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Multiple myeloma (MM) is an aggressive plasma cell neoplasm characterized by genomic heterogeneity. Superenhancers (SEs) are defined as large clusters of enhancers in close genomic proximity, which regulate genes for maintaining cellular identity and promote oncogenic transcription to which cancer cells highly addicted. Here, we analyzed cis-regulatory elements in MM samples with H3K27ac ChIP-seq, to identify novel SE-associated genes involved in the myeloma pathogenesis. SEs and their associated genes in cancerous tissue were compared with the control samples, and we found SE analysis alone uncovered cell-lineage-specific transcription factors and well-known oncogenes ST3GAL6 and ADM. Using a transcriptional CDK7 inhibitor, THZ1, coupled with H3K27ac ChlP-seq, we identified MAGI2 as a novel SE-associated gene of myeloma cells. Elevated MAGI2 was related to myelomagenesis with gradual increased expression from MGUS, SMM to newly diagnosed and relapsed MM. High prevalence of MAGI2 was also associated with poor survival of MM patients. Importantly, inhibition of the SE activity associated with MAGI2 decreased MAGI2 expression, inhibited cell growth and induced cell apoptosis. Mechanistically, we revealed that the oncogenic transcription factor, MAF, directly bound to the SE region and activated gene transcription. In summary, the discoveries of these acquired SEs-associated genes and the novel mechanism by which they are regulated provide new insights into MM biology and MAGI2-MAF-SE regulatory circuit offer potential novel targets for disease treatment.

Multiple myeloma (MM) is a plasma cell neoplasm with significant molecular heterogeneity. Gene expression profiling (GEP) has contributed significantly to our understanding of the underlying biology and has led to several prognostic gene signatures. However, the best way to apply these GEP signatures in clinical practice is unclear. In this study, we investigated the integration of proven prognostic signatures for improved patient risk stratification. Three publicly available MM GEP data sets that encompass newly diagnosed as well as relapsed patients were analyzed using standardized estimation of nine prognostic MM signature indices and simulations of signature index combinations. Cox regression analysis was used to assess the performance of simulated combination indices. Taking the average of multiple GEP signature indices was a simple but highly effective way of integrating multiple GEP signatures. Furthermore, although adding more signatures in general improved performance substantially, we identified a core signature combination, EMC92+HZDCD, as the top-performing prognostic signature combination across all data sets. In this study, we provided a rationale for gene signature integration and a practical strategy to choose an optimal risk score estimation in the presence of multiple prognostic signatures.