Demographic disparities in genomic data and clinical trials for multiple myeloma.

person Nidhi Aggarwal Medical College of Georgia at Augusta University, Augusta, GA info_outline Nidhi Aggarwal, Pankaj Kumar Ahluwalia, Ravindra B. Kolhe, Jorge E. Cortes

Authors person Nidhi Aggarwal Medical College of Georgia at Augusta University, Augusta, GA info_outline Nidhi Aggarwal, Pankaj Kumar Ahluwalia, Ravindra B. Kolhe, Jorge E. Cortes Organizations Medical College of Georgia at Augusta University, Augusta, GA, Department of Pathology, Medical College of Georgia at Augusta University, Augusta, GA, Georgia Cancer Center, Medical College of Georgia at Augusta University, Augusta, GA Abstract Disclosures Research Funding No funding received Background: There are considerable outcome disparities among patients with multiple myeloma (MM). African-Americans have higher risk of developing MM, earlier age at diagnosis, and higher mortality compared to Whites. While outcomes have multifactorial socioeconomic etiologies, race and ethnicity (R&E) correlate with genomic ancestry, and any role of genetics should be explored. This requires equitable representation in biological databases like The Cancer Genome Atlas (TCGA). Here, we characterize disparities in MM cases of TCGA and clinical trials (CT). Methods: MM incidence was obtained from North American Association of Central Cancer Registries (NAACCR) for R&E, sex and age ( < 50, 50-64, 65+ years). Race includes White, Black, and Asian, along with Hispanic ethnicity, as TCGA MM is limited to these groups. Genes with oncogenic potential and > 5% mutation were stratified by R&E and age. TCGA MM cases are from USA, Canada, Italy, and Spain. On ClinicalTrials.gov, completed MM CT limited to these countries were identified. Student’s t-test and chi-square test were used to analyze disparities and gene mutations. Kaplan-Meier curve was generated to evaluate survival. Results: TCGA MM representation is in Table, calculated as (demographic TCGA cases divided by total TCGA cases) divided by (demographic incidence divided by total incidence). Race was not reported in 20% of TCGA, 4% of CT, and 3% of NAACCR. Of cases reporting R&E, Hispanics are underrepresented by 26% in TCGA and 47% in CT relative to incidence, Blacks by 22% in TCGA and 41% in CT, and Asians by 40% in TCGA and 10% in CT. Whites are overrepresented, by 8% in TCGA and 7% in CT. More men are in TCGA than women, in all R&E. Less than 25% of patients age < 50 relative to incidence are in TCGA, in all R&E. BCL7A is mutated more in Blacks age < 50 (n = 20, 50%) than Whites (n = 39, 10%) (p < 0.01). FAT4 is mutated in Whites age 65+ (n = 31, 10%) but not Blacks. In all ages, KRAS is mutated more in Blacks than Whites (31-35% vs. 20-25%, p < 0.05). Black patients have lower survival than Whites (p < 0.02). In patients age 65+, KRAS mutation is associated with 10% lower 4.5-year survival. Conclusions: Substantial disparities in Black, Hispanic, women, and age representation exist for MM cases in TCGA and CT. This stratification by R&E and age offers new insight on BCL7A, FAT4, and KRAS mutation in MM. Mutation status is associated with survival in older patients. Equitable demographic representation should be pursued to improve quality of available data and access to medical resources for all populations. Percent of All Demographic Incidence Represented in TCGA All Ages (TCGA n=1065, NAACCR n=26843) Age <50 (TCGA n=94, NAACCR n=1723) Age 50-64 (TCGA n=441, NAACCR n=7749) Age 65+ (TCGA n=418, NAACCR n=17371) Asian Women 32% 2% 19% 43% Asian Men 63% 6% 106% 18% Black Women 54% 20% 53% 52% Black Men 74% 9% 51% 114% White Women 82% 21% 77% 93% White Men 93% 25% 92% 107% Hispanic Women 49% 2% 30% 92% Hispanic Men 74% 2% 69% 112%