Intraclonal heterogeneity and distinct molecular mechanisms characterize the development of t(4;14) and t(11;14) myeloma.

We have used whole exome sequencing to compare a group of presentation t(4;14) with t(11;14) cases of myeloma to define the mutational landscape. Each case was characterized by a median of 24.5 exonic nonsynonymous single-nucleotide variations, and there was a consistently higher number of mutations in the t(4;14) group, but this number did not reach statistical significance. We show that the transition and transversion rates in the 2 subgroups are similar, suggesting that there was no specific mechanism leading to mutation differentiating the 2 groups. Only 3% of mutations were seen in both groups, and recurrently mutated genes include NRAS, KRAS, BRAF, and DIS3 as well as DNAH5, a member of the axonemal dynein family. The pattern of mutation in each group was distinct, with the t(4;14) group being characterized by deregulation of chromatin organization, actin filament, and microfilament movement. Recurrent RAS pathway mutations identified subclonal heterogeneity at a mutational level in both groups, with mutations being present as either dominant or minor subclones. The presence of subclonal diversity was confirmed at a single-cell level using other tumor-acquired mutations. These results are consistent with a distinct molecular pathogenesis underlying each subgroup and have important impacts on targeted treatment strategies. The Medical Research Council Myeloma IX trial is registered under ISRCTN68454111.

A compendium of myeloma-associated chromosomal copy number abnormalities and their prognostic value.

To obtain a comprehensive genomic profile of presenting multiple myeloma cases we performed high-resolution single nucleotide polymorphism mapping array analysis in 114 samples alongside 258 samples analyzed by U133 Plus 2.0 expression array (Affymetrix). We examined DNA copy number alterations and loss of heterozygosity (LOH) to define the spectrum of minimally deleted regions in which relevant genes of interest can be found. The most frequent deletions are located at 1p (30%), 6q (33%), 8p (25%), 12p (15%), 13q (59%), 14q (39%), 16q (35%), 17p (7%), 20 (12%), and 22 (18%). In addition, copy number-neutral LOH, or uniparental disomy, was also prevalent on 1q (8%), 16q (9%), and X (20%), and was associated with regions of gain and loss. Based on fluorescence in situ hybridization and expression quartile analysis, genes of prognostic importance were found to be located at 1p (FAF1, CDKN2C), 1q (ANP32E), and 17p (TP53). In addition, we identified common homozygously deleted genes that have functions relevant to myeloma biology. Taken together, these analyses indicate that the crucial pathways in myeloma pathogenesis include the nuclear factor-κB pathway, apoptosis, cell-cycle regulation, Wnt signaling, and histone modifications. This study was registered at http://isrctn.org as ISRCTN68454111.

Integration of global SNP-based mapping and expression arrays reveals key regions, mechanisms, and genes important in the pathogenesis of multiple myeloma.

Multiple myeloma is characterized by genomic alterations frequently involving gains and losses of chromosomes. Single nucleotide polymorphism (SNP)-based mapping arrays allow the identification of copy number changes at the sub-megabase level and the identification of loss of heterozygosity (LOH) due to monosomy and uniparental disomy (UPD). We have found that SNP-based mapping array data and fluorescence in situ hybridization (FISH) copy number data correlated well, making the technique robust as a tool to investigate myeloma genomics. The most frequently identified alterations are located at 1p, 1q, 6q, 8p, 13, and 16q. LOH is found in these large regions and also in smaller regions throughout the genome with a median size of 1 Mb. We have identified that UPD is prevalent in myeloma and occurs through a number of mechanisms including mitotic nondisjunction and mitotic recombination. For the first time in myeloma, integration of mapping and expression data has allowed us to reduce the complexity of standard gene expression data and identify candidate genes important in both the transition from normal to monoclonal gammopathy of unknown significance (MGUS) to myeloma and in different subgroups within myeloma. We have documented these genes, providing a focus for further studies to identify and characterize those that are key in the pathogenesis of myeloma.