Similar Datasets
Project description:Purpose: Increasing genomics-based evidence suggests that synchronous endometrial and ovarian cancer (SEOC) represents clonally related primary and metastatic tumors. A systematic analysis of the global protein landscape of SEOCs, heretofore lacking, could reveal functional and disease-specific consequences of known genetic alterations, the directionality of metastasis, and accurate histological markers to distinguish SEOCs from single-site tumors. Experimental Design: We performed a systematic proteogenomic analysis of 29 patients diagnosed with SEOC at three international gynecologic oncology treatment centers (Chicago, Vancouver, Tübingen). For direct comparison to single-site tumors, we included 9 patients with single-site endometrioid ovarian and 26 patients with single-site endometrial endometrioid cancer. For all 64 patients, we performed sequencing of a 275-gene cancer panel combined with compartment-resolved mass spectrometry (MS) based proteomics of consecutive tissue sections to compare global (6,000+ proteins), tumor, and stromal proteomes. Results: DNA-based panel sequencing confirmed that most SEOCs are clonally related, suggesting primary and metastatic disease. These findings were further substantiated on the global proteome level, uncovering pronounced differences between SEOCs and single tumors and underscoring the importance of the stromal proteome in defining and identifying SEOCs. Our integrated proteogenomic approach confirmed that SEOCs more closely resemble endometrial endometrioid than endometrioid ovarian cancers. Conclusions: The integrated proteogenomic data show that SEOCs are distinguishable from endometrial endometrioid or endometrioid ovarian cancers. Based on their proteogenomic similarity to endometrial endometrioid cancers, we conclude that most synchronous endometrial and ovarian cancers represent primary endometrial endometrioid cancers that have metastasized to the ovary.
2025-03-11 | PXD059172 | Pride
Project description:The distinction between primary and secondary ovarian tumors may be challenging for pathologists. We performed transcriptomic analysis in order to discriminate between primary ovarian tumors and ovarian metastases after primary breast cancer. We performed genomic analysis on tumor paired samples (breast/ovary) in order to know if genomic profiles could help for the discrimination of primary ovarian tumors and ovarian metastases after primary breast cancer.
2010-04-14 | GSE20565 | GEO
Project description:High-grade serous ovarian cancer presents significant challenges due to its poor prognosis and high heterogeneity, both of which complicate treatment responses. This project aims to understand intra-patient tumor evolution by investigating different sampling sites (primary and metastatic) at the time of diagnosis and during disease recurrence. A total of 183 biopsies from 50 patients were collected for this purpose, and bulk mRNA sequencing was performed. The majority of samples originated from following tissue types: omentum, ovary, and ascites.
2025-02-08 | GSE289067 | GEO
Project description:Fifty million plaque-forming units of AdCre was injected into the right ovarian bursal cavity of 56- 70 day old female mice. Mice were euthanized 63 days later to obtain ovary tumors and normal ovary tissue. Seven individual ovarian tumors and 4 individual normal ovary samples were each assayed on an Affymetrix Mouse Genome 430 2.0 array. Keywords: Tumor vs normal comparisons
2007-04-09 | GSE5987 | GEO
Project description:The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We provide a bioinformatic analysis of copy number variation and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We individually examined the copy number variation and DNA methylation for 44 primary ovarian cancer samples and 7 ovarian normal samples using our MOMA-ROMA technology and Affymetrix expression data as well as 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with significantly altered copy number and correlated changes in expression. We identify changes in DNA methylation and expression for all amplified and deleted genes. We predicted 615 potential oncogenes and tumor suppressors candidates by integrating these multiple genomic and epigenetic data types. Expression data accompaniment to CSHL ROMA and MOMA3 human ovarian analysis. Correlation of expression to Methylation and Copy Number Variation in ovarian cancer.
2011-10-16 | E-GEOD-27943 | biostudies-arrayexpress
Project description:The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We provide a bioinformatic analysis of copy number variation and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We individually examined the copy number variation and DNA methylation for 44 primary ovarian cancer samples and 7 ovarian normal samples using our MOMA-ROMA technology and Affymetrix expression data as well as 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with significantly altered copy number and correlated changes in expression. We identify changes in DNA methylation and expression for all amplified and deleted genes. We predicted 615 potential oncogenes and tumor suppressors candidates by integrating these multiple genomic and epigenetic data types. Expression data accompaniment to CSHL ROMA and MOMA3 human ovarian analysis.
2011-10-17 | GSE27943 | GEO
Project description:Matched high-grade serous ovarian carcinoma samples collected from the ovary (ov), omental metastasis (om-met), and non-omental intraperitoneal metastasis (met) from 10 patients at the time of primary debulking surgery were analyzed for RNA expression by RNA sequencing.
2020-07-02 | GSE133296 | GEO
Project description:99 individual ovarian tumors (37 endometrioid, 41 serous, 13 mucinous, and 8 clear cell carcinomas) and 4 individual normal ovary samples, each assayed on an Affymetrix HG_U133A array Keywords: Individual tumor comparisons
2007-04-09 | GSE6008 | GEO
Project description:Carcinoma-associated mesenchymal stem cells (CA-MSCs) are critical stromal progenitor cells within the tumor microenvironment. We previously demonstrated that CA-MSCs differentially express BMP genes, promote tumor cell growth, increase cancer ‘stemness’ and chemotherapy resistance. Here we use RNA sequencing of normal omental MSCs and ovarian CA-MSCs to demonstrate CA-MSCs have global changes in gene expression. Using these expression profiles we create a unique predictive algorithm to classify CA-MSCs. Our classifier, accurately distinguishes normal omental, ovary and bone marrow MSCs from ovarian cancer CA-MSCs. Suggesting broad applicability, the model correctly classifies pancreatic and endometrial cancer CA-MSCs and distinguishes cancer associated fibroblasts (CAFs) from CA-MSCs. Using this classifier, we definitively demonstrate ovarian CA-MSCs arise from tumor mediated reprograming of local tissue MSCs. While cancer cells alone cannot induce a CA-MSC phenotype, the in vivo ovarian tumor micoenvironment (TME) can reprogram omental or ovary MSCs to protumorigenic CA-MSC (classifier score of >0.96). In vitro studies suggest that both tumor secreted factors and hypoxia are critical to induce the CA-MSC phenotype. Interestingly, while the breast cancer TME can reprogram BM MSCs into CA-MSCs, the ovarian TME cannot, demonstrating for the first time that tumor mediated CA-MSC conversion is tissue and cancer type dependent. Together these findings (1) provide a critical tool to define CA-MSCs and (2) highlight cancer cell influence on distinct normal tissues providing powerful insights into the mechanisms underlying cancer specific metastatic niche formation. Carcinoma-associated mesenchymal stem cells (CA-MSCs) are critical stromal progenitor cells within the tumor microenvironment. We previously demonstrated that CA-MSCs differentially express BMP genes, promote tumor cell growth, increase cancer ‘stemness’ and chemotherapy resistance. Here we use RNA sequencing of normal omental MSCs and ovarian CA-MSCs to demonstrate CA-MSCs have global changes in gene expression. Using these expression profiles we create a unique predictive algorithm to classify CA-MSCs. Our classifier, accurately distinguishes normal omental, ovary and bone marrow MSCs from ovarian cancer CA-MSCs. Suggesting broad applicability, the model correctly classifies pancreatic and endometrial cancer CA-MSCs and distinguishes cancer associated fibroblasts (CAFs) from CA-MSCs. Using this classifier, we definitively demonstrate ovarian CA-MSCs arise from tumor mediated reprograming of local tissue MSCs. While cancer cells alone cannot induce a CA-MSC phenotype, the in vivo ovarian tumor micoenvironment (TME) can reprogram omental or ovary MSCs to protumorigenic CA-MSC (classifier score of >0.96). In vitro studies suggest that both tumor secreted factors and hypoxia are critical to induce the CA-MSC phenotype. Interestingly, while the breast cancer TME can reprogram BM MSCs into CA-MSCs, the ovarian TME cannot, demonstrating for the first time that tumor mediated CA-MSC conversion is tissue and cancer type dependent. Together these findings (1) provide a critical tool to define CA-MSCs and (2) highlight cancer cell influence on distinct normal tissues providing powerful insights into the mechanisms underlying cancer specific metastatic niche formation.
2018-08-17 | GSE118624 | GEO
Project description:Ovarian cancer is a common, malignant cancer in the female reproductive system. Despite the commonly affected tissue ovary, ovarian cancer is a heterogeneous disease consisting of at least five different histological subtypes and varying clinical features, cells of origin, molecular composition, risk factors, and treatments. With cumulative studies on the tumor microenvironment, a comprehensive landscape of the constituent cell types, and their interactions are yet to be established in ovarian cancer and its histotypes. Further characterization of tumor progression, metastasis, and various histotypes is needed to connect molecular signatures to pathological grading for tailored diagnosis and treatment. In this study, we leveraged high-resolution single-cell RNA sequencing technology to elucidate the cellular compositions on 21 solid tumor samples collected from 12 patients with six ovarian cancer histotypes and both primary (ovaries) and metastatic (omentum, rectum) sites. The diverse collection allows us to zoom in on histotype and tumor site-specific expression patterns of cells in the tumor and identify key marker genes and ligand-receptor pairs that are active in the ovarian tumor microenvironment. Our findings can be used in improving disease stratification and design of customized treatment.
2023-12-15 | GSE235931 | GEO