Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Classification of a large micro-array dataset. Algorithm comparison and analysis of drug signatures.

ABSTRACT: Classification of a large micro-array dataset. Algorithm comparison and analysis of drug signatures. These data support the publication titled "Classification of a large micro-array dataset. Algorithm comparison and analysis of drug signatures.". Some of the calculations in the publication were derived from an older version of the data available at http://www.iconixpharm.com Copyright (c) 2005 by Iconix Pharmaceuticals, Inc. Guidelines for commercial use: http://www.iconixbiosciences.com/guidelineCommUse.pdf

ORGANISM(S): Rattus norvegicus

SUBMITTER: Mark Fielden

PROVIDER: E-GEOD-2187 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

Natsoulis Georges G El Ghaoui Laurent L Lanckriet Gert R G GR Tolley Alexander M AM Leroy Fabrice F Dunlea Shane S Eynon Barrett P BP Pearson Cecelia I CI Tugendreich Stuart S Jarnagin Kurt K

Genome research 20050501 5

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines ...[more]

PMID: 15867433

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Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.

Dataset Information

Classification of a large micro-array dataset. Algorithm comparison and analysis of drug signatures.

Publications

Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

Similar Datasets

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