Assistant Professor Memorial Sloan Kettering New York City, New York
Introduction:
Multiple myeloma (MM) is always preceded by precursor conditions—monoclonal gammopathy of undetermined significance (MGUS) or smoldering myeloma (SMM). While common in adults, a subset of cases precursor conditions progress to MM
Methods:
To identify genomic features underlying clinical behavior in MGUS and SMM, a total of 375 patients with SMM (n= 295) or MGUS (n=80) with available whole exome (WES, n=191) or whole genome sequencing (WGS, n=184) were included in the study. Overall, none of the patients had evidence of SLiM or CRAB criteria at the time of the biopsy. Based on the center of origin, 98 patients were used to form our study validation set. The remaining patients were assigned to the training set (n=277, 68 of were either lost to follow-up or enrolled in a clinical trial).
Results:
Based on the distribution of established MM-defining genomic events (Maura et al. JCO 2024), we identified in the training set 28 genomic features associated with progression into MM. Next, we used the training set to develop a workflow that differentiates MGUS and SMM into two genomically distinct entities: one with evidence of neoplastic transformation (i.e.,genomic MM) and one without (i.e., genomic MGUS). Overall, 39% of MGUS and 91% of SMM cases had genomic evidence of neoplastic transformation in the training set (i.e., genomic MM, gMM). Importantly, none of the patients with genomic-MGUS (gMGUS) experienced progression into MM in the training set. When we applied the same workflow and classification to the validation set, 46% and 83% of MGUS and SMM had evidence of neoplastic transformation, respectively. Importantly, also in the validations set none of the gMGUS experiences progression. Moreover, all patients with IMWG 2020 high risk SMM were classified as gMM in both the training and validation set. Overall, these data demonstrated that the MM genomic background and neoplastic transformation can be acquired early in pathogenesis, even before the SMM phase. Finally, we identified four genomic features significantly associated with a shorter time to progression in patients with evidence of neoplastic transformation: RAS mutations, MYC translocations, APOBEC mutagenesis, and copy number complexity. Integrating these features with the 2020 International Myeloma Working Group (IMWG) model improved its predictive accuracy for progression risk to MM in both the training and validations set.
Conclusions:
Herein, we are introducing the concept that gMM can be identified much earlier in the MGUS/SMM stage. This observation and distinction will be of high importance in recognizing patients that may be considered for interventional trials. Furthermore, we demonstrated that integrating genomic and clinical features significantly enhances the prediction of clinical progression amongst MGUS/SMM patients who already have established gMM.
