Gene expression profiling of plasma cells (GEP-PC) has provided major insights into myeloma pathobiology. However the data about GEP-PC in preneoplastic gammopathy (MGUS) or asymptomatic myeloma (AM) are limited, and gene expression patterns that might predict outcome in these patients have not been defined. We analyzed GEP (using U133Plus Affymetrix microarrays), of plasma cells isolated by immuno-magnetic bead selection with CD138 microbeads, from the bone marrow of patients with MGUS (n=16) and asymptomatic myeloma (AM; n=18) enrolled in a prospective South West Oncology Group (SWOG) observational study. Data from normal plasma cells (PCs) and from 105 myeloma PCs were included as controls. Myeloma PCs were randomly selected to include at least 15 patients from each of the 7 subgroups previously identified based on GEP of myeloma tumor cells (Zhan and Shaughnessy, ASH 2004). After the suppression of immunoglobulin (Ig) genes, there were 1297 genes that significantly differed in expression between MGUS-PCs and MM-PCs, and 1099 genes that differed between MGUS-PCs and normal PCs with a 1% false discovery rate. Hierarchical cluster analysis of all samples was performed using 1000 plasma cell signature genes that were most differentially expressed between normal and myeloma PCs. These data demonstrated that both MGUS and AM samples were distributed between normal and MM samples. A prediction analysis of microarrays (PAM) model (

) utilizing 134 genes was then developed to determine if the signature from these genes in MGUS/AM was more similar to normal or to myeloma plasma cells. In this analysis, 11/16 (69%) of the MGUS samples were more similar to normal PC, compared to 6/18 (33%) of the AM samples (p=0.04). At present, there are no reliable phenotypic markers to distinguish between normal and malignant PCs within the bulk CD138+ population. Gene expression spikes for cyclin D1 and MAF/MAF-B were seen in both MGUS and AM cohorts, including in some patients with normal PC signature. These data provide the largest comparison to date, of GEP of PCs in preneoplastic versus malignant gammopathies and suggest that GEP may be a useful tool to prospectively identify subsets of patients within the MGUS/AM population with dominant normal PC or MM PC signatures, and potentially differing prognosis. Further analysis of differentially expressed genes between MGUS/MM PCs identified in this dataset may allow insights into the genomic changes in tumor cells underlying the malignant progression of myeloma.

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