Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

A 50-Gene Predictor of Recurrence as a Guide to Therapy in Early-Stage Colon Cancer.

ABSTRACT: Gene expression profiles reflect unique aspects of individual biologic phenotypes and may characterize the heterogeneity of solid tumors. Using previously-described methodologies that employ DNA microarray data, a 50-gene expression profile (metagene) that predicts risk of recurrence in early stage colon carcinoma was identified. This analysis used an initial discovery cohort of 52 patients. The performance of the 50-gene predictor was evaluated in an independent validation cohort of 73 patients. Using a connectivity map analysis of the 50-gene model, we identified candidate agents and then tested the in vitro efficacy of these compounds in colon cancer cell lines. 73 samples that had patient recurrence data with stage information were used in the analysis. Keywords: Disease state analysis A total of 73 samples were spotted on microarray slides. No replicates are included in the study.

ORGANISM(S): Homo sapiens

SUBMITTER: Anil Potti

PROVIDER: E-GEOD-10402 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

A genomic approach to colon cancer risk stratification yields biologic insights into therapeutic opportunities.

Garman Katherine S KS Acharya Chaitanya R CR Edelman Elena E Grade Marian M Gaedcke Jochen J Sud Shivani S Barry William W Diehl Anna Mae AM Provenzale Dawn D Ginsburg Geoffrey S GS Ghadimi B Michael BM Ried Thomas T Nevins Joseph R JR Mukherjee Sayan S Hsu David D Potti Anil A

Proceedings of the National Academy of Sciences of the United States of America 20081202 49

Gene expression profiles provide an opportunity to dissect the heterogeneity of solid tumors, including colon cancer, to improve prognosis and predict response to therapies. Bayesian binary regression methods were used to generate a signature of disease recurrence in patients with resected early stage colon cancer validated in an independent cohort. A 50-gene signature was developed that effectively distinguished early stage colon cancer patients with a low or high risk of disease recurrence. RT ...[more]

PMID: 19050079

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Project description:Hepatocarcinogenesis is a multi-stage process in which precursor lesions progress into early hepatocellular carcinomas (eHCC) by sequential accumulation of multiple genetic and epigenetic alterations. To decode the molecular events during early stages of liver carcinogenesis, we performed gene expression profiling on cirrhotic (regenerative) and dysplastic nodules (DN) as well as eHCC. Although considerable heterogeneity was observed at the regenerative and dysplastic stages, clear differences were detected between DN and eHCC which included 460 differentially expressed genes. Functional analysis of the significant gene set identified the MYC oncogene as a plausible driver gene for malignant conversion of the dysplastic nodules. In addition, gene set enrichment analysis (GSEA) revealed a remarkable enrichment of MYC up-regulated gene set in eHCC versus dysplasia. Presence of the MYC signature significantly correlated with increased expression of CSN5 as well as with the higher overall transcription rate of genes located in the 8q chromosome region. Furthermore, a classifier constructed from MYC target genes could robustly discriminate eHCC from high- and low-grade dysplastic nodules. In conclusion, our study identified unique expression patterns associated with the transition of high-grade dysplastic nodules to early HCC and demonstrated that activation of the MYC transcription signature is critical for the malignant conversion of pre-neoplastic liver lesions. Samples from forty-nine nodular liver lesions including 24 regenerative (cirrhotic) nodules (CN), 3 low-grade (LGDN), 12 high-grade dysplastic nodules (HGDN) and 10 early hepatocellular carcinomas (Early HCC) were used. The Human Operon V2 oligonucleotide library containing 22K features representing expressed sequences was printed to glass arrays in the Advanced Technology Center (National Cancer Institute, Gaithersburg, MD). The aRNA probes were fragmented and hybridized to the microarray slides following the standard procedures. All samples were hybridized against a common amplified reference RNA pooled from normal liver samples. Experimental duplicates were prepared following a reverse-flour design.

Dataset Information

A 50-Gene Predictor of Recurrence as a Guide to Therapy in Early-Stage Colon Cancer.

Publications

A genomic approach to colon cancer risk stratification yields biologic insights into therapeutic opportunities.

Similar Datasets

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