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.

Chromosomal abnormalities and molecular landscape of metastasizing mucinous salivary adenocarcinoma.
Paper ID
Panaccione A, Zhang Y, Mi Y, Mitani Y, Yan G, Prasad ML, McDonald WH, El-Naggar AK, Yarbrough WG and Ivanov SV
Section of Otolaryngology, Department of Surgery, Yale School of Medicine, 789 Howard Avenue, New Haven, CT 06519, USA.
Oral oncology, 2017;66:38-45
ISSN: 1879-0593
PMID: 28249646 (view at PubMed or Europe PMC)
Background: Mucinous adenocarcinoma of the salivary gland (MAC) is a lethal cancer with unknown molecular etiology and a high propensity to lymph node metastasis. Mostly due to its orphan status, MAC remains one of the least explored cancers that lacks cell lines and mouse models that could help translational and pre-clinical studies. Surgery with or without radiation remains the only treatment modality but poor overall survival (10-year, 44%) underscores the urgent need for mechanism-based therapies.Methods: We developed the first patient-derived xenograft (PDX) model for pre-clinical MAC studies and a cell line that produces aggressively growing tumors after subcutaneous injection into nude mice. We performed cytogenetic, exome, and proteomic profiling of MAC to identify driving mutations, therapeutic targets, and pathways involved in aggressive cancers based on TCGA database mining and GEO analysis.Results: We identified in MAC KRAS (G13D) and TP53 (R213X) mutations that have been previously reported as drivers in a variety of highly aggressive cancers. Somatic mutations were also found in KDM6A, KMT2D, and other genes frequently mutated in colorectal and other cancers: FAT1, NBEA, RELN, RLP1B, and ZFHX3. Proteomic analysis of MAC implied epigenetic up-regulation of a genetic program involved in proliferation and cancer stem cell maintenance.Conclusion: Genomic and proteomic analyses provided the first insight into potential molecular drivers of MAC metastases pointing at common mechanisms of CSC propagation in aggressive cancers. The in vitro/in vivo models that we created should aid in the development and validation of new treatment strategies against MAC.
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)