Characterizing the mutational landscape of MM and its precursor MGUS

Abstract

Mutational Signatures and Tumor mutational burden (TMB) have emerged as prognostic biomarkers in cancer genomics. However, the association of TMB with overall survival (OS) is still unknown in newly diagnosed multiple myeloma (NDMM) patients. Further, the change in the mutational spectrum involving both synonymous and non-synonymous mutations as MGUS progresses to MM is unexplored. This study addresses both these aspects via extensive evaluation of the mutations in MGUS and NDMM. WES data of 1018 NDMM patients and 61 MGUS patients collected from three different global regions were analyzed in this study. Single base substitutions, mutational signatures and TMB were inferred from the variants identified in MGUS and MM patients. The cutoff value for TMB was estimated to divide patients into low TMB and high TMB (hypermutators) groups. This study finds a change in the mutational spectrum with a statistically significant increase from MGUS to MM. There was a statistically significant increase in the frequency of all the three categories of variants, non-synonymous (NS), synonymous (SYN), and others (OTH), from MGUS to MM (P<0.05). However, there was a statistically significant rise in the TMB values for TMB_NS and TMB_SYN only. We also observed that 3' and 5'UTR mutations were more frequent in MM and might be responsible for driving MGUS to MM via regulatory binding sites. NDMM patients were also examined separately along with their survival outcomes. The frequency of hypermutators was low in MM with poor OS and PFS outcome. We observed a statistically significant rise in the frequency of C>A and C>T substitutions and a statistically significant decline in T>G substitutions in the MM patients with poor outcomes. Additionally, there was a statistically significant increase in the TMB of the patients with poor outcome compared to patients with a superior outcome. A statistically significant association between the APOBEC activity and poor overall survival in MM was discovered. These findings have potential clinical relevance and can assist in designing risk-adapted therapies to inhibit the progression of MGUS to MM and prolong the overall survival in high-risk MM patients.

Keywords: Multiple myeloma; NGS; exome sequencing; monoclonal gammopathy of undetermined significance; mutational landscape; progression; tumor mutation burden.

Figure 1

Workflow of the study and data analysis. Four different variant callers were used to identify variants in the MM and MGUS patients. Variants were finalized using the majority voting scheme. Variants were then annotated with Annovar for deducing TMB. Mutational signatures were inferred using Sigprofiler tool.

Figure 2

Boxplot shows the difference in the frequency of the single base substitutions between MGUS and MM patients. Wilcoxon rank-sum test was applied to determine if the change is statistically significant or not. For all the substitutions, there is significant variation in the frequency with p-values less than 0.05 between the two groups.

Figure 3

Boxplot showing the variation in the frequency of the three different categories of variants-Nonsynonymous, Synonymous, and Others between MGUS and MM. Wilcoxon rank-sum test was applied to determine if the change is statistically significant or not. There was a statistically significant variation in all the categories of variants with p-values less than 0.05.

Figure 4

A. Boxplot showing the variation in the frequency of the variants under the nonsynonymous category. There was a statistically significant variation in the frequency of nonsynonymous_snv and stop_gain variants with p-values less than 0.05. B. Boxplot showing the variation in the frequency of the variants under the synonymous category. There was a statistically significant variation in the frequency of UTR3 and UTR5 variants with p-values less than 0.05. Wilcoxon rank-sum test was applied to determine if the change is statistically significant or not.

Figure 5

Boxplot reveals that the difference in the low TMB and high TMB groups is statistically significant with p-values less than 0.05 for TMB_NS and TMB_SYN. Wilcoxon rank-sum test was applied to determine if the change is statistically significant or not.

Figure 6

High TMB is associated with poor overall survival in NDMM patients. The difference in the overall survival probability between low and high TMB_NS is statistically significant with p-values 0.045 and 0.022 for PFS and OS respectively.