Gene expression profiling of plasma cell dyscrasias reveals molecular patterns associated with distinct IGH translocations in multiple myeloma.
ABSTRACT Multiple myeloma (MM) is the most common form of plasma cell dyscrasia, characterized by a marked heterogeneity of genetic lesions and clinical course. It may develop from a premalignant condition (monoclonal gammopathy of undetermined significance, MGUS) or progress from intramedullary to extramedullary forms (plasma cell leukemia, PCL). To provide insights into the molecular characterization of plasma cell dyscrasias and to investigate the contribution of specific genetic lesions to the biological and clinical heterogeneity of MM, we analysed the gene expression profiles of plasma cells isolated from seven MGUS, 39 MM and six PCL patients by means of DNA microarrays. MMs resulted highly heterogeneous at transcriptional level, whereas the differential expression of genes mainly involved in DNA metabolism and proliferation distinguished MGUS from PCLs and the majority of MM cases. The clustering of MM patients was mainly driven by the presence of the most recurrent translocations involving the immunoglobulin heavy-chain locus. Distinct gene expression patterns have been found to be associated with different lesions: the overexpression of CCND2 and genes involved in cell adhesion pathways was observed in cases with deregulated MAF and MAFB, whereas genes upregulated in cases with the t(4;14) showed apoptosis-related functions. The peculiar finding in patients with the t(11;14) was the downregulation of the alpha-subunit of the IL-6 receptor. In addition, we identified a set of cancer germline antigens specifically expressed in a subgroup of MM patients characterized by an aggressive clinical evolution, a finding that could have implications for patient classification and immunotherapy.
Article: Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways.[show abstract] [hide abstract]
ABSTRACT: Interpretation of lists of genes or proteins with altered expression is a critical and time-consuming part of microarray and proteomics research, but relatively little attention has been paid to methods for extracting biological meaning from these output lists. One powerful approach is to examine the expression of predefined biological pathways and gene sets, such as metabolic and signaling pathways and macromolecular complexes. Although many methods for measuring pathway expression have been proposed, a systematic analysis of the performance of multiple methods over multiple independent data sets has not previously been reported. Five different measures of pathway expression were compared in an analysis of nine publicly available mRNA expression data sets. The relative sensitivity of the metrics varied greatly across data sets, and the biological pathways identified for each data set are also dependent on the choice of pathway activation metric. In addition, we show that removing incoherent pathways prior to analysis improves specificity. Finally, we create and analyze a public map of pathway expression in human tissues by gene-set analysis of a large compendium of human expression data. We show that both the detection sensitivity and identity of pathways significantly perturbed in a microarray experiment are highly dependent on the analysis methods used and how incoherent pathways are treated. Analysts should thus consider using multiple approaches to test the robustness of their biological interpretations. We also provide a comprehensive picture of the tissue distribution of human gene pathways and a useful public archive of human pathway expression data.Genome biology 02/2006; 7(10):R93. · 6.63 Impact Factor