Abstract

Abstract The advent of single cell techniques has advanced our understanding of glioblastoma (GBM) evolution and cell lineage relationships. However, study of mutational co-occurrence and clonal phylogeny has been hampered by limitations in single cell genotyping. Here, we perform semi-automated, rapid single nuclear dissociation followed by targeted single nucleus DNA sequencing (snDNA-seq) of eleven retrospectively identified somatic NF1 mutant IDH-wildtype glioblastomas. Bulk DNA sequencing was performed as part of routine clinical care using a CLIA-certified targeted sequencing panel. For snDNA-seq, tissue cores with greater than 30% tumor were dissociated on a S2 genomics S100 Singulator followed by snDNA-seq using a targeted 361 amplicon panel (Tapestri, Mission Bio, USA) to sequence for recurrently observed single nucleotide substitutions and copy number alterations. A mean of 3,661 cells were recovered per sample with a mean of 96 reads per cell per amplicon and 87% mean panel uniformity. SnDNA-seq validated point mutations and copy number alterations observed in bulk DNA-sequencing and revealed novel alterations and subclonal copy number alterations not detected on bulk analysis. Within individual samples, snDNA-seq based phylogenetic reconstruction identified mutually exclusive patterns of mutation between NF1 and PI3K signaling alterations, suggesting these events may occur in distinct tumor subclones. With regard to copy number alterations (CNAs), snDNA-seq demonstrated intra-tumoral heterogeneity for chromosome 9p loss, chromosome 7 gain, and chromosome 10 monosomy. Subclonal CNA clones not detected by bulk sequencing were identified in 5 samples. Analysis of a matched primary-recurrent tumor pair revealed expansion of a specific mutational clone (loss of 9p, 10p, gains of 1p, 7, 15, and 19q) at recurrence. SnDNA-seq recapitulated bulk DNA sequencing alterations and identified distinct patterns of mutational co-occurrence, CNAs, and tumor evolution over time. Reconstructing GBM phylogeny at single cell resolution has potential translational implications for understanding tumor evolution and treatment resistance.

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