Diagnosis of pediatric central nervous system tumors using methylation profiling of cfDNA from cerebrospinal fluid

Abstract

Pediatric central nervous system tumors remain challenging to diagnose. Imaging approaches do not provide sufficient detail to discriminate between different tumor types, while the histopathological examination of tumor tissue shows high inter-observer variability. Recent studies have demonstrated the accurate classification of central nervous system tumors based on the DNA methylation profile of a tumor biopsy. However, a brain biopsy holds significant risk of bleeding and damaging the surrounding tissues. Liquid biopsy approaches analyzing circulating tumor DNA show high potential as an alternative and less invasive tool to study the DNA methylation pattern of tumors. Here, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the circulating cell-free DNA (cfDNA) in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected cerebrospinal fluid samples from 19 pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. Analyses on the cfDNA showed high variability of cfDNA quantities across patients ranging from levels below the limit of quantification to 40 ng cfDNA per milliliter of CSF. Classification based on methylation profiling of cfDNA from CSF was correct for 7 out of 20 samples in our cohort. Accurate results were mostly observed in samples of high quality, more specifically those with limited high molecular weight DNA contamination. Interestingly, we show that centrifugation of the CSF prior to processing increases the fraction of fragmented cfDNA to high molecular weight DNA. In addition, classification was mostly correct for samples with high tumoral cfDNA fraction as estimated by computational deconvolution (> 40%). In summary, analysis of cfDNA in the CSF shows potential as a tool for diagnosing pediatric nervous system tumors especially in patients with high levels of tumoral cfDNA in the CSF. Further optimization of the collection procedure, experimental workflow and bioinformatic approach is required to also allow classification for patients with low tumoral fractions in the CSF.

Keywords: Central nervous system tumor; Cerebrospinal fluid; DNA methylation; Liquid biopsy; Pediatric oncology; Precision medicine.

Fig. 1

A UMAP visualization of the DNA methylation reference dataset that is built for computational deconvolution of pediatric brain tumor classification. Only data from regions covered by both cfRRBS and Illumina 450 k arrays are included. The two sample groups supplemented to the published dataset of Capper et al. are indicated with an *. B Zoom-in on the low-grade glioma (LGG) clusters that overlap with other tumor types

Fig. 2

Visualization of the estimated tumor fraction according to computational deconvolution based on DNA methylation profiles of the tumor tissue samples. The fraction of the histopathological diagnosis is indicated in brown; the other estimated tumor fractions that do not correspondent with the histopathological diagnosis are indicated in orange and the non-tumoral fraction in beige. Samples are classified correctly when the highest estimated tumoral fraction corresponds to the diagnosis, indicated with an asterisk. Table with full classification results for each sample is available in supplemental Table 4

Fig. 3

Percentage of cfDNA (70–700 bp) over total DNA isolated from CSF. A cfDNA fraction over total DNA in whole CSF (n = 12) versus the matched centrifuged CSF samples (n = 12). B cfDNA fraction over total DNA per tumor type for centrifuged samples. Included tumor types are atypical teratoid rhabdoid tumor (ATRT), adamantinomatous craniopharyngioma (CPH), diffuse midline glioma (DMG), ependymoma (EPN), low-grade glioma tumors (LGG), medulloblastoma (MB), choroid plexus papilloma (PLEX)

Fig. 4

Plot of the cfDNA fraction based on length profiling before library preparation and the estimated tumor burden after deconvolution. Samples that score high on both these variables show a classification that corresponds with the pathological diagnosis. Correct classification in blue and incorrect classification in orange

Fig. 5

DNA copy number profiles of four included patients, with corresponding estimated tumor fractions (ETF) and cfDNA fraction (cfDNA) of the CSF-cfDNA. Overlapping profiles of the tumor formalin-fixed paraffin-embedded (FFPE) material in blue and CSF material in orange show both corresponding aberrations as well as some heterogeneity