Stage III serous ovarian adenocarcinomas
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE12418 | GEO | 2008/08/12
SECONDARY ACCESSION(S): PRJNA113113
REPOSITORIES: GEO
Similar Datasets
Project description:The clinical outcome for patients with ovarian cancer is difficult to predict. However, the use of biomarkers as additional prognostic factors may improve the outcome for these patients. In order to find novel candidate biomarkers, differences in gene expressions were analyzed in 54 stage III serous ovarian adenocarcinomas with oligonucleotide microarrays containing 27,000 unique probes. The microarray data was verified with quantitative real-time polymerase chain reaction for the genes TACC1, MUC5B and PRAME. With a hierarchical cluster analysis we detected a sub-group including 60% of the survivors. The gene expressions in tumours from patients in this sub-group of survivors were compared to the remaining tumours, and 204 genes were found to be differently expressed. We conclude that the sub-group of survivors might represent patients with favourable tumour biology and sensitivity to treatment. A selection of the 204 genes might be used as a predictive model to distinguish patients within and outside of this group. Alternative chemotherapy strategies could then be offered as first-line treatment, which could improve the clinical outcome for these patients. Keywords: Comparison between groups of samples 20 fresh frozen tumors from 5 year survivors were compared to 34 tumors from deceased patients in order to find genes where the expression differed between the two groups
2010-05-25 | E-GEOD-12418 | biostudies-arrayexpress
Project description:Goal of the experiment: To identify correlated genes, pathways and groups of patients with systemic inflammatory response syndrome and septic shock that is indicative of biologically important processes active in these patients. Background: We measured gene expression levels and profiles of children with systemic inflammatory response syndrome (SIRS) and septic shock as a means for discovering patient sub-groups and gene signatures that are active in disease-affected individuals and potentially in patients with poor outcomes. Methods: Microarray and bioinformatics analyses of 123 microarray chips representing whole blood derived RNA from controls, children with SIRS, and children with septic shock. Results: A discovery-based filtering approach was undertaken to identify genes whose expression levels were altered in patients with SIRS or septic shock. Clustering of these genes identified 3 Major and several minor sub-groups of patients with SIRS or septic shock. The three groups differed with respect to incidence of septic shock and trended toward differences in mortality. Statistical analyses demonstrated that 6,435 gene probes were differentially regulated between the three patient sub-groups (false discovery rate < 0.001%). Of these gene probes, 623 gene probes within 7 major gene ontologies accounted for the majority of group differentiation. Network analyses of these 623 gene probes demonstrated 5 major gene networks that were differentially expressed between the 3 groups. Statistical comparison of septic shock survivors and non-survivors identified one major gene network that was under expressed in a high fraction of the non-survivors and identified potential biomarkers for poor outcome. Conclusions: This is the first genome-level demonstration of pediatric patient sub-groups with SIRS and septic shock. The sub-groups differ clinically and differentially express 5 major gene networks. We have identified gene signatures and potential biomarkers associated with poor outcome in children with septic shock. These data represent a major advancement in our genome-level understanding of pediatric SIRS and septic shock. Keywords: Septic shock, SIRS, pediatrics, outcome, infection, inflammation
2006-04-30 | GSE4607 | GEO
Project description:Trastuzumab improves survival outcomes in patients with HER2+ metastatic breast cancer. Some of these patients may become long-term survivors. The Long-Her study was designed to identify clinical and molecular markers that could differentiate long-term survivors from patients having early progression to trastuzumab. 103 patients were registered in the Long-HER study, of whom 71 had obtained a durable complete response. Median age was 58 years. Metastatic disease was diagnosed after a median of 24.7 months since primary diagnosis. Sixty-five per cent of tumours were poorly differentiated ductal carcinomas and hormonal receptors were present in 47%. Metastases were present in the liver (25%), lungs (25%), bones (23%) and soft tissues (23%), with 20% of patients having multiple locations of metastases. Median progression-free survival was 10.6 years after the diagnosis of metastatic disease. Absence of trastuzumab as part of adjuvant therapy was the only clinical factor associated with long-term survival . Gene ontology analysis demonstrated that genes involved in HIF activation, EGF receptor signalling pathway, PI3 kinase pathway, apoptosis signalling pathway and p53 pathway significantly correlated with response. The PI3K pathway was the most strongly associated with poor response to trastuzumab-based therapy: tumours in the control group usually had four or five alterations in this pathway, whereas tumours in the Long-HER group had two alterations at most. These findings support that trastuzumab may provide a substantial long-term survival benefit in a selected group of patients. Whole genome expression analysis comparing long-term survivors vs. a control group predicted early progression to trastuzumab-based therapy. Multiple alterations in genes related to the PI3K-mTOR pathway seem to be required to confer resistance to this therapy. 53 FFPE samples, which passed the RNA quality control criteria, were analyzed (45 primary tumours and 8 metastases). Among these 53 samples, 35 samples corresponded to long-term responders and 18 to the control group.
2016-02-11 | E-GEOD-44272 | biostudies-arrayexpress
Project description:Trastuzumab improves survival outcomes in patients with HER2+ metastatic breast cancer. Some of these patients may become long-term survivors. The Long-Her study was designed to identify clinical and molecular markers that could differentiate long-term survivors from patients having early progression to trastuzumab. 103 patients were registered in the Long-HER study, of whom 71 had obtained a durable complete response. Median age was 58 years. Metastatic disease was diagnosed after a median of 24.7 months since primary diagnosis. Sixty-five per cent of tumours were poorly differentiated ductal carcinomas and hormonal receptors were present in 47%. Metastases were present in the liver (25%), lungs (25%), bones (23%) and soft tissues (23%), with 20% of patients having multiple locations of metastases. Median progression-free survival was 10.6 years after the diagnosis of metastatic disease. Absence of trastuzumab as part of adjuvant therapy was the only clinical factor associated with long-term survival . Gene ontology analysis demonstrated that genes involved in HIF activation, EGF receptor signalling pathway, PI3 kinase pathway, apoptosis signalling pathway and p53 pathway significantly correlated with response. The PI3K pathway was the most strongly associated with poor response to trastuzumab-based therapy: tumours in the control group usually had four or five alterations in this pathway, whereas tumours in the Long-HER group had two alterations at most. These findings support that trastuzumab may provide a substantial long-term survival benefit in a selected group of patients. Whole genome expression analysis comparing long-term survivors vs. a control group predicted early progression to trastuzumab-based therapy. Multiple alterations in genes related to the PI3K-mTOR pathway seem to be required to confer resistance to this therapy.
2016-02-11 | GSE44272 | GEO
Project description:RNA-seq dataset of high-grade serous ovarian cancer (HGSC) tumours from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study.
The dataset includes fastq files from 56 HGSC tumours (53 primary tumours and 3 recurrent tumours) from 53 long-term survivor patients.
Libraries were generated using the Illumina Stranded mRNA Prep and 150 bp paired-end sequencing was performed to a minimum of 100 million reads on Illumina NovaSeq 6000 instruments.
| EGAD00001008537 | EGA
Project description:Endometrial cancer is the most common malignancy of the female reproductive tract. In many cases the prognosis is favourable, but 22% of affected women die from the disease. We aimed to study potential differences in gene expression between endometrioid adenocarcinomas from survivors (5-year survival) and non-survivors. Forty-five patients were included in the investigation, of which 21 were survivors and 24 were non-survivors. The tumours were analysed with genome-wide expression array analysis, represented by 13526 genes. Distinct differences in gene expression were found between the groups. A t-test established that a set of 218 genes were significantly differentially expressed (P < 0.001) between the two survival groups, and in a cross validation test 40 of the 45 (89%) tumours were classified correctly. The 218 differentially expressed genes were subjected to hierachical clustering analysis which yielded two clusters both exhibiting over 80% homogeneity with respect to survival. When the additional constraint of fold change (FC>2) was added the hierachical clustering yielded similar results. Tumour cDNA from both groups (survivors vs. non-survivors) were hybridised together with the same reference, and we were able to calculate the relative expression level value for the sample Stage I tumours are expected to have a favourable prognosis. However, in our tumour material there were six non-survivors with stage I tumours. Five out of six stage I non-survivors clustered in the non-survival fraction. Our findings suggest that a subgroup of early stage endometroid adenocarcinomas can be correctly classified as potentially aggressive by using molecular biology in combination with conventional markers, thereby providing a tool for a more accurate classification and risk evaluation of the individual patient.
2011-05-17 | E-GEOD-21882 | biostudies-arrayexpress
Project description:Goal of the experiment: To identify correlated genes, pathways and groups of patients with systemic inflammatory response syndrome and septic shock that is indicative of biologically important processes active in these patients. Background: We measured gene expression levels and profiles of children with systemic inflammatory response syndrome (SIRS) and septic shock as a means for discovering patient sub-groups and gene signatures that are active in disease-affected individuals and potentially in patients with poor outcomes. Methods: Microarray and bioinformatics analyses of 123 microarray chips representing whole blood derived RNA from controls, children with SIRS, and children with septic shock. Results: A discovery-based filtering approach was undertaken to identify genes whose expression levels were altered in patients with SIRS or septic shock. Clustering of these genes identified 3 Major and several minor sub-groups of patients with SIRS or septic shock. The three groups differed with respect to incidence of septic shock and trended toward differences in mortality. Statistical analyses demonstrated that 6,435 gene probes were differentially regulated between the three patient sub-groups (false discovery rate < 0.001%). Of these gene probes, 623 gene probes within 7 major gene ontologies accounted for the majority of group differentiation. Network analyses of these 623 gene probes demonstrated 5 major gene networks that were differentially expressed between the 3 groups. Statistical comparison of septic shock survivors and non-survivors identified one major gene network that was under expressed in a high fraction of the non-survivors and identified potential biomarkers for poor outcome. Conclusions: This is the first genome-level demonstration of pediatric patient sub-groups with SIRS and septic shock. The sub-groups differ clinically and differentially express 5 major gene networks. We have identified gene signatures and potential biomarkers associated with poor outcome in children with septic shock. These data represent a major advancement in our genome-level understanding of pediatric SIRS and septic shock. Experiment Overall Design: Children < 10 years of age admitted to the pediatric intensive care unit and meeting the criteria for either SIRS or septic shock were eligible for the study. SIRS and septic shock were defined based on pediatric-specific criteria. We did not use separate categories of "sepsis" or "severe sepsis". Patients meeting criteria for "sepsis" or "severe sepsis" were placed in the categories of SIRS and septic shock, respectively, for study purposes. Control patients were recruited from the outpatient or inpatient departments of the participating institutions using the following exclusion criteria: a recent febrile illness (within 2 weeks), recent use of anti-inflammatory medications (within 2 weeks), or any history of chronic or acute disease associated with inflammation. Experiment Overall Design: After obtaining informed consent, blood samples were obtained on Day 1 of the study, and when possible on Day 3 of the study. Blood samples were divided for RNA extraction and isolation of serum. Severity of illness was calculated based on the PRISM III score. Organ failure was defined based on pediatric-specific criteria. Annotated clinical and laboratory data were collected daily while in the intensive care unit. Study patients were placed in the study categories of SIRS or Septic Shock on Day 1 of the study. On Day 3 of the study, patients were classified as SIRS, Septic Shock, or SIRS resolved (no longer meeting criteria for SIRS). All study patients were followed for 28 days to determine mortality or survival. Clinical, laboratory, and biological data were entered and stored using a web-based data base developed locally.
2008-06-13 | E-GEOD-4607 | biostudies-arrayexpress
Project description:Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did. Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification 23 fresh frozen primary node negative breast cancer tumours from 10 year survivors were compared to 23 tumours from deceased patients in order to find genes where the expression differed between the two groups Microarrays were produced at the Swegene DNA Microarray Resource Center, Department of Oncology, Lund University, Sweden (http://swegene.onk.lu.se). Frozen tumours were homogenized together with TRIzol Reagent (Invitrogen, Carlsbad, CA, USA). From the cell-suspension total RNA was extracted using RNeasy mini kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s protocol. The quality of the RNA was evaluated with the Agilent 2100 BioAnalyser (Agilent Technologies, Palo Alto, CA, USA). Specimens where the 28S/16S ratio was lower than 1.0 or the RIN-value was lower than 6.7 were excluded from the study. For each sample, Cy3-dCTP (Amersham Biosciences, Buckinghamshire, UK) labelled probes were synthesized from 5 µg of the total tumour RNA and Cy5-dCTP (Amersham Biosciences) labelled reference were synthesized from 5 µg of commercial reference RNA (Universal Human Reference RNA, Stratagene, La Jolla, CA, USA) by reverse transcription. The probes were purified using ChipShot™ labelling cleanup system (Promega, Madison, WI, USA). The hybridizations were carried out using Pronto! Micro Array reagent systems (Corning Inc., Corning, NY, USA). For each sample, labelled tumour cDNA and reference cDNA were co-precipitated and hybridised to the microarray slide. The microarray slides were scanned with Agilent microarray scanner G2565AA (Agilent Technologies) and image analysis was performed using the Genepix 6.0.0.45 software (Axon Instruments, Union City, CA, USA).
2008-08-29 | E-GEOD-12071 | biostudies-arrayexpress
Project description:Ovarian cancer is the leading cause of death in gynaecological malignancies in women. However, currently there are no clinical or pathologic parameters available that can reliably predict clinical outcome. In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict disease stage with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs). In the current study we prospectively evaluate models, built on the pilot data set, on a set of 49 new patients. Principal component analysis showed that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the prospective study did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study, only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours but this model was not able to predict resistance to platin-based chemotherapy. We discuss possible reasons for prospective failure of these models and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before this technology could be considered ready for clinical use.
2007-12-31 | E-MEXP-995 | biostudies-arrayexpress
Project description:Background: Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression profiling to delineate major deregulated pathways and biomarkers associated with the development of intrinsic chemotherapy resistance upon exposure to standard first-line therapy for ovarian cancer. Methods: The study cohort comprised 28 patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. All 28 patients had advanced stage, high-grade serous ovarian cancer, and were treated with the same standard platinum-based chemotherapy. Twelve patient tumors demonstrating relative resistance to platinum chemotherapy corresponding to shorter PFS (< eight months) were compared to sixteen tumors from platinum-sensitive patients (PFS > eighteen months). Whole transcriptome profiling was performed using a Affymetrix high-resolution microarray platform to permit global comparisons of gene expression profiles between tumors from the resistant group and the sensitive group. Results: Microarray data analysis revealed a set of 204 discriminating genes possessing expression levels, which could influence differential chemotherapy response between the two groups. Robust statistical testing was then performed which eliminated a dependence on the normalization algorithm employed, producing a restricted list of differentially regulated genes, and which found IGF1 to be the most strongly differentially expressed gene. Pathway analysis, based on the list of 204 genes, revealed enrichment in genes primarily involved in the IGF1/PI3K/NFκB/ERK gene signalling networks. Conclusions: This study has identified pathway specific prognostic biomarkers possibly underlying a differential chemotherapy response in patients undergoing standard platinum-based treatment of serous epithelial ovarian cancer. Future studies to validate these markers are necessary to apply this knowledge to biomarker-based clinical trials. Total RNA from 12 chemotherapy resistant and 16 sensitive chemotherapy sensitive high-grade serous epithelial ovarian cancer samples was subjected to whole transcriptome profiling using Affymetrix U133 Plus 2.0 arrays
2014-01-01 | E-GEOD-51373 | biostudies-arrayexpress