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

Colin Watts,Richard Mair,Florent Moulière ,Hui Zhao,Marta Grzelak,Jing Su,Irena Hudecova,Dominique‐Laurent Couturier,Ymke van der Pol,Wendy N. Cooper ,Kevin M. Brindle ,James A. Morris ,Dineika Chandrananda,Mark Thompson ,Christopher G. Smith ,Nitzan Rosenfeld,Matthew Eldridge,James Hadfield,Jonathan C. M. Wan ,Davina Gale,Katrin Heider

doi:10.15252/emmm.202012881

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.

Highlights

Primary brain tumors, which are diagnosed in over 260,000 patients worldwide annually (Wesseling & Capper, 2018), have a poor prognosis and lack effective treatments
Analyzing urine fragmentation in samples from five patients with low-grade glioma (LGG) and 30 with high-grade glioma (HGG), and 53 individuals without glioma, we demonstrated that urine samples from glioma patients could be identified by analyzing specific fragmentation patterns from shallow whole-genome sequencing data using machine learning classifiers
Despite the small cohort size (n = 93), which might affect the reproducibility of the models with an independent dataset, these results suggest that the cell-free DNA (cfDNA) fragmentation patterns in urine samples may be a useful tool to provide information that can aid in the diagnosis of gliomas

Summary

Introduction

Primary brain tumors, which are diagnosed in over 260,000 patients worldwide annually (Wesseling & Capper, 2018), have a poor prognosis and lack effective treatments. Better methods for early detection and identification of tumor recurrence may enable the development of novel treatment strategies. The development of new treatments would benefit from minimally invasive methods that characterize the evolving glioma genome (Brennan et al, 2013; Westphal & Lamszus, 2015). DNA analysis in liquid biopsies has the potential to replace or supplement current imaging-based monitoring techniques, which have limited effectiveness, and to provide the genomic information required for precision medicine while reducing the morbidity associated with repeated biopsy (Mouliere et al, 2014; Kros et al, 2015; Westphal & Lamszus, 2015). Cell-free tumor DNA (ctDNA) is extremely challenging to detect in the plasma of patients with brain tumors as its fractional concentration (mutant allele fractions, MAF) is low and appears to be in the same range as that observed in plasma of patients with early-stage a 2021 The Authors.

Methods

Results

Conclusion