Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression data from SKOV3 cells treated with SAHA or vehicle control

ABSTRACT: We performed a microarray experiment to assess SAHA-induced changes in expression of genes of the homologous recombination DNA repair pathway SKOV-3 ovarian cancer cells were treated with 1ÂµM SAHA or vehicle-control (0.01% DMSO) for 6 or 24 hours, harvested and processed for RNA isolation. Data for both time-points for SAHA and vehicle-control treated cells were obtained in duplicate. Total RNA was isolated using the Qiagen RNeasy Kit (Qiagen, Valencia, CA) according to manufacturer's instructions. cDNA synthesis and hybridization on oligonucleotide microarrays (U133 Plus 2.0 Array GeneChip, Affymetrix, Inc., Santa Clara, CA) were carried out using standard protocols.

ORGANISM(S): Homo sapiens

SUBMITTER: Panagiotis Konstantinopoulos

PROVIDER: E-GEOD-53603 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Suberoylanilide hydroxamic acid (SAHA) enhances olaparib activity by targeting homologous recombination DNA repair in ovarian cancer.

Konstantinopoulos Panagiotis A PA Wilson Andrew J AJ Saskowski Jeanette J Wass Erica E Khabele Dineo D

Gynecologic oncology 20140311 3

<h4>Objectives</h4>Approximately 50% of serous epithelial ovarian cancers (EOC) contain molecular defects in homologous recombination (HR) DNA repair pathways. Poly(ADP-ribose) polymerase inhibitors (PARPi) have efficacy in HR-deficient, but not in HR-proficient, EOC tumors as a single agent. Our goal was to determine whether the histone deacetylase inhibitor, suberoylanilide hydroxamic acid (SAHA), can sensitize HR-proficient ovarian cancer cells to the PARPi AZD-2281 (olaparib).<h4>Methods</h4 ...[more]

PMID: 24631446

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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

Dataset Information

Expression data from SKOV3 cells treated with SAHA or vehicle control

Publications

Suberoylanilide hydroxamic acid (SAHA) enhances olaparib activity by targeting homologous recombination DNA repair in ovarian cancer.

Similar Datasets

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