosenfeld(University of Cambridge), Richard Mair(University of Cambridge)
EMBO Molecular Medicine
July 22, 2021
Cited by 124Open Access
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Abstract

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.


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