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Abstract Hedgehog signaling mediates embryologic development of the central nervous system and other tissues and is frequently hijacked by neoplasia to facilitate uncontrolled cellular proliferation. Meningiomas, the most common primary brain tumor, exhibit Hedgehog signaling activation in 6.5% of cases, triggered by recurrent mutations in pathway mediators such as SMO. In this study, we find 35.6% of meningiomas that lack previously known drivers acquired various types of somatic structural variations affecting chromosomes 2q35 and 7q36.3. These cases exhibit ectopic expression of Hedgehog ligands, IHH and SHH, respectively, resulting in Hedgehog signaling activation. Recurrent tandem duplications involving IHH permit de novo chromatin interactions between super-enhancers within DIRC3 and a locus containing IHH. Our work expands the landscape of meningioma molecular drivers and demonstrates enhancer hijacking of Hedgehog ligands as a route to activate this pathway in neoplasia.

Meningiomas are mostly benign brain tumours with the potential for becoming atypical or malignant. Here, the authors show that primary atypical meningiomas are epigenetically and genetically distinct from benign and progressed tumours, highlighting possible therapeutic targets such as PRC2.

Nature Communications 8: Article number: 14433 (2017) Published online 14 February 2017; Updated 20 April 2018 In this Article, a subset of the H3K27ac ChIP-seq data (15 benign meningiomas and 2 dura samples (Sample IDs: MN-297, MN-288, MN-292, MN-163, MN-1037, MN-105, MN-201, MN-249, MN-191, MN-1066, MN-169, MN-291, MN-24, MN-79, MN-1044, CONTROL1, CONTROL2) was reported previously in a publication by the corresponding author1.

Abstract Background RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states. Results Here, we introduce XCVATR (eXpressed Clusters of Variant Alleles in Transcriptome pRofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local “clumps” of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors. Conclusions XCVATR is publicly available to download from https://github.com/harmancilab/XCVATR .

RNA-sequencing is mostly used to assess gene expression; however, it can also give information about genetic variants. Here, the authors present CaSpER, a statistical framework that utilises RNA-sequencing reads to identify and visualise CNV events by integrating transcriptome-wide expression and allelic shift profiles.