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Reference

Identification of Molecular Pathway Aberrations in Uterine Serous Carcinoma by Genome-wide Analyses.

Paper Id
COSP29619
Authors
Kuhn E,Wu RC,Guan B,Wu G,Zhang J,Wang Y,Song L,Yuan X,Wei L,Roden RB,Kuo KT,Nakayama K,Clarke B,Shaw P,Olvera N,Kurman RJ,Levine DA,Wang TL and Shih IM
Affiliation
Affiliations of Authors:Departments of Pathology, Oncology, and Gynecology/Obstetrics, Johns Hopkins Medical Institutions, Baltimore, MD (EK, R-CW, BG, RBR, RJK, T-LW, I-MS); St. Jude Children's Research Hospital, Memphis, TN (GW, JZ, LW); Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA (YW, LS, XY); Department of Pathology, National Taiwan University Hospital, Medical College, National Taiwan University, Taipei, Taiwan (K-TK); Shimane University, Japan (KN); Department of Pathology, Toronto Health Care system, Toronto, ON, Canada (BC, PS); Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan (R-CW); Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY (NO, DAL).
Journal
Journal of the National Cancer Institute 2012
ISSN:1460-2105
PUBMED:22923510
Abstract
BackgroundUterine cancer is the fourth most common malignancy in women, and uterine serous carcinoma is the most aggressive subtype. However, the molecular pathogenesis of uterine serous carcinoma is largely unknown. We analyzed the genomes of uterine serous carcinoma samples to better understand the molecular genetic characteristics of this cancer. MethodsWhole-exome sequencing was performed on 10 uterine serous carcinomas and the matched normal blood or tissue samples. Somatically acquired sequence mutations were further verified by Sanger sequencing. The most frequent molecular genetic changes were further validated by Sanger sequencing in 66 additional uterine serous carcinomas and in nine serous endometrial intraepithelial carcinomas (the preinvasive precursor of uterine serous carcinoma) that were isolated by laser capture microdissection. In addition, gene copy number was characterized by single-nucleotide polymorphism (SNP) arrays in 23 uterine serous carcinomas, including 10 that were subjected to whole-exome sequencing. ResultsWe found frequent somatic mutations in TP53 (81.6%), PIK3CA (23.7%), FBXW7 (19.7%), and PPP2R1A (18.4%) among the 76 uterine serous carcinomas examined. All nine serous carcinomas that had an associated serous endometrial intraepithelial carcinoma had concordant PIK3CA, PPP2R1A, and TP53 mutation status between uterine serous carcinoma and the concurrent serous endometrial intraepithelial carcinoma component. DNA copy number analysis revealed frequent genomic amplification of the CCNE1 locus (which encodes cyclin E, a known substrate of FBXW7) and deletion of the FBXW7 locus. Among 23 uterine serous carcinomas that were subjected to SNP array analysis, seven tumors with FBXW7 mutations (four tumors with point mutations, three tumors with hemizygous deletions) did not have CCNE1 amplification, and 13 (57%) tumors had either a molecular genetic alteration in FBXW7 or CCNE1 amplification. Nearly half of these uterine serous carcinomas (48%) harbored PIK3CA mutation and/or PIK3CA amplification. ConclusionMolecular genetic aberrations involving the p53, cyclin E-FBXW7, and PI3K pathways represent major mechanisms in the development of uterine serous carcinoma.
Paper Status
Curated
Genes Analysed
491
Mutated Samples
10
Total No. of Samples
10
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Genes Samples CDS Mutation AA Mutation
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Non-Mutant Genes Gene Id (COSG)
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Non-Mutant Samples Sample Id (COSS)
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Sample Name Mutation Count
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
This tab shows the copy number variation data for this study. Only variants (classified as gain or loss) are listed. [more details]
CNV Gene Sample Position Minor Allele Copy Number Average Ploidy

1. N/A represents cases where average ploidy value is not available( mostly ICGC samples). For some TCGA samples where minor allele information is not available the average ploidy value could not be calculated.

2. For TCGA samples, Ascat algorithm is used to calculate the average ploidy.

3. For CGP samples, Picnic algorithm is used to calculate the average ploidy.

Type
This tab shows a table of count of samples having gain or loss for all genes [more details]
Gene Gain Samples Loss Samples Samples Tested
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