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
Alteration of SF3B1 and SRSF2 Genes in Myelodysplastic Syndromes Patients in Upper Northern Thailand
Paper ID
COSP47539
Authors
Yimpak P, Tantiworawit A, Rattanathammethee T, Angsuchawan S, Laowatthanapong S, Tasuya W and Bumroongkit K
Affiliation
Department of Anatomy, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand. Email: kanokkan.bumr@cmu.ac.th
Journal
Asian Pacific journal of cancer prevention : APJCP, 2019;20(4):1215-1221
ISSN: 2476-762X
PMID: 31030497 (view at PubMed or Europe PMC)
Abstract
Background: The frequency and pattern of mutation in SF3B1 and SRSF2 RNA splicing machinery genes were found to vary among myelodysplastic syndrome (MDS) patients in different populations. There have been less reports of incidence of these gene mutations in Thailand especially in upper northern Thailand. This study therefore had aims to investigate the frequency and pattern of mutation in mutational hotspot of SF3B1 and SRSF2 genes among MDS patients in upper northern Thailand and to investigate the clinical features associated with the mutations. Methods: Fifty-five MDS patients who underwent treatment at Maharaj Nakorn Chiang Mai Hospital participated in this study. The detection of SF3B1 and SRSF2 hotspot mutations was carried out using polymerase chain reaction followed by Sanger sequencing. In addition, clinical features of individual patients with these gene mutations were also investigated. Results: SF3B1 mutations (SF3B1mut) were found in 9 patients (16.4%) including E622D (1/9), R625C (1/9), H662Q (1/9), K700E (5/9), and Q699H co-mutation with K700E (1/9). SRSF2 mutations (SRSF2mut) were found in 4 patients (7.3%) which included P95H (3/4) and P95L (1/4). The SF3B1mut was associated with lower hemoglobin levels (p = 0.023) and higher platelet counts (p = 0.047) when compared with MDS patients without SF3B1mut, while SRSF2mut tended to occur in patients with a higher percentage of bone marrow blasts (p = 0.074). Conclusion: The findings confirmed the difference in frequency of SF3B1 and SRSF2 mutations among different populations. Specifically, we found a co-mutation of Q699H and K700E that has not been previously reported in MDS patients in the COSMIC database. It was also found that SF3B1mut was strongly associated with low hemoglobin level, and high platelet counts whereas SRSF2mut was mostly clustered in MDS with excess blasts subsequently increasing the probability of progression to acute myeloid leukemia.
Paper Status
Curated
Genes Analysed
28
Mutated Samples
13
Total No. of Samples
55

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]
Non-Mutant Genes Gene Id (COSG)

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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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)