Background: Multiple Myeloma (MM) is an incurable plasma cell malignancy with significant genomic heterogeneity. It is usually preceded by the asymptomatic stage known as smoldering multiple myeloma (SMM). SMM patients have a 10% annual risk of progression to MM. Genomic alterations that are observed in SMM patients include chromosomal gains and losses, translocations, and point mutations. However, current SMM risk models rely solely on clinical markers that do not accurately capture the progression risk. While incorporating some genomic biomarkers improves prediction, using all MM genomic features to comprehensively stratify patients may increase the precision of risk models.

Methods: We obtained a total of 214 patients' samples at SMM diagnosis in the US and Europe. We performed whole exome sequencing on 166 tumors; of these, RNA sequencing was performed on 100. Targeted capture with a MM gene panel was done on an additional 48 tumors. We identified subgroups using binarized DNA features and performing consensus binary non-negative matrix factorization.

Results: We identified six clusters (C1-C6) with the following features: four with a hyperdiploidy (HD) (>48 chromosomes) and two with IgH translocations. These subgroups have unique transcriptomic profiles overlapping with known MM signatures and biological pathways. One of the clusters harboring translocation (11;14), which we call C4-CCND1, was enriched with the previously defined CD-2 MM signature that uniquely expresses B cell markers CD20 and CD79A; shows upregulation of CCND1 and E2F7; and is enriched with pathways like DNA replication, heme metabolism, and NFkB signaling. The C3-MS_MF cluster with the IgH translocations (4;14) and (14;16) shows downregulation of ribosomal genes, TRAF2, and DUSP2. The MYC oncogene was highly expressed in the four HD clusters: C1-HD_NRAS, C2-HD_MAFB, C5-HD_KRAS, and C6-HD_1q (BH-P = 0.037). The clusters also showed different outcomes in terms of time to progression (TTP) to active MM (P = 0.005). Median TTP for patients in C2-HD_MAFB, C3-MS_MF, and C5-HD_KRAS was 3.7, 2.6, and 2.2 years, respectively; TTP for C1-HD_NRAS, C4-CCND1, and C6-HD_1q was 4.3, 11, and not reached, respectively. In multivariate analysis, C2-HD_MAFB, C3-MS_MF, and C5-HD_KRAS were independent predictors of progression after accounting for the clinical risk stage. Moreover, the odds of having evolving hemoglobin and monoclonal protein levels in these three clusters were 3.5 and 12.3 times higher than the other clusters, respectively (P = 0.01 and 0.002).

Conclusion: We identified six distinct SMM molecular groups with corresponding transcription profiles and dysregulated pathways. These groups have different progression risks to active MM, with three groups being independent predictors of progression. Our results underscore the importance of molecular classification in MM to better understand and target various tumor vulnerabilities.

Citation Format: Shankara Anand, Mark Bustoros, François Aguet, Romanos Sklavenitis-Pistofidis, Robert Redd, Benny Zhitomirsky, Andrew J. Dunford, Yu-Tzu Tai, Selina J. Chavda, Cody Boehner, Carl J. Neuse, Tineke Casneuf, Lorenzo Trippa, Chip Stewart, Kwee Yong, Irene Ghobrial, Gad Getz. Genomic profiling of smoldering multiple myeloma classifies distinct molecular groups [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2240.