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Molecular Dissection of Hyperdiploid Multiple Myeloma by Gene Expression Profiling

¹ Department of Hematology-Oncology, Mayo Clinic, Scottsdale, Arizona; ² Division of Hematology, Mayo Clinic, Rochester, Minnesota; ³ Translational Genomics Research Institute, Phoenix, Arizona; ⁴ Department of Computer Science and Engineering, Arizona State University, Tempe, Arizona; and ⁵ Millenium Pharmaceuticals, Inc., Cambridge, Massachusetts

Requests for reprints: Wee J. Chng, Division of Hematology-Oncology, Mayo Clinic, Johnson Research Building, 13400 East Shea Boulevard, Scottsdale, AZ 85260. Phone: 480-301-6363; Fax: 480-301-9162; E-mail: Chng.wee{at}mayo.edu.

Hyperdiploid multiple myeloma (H-MM) is the most common form of myeloma. In this gene expression profiling study, we show that H-MM is defined by a protein biosynthesis signature that is primarily driven by a gene dosage mechanism as a result of trisomic chromosomes. Within H-MM, four independently validated patient clusters overexpressing nonoverlapping sets of genes that form cognate pathways/networks that have potential biological importance in multiple myeloma were identified. One prominent cluster, cluster 1, is characterized by high expression of cancer testis antigen and proliferation-associated genes. Tumors from these patients were more proliferative than tumors in other clusters (median plasma cell labeling index, 3.8; P < 0.05). Another cluster, cluster 3, is characterized by genes involved in tumor necrosis factor/nuclear factor-B signaling and antiapoptosis. These patients have better response to bortezomib as compared with patients within other clusters (70% versus 29%; P = 0.02). Furthermore, for a group of patients generally thought to have better prognosis, a cluster of patients with short survival (cluster 1; median survival, 27 months) could be identified. This analysis illustrates the heterogeneity within H-MM and the importance of defining specific cytogenetic prognostic factors. Furthermore, the signatures that defined these clusters may provide a basis for tailoring treatment to individual patients. [Cancer Res 2007;67(7):2982–9]

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