GRCh38 · COSMIC v87

Overview

This section shows a general overview of information for the selected study (COSU identifier) or publication (COSP identifier). Studies may have been performed by the Sanger Institute Cancer Genome Project, or imported from the ICGC/TCGA. You can see more information on the help pages.

Reference
Insights into beta cell regeneration for diabetes via integration of molecular landscapes in human insulinomas
Paper ID
COSP44132
Authors
Wang H, Bender A, Wang P, Karakose E, Inabnet WB, Libutti SK, Arnold A, Lambertini L, Stang M, Chen H, Kasai Y, Mahajan M, Kinoshita Y, Fernandez-Ranvier G, Becker TC, Takane KK, Walker LA, Saul S, Chen R, Scott DK, Ferrer J, Antipin Y, Donovan M, Uzilov AV, Reva B, Schadt EE, Losic B, Argmann C and Stewart AF
Affiliation
The Department of Genetics and Genomic Sciences and The Icahn Institute for Genomics and Multiscale Biology, The Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Journal
Nature communications 2017;8(1):767
ISSN:2041-1723
PUBMED:28974674
Abstract
Although diabetes results in part from a deficiency of normal pancreatic beta cells, inducing human beta cells to regenerate is difficult. Reasoning that insulinomas hold the "genomic recipe" for beta cell expansion, we surveyed 38 human insulinomas to obtain insights into therapeutic pathways for beta cell regeneration. An integrative analysis of whole-exome and RNA-sequencing data was employed to extensively characterize the genomic and molecular landscape of insulinomas relative to normal beta cells. Here, we show at the pathway level that the majority of the insulinomas display mutations, copy number variants and/or dysregulation of epigenetic modifying genes, most prominently in the polycomb and trithorax families. Importantly, these processes are coupled to co-expression network modules associated with cell proliferation, revealing candidates for inducing beta cell regeneration. Validation of key computational predictions supports the concept that understanding the molecular complexity of insulinoma may be a valuable approach to diabetes drug discovery.Diabetes results in part from a deficiency of functional pancreatic beta cells. Here, the authors study the genomic and epigenetic landscapes of human insulinomas to gain insight into possible pathways for therapeutic beta cell regeneration, highlighting epigenetic genes and pathways.
Paper Status
Curated
Genes Analysed
357
Mutated Samples
22
Total No. of Samples
22

Mutation Matrix

This section shows the correlation plot between the top 20 genes and samples. There is more information in our help pages.

Genes

This table shows genes with mutations in the selected study/paper [more details]
Genes Mutated Samples
This table shows genes without mutations in the selected study/paper [more details]

Table Information

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This is a whole exome/systematic screen paper and the negatives for this paper should be inferred.

Variants

This tab shows genes with mutations in the selected study/paper [more details]

Genes Samples CDS Mutation AA Mutation

This tab shows non coding variant in the selected study/paper [more details]

Sample ID Sample Name ID NCV Annotation Zygosity Chromosome Genome start Genome stop Genome version Strand WT seq Mut seq FATHMM-MKL

This tab shows the gene expression and copy number variation data for this study [more details]

Table Information

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The table currently shows only high value (numeric) copy number data. Copy number segments are excluded if the total copy number and minor allele values are unknown.

Click here to include all copy number data. For more detailed information about copy number data and gain/loss definitions click here.

Sample Gene Expression Expr Level (Z-Score)

Over Expressed; Z-Score > 2.0

Under Expressed; Z-Score < -2.0

Normal; Z-Score within the range -2.0 to 2.0

CN Type Minor Allele Copy Number CN Segment Posn. Average Ploidy

1. N/A represents cases where the average ploidy value is not available( mostly ICGC samples). For some TCGA samples where the minor allele information is not available the average ploidy value could not be calculated.

2. For TCGA samples, the ASCAT algorithm was used to calculate the average ploidy.

3. For CGP samples, the PICNIC algorithm was used to calculate the average ploidy.

CNV

This table lists the samples in the selected study which have low/high methylation for each gene. [more details]

No data

This tab shows the fusion mutations observed in this sample [more details]

Gene Sample Name Id Sample(COSS) CDS Mutation Somatic status Zygosity Validated Type

Samples

This table shows mutated samples in the selected study/paper.

Sample Name Mutation Count

This table shows samples without mutations in the selected study/paper.

Non-Mutant Samples Sample Id (COSS)