Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Identification of Novel Therapeutic Targets in Microdissected Clear Cell Ovarian Cancers

ABSTRACT: To identify the gene signature accounting for the distinct clinical outcomes in ovarian clear cell cancer patients Clear cell ovarian cancer is an epithelial ovarian cancer histotype that is less responsive to chemotherapy and carries poorer prognosis than serous and endometrioid histotypes. Despite this, patients with these tumors are treated in a similar fashion as all other ovarian cancers. Previous genomic analysis has suggested that clear cell cancers represent a unique tumor subtype. Here we generated the first whole genomic expression profiling using epithelial component of clear cell ovarian cancers and normal ovarian surface specimens isolated by laser capture microdissection. Arrays analyzed using BRB ArrayTools and PathwayStudio software was used to identify the signaling pathways. Gene expression profiling was completed for 10 clear cell ovarian cancer specimens and 10 normal ovarian surface epithelium using the Affymetrix human U133 Plus 2.0 Arrays

ORGANISM(S): Homo sapiens

SUBMITTER: Michael Birrer

PROVIDER: E-GEOD-29450 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Identification of novel therapeutic targets in microdissected clear cell ovarian cancers.

Stany Michael P MP Vathipadiekal Vinod V Ozbun Laurent L Stone Rebecca L RL Mok Samuel C SC Xue Hui H Kagami Takashi T Wang Yuwei Y McAlpine Jessica N JN Bowtell David D Gout Peter W PW Miller Dianne M DM Gilks C Blake CB Huntsman David G DG Ellard Susan L SL Wang Yu-Zhuo YZ Vivas-Mejia Pablo P Lopez-Berestein Gabriel G Sood Anil K AK Birrer Michael J MJ

PloS one 20110706 7

Clear cell ovarian cancer is an epithelial ovarian cancer histotype that is less responsive to chemotherapy and carries poorer prognosis than serous and endometrioid histotypes. Despite this, patients with these tumors are treated in a similar fashion as all other ovarian cancers. Previous genomic analysis has suggested that clear cell cancers represent a unique tumor subtype. Here we generated the first whole genomic expression profiling using epithelial component of clear cell ovarian cancers ...[more]

PMID: 21754983

Similar Datasets

Project description:To identify a prognostic gene signature accounting for the distinct clinical outcomes in ovarian cancer patients Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185(90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population. Gene expression profiling was completed for an extensive set of 185 primary ovarian tumors and 10 normal ovarian surface epithelium using the Affymetrix human U133A microarray

Project description:Ovarian cancer is a major cause of cancer mortality among women largely due to late diagnosis of advanced stage metastatic disease. More extensive molecular analysis of metastatic ovarian cancer is needed to identify post-translational modifications of proteins particularly associated with metastatic disease so that we can better understand the metastatic process and identify potential therapeutic targets. Proteins are the target of many recently developed cancer treatments, but an often neglected aspect of biomarker discovery is protein glycosylation, especially those involving tumor-associated carbohydrate antigens (TACAs) such as sialyl-Lewis(x) (SLe(x) and sialyl-Lewis(a) (SLe(a), which have been identified in various cancers. Although it is already known that considerable changes in protein glycosylation are major contributors to the initiation, progression and metastasis of tumors, specifics about glycosylation changes particularly important to the metastatic process are still lacking. In this report we describe the results of a combined glycomic and proteomic study of metastatic ovarian cancer (OC) ascites fluids. Glycoproteins in ascites fluid were enriched by affinity binding to lectins (ConA or WGA) and other affinity matrices. Separate glycomic and proteomic analyses were performed as well as glycopeptide analyses. Relative abundances of different N-glycan groups and proteins were identified from original ascites fluids and corresponding lectin bound samples. Levels of biomarkers CA125, MUC1 and fibronectin were also monitored in these samples by Western blot analysis. N-glycan analysis of ascites fluids showed the presence of large, highly fucosylated and sialylated, complex and hybrid glycans, some of which were not observed in normal serum. Proteins in OC ascites that were more abundant or not present in the serum control, were haptoglobin, fibronectin, lumican, fibulin, hemopexin, ceruloplasmin, alpha-1-antitrypsin and alpha-1-antichymotrypsin. Glycopeptide analysis identified N- and O-glycans in clusterin, hemopexin, and fibulin that were present in OC ascites.

Dataset Information

Identification of Novel Therapeutic Targets in Microdissected Clear Cell Ovarian Cancers

Publications

Identification of novel therapeutic targets in microdissected clear cell ovarian cancers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure