MiROvaR, a microRNA-based predictor of early relapse for ovarian cancer patients [validation set; OC133]
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE73582 | GEO | 2016/10/31
SECONDARY ACCESSION(S): PRJNA297361
REPOSITORIES: GEO
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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 | GSE73581 | 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:<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: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
Project description:Early-stage epithelial ovarian cancer (eEOC) patients have a generally favorable prognosis but heterogeneous behavior at recurrence. Accurate prediction of the risk of relapse is still a major concern, essentially to avoid overtreatment. We have identified a robust miRNA-based signature named MiROvaR able to predict early disease recurrence in case materials of mostly advanced-stage EOC patients. We challenged MiROvaR in the eEOC sub-setting (stage IA-IIB) and it proved to accurately classify eEOC patients according to their risk of relapse.
2022-12-21 | GSE169314 | GEO
Project description:Chemotherapy (CT) resistance in ovarian cancer is broad and encompasses diverse, unrelated drugs, suggesting more than one mechanism of resistance. We aimed to analyze the gene expression patterns in primary serous epithelial ovarian cancer (EOC) samples displaying different responses to first-line CT in an attempt to identify specific molecular signatures associated with response to CT. Initially, the expression profiles of 15 chemoresistant serous EOC tumors [time to recurrence (TTR) ≤6 months] and 10 chemosensitive serous EOC tumors (TTR ≥30 months) were independently analyzed which allowed the identification of specific sets of differentially expressed genes that might be functionally implicated in the evolution of the chemoresistant or the chemosensitive phenotype. Our data suggest that the intrinsic chemoresistance in serous EOC cells may be attributed to the combined action of different molecular mechanisms and factors linked with drug influx and efflux and cell proliferation, as possible implications of other molecular events including altered metabolism, apoptosis and inflammation cannot be excluded. Next, gene expression comparison using hierarchical clustering clearly distinguished chemosensitive and chemo- resistant tumors from the 25 serous EOC samples (training set), and consecutive class prediction analysis was used to develop a 43-gene classifier that was further validated in an independent cohort of 15 serous EOC patients and 2 patients with other ovarian cancer histotypes (test set). The 43-gene predictor set properly classified serous EOC patients at high risk for early (≤22 months) versus late (>22 months) relapse after initial CT. Thus, gene expression array technology can effectively classify serous EOC tumors according to CT response. The proposed 43-gene model needs further validation. 2 condition experiment: chemoresistant clinical samples versus chemosensitive samples
2011-07-23 | E-GEOD-28739 | biostudies-arrayexpress
Project description:PAM50 intrinsic subtype classification (Parker JS, Mullins M, Cheang MCU, et al: Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J Clin Oncol 27:1160-1167, 2009) was used for the Z1031 patient cohort. However, to account for potential technical biases such as differences in platforms and laboratory protocols between the Z1031 data set and the PAM50 training set2, we normalized the Z1301 data set before applying the PAM50 algorithm. This is a critical step that must be taken into consideration for appropriate subtype predictions as our group and others have previously shown3,4. To appropriately normalize the Z1301 microarray data, which is entirely composed of ER-positive tumors, we needed to estimate technical bias from a cohort with equivalent subtype representation as the PAM50 training set. A set including ER-negative, HER2-positive and true normal breast samples were profiled under the same platform, protocol and laboratory (i.e. WashU) as the Z1301 data set. A total of 40 prototypic samples of the various molecular subtypes were selected to match the distribution in the PAM50 training set. The mean expression of each PAM50 gene was calculated in this set of 40. These values obtained from the prototypical sample set were used for calibration purposes and were included as a new WashU_4X44K_Calibration column in the CalibrationParameter file, which has been already provided in the PAM50 algorithm2. This adjustment step assures that each sample is placed in the appropriate ‘prototypic space’ by subtracting the calibration value of each PAM50 gene from the expression value of the corresponding gene in each tested sample. Finally, single sample predictions were performed in every Z1301 sample using the WashU_4X44K_Calibration parameter. The complete data set for the 40 prototypic arrays is submitted under GSE26082. 40 experiments were used as prototypical cases for subtype calibration purposes.
2011-05-10 | E-GEOD-26082 | biostudies-arrayexpress