Introduction: Multiple Myeloma (MM) is a complex malignancy of plasma cells triggered by immunoglobulin gene rearrangements and well-described hyperdyploidy, accounting for slightly more than 10% of all hematologic cancers. MM is the most common hematologic malignancy in African American population with conspicuous racial disparities in both, mortality rates and incidence in cancer compared to European American. This observation evolved into a central hypothesis that MM has distinct biological differences across different ethnicities with yet unidentified race specific markers of tumor heterogeneity. Clear understanding of these molecular differences among ethnic minorities with MM will fulfill a major unmet medical need and eliminate racial disparity.
Methods: We acquired data from the Multiple Myeloma Research Foundation (MMRF) initiated comprehensive longitudinal study (CoMMpass) with an overall goal to profile 1,000 multiple myeloma patients at diagnosis, with multiple follow-up points throughout the course of the disease. To generate population subgroups based on genetic ancestry, we used a population stratification principle component analysis (PCA) and STRUCTURE to stratify myeloma patients by Ancestry Informative Markers. These well-established methods have allowed us to avoid confounders associated with self-reporting, and thus stratify the myeloma samples by genetic ancestry mapped along with anchor populations developed by 1000 genome project. We then assessed mutational frequencies as a function of PCA for each ancestry group using complex bioinformatics algorithms.
Results: We confirmed known commonly mutated genes in MM including KRAS, NRAS, FAM46C, and DIS3. Among the most striking and novel observations in our preliminary analysis of CoMMpass data using genetic ancestry and PCA was a significant difference in the frequency of nonsilent mutations in TP53, with a frequency of 7.1% (33/464) in patients clustering within the European ancestry compared to none (0/142) in African ancestry populations. Further analysis of enrichment of differentially mutated key factors within the TP53 pathway showed ATM as another gene with a significantly (p= 0.019) higher mutation frequency in EA PCA 4.7% (22/464) compared to AA PCA 0.7% (1/142). Analysis of clinical outcomes data showed poorer overall survival in patients harboring TP53 alterations. Furthermore, a comprehensive mutation analysis across samples identified a novel candidate PTCHD3 (p = 7.07E-06) with a significantly higher mutation occurrence in patients of African ancestry. Moreover, the frequency of copy number alterations known to be associated with poor prognosis revealed notable, but not significant (p=0.259) lower frequency of 1q gain in tumors from African compared to European descent. Lastly, we also observed a significant (p=0.0157) two-fold increase in early age of onset of MM in patients of African descent compared to those of European descent.
Conclusion: CoMMpass has constructed a fruitful discovery environment at nexus of high-resolution next generation deep sequencing with detailed clinical data allowing to elucidate potential ancestral drivers of MM paving the way to personalized treatments. Ultimately, these data may help us further delineate the influence of percent admixture on biological factors that drive differences in incidence and outcomes among multi-ethnic MM patients.
Citation Format: Zarko Manojlovic, Austin Christofferson, Gil Speyer, Seungchan Kim, Winnie Liang, Mary Derome, Daniel Auclair, David Craig, Jonathan Keats, John Carpten. Comprehensive analysis of molecular pathogenesis of multiple myeloma by genetic ancestry [abstract]. In: Proceedings of the AACR International Conference: New Frontiers in Cancer Research; 2017 Jan 18-22; Cape Town, South Africa. Philadelphia (PA): AACR; Cancer Res 2017;77(22 Suppl):Abstract nr A32.