In-Depth Molecular Profiling of Multiple Myeloma in African Americans

Introduction: Multiple Myeloma (MM) is a complex malignancy of plasma cells well-described hyperdyploidy, and immunoglobulin gene rearrangements. To promote rapid advances in the field, Multiple Myeloma Research Foundation (MMRF) initiative is an intensive and comprehensive longitudinal study (CoMMpass) designed to create a rich discovery ecosystem to through in-depth clinical and molecular profiling to understand the molecular perturbations of the disease in the context of therapy. The large study population empowered us to stratify mutational landscapes among different ethnicities to influence on broader disparities in tumor dynamics.

Methods: Clinical data, tumor/normal sample collection, and mutational landscape analysis described in this abstract are derived from MMRF CoMMpass IA7 release that is composed of self-identified 93 African American (AA) and 377 European American (EA). The post-processing and primary analysis was done on baseline samples only. Whole exome sequencing was analyzed for the detection of somatic events. Secondary analysis was performed using MutSigCV (Mutation Significance) algorithm and GISTIC (The Genomic Identification of Significant Targets in Cancer) to determine the significance of coding mutations and copy number events.

Results: Our preliminary comparison analysis of CoMMpass IA7 data demonstrated that overall there was no statistical difference (p=0.5973) in nonsilent mutation burden between the two stratified groups, AA (μ=63.9 mutations/patient) vs EA (μ=73.7 mutations/patient). However, we have observed several notable differences.  The most notable population difference (p<0.0001) was TP53 mutation occurrence, which was more common in EA 5.6% (21/377) compared AA 2.1% (2/93).

 Furthermore, MutSig analysis also revealed a novel candidate PTCHD3 (p = 7.07E-06, q = 3.33E-02) with 6% occurrence in AA compared to only 0.04% in EA. Furthermore, we looked at mutation occurrence difference of at least 5 fold between the two stratified populations. We have identified several additional genes that have higher mutation frequencies in tumors from AA patients including: ANKRD26, BCL7A, BRWD3. Interestingly, majority is implicated in epigenetic regulatory mechanisms.

Moreover, despite the complex karyotype in MM translocations, our analysis demonstrated a lower frequency of 14q32 translocations in tumors from AA patients (10%) compared to tumors from EA patients (37%).  These data would independently validate our previously reported differences in 14q32 breakpoints between these two populations.

Conclusion: Data from this comprehensive diverse multi-institutional longitudinal study has afforded us the opportunity to study population differences among MM patients in an unprecedented way. Taking advantage of high quality, high-resolution whole exome and whole-genome data has allowed us to identify potential differences in the genomic and molecular landscape of MM from AA and EA patients.  These data may help us further understand the incidence and outcomes among patients from these populations.  As CoMMpass continues to mature these datasets will be retested that may result in some of these preliminary reported events to either level off, or increase in power.