Citations

With improved medical treatments, the prognosis for many malignancies has improved, and more patients are presenting for transplant evaluation with a history of treated cancer. Solid organ transplant (SOT) recipients with a prior malignancy are at higher risk of posttransplant recurrence or de novo malignancy, and they may require a cancer surveillance program that is individualized to their specific needs. There is a dearth of literature on optimal surveillance strategies specific to SOT recipients. A working group of transplant physicians and cancer-specific specialists met to provide expert opinion recommendations on optimal cancer surveillance after transplantation for patients with a history of malignancy. Surveillance strategies provided are mainly based on general population recurrence risk data, immunosuppression effects, and limited transplant-specific data and should be considered expert opinion based on current knowledge. Prospective studies of cancer-specific surveillance models in SOT recipients should be supported to inform posttransplant management of this high-risk population.

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.

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.

Abstract Twenty-eight patients with relapsed or refractory CD20+ NHL have been enrolled in an ongoing phase I trial of dose-escalated 90YZ followed by high-dose BEAM and autotransplant in which the 90YZ dose is patient-specific based on dosimetry. 90YZ doses are calculated to deliver cohort-defined radiation doses (100, 300, 500, ... cGy) to critical organs (liver, lung or kidney), with 3–6 patients per group. On D -22, rituximab (R) 250 mg/m2 is infused followed by the imaging dose of 111In Zevalin® (5 mCi). Imaging is performed immediately post-injection and at 4, 24, 72, and 144 hours; dosimetry is performed on D -15. On D -14, R 250 mg/m2 is administered followed immediately by 90YZ at the dose calculated to deliver the cohort-prescribed absorbed radiation dose to the critical organ. On D -6 through -1, patients receive high-dose BEAM. On D0, a minimum of 2.0 X 106 CD34+ cells/kg is infused and G-CSF 5 μg/kg SQ daily begun. The median age was 54 (range: 25–72) years. NHL histologic subtypes were as follows: mantle cell 5, diffuse aggressive 13, low grade 5, and transformed 5. Most had received 3 or more treatment regimens, including R. The toxicity profile was similar to that associated with high-dose BEAM and included a decrease in DLCO for most patients with one patient at the 500 cGy dose level experiencing a transient decline to below 50% of the predicted value corrected for hemoglobin. The most common grade III/IV toxicities were infection, fever, stomatitis, nausea, vomiting, diarrhea, hemorrhage, and edema. One patient experienced transient veno-occlusive disease at the 700 cGy dose level. Engraftment occurred at a median of 10 days (range:8–18) to granulocytes ≥ 500/μL, and 21 days (range:13–40days) to platelets ≥20,000/μL . With a median follow-up of one year, the 3 year overall and progression-free survivals are 60% and 50%, respectively. Figure Figure 90-Y Zevalin Dosing by Cohort (median; range) Cohort (cGy) Total Dose (mCi) mCi/kg 100 (n=3) 5 (2–14) .06(.05–.12) 300 (n=7) 22(14–57) .25(.18–.63) 500 (n=6) 31(16–48) .40(.14–.63) 700 (n=6) 37(26–55) .38(.27–.73) 900 (n=3) 28(27–37) .32(.27–.44) 1100 (n=3) 48(29–65) .57(.50–.75) The liver was the critical organ in nearly all cases. Patient-specific doses calculated to deliver a cohort-prescribed absorbed radiation dose to the critical organ were highly variable suggesting that dosing based on weight and not dosimetry is likely to result in a wide range of absorbed dose to critical organs. In the context of this study, 90YZ has been administered to eight patients at doses of .5 mCi/kg or greater. We conclude that with careful dosimetry, 90YZ doses higher than the conventional .4 mCi/kg may be safely combined with BEAM and autotransplant. Accrual continues at the 1300 cGy dose level.

8037 Background: Advances in the treatment of MM have greatly improved clinical outcomes for patients (pts). SPM occurrence has been observed in early and late stage MM as well as with increasing age. US SEER Cancer Registry reports a background incidence rate of SPM 2.1/100 person-yrs (PY) among persons ≥ 65 yrs of age. However, incidence of SPM in MM pts and the relationship to therapy still warrants further exploration. Methods: Connect MM is a US-based observational registry designed to characterize pts with newly diagnosed MM from 266 US sites. Initiated in Sep 2009, patient data were collected at baseline and each subsequent quarter with a standardized form. On Dec 14, 2011, Connect MM reached full enrollment at 1,500 pts. Results: As of Jan 13, 2012, preliminary retrospective SPM data is available for 1015 pts. Median age was 67 yrs, 56.8% male, and median follow-up was 10.6 mo (0.03-24.9 mo). 12 pts (7 male) with median age of 68.5 yrs developed SPM. Median time to SPM was 8.5 mo (0.8-17.7 mo) after treatment initiation. 11 invasive SPM were observed - 4 hematological (heme): 1 AML, 1 MDS, 1 CMML and 1 DLBCL, and 7 solid: 2 melanoma skin, 1 bronchus, 1 breast, 1 prostate, 1 tonsil, and 1 gastric carcinoid. Also, 1 pt had non-melanoma skin cancer (NMSC), and 1 pt with invasive skin cancer also had NMSC. Of the 4 pts developing heme SPM, 3 had bortezomib (BORT), 1 had thalidomide (THAL), 1 had lenalidomide (LEN), 2 had melphalan (MEL), and all 4 pts had steroids. Of the 7 pts who developed solid tumor SPM, 5 had BORT, 5 had LEN, 2 had doxorubicin, 1 had MEL, 5 had steroids, 1 had irradiation, and 1 had not received MM treatment. 2 pts with prior history of invasive malignancy developed solid tumor SPM (1 pt also developed NMSC).1 pt who received irradiation developed NMSC. Early overall incidence of invasive SPM was 1.21/100 PY (95% CI 0.60, 2.16) for all pts and 1.20/100 PY (95% CI 0.44,2.62) for pts ≥ 65 yrs of age. Conclusions: This preliminary analysis shows that SPM occurred at an expected rate in this disease specific registry of patients with NDMM and appeared to occur irrespective of MM treatment administered. Incidence rates of SPM may increase over time as patients receive transplantation and alkylators. Prospective observation will continue.

e18556 Background: Studies in the United States (US) have identified variation in incidence and survival of multiple myeloma (MM) patients of different races, and noted that MM is the most common hematologic cancer among African Americans. This analysis evaluated whether health-related quality of life (HRQOL) of patients in the US varies by race as they initiate treatment having been newly diagnosed with active, symptomatic MM. Methods: Data were collected in Connect MM, a prospective US observational registry begun in September 2009. Data on patient demographics and clinical characteristics were provided by clinicians. HRQOL was reported by patients at enrollment within 2 months of diagnosis. Patients completed the Brief Pain Inventory (BPI), EQ-5D, and Functional Assessment of Cancer Therapy-Multiple Myeloma (FACT-MM). Mean reported scores for BPI, EQ-5D and FACT-MM were analyzed by patient race. Statistical significance was ascertained by ANOVA using SAS 9.1. Results: Baseline HRQOL data were reported by 1144 patients (enrolled in 228 centers) of whom 82% were White, 13% Black, and 6% Other race. The cohorts did not differ statistically by ECOG status or by multiple myeloma stage (assessed by either the International Staging System or the Durie and Salmon system). Compared to non-Blacks, Black patients reported less anxiety/depression on the EQ-5D (p=0.030) and better emotional well-being on the FACT-MM (p=0.003). No other statistically significant differences were noted between cohorts on the BPI, other EQ-5D domains (mobility, self care, usual activities, pain/discomfort), or other FACT-MM domains (physical, social/family, functional, and MM-specific considerations). Conclusions: Initial results from the Connect MM Registry indicate that baseline HRQOL prior to initiation of treatment may vary by patient race with respect to emotional well-being, and specifically anxiety/depression. These results serve as a baseline reference for future analyses. As patients in the Connect MM Registry proceed through therapy, analyses should be conducted of patients by race, among other characteristics and factors, to determine whether it may be associated with subsequent clinical outcomes and HRQOL over time.

Abstract Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs of MM in 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We 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. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10−6; however, 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. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better capture MM risk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with 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). Our study shows that common genetic variation contributes to MM risk in individuals with AA.

Multiple myeloma (MM) is a neoplasm thought to arise from a damaged germinal center B-cell that progresses to a plasma cell clone arising in bone marrow. MM comprises 20% of all hematologic cancer deaths. Persons of African ancestry (AA) have a 1.5 to 2-fold higher risk compared to individuals of European ancestry (EA). Genetically driven differences in hematopoiesis may lead to variation in the levels white blood cell (WBC) subsets which could, in turn, be associated with MM etiology. There are differences in genetic determinants of WBC traits between EA and AA populations, with possible implications for the racial disparity in risk. We tested the above hypothesis using Mendelian randomization (MR), an approach that leverages genetic determinants of specific traits (i.e. WBC counts) to estimate their effects on the risk of an outcome; and a transcriptome-wide association study (TWAS), which utilizes genetic predictors of gene expression to identify susceptibility genes. These analytic approaches were applied to data from the African American Multiple Myeloma Study (AAMMS) consisting of 1813 cases and 8871 AA cancer-free controls to examine how differences in heritable WBC gene expression profiles influence MM risk. Genetic determinants of variation in WBC subsets in AA were obtained from the literature and supplemented with new genome-wide association findings in AA subjects from the UK Biobank cohort (n=6108). Odds ratios (OR) for MM per 1 standard deviation (SD) increase in each WBC phenotype were estimated using independent (linkage disequilibrium (LD) r2<0.10) variants with P<10-6 as genetic instruments. Analyses based on variants associated with WBC traits in AA populations did not identify any statistically-significant associations between MM risk and WBC overall (p=0.81, using 15 SNPs) or subsets (lymphocytes, monocytes, eosinophils, neutrophils and basophils, p>0.05 for each). However, when we applied genetic determinants of WBC identified in 330,000 cancer-free EA UK Biobank participants (P<10-8, replication P<0.05, LD r2<0.05), a statistically significant inverse relationship emerged between increasing lymphocyte counts and MM risk (OR=0.80, 95% CI: 0.66-0.97, p=0.02, using 385 SNPs), as well as increasing basophil counts (OR=0.63, 95% CI: 0.41-0.96, p=0.03, using 140 SNPs). Next, we examined the association between WBC gene expression profiles and MM risk in AAMMS data. We applied published and validated ancestry-specific models developed using the PrediXcan approach, which leverage germline genetic and transcriptomic data from the Multi-Ethnic Study of Atherosclerosis (MESA) (Mogil et al. PMID: 30096133). The primary TWAS used gene expression models trained in AA subjects (n=233), with sensitivity analyses using models developed in AA and Hispanic subjects (n=585). The TWAS significance threshold was based on the number of genes with significant germline prediction models (p<0.05 and R2 ≥0.05) in AA, corresponding to P < 0.05/2700 = 1.85×10-5. The expression of two genes was significantly associated with MM risk: KANK1at 9p24.3 (P = 1.01×10-5) and DNAJC27at 2p23.3 (P = 1.56×10-5). KANK1is a candidate tumor suppressor gene for renal cell carcinoma and has recently been associated with MM risk in AA [Du, Blood 2017 130:3058]. Here we provide additional evidence for its role in MM etiology via gene expression-mediated mechanisms. DNAJC27 (previously known as RBJ) is a novel MM risk gene linked to constitutive activation of ERK in solid tumors. We also identified two suggestively associated genes: PRR14 (P = 1.34×10-4; combined AA-Hispanic sample: P = 1.56×10-6), which has been linked to MM risk in EA populations, and PARP16 (P = 9.46×10-5). To our knowledge this is the first study to comprehensively examine variation in WBC traits and gene expression profiles with respect to MM risk in AA. Our TWAS analysis leveraged data from the largest collection of genetic and gene expression data in AA, enabling ancestry-matched inference and identification of two novel risk genes. Although the limited availability of genetic instruments for WBC limited the power of MR analysis, findings using variants identified in European populations may offer some insight into trans-ethnic etiologic pathways and contribute to risk stratification strategies using genetic and blood cell count biomarkers. Future studies, particularly with MGUS-free controls, are needed to validate these results. Disclosures Song: Millennium Pharmaceuticals Inc: Employment. Rand:Ancestry.com: Employment. Ailawadhi:Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy. Nooka:Takeda: Honoraria, Other: advisory board participation; Janssen: Honoraria, Other: advisory board participation; GSK: Honoraria, Other: advisory board participation; Spectrum pharmaceuticals: Honoraria, Other: advisory board participation; Adaptive technologies: Honoraria, Other: advisory board participation; Amgen: Honoraria, Other: advisory board participation; Celgene: Honoraria, Other: advisory board participation; BMS: Honoraria, Other: advisory board participation. Singhal:Bureau of Millennium/Takeda, Celgene, Janssen, Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. van Rhee:Takeda: Consultancy; Sanofi Genzyme: Consultancy; Castleman Disease Collaborative Network: Consultancy; EUSA: Consultancy; Adicet Bio: Consultancy; Kite Pharma: Consultancy; Karyopharm Therapeutics: Consultancy. Mehta:Millennium/Takeda, Celgene; stock in Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. Wolf:Takeda: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Amgen: Consultancy. Martin:Roche and Juno: Consultancy; Amgen, Sanofi, Seattle Genetics: Research Funding. Fiala:Incyte: Research Funding. Terebelo:Jannsen: Speakers Bureau; Celgene: Honoraria; Newland Medical Asociates: Employment. Anderson:Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Sanofi-Aventis: Other: Advisory Board. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding. Bernal-Mizrachi:TAKEDA: Research Funding; Kodikas Therapeutic Solutions, Inc: Equity Ownership; Winship Cancer Institute: Employment, Patents & Royalties. Morgan:Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Other: research grant, Research Funding. Zonder:Celgene Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Intellia: Consultancy, Membership on an entity's Board of Directors or advisory committees; Caelum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Alnylam: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees. Huff:Member of Safety Monitoring Board for Johnson and Johnson: Membership on an entity's Board of Directors or advisory committees; Karyopharm, Sanofi, MiDiagnostics: Consultancy. Lonial:Karyopharm: Consultancy; Takeda: Consultancy, Research Funding; Amgen: Consultancy; BMS: Consultancy; Janssen: Consultancy, Research Funding; GSK: Consultancy; Celgene Corporation: Consultancy, Research Funding; Genentech: Consultancy. Orlowski:Poseida Therapeutics, Inc.: Research Funding.

Abstract Abstract 3150 Introduction. Affordability of health care in the United States (US), and the impact of patient insurance coverage on it have been shown to be associated with the delivery and timeliness of access to care. With increasing costs of novel therapies in hematology and oncology, this concern has been raised in hematologic cancers. This analysis evaluates whether the health-related quality of life (HRQOL) of patients with active, symptomatic multiple myeloma (MM) patients in the US varies by their insurance coverage as they initiate treatment. Methods. Baseline data were collected in Connect MM®, a prospective observational registry initiated in September 2009 involving centers in the US. Data on patient demographics and clinical characteristics were provided by clinicians. HRQOL was reported by patients in the clinic at enrollment, within two months of diagnosis. Patients completed 3 psychometrically validated instruments: the Brief Pain Inventory (BPI), EQ-5D, and Functional Assessment of Cancer Therapy-Multiple Myeloma (FACT-MM). Standard analyses were conducted of each instrument given clinical characteristics at that time. Patients were characterized as to the main source of their insurance coverage: Medicare, Medicaid or Commercial. The Medicare cohort included those with supplemental commercial coverage, and the Medicaid cohort included those with dual Medicare-Medicaid coverage. Reported mean BPI, EQ-5D and FACT-MM scores were analyzed by patient insurance coverage. Statistical significance of score differences among sub-cohorts was ascertained by ANOVA using SAS 9.1. Results. HRQOL data and insurance status were reported by 863 patients, enrolled from 189 centers. Patients were predominantly male (57%) and white (82%) with mean age at 67.0 (standard deviation [SD] 11.3) yrs. HRQOL scores by insurance status are presented: Overall FACT-MM results indicate that Medicaid insurance status is associated with greater decrement in baseline HRQOL compared to the other groups. Statistically significant differences are noted in the FACT-G general cancer HRQOL score, and its physical, social/family and emotional component scores. BPI data (on a scale of 0 [no pain] to 10 [worst pain] indicate that average pain may be worse among Medicaid patients compared to either Medicare or commercially insured patients. There are no statistically significant differences on EQ-5D scores (on a scale of 1 [no problem] to 2 [some problems] to 3 [incapacity] between cohorts based on insurance status. Conclusions. Initial results from the Connect MM® Registry indicate that HRQOL at baseline prior to initiation of treatment is worse among Medicaid patients compared to those with either Medicare or commercial coverage. This should be investigated to determine whether Medicaid patients are accessing care later in disease progression than others covered by Medicare or commercial insurance. Furthermore, future analyses should be conducted of patients by insurance coverage to determine whether status may be associated with access to medical care, subsequent clinical outcomes, and HRQOL over time. Results reported here should serve as a baseline reference. Disclosures: Pashos: Celgene: Membership on an entity's Board of Directors or advisory committees. Durie:Celgene: Membership on an entity's Board of Directors or advisory committees. Rifkin:Celgene: Membership on an entity's Board of Directors or advisory committees. Terebelo:Celgene: Membership on an entity's Board of Directors or advisory committees. Gasparetto:Celgene: Membership on an entity's Board of Directors or advisory committees. Mehta:Celgene: Membership on an entity's Board of Directors or advisory committees. Toomey:Celgene: Membership on an entity's Board of Directors or advisory committees. Abonour:Celgene: Membership on an entity's Board of Directors or advisory committees. Fonseca:Celgene: Membership on an entity's Board of Directors or advisory committees. Narang:Celgene: Membership on an entity's Board of Directors or advisory committees. Shah:Celgene: Membership on an entity's Board of Directors or advisory committees. Swern:Celgene Corporation: Employment, Equity Ownership. Sullivan:Celgene: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Khan:Celgene: Employment, Equity Ownership.

Abstract Abstract 5070 Introduction. Studies in the United States (US) have identified variation in incidence and survival of multiple myeloma (MM) patients of different races, and noted that MM is the most common hematologic cancer among African Americans. This analysis was conducted to evaluate whether the health-related quality of life (HRQOL) of patients in the US as they initiate treatment with active, symptomatic MM varies by race. Methods. Data were collected in Connect MM®, a prospective observational registry begun in September 2009 involving centers in the US. Data on patient demographics and clinical characteristics were provided by clinicians. HRQOL was reported by patients in the clinic at enrollment, within two months of diagnosis. Patients completed 3 psychometrically validated instruments: the Brief Pain Inventory (BPI), EQ-5D, and Functional Assessment of Cancer Therapy-Multiple Myeloma (FACT-MM). Standard analyses were conducted of each instrument. Reported mean BPI, EQ-5D and FACT-MM scores were analyzed by patient race. Statistical significance of score differences was ascertained by ANOVA using SAS 9.1 in two ways: (1) among three different racial categories (White, Black and Other), and (2) between Blacks and non-Blacks. Hispanic/Latino patients and Asian patients comprise most of the Other cohort. Results. HRQOL data at baseline were reported by 916 pts, enrolled from 189 centers. Of these patients, 82% were White, 12% were Black, and 5% were of another race (Other). Among evaluable patients, the different race groups did not differ statistically by ECOG status or multiple myeloma stage (measured by either the International Staging System or the Durie and Salmon system). The three racial groups differed by age, with the mean (SD) age of Blacks (63 (11)) and Others (63 (13)) being less than that of Whites (68 (11), p<0.0001). Gender was trending, but not statistically significantly different, between races, with males comprising 58% of Whites, 52% of Blacks, and 46% of Others (p=0.1803). HRQOL scores by race are presented here: Overall FACT-MM results indicate that baseline HRQOL does not vary between races, as the main summary scores do not differ statistically, although the FACT-MM Total scores of Blacks and Whites are trending higher than those of Others. Only the emotional and social/family domain scores of the FACT-MM vary statistically, as Blacks show a worse mean score in the social/family domain and a better score in the emotional domain. BPI data (on a scale of 0 [no pain] to 10 [worst pain] do not show a statistically significant difference between races, and the same is true of the five EQ-5D domains (measured on a scale of 1 [no problem] to 2 [some problems] to 3 [incapacity]. Conclusions. Initial results from the Connect MM® Registry indicate that HRQOL at baseline prior to initiation of treatment does not vary by patient race. As such, these results may serve as a baseline reference for future analyses. In particular, as patients proceed through therapy, analyses should be conducted of patients by race, among other factors, to determine whether it may be associated with subsequent clinical outcomes and HRQOL over time. Disclosures: Pashos: Celgene: Membership on an entity's Board of Directors or advisory committees. Durie:Celgene: Membership on an entity's Board of Directors or advisory committees. Rifkin:Celgene: Membership on an entity's Board of Directors or advisory committees. Terebelo:Celgene: Membership on an entity's Board of Directors or advisory committees. Gasparetto:Celgene: Membership on an entity's Board of Directors or advisory committees. Mehta:Celgene: Membership on an entity's Board of Directors or advisory committees. Toomey:Celgene: Membership on an entity's Board of Directors or advisory committees. Abonour:Celgene: Membership on an entity's Board of Directors or advisory committees. Fonseca:Celgene: Membership on an entity's Board of Directors or advisory committees. Narang:Celgene: Membership on an entity's Board of Directors or advisory committees. Shah:Celgene: Membership on an entity's Board of Directors or advisory committees. Swern:Celgene Corporation: Employment, Equity Ownership. Sullivan:Celgene: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Khan:Celgene: Employment, Equity Ownership.