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. 2024 Jul 15;30(14):2974-2985.
doi: 10.1158/1078-0432.CCR-23-2907.

Cerebrospinal Fluid cfDNA Sequencing for Classification of Central Nervous System Glioma

Florian Iser  1 , Felix Hinz  2   3 , Dirk C Hoffmann  1   4 , Niklas Grassl  5   6 , Cansu Güngoör  7 , Jochen Meyer  2   3 , Laura Dörner  2   3 , Lea Hofmann  2   3 , Vanessa Kelbch  2   3 , Kirsten Göbel  2   3 , Mustafa Ahmed Mahmutoglu  8 , Philipp Vollmuth  8 , Areeba Patel  2   3 , Duy Nguyen  9 , Leon D Kaulen  1   7 , Iris Mildenberger  5   6 , Katharina Sahm  5   6 , Kendra Maaß  10   11 , Kristian W Pajtler  10   11 , Ganesh M Shankar  12 , Markus Weiler  7 , Brigitte Wildemann  13 , Frank Winkler  1   14 , Andreas von Deimling  2   3 , Michael Platten  5   6 , Wolfgang Wick  1   14 , Felix Sahm #  2   3 , Tobias Kessler #  1   14
Affiliations

Cerebrospinal Fluid cfDNA Sequencing for Classification of Central Nervous System Glioma

Florian Iser et al. Clin Cancer Res. .

Abstract

Purpose: Primary central nervous system (CNS) gliomas can be classified by characteristic genetic alterations. In addition to solid tissue obtained via surgery or biopsy, cell-free DNA (cfDNA) from cerebrospinal fluid (CSF) is an alternative source of material for genomic analyses.

Experimental design: We performed targeted next-generation sequencing of CSF cfDNA in a representative cohort of 85 patients presenting at two neurooncological centers with suspicion of primary or recurrent glioma. Copy-number variation (CNV) profiles, single-nucleotide variants (SNV), and small insertions/deletions (indel) were combined into a molecular-guided tumor classification. Comparison with the solid tumor was performed for 38 cases with matching solid tissue available.

Results: Cases were stratified into four groups: glioblastoma (n = 32), other glioma (n = 19), nonmalignant (n = 17), and nondiagnostic (n = 17). We introduced a molecular-guided tumor classification, which enabled identification of tumor entities and/or cancer-specific alterations in 75.0% (n = 24) of glioblastoma and 52.6% (n = 10) of other glioma cases. The overlap between CSF and matching solid tissue was highest for CNVs (26%-48%) and SNVs at predefined gene loci (44%), followed by SNVs/indels identified via uninformed variant calling (8%-14%). A molecular-guided tumor classification was possible for 23.5% (n = 4) of nondiagnostic cases.

Conclusions: We developed a targeted sequencing workflow for CSF cfDNA as well as a strategy for interpretation and reporting of sequencing results based on a molecular-guided tumor classification in glioma. See related commentary by Abdullah, p. 2860.

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Conflict of interest statement

G.M. Shankar reports grants from NIH during the conduct of the study as well as personal fees from Depuy Synthes/J&J and grants from NuVasive and Cervical Spine Research Society outside the submitted work; in addition, G.M. Shankar has a patent for US20170369939A1 issued. B. Wildemann reports grants from Deutsche Forschungsgemeinschaft, German Ministry of Education and Research, Baden-Württemberg Ministry for Science, Research and Art, Dietmar Hopp Foundation, Klaus Tschira Foundation; grants and personal fees from Merck and Novartis; and personal fees from Alexion, INSTAND, and Roche outside the submitted work. W. Wick reports personal fees from Servier and Roche outside the submitted work. F. Sahm reports grants from Assmus Foundation during the conduct of the study. T. Kessler reports grants from Else Kröner-Fresenius Foundation and German Research Foundation during the conduct of the study as well as grants from Hertie Foundation outside the submitted work. No disclosures were reported by the other authors.

Figures

Figure 1.
Figure 1.
cfDNA sequencing procedure and molecular-guided tumor classification. A, Key steps of the wet lab procedure and sequencing data analysis. CNVs, SNVs at predefined gene loci (special positions), and uninformed variant calling were combined to obtain a molecular-guided tumor classification. B, Combinations of genetic alterations sufficient for the respective classification categories. C, CNVs, special positions, and variants identified via uninformed calling for representative examples of class A (top), class B (middle), and ctDNA negative (bottom) cases.
Figure 2.
Figure 2.
Classification of glioblastoma, other glioma, and nonmalignant cohorts. A, Proportion of class A, B, or ctDNA negative cases in the glioblastoma, other glioma, and nonmalignant cohorts. B, Oncoprint of all identified genetic alterations. Variants were either identified via manual review of CNV profiles, SNVs at predefined loci (special positions), or uninformed variant calling. In some cases, SNVs at predefined loci were also identified via uninformed variant calling. Samples are grouped by molecular-guided classification within each cohort. P, primary tumor; R, recurrent tumor.
Figure 3.
Figure 3.
Comparison between CSF and matching solid tumor. A, For cases where matching solid tissue was available, the overlap between CSF and solid tissue calls was analyzed separately for CNVs, SNVs at predefined gene loci (special positions), and SNVs/indels identified via uninformed variant calling. B and C, Oncoprints of identified variants that were either shared between CSF and matching solid or private to one or the other. P, primary tumor; R, recurrent tumor. B, Glioblastoma cohort. C, Other glioma cohort.
Figure 4.
Figure 4.
Nondiagnostic cases. A, Molecular-guided tumor classification for all samples in the nondiagnostic cohort. B, Oncoprint of identified CNVs, SNVs at predefined loci (special positions), and SNVs/indels identified via uninformed variant calling. P, Primary tumor; R, recurrent tumor. C and D, Representative MRI and VAF for two cases where integrated clinical diagnosis was made based on CSF cfDNA sequencing results and MRI findings. C, Case SUSP_10: H3 K28M-mutated DMG. D, Case SUSP_11: IDH-mutant glioma.
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