Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes

Abstract

Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with significant heterogeneity in disease progression. Existing clinical models of progression risk do not fully capture this heterogeneity. Here we integrate 42 genetic alterations from 214 SMM patients using unsupervised binary matrix factorization (BMF) clustering and identify six distinct genetic subtypes. These subtypes are differentially associated with established MM-related RNA signatures, oncogenic and immune transcriptional profiles, and evolving clinical biomarkers. Three genetic subtypes are associated with increased risk of progression to active MM in both the primary and validation cohorts, indicating they can be used to better predict high and low-risk patients within the currently used clinical risk stratification models.

Fig. 1. Outline of the study and the six molecular subtypes identified based on DNA alterations in tumor samples from smoldering myeloma patients.

A Flowchart of analyses was performed in this study. Clusters were generated based on the tumor genetic alterations from DNA sequencing data, then they were analyzed for correlations with transcriptomic and clinical data. This flowchart was created with BioRender.com. B Identification of groups of tumors with corresponding genetic events. Binary matrix factorization consensus clustering was performed using somatic mutations, somatic copy number alterations, and translocations from 214 SMM tumor samples (columns). Clusters HL1–4, TL1, and TL2 with their associated landmark genetic alterations are visualized (boxed for each cluster). Genetic alterations that were positively associated with each cluster were identified by a one-sided Fisher test and ranked by significance (Benjamini–Hochberg adjusted p value < 0.1, red line, bar graph to the right). C Summary table of the six subtypes identified with selected enriched genetic features.

Fig. 2. Differential gene expression and gene set enrichment analysis for tumors from the six genetic subtypes (n = 89).

A–D Comparison of gene expression levels of MCL1, MYC, CCND1, and CCND2 among the six genetic subtypes. Two-sided p value derived from Kruskal–Wallis test. E Comparison of expression levels of MYC oncogene expression between the two non hyperdiploid ones (-HL) and the four hyperdiploid (+HL) subtypes. Two-sided p-value was calculated using the Wilcoxon rank-sum test. Expression is measured by the log₂ value of transcript per million of each gene (log₂ TPM + 1). Boxplots representing median, and interquartile range, whiskers representing first, and fourth quartile. F Volcano plot showing fold changes for genes differentially expressed between tumors of TL1 subtype and the other subtypes. G Volcano plot showing fold changes for genes differentially expressed between tumors of TL2 subtype and the other subtypes. X axis = Log₂ fold change, Y axis = −log₁₀ adjusted p value. H Gene set enrichment analysis of different molecular and oncogenic pathways (top), immune cell signatures (middle), and MM-specific signatures (bottom) among the six genetic subtypes.

Fig. 3. Clinical outcomes of the six molecular and the risk groups in the primary and validation cohorts.

A Kaplan-Meier curves for analysis of TTP in patients (n = 87) belonging to the three genetic risk groups (Low: TL2, Intermediate: HL1, HL4, High: HL2, TL1, HL3); log-rank p value = 0.0005. B Multivariate cox regression analysis of the low, intermediate, and high-risk genetic subtypes and clinical risk stages according to the IMWG 20/2/20 model in the primary cohort (n = 87). C Multivariate cox regression analysis of the low, intermediate, and high-risk genetic subtypes and clinical risk stages according to the IMWG 20/2/20 model in the first validation cohort (n = 74). D Multivariate cox regression analysis of the genetic subtypes and clinical risk stages according to the IMWG 20/2/20 model in the two validation cohorts (n = 142). E Kaplan-Meier curves for analysis of TTP in patients from the six genetic subtypes in the combined cohort (n = 229); log-rank p value = 0.002. F Kaplan-Meier curves for analysis of TTP in patients belonging to the three genetic risk groups of the combined cohort (n = 229); log-rank p value = 0.0002. G Multivariate cox regression analysis of the low, intermediate, and high-risk genetic subtypes and clinical risk stages according to the IMWG 20/2/20 model in the combined cohorts (n = 229). Forest plots are used to visualize the multivariate analysis. IMWG International Myeloma Working Group, N number of patients with event and percentages from the total number of patients evaluable, HR hazards ratio, error bars indicate 95% CI. All p values are two-sided. Differences in survival curves and subsequent two-sided p values were calculated using the log-rank test.