Abstract: 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: Multiple myeloma (MM) is 2-3 times more common among African-Americans compared to non-Hispanic whites. The 2-3-fold increased risk among family members of cases suggests a genetic contribution to risk. Genome-wide association studies (GWAS) in populations of European ancestry have identified seven novel risk loci at 2p23.3 (rs6746082), 3q26.2 (rs10936599), 3p22.1 (rs1052501), 6p21.32 (rs2285803), 7p15.3 (rs4487645), 17p11.2 (rs4273077) and 22q13.1 (rs877529) (Broderick, et al. Nat Genet, 2011, Chubb, et al. Nat Genet, 2013), three of which were replicated in another European series (Martino et al., Br J Haematol, 2012). Here we examined the index signals and conducted fine-mapping for each locus in a case-control study of 1,049 multiple myeloma cases and 7,084 controls of African ancestry to identify better markers of risk and novel independent loci in seven previously reported regions in this high risk population. Incident cases were recruited from 10 clinical centers and SEER cancer registries from 2011 to 2013 and genotyped using the Illumina HumanCore GWAS array. Control data were obtained from previous genome-wide studies of breast and prostate cancer, genotyped using the Illumina 1M-Duo in 4425 male controls from the African Ancestry Prostate Cancer Consortium (consisting of 14 independent studies) and 2632 female controls from a breast cancer GWAS of African-American women (consisting of 9 independent studies). Imputation to 1000 Genomes (March 2012 release) was conducted for regions around six of the previously identified single nucleotide polymorphisms [SNPs] (the HLA region harboring rs2285803 is still being imputed, results will be presented). A case-control analysis of SNPs/indels 〉 1% frequency within 250 kb of each index variant was conducted using unconditional multivariable logistic regression adjusting for age, sex and five leading principal components. Region-specific alpha levels were determined through permutation tests. The minimum alpha level across the six regions was α=0.002. All previously reported risk variants were common in African-Americans (minor allele frequency [MAF] 〉 0.05). For five of the six SNPs, we had ≥94% power to detect the same effect observed in non-Hispanic whites, and 64% power for the less common variant rs10936599 (MAF=0.07). We observed directionally consistent effects (odds ratio [OR] 〉 1) for the six risk variants tested, with three replicating at p≤0.05 (7p15.3, p=1.4x10-7; 17p11.2, p=0.05; 22q13.1, p=0.02). For three of the six regions, we observed better markers of risk in African-Americans that were correlated with the index SNP in Europeans (7p15.3, rs56333627, p=1.5x10-5, r2=0.89; 17p11.2, rs34562254, p=2.9x10-3, r2=0.90; 22q13.1, rs2092410, p=1.1x10-4 r2=.71). The missense variant identified in the 17p11.2 region (rs34562254, Pro251Leu) is located in TNFRSF13B, which encodes the protein TACI, a B cell surface receptor which plays a role in B cell maturation, apoptosis and antibody production by inducing activation of transcription factors including NFAT and NFκβ. In addition, there is evidence suggesting that TACI is involved in MM pathogenesis. Our results demonstrate that many of the risk loci for MM found in European ancestry populations are also risk loci in men and women of African ancestry and that by fine-mapping, we are able to identify variants that better capture risk in populations of African ancestry. Disclosures Terebelo: Celgene Corp: Membership on an entity's Board of Directors or advisory committees. Lonial:Millennium: The Takeda Oncology Company: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Onyx Pharmaceuticals: Consultancy, Research Funding.

Abstract: Background: Persons of African ancestry (AA) have a 2-3-fold higher risk of multiple myeloma (MM) than persons of European ancestry (EA). Like other B-cell malignancies, genome-wide association scans (GWAS) have identified MM risk variants in the HLA region in persons of EA. We conducted a case-control analysis with data from the National Marrow Donor Program (NMDP)1comprising MM patients typed for bone marrow transplant to donor controls matched by race-ethnicity, and found associations between specific HLA alleles/haplotypes and MM risk that varied by race and ethnicity. To confirm our results and identify additional novel signals, we have now investigated associations between HLA alleles and haplotypes and MM risk in the African American Multiple Myeloma Study (AAMMS) Cohort. Methods: The source of subjects was the AAMMS, in which AA MM patients were identified from 10 cancer centers and 4 Surveillance, Epidemiology and End-Results (SEER) Program cancer registries in order to identify genetic risk factors for MM among AAs. A GWAS was conducted using the Illumina Human Core BeadChip array on DNA samples from 1,305 AA MM patients in the AAMMS comparing results to those from 7,078 AA controls with GWAS data generated from the Illumina 1MDuo2. The major histocompatibility complex (MHC) region single nucleotide polymorphisms (SNPs) were imputed to classical HLA variants using HIBAG. Unconditional logistic regression was used to estimate HLA associations, adjusting for sex, age and the first 2 principal components. P-values were adjusted for false discovery rate (FDR) for each locus group. Results: We did not identify any single HLA alleles associated with MM risk among AAs. However, several B*07:02-containing haplotypes were associated with MM risk (odds ratios [OR] ranging from 2.38 to 2.64 and FDR P-values ranging from 1.43 x 10-6 to 3.57 x 10-8). We found associations between MM risk and genotypes containing DRB3*02:02, including DRB3*02:02~DRB1*11:01+ DRB3*02:02~DRB1*11:01 (OR=1.93, PFDR= 9.36 x 10-5) similar to those observed in the NMDP study1. Novel findings included associations between MM risk and HLA Class I haplotypes B*53:01+ B*57:01 (OR=1.94, PFDR= 0.003) and C04:01~B*53:01+C*06:02~B*57:01 (OR=1.96, PFDR= 0.0050). Results from an ongoing meta-analysis between the two data sets (one based on an imputed GWAS and one based on NMDP HLA typing) will be presented. Conclusions: This study is the second to examine HLA alleles and risk of MM among AA's and is by far the largest. We confirmed a previously observed association between an HLA Class II DRB3 variant and MM risk and confirmed an association with B*07 haplotypes previously observed among EAs1. We also identified novel associations between other HLA Class I haplotypes and MM risk in AA's. Because HLA is highly polymorphic, many HLA alleles are rare variants for which genetic associations are difficult to detect without very large sample sizes. Further investigation with large sample sizes will be necessary to refine these associations in order to better identify the underlying causal alleles and determine the functional significance of these HLA associations. 1Beksac M, Gragert L, Fingerson S, et al.: HLA polymorphism and risk of multiple myeloma.Leukemia. 2016 Jul 27. doi: 10.1038/leu.2016.199. 2Rand KA, Song C, Hwang AE, et al. Genetic susceptibility markers of multiple myeloma in African-Americans. Abstract # 2030, 56th Annual American Society of Hematology Meeting, San Francisco, California, 2014. Disclosures Ailawadhi: Pharmacyclics: Consultancy; Novartis: Consultancy; Amgen Inc: Consultancy; Takeda Oncology: Consultancy. Nooka:Spectrum, Novartis, Onyx pharmaceuticals: Consultancy. Zonder:Pharmacyclics: Other: DSMC membership; Prothena: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Lonial:BMS: Consultancy; Novartis: Consultancy; Millenium: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Merck: Consultancy; Celgene: Consultancy; BMS: Consultancy; Novartis: Consultancy; Onyx: Consultancy; Janssen: Consultancy; Onyx: Consultancy.

Abstract: Abstract 4002 African-Americans (AA) have a 2–3-fold higher risk of multiple myeloma (MM) relative to Whites (Gebregziabher, 2006). We have formed a consortium and are conducting a multi-center study with 9 clinical centers and 4 NCI Surveillance, Epidemiology and End-Results (SEER) Program population-based cancer registries to determine the causes of the disease in this population and explain the excess risk (Myeloma in African-American Patients, MAP). Participation involves providing a blood or saliva specimen for DNA and answering a lifestyle and medical history questionnaire. At the end of the data collection period, a genome-wide scan will be performed and our results compared to those from 2,000 African-American controls participating in cohort studies. Patients with African ancestry (predominantly African-Americans) are identified from outpatient clinic rosters or from population-based cancer registries. For patients recruited at clinics, information on subtype, cytogenetics, FISH and lytic bone lesions is abstracted from medical records. To date, 601 patients have agreed to be in the study and we have received DNA samples from 592 patients; 54.6% are female and 45.4% are male. The mean age at diagnosis is 57 years (SD =11.2) with a median age at diagnosis of 58 years (range 27 to 90 years of age). Of the 514 subjects who completed a questionnaire, 7.8% were obese at age 20 (body mass index 〉 30) and 39% were obese 5 years prior to diagnosis. A first-degree relative with MM was reported by 17 cases (3%), 74% higher than the lifetime risk of 1.7% in the general population based on SEER data. In addition, cases reported 21 first-degree relatives with leukemia (4%), 7 with non-Hodgkin lymphoma (1%) and 14 with Hodgkin lymphoma (3%). To date, clinical information has been abstracted for 351 patients. Of these, 207 (58%) have active disease with the following distribution: stage I (30%), stage II (27%) and stage III (43%). The remainder have relapsed (13%), refractory (1%), relapsed and refractory (4%), or smoldering myeloma (6%), or are in remission (18%). The subtype distribution is: IgG (74%), IgA (11.4%), IgD (0.9%) and IgM (0.3%), and light chain only (13.5%); a distribution significantly different from that observed in a predominantly White population (P 〈 0.007) (Kyle, 2003), (Table 1). Lytic bone lesions were present in 67% of patients, similar to the prevalence observed in other series. FISH and cytogenetics data on hyperdiploidy, deletions in chromosome 13 and 17p, and IGH translocations are being collected on this large cohort of African-Americans patients and will be presented at the ASH meeting. Disease characteristics in AA patients appear to be different than those previously reported in predominantly White populations. Table 1. Type of multiple myeloma and presence of bony lesions in 351 African-American patients. African-American Patients Mayo Clinic1 n % % Type     IgA 38 11.4 20     IgD 3 0.9 〈 10     IgG 247 74 50     IgM 1 0.3 〈 10     Light Chain only 45 13.5 20     Total 334     Not Available 17 Lytic Bone Lesions     Present 160 67 70     Absent 79 33     Total 239     Not Available 112 1 Kyle RA, Gertz MA, Witzig TE, Lutz JA, Lacy MQ, Dispenzieri A, Fonseca R, Rajkumar SV, Offord JR, Larson DR, Plevak ME, Themeau TM, Gregg PR, Review of 1,027 patients with newly diagnosed multiple myeloma. Mayo Clin Proc, 78: 21–33, 2003. Disclosures: No relevant conflicts of interest to declare.

Abstract: African-American ethnicity, male sex, older age and obesity are accepted risk factors for multiple myeloma (MM). Obesity early in life is a risk factor for many cancers, including MM; most studies have focused on populations of European origin. African-Americans have a higher prevalence of obesity than other populations, and may have a distinct genetic contribution to this condition. We established a multi-center collaborative study to investigate possible explanations for the excess risk of MM among African-Americans. The aim of the present case-case analysis was to determine whether body mass index (BMI) was associated with risk factors and clinical characteristics at presentation in African-American MM patients. Methods Patients diagnosed with active MM since January 1, 2009 were recruited from nine outpatient centers and three Surveillance, Epidemiology, End-Results Program (SEER) population-based cancer registries. Information on weight and height at 20 years of age and at 5 years prior to diagnosis was obtained from questionnaires. Clinical information collected included age at diagnosis, stage, percent plasmacytosis on bone marrow biopsy, β2 microglobulin level, Ig serotype, light vs. heavy chain disease, and presence of lytic bone lesions. BMI (ht/wt2) was categorized into 3 levels (normal 〈 25, overweight 25-29, obese 〉 30) according to World Health Organization standard. The Pearson chi-square test was used to test the association between BMI category, and risk factors and clinical characteristics. Mean ages at diagnosis across BMI categories were compared using linear regression and a t-test for trend calculated. Results To date, 1,044 African-American MM patients have been enrolled and of those, 1,014 provided a DNA sample. At present, 970 patients have completed a questionnaire, clinical records have been abstracted for 823 patients, and 509 patients have some information on gender, age at diagnosis, weight, height and clinical characteristics.The mean age at diagnosis was 59. Increasing BMI at age 20 was associated with younger age at diagnosis (p= 0.0004), whereas BMI at 5 years prior to diagnosis was not associated with age at diagnosis (p=0.9477). Among men, mean age at diagnosis decreased with increasing BMI at age 20 (p= 0.0125) (Table 1a) and at 5 years prior to diagnosis (p=0.0252) (Table 1b). Among women, the trend was signficant at age 20 (p=0.0018) (Table 1a) but not at 5 years prior to diagnosis (p= 0.7094) (Table 1b). Increasing BMI was not significantly associated with any other clinical characteristics. Conclusion/Discussion In a large collection of African-American MM patients, we observed a strong association between increasing BMI at age 20 and younger age at diagnosis. A similar trend was observed in men only at 5 years prior to diagnosis, consistent with previous reports. Obesity is one of the few known potentially modifiable risk factors for MM. Younger age at diagnosis reflects an earlier accumulation of either or both genetic and environmental risk factors. Obesity at an early age may influence MM risk through shared biological pathways such as interleukin-6 and insulin-like growth factor, by contributing to chronic B-cell activation, thereby increasing susceptibilty for MM later in life. The significance of the gender difference for the association closer to diagnosis is unclear and requires additional study. Disclosures: Terebelo: Amgen: Honoraria; Millennium: Honoraria. Mehta:Celgene: Speakers Bureau; Millennium: Speakers Bureau. Zonder:Skyline: Consultancy. Orlowski:Bristol-Myers Squibb: Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding; Millennium: The Takeda Oncology Company: Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding; Onyx: Honoraria, Membership on an entity’s Board of Directors or advisory committees, Research Funding; Resverlogix: Research Funding; Array Biopharma: Honoraria, Membership on an entity’s Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity’s Board of Directors or advisory committees; Merck: Membership on an entity’s Board of Directors or advisory committees. Lonial:Celgene Corporation: Consultancy; Millennium: Consultancy; Novartis: Consultancy; Bristol Myers Squibb: Consultancy; Sanofi: Consultancy; Onyx: Consultancy.

Abstract: EVs cause accumulation of activated maternal platelets within the placenta, resulting in a thromboinflammatory response and PE. Activated maternal platelets cause NLRP3-inflammasome activation in trophoblast cells via ATP release and purinergic signaling.

Abstract: Abstract 2367 Hematopoietic stem cell (HSC) Aging is a complex process linked to number of changes in gene expression and functional decline of self-renewal and differentiation potential. While epigenetic changes have been implicated in HSC aging, little direct evidence has been generated. DNA methylation is one of the major underlying mechanisms associated with the regulation of gene expression, but changes in DNA methylation patterns with HSC aging have not been characterized. We hypothesize that revealing the genome-wide DNA methylation and transcriptome signatures will lead to a greater understanding of HSC aging. Here, we report the first genome-scale study of epigenomic dynamics during normal mouse HSC aging. We isolated SP-KSL-CD150+ HSC populations from 4, 12, 24 month-old mouse bone marrow and carried out genome-wide reduced representative bisulfite sequencing (RRBS) and identified aging-associated differentially methylated CpGs. Three biological samples were sequenced from each aging group and we obtained 30–40 million high-quality reads with over 30X total coverage on ∼1.1M CpG sites which gives us adequate statistical power to infer methylation ratios. Bisulfite conversion rate of non-CpG cytosines was 〉 99%. We analyzed a variety of genomic features to find that CpG island promoters, gene bodies, 5'UTRs, and 3'UTRs generally were associated with hypermethylation in aging HSCs. Overall, out of 1,777 differentially methylated CpGs, 92.8% showed age-related hypermethylation and 7.2% showed age-related hypomethylation. Gene ontology analyses have revealed that differentially methylated CpGs were significantly enriched near genes associated with alternative splicing, DNA binding, RNA-binding, transcription regulation, Wnt signaling and pathways in cancer. Most interestingly, over 579 splice variants were detected as candidates for age-related hypermethylation (86%) and hypomethylation (14%) including Dnmt3a, Runx1, Pbx1 and Cdkn2a. To quantify differentially expressed RNA-transcripts across the entire transcriptome, we performed RNA-seq and analyzed exon arrays. The Spearman's correlation between two different methods was good (r=0.80). From exon arrays, we identified 586 genes that were down regulated and 363 gene were up regulated with aging (p 〈 0.001). Most interestingly, overall expression of DNA methyl transferases Dnmt1, Dnmt3a, Dnmt3b were down regulated with aging. We also found that Dnmt3a2, the short isoform of Dnmt3a, which lacks the N-terminal region of Dnmt3a and represents the major isoform in ES cells, is more expressed in young HSC. For the RNA-seq analysis, we focused first on annotated transcripts derived from cloned mRNAs and we found 307 genes were down regulated and 1015 gene were up regulated with aging (p 〈 0.05). Secondly, we sought to identify differentially expressed isoforms and also novel transcribed regions (antisense and novel genes). To characterize the genes showing differential regulation, we analyzed their functional associations and observed that the highest scoring annotation cluster was enriched in genes associated with translation, the immune network and hematopoietic cell lineage. We expect that the results of these experiments will reveal the global effect of DNA methylation on transcript stability and the translational state of target genes. Our findings will lend insight into the molecular mechanisms responsible for the pathologic changes associated with aging in HSCs. Disclosures: No relevant conflicts of interest to declare.

Abstract: Introduction: VEXAS syndrome (vacuoles, E1 ubiquitin ligase, X-linked, autoinflammatory, somatic) is a newly recognized inflammatory disorder caused by somatic mutations in the UBA1 gene. Bone marrows from these patients reveal a range of morphological changes in hematopoietic precursor cells. In this study, we aim to assess the laboratory indices and morphologic spectrum of bone marrow pathology in VEXAS syndrome. Methods: We identified 16 cases of VEXAS syndrome. All cases had confirmed UBA1 mutation. We reviewed bone marrow biopsies corresponding to the date of diagnosis. This study was approved by the Mayo Clinic Institutional Review Board. Results: All patients were male with a median age of 73 years - associated autoimmune disorders included Sweet syndrome, inflammatory arthritis, relapsing polychondritis and granulomatosis with polyangiitis. 14/16 patients had anemia with median hemoglobin of 10.4 (Range: 6.7- 14.1 g/dL). 15/16 had macrocytosis with median MCV 110.4 (Range: 94.8- 123.1 /fL). 5/16 had thrombocytopenia with median platelet count 174 (Range: 20- 500 x10^9/L). 7/16 had leukopenia with median WBC 3.65 (Range: 2.4- 11.6 x10^9/ L). The ESR and CRP medians were 61.0 mm/hr and 81.5 mg/L, respectively. Karyotype was performed in 12 patients of which 11 were normal and the remaining case showed a complex karyotype. An NGS panel targeting the most frequent myeloid disorder associated gene mutations was negative in 10/15 cases. GS for myeloid mutations revealed pathogenic mutations in 5 patients, involving genes TET2 (2/5), DNMT3A (2/5), and TP53 (1/5). Conclusions: Bone marrow findings in VEXAS syndrome, in this series of 16 patients, are individually non-specific, yet when taken altogether in the overtly abnormal cases, are very suggestive when the clinical index of suspicion is high. In such scenarios, the combined clinical and bone marrow findings should prompt discussion and consideration for UBA1 mutation testing given the significant clinical implications for patient management and prognosis. Disclosures Patnaik: StemLine: Research Funding; Kura Oncology: Research Funding. Warrington: Eli Lilly: Research Funding; Kiniksa: Research Funding.