4.7 Article Proceedings Paper

Specific secondary genetic alterations in mantle cell lymphoma provide prognostic information independent of the gene expression-based proliferation signature

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JOURNAL OF CLINICAL ONCOLOGY
卷 25, 期 10, 页码 1216-1222

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AMER SOC CLINICAL ONCOLOGY
DOI: 10.1200/JCO.2006.08.4251

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  1. NCI NIH HHS [U01 CA084967, UO1-CA84967, U01 CA114778-01] Funding Source: Medline

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Purpose To compare the genetic relationship between cyclin D1 - positive and cyclin D1 - negative mantle cell lymphomas (MCLs) and to determine whether specific genetic alterations may add prognostic information to survival prediction based on the proliferation signature of MCLs. Patients and Methods Seventy-one cyclin D1 - positive and six cyclin D1 - negative MCLs previously characterized by gene expression profiling were examined by comparative genomic hybridization (CGH). Results Cyclin D1 - negative MCLs were genetically characterized by gains of 3q, 8q, and 15q, and losses of 1p, 8p23- pter, 9p21- pter, 11q21- q23, and 13q that were also the most common alterations in conventional MCLs. Parallel analysis of CGH aberrations and locus-specific gene expression profiles in cyclin D1 - positive patients showed that chromosomal imbalances had a substantial impact on the expression levels of the genes located in the altered regions. The analysis of prognostic factors revealed that the proliferation signature, the number of chromosomal aberrations, gains of 3q, and losses of 8p, 9p, and 9q predicted survival of MCL patients. A multivariate analysis showed that the gene expression-based proliferation signature was the strongest predictor for shorter survival. However, 3q gains and 9q losses provided prognostic information that was independent of the proliferative activity. Conclusion Cyclin D1 - positive and - negative MCLs share the same secondary genetic aberrations, supporting the concept that they correspond to the same genetic entity. The integration of genetic information on chromosome 3q and 9q alterations into a proliferation signature-based model may improve the ability to predict survival in patients with MCL.

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