Immunoglobulin heavy chain sequence analysis in Waldenstrom's macroglobulinemia and immunoglobulin M monoclonal gammopathy of undetermined significance.

In this study, we used IGH sequence analysis to assess the maturational status of Waldenstrom's (WM) macroglobulinemia and its putative precursor immunoglobulin (Ig)-M monoclonal gammopathy of undetermined significance (MGUS). IGH sequence analysis was performed using standard methods in 23 cases (20 WM and 3 IgM MGUS as defined by consensus panel criteria). Waldenstrom's macroglobulinemia cases were characterized by heavily mutated IGH genes (median, 6.3%; range, 3.8%-13.9%) but without intraclonal variation (ICV). IgM MGUS was similarly characterized by somatic hypermutation (median, 7.5%; range, 7%-7.7%), but ICV was evident in 1 of the 3 cases. We would therefore conclude that WM is characterized by somatic hypermutation without ICV, which supports a derivation from postgerminal center/memory B cells. IgM MGUS is also characterized by somatic hypermutation but, in a manner similar to IgA/IgG MGUS, can be associated with ICV, although the significance of this remains unclear.

Frequency of mutations and polymorphisms in borderline ovarian tumors of known cancer genes.

Borderline ovarian tumors represent an understudied subset of ovarian tumors. Most studies investigating aberrations in borderline tumors have focused on KRAS/BRAF mutations. In this study, we conducted an extensive analysis of mutations and single-nucleotide polymorphisms (SNPs) in borderline ovarian tumors. Using the Sequenom MassArray platform, we investigated 160 mutations/polymorphisms in 33 genes involved in cell signaling, apoptosis, angiogenesis, cell cycle regulation and cellular senescence. Of 52 tumors analyzed, 33 were serous, 18 mucinous and 1 endometrioid. KRAS c.35G>A p.Gly12Asp mutations were detected in eight tumors (six serous and two mucinous), BRAF V600E mutations in two serous tumors, and PIK3CA H1047Y and PIK3CA E542K mutations in a serous and an endometrioid BOT, respectively. CTNNB1 mutation was detected in a serous tumor. Potentially functional polymorphisms were found in vascular endothelial growth factor (VEGF), ABCB1, FGFR2 and PHLPP2. VEGF polymorphisms were the most common and detected at four loci. PHLPP2 polymorphisms were more frequent in mucinous as compared with serous tumors (P=0.04), with allelic imbalance in one case. This study represents the largest and most comprehensive analysis of mutations and functional SNPs in borderline ovarian tumors to date. At least 25% of borderline ovarian tumors harbor somatic mutations associated with potential response to targeted therapeutics.

Gene mapping and expression analysis of 16q loss of heterozygosity identifies WWOX and CYLD as being important in determining clinical outcome in multiple myeloma.

We performed fluorescent in situ hybridization (FISH) for 16q23 abnormalities in 861 patients with newly diagnosed multiple myeloma and identified deletion of 16q [del(16q)] in 19.5%. In 467 cases in which demographic and survival data were available, del(16q) was associated with a worse overall survival (OS). It was an independent prognostic marker and conferred additional adverse survival impact in cases with the known poor-risk cytogenetic factors t(4;14) and del(17p). Gene expression profiling and gene mapping using 500K single-nucleotide polymorphism (SNP) mapping arrays revealed loss of heterozygosity (LOH) involving 3 regions: the whole of 16q, a region centered on 16q12 (the location of CYLD), and a region centered on 16q23 (the location of the WW domain-containing oxidoreductase gene WWOX). CYLD is a negative regulator of the NF-kappaB pathway, and cases with low expression of CYLD were used to define a "low-CYLD signature." Cases with 16q LOH or t(14;16) had significantly reduced WWOX expression. WWOX, the site of the translocation breakpoint in t(14;16) cases, is a known tumor suppressor gene involved in apoptosis, and we were able to generate a "low-WWOX signature" defined by WWOX expression. These 2 genes and their corresponding pathways provide an important insight into the potential mechanisms by which 16q LOH confers poor prognosis.

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.

Homozygous deletion mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome.

PURPOSE: Myeloma is a clonal malignancy of plasma cells. Poor-prognosis risk is currently identified by clinical and cytogenetic features. However, these indicators do not capture all prognostic information. Gene expression analysis can be used to identify poor-prognosis patients and this can be improved by combination with information about DNA-level changes. EXPERIMENTAL DESIGN: Using single nucleotide polymorphism-based gene mapping in combination with global gene expression analysis, we have identified homozygous deletions in genes and networks that are relevant to myeloma pathogenesis and outcome. RESULTS: We identified 170 genes with homozygous deletions and corresponding loss of expression. Deletion within the "cell death" network was overrepresented and cases with these deletions had impaired overall survival. From further analysis of these events, we have generated an expression-based signature associated with shorter survival in 258 patients and confirmed this signature in data from two independent groups totaling 800 patients. We defined a gene expression signature of 97 cell death genes that reflects prognosis and confirmed this in two independent data sets. CONCLUSIONS: We developed a simple 6-gene expression signature from the 97-gene signature that can be used to identify poor-prognosis myeloma in the clinical environment. This signature could form the basis of future trials aimed at improving the outcome of poor-prognosis myeloma.

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.

Insights into the multistep transformation of MGUS to myeloma using microarray expression analysis.

To define specific pathways important in the multistep transformation process of normal plasma cells (PCs) to monoclonal gammopathy of uncertain significance (MGUS) and multiple myeloma (MM), we have applied microarray analysis to PCs from 5 healthy donors (N), 7 patients with MGUS, and 24 patients with newly diagnosed MM. Unsupervised hierarchical clustering using 125 genes with a large variation across all samples defined 2 groups: N and MGUS/MM. Supervised analysis identified 263 genes differentially expressed between N and MGUS and 380 genes differentially expressed between N and MM, 197 of which were also differentially regulated between N and MGUS. Only 74 genes were differentially expressed between MGUS and MM samples, indicating that the differences between MGUS and MM are smaller than those between N and MM or N and MGUS. Differentially expressed genes included oncogenes/tumor-suppressor genes (LAF4, RB1, and disabled homolog 2), cell-signaling genes (RAS family members, B-cell signaling and NF-kappaB genes), DNA-binding and transcription-factor genes (XBP1, zinc finger proteins, forkhead box, and ring finger proteins), and developmental genes (WNT and SHH pathways). Understanding the molecular pathogenesis of MM by gene expression profiling has demonstrated sequential genetic changes from N to malignant PCs and highlighted important pathways involved in the transformation of MGUS to MM.