Detection of diagnostic somatic copy number alterations from cerebrospinal fluid cell-free DNA in brain tumor patients

Abstract

The gold standard for precise diagnostic classification of brain tumors requires tissue sampling, which carries relevant procedural risks. Brain biopsies often have limited sensitivity and fail to address tumor heterogeneity, because small tissue parts are being examined. This study aims to explore the detection and quantification of diagnostically relevant somatic copy number aberrations (SCNAs) in cell-free DNA (cfDNA) extracted from cerebrospinal fluid (CSF) in a real-world cohort of patients with defined brain tumor subtypes. A total of 33 CSF samples were collected from 30 patients for cfDNA extraction. Shallow whole-genome sequencing was conducted on CSF samples containing > 3ng of cfDNA and corresponding tissue DNA from nine patients. The sequencing cohort encompassed 26 samples of 23 patients, comprising 12 with confirmed CNS cancer as compared to 11 patients with either ambiguous CNS lesions (n = 5) or non-cancer CNS lesions (n = 6). After mapping and quality filtering SCNAs were called by depth-of-coverage analyses with a binning of 5.5 Mbp. SCNAs were exclusively identified in CSF cfDNA from brain tumor patients (10/12, 83%). In tumor patients, SCNAs were detectable in cfDNA from all patients with, but also in five of seven patients without tumor cells detected by CSF cytopathology. A substantial number of shared SCNAs were traceable between tissue and CSF in matched pair analyses. Additionally, some SCNAs unique to either CSF or tissue indicating spatial heterogeneity or tumor evolution. Also, diagnostically relevant genomic alterations as well as essential and desirable SCNAs as implemented in the current WHO classification of CNS tumors for certain primary brain tumor subtypes were traceable. In summary, this minimally invasive cfDNA-based LB approach employing shallow whole genome sequencing demonstrates potential for providing a molecularly informed diagnosis of CNS cancers, mapping tumor heterogeneity, tracking tumor evolution, and surveilling tumor patients. Further prospective trials are warranted.

Keywords: Brain tumor; Cell-free DNA; Cerebrospinal fluid; Liquid biopsy; Next-generation sequencing.

Fig. 1

Study workflow and consort diagram. A Study workflow. CSF, cerebrospinal fluid; DNA, Deoxyribonucleic acid. B The consort flow diagram illustrates the study approach leading to a real-life cohort of patients with different types of CNS cancers with the purpose of analyzing somatic copy number aberrations (SCNAs) through next-generation sequencing

Fig. 2

Somatic copy number aberrations in cell-free DNA from cerebrospinal fluid of patients with CNS cancers. A Overview clinical diagnosis and SCNA parameters. CSF cfDNA SCNA positivity vs. negativity and comparison of SCNA profiles in CSF and corresponding tissue (shared SCNAs) are depicted. B Frequency of detection of somatic copy number aberrations (SCNAs) across various CNS cancer types. SCNA positive samples are shown in green, negative samples in red. C SCNA parameters (CNI score, aberrant bin count, tumor cfDNA fraction) across the tumor patients. D Circos plots showing the copy number profile of cfDNA from cerebrospinal fluid of three exemplarily tumor patients with a high or medium CNI score in contrast to a patient without SCNAs

Fig. 3

Correlation of CSF liquid biopsies with sampling and clinical variables. Box-plots depict the distribution of the NGS parameters (CNI score, aberrant bin count, tumor cfDNA fraction) considering defined sampling and clinical variables. Significant p-values < 0.05 are shown in the charts, otherwise no statistically significant differences were observed. LB = liquid biopsy; PD = progressive disease, LMD = leptomeningeal disease, cytology-confirmed; ref. = reference; CSF = cerebrospinal fluid.

Fig. 4

Diagnostic value of SCNA profiling of cell-free DNA from cerebrospinal fluid of patients with CNS cancers. A Concordance analyses between CSF and tumor tissue. Depicted is the fraction of SCNAs private to CSF (blue), private to tissue (gray) or shared between the two (red). B Circos plots with copy number profiles of CSF cfDNA compared to tissue DNA exemplifying the usefulness of SNCA profiling for minimal invasive detection of CNS cancer, molecularly informed diagnostic assessment, mapping of tumor heterogeneity and tracking tumor evolution as well as surveilling patients with a previous cancer diagnosis