scholarly journals Genome-wide analysis of gynecologic cancer: The Cancer Genome Atlas in ovarian and endometrial cancer

2017 ◽  
Vol 13 (3) ◽  
pp. 1063-1070 ◽  
Author(s):  
Moito Iijima ◽  
Kouji Banno ◽  
Ryuichiro Okawa ◽  
Megumi Yanokura ◽  
Miho Iida ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Gynecologic Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Genome Wide Analysis ◽  
Genome Wide ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Racial differences in endometrial cancer molecular portraits in The Cancer Genome Atlas

Oncotarget ◽  
2018 ◽  
Vol 9 (24) ◽  
pp. 17093-17103 ◽  
Author(s):  
David S. Guttery ◽  
Kevin Blighe ◽  
Konstantinos Polymeros ◽  
R. Paul Symonds ◽  
Salvador Macip ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Racial Differences ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Possible Human Papillomavirus 38 Contamination of Endometrial Cancer RNA Sequencing Samples in The Cancer Genome Atlas Database

2015 ◽  
Vol 89 (17) ◽  
pp. 8967-8973 ◽  
Author(s):  
Majid Kazemian ◽  
Min Ren ◽  
Jian-Xin Lin ◽  
Wei Liao ◽  
Rosanne Spolski ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Human Papillomavirus ◽  
Rna Sequencing ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Clinical Samples ◽  
Cross Contamination ◽  
Rna Seq ◽  
Cancer Genome Atlas ◽  
Genome Atlas

ABSTRACTViruses are causally associated with a number of human malignancies. In this study, we sought to identify new virus-cancer associations by searching RNA sequencing data sets from >2,000 patients, encompassing 21 cancers from The Cancer Genome Atlas (TCGA), for the presence of viral sequences. In agreement with previous studies, we found human papillomavirus 16 (HPV16) and HPV18 in oropharyngeal cancer and hepatitis B and C viruses in liver cancer. Unexpectedly, however, we found HPV38, a cutaneous form of HPV associated with skin cancer, in 32 of 168 samples from endometrial cancer. In 12 of the HPV38-positive (HPV38+) samples, we observed at least one paired read that mapped to both human and HPV38 genomes, indicative of viral integration into the host DNA, something not previously demonstrated for HPV38. The expression levels of HPV38 transcripts were relatively low, and all 32 HPV38+samples belonged to the same experimental batch of 40 samples, whereas none of the other 128 endometrial carcinoma samples were HPV38+, raising doubts about the significance of the HPV38 association. Moreover, the HPV38+samples contained the same 10 novel single nucleotide variations (SNVs), leading us to hypothesize that one patient was infected with this new isolate of HPV38, which was integrated into his/her genome and may have cross-contaminated other TCGA samples within batch 228. Based on our analysis, we propose guidelines to examine the batch effect, virus expression level, and SNVs as part of next-generation sequencing (NGS) data analysis for evaluating the significance of viral/pathogen sequences in clinical samples.IMPORTANCEHigh-throughput RNA sequencing (RNA-Seq), followed by computational analysis, has vastly accelerated the identification of viral and other pathogenic sequences in clinical samples, but cross-contamination during the processing of the samples remain a major problem that can lead to erroneous conclusions. We found HPV38 sequences specifically present in RNA-Seq samples from endometrial cancer patients from TCGA, a virus not previously associated with this type of cancer. However, multiple lines of evidence suggest possible cross-contamination in these samples, which were processed together in the same batch. Despite this potential cross-contamination, our data indicate that we have detected a new isolate of HPV38 that appears to be integrated into the human genome. We also provide general guidelines for computational detection and interpretation of pathogen-disease associations.

scholarly journals Classification Systems of Endometrial Cancer: A Comparative Study about Old and New

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 33
Author(s):  
Camelia Alexandra Coada ◽  
Giulia Dondi ◽  
Gloria Ravegnini ◽  
Antonio De Leo ◽  
Donatella Santini ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
European Society ◽  
Medical Oncology ◽  
Cancer Genome ◽  
Classification Systems ◽  
The Cancer Genome Atlas ◽  
Study Cohort ◽  
Type I ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Endometrial cancer is the most common gynecological malignancy of the female reproductive organs. Historically it was divided into type I and type II, until 2013 when the Cancer Genome Atlas molecular classification was proposed. Here, we applied the different classification types on our endometrial cancer patient cohort in order to identify the most predictive one. We enrolled 117 endometrial cancer patients available for the study and collected the following parameters: age, body mass index, stage, menopause, Lynch syndrome status, parity, hypertension, type of localization of the lesion at hysteroscopy, type of surgery and complications, and presence of metachronous or synchronous tumors. The tumors were classified according to the European Society for Medical Oncology, Proactive Molecular Risk Classifier for Endometrial Cancer, Post-Operative Radiation Therapy in Endometrial Carcinoma, and Cancer Genome Atlas classification schemes. Our data confirmed that European Society for Medical Oncology risk was the strongest predictor of prognosis in our cohort. The parameters correlated with poor prognosis were the histotype, FIGO stage, and grade. Our study cohort shows that risk stratification should be based on the integration of histologic, clinical, and molecular parameters.

scholarly journals L1CAM is an independent predictor of poor survival in endometrial cancer — An analysis of The Cancer Genome Atlas (TCGA)

2016 ◽  
Vol 141 (2) ◽  
pp. 336-340 ◽  
Author(s):  
Thanh H. Dellinger ◽  
David D. Smith ◽  
Ching Ouyang ◽  
Charles D. Warden ◽  
John C. Williams ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Independent Predictor ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Poor Survival ◽  
Cancer Genome Atlas ◽  
Genome Atlas

A new unsupervised clustering algorithm applied to genome-wide profiles of breast cancers in The Cancer Genome Atlas proper subsets triple-negative samples.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23195-e23195
Author(s):  
Jason Mezey ◽  
Steven Schwager ◽  
Sushila Shenoy ◽  
Jef Benbanaste ◽  
Michael Elashoff ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Genomic Data ◽  
Cancer Genome ◽  
Proper Subset ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Genome Wide ◽  
A Genome ◽  
Cancer Genome Atlas ◽  
Genome Atlas

e23195 Background: Clustering algorithms have identified subtypes of major cancers from analysis of genome-wide gene expression (GE) and somatic mutation (SM) profiles. These algorithms almost never discover a proper subset cluster, a recovered cluster that includes all the samples of a specific subtype. For breast cancer (BC), clustering of genome-wide profiles has been unable to proper subset triple negatives (TNs), TN subtypes, or other major subtypes. Methods: To search for a proper subset cluster for TNs, we applied a new clustering algorithm to the public domain GE and SM data of BC samples in The Cancer Genome Atlas (TCGA). A module of Medidata’s Clinical Trial Genomics (CTG) platform for automated clinical and genomic data integration and analysis, it uses a hierarchical component with tree learned cut points applied to a principal component dimension reduced similarity matrix calculated from a genome-wide data profile. Results: Our analysis of 540 TCGA BC samples run without human supervision produced a proper subset cluster that included all 55 TN samples and only 74 non-TN samples. GE data have previously indicated TN status, but this is the first demonstration that these TCGA BC data contain enough information to proper subset TNs, implying that this broad BC subtype has a strong, quantifiable impact on GE. We show that the genome-wide SMs of TCGA BC samples can be used to proper subset 4 novel subtypes distinguished as classes “TP53 mutated”, “PIK3CA mutated”, “both TP53 and PIK3CA mutated”, and “neither mutated”, signifying an important role for these known driver mutations in producing the subtypes’ genome-wide mutation profiles. We find that most ( > 80%) TN BCs are in “TP53 mutated” but only 1 TN sample ( < 2%) is in “PIK3CA mutated”, indicating distinct biology for these TNs with potential implications for TN therapy. Conclusions: CTG clustering achieves proper subset cancer subtype clustering of TCGA BC samples. These results illustrate the therapeutic discovery potential possible from genomic data of the high quality present in TCGA if combined with detailed clinical data with the Medidata CTG integration and annotation platform.

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