Whole-Genome Sequencing Reveals Distinct Mutational Signatures in Multiple Myeloma Patients with African Ancestry Compared with Those of Non-African Ancestry

INTRODUCTION: Improving health outcomes for patients with African ancestry (AA) is a key healthcare aim, but there is uncertainty in whether disparities arise predominantly from socioeconomic differences or from genetic differences in tumor biology. It remains an open question as to whether multiple myeloma (MM) occurring in AA patients has a similar or different spectrum of genomic abberations when compared to patients having European ancestry. To date, studies have suggested that AA have an excess of t(11;14) and a deficiency of TP53 mutations. We have established a series of 302 cases of MM precursor and newly diagnosed MM with whole-genome sequencing (WGS) available that is enriched for self-declared AA or diverse ethnic background.

METHODS: In collaboration with the NYGC and Polyethnic-1000 consortium, we sequenced tumor samples to a depth of 60-80X and normal tissue to 30-40X. We a employed bioinformatics pipeline with a consensus mechanism for somatic variant calling, including Mutect2, Strelka2, and VarScan2 for SNVs; Mutect2, Strelka2, VarScan2, and SvABA for InDels; Battenberg and FACETS for CNVs; Manta, SvABA, DELLY2, and IgCaller for SVs. Additionally, an admixture workflow was used to estimate each individual’s ancestral lineage using continentally-distinct references, comprising 23 regional populations within 5 super-populations from the 1000 Genomes Project (https://github.com/pblaney/mgp1000). Mutational signature were calculated using the R package mmsig (Rustad et al. Comm. Bio. 2021).

RESULTS: Using admixture estimations from 302 patients with high-coverage WGS together with 941 patients from the CoMMpass trial, we identified five clusters corresponding to single dominant genetic ancestries (median proportion >75% assignment to reference super-population), together with a cluster characterized by highly admixed individuals with no dominant genetic ancestry (median proportion <50%). Of the total, 53.0% are in the European dominant (EUR) cluster, 26.5% African dominant (AFR), 8.6% American dominant (AMR), 7.9% highly admixed, 2.0% East Asian dominant (EAS) and South-East Asian dominant (SAS) clusters, respectively.

Stratifying patients by their cluster assignment and calculating the frequency of subtype translocations, we show that t(11;14) occurred in 25.8% of EUR patients while in only 14.6% of AFR (p=0.045) and 7.7% of AMR (p=0.05) patients. The frequency of the t(4;14) was more closely distributed, with 15.7% in EUR, 11.5% in AMR, and 11% in AFR clusters. For the acquired somatic mutations, the tumor mutational burden (TMB) was lowest in the AFR cluster 2.21 (median, somatic mutations per Mb), significantly lower by comparison to the EUR cluster at 2.94 (FDR adj. P=4.3x10^-6).

The most striking genomic difference was observed when comparing the mutational signatures landscape between AA and the other racial groups. Using WGS, AA had lower SBS1 and SBS5 absolute contribution compared to EUR, and this was largely responsible for the difference in TMB. SBS1 and SBS5 are known to be clock-like signatures, accumulating at a constant rate over time. Because no differences in age, cancer cell fraction, and coverage were observed between AA and EUR, this finding suggest different mutational clock-like rate between AA and EUR. The higher TMB observed in EUR was also driven by the higher APOBEC-mutational activity (SBS2 and SBS13) compared to AA (p=0.05). Interestingly, 72% of all EUR had APOBEC-activity evident, in contrast to 45% of the AA (p=0.001). This difference was confirmed after excluding the MM precursor patients, previously demonstrated to have lower APOBEC-activity (Oben et al. Natur Comm. 2021), and was validated on CoMMpass whole-exomes.

CONCLUSIONS: Leveraging one of the largest series of diverse patients with WGS, and integrating genomic data with comprehensive ancestry information, AA MM emerged as biologically different in term of genomic drivers and mutational signatures, suggesting potential differences in etiology and genomic evolution over time. Further analysis will include molecular timing of clonal copy number gains, and reconstruction of phylogenetic trees, with view to improving our understanding of the etiology of MM development across patient genetic backgrounds.

FIGURE: Genomic characteristics of myeloma across ancestries. A) Translocation percentage (number per cluster) and B) Tumor mutational burden across clusters.