Vachon, Celine M.

Abstract Background: The clinical utility of routine annual mammogram after curative-intent treatment for male breast cancer is uncertain. There is potentially greater value after lumpectomy (as surveillance for ipsilateral recurrence) than mastectomy (as screening for a new contralateral cancer). The goal of this study was to assess real world use of mammography in men during the first year after lumpectomy or mastectomy to treat breast cancer. Methods: Administrative claims data from OptumLabs Data Warehouse (a large US database that includes privately insured patients and Medicare Advantage-insured enrollees from all 50 states and of all ages and ethnic and racial groups) were used to identify men treated with breast surgery for a new diagnosis of breast cancer between 2007 and 2017. We required continuous coverage starting at least 6 months prior to the non-metastatic breast cancer diagnosis and continuing until at least 13 months after the breast surgery. Our primary endpoint was the proportion of patients who had at least one mammogram during the year (13-month period, to allow for scheduling and other logistical delays) after lumpectomy or mastectomy. Univariate and multivariate testing were performed to identify predictors of mammography (with p<0.05 used as the threshold for statistical significance for both). Our secondary endpoint was the proportion with at least one mammogram within 24 months of surgery, performed in a subset who maintained their insurance coverage for at least that duration. Results: The 13-month analysis included 730 men with a median age at diagnosis of 62 years (Range: 25 to 87 years) and a median follow-up duration of 35 months (Range: 13 to 134 months). 209 (29%) of these men underwent mammography within 13 months after surgery. The characteristics of patients who underwent mammography and those who did not are shown in Table 1. Mammography was more likely after lumpectomy than mastectomy (41% vs. 27%) and after radiation therapy (41% vs. 32% in those who did not receive radiation). In a multivariate logistic regression model, more recent diagnosis (2015+) was associated with lower odds of undergoing mammography, while receipt of radiation was associated with higher odds of undergoing mammography. In the subset of patients with two or more years of post-surgery coverage (n=527), the proportion who had at least one mammogram during that 24-month period was 49% after lumpectomy and 40% after mastectomy. Conclusions: In this insured cohort, 73% of men did not undergo mammography within a year after mastectomy, and 59% did not within a year after lumpectomy. Mammography was less likely in patients diagnosed more recently (perhaps due to acknowledgment of the unique aspects of male breast cancer including a relatively low risk of contralateral second primary tumors), and more likely in those who received radiation. These variations in practice likely result from the paucity of evidence-based guidelines for male breast cancer survivorship care. More research is needed pertaining to whether or not mammograms improve clinical outcomes after curative intent treatment for male breast cancer. Table 1: Patient characteristics associated with receipt of mammogram within first 13 months after male breast cancer surgeryUnivariate AnalysisMultivariate AnalysisNo Mammogram (N=521)Mammogram (N=209)P-value, chi-square testOdds Ratio (OR) and 95% CIP-value for ORAge Group:0.1225-4966 (12.7%)32 (15.3%)Reference50-64216 (41.5%)98 (46.9%)0.99 (0.60, 1.63)0.9665-74112 (21.5%)44 (21.1%)0.87 (0.48, 1.57)0.6575+127 (24.4%)35 (16.7%)0.57 (0.30, 1.07)0.08Census Region:0.52Midwest138 (26.5%)59 (28.2%)ReferenceNortheast97 (18.6%)47 (22.5%)1.10 (0.68, 1.78)0.69South223 (42.8%)79 (37.8%)0.74 (0.49, 1.11)0.15West63 (12.1%)24 (11.5%)0.83 (0.47, 1.47)0.51Year of diagnosis:0.072007-2010126 (24.2%)65 (31.1%)Reference2011-2014199 (38.2%)82 (39.2%)0.80 (0.54, 1.20)0.292015+196 (37.6%)62 (29.7%)0.63 (0.41, 0.96)0.03Elixhauser Category:0.250148 (28.4%)69 (33.0%)Reference1-2218 (41.8%)74 (35.4%)0.85 (0.56, 1.28)0.433+155 (29.8%)66 (31.6%)1.18 (0.75, 1.87)0.47Surgery Type:0.005Lumpectomy55 (10.6%)38 (18.2%)ReferenceMastectomy466 (89.4%)171 (81.8%)1.57 (0.97, 2.55)0.07Chemotherapy:0.79No301 (57.8%)123 (58.9%)ReferenceYes220 (42.2%)86 (41.1%)0.79 (0.54, 1.16)0.23Radiation:0.02No355 (68.1%)124 (59.3%)ReferenceYes166 (31.9%)85 (40.7%)1.51 (1.03, 2.20)0.03 Citation Format: Siddhartha Yadav, Lindsey Sangaralingham, Stephanie R. Payne, Karthik V. Giridhar, Tina J. Hieken, Judy C. Boughey, Robert W. Mutter, John R. Hawse, Rafael E. Jimenez, Rachel A. Freedman, Sadia Choudhery, Fergus J. Couch, Celine M. Vachon, Nilay Shah, Roberto A. Leon-Ferre, Kathryn J. Ruddy. Surveillance mammography after treatment for male breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-17-08.

1002 Background: Physical activity is a modifiable breast cancer risk factor, perhaps mediating risk reduction through regulation of estrogen metabolism. Evidence regarding effect of physical activity is conflicting partly because breast cancer is a heterogenous constellation of different tumor subtypes with differing etiologies. No prospective study has examined the relationship between physical activity and breast cancer incidence based on ER/PR status or histological subtype. Objective: Examine effect of physical activity on breast cancer incidence based on ER/PR status and histological subtypes of breast cancer. Methods: The Iowa Women’s Health Study is a prospective cohort study of postmenopausal women (N=41,837). Physical activity was self-reported on baseline questionnaire, and three levels (high, medium and low) were defined. Breast cancer incidence, histologic subtype and ER/PR status, through 18 years of follow-up, were ascertained by linkage with the Iowa SEER Cancer Registry. Cox proportional hazards models were used to estimate multivariate relative risks (RRs) and 95% confidence intervals (CIs) of breast cancer, adjusting for other breast cancer risk factors. Results: During 554,819 person-years of follow-up, 2548 incident cases of breast cancer were observed. High physical activity was associated with decreased risk for breast cancer (RR 0.91, 95 % CI 0.81–1.01) compared to low activity. The protective effect was most marked in ER+/PR− (RR 0.66, CI 0.46–0.94), intermediate in ER−/PR− (RR 0.80, CI 0.56–1.15), weakest in ER+/PR+ (RR 0.94, CI 0.81–1.08), and elevated in ER-/PR+ (RR 1.42, CI 0.67–3.01) tumors. Higher physical activity was also associated with a decreased risk of invasive ductal/lobular carcinoma (RR 0.90, CI 0.80–1.02), but not with invasive breast cancer with a favorable histology (RR 1.19, CI 0.78–1.81). Conclusions: Higher physical activity was associated with a 10% decreased risk of breast cancer. Unexpectedly, risk reduction was most marked in PR- tumors, particularly ER+/PR-, and the more aggressive histologic forms. Further studies are needed to confirm these findings, and also evaluate other risk factors based on ER/PR status and histological subtypes. No significant financial relationships to disclose.

AbstractPrevious transcriptome‐wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome‐wide association studies (GWAS), but analyses of breast cancer subtype‐specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta‐analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER−). We further compared associations with ER+ and ER− subtypes, using a case‐only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER– breast cancer. We further identified 30 TWAS‐significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast‐cancer gene in three of six regions harboring multiple TWAS‐significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER− breast cancer.

Background The CLL international prognostic index (CLL-IPI) is a validated staging system for newly diagnosed CLL patients that combines age, clinical stage, serum Beta-2 microglobulin, TP53, and IGHV mutation status into a single score and stratifies CLL patients into four prognostic risk groups with a c-statistic=0.72. Next-generation sequencing identified ~60 putative driver genes recurrently mutated in CLL, and genome-wide association studies (GWAS) identified 41 single nucleotide polymorphisms (SNPs) associated with CLL risk. We previously showed that a polygenic risk score (PRS) of the weighted average of the number of risk alleles among these 41 SNPs is associated with risk of both CLL and MBL. We and others have also shown that the tumor mutational load (TML), i.e., the total number of somatically mutated driver genes, is associated with time to first treatment (TTT). Herein, we examine whether TML and PRS add prognostic value beyond CLL-IPI for disease progression in a cohort of CLL and high-count MBLs. Methods We used the Mayo Clinic CLL resource to identify newly diagnosed (<2 years) previously untreated CLL and high-count MBL patients based on the 2008 International Workshop CLL (IWCLL) criteria . We extracted DNA from CD5+/CD19+ clonal B-cells and sequenced the coding regions of 59 recurrently mutated CLL driver genes using a custom SureSelect panel and Illumina HiSeq 4000. The median coverage depth per sample was 2737X. Somatic mutations were called using MuTect2 in tumor-only mode. Germline variants were eliminated based on minor allele frequencies >0.01%, and by those identified in 1000 Genomes Project, ExAC and/or ESP6500 databases, unless present in known mutation hotspots or COSMIC. After filtering high/moderate impact mutations, we calculated the TML. Peripheral blood DNA was genotyped on Illumina Omni Express array; after extensive quality control metrics, we calculated the germline PRS as the weighted average of the number of risk alleles among the CLL susceptibility SNPs. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) to test for associations with TTT. We analyzed the low/intermediate risk groups and the high/very high risk groups in stratified analyses and tested for interactions between CLL-IPI and TML or PRS. Survival curves are displayed using the Kaplan-Meier method using P-values from the log-rank test; the PRS is displayed by tertile categories. Results Of 166 patients (120 CLL, 46 MBL), 67% were male and the median age at diagnosis was 62 years (range: 28-85). The median follow-up was 5.3 years, and 73 were treated for progressive disease as defined by the 2018 IWCLL criteria. Low risk CLL-IPI comprised 51%, intermediate risk 27%, high risk 18%, and very-high risk 4%. The most commonly somatically mutated genes were TP53 (8%), CHD2 (8%), BIRC3 (8%), SF3B1 (7%), and NOTCH1 (7%). We found that 42%, 32%, and 26% of patients had 0, 1, or 2+ genes with somatic mutations, respectively. The median PRS was 8.3 (range: 6.1-11.3) which is consistent with our prior PRS study on CLL/MBL and higher than 7.53 found in 7983 controls from our prior work. When we modeled TML, PRS, sex, CLL/MBL status and CLL-IPI together with the joint effects of CLL-IPI with TML and PRS, both interactions were significant in the model (P=0.008 for TML*CLL-IPI and P=0.014 for PRS*CLL-IPI), with c-statistic=0.81 (CI:0.77-0.85). When we stratified by CLL-IPI, TML was associated with shorter TTT in the low/intermediate risk (N=129, continuous HR=2.36 per TML category, CI: 1.61-3.47, P=1.3x10-5, Figure 1.A), but there was no evidence of an association in the high/very-high risk (N=37, continuous HR=1.12 per TML category, CI: 0.70-1. 78, P=0.64, Figure 1.B), after adjusting for PRS, sex, and CLL/MBL status. However, the PRS was associated with a shorter TTT in the high/very-high risk (continuous HR=1.77 per SD, CI: 1.00-3.12, P=0.048, Figure 1.D), with no evidence of an association in the low/intermediate risk (continuous HR=0.95 per SD, CI: 0.95-1.29, P=0.74, Figure 1.C), after adjusting for TML, sex, and CLL/MBL status. Conclusions In our study, TML was prognostic among the low/intermediate CLL-IPI risk groups while the PRS was prognostic among the high/very-high CLL-IPI risk groups. Sequencing CLL driver genes and genotyping relevant germline SNPs at time of diagnosis may provide incremental value over the CLL-IPI in predicting TTT among patients with CLL/MBL. Disclosures Parikh: MorphoSys: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; Janssen: Research Funding; Ascentage Pharma: Research Funding; Acerta Pharma: Research Funding. Ding:Merck: Research Funding; DTRM Biopharma: Research Funding. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Kay:MorphoSys: Other: Data Safety Monitoring Board; Infinity Pharmaceuticals: Other: DSMB; Celgene: Other: Data Safety Monitoring Board; Agios: Other: DSMB.

Abstract Background: The link between modifiable breast cancer risk factors and tumor genomic alterations remains largely unexplored. We evaluated the association of prediagnostic body mass index (BMI), cigarette smoking, and alcohol consumption with somatic copy number variation (SCNV), total somatic mutation burden (TSMB), seven single base substitution (SBS) signatures (SBS1, SBS2, SBS3, SBS5, SBS13, SBS29, and SBS30), and nine driver mutations (CDH1, GATA3, KMT2C, MAP2K4, MAP3K1, NCOR1, PIK3CA, RUNX1, and TP53) in a subset of The Cancer Genome Atlas (TCGA). Methods: Clinical and genomic data were retrieved from the TCGA database. Risk factor information was collected from four TCGA sites (n = 219 women), including BMI (1 year before diagnosis), cigarette smoking (smokers/nonsmokers), and alcohol consumption (current drinkers/nondrinkers). Multivariable regression analyses were conducted in all tumors and stratified according to estrogen receptor (ER) status. Results: Increasing BMI was associated with increasing SCNV in all women (P = 0.039) and among women with ER− tumors (P = 0.031). Smokers had higher SCNV and TSMB versus nonsmokers (P < 0.05 all women). Alcohol drinkers had higher SCNV versus nondrinkers (P < 0.05 all women and among women with ER+ tumors). SBS3 (defective homologous recombination-based repair) was exclusively found in alcohol drinkers with ER− disease. GATA3 mutation was more likely to occur in women with higher BMI. No association was significant after multiple testing correction. Conclusions: This study provides preliminary evidence that BMI, cigarette smoking, and alcohol consumption can influence breast tumor biology, in particular, DNA alterations. Impact: This study demonstrates a link between modifiable breast cancer risk factors and tumor genomic alterations.

Background Genome-wide association studies (GWAS) conducted in populations of European ancestry (EA) have identified and confirmed 23 germline susceptibility loci for multiple myeloma (MM). The effect sizes of single nucleotide polymorphisms (SNPs) at these loci are small, therefore combining them into a single summary measure, known as a polygenic risk score (PRS), may provide a more meaningful risk factor. We have previously shown a PRS comprised of the 23 SNPs for MM contributes to increased risk of MM, with a 2.7-fold increase for highest vs. lowest PRS quintiles. Whether the MM-PRS is also associated with overall survival (OS) in MM cases has not been evaluated. We examined the association between MM-PRS and OS in two EA studies. Methods The first study consisted of 2,179 EA MM cases from ten studies included in the Multiple Myeloma Working Group within the International Lymphoma Consortium (InterLymph). Cases were diagnosed between 1970 and 2015 and genotyped using multiple platforms (Oncoarray, Affymetrix, Human660W-quad Beadchip, and Illumina arrays); 885 cases also had stage [based on International Staging System (ISS)] available. Each of the GWAS was subjected to rigorous standard quality control independently (prior to imputation via the Michigan imputation server based on the Haplotype Reference Consortium (HRC). The second study consisted of 515 newly diagnosed EA MM cases from CoMMpass (Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile), diagnosed from 2011-2013, who had whole genome sequencing (WGS) performed on germline DNA. The WGS data was used to call common germline genetic variants through the Mayo Clinic bioinformatics pipeline. Briefly, genetic variants were detected with GenomeGPS, aligned to the hg19 reference genome, called using the GATK (V3.6) Haplotype Caller, and merged for multiple-sample joint calling. To reduce the false positive variants, variant quality score recalibration (VQSR) was applied for both SNPs and INDELs. After quality control, 458 EA samples remained. Follow-up was available for both studies and consisted of time from MM diagnosis date until death or date of last known follow-up. The PRS was constructed from the 23 MM SNPs using the published per allele odds ratio associated with MM risk. The published log odds ratios for each SNP were multiplied by the number of risk alleles (0, 1, 2) for the corresponding SNP, and summed, resulting in a unique score per person. Kaplan-Meier curves and Cox proportional hazard models were used to assess the association between PRS with MM OS considering two models: 1) adjusted for age, sex, study and 2) additional adjustment by stage (ISS). Hazard ratios (OR) and 95% confidence intervals (CI) were estimated. The PRS was evaluated both as a continuous variable, per standard deviation (SD), and as a categorical variable (quintiles). Results MM cases (N=2,179) in the InterLymph study were 59% male and 41% female and the median age was 61.0 years (26-90 years). Median follow-up time was 57.2 months (1.0-509.0 months) with 868 reported deaths. MM cases with stage information available consisted of 20% stage I (n=178), 53% stage II (n=466), and 27% stage III (n=241). No association was observed between PRS and OS in MM patients regardless of adjustment for stage (continuous PRS (HR: 1.03, 95% CI: 0.83-1.28, P=0.80) or by quintile PRS (p>0.05)) (Table). The CoMMpass EA MM cases (n=458) had similar distributions for sex (61% male and 39% females) but were slightly older 65 years (27-93 years) and had shorter follow-up time (median=39.75 months (0.13-77.2)) with 117 deaths. Stage was available for 96% of CoMMpass cases including 36% stage I (n=159), 33% stage II (n=146), and 31% stage III (n=134). We also observed no association of PRS and OS in the CoMMpass study (HR=1.02, 95% CI: 0.72 -1.46, P= 0.89), adjusted for age, sex, and stage (Table). Discussion A PRS score for MM risk is not associated with OS for MM cases in two EA populations. Given that prior studies have shown association of genetic variation with MM survival, efforts to identify additional loci associated with OS or MM specific survival are warranted. Future studies should also consider germline variants impact on molecular subtypes, specific therapies, and outcomes. Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Background: Multiple myeloma (MM) treatment has advanced considerably with proteasome inhibitors, immunomodulatory drugs (IMiDs), and, most recently, monoclonal antibodies. However, treatment response is quite heterogeneous, and many patients still progress even with novel combination therapies. Thus, understanding the factors that underlie response to treatment is a priority. Several somatic genomic aberrations and mutations predict poor response to therapy. However, germline variation has not previously been investigated as a predictor of treatment response in MM. We used genome-wide association and transcriptome-wide association approaches to identify germline genetic predictors of treatment response in MM. Methods: We included 510 MM patients from Mayo Clinic and 324 MM patients from UCSF diagnosed from 1999-2015. Basic demographics, laboratory values and pathology at diagnosis, type of initial therapy, duration of therapy, and follow-up were ascertained by chart review. Patients were grouped into categories of treatment based on their first line therapy: proteasome inhibitor-based regimen, IMiD based regimen, combination proteasome and IMiD based regimen, or other. Response was assessed using International Myeloma Working Group (IMWG) response criteria after 4-6 cycles of induction. As such, response was categorized into progressive disease (PD), minimal response (MR), partial response (PR), very good partial response (VGPR), or complete response (CR). Germline samples were genotyped using Illumina or Affymetrix arrays and were imputed based on the Haplotype Reference Consortium (HRC). For the genome-wide association study (GWAS), we included all SNPs with minor allele frequency >0.01 and imputation r2 of >0.5. We tested each SNP for association with treatment response using a linear regression model that adjusted for age, gender, and genetic ancestry (from principal components analysis (PCA)). To perform the transcriptome-wide association study (TWAS), we calculated predicted gene expression data using PREDIXCAN software on a reference cohort of 922 individuals with genotype and RNA expression data from peripheral blood. We then tested the association between predicted gene expression and response to therapy using linear regression models. Both the GWAS and TWAS were performed on subgroups of patients who received either proteasome inhibitors or IMiD therapies. The analyses of patients on proteasome inhibitors were adjusted for IMiD use as a covariate and analyses of patients on IMiDs were adjusted for proteasome inhibitor use. The threshold for genome-wide significance loci was set at P = 5 x 10-8 and the threshold for suggestive loci was set at P = 10-6. The threshold for significance for TWAS was set at P = 4 x 10-6 by using a Bonferroni correction for the number of genes for which genetic models of expression could be developed by PREDIXCAN. Results: Overall, 42.7% (59 of 138) of patients on proteasome inhibitors alone, 32.5% (66 of 203) of patients on IMiDs alone, and 58.1% (50 of 86) of patients on combination achieved at least a VGPR. There were no significant differences in response across centers in analyses that adjusted for age, sex and types of therapy. There were no genome wide significant loci to predict for response. We identified 8 suggestive SNPs associated with proteasome inhibitor response and 4 suggestive SNPs associated with IMiD response. TWAS identified ZNF622 as a candidate genetic modifier of proteasome inhibitor effect that was significant after correction for multiple hypothesis testing (P = 1.6 x 10-6). Higher genetically predicted expression was associated with improved response to proteasome inhibitor therapy. Among patients above the median of predicted expression of ZNF622 on proteasome inhibitors, 62.6% achieved at least VGPR; among patients at or below the median of expression, only 37.6% achieved at least VGPR. Conclusions: We identified an association between predicted expression of ZNF622 and clinical response to proteasome inhibitor therapy among MM patients. ZNF622 is a zinc finger binding protein which is known to be co-activator of B Myb activity and to affect apoptosis in response to oxidative stress. Our work highlights the potential importance of pharmacogenetic modifiers of treatment response in MM. Disclosures Shah: Nkarta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Indapta Therapeutics: Equity Ownership; University of California, San Francisco: Employment; Celgene, Janssen, Bluebird Bio, Sutro Biopharma: Research Funding; Genentech, Seattle Genetics, Oncopeptides, Karoypharm, Surface Oncology, Precision biosciences GSK, Nektar, Amgen, Indapta Therapeutics, Sanofi: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Poseida: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees; Teneobio: Consultancy, Membership on an entity's Board of Directors or advisory committees. Wong:Celgene: Research Funding; Janssen: Research Funding; Roche: Research Funding; Fortis: Research Funding. Martin:Roche and Juno: Consultancy; Amgen, Sanofi, Seattle Genetics: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding.

Purpose: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the US. Epidemiological studies demonstrate a 2-3 fold higher incidence of the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) and MM with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). With equal access to care, AAs have better overall survival compared to EAs (Waxman, Blood, 2010). This disparity may be explained by ancestral-associated genetic predisposition of AAs to development of monoclonal gammopathies and to specific acquired, cytogenetically-defined subtypes. Using calculated genetic ancestry data, we have previously identified a higher prevalence of IgH translocations t(11;14), t(14;16) and t(14;20) in individuals with >80% African ancestry (Baughn, BCJ, 2018). Since SNP rs9344 encoding the CCND1 870G>A polymorphism has been reported in association with increased risk of t(11;14) (Weinhold, Nature Genetics, 2013), we investigated whether rs9344 correlates with African ancestry and with t(11;14) in our cohort of patients with plasma cell dyscrasias. Methods: We studied 898 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014). Plasma cell proliferative disorder FISH of immunoglobulin (cIg)-stained positive plasma cells was performed as described (Baughn, BCJ, 2018). Individuals were divided into three ancestral groups: 1. EAs (<0.1% African ancestry and <30% Asian ancestry); 2. AAs (> 80% African ancestry); and 3. Other. Chi-squared test was used to determine the overall comparison between the 3 ancestral groups and also between ancestral groups 1 vs 2, and 2 vs 3 using pairwise comparison. All tests were two-sided with alpha level set at 0.05 for overall statistical significance. Pairwise comparison was considered statistically significant when p<0.025 based on Bonferroni method for multiple comparisons. Results: We identify increased risk of development of either a t(11;14), t(14;16) or t(14;20) in AAs (48.8%) compared to EAs (33.6%) (p-value=0.0051). To explore the genetic basis of increased t(11;14) specifically (37.4% AAs vs. 27.3% p-value=0.049), we evaluated the frequency of the G risk allele of rs9344 in relation to African ancestry. The frequency of the G risk allele was higher in AAs (0.81) compared to EAs (0.59) (p-value <0.0001) and also higher in t(11;14) cases (0.73) compared to non-t(11;14) controls (0.58) (p-value <0.0001). A multivariate model identified only rs9344 as significantly associated with t(11;14) after adjusting for age, gender and race group suggesting that it plays a role in the development of t(11;14) (p-value <0.001 for GG, p-value=0.005 for AG). To test if these results are replicable, we studied the MMRF CoMMpass cohort. This cohort includes individuals with newly diagnosed MM along with self-report race information and translocation data from long insert whole genome sequencing. Although t(11;14) was not enriched in self-reported Black individuals from this cohort (17.5% Black vs. 20.4% White, p-value=0.47), there was association between the rs9344 and self-reported Black race (p-value <0.0001) and with the presence of t(11;14) when the t(11;14) was analyzed by long insert whole genome sequencing (p-value=0.0001). Conclusions: To our knowledge, this study includes the largest group of African individuals with an abnormal plasma cell clone along with uniformly collected FISH, genotyping and ancestry data. We have identified in this diverse population the association of the CCND1 870G>A polymorphism (rs9344) with African ancestry and with t(11;14) suggesting that it plays a role in the development of t(11;14) plasma cell dyscrasias. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Research Funding.

Abstract Introduction Multiple myeloma (MM) is a result of a malignant transformation of plasma cells that is preceded by the presence of an asymptomatic clonal plasma cell expansion, a condition referred to as monoclonal gammopathy of undetermined significance (MGUS). We and others have shown familial aggregation of MM and MGUS. Evidence from epidemiologic, family and genome-wide association studies (GWAS) suggests a genetic component underlying MM etiology. GWAS have successfully established 17 common genetic risk loci for MM to date and recently, rare inherited susceptibility variants in the LSD1 / KDM1A and USP45 genes were identified in familial MM / MGUS kindreds. Family-based approaches may be used to elucidate genetic variation contributing to familial MM. Genetic linkage analysis has historically been used to detect the chromosomal location of disease genes. The objective of this study was to conduct a linkage analysis of MM / MGUS families to identify genomic regions for MM / MGUS. Methods Linkage analysis was performed on whole exome sequencing (WES) data generated from germline DNA extracted from peripheral blood from MM / MGUS families ascertained at four sites, of which 79 were selected with 2 or more affected members with any combination of MM or MGUS. Whole exome capture was performed using Agilent SureSelect 38 Mb paired end sequencing and ran on Illumina HiSeq 2000s/2500s; standard analyses for alignment (GRCh37) and quality control were conducted. The Genome Analysis Toolkit's (GATK) HaplotypeCaller in per-sample mode was used to jointly call all the samples together. Quality control included removing variants that had <75% call rate, <8x coverage, or minor allele frequency <0.01 (1KGenomes). The jointly called WES data from the 79 families consisted of 230 individuals, including 119 MM, 93 MGUS and 18 unaffected relatives. Additional early onset (defined as age <60) MM cases (n=400), sporadic MM cases (n=879) and controls (n=2389) were also included in the joint calling to be used for follow-up of linkage regions. We included independent variants defined as linkage disequilibrium (LD) r2 <0.05 using both PLINK 1.07's LD based variant pruner and also MERLIN's pairwise r2 cluster marker-marker modeling to ensure variant independence. After quality control and LD filtering: 12,946 variants remained. We conducted multipoint linkage analysis using MERLIN (Multipoint Engine for Rapid Likelihood Inference using the Lander-Green approach) and considered both non-parametric and parametric (dominant and recessive) models to test for co-segregation of chromosomal regions with MM / MGUS. A logarithm of the odds (LOD) score greater than 3.3 was significant evidence for linkage. Within the same families, we interrogated any suggestive regions by performing gene-level association tests using the PEDGENE R package to calculate burden and kernel statistics, accounting for family relationships. Follow-up of these genes in the early onset and sporadic MM cases and controls and interrogation of single variants driving the associations of these genes is ongoing. Results Among the 79 families, 28 consisted of two or more MM, 41 had two or more MM and MGUS cases and 10 had two or more MGUS. Analyses of the 79 MM / MGUS families identified chromosome 6 at 123420001-149070000 BP region, with evidence for linkage by the non-parametric model (LOD score=3.3) and supportive evidence for linkage by the dominant parametric model (LOD=2.5) (Figure). The chromosome 6 region, which is outside the HLA-region, contains a total of 72 genes. Using gene-level association tests, we identified four genes within the region (MOXD1, TRDN, ADAT2, LTV1) that were associated within MM / MGUS in the family cases (p<0.05). Discussion We found evidence for a locus on chromosome 6 linked to MM / MGUS. Four genes in this region may be associated with MM / MGUS in these families. Follow-up of these genes/variants in high-risk individuals (early-onset / age <60), sporadic cases and controls will help elucidate genetic mechanisms contributing to familial MM / MGUS. In addition, we demonstrate the benefit of applying a linkage analysis framework to familial WES data for discovery of genomic regions influencing MM / MGUS. Disclosures Dumontet: Roche: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria; Sanofi: Honoraria. Kumar:AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Roche: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding.