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Profiles of genomic instability in high-grade serous ovarian cancer (HGSC) predict treatment outcome

ABSTRACT: Profiling of loss of heterozygosity (LOH) in HGSC, subcrouping HGSC by LOH-based clustering and comparing to the LOH profiles of triple-negative breast cancer [previously submitted; GSE19594]. Study for the correlation of LOH burdern and LOH-based subgroups to clinical response to platinum-based chemotherapy in patients suffered from HGSC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE39130 | GEO | 2012/11/06

SECONDARY ACCESSION(S): PRJNA170063

REPOSITORIES: GEO

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Profiles of genomic instability in high-grade serous ovarian cancer (HGSC) predict treatment outcome

Project description:Profiling of loss of heterozygosity (LOH) in HGSC, subcrouping HGSC by LOH-based clustering and comparing to the LOH profiles of triple-negative breast cancer [previously submitted; GSE19594]. Study for the correlation of LOH burdern and LOH-based subgroups to clinical response to platinum-based chemotherapy in patients suffered from HGSC. SNP data (Affymetrix GenChip 250K SNP Nsp) from 47 high grade serous ovarian cancer were generated and used for LOH and copy number analysis, LOH-based hierarchical clustering to subclassify HGSC, and comparison to the chromosomal alterations in high grade brest cancer. The associstion between LOH-based subgroups and LOH burden and clinical resposne to platinum-based chemotherapy was investigated. The results were validated in two independent public opening datasets.

2012-11-06 | E-GEOD-39130 | biostudies-arrayexpress

Expression data from high-grade serous ovarian carcinomas (HGSC), clear cell ovarian carcinomas (CCC) and ovarian surface epithelium (OSE)

Project description:Background: The aim of this study was to identify differentially expressed miRNAs in high-grade serous ovarian carcinoma (HGSC), clear cell ovarian carcinoma (CCC) and ovarian surface epithelium (OSE). Selected miRNAs were evaluated for association with clinical parameters including survival, and miRNA/mRNA interactions were mapped. Results: Differentially expressed miRNAs between HGSC, CCC and OSE were identified, of which 18 were validated (p<0.01) using RT-qPCR in an extended cohort. Compared with OSE, miR-205-5p was the most overexpressed miRNA in HGSC. miR-200 family members and miR-182-5p were the most overexpressed in HGSC and CCC compared with OSE, whereas miR-383 was the most underexpressed. miR-509-3-5p, miR-509-5p, miR-509-3p and miR-510 were among the strongest differentiators between HGSC and CCC, all being significantly overexpressed in CCC compared with HGSC. High miR-200c-3p expression was associated with poor progression-free (p=0.031) and overall (p=0.026) survival in HGSC. Interacting miRNAs and mRNA targets, including those of a TP53-related pathway presented previously, were identified in HGSC. Conclusions: Several miRNAs are overexpressed in HGSC and CCC compared with OSE, including the miR-200 family, among which miR-200c-3p is associated with survival in HGSC. A set of miRNAs differentiates CCC from HGSC, of which miR-509-3-5p and miR-509-5p are the strongest classifiers. Several interactions between miRNAs and mRNAs in HGSC were mapped. Methods: Differences in miRNA expression between HGSC, CCC and OSE scrapings were analyzed by global miRNA expression profiling (Affymetrix GeneChip miRNA 2.0 Arrays, n=30), validated by RT-qPCR (n=63), and evaluated for associations with clinical parameters. For HGSC, differentially expressed miRNAs were linked to differentially expressed mRNAs identified previously (GSE36668).

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A combination of molecular and clinical parameters provides a new strategy for high-grade serous ovarian cancer patient management

Project description:High-grade serous carcinoma (HGSC) is the most common and deadly subtype of ovarian cancer. Although most patients will initially respond to first-line treatment with a combination of surgery and platinum-based chemotherapy, up to a quarter will be resistant to treatment. We aimed to identify a new strategy to improve HGSC patient management at the time of cancer diagnosis (HGSC-1LTR). Using proteomics in ready-available HGSC tissues, we have identified a molecular signature (TKT, LAMC1 and FUCO) that combined with ready available clinical data is able to predict patient response to first-line treatment (AUC: 0.82). Identification of chemoresistance at the time of diagnosis can facilitate the study of alternative treatments aimed at improving patient outcome. In addition, those patients classified as chemosensitive could undergo standard care with platinum-based agents. Therefore, the HGSC-1LTR strategy can allow optimization of therapeutic decision making and individualize HGSC patients’ care

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Gene Expression Profiling in progesterone-induced HGSC development.

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