Literature DB >> 18042147

Diagnostic and prognostic significance of gene expression profiling in lymphomas.

Ellen Leich1, Elena Maria Hartmann, Christof Burek, German Ott, Andreas Rosenwald.   

Abstract

Gene expression profiling is a powerful tool to uncover complex molecular networks in cancer and, specifically, in malignant lymphomas. Within diffuse large B-cell lymphomas (DLBCL), two major molecular subtypes, the activated B-cell-like (ABC) and the germinal center B-cell-like (GCB) DLBCL, can be defined. Compared to GCB DLBCL, ABC DLBCL shows a vast difference in gene expression and constitutively expresses NFkappaB and its target genes. In retrospective analyses, the molecular phenotype of ABC DLBCL is associated with inferior survival. Gene expression profiling furthermore allows the molecular separation of Burkitt lymphoma (BL) from DLBCL and reveals a Burkitt-specific signature which is also expressed by a subset of tumors that are currently classified as DLBCL. Whether patients with a DLBCL displaying a Burkitt-specific gene expression signature may benefit from alternative therapeutic approaches will have to be determined in future prospective clinical trials. In follicular lymphoma (FL), two outcome-related signatures, termed Immune response 1 (IR1) and Immune response 2 (IR2), have been identified by gene expression profiling, indicating a significant role of the microenvironment in tumor development and progression. IR1, composed of genes mostly expressed by T-cells, was found to be associated with a more favorable clinical course, and IR2, enriched for genes expressed by macrophages and follicular dendritic cells, was found to be associated with an inferior clinical course. In mantle cell lymphoma (MCL), a gene expression-based proliferation signature of 20 different genes was identified that is able to predict survival of MCL patients in a linear fashion. Future efforts will have to be directed towards the translation of relevant molecular diagnostic and prognostic markers derived from the wealth of gene expression data into clinical tests and towards the development of novel, targeted therapies.

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Year:  2007        PMID: 18042147     DOI: 10.1111/j.1600-0463.2007.apm_867.xml.x

Source DB:  PubMed          Journal:  APMIS        ISSN: 0903-4641            Impact factor:   3.205


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