To identify genetic events underlying the genesis and progression of multiple myeloma (MM), we conducted a high-resolution analysis of recurrent copy number alterations (CNAs) and expression profiles in a collection of MM cell lines and outcome-annotated clinical specimens. Attesting to the molecular heterogeneity of MM, unsupervised classification using nonnegative matrix factorization (NMF) designed for array comparative genomic hybridization (aCGH) analysis uncovered distinct genomic subtypes. Additionally, we defined 87 discrete minimal common regions (MCRs) within recurrent and highly focal CNAs. Further integration with expression data generated a refined list of MM gene candidates residing within these MCRs, thereby providing a genomic framework for dissection of disease pathogenesis, improved clinical management, and initiation of targeted drug discovery for specific MM patients.