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 a-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.

Cancer/Testis Genes in Multiple Myeloma: Expression Patterns and Prognosis Value Determined by Microarray Analysis

“…Using DNA microarrays, we and others have shown that genes belonging to the MAGE and SSX families and NY-ESO-1 were aberrantly expressed in a certain percentage of primary MMC (29,30). The expression of NY-ESO-1 has been correlated with a poor prognosis and its frequency is increased in patients displaying genetic abnormalities determined by conventional cytogenetic analyses (31).…”

Functional validation of the anaplastic lymphoma kinase signature identifies CEBPB and Bcl2A1 as critical target genes

“…The RNA integrity was verified by means of an Agilent Technologies 2100 Bioanalyzer. The biotin-labeled cRNA was synthesized as previously described (49). In accordance with the Affymetrix protocols, 15 μg of fragmented cRNA was hybridized on GeneChip Human Genome U133A Arrays (Affymetrix) after quality checking on GeneChip Test3 Arrays (Affymetrix).…”

“…Unsupervised analyses were applied to a subset of genes whose average change in expression levels varied at least 2-fold from the mean across the whole panel. Hierarchical agglomerative clustering and dendrogram generation were used to search for natural groupings in the profiles using DNA-Chip Analyzer (dChip) software (25); in order to perform the clustering of the selected probe lists, Pearson's correlation coefficient and average linkage were used as distance and linkage methods, respectively (49,50). The supervised gene expression analyses were performed using the Gene@Work software platform as previously described (49).…”

“…Hierarchical agglomerative clustering and dendrogram generation were used to search for natural groupings in the profiles using DNA-Chip Analyzer (dChip) software (25); in order to perform the clustering of the selected probe lists, Pearson's correlation coefficient and average linkage were used as distance and linkage methods, respectively (49,50). The supervised gene expression analyses were performed using the Gene@Work software platform as previously described (49). Briefly, Gene@Work discovers global gene expression signatures that are common to an entire set of at least n experiments (the support set), where n was chosen as n = n0; n0 being the total number of samples in the set.…”

“…The probe level data were converted to expression values using the MAS 5.0 algorithm and normalized using the "global scaling" procedure (49), which normalizes the signals of different experiments to the same target intensity. Unsupervised analyses were applied to a subset of genes whose average change in expression levels varied at least 2-fold from the mean across the whole panel.…”