GRCh38 · COSMIC v92


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.

The Landscape of Somatic Genetic Alterations in Metaplastic Breast Carcinomas.
Paper ID
Ng CK, Piscuoglio S, Geyer FC, Burke KA, Pareja F, Eberle C, Lim R, Natrajan R, Riaz N, Mariani O, Norton L, Vincent-Salomon A, Wen YH, Weigelt B and Reis-Filho JS
Institute of Pathology, University Hospital Basel.
Clinical cancer research : an official journal of the American Association for Cancer Research, 2017
ISSN: 1078-0432
PMID: 28153863 (view at PubMed or Europe PMC)
Purpose: Metaplastic breast carcinoma (MBC) is a rare and aggressive histologic type of breast cancer predominantly of triple-negative phenotype, and characterized by the presence of malignant cells showing squamous and/or mesenchymal differentiation. We sought to define the repertoire of somatic genetic alterations and the mutational signatures of MBCs.Whole-exome sequencing was performed in 35 MBCs, with 16, ten and nine classified as harboring chondroid, spindle and squamous metaplasia as the predominant metaplastic component. The genomic landscape of MBCs was compared to that of triple-negative invasive ductal carcinomas of no special type (IDC-NSTs) from The Cancer Genome Atlas. Wnt pathway activity was assessed using a quantitative PCR assay.Results: MBCs harbored complex genomes with frequent TP53 (69%) mutations. In contrast to triple-negative IDC-NSTs, MBCs more frequently harbored mutations in PIK3CA (29%), PIK3R1 (11%), ARID1A (11%), FAT1 (11%) and PTEN (11%). PIK3CA mutations were not found in MBCs with chondroid metaplasia. Compared to triple-negative IDC-NSTs, MBCs significantly more frequently harbored mutations in PI3K/AKT/mTOR pathway-related (57% vs 22%) and canonical Wnt pathway-related (51% vs 28%) genes. MBCs with somatic mutations in PI3K/AKT/mTOR or Wnt pathway-related genes displayed increased activity of the respective pathway.Conclusion: MBCs are genetically complex and heterogeneous, and are driven by a repertoire of somatic mutations distinct from that of triple-negative IDC-NSTs. Our study highlights the genetic basis and the importance of PI3K/AKT/mTOR and Wnt pathway dysregulation in MBCs, and provides a rationale for the metaplastic phenotype and the reported responses to PI3K/AKT/mTOR inhibitors in these tumors.
Paper Status
Genes Analysed
Mutated Samples
Total No. of Samples

Mutation Matrix

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


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


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


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


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.


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


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)