Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

CaArray_cho-00156: Gene Expression in Ovarian Cancer Reflects Both Morphology and Biological Behavior

ABSTRACT: Biologically and clinically meaningful tumor classification schemes have long been sought. Some malignant epithelial neoplasms, such as those in the thyroid and endometrium, exhibit more than one pattern of differentiation, each associated with distinctive clinical features and treatments. In other tissues, all carcinomas, regardless of morphological type, are treated as though they represent a single disease. To better understand the biological and clinical features seen in the four major histological types of ovarian carcinoma (OvCa), we analyzed gene expression in 113 ovarian epithelial tumors using oligonucleotide microarrays. Global views of the variation in gene expression were obtained using PCA. These analyses show that mucinous and clear cell OvCas can be readily distinguished from serous OvCas based on their gene expression profiles, regardless of tumor stage and grade. In contrast, endometrioid adenocarcinomas show significant overlap with other histological types. Although high-stage/grade tumors are generally separable from low-stage/grade tumors, clear cell OvCa has a molecular signature that distinguishes it from other poor-prognosis OvCas. Indeed, 73 genes, expressed 2- to 29-fold higher in clear cell OvCas compared with each of the other OvCa types, were identified. Collectively, the data indicate that gene expression patterns in ovarian adenocarcinomas reflect both morphological features and biological behavior. Moreover, these studies provide a foundation for the development of new type-specific diagnostic strategies and treatments for ovarian cancer. cho-00156 Assay Type: Gene Expression Provider: Affymetrix Array Designs: Hu6800 Organism: Homo sapiens (ncbitax) Material Types: synthetic_RNA, organism_part, whole_organism, total_RNA Disease States: Ovary cancer

ORGANISM(S): Homo sapiens

SUBMITTER: Mervi Heiskanen

PROVIDER: E-GEOD-68600 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas.

Schwartz Donald R DR Kardia Sharon L R SL Shedden Kerby A KA Kuick Rork R Michailidis George G Taylor Jeremy M G JM Misek David E DE Wu Rong R Zhai Yali Y Darrah Danielle M DM Reed Heather H Ellenson Lora H LH Giordano Thomas J TJ Fearon Eric R ER Hanash Samir M SM Cho Kathleen R KR

Cancer research 20020801 16

Biologically and clinically meaningful tumor classification schemes have long been sought. Some malignant epithelial neoplasms, such as those in the thyroid and endometrium, exhibit more than one pattern of differentiation, each associated with distinctive clinical features and treatments. In other tissues, all carcinomas, regardless of morphological type, are treated as though they represent a single disease. To better understand the biological and clinical features seen in the four major histo ...[more]

PMID: 12183431

Similar Datasets

Project description:Migrated from 1.6 id: 1015897590491013 GEDP id: 760 In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions. nelso-00262 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A, HG-U133B Organism: Homo sapiens (ncbitax) Material Types: total RNA, synthetic_RNA, organism_part, whole_organism Disease States: Glioma, Glioblastoma, Oligodendroglial Tumor, astrocytomas

Project description:To assess pathologic complete response (pCR), clinical response, feasibility, safety, and potential predictors of response to preoperative trastuzumab plus vinorelbine in patients with operable, human epidermal growth factor receptor 2 (HER2)-positive breast cancer. EXPERIMENTAL DESIGN: 48 patients received preoperative trastuzumab and vinorelbine weekly for 12 weeks. Single and multigene biomarker studies were done in an attempt to identify predictors of response. RESULTS: 8 of 40 (20%) patients achieved pCR (95% confidence interval, 9-36%). Of 9 additional patients recruited for protocol-defined toxicity analysis, 8 were evaluable; 42 of 48 (88%) patients had clinical response (16 patients, clinical complete response; 26 patients, clinical partial response). T(1) tumors more frequently exhibited clinical complete response (P = 0.05) and showed a trend to exhibit pCR (P = 0.07). 5 (13%) patients experienced grade 1 cardiac dysfunction during preoperative treatment. Neither HER2 nor estrogen receptor status changed significantly after exposure to trastuzumab and vinorelbine. RNA profiling identified three top-level clusters by unsupervised analysis. Tumors with extremes of response [pCR (n = 3) versus nonresponse (n = 3)] fell into separate groups by hierarchical clustering. No predictive genes were identified in pCR tumors. Nonresponding tumors were more likely to be T(4) stage (P = 0.02) and express basal markers (P < 0.00001), growth factors, and growth factor receptors. Insulin-like growth factor-I receptor membrane expression was associated with a lower response rate (50% vs 97%; P = 0.001). CONCLUSIONS: Preoperative trastuzumab plus vinorelbine is active and well tolerated in patients with HER2-positive, operable, stage II/III breast cancer. HER2-overexpressing tumors with a basal-like phenotype, or with expression of insulin-like growth factor-I receptor and other proteins involved in growth factor pathways, are more likely to be resistant to this regimen. harri-00137 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133_Plus_2 Organism: Homo sapiens (ncbitax)

Project description:A key step in bringing gene expression data into clinical practice is the conduct of large studies to confirm preliminary models. The performance of such confirmatory studies and the transition to clinical practice requires that microarray data from different laboratories are comparable and reproducible. We designed a study to assess the comparability of data from four laboratories that will conduct a larger microarray profiling confirmation project in lung adenocarcinomas. To test the feasibility of combining data across laboratories, frozen tumor tissues, cell line pellets, and purified RNA samples were analyzed at each of the four laboratories. Samples of each type and several subsamples from each tumor and each cell line were blinded before being distributed. The laboratories followed a common protocol for all steps of tissue processing, RNA extraction, and microarray analysis using Affymetrix Human Genome U133A arrays. High within-laboratory and between-laboratory correlations were observed on the purified RNA samples, the cell lines, and the frozen tumor tissues. Intraclass correlation within laboratories was only slightly stronger than between laboratories, and the intraclass correlation tended to be weakest for genes expressed at low levels and showing small variation. Finally, hierarchical cluster analysis revealed that the repeated samples clustered together regardless of the laboratory in which the experiments were done. The findings indicate that under properly controlled conditions it is feasible to perform complete tumor microarray analysis, from tissue processing to hybridization and scanning, at multiple independent laboratories for a single study. dobbi-00100 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A Organism: Homo sapiens (ncbitax) Tissue Sites: Kidney, Lung, Stomach, Uterus, Liver, Lymphoid tissue, Ovary, Skin, Adrenal Gland, Lymph_Node Material Types: cell, nuclear_RNA, synthetic_RNA, organism_part, total_RNA Disease States: Recurrent Renal Cell Carcinoma, Squamous Cell Carcinoma,Conventional_Clear_Cell_Renal_Cell_Carcinoma,Gastrointestinal_Stromal_Tumor, Lung_Adenocarcinoma, Leiomyoma, Non neoplastic liver with cirrosis, Stomach Adenocarcinoma, Large Cell Lymphoma, Ovarian Adenocarcinoma, Melanoma, Malignant G1 Stromal Tumor, Adrenal Cortical Adenoma, Metastatic Renal Cell Carcinoma, Malignant Melanoma

Dataset Information

CaArray_cho-00156: Gene Expression in Ovarian Cancer Reflects Both Morphology and Biological Behavior

Publications

Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure