MiROvaR, a microRNA-based predictor of early relapse for ovarian cancer patients [training set; OC179]
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE73581 | GEO | 2016/10/31
SECONDARY ACCESSION(S): PRJNA297360
REPOSITORIES: GEO
Similar Datasets
Project description:Despite a pathological complete response, risk-of-relapse remains a challenge for most of epithelial ovarian cancer (EOC) patients and an ad-hoc predictor can be a valuable clinical tool. We developed a 35 miRNAs-based predictor of Risk of Ovarian Cancer Relapse (MiROvaR) using a training set of patients from MITO-2 (Multicentre Italian Trials in Ovarian Cancer-2; Pignata S et al. J Clin Oncol. 2011 Sep 20). MiROvaR performance was confirmed in two independent validation cohorts.
2016-10-31 | GSE73582 | GEO
Project description:Integration of several ovarian cancer datasets to identify a reproducible predictors of survival Four microarray datasets from different institutions were reprocessed in a uniform manner, into a single training dataset. Survival analysis was performed and a validation cohort (61 patients from 3 institutions) was profiled using a custom array to confirm the prognostic value of the predictors. The four datasets were obtained from the following reports: Spentzos D, Levine DA, Ramoni MF, et al. Gene expression signature with independent prognostic significance in epithelial ovarian cancer. J Clin Oncol 2004;22:4700-10 Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006;439:353-7 Marquez RT, Baggerly KA, Patterson AP, et al. Patterns of gene expression in different histotypes of epithelial ovarian cancer correlate with those in normal fallopian tube, endometrium, and colon. Clin Cancer Res 2005;11:6116-26 Zhang L, Volinia S, Bonome T, et al. Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer. Proc Natl Acad Sci U S A 2008;105:7004-9 61 samples
2011-06-15 | E-GEOD-19161 | biostudies-arrayexpress
Project description:Integration of several ovarian cancer datasets to identify a reproducible predictors of survival Four microarray datasets from different institutions were reprocessed in a uniform manner, into a single training dataset. Survival analysis was performed and a validation cohort (61 patients from 3 institutions) was profiled using a custom array to confirm the prognostic value of the predictors. The four datasets were obtained from the following reports: Spentzos D, Levine DA, Ramoni MF, et al. Gene expression signature with independent prognostic significance in epithelial ovarian cancer. J Clin Oncol 2004;22:4700-10 Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006;439:353-7 Marquez RT, Baggerly KA, Patterson AP, et al. Patterns of gene expression in different histotypes of epithelial ovarian cancer correlate with those in normal fallopian tube, endometrium, and colon. Clin Cancer Res 2005;11:6116-26 Zhang L, Volinia S, Bonome T, et al. Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer. Proc Natl Acad Sci U S A 2008;105:7004-9
2011-06-15 | GSE19161 | GEO
Project description:MiROvaR, a microRNA-based predictor of early relapse for ovarian cancer patients
| PRJNA297358 | ENA
Project description:<p><strong>PURPOSE:</strong> Detecting signals of micrometastatic disease in early breast cancer (EBC) patients could improve risk stratification and allow better tailoring of adjuvant therapies. We have previously shown that postoperative serum metabolomic profiles are predictive of relapse in a single-centre cohort of ERnegative EBC patients. Here, we investigated this further using pre-operative serum samples from ER-positive, premenopausal women with EBC who were enrolled in an international phase III trial. </p><p><strong>METHODS:</strong> Proton nuclear magnetic resonance (NMR) spectroscopy of 590 EBC samples (319 with relapse or =6 years clinical follow up) and 109 metastatic breast cancer (MBC) samples was performed. A Random Forest (RF) classification model was built using a training set of 85 EBC and all MBC samples. The model was then applied to a test set of 234 EBC samples, and a risk of recurrence score was generated based on the likelihood of the sample being misclassified as metastatic. </p><p><strong>RESULTS:</strong> In the training set, the RF model separated EBC from MBC with discrimination accuracy of 84.9%. In the test set, the RF recurrence risk score correlated with relapse, with an area under the curve of 0.747 in receiver operator characteristics analysis. Accuracy was maximised at 71.3% (sensitivity 70.8%, specificity 71.4%). The model performed independently of age, tumor size, grade, HER2 status and nodal status, and also of AdjuvantOnline risk of relapse score<strong>.</strong></p><p><strong>CONCLUSIONS:</strong> In a multicentre group of EBC patients, we developed a model based on preoperative serum metabolomic profiles that was prognostic for disease recurrence, independent of traditional clinicopathological risk factors.</p>
2017-03-02 | MTBLS424 | MetaboLights
Project description:Epithelial ovarian cancer is the leading cause of death from gynecological malignancies. Currently platinum-based chemotherapy, coupled with a taxane based drug is the primary treatment for ovarian cancer. Approximately 25% of patients either present with or rapidly develop resistance to platinum based chemotherapy and all recurrent tumors ultimately become resistant. Epigenetic modifications have been associated with tumor formation and progression and may contribute to therapy response. We performed a methylation screen on a set of tumors and have found a number of genes and family members differentially methylated between resistant patients and sensitive patients. Here we show that loss of expression of CHD3, a member of the Mi-2/NuRD complex, causes increased resistance to platinum chemotherapy drugs. Additionally, ovarian cell lines transcriptionally silenced for CHD3 are more invasive, have migratory ability, and display a transformed epithelial to mesenchymal (EMT) phenotype. Taken together, we provide the first evidence of a role for CHD3 as an important epigenetic regulator of chemoresistance in ovarian cancer and hypothesize EMT as one of the underlying mechanisms. Furthermore, CHD3 expression might represent a therapy response predictor and could be a future therapeutic target for ovarian cancer. We have developed a method to profile genome wide methylation. 71 ovarian tumor samples and 9 normal tissue samples from individuals were analyzed for CpG methylation. Some of these regions were validated for their methylation as a proof of principle for the method. Kamalakaran S., et. al. Mol Oncol. 2011;5:77-92 (PMID: 21169070).
2012-05-14 | E-GEOD-33011 | biostudies-arrayexpress
Project description:Extracellular, cancer-specific methylated DNA has been shown to be a prognostic marker when detected in serum or plasma. In this study we investigated the effect of treating cancer cells with differentially methylated CpG DNA. When breast cancer cell lines were treated with methylated CpG DNA, a consistent upregulation of CHAC1 mRNA expression was observed. CHAC1 was recently described to be a novel component of the unfolded protein response pathway. To elucidate the role of CHAC1 mRNA expression in cancer in more detail, we analyzed expression of this gene in breast (n=107) and ovarian cancer (n=107) and found a strong correlation with tumor differentiation. Poorly differentiated tumors exhibited higher CHAC1 expression levels (p=0.004 for breast and p=0.031 for ovarian cancer). Additionally, hormone receptor (HR)-negative breast cancers (p<0.001) and advanced stage disease ovarian cancers (p=0.026) also demonstrated high CHAC1 mRNA levels. mRNA expression analysis of the two known CHAC1 isoforms showed a strong association of expression above the median with poor outcome in breast cancer patients in a multivariate analysis (isoform a: relative risk (RR) of death 3.2 (95% CI 1.6-6.5; p<0.01); RR of relapse 3.9 (95% CI 1.6-9.8; p<0.01); isoform b: relative risk (RR) of death 3.5 (95% CI 1.6-7.3; p<0.01); RR of relapse 6.6 (95% CI 2.4-18.5; p<0.01)). Univariate analysis in ovarian cancer showed that CHAC1 mRNA expression above the median was associated with a poor relapse free survival (p=0.03). In younger ovarian cancer patients (age < median age), a high CHAC1 mRNA expression was associated with overall survival (p=0.007) and relapse free survival (p=0.015). Finally, we show that downregulation of CHAC1 by small interfering RNA suppressed breast cancer cell migration and proliferation, whereas overexpression resulted in an observed increase in these cellular behaviours. This is the first report demonstrating that a gene (CHAC1) whose expression is triggered by methylated, but not unmethylated DNA, is involved in tumour biology. Human breast cancer cell lines (BT-20, Hs578T) were treated with methylated and unmethylated DNA oligos, and differentially expressed genes were identified between treatments with methylated and un-methylated oligos.
2011-12-01 | E-GEOD-22865 | biostudies-arrayexpress
Project description:MiROvaR, a microRNA-based predictor of early relapse for ovarian cancer patients [validation set OC133]
| PRJNA297361 | ENA
Project description:MiROvaR, a microRNA-based predictor of early relapse for ovarian cancer patients [training set OC179]
| PRJNA297360 | ENA
Project description:Triple negative breast cancer (TNBC) accounts for 15-20% of all breast carcinomas and it is clinically characterized by an aggressive phenotype and bad prognosis. TNBC does not benefit from any targeted therapy, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of one hundred twenty-five formalin-fixed paraffin-embedded samples from patients diagnosed with triple negative breast cancer were analyzed by mass spectrometry using data-independent acquisition. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were used to characterize molecular groups. Additionally, a predictive signature related with relapse was defined. Two molecular groups with differences in several biological processes as glycolysis, translation and immune response, were defined in this cohort, and a prognostic signature based on the abundance of proteins RBM3 and NIPSNAP1 was defined. This predictor split the population into low-risk and high-risk groups. The differential processes identified between the two molecular groups may serve to design new therapeutic strategies in the future and the prognostic signature could be useful to identify a population at high-risk of relapse that could be directed to clinical trials.
2022-02-14 | PXD021491 | Pride