Abstract
Background: Outcomes in multiple myeloma (MM) have improved significantly over the years but disparities in outcomes exist among MM patients (pts) belonging to different racial subgroups. Previous studies have explored healthcare access and utilization disparities for pts of different races as potential causes, but genomic differences, which could alter disease biology and clinical behavior, have not been studied. We utilized the MMRF CoMMpass database, which prospectively captures clinical and molecular characteristics of MM patients to explore race-based differences at the genomic level.
Methods: The publically available CoMMpass trial database (data release version IA8) was utilized. Pts with race category of White (total n=698) and non-White (total n=223, comprising 129 Blacks, 19 Asians and 45 others) were analyzed for genomics including cytogenetics (fluorescent in situ hybridization; FISH), tumor diploidy (conventional karyotyping), gene expression profiling (GEP), gene copy number variations (CNV), gene single nucleotide variations (SNV) and genomic translocations analysis focused on genes from the FoundationOne Heme panel¨ (402 genes). GEP was considered significantly different between categories for more than or equal to a 2-fold change for a given gene (p<0.05). Categorical variables for FISH probes and diploidy status were compared by Chi-square or Fischer exact test, wherever applicable.
Results: GEP clustering analysis uncovered 67 genes differentially expressed between Whites vs. non-Whites (Fig 1A). Baseline cytogenetics aberrations between both race cohorts showed statistically insignificant differences. Multiple cytogenetic abnormalities were seen most commonly in both race groups (~31%) with the distribution of others (in descending order) as such: Del13 > No abnormalities > t(11;14) > 1q amplification > Del17p or p53 > t(4;14) > Del1p > t(14;16) > t(14;20). Aneuploidy status showed insignificant differences between race groups with ~34% of patients from both groups having no aneuploidy. SNV data was present for 90% of Whites and 57% of non-Whites. Overall, 84% of genes had>1 SNV (84%), 2% translocations only and 14% had neither SNVs or translocations. SNVs were classified as Non-specific Non-Synonymous coding (NNSCs), Splice-site (SS) or Tier 1 (T1, codon deletion, insertion, frame-shift, etc.). A total of 880 NNSCs SNVs were observed: 670 in Whites across 227 genes, 210 in non-Whites across 120 genes. In whites, 57% of NNSC SNVs consisted of transition-type mutations and 43% as transversions. In non-Whites, transition mutations comprised 64% of all NNSC SNVs and transversions in the remaining 36%. A total of 38 SS SNVs were observed, 30 in Whites and 8 in non-Whites observed in 25 and 6 genes, respectively. In Whites, 57% of SS SNVs were transition type and remaining 43% were transversion. In non-Whites, an equal % of SS transition and transversion mutations were noted. A total of 240 T1 SNVs were noted; 182 in Whites seen in 82 genes; 58 in non-Whites seen 6 genes. In both Whites and non-Whites, frameshifts (41% in Whites, 47% in non-Whites) and stop-gained (46% in Whites, 45% in non-Whites) SNVs were most common. A total of 17 genes with>10 distinct SNVs were identified in Whites; 5 genes in non-Whites and 4 genes commonly seen in both cohorts (Fig 1B). In Whites, 17 genes were found to have translocations (intrachromosomal TRAF3 and PIK3R2 most common); in non-Whites, 5 genes had translocations (intrachromosomal CIC and EMSY most common). CNV analysis demonstrated 21 genes that were significantly altered with either copy number gain or loss between Whites vs. non-Whites (Fig 1C).
Conclusions: We present the first race-based comprehensive genomic analysis utilizing the public interface of the CoMMpass trial. We discovered genomic differences between Whites and non-Whites at several levels including GEP, SNV and CNV, but not at the level of cytogenetics, which are in fact the most commonly performed clinical genomic analysis. Correlation of these variations with MM pt outcomes would be invaluable in understanding disease biology and hopefully mitigating some of the outcome disparities by pt race. As the database grows and becomes more enriched, separate analyses for Blacks, Asians and Hispanics would be attempted.
Acknowledgments: We would like to thank the MMRF, Ms. Mary Derome and Dr. Jonathan Keats for providing scientific overview and technical support.
Fonseca:Millennium, a Takeda Company: Consultancy; Millennium, a Takeda Company: Consultancy; AMGEN: Consultancy; AMGEN: Consultancy; Patent: Patents & Royalties: Prognostication of MM based on genetic categorization of FISH of the disease; Patent: Patents & Royalties: Prognostication of MM based on genetic categorization of FISH of the disease; Patent Pending: Patents & Royalties: The use of calcium isotopes as biomarkers for bone metabolisms; Patent Pending: Patents & Royalties: The use of calcium isotopes as biomarkers for bone metabolisms; Celgene: Consultancy; BMS: Consultancy; Bayer: Consultancy; Novartis: Consultancy; Sanofi: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Millennium, a Takeda Company: Consultancy; BMS: Consultancy; AMGEN: Consultancy; Bayer: Consultancy; Patent: Patents & Royalties: Prognostication of MM based on genetic categorization of FISH of the disease; Novartis: Consultancy; Patent Pending: Patents & Royalties: The use of calcium isotopes as biomarkers for bone metabolisms; Sanofi: Consultancy; Janssen: Consultancy; Millennium, a Takeda Company: Consultancy; AMGEN: Consultancy; Patent: Patents & Royalties: Prognostication of MM based on genetic categorization of FISH of the disease; Patent Pending: Patents & Royalties: The use of calcium isotopes as biomarkers for bone metabolisms. Ailawadhi:Takeda Oncology: Consultancy; Amgen Inc: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy.
