Browse
Submit Data
Databases
API
Help

Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

15 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Characterization of novel ovarian tumor cell lines that retain the phenotype of primary tumors [MIP]

ABSTRACT: The inability to establish stable cell lines from the vast majority of human tumors has limited the use of in vitro models to study human cancer. Currently available tumor cell lines fail to represent the biological diversity of human tumors. We have developed a cell culture medium that enabled us to routinely establish cell lines from diverse subtypes of ovarian tumors. Importantly, the twenty-five ovarian tumor cell lines described here retain the genomic landscape, histopathology, and molecular features of the original tumors. Furthermore, the molecular profile and drug response of these cell lines correlated with distinct groups of primary tumors with different outcomes. Thus, tumor cell lines derived using this methodology represent a significantly improved new platform to study human tumor biology and treatment. DNA was extracted from 25 OCI (human ovarian cancer) cell lines.

ORGANISM(S): Homo sapiens

SUBMITTER: Tan Ince

PROVIDER: E-GEOD-40786 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

Identification of nine genomic regions of amplification in bladder cancer, correlation with stage, and potential prognositic and therapeutic value

Project description:We sought to test the hypothesis that ascertainment of genome-wide copy number alterations in bladder cancer may have value for clinical management. We performed a genome wide analysis of 164 bladder cancer samples and 27 bladder cancer cell lines to identify genetic changes associated with disease characteristics. Multiplex inversion probe (MIP) analysis, a newly developed genomic technique, was used to study 80 bladder cancers to identify mutations and copy number changes. Selected amplification events were then analyzed in a validation cohort of 84 bladder cancers by multiplex ligation-dependent probe assay (MLPA). In the MIP analysis, 44 regions of significant copy number change were identified using GISTIC. Nine gene-containing regions of amplification were selected for validation in the second cohort by MLPA. Amplification events at these 9 genomic regions were found to correlate strongly with stage, being seen in only 2 of 23 (9%) Ta grade 1 or 1-2 cancers, in contrast to 31 of 61 (51%) Ta grade 3 and T2 grade 2 cancers, p < 0.001. These observations suggest that analysis of genomic amplification of these 9 regions might serve as a guide for clinical decision making, in terms of management decisions following transurethral resection of bladder cancers. This approach is facilitated by the capability of each of the MIP and MLPA methods to analyze formalin-fixed paraffin-embedded DNA, as done here. Furthermore several of the amplified genes identified here (ERBB2, MDM2, CCND1) render the bladder cancers potentially sensitive to targeted therapy. Copy number profiling was completed for 80 urothelial carcinoma samples using the Affymetrix OncoScan(TM) FFPE Express platform.

2013-04-29 | E-GEOD-44323 | biostudies-arrayexpress

Characterization of novel ovarian tumor cell lines that retain the phenotype of primary tumors [SNP]

2015-06-16 | E-GEOD-40787 | biostudies-arrayexpress

Characterization of novel ovarian tumor cell lines that retain the phenotype of primary tumors [MIP]

2015-06-16 | GSE40786 | GEO

Characterization of novel ovarian tumor cell lines that retain the phenotype of primary tumors [SNP]

2015-06-16 | GSE40787 | GEO

Characterization of novel ovarian tumor cell lines that retain the phenotype of primary tumors [Illumina]

2015-06-16 | GSE40785 | GEO

DNA copy number gain at 1q23.3 is associated with poor survival in metastatic bladder cancer [Affymetrix]

Project description:Metastatic urothelial carcinoma (UC) of the bladder is associated with multiple somatic copy number alterations (SCNAs). We evaluated SCNAs to identify predictors of poor survival in patients with metastatic UC treated with platinum chemotherapy. We obtained overall survival (OS) and DNA copy number data from UC patients in a Spanish discovery cohort and three validation cohorts. GISTIC was used to identify altered regions, and associations between copy number gains/losses and OS were determined by a Cox proportional hazard model adjusting for performance status and visceral disease The output files of the GISTIC 2.0 software are linked as supplementary files on Series record along with readme.txt file describing the contents of each file/column.

2013-07-07 | E-GEOD-39280 | biostudies-arrayexpress

Characterization of novel ovarian tumor cell lines that retain the phenotype of primary tumors [Illumina]

Project description:The inability to establish stable cell lines from the vast majority of human tumors has limited the use of in vitro models to study human cancer. Currently available tumor cell lines fail to represent the biological diversity of human tumors. We have developed a cell culture medium that enabled us to routinely establish cell lines from diverse subtypes of ovarian tumors. Importantly, the twenty-five ovarian tumor cell lines described here retain the genomic landscape, histopathology, and molecular features of the original tumors. Furthermore, the molecular profile and drug response of these cell lines correlated with distinct groups of primary tumors with different outcomes. Thus, tumor cell lines derived using this methodology represent a significantly improved new platform to study human tumor biology and treatment. Total RNA was obtained from untreated ovarian cancer cell lines and clustering analysis was performed. The 'Median-Cent_5147_probes.txt' is a list of the 5147 gene probes that has at least 2-fold difference relative to median value across cell lines in at least 4 samples. These are the probes that were used for hierarchial clustering analysis. The 'Combined_and_GSE9891.txt' is a combination of gene probes from this study of ovarian cell lines and of human ovarian cancer cell lines (GSE9891[GPL570]: Expression profile of 285 ovarian tumour samples). Only the 3832 overlapping gene probes between the this study and the GSE9891[GPL570] study are listed. The 3832 gene probes listed have at least 2-fold difference relative to median value across samples in at least 4 samples.

2015-06-16 | E-GEOD-40785 | biostudies-arrayexpress

DNA copy number gain at 1q23.3 is associated with poor survival in metastatic bladder cancer

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2013-07-07 | E-GEOD-39282 | biostudies-arrayexpress

Integrative proteomic profiling of ovarian cancer cell lines reveals precursor-cell associated proteins and functional status

Project description:A cell line representative of human high-grade serous ovarian cancer (HGSOC) should not only resemble its tumor of origin at the molecular level, but also demonstrate functional utility in pre-clinical investigations. Here we report the integrated proteomic analysis of 26 ovarian cancer cell lines, HGSOC tumors, immortalized ovarian surface epithelial cells, and fallopian tube epithelial cells via a single-run mass spectrometric workflow. The in-depth quantitation of > 10,000 proteins results in three distinct cell line categories: epithelial (group I), clear cell (group II), and mesenchymal (group III). We identify a 67-protein cell line signature, which separates our entire proteomic dataset, as well as a confirmatory publicly available CPTAC/TCGA tumor proteome dataset, into a predominantly epithelial and mesenchymal HGSOC tumor cluster. This proteomics-based epithelial/mesenchymal stratification of cell lines and human tumors indicates a possible origin of HGSOC either from the fallopian tube or from the ovarian surface epithelium.

2016-08-24 | PXD003668 | Pride

Integrative proteomic profiling of ovarian cancer cell lines reveals precursor-cell associated proteins and functional status

2017-03-30 | MSV000080814 | MassIVE

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data