20f01ede-79e9-4bec-8dfc-c4237f1c5cc1 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001009330 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Sequencing data of HGSC patient-derived cell lines and organoids, and possible fresh frozen tumor samples from same patients.
| EGAS00001006557 | EGA
Project description:Improved biomarkers of treatment response are needed for patients with High-Grade Serous Ovarian Cancer (HGSC). A challenge is substantial anatomical site-to-site variation in expression. We completed Data Independent Acquisition – Mass Spectrometry (DIA-MS) analysis of over 404 fresh frozen and 78 formalin fixed, paraffin-embedded HGSC tissue samples from ovary (adnexal) and a common secondary site (omentum) in 11 patients. This was compared with mutation testing, gene expression and whole genome copy number profiling. Proteins with relatively stable intra-, and variable inter-individual expression (n=1,651), included a 52-protein module reflecting interferon mediated tissue inflammation indicative of a cGAS-STING pathway cytosolic double-stranded (ds) DNA response. The dsDNA sensing / inflammation (DSI) score was higher in omentum compared with ovary. Ovarian HGSC samples showed marked inter-individual differences in inflammatory and immune responses to DNA damage. Stable discriminative features of the HGSC proteome, a prerequisite for clinical predictive biomarkers, are detectable in ovary (adnexal) tissue samples.
2025-06-11 | GSE277107 | GEO
Project description:High-grade serous ovarian cancer (HGSC) has a disproportionate impact on cancer-related mortality in women. There is an urgent need to improve treatment options for HGSC. This includes better selection of patients who will benefit from existing treatments, including HRD targeted therapy, and discovery of new targeted therapeutic options. Reliable tissue-based biomarkers that can guide treatment selection are key to progress in this area. A challenge for their definition is the large volume of disease that is characteristically present at HGSC diagnosis and that can show considerable heterogeneity within and between anatomical sites. In order to address this issue, we carried out a detailed study of the HGSC proteome in eleven individuals by comparing genomic and gene-expression features with multiple independent proteomic results derived from fresh frozen (FF) and formalin fixed paraffin embedded (FFPE) samples from both ovary and a common metastatic site, omentum. Our findings gave emphasis to the influence of sample site of origin on biomarker expression, and revealed opportunities to detect mechanisms shaping the HGSC tumour immune microenvironment from tissue proteomics. The dataset also forms a useful resource to investigate molecular heterogeneity in HGSC.
2025-06-13 | PXD042150 | Pride
Project description:Purpose: Gene expression signatures developed to measure the activity of oncogenic signaling pathways have been used to dissect the heterogeneity of tumor samples and to predict sensitivity to various cancer drugs that target components of the relevant pathways, thus potentially identifying therapeutic options for subgroups of patients. To facilitate broad use, including in a clinical setting, the ability to generate data from formalin-fixed, paraffin-embedded (FFPE) tissues is essential. Experimental Design: Patterns of pathway activity in matched fresh-frozen and FFPE xenograft tumor samples were generated using the MessageAmp Premier methodology in combination with assays using Affymetrix arrays. Results generated were compared with those obtained from fresh-frozen samples using a standard Affymetrix assay. In addition, gene expression data from patient matched fresh-frozen and FFPE melanomas were also utilized to evaluate the consistency of predictions of oncogenic signaling pathway status. Results: Significant correlation of pathway activity predictions was observed between paired fresh-frozen and FFPE xenograft tumor samples. In addition, significant concordance of pathway activity predictions was also observed between patient matched fresh-frozen and FFPE melanomas. Conclusion: Reliable and consistent predictions of oncogenic pathway activities can be obtained from FFPE tumor tissue samples. The ability to reliably utilize FFPE patient tumor tissue samples for genomic analyses will lead to a better understanding of the biology of disease progression and, in the clinical setting, will provide tools to guide the choice of therapeutics to those most likely to be effective in treating a patient’s disease.
2011-06-24 | GSE29598 | GEO
Project description:Gene expression profiling of CMS4 colon cancer treated with Imatinib in the ImPACCT study. Fresh-frozen biopsies were taken during diagnostic colonoscopy for CMS4 identification. Five patients gave informed consent for two week of neoadjuvant Imatinib therapy prior to removal of the primary tumor. Fresh-frozen samples were taken from surgically resected colon cancer specimens.
2022-06-01 | GSE196263 | GEO
Project description:To investigate the microRNA profiles of ovarian clear cell carcinoma (OCCC), microRNA sequencing was performed using formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen clinical samples. Moreover, patient-derived xenograft (PDX) tumors and cell lines were also investigated.
2022-04-16 | GSE200852 | GEO
Project description:Fresh frozen Melanoma patient samples were profiled on Affymetrix GeneChip Mapping 100K Set Arrays
2013-10-15 | GSE41592 | GEO
Project description:Proteomic analyses of human tissues are sometimes conducted on autopsy samples. However, no comparative analysis between proteomic data derived from autopsy samples and fresh frozen samples has been undertaken, nor has there been an assessment of the post-mortem interval (PMI) influences on protein quantification. In the current study, 94 human left anterior descending (LAD) coronary artery were collected from deceased patients. Proteins were analysed using nanoflow liquid chromatography-tandem mass spectrometry. Data were processed with Proteome Discoverer and Mascot. The correlations between the protein abundances and the PMI were calculated. DAVID software was used for pathway and GO annotation enrichment. Among consistently quantified proteins, approximately 40% of the protein abundances exhibited significant correlations with PMI, most of which being inverse. Notably, smooth muscle cell markers displayed substantial reduction with prolonged PMI. Conversely, positive correlations with PMI were observed for immunoglobulins, coagulation factors, and complement factors, including coagulation factor XII, plasminogen, and lactotransferrin. Comparative analyses of sex differences between autopsy LAD samples and fresh-frozen LAD samples (n=65) showed no concordance in protein quantification. However, a robust correlation was observed within a sex comparison conducted between fresh-frozen carotid endarterectomies (CEA) from 2 different cohorts (n=104 and n=200). This study represents the first large-scale proteomics investigation into the influence of PMI on the protein composition of human vasculature. We observed significant correlations with PMI for nearly 40% of the consistently quantified proteins. Our findings underscore potential discrepancies in the quantitative accuracy of proteomics data derived from autopsy samples. Consequently, results obtained from post-mortem specimens may not be reproducible in fresh-frozen samples.
2025-06-09 | PXD052071 | Pride
Project description:Sequencing data of 10 high-grade serous carcinoma (HGSC) patients (58 samples including blood derived normal samples as germline controls, fresh frozen tissue samples as tissue controls and organoids) sequenced with HiSeq X Ten / BGISEQ-500 / MGISEQ-2000.
| EGAD00001010019 | EGA
Project description:Ovarian cancer is one of the leading causes of death among patients with gynecological malignancies worldwide. To identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. To predict the prognosis of HGSC patients, we created machine learning-based models and identified RPL23 and USP19 as candidate prognostic markers. Specifically, patients with higher RPL23 mRNA levels had a worse prognosis and patients with higher USP19 mRNA levels had a better prognosis. This model was then used to analyze HGSC patient data hosted on The Cancer Genome Atlas; this exercise validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of RPL23 and USP19 determined using a machine-learning model could serve as prognostic markers in HGSC patients receiving conventional therapy.
2021-08-09 | GSE165808 | GEO