Genetic Analysis of Multiple Myeloma Identifies Cytogenetic Alterations Implicated in Disease Complexity and Progression

SIMPLE SUMMARY: Multiple myeloma (MM) is the second most common hematological neoplasia with a high incidence in elderly populations. The disease is characterized by a severe chaos of genomic abnormality. Comprehensive examinations of myeloma cytogenetics are needed for better understanding of MM and potential application to the development of novel therapeutic regiments. Here we utilized gene expression profiling and CytoScan HD genomic arrays to investigate molecular alterations in myeloma leading to disease progression and poor clinical outcomes. We demonstrates that genetic abnormalities within MM patients exhibit unique protein network signatures that can be exploited for implementation of existing therapies targeting key pathways and the development of novel therapeutics. ABSTRACT: Multiple myeloma (MM) is a genetically heterogeneous disease characterized by genomic chaos making it difficult to distinguish driver from passenger mutations. In this study, we integrated data from whole genome gene expression profiling (GEP) microarrays and CytoScan HD high-resolution genomic arrays to integrate GEP with copy number variations (CNV) to more precisely define molecular alterations in MM important for disease initiation, progression and poor clinical outcome. We utilized gene expression arrays from 351 MM samples and CytoScan HD arrays from 97 MM samples to identify eight CNV events that represent possible MM drivers. By integrating GEP and CNV data we divided the MM into eight unique subgroups and demonstrated that patients within one of the eight distinct subgroups exhibited common and unique protein network signatures that can be utilized to identify new therapeutic interventions based on pathway dysregulation. Data also point to the central role of 1q gains and the upregulated expression of ANP32E, DTL, IFI16, UBE2Q1, and UBE2T as potential drivers of MM aggressiveness. The data presented here utilized a novel approach to identify potential driver CNV events in MM, the creation of an improved definition of the molecular basis of MM and the identification of potential new points of therapeutic intervention.