Multiple myeloma (MM), a terminally differentiated B cell malignancy, remains difficult to cure. Understanding the molecular mechanisms underlying the progression of MM may identify therapeutic targets and lead to a fundamental shift in treatment of the disease. Deubiquitination, like ubiquitination, is a highly regulated process, implicated in almost every cellular process. Multiple deubiquitinating enzymes (DUBs) have been identified, but their regulation is poorly defined. Here, we determined that TRIP13 increases cellular deubiquitination. Overexpression of TRIP13 in mice and cultured cells resulted in excess cellular deubiquitination by enhancing the association of the DUB USP7 with its substrates. We show that TRIP13 is an oncogenic protein because it accelerates B cell tumor development in transgenic mice. TRIP13-induced resistance to proteasome inhibition can be overcome by a USP7 inhibitor in vitro and in vivo. These findings suggest that TRIP13 expression plays a critical role in B cell lymphoma and MM by regulating deubiquitination of critical oncogenic (NEK2) and tumor suppressor (PTEN, p53) proteins. High TRIP13 identifies a high-risk patient group amenable to adjuvant anti-USP7 therapy.

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.