Pediatric high-grade glioma resources from the Children’s Brain Tumor Tissue Consortium

Ijaz, Heba; Koptyra, Mateusz; Gaonkar, Krutika S.; Rokita, Jo Lynne; Baubet, Valérie; Tauhid, Lamiya; Zhu, Yuankun; Brown, Miguel; Lopez, Gonzalo; Zhang, Bo; Diskin, Sharon J.; Vaksman, Zalman; Mason, J. L.; Appert, Elizabeth; Lilly, Jena; Lulla, Rishi; Raedt, Thomas De; Heath, Allison P.; Felmeister, Alex; Raman, Pichai; Nazarian, Javad; Santi, Mariarita; Storm, Phillip B.; Resnick, Adam; Waanders, Angela J.; Cole, Kristina A.

doi:10.1093/neuonc/noz192

Cited by 34 publications

(36 citation statements)

References 4 publications

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“…To measure the diversity of the nerve sheath tumors at the transcriptomic level, we re-processed RNA-seq data from three published datasets [37][38][39] and one unpublished dataset. Despite having four types of nerve sheath tumors (cNFs, pNFs, NFs, and MPNSTs) across four datasets, we observed confounding batch effects ( Figure 1A) as some tumor types (e.g., cNF and NF) were derived from separate studies.…”

Section: Pan-nf Transcriptomic Analysis Identified Most Variable Latementioning

confidence: 99%

Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1

Banerjee

Allaway

Taroni

et al. 2020

Genes

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Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40–60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions.

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Section: Pan-nf Transcriptomic Analysis Identified Most Variable Latementioning

confidence: 99%

Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1

Banerjee

Allaway

Taroni

et al. 2020

Genes

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show abstract

“…To measure the diversity of the nerve sheath tumors at the transcriptomic level, we re-processed RNA-seq data from three published datasets [33][34][35] and one unpublished dataset. Despite having four types of nerve sheath tumors (cNFs, pNFs, NFs and MPNSTs) across four datasets, we observed confounding batch effects ( Figure 1A) as some tumor types (eg.…”

Section: Pan-nf Transcriptomic Analysis Identifies Most Variable Latementioning

confidence: 99%

Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

Banerjee

Allaway

Taroni

et al. 2020

Preprint

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Neurofibromatosis type 1 is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, while 40-60% of patients develop plexiform neurofibromas (pNFs) which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions.

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“…As proof of concept, we utilized RNA expression generated by STAR-RSEM (43) and fusion calls generated by Arriba v1.1.0 (33) and/or STAR-Fusion 1.5.0 (14) which were released as part of the Pediatric Brain Tumor Atlas (44). Briefly, RNA from fresh-frozen tissue was extracted and libraries were prepped and sequenced at 2x100 or 2x150 bp to an average of 200M+ total reads, and at least 60% of reads were required to map to the human genome for fusion analysis to proceed.…”

Section: Case Study With Annofuse Shinyfuse and Reportfuse Using Opmentioning

confidence: 99%

annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions

Gaonkar

Marini²,

Rathi

et al. 2019

Preprint

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BackgroundGene fusion events are a significant source of somatic variation across adult and pediatric cancers and have provided some of the most effective clinically relevant therapeutic targets, yet computational algorithms for fusion detection from RNA sequencing data show low overlap of predictions across methods. In addition, events such as polymerase read-throughs, mis-mapping due to gene homology, and fusions occurring in healthy normal tissue require stringent filtering, making it difficult for researchers and clinicians to discern gene fusions that might be true underlying oncogenic drivers of a tumor and in some cases, appropriate targets for therapy. ResultsHere, we present annoFuse, an R package developed to annotate and identify biologically-relevant expressed gene fusions, along with highlighting recurrent novel fusions in a given cohort. We applied annoFuse to STAR-Fusion and Arriba results for 1028 pediatric brain tumor samples provided as part of the Open Pediatric Brain Tumor Atlas (OpenPBTA) Project.First, we used FusionAnnotator to identify and filter "red flag" fusions found in healthy tissues or in gene homology databases. Using annoFuse, we filtered out fusions known to be artifactual and retained high-quality fusion calls using support of at least one junction read and if there is disproportionate spanning fragment support of more than 10 reads compared to the junction read count, we removed them to remove false positives from background noise. Second, we prioritized and captured known, as well as putative oncogenic driver, fusions previously reported in TCGA, or fusions containing gene partners that are known oncogenes, tumor suppressor genes, or COSMIC genes. Finally, using annoFuse, we determined recurrent fusions across the cohort and recurrently-fused genes within each histology. ConclusionsannoFuse provides a standardized filtering and annotation method for gene fusion calls from STAR-Fusion and Arriba by merging, filtering and prioritizing putative oncogenic fusions across large cancer datasets, as demonstrated here with the OpenPBTA dataset. We are expanding the package to be widely-applicable to other fusion algorithms, adding functionalities, and expect annoFuse to provide researchers a method for quickly evaluating and prioritizing fusions in patient tumors. BackgroundGene fusions arise in cancer as a result of aberrant chromosomal rearrangements or defective splicing, which bring together two unrelated genes that are then expressed as a novel fusion transcript. Detection of therapeutically-targetable fusion calls is of clinical importance and computational methods are constantly being developed to detect these events in real-time.Recent comparative studies show low concordance of fusion predictions across methods (1), suggesting that many predictions may not represent true events. Additionally, transcriptional readthroughs (2), in which the polymerase machinery skips a stop codon and reads through a neighbouring gene, as well as fusions that involve non-canonical transcripts or ...

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Pediatric high-grade glioma resources from the Children’s Brain Tumor Tissue Consortium

Cited by 34 publications

References 4 publications

Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1

Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1

Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions

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