GRCh38 · COSMIC v91

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
The molecular genetic make-up of male breast cancer.
Paper ID
COSP46952
Authors
Moelans CB, de Ligt J, van der Groep P, Prins P, Besselink N, Hoogstraat M, Ter Hoeve N, Lacle M, Kornegoor R, van der Pol C, de Leng W, Barbe E, van der Vegt B, Martens J, Bult P, Smits VT, Koudijs M, Nijman I, Voest E, Selenica P, Weigelt B, Reis-Filho J, van der Wall E, Cuppen E and van Diest PJ
Affiliation
C Moelans, Pathology, UMC Utrecht, Utrecht, Netherlands.
Journal
Endocrine-related cancer, 2019
ISSN: 1479-6821
PMID: 31340200 (view at PubMed or Europe PMC)
Abstract
Male breast cancer (MBC) is extremely rare and accounts for less than 1% of all breast malignancies. Therefore, clinical management of MBC is currently guided by research on the disease in females. In this study, DNA obtained from 45 formalin-fixed paraffin-embedded (FFPE) MBCs with and 90 MBCs (52 FFPE and 38 fresh-frozen) without matched normal tissues was subjected to massively parallel sequencing targeting all exons of 1943 cancer-related genes. The landscape of mutations and copy number alterations was compared to that of publicly available estrogen receptor (ER)-positive female breast cancers (smFBCs) and correlated to prognosis. From the 135 MBCs, 90% showed ductal histology, 96% were ER-positive, 66% were progesterone receptor (PR)-positive, and 2% HER2-positive, resulting in 50%, 46% and 4% luminal A-like, luminal B-like and basal-like cases, respectively. Five patients had Klinefelter syndrome (4%) and 11% of patients harbored pathogenic BRCA2 germline mutations. The genomic landscape of MBC to some extent recapitulated that of smFBC, with recurrent PIK3CA (36%) and GATA3 (15%) somatic mutations, and with 40% of the most frequently amplified genes overlapping between both sexes. TP53 (3%) somatic mutations were significantly less frequent in MBC compared to smFBC whereas somatic mutations in genes regulating chromatin function and homologous recombination deficiency-related signatures were more prevalent. MDM2 amplifications were frequent (11%), correlated with protein overexpression (p=0.001) and predicted poor outcome (p=0.007). In conclusion, despite similarities in the genomic landscape between MBC and smFBC, MBC is a molecularly unique and heterogeneous disease requiring its own clinical trials and treatment guidelines.
Paper Status
Curated
Genes Analysed
7726
Mutated Samples
133
Total No. of Samples
134

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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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)