Terebelo, Howard R.

8027 Background: The Connect MM Registry is a large, US, multicenter, prospective observational cohort study of pts with newly diagnosed multiple myeloma (NDMM) and one of the largest, longest running MM registries. Long-term survivors (LTS), defined as patients (pts) who have ≥ 8 years of follow-up, comprise a large portion of the Connect MM Registry. The purpose of this analysis was to assess LTS demographic and clinical characteristics. Methods: Adult pts with NDMM (N = 3011) were enrolled from 250 community, academic, and government sites: Cohort 1 from 2009 – 2011 and Cohort 2 from 2012 – 2016. As 99% of LTS were enrolled in Cohort 1, only pts from Cohort 1 were included in this analysis. Pt data were unevaluable if there were missing treatments, disease assessments, or large gaps in activity during follow-up (n = 28). Baseline characteristics, treatment patterns, and quality of life (QoL) form completion rates were examined. Results: At data cutoff (5/17/21), of the 1493 pts in Cohort 1 with evaluable data, 279 were LTS and 1186 were non-LTS. LTS were generally younger and had better performance status at enrollment (Table). Most (66%) LTS received stem cell transplants and few experienced progression within the first 6 months (3%). Top first-line (1L) regimen for LTS was lenalidomide/bortezomib/dexamethasone (31%) vs bortezomib/dexamethasone (22%) in non-LTS. At data cutoff, 73% of LTS were still on treatment at their most recent visit. LTS underwent disease assessments more frequently (2.0 vs 1.3 per year) and had a higher QoL completion rate by year 5 (58% vs 46%). This analysis showed an estimated 8-year survival of 36% vs an observed 8-year survival of 39% from the SEER database. Additional analyses are ongoing. Conclusions: LTS were younger and healthier than non-LTS. Most LTS received triplets at induction, stem cell transplants in 1L, and were less likely to relapse within the first 6 months of treatment than non-LTS. These findings are consistent with what has been observed in MM clinical trials. Further, this analysis demonstrates the value of longitudinal data in the CONNECT MM Registry and provides insights on long surviving pts with MM. Clinical trial information: NCT01081028. [Table: see text]

Abstract Introduction: Treatment resistance remains a challenge that most patients (pts) with MM will encounter in their disease course. With each subsequent line of therapy (LOT), pt outcomes and health-related quality of life (HRQoL) worsen. Pts with MM who are refractory to immunomodulatory drugs, proteasome inhibitors (PIs), and anti-CD38 antibodies (triple-class refractory; TCR) have a poor prognosis and the holistic burden is not well understood. To capture these features, the ongoing prospective Connect ® MM Disease Registry was used to report treatment patterns and outcomes in pts with TCR MM. Methods: Data from September 28, 2009 to February 4, 2021 were used. Eligible pts were ≥18 years of age at first documented diagnosis of MM, which became TCR during their disease course. MM was defined as refractory to therapies based on the International Myeloma Working Group Criteria. Index date was defined as first record of TCR during the disease course. Data analyzed included pt characteristics, treatment patterns pre- and post-index, overall survival (OS), healthcare resource utilization (HCRU), and HRQoL. General HRQoL was captured by EQ-5D-3L utility index (range −0.11-1, higher score indicated better HRQoL), EQ-5D visual analog scale (VAS) score (range 0-100, "worst imaginable" to "best imaginable" health status), and FACT-G total score (range 0-108). Disease-specific HRQoL was measured by FACT-MM total score (range 0-164), FACT-MM Trial Outcome Index (TOI) (range 0-112), and the Brief Pain Inventory (BPI) average pain item (range 0-10, "no pain" to "worst pain you can imagine"). Higher scores indicated better HRQoL for all FACT-related index scores. Cohort-level clinically meaningful change in HRQoL during post-index year 1 was defined as 0.08 for the EQ-5D utility index, 7 for the EQ-5D VAS, and 6-8 points for the FACT-G total score. Validated thresholds were unavailable for other index scores. All statistical analyses were descriptive. Results: This analysis included 240 pts identified from the Registry. Mean age at index date was 68.1 (standard deviation [SD] 10.4) years. Mean time from initial MM diagnosis was 64.1 (SD 26.9) months. 63.3% of pts had prior stem cell transplantation. Mean number of prior LOTs was 4 (SD 1.7). 64.6% of pts had received a subsequent MM LOT since index date. Median follow-up time post-index date was 4.6 months. Treatment pattern analysis of post-index treated pts (Table A) revealed that retreatment with an immunomodulatory drug, PI, or anti-CD38 agent post-index date was common, led by carfilzomib (47.1%) and pomalidomide (40.0%). However, no therapeutic combination was preferred in this pt population. Regarding their index therapy (first post-index LOT), 70.3% and 22.6% were on triplet and quadruplet combination therapy; 3.2% initiated belantamab mafodotin, selinexor, melflufen, and/or idecabtagene vicleucel; mean duration of treatment was 5.6 (SD 6.1) months. For all pts, median OS was 8.9 months; median OS for those who received < 4 prior LOTs (43.8%) versus those who received 4 or more prior LOTs (56.2%) was 10.0 and 8.0 months, respectively. Regarding HCRU during the post-index follow-up, all-cause and MM-related hospitalizations were observed in 49.6% and 23.8% of all eligible pts, respectively. Among these pts, annualized mean number of all-cause/MM-related hospitalization and length of hospital stay were estimated as 8.5 (SD 18.2)/11.0 (SD 25.2) and 64.0 days (SD 80.0)/ 69.2 days (SD 83.7), respectively. Analysis of HRQoL showed a numerical decline in HRQoL relative to index during the 1-year post-index period across all studied HRQoL instruments. There was a clinically meaningful decline in HRQoL from baseline following the index date based on the EQ-5D utility index, EQ-5D VAS, and FACT-G total score (Table B). Conclusion: Pts with TCR MM experienced poor survival, substantial hospitalizations, and a clinically meaningful decline in HRQoL. 35.4% of pts did not receive any new LOTs since the disease became TCR. Among those who did receive subsequent LOTs, re-treatment with an immunomodulatory drug, PI, or anti-CD38 antibody therapy occurred, showing a lack of novel treatment options. This study suggests novel tolerable and efficacious therapeutic agents are needed to address the burden of illness in pts with MM. Figure 1 Figure 1. Disclosures Tang: Bristol-Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Hari: Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Celgene-BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Adaptive Biotech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Karyopharm: Consultancy; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Millenium: Membership on an entity's Board of Directors or advisory committees, Research Funding. Ramasamy: Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene - Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Weisel: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy; Novartis: Honoraria; Pfizer: Honoraria. Joshi: Bristol Myers Squibb: Current Employment. Liu: Bristol Myers Squibb: Current Employment. Che: Bristol Myers Squibb: Current Employment. Hernandez: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Abonour: Jensen: Honoraria, Research Funding; Celgene-BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Honoraria, Research Funding; Takeda: Research Funding. Hardin: Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees. Rifkin: Sanofi: Membership on an entity's Board of Directors or advisory committees; McKesson: Current Employment, Current equity holder in publicly-traded company; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Fresenius-Kabi: Membership on an entity's Board of Directors or advisory committees; Coherus: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb (Celgene): Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Ailawadhi: BeiGene, Ltd.: Consultancy; Bristol Myers Squibb: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Ascentage: Research Funding; Medimmune: Research Funding; Cellectar: Research Funding; Xencor: Research Funding; GSK: Consultancy, Research Funding; Takeda: Consultancy; Genentech: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Sanofi: Consultancy; Oncopeptides: Consultancy. Lee: Legend Biotech: Consultancy; Sanofi: Consultancy; Oncopetides: Consultancy; Bristol Myers Squibb: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Consultancy; Janssen: Consultancy, Research Funding; Takeda Pharmaceuticals: Consultancy, Research Funding; Karyopharm: Consultancy; Regeneron: Research Funding; Genentech: Consultancy; GlaxoSmithKline: Consultancy, Research Funding. Terebelo: Pharmacylics: Speakers Bureau; Celgene-BMS: Consultancy; Takeda: Speakers Bureau; Janssen: Speakers Bureau. Durie: Amgen, Celgene/Bristol-Myers Squibb, Janssen, and Takeda: Consultancy; Amgen: Other: fees from non-CME/CE services . Narang: Bristol Myers Squibb: Consultancy; TG Pharma: Consultancy. Toomey: Bristol Myers Squibb: Consultancy. Gasparetto: Oncopeptide: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy; Sanofi: Consultancy, Honoraria, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Speakers Bureau; Gsk: Consultancy, Honoraria, Speakers Bureau. Wagner: Celgene-BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Athenex Inc: Consultancy; Eli Lilly, Johnson & Johnson: Other: Spouse, previously held individual stocks;. Jagannath: Janssen Pharmaceuticals: Consultancy; Bristol Myers Squibb: Consultancy; Karyopharm Therapeutics: Consultancy; Legend Biotech: Consultancy; Sanofi: Consultancy; Takeda: Consultancy.

Background: Multiple myeloma (MM) is typically diagnosed in patients (pts) aged 65-74 y (SEER: Myeloma. 2019); therefore, many pts present with concomitant metabolic disorders such as diabetes (18%-22% prevalence of type 2 diabetes [T2D; BMC Cancer. 2020;20:489]). Obesity (body mass index [BMI], ≥ 30 kg/m2) is associated with increased prevalence of T2D (Int J Clin Pract. 2007;61:737), and both are suggested risk factors for MM (Blood. 2012;119:4845; Bone Marrow Transpl. 2014;49:1009). Results from single-center, retrospective studies have shown poorer clinical features and survival outcomes in diabetic pts with MM vs their nondiabetic counterparts (Eur J Haematol. 2012;89:320, Br J Cancer. 2014;111:628). The Connect® MM Registry (NCT01081028) is a large, US, multicenter, prospective observational cohort study of largely community-based pts with NDMM. Reported here is a descriptive analysis assessing differences in baseline (BL) characteristics, treatment patterns, and survival outcomes in diabetic vs nondiabetic pts enrolled in the Connect MM Registry. Methods: Pts with NDMM (N = 3011) were enrolled at 250 community, academic, and government sites. Eligible pts were aged ≥ 18 y with symptomatic MM diagnosed ≤ 2 months (mo) pre-enrollment per International Myeloma Working Group criteria (Leukemia. 2009;23:3). Diabetic pts were defined as those with a history of diabetes and also receiving antidiabetic treatment at BL; those without a history of diabetes and not receiving antidiabetic treatment at BL were defined as nondiabetic pts. Descriptive statistics were used for BL characteristics and treatment regimens. Survival outcomes (progression-free survival [PFS] and overall survival [OS]) were analyzed using Cox regression and adjusted for covariates. Results: Of the 2845 pts included in this analysis, 467 (16%) were diabetic, and 2378 (84%) were nondiabetic. The median age was 69 y for diabetic pts and 66 y for nondiabetic pts, with fewer Black pts (12.2% vs 19.5%) in the nondiabetic group. The proportion of men was higher in the diabetic vs the nondiabetic group (61.9% vs 56.6%). Similar percentages of pts had ECOG PS of 0-1 in both groups (53.3% vs 55.3%). Obesity was more prevalent in diabetic vs nondiabetic pts (55.9% vs 30.4%). More diabetic pts had a history of hypertension requiring treatment vs nondiabetic pts (84.2% vs 52.5%). The prevalence of chromosomal abnormalities was comparable between the groups (within the limitation that data were missing in a substantial number of pts). Among MM-defining criteria, diabetic pts had a higher incidence of hypercalcemia (11.8% vs 8.5%), anemia (50.3% vs 46.5%), and increased creatinine (23.8% vs 19%), whereas measured kidney function was similar. A lower proportion of diabetic than nondiabetic pts received stem cell transplantation (SCT; 28.9% vs 41.5%) and triplet regimens during first induction (48.2% vs 52.8%%). The use of ≥ 2 novel agents during first line (1L) was lower in diabetic than nondiabetic pts (27.8% vs 34.6%), but use of a single novel agent (63.4% vs 60%) and alkylator in 1L (20.6% vs 22.4%) was comparable. The top five 1L regimens were similar for both groups, with lenalidomide + bortezomib + dexamethasone being the most common 1L regimen, and lenalidomide the most common maintenance therapy. Diabetic pts had significantly shorter adjusted median PFS (24.4 vs 31.5 mo; hazard ratio [HR], 1.24; P = 0.001; Fig. 1A) and median OS (60 vs 72.6 mo; HR, 1.29; P = 0.001; Fig. 1B) than nondiabetic pts. In a subgroup analysis by BMI, diabetic pts had significantly shorter OS than nondiabetic pts (for BMI < 30 [50.8 vs 73.1 mo; HR, 1.51; P = 0.0001] and BMI ≥ 30 [56.4 vs 78.6 mo; HR, 1.51; P < 0.0001]). Conclusions: Diabetic pts with NDMM had poorer BL features and were less likely to receive SCT and triplet regimens and/or aggressive upfront treatment. Regardless of BMI, diabetic pts had shorter median OS than nondiabetic pts, suggesting that diabetes might be a stronger factor for survival than obesity. These results support a previous analysis of the Connect MM Registry that identified history of diabetes as a predictive prognostic factor for poor OS (Br J Haematol. 2019;187:602). Limitations of this analysis include limited detail on the care/management of diabetes and steroid use/dosage (which can be challenging for glycemic control). These findings highlight the need to provide better supportive care for diabetes management in MM pts to improve survival. Disclosures La: Celgene BMS: Consultancy. Jagannath:BMS, Janssen, Karyopharm, Legend Biotech, Sanofi, Takeda: Consultancy. Ailawadhi:Amgen: Research Funding; Celgene: Honoraria; Takeda: Honoraria; Janssen: Research Funding; Pharmacyclics: Research Funding; Cellectar: Research Funding; BMS: Research Funding; Medimmune: Research Funding; Phosplatin: Research Funding. Durie:Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy. Gasparetto:Adaptive Biotechnologies: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company), Speakers Bureau; Karyopharm: Consultancy, Honoraria, Other: TRAVEL, ACCOMODATIONS, EXPENSES (paid by any for-profit health care company), Speakers Bureau; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; GSK: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Consultancy, Honoraria. Hardin:Celgene Pharmaceutical Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: TRAVEL, ACCOMMODATIONS, EXPENSES. Lee:Sanofi: Consultancy; Janssen: Consultancy, Research Funding; GlaxoSmithKline: Consultancy, Research Funding; Genentech: Consultancy; Takeda: Consultancy, Research Funding; Regeneron: Research Funding; Genentech: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Daiichi Sankyo: Research Funding. Narang:Maryland Oncology Hematology: Current Employment; Beigene: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; J&J: Consultancy, Honoraria. Omel:Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; BMS/Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Rifkin:McKesson: Current equity holder in publicly-traded company, Ended employment in the past 24 months, Other: Stock ownership; Takeda, Amgen, Celgene, BMS, Mylan, Coherus BioSciences, Fresenius: Consultancy; AbbVie: Other: Investigator in AbbVie sponsored clinical trials; Takeda, Amgen, BMS (Celgene): Membership on an entity's Board of Directors or advisory committees. Terebelo:Newland Medical Associates: Current Employment; BMS: Membership on an entity's Board of Directors or advisory committees. Toomey:Celgene/BMS: Membership on an entity's Board of Directors or advisory committees. Wagner:Celgene Inc.: Membership on an entity's Board of Directors or advisory committees; Connect Multiple Myeloma Registry: Membership on an entity's Board of Directors or advisory committees. Dhalla:BMS: Current Employment, Current equity holder in publicly-traded company. Liu:TechData Inc: Consultancy. Yang:BMS: Current Employment, Current equity holder in publicly-traded company. Joshi:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company; Eisai Inc.: Ended employment in the past 24 months. Abonour:Takeda: Consultancy; Janssen: Honoraria, Research Funding; Celgene: Consultancy; BMS: Consultancy, Research Funding.

SummaryAlthough new multiple myeloma (MM) therapies are effective in alleviating some disease‐associated symptoms (e.g. bone pain, fatigue, functional decline), they can result in additional toxicities, further impacting health‐related quality of life (HRQoL). Here, we compared HRQoL and safety of lenalidomide‐bortezomib‐dexamethasone [RVd (n = 445)], bortezomib‐melphalan‐prednisone [VMP (n = 77)] and Vd or VMP (n = 588) in patients with newly diagnosed MM (NDMM) from the Connect® MM Registry, a large, USA, multicentre, prospective observational cohort study. Functional Assessment of Cancer Therapy‐Multiple Myeloma subscale, EuroQol‐5D overall score and Bone Pain Inventory HRQoL scores were significantly improved with RVd versus Vd/VMP. Serious adverse event rates were similar in all groups. Treatment with RVd maintained HRQoL in this real‐world, largely community‐based population of patients with NDMM.

Abstract Background: Persons of African ancestry (AA) experience a 1.5-2-fold risk of multiple myeloma (MM) compared to persons of European ancestry (EA). We assembled a set of MM patients with self-reported AA in order to evaluate the contribution of genetics to etiology in this high-risk group. Methods: Here we present the results of a meta-analysis of two GWAS in 1,813 cases and 8,871 controls of AA. We also conducted an admixture mapping scan to identify risk alleles associated with local ancestry, fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA, and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. Finally, we conducted an eQTL analysis measuring gene expression in those genes harboring a risk variant in malignant plasma cells from 292 of the patients from a single site. Results: In GWAS analysis, we identified two suggestive novel loci located at 9p24.3 and 9p13.1 at P<1 × 10-6, but no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. 20 of the 23 known EA risk variants showed directional consistency and 9 replicated at P<0.05 in AA individuals. In eight regions, we identified markers that better capture MM risk in persons of AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95%CI: 1.56, 2.11) increased MM risk compared to those with average risk (25-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P= 5.1 × 10–12). Conclusion: Our study shows that common genetic variation contributes to MM risk individuals of AA. This abstract is also being presented as Poster C040. Citation Format: Zhaohui Du, Niels Weinhold, Gregory Chi Song, Kristen A. Rand, David J. Van Den Berg, Amie E. Hwang, Xin Sheng, Victor Hom, Sikander Ailawadhi, Ajay K. Nooka, Seema Singhal, Karen Pawlish, Edward Peters, Cathryn Bock, Ann Mohrbacher, Alexander Stram, Sonja I. Berndt, William J. Blot, Graham Casey, Victoria L. Stevens, Rick Kittles, Phyllis J. Goodman, W. Ryan Diver, Anselm Hennis, Barbara Nemesure, Eric A. Klein, Benjamin A. Rybicki, Janet L. Stanford, John S. Witte, Lisa Signorello, Esther M. John, Leslie Bernstein, Antoinette Stroup, Owen W. Stephens, Maurizio Zangari, Frits Van Rhee, Andrew Olshan, Wei Zheng, Jennifer J. Hu, Regina Ziegler, Sarah J. Nyante, Sue Ann Ingles, Michael Press, John David Carpten, Stephen Chanock, Jayesh Mehta, Graham A Colditz, Jeffrey Wolf, Thomas G. Martin, Michael Tomasson, Mark A. Fiala, Howard Terebelo, Nalini Janakiraman, Laurence Kolonel, Kenneth C. Anderson, Loic Le Marchand, Daniel Auclair, Brian C.-H. Chiu, Elad Ziv, Daniel Stram, Ravi Vij, Leon Bernal-Mizrachi, Gareth J. Morgan, Jeffrey A. Zonder, Carol Ann Huff, Sagar Lonial, Robert Z. Orlowski, David V. Conti, Christopher A. Haiman, Wendy Cozen. A meta-analysis of genome-wide association study and eQTL analysis of multiple myeloma among African Americans [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr PR05.

BackgroundStudies have reported racial disparities in access to and use of multiple myeloma (MM) treatments between African American (AA) and White patients. Although AA patients demonstrate longer disease‐specific survival, this has not uniformly translated into improved survival over time. The association between race and treatment patterns and survival outcomes was analyzed using data from the Connect MM Registry.MethodsThe Connect MM Registry is a large US, multicenter, prospective observational cohort study of patients with newly diagnosed MM. Patients who received first‐line (1L) stem cell transplantation (SCT) or who did not receive SCT (non‐SCT or non–stem cell transplantation [NSCT]) were grouped by raceEffects of race and transplantation status on the use of triplet treatment were estimated using logistic regression.ResultsTreatment patterns in 1L (types and duration of induction, posttransplantation maintenance) were similar between AA and White patients. SCT rates in 1L (32% vs 36%) and triplet treatment use (AA: 44% for NSCT patients and 72% for SCT patients; and White: 48% for NSCT patients and 72% for SCT patients) during first induction were similar. No significant effect of race or transplantation status on 1L triplet treatment use was observed. Race was not found to be associated with survival outcomes among patients who underwent NSCT; however, AA patients who received SCT had significantly longer overall survival compared with White patients who underwent SCT (not reached vs 88.2 months; hazard ratio, 0.56; 95% CI, 0.35‐0.89 [P = .0141]).ConclusionsAA and White patients were found to have similar treatment patterns in the Connect MM Registry, suggesting that both groups had equal access to health care. In this real‐world setting, AA patients received standard‐of‐care treatment, which might have contributed to better MM‐specific survival compared with White patients.

Purpose: Multiple myeloma (MM) treatment has changed tremendously, with significant improvement in patient out-comes. One group with a suboptimal benefit is patients with high-risk cytogenetics, as tested by conventional karyotyping or fluorescence in situ hybridization (FISH). Methodology for these tests has been published, but not necessarily standardized. Methods: We address variability in the testing and reporting methodology for MM cytogenetics in the United States using the ongoing African American Multiple Myeloma Study (AAMMS). We evaluated clinical and cytogenetic data from 1,221 patients (1,161 with conventional karyotyping and 976 with FISH) tested between 1998 and 2016 across 58 laboratories nationwide. Results: Interlab and intralab variability was noted for the number of cells analyzed for karyotyping, with a significantly higher number of cells analyzed in patients in whom cytogenetics were normal (P 5.0025). For FISH testing, CD138-positive cell enrichment was used in 29.7% of patients and no enrichment in 50% of patients, whereas the remainder had unknown status. A significantly smaller number of cells was analyzed for patients in which CD138 cell enrichment was used compared with those without such enrichment (median, 50 v 200; P, .0001). A median of 7 loci probes (range, 1-16) were used for FISH testing across all laboratories, with variability in the loci probed even within a given laboratory. Chromosome 13–related abnormalities were the most frequently tested abnormality (n5956; 97.9%), and t(14;16) was the least frequently tested abnormality (n 5 119; 12.2%). Conclusions: We report significant variability in cytogenetic testing across the United States for MM, potentially leading to variability in risk stratification, with possible clinical implications and personalized treatment approaches.