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.

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.

A global expression-based analysis of the consequences of the t(4;14) translocation in myeloma

Purpose: Our purpose in this report was to define genes and pathways dysregulated as a consequence of the t(4;14) in myeloma, and to gain insight into the downstream functional effects that may explain the different prognosis of this subgroup.Experimental Design: Fibroblast growth factor receptor 3 (FGFR3) overexpression, the presence of immunoglobulin heavy chain-multiple myeloma SET domain (IgH-MMSET) fusion products and the identification of t(4;14) breakpoints were determined in a series of myeloma cases. Differentially expressed genes were identified between cases with (n = 55) and without (n = 24) a t(4;14) by using global gene expression analysis.Results: Cases with a t(4;14) have a distinct expression pattern compared with other cases of myeloma. A total of 127 genes were identified as being differentially expressed including MMSET and cyclin D2, which have been previously reported as being associated with this translocation. Other important functional classes of genes include cell signaling, apoptosis and related genes, oncogenes, chromatin structure, and DNA repair genes. Interestingly, 25% of myeloma cases lacking evidence of this translocation had up-regulation of the MMSET transcript to the same level as cases with a translocation.Conclusions: t(4;14) cases form a distinct subgroup of myeloma cases with a unique gene signature that may account for their poor prognosis. A number of non-t(4;14) cases also express MMSET consistent with this gene playing a role in myeloma pathogenesis.

Proteomic analysis of coronary sinus serum reveals leucine-rich α2-glycoprotein as a novel biomarker of ventricular dysfunction and heart failure

BACKGROUND: Heart failure (HF) prevention strategies require biomarkers that identify disease manifestation. Increases in B-type natriuretic peptide (BNP) correlate with increased risk of cardiovascular events and HF development. We hypothesize that coronary sinus serum from a high BNP hypertensive population reflects an active pathological process and can be used for biomarker exploration. Our aim was to discover differentially expressed disease-associated proteins that identify patients with ventricular dysfunction and HF.

METHODS AND RESULTS: Coronary sinus serum from 11 asymptomatic, hypertensive patients underwent quantitative differential protein expression analysis by 2-dimensional difference gel electrophoresis. Proteins were identified using mass spectrometry and then studied by enzyme-linked immunosorbent assay in sera from 40 asymptomatic, hypertensive patients and 105 patients across the spectrum of ventricular dysfunction (32 asymptomatic left ventricular diastolic dysfunction, 26 diastolic HF, and 47 systolic HF patients). Leucine-rich α2-glycoprotein (LRG) was consistently overexpressed in high BNP serum. LRG levels correlate significantly with BNP in hypertensive, asymptomatic left ventricular diastolic dysfunction, diastolic HF, and systolic HF patient groups (P≤0.05). LRG levels were able to identify HF independent of BNP. LRG correlates with coronary sinus serum levels of tumor necrosis factor-α (P=0.009) and interleukin-6 (P=0.021). LRG is expressed in myocardial tissue and correlates with transforming growth factor-βR1 (P<0.001) and α-smooth muscle actin (P=0.025) expression.

CONCLUSIONS: LRG was identified as a serum biomarker that accurately identifies patients with HF. Multivariable modeling confirmed that LRG is a stronger identifier of HF than BNP and this is independent of age, sex, creatinine, ischemia, β-blocker therapy, and BNP.

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.