scholarly journals 852P Exploring the correlation between AXL expression and gene expression molecular subtyping (GEMS) in high grade serous ovarian cancer (HGSOC)

2020 ◽  
Vol 31 ◽  
pp. S635
Author(s):  
N.Y.L. Ngoi ◽  
T.Z. Tan ◽  
N.M. Lee ◽  
D. Micklem ◽  
A. Rayford ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Molecular Subtyping

scholarly journals Prognostic gene expression signature for high-grade serous ovarian cancer

2020 ◽  
Vol 31 (9) ◽  
pp. 1240-1250 ◽  
Author(s):  
J. Millstein ◽  
T. Budden ◽  
E.L. Goode ◽  
M.S. Anglesio ◽  
A. Talhouk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Signature ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Expression Signature ◽  
Prognostic Gene

scholarly journals Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes

2016 ◽  
Author(s):  
Gregory P. Way ◽  
James Rudd ◽  
Chen Wang ◽  
Habib Hamidi ◽  
Brooke L. Fridley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Conflicts Of Interest ◽  
Population Analysis ◽  
Expression Patterns ◽  
Rocky Mountain ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Bioinformatics Pipeline ◽  
Early Results

AbstractFour gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and non-negative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by including serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.CONFLICTS OF INTERESTThe authors do not declare any conflicts of interest.OTHER PRESENTATIONSAspects of this study were presented at the 2015 AACR Conference and the 2015 Rocky Mountain Bioinformatics Conference.

scholarly journals Tumor specific methylome in Chinese high-grade serous ovarian cancer characterized by gene expression profile and tumor genotype

2020 ◽  
Vol 158 (1) ◽  
pp. 178-187
Author(s):  
Fangfang Song ◽  
Lian Li ◽  
Baifeng Zhang ◽  
Yanrui Zhao ◽  
Hong Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
High Grade ◽  
Serous Ovarian Cancer

A stromal-associated gene expression signature predicting for survival in a series of patients with advanced high-grade serous ovarian cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5552-5552
Author(s):  
T. Bonome ◽  
G. Samimi ◽  
M. Randonovich ◽  
J. Brady ◽  
S. Ghosh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cox Regression ◽  
Patient Survival ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Expression Data ◽  
Prognostic Signature ◽  
Qrt Pcr ◽  
Microarray Expression

5552 Background: Prognostic gene expression signatures have been derived for undissected serous ovarian epithelial tumors, yet the specific contribution of stromal cells to patient survival has not been addressed. The aim of this study is to identify stromal genes impacting patient survival in the context of serous ovarian cancer. Methods: Expression profiling utilizing Affymetrix U133 Plus 2.0 oligonucleotide arrays was completed for 50 microdissected stromal samples derived from high-grade, late-stage serous tumors displaying a broad spectrum of survival endpoints. A semi-supervised dimension reduction method employing multivariate Cox regression and principal components analysis was applied to the expression data to identify genes associated with patient survival and establish a predictive model. qRT-PCR was employed to validate the microarray expression data. Results: Cox regression analysis identified 267 significant genes. The first 6 principal components of these genes, representing >65% of total variance, entered a multivariate Cox model through which the relative hazard of future patients can be predicted. To confirm our finding, the microarray data underwent leave-one-out validation. The patients were equally divided into low- and high-risk groups and non-parametric Kaplan-Meier analysis and log rank test demonstrated the two groups were significantly different in survival (p = 0.0115). Genes associated with cell survival and migration were identified in the prognostic signature. For validation, qRT-PCR data for all 50 specimens was correlated with microarray expression values for a series of select prognostic genes. Conculsions: In this study, we characterized and validated a stromal dervied prognostic signature associated with poor patient survival. Contained in this novel predictor may be stromal targets suitable for the design of new therapeutic interventions, or use as independent diagnostic markers. No significant financial relationships to disclose.

Abstract 3407: Gene expression subtypes of high grade serous ovarian cancer in African American women

2016 ◽  
Author(s):  
Jennifer A. Doherty ◽  
Casey S. Greene ◽  
James E. Rudd ◽  
Laura J. Tafe ◽  
Anthony J. Alberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
African American ◽  
African American Women ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
American Women

scholarly journals Comprehensive Cross-Population Analysis of High-Grade Serous Ovarian Cancer Supports No More Than Three Subtypes

2016 ◽  
Vol 6 (12) ◽  
pp. 4097-4103 ◽  
Author(s):  
Gregory P Way ◽  
James Rudd ◽  
Chen Wang ◽  
Habib Hamidi ◽  
Brooke L Fridley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Population Analysis ◽  
Expression Patterns ◽  
Nonnegative Matrix ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Bioinformatics Pipeline ◽  
Early Results ◽  
Population Comparison

Abstract Four gene expression subtypes of high-grade serous ovarian cancer (HGSC) have been previously described. In these early studies, a fraction of samples that did not fit well into the four subtype classifications were excluded. Therefore, we sought to systematically determine the concordance of transcriptomic HGSC subtypes across populations without removing any samples. We created a bioinformatics pipeline to independently cluster the five largest mRNA expression datasets using k-means and nonnegative matrix factorization (NMF). We summarized differential expression patterns to compare clusters across studies. While previous studies reported four subtypes, our cross-population comparison does not support four. Because these results contrast with previous reports, we attempted to reproduce analyses performed in those studies. Our results suggest that early results favoring four subtypes may have been driven by the inclusion of serous borderline tumors. In summary, our analysis suggests that either two or three, but not four, gene expression subtypes are most consistent across datasets.

scholarly journals Differential DNA methylation in high-grade serous ovarian cancer (HGSOC) is associated with tumor behavior

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Henry D. Reyes ◽  
Eric J. Devor ◽  
Akshaya Warrier ◽  
Andreea M. Newtson ◽  
Jordan Mattson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Dna Methylation ◽  
Surgical Outcomes ◽  
Methylation Status ◽  
Hippo Signaling ◽  
Gene Promoters ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Differentially Methylated Genes

AbstractThe epigenome offers an additional facet of cancer that can help categorize patients into those at risk of disease, recurrence, or treatment failure. We conducted a retrospective, nested, case-control study of advanced and recurrent high-grade serous ovarian cancer (HGSOC) patients in which we assessed epigenome-wide association using Illumina methylationEPIC arrays to characterize DNA methylation status and RNAseq to evaluate gene expression. Comparing HGSOC tumors with normal fallopian tube tissues we observe global hypomethylation but with skewing towards hypermethylation when interrogating gene promoters. In total, 5,852 gene interrogating probes revealed significantly different methylation. Within HGSOC, 57 probes highlighting 17 genes displayed significant differential DNA methylation between primary and recurrent disease. Between optimal vs suboptimal surgical outcomes 99 probes displayed significantly different methylation but only 29 genes showed an inverse correlation between methylation status and gene expression. Overall, differentially methylated genes point to several pathways including RAS as well as hippo signaling in normal vs primary HGSOC; valine, leucine, and isoleucine degradation and endocytosis in primary vs recurrent HGSOC; and pathways containing immune driver genes in optimal vs suboptimal surgical outcomes. Thus, differential DNA methylation identified numerous genes that could serve as potential biomarkers and/or therapeutic targets in HGSOC.

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