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The transcriptional landscape of Shh medulloblastoma.

Patryk Skowron | Hamza Farooq | Florence M G Cavalli | A Sorana Morrissy | Michelle Ly | Liam D Hendrikse | Evan Y Wang | Haig Djambazian | Helen Zhu | Karen L Mungall | Quang M Trinh | Tina Zheng | Shizhong Dai | Ana S Guerreiro Stucklin | Maria C Vladoiu | Vernon Fong | Borja L Holgado | Carolina Nor | Xiaochong Wu | Diala Abd-Rabbo | Pierre Bérubé | Yu Chang Wang | Betty Luu | Raul A Suarez | Avesta Rastan | Aaron H Gillmor | John J Y Lee | Xiao Yun Zhang | Craig Daniels | Peter Dirks | David Malkin | Eric Bouffet | Uri Tabori | James Loukides | François P Doz | Franck Bourdeaut | Olivier O Delattre | Julien Masliah-Planchon | Olivier Ayrault | Seung-Ki Kim | David Meyronet | Wieslawa A Grajkowska | Carlos G Carlotti | Carmen de Torres | Jaume Mora | Charles G Eberhart | Erwin G Van Meir | Toshihiro Kumabe | Pim J French | Johan M Kros | Nada Jabado | Boleslaw Lach | Ian F Pollack | Ronald L Hamilton | Amulya A Nageswara Rao | Caterina Giannini | James M Olson | László Bognár | Almos Klekner | Karel Zitterbart | Joanna J Phillips | Reid C Thompson | Michael K Cooper | Joshua B Rubin | Linda M Liau | Miklós Garami | Peter Hauser | Kay Ka Wai Li | Ho-Keung Ng | Wai Sang Poon | G Yancey Gillespie | Jennifer A Chan | Shin Jung | Roger E McLendon | Eric M Thompson | David Zagzag | Rajeev Vibhakar | Young Shin Ra | Maria Luisa Garre | Ulrich Schüller | Tomoko Shofuda | Claudia C Faria | Enrique López-Aguilar | Gelareh Zadeh | Chi-Chung Hui | Vijay Ramaswamy | Swneke D Bailey | Steven J Jones | Andrew J Mungall | Richard A Moore | John A Calarco | Lincoln D Stein | Gary D Bader | Jüri Reimand | Jiannis Ragoussis | William A Weiss | Marco A Marra | Hiromichi Suzuki | Michael D Taylor
Nature communications | 2021

Sonic hedgehog medulloblastoma encompasses a clinically and molecularly diverse group of cancers of the developing central nervous system. Here, we use unbiased sequencing of the transcriptome across a large cohort of 250 tumors to reveal differences among molecular subtypes of the disease, and demonstrate the previously unappreciated importance of non-coding RNA transcripts. We identify alterations within the cAMP dependent pathway (GNAS, PRKAR1A) which converge on GLI2 activity and show that 18% of tumors have a genetic event that directly targets the abundance and/or stability of MYCN. Furthermore, we discover an extensive network of fusions in focally amplified regions encompassing GLI2, and several loss-of-function fusions in tumor suppressor genes PTCH1, SUFU and NCOR1. Molecular convergence on a subset of genes by nucleotide variants, copy number aberrations, and gene fusions highlight the key roles of specific pathways in the pathogenesis of Sonic hedgehog medulloblastoma and open up opportunities for therapeutic intervention.

Pubmed ID: 33741928 RIS Download

Associated grants

  • Agency: NCI NIH HHS, United States
    Id: R01 CA235162
  • Agency: NCI NIH HHS, United States
    Id: T32 CA151022
  • Agency: NIGMS NIH HHS, United States
    Id: P41 GM103504
  • Agency: NCI NIH HHS, United States
    Id: P50 CA097257
  • Agency: NCI NIH HHS, United States
    Id: P30 CA014236
  • Agency: CCR NIH HHS, United States
    Id: HHSN261200800001C
  • Agency: NIGMS NIH HHS, United States
    Id: T32 GM007618
  • Agency: NIGMS NIH HHS, United States
    Id: T32 GM141323
  • Agency: NINDS NIH HHS, United States
    Id: R01 NS106155
  • Agency: NCI NIH HHS, United States
    Id: R01 CA159859
  • Agency: NCI NIH HHS, United States
    Id: P30 CA015083
  • Agency: NCI NIH HHS, United States
    Id: P50 CA211015
  • Agency: NCI NIH HHS, United States
    Id: R01 CA148699
  • Agency: NHLBI NIH HHS, United States
    Id: HHSN268201000029C
  • Agency: Cancer Research UK, United Kingdom
  • Agency: NCI NIH HHS, United States
    Id: HHSN261200800001E

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This is a list of tools and resources that we have found mentioned in this publication.


GATK (tool)

RRID:SCR_001876

A software package to analyze next-generation resequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size. This software library makes writing efficient analysis tools using next-generation sequencing data very easy, and second it's a suite of tools for working with human medical resequencing projects such as 1000 Genomes and The Cancer Genome Atlas. These tools include things like a depth of coverage analyzers, a quality score recalibrator, a SNP/indel caller and a local realigner. (entry from Genetic Analysis Software)

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SAMTOOLS (tool)

RRID:SCR_002105

Original SAMTOOLS package has been split into three separate repositories including Samtools, BCFtools and HTSlib. Samtools for manipulating next generation sequencing data used for reading, writing, editing, indexing,viewing nucleotide alignments in SAM,BAM,CRAM format. BCFtools used for reading, writing BCF2,VCF, gVCF files and calling, filtering, summarising SNP and short indel sequence variants. HTSlib used for reading, writing high throughput sequencing data.

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COSMIC - Catalogue Of Somatic Mutations In Cancer (tool)

RRID:SCR_002260

Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.

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Cytoscape (tool)

RRID:SCR_003032

Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.

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Gene Set Enrichment Analysis (tool)

RRID:SCR_003199

Software package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.

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Reactome (tool)

RRID:SCR_003485

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

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RefSeq (tool)

RRID:SCR_003496

Collection of curated, non-redundant genomic DNA, transcript RNA, and protein sequences produced by NCBI. Provides a reference for genome annotation, gene identification and characterization, mutation and polymorphism analysis, expression studies, and comparative analyses. Accessed through the Nucleotide and Protein databases.

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STAR (tool)

RRID:SCR_004463

Software performing alignment of high-throughput RNA-seq data. Aligns RNA-seq reads to reference genome using uncompressed suffix arrays.

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Bioconductor (tool)

RRID:SCR_006442

Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.

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BEDTools (tool)

RRID:SCR_006646

A powerful toolset for genome arithmetic allowing one to address common genomics tasks such as finding feature overlaps and computing coverage. Bedtools allows one to intersect, merge, count, complement, and shuffle genomic intervals from multiple files in widely-used genomic file formats such as BAM, BED, GFF/GTF, VCF. While each individual tool is designed to do a relatively simple task (e.g., intersect two interval files), quite sophisticated analyses can be conducted by combining multiple bedtools operations on the UNIX command line.

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EBCall (tool)

RRID:SCR_006791

A software package for somatic mutation detection (including InDels). EBCall uses not only paired tumor/normal sequence data of a target sample, but also multiple non-paired normal reference samples for evaluating distribution of sequencing errors, which leads to an accurate mutaiton detection even in case of low sequencing depths and low allele frequencies.

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ENCODE (tool)

RRID:SCR_006793

Encyclopedia of DNA elements consisting of list of functional elements in human genome, including elements that act at protein and RNA levels, and regulatory elements that control cells and circumstances in which gene is active. Enables scientific and medical communities to interpret role of human genome in biology and disease. Provides identification of common cell types to facilitate integrative analysis and new experimental technologies based on high-throughput sequencing. Genome Browser containing ENCODE and Epigenomics Roadmap data. Data are available for entire human genome.

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1000 Genomes: A Deep Catalog of Human Genetic Variation (tool)

RRID:SCR_006828

International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes

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1000 Genomes Project and AWS (tool)

RRID:SCR_008801

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

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FISHER (tool)

RRID:SCR_009181

THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 1st, 2022. Software application for genetic analysis of classical biometric traits like blood pressure or height that are caused by a combination of polygenic inheritance and complex environmental forces. (entry from Genetic Analysis Software)

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Circos (tool)

RRID:SCR_011798

A software package for visualizing data and information. It visualizes data in a circular layout - this makes Circos ideal for exploring relationships between objects or positions.

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BLAT (tool)

RRID:SCR_011919

Software designed to quickly find sequences of 95% and greater similarity of length 25 bases or more.

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SIFT (tool)

RRID:SCR_012813

Data analysis service to predict whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids. SIFT can be applied to naturally occurring nonsynonymous polymorphisms and laboratory-induced missense mutations. (entry from Genetic Analysis Software) Web service is also available.

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ANNOVAR (tool)

RRID:SCR_012821

An efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, as well as mouse, worm, fly, yeast and many others). Given a list of variants with chromosome, start position, end position, reference nucleotide and observed nucleotides, ANNOVAR can perform: 1. gene-based annotation. 2. region-based annotation. 3. filter-based annotation. 4. other functionalities. (entry from Genetic Analysis Software)

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GENCODE (tool)

RRID:SCR_014966

Human and mouse genome annotation project which aims to identify all gene features in the human genome using computational analysis, manual annotation, and experimental validation.

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DESeq2 (tool)

RRID:SCR_015687

Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.

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