GRCh38 · COSMIC v92

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
Ring sideroblasts in AML are associated with adverse risk characteristics and have a distinct gene expression pattern.
Paper ID
COSP47520
Authors
Berger G, Gerritsen M, Yi G, Koorenhof-Scheele TN, Kroeze LI, Stevens-Kroef M, Yoshida K, Shiraishi Y, van den Berg E, Schepers H, Huls G, Mulder AB, Ogawa S, Martens JHA, Jansen JH and Vellenga E
Affiliation
Department of Hematology, Cancer Research Center Groningen, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Journal
Blood advances, 2019;3(20):3111-3122
ISSN: 2473-9537
PMID: 31648334 (view at PubMed or Europe PMC)
Abstract
Ring sideroblasts (RS) emerge as result of aberrant erythroid differentiation leading to excessive mitochondrial iron accumulation, a characteristic feature for myelodysplastic syndromes (MDS) with mutations in the spliceosome gene SF3B1. However, RS can also be observed in patients diagnosed with acute myeloid leukemia (AML). The objective of this study was to characterize RS in patients with AML. Clinically, RS-AML is enriched for ELN adverse risk (55%). In line with this finding, 35% of all cases had complex cytogenetic aberrancies, and TP53 was most recurrently mutated in this cohort (37%), followed by DNMT3A (26%), RUNX1 (25%), TET2 (20%), and ASXL1 (19%). In contrast to RS-MDS, the incidence of SF3B1 mutations was low (8%). Whole-exome sequencing and SNP array analysis on a subset of patients did not uncover a single genetic defect underlying the RS phenotype. Shared genetic defects between erythroblasts and total mononuclear cell fraction indicate common ancestry for the erythroid lineage and the myeloid blast cells in patients with RS-AML. RNA sequencing analysis on CD34+ AML cells revealed differential gene expression between RS-AML and non RS-AML cases, including genes involved in megakaryocyte and erythroid differentiation. Furthermore, several heme metabolism-related genes were found to be upregulated in RS- CD34+ AML cells, as was observed in SF3B1mut MDS. These results demonstrate that although the genetic background of RS-AML differs from that of RS-MDS, they have certain downstream effector pathways in common.
Paper Status
Curated
Genes Analysed
812
Mutated Samples
16
Total No. of Samples
16

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

Hide

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

Hide

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)