Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcriptional profiling of human kidney samples to discover gene signatures of progression and metastasis in renal cell cancer

ABSTRACT: In order to address the progression, metastasis, and clinical heterogeneity of renal cell cancer (RCC), transcriptional profiling with oligonucleotide microarrays (22,283 genes) was done on 49 RCC tumors, 20 non-RCC renal tumors, and 23 normal kidney samples. Samples were clustered based on gene expression profiles and specific gene sets for each renal tumor type were identified. Gene expression was correlated to disease progression and a metastasis gene signature was derived. Gene signatures were identified for each tumor type with 100% accuracy. Differentially expressed genes during early tumor formation and tumor progression to metastatic RCC were found. Subsets of these genes code for secreted proteins and membrane receptors and are both potential therapeutic or diagnostic targets. A gene pattern ("metastatic signature") derived from primary tumors was very accurate in classifying tumors with and without metastases at the time of surgery. A previously described "global" metastatic signature derived by another group from various non-RCC tumors was validated in RCC. Unlike previous studies, we describe highly accurate and externally validated gene signatures for RCC subtypes and other renal tumors. Interestingly, the gene expression of primary tumors provides us information about the metastatic status in the respective patients and has the potential, if prospectively validated, to enrich the armamentarium of diagnostic tests in RCC. We validated in RCC, for the first time, a previously described metastatic signature and further showed the feasibility of applying a gene signature across different microarray platforms. Transcriptional profiling allows a better appreciation of the molecular and clinical heterogeneity in RCC. We used the following tissue samples to obtain transcriptional profiling of kidney tumors using Affymetrix HGU-133A chips: 23 Normal, 32 clear cell RCC (cRCC), 11 papillary RCC (pRCC), 6 chromophobe RCC (chrRCC), 12 Oncocytoma (OC), and 8 transitional cell carcinoma (TCC). The supplementary file 'GSE15641_mas5_data.txt' contains MAS5 signal values for the Samples included in Series GSE15641. This dataset is part of the TransQST collection.

ORGANISM(S): Homo sapiens

SUBMITTER: Hasan Otu

PROVIDER: E-GEOD-15641 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Gene signatures of progression and metastasis in renal cell cancer.

Jones Jon J Otu Hasan H Spentzos Dimitrios D Kolia Shakirahmed S Inan Mehmet M Beecken Wolf D WD Fellbaum Christian C Gu Xuesong X Joseph Marie M Pantuck Allan J AJ Jonas Dietger D Libermann Towia A TA

Clinical cancer research : an official journal of the American Association for Cancer Research 20050801 16

<h4>Purpose</h4>To address the progression, metastasis, and clinical heterogeneity of renal cell cancer (RCC).<h4>Experimental design</h4>Transcriptional profiling with oligonucleotide microarrays (22,283 genes) was done on 49 RCC tumors, 20 non-RCC renal tumors, and 23 normal kidney samples. Samples were clustered based on gene expression profiles and specific gene sets for each renal tumor type were identified. Gene expression was correlated to disease progression and a metastasis gene signatu ...[more]

PMID: 16115910

Similar Datasets

Project description:Purpose: Discovery of curative therapies for renal cell carcinoma (RCC) is hampered by lack of authentic preclinical models. Tumorgrafts, generated by direct implantation of patient-derived tissues into mice, have demonstrated superior ability to predict therapeutic response. We evaluated âtissue slice graftsâ (TSGs) as an improved tumorgraft model of RCC. Experimental Design: Cores of fresh RCC were precision-cut at 300-Î¼m and implanted under the renal capsule of RAG2-/-Î³C-/- mice. Engraftment rate, histology, biomarker expression, genetic fidelity and metastatic potential were evaluated. Magnetic resonance imaging (MRI) was tested as a non-invasive method to measure tumor volume, and response to a targeted therapy was determined. Results: All 13 cases of RCC engrafted and displayed characteristic histology and biomarkers. TSG volume quantified non-invasively by MRI highly correlated with graft weights, providing a unique tool for monitoring orthotopic growth. Moreover, in 2 cases, cancer cells from TSGs metastasized to clinically relevant sites, including bone. Microarray analysis and DNA sequencing demonstrated a high degree of correlation of global gene expression and VHL status between TSGs and parental tumors. Treatment of TSGs with sunitinib significantly decreased graft weight and mean vessel density compared to controls. Conclusions: The TSG model of RCC faithfully recapitulates tumor pathology, gene expression, genetic mutation and drug response. The high engraftment rate and metastatic potential of this authentic model, in conjunction with the ability to generate large first-generation animal cohorts and to quantitate tumor volume at the orthotopic site by MRI, proffer significant advantages compared to other preclinical platforms. Three pairs of tissue slice graftes and parent tumors were included in the study. Tissue slice grafts and parent tumors were preserved in Allprotect tissue reagent (Qiagen, Valencia, CA) at -20Â°C prior to RNA extraction using an AllPrep DNA/RNA/Protein Mini Kit (Qiagen, Valencia, CA). The quality of RNA was determined using an Agilent 2100 Bioanalyzer (Agilent Biotechnologies, Santa Clara, CA). Microarray hybridization was performed using Illumina Human HT-12 v4 Beadchips (Illumina Inc., San Diego, CA) according to the manufacturerâs directions. Expression data was rank invariant normalized using BeadStudio software (Illumina Inc.).

Dataset Information

Transcriptional profiling of human kidney samples to discover gene signatures of progression and metastasis in renal cell cancer

Publications

Gene signatures of progression and metastasis in renal cell cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure