Differentiating between MM like Smoldering Myeloma (SMM) and Non-Progressor SMM

Identifying patients with SMM who are genomically similar to MM and progress quickly, and those who are likely not to progress to MM for a longer period, is crucial for intervention and treatment decisions, as well as defining potential clinical trials. Risk stratification tools such as the 20/2/20 criteria and genomic features have been developed to identify high-risk patients who may progress rapidly, typically within two years. However, the challenge remains in predicting SMM patients with low-risk disease. Despite advancements in risk assessment, there is still need for more reliable markers to identify low-risk populations. We profiled SMM samples from 175 patients, of which 30 have progressed (P-SMM) within 5 years. We genomically profiled SMM and MM samples from the same patients and we have further compared progressed SMM to 800 deep whole genome sequencing data from newly-diagnosed MM patients. Between paired SMM and MM samples from the same patients, we did not find significant large-scale copy number or structural variant changes. All driver mutations and copy number/translocation events were detected at both timepoints. There was a slight but significant increase in total mutations (paired sample p = 0.03) at MM stage. Among all 30 paired samples, only 7 had a clonal selection that was not detected at SMM stage. No significant differences were detected in the utilization of the mutational signatures, and chromotripsis at SMM and MM stage. This suggests that the majority of progressed SMMs were genomically similar to MM at diagnosis. RNAseq data from the same samples suggest changes in MYC target gene activity (p = 2.4e-07) and proliferation signatures (p = 8.1e-04). We next compared whole genome sequencing from non-progressor (NP) patients (no progression to symptomatic MM and a minimum follow-up of more than 5 years) with those who progressed within five years. We observed that cells from non-progressor MM cells (NP-SMM) exhibited significantly lower DNA damage, including mutational load (p = 6e-05), genome-wide focal deletions (p = 0.002), and lower genomic scar score (p = 0.02) than P-SMM plasma cells. The absence of any copy number alteration on chromosome 8 alone strongly predicted low-risk SMM (OR=8.2, p = 2e-04). Mutations in NRAS, BRAF, FAM46C (TENT5C), and ATM were significantly (FDR < 0.1) more frequent in P-SMM compared to NP-SMM. Mutational signature analysis identified all signatures in both groups; however, signature utilization was significantly (adj. p < 0.05) different. Lower APOBEC (p = 0.002) and DNA damage by HR (p = 0.03) were detected in NP-SMM. Similarly, lower focal LOH and chromosomal instability (p = 0.039) were also found in NP-SMM. Primary translocations involving IgH were observed in both progressor and non-progressor samples with no significant difference. However, there was an exception with MYC translocations, which were almost mutually exclusive in progressor patients (OR=7.5, p = 0.005). By combining all genomic differences between the two groups, we constructed a C5.0 classification model. This model, in turn, was used to develop a decision tree that predicts low-risk SMM patients based on their genomic profiles. The final decision tree incorporates chromosome 8 CNAs with focal deletions and gains, genome integrity pathway mutations, and gain1q. We then validated our tree using WGS data from an independent validation cohort of 42 SMM patients. The results showed that this simple decision tree can achieve high sensitivity (91%) and moderate specificity (70%) in an independent validation cohort. Our results suggest that the majority of the progressor SMMs are very similar to their MM genome and indicate that the dominant clone at MM diagnosis is already present at SMM stage. The lack of differences between MM and SMM diagnosis in this group suggests the need to revisit the diagnosis and define them as myeloma. On the other hand, a longer time without progression and significant differences in genomic changes in non-progressor low-risk SMM cases suggests that these patients may have more indolent course like MGUS. The validation of the genomic model using an independent dataset not only strengthens its reliability and generalizability but also underscores the robustness of our approach and potentially provides critical information for future redefinition of the disease as well as trial designs based on genomic changes.