Waller, Rosalie

Abstract Low count (LC) MBL is characterized by a circulating population of clonal B-cells and is a precursor state to CLL. Most individuals with LC MBL do not progress to CLL and never come to clinical attention. An active area of research is to identify factors that distinguish those individuals with MBL who progress from those who do not. Currently, 41 single nucleotide polymorphisms (SNPs) are known to be associated with CLL risk, and a portion of these SNPs have also been found to be associated with MBL risk. We hypothesize that these CLL-susceptibility SNPs associated with both MBL and CLL risk are SNPs associated with developing a B-cell clone; whereas the remaining SNPs found not to be associated with MBL risk are associated with progression of the B-cell clone (i.e., progression from MBL to CLL). To test this hypothesis, we evaluated the association of these 41 SNPs with risk of progression to CLL using a case-control study of 1,911 CLL cases and 1,579 individuals with MBL from Mayo Clinic. Individuals with MBL were identified through screening using 8-color flow cytometry assay. Individuals with LC MBL (defined by having a clonal B-cell population with the percent clonal B-cells out of total B-cell count <85%) who had an immunophenotype consistent with CLL were included as controls. CLL cases were obtained through the Mayo Clinic hematology practice. DNA was genotyped from whole blood. We analyzed the 41 CLL susceptibility SNPs with risk of progression to CLL using logistic regression and estimated odds ratio (OR) and 95% confidence interval (CI), adjusting for sex and age. We evaluated the CLL polygenic risk score (CLL-PRS), comprised of a weighted average of the 41 SNPs, with risk of CLL. Out of the 41 established CLL susceptibility SNPs, 15 were associated with CLL progression from MBL to CLL (all P<0.05). Nine of these 15 significant SNPs were not previously found to be associated with risk of MBL. The most significant SNPs were rs9880772 (OR=1.23, CI: 1.11-1.36, P=6.1 × 10−5), rs888096 (OR=1.25, CI: 1.12-1.38, P=3.0 × 10−5), and rs305065 (OR=1.28, CI: 1.14-1.43, P=1.5 × 10−5). One SNP (rs77551289) had a suggestive association (OR=1.21, P=0.069). The remaining 25 SNPs had no evidence of association with progression (ORs ranging between 0.9-1.1 and all P>0.1). The CLL-PRS was also associated with CLL risk (continuous OR = 1.25, CI: 1.15 - 1.35, P = 6.7 × 10−8). In our study accounting for the presence of B-cell clonal population, we identified 15 out of 41 CLL-susceptibility SNPs to be significantly associated with progression from LC MBL to CLL. The remaining 25 non-significant CLL-SNPs were not associated with progression and may instead be associated with development or initiation of the B-cell clone. These findings suggest that the established CLL susceptibility SNPs may play differing role (i.e., development or progression of the clone) in the etiology of CLL. Citation Format: Raphael Mwangi, Geffen Kleinstern, Sara J. Achenbach, Dennis Robinson, Aaron D. Norman, Kari G. Rabe, Janet E. Olson, Neil E. Kay, Rosalie G. Waller, Nicholas J. Boddicker, James R. Cerhan, Esteban Braggio, Sameer A. Parikh, Curtis A. Hanson, Celine M. Vachon, Tait Shanafelt, Susan L. Slager. Genetic variants associated with progression from monoclonal B-cell lymphocytosis (MBL) to chronic lymphocytic leukemia (CLL). [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5222.

Abstract Diffuse large B-cell lymphoma (DLBCL) is clinically and biologically heterogenous. Based on gene expression profiling of the tumor, DLBCL is classified into two major molecular subtypes linked to cell-of-origin (COO): germinal center (GCB) and activated B-Cell (ABC). The GCB subtype is characterized by a high prevalence of somatic mutations, particularly in apoptosis genes (e.g., BCL2) which is characteristic of follicular lymphoma (FL, another GCB lymphoma). The development of the validated NanoString gene expression test as well as immunohistochemistry markers that work in formalin-fixed, paraffin-embedded tissue allows for the classification of DLBCL by COO in epidemiological studies. We hypothesized that integration of COO with germline genetics could provide insight into the underlying biology driving this heterogeneity, as well as insight into putative function of germline risk variants. Prior genome wide association studies (GWAS) have identified 7 SNPs in 6 susceptibility loci for DLBCL, including: 2p23.3 (rs79480871 NCOA1), 3q13.33 (rs9831894 CD86), 3p24.1 (rs6773363 EOMES), 6p21.33 (rs2523607 HLA-B), 6p25.3 (rs116446171 EXOC2), and 8q24.21 (rs13255292 and rs4733601 PVT1 and MYC). Two of these loci were also identified in FL GWAS (different lead SNPs): 6p21.33 (rs3130437) and 8q24.21 (rs13254990 PVT1). Additional published FL susceptibility loci include: 3q28 (rs6444305 LPP), 6p21.32 (rs9268839), 11q23.3 (rs4938573 CXCR5), 11q24.3 (rs4937362 ETS1), and 18q21.33 (rs17749561 BCL2). Here we evaluated whether the published DLBCL/FL loci differ by COO molecular subtypes. We evaluated each of the 14 DLBCL/FL SNPs by case-case (GCB vs. nonGCB DLBCL) and polytomous case-control (GCB, nonGCB, and FL vs. controls) analyses. DLBCL cases were classified into GCB vs. nonGCB based on best available data from NanoString, RNAseq, followed by the Hans algorithm. A total of 778 DLBCL cases (470 GCB and 308 nonGCB), 1,050 FL cases, and 1,383 controls were analyzed. Odds ratios (OR) based on ordinal encoding of each SNP and 95% CI were calculated for each of the comparison groups. The ORs for four GWAS SNPs were significantly elevated in GCB vs. nonGCB DLBCL as well as in FL; two were FL GWAS SNPs from the HLA region (6p21.32 and 6p21.33) and the other two (BCL2 and ETS1) are important in the germinal center program. In contrast, one SNP (EXOC2) was significantly elevated in nonGCB-DLBCL and was not associated with GCB-DLBCL or FL; this SNP is also near IRF4 (MUM1), a gene important in the ABC program. For the remaining SNPs, there were suggestive associations for GCB-DLBCL and FL versus nonGCB DLBCL (CD86, CXCR5); nonGCB-DLBCL vs. GCB-DLBCL and FL (NCOA1, PVT1); and associations that showed no pattern with COO (EOMES, LPP, HLA-B and MYC). These findings provide insight into the shared pathogenesis of FL and GCB-DLBCL to inform etiologic mechanisms and risk assessment. Citation Format: Rosalie Griffin Waller, Dennis P. Robinson, Anne J. Novak, Lisa M. Rimsza, Kerstin Wenzl, Rebecca L. King, Andrew L. Feldman, Matthew J. Maurer, Grzegorz S. Nowakowski, Brian K. Link, Thomas M. Habermann, Susan L. Slager, James R. Cerhan. Etiologic heterogeneity of genetic risk for DLBCL cell-of origin molecular subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1181.

AbstractPleiotropy, which consists of a single gene or allelic variant affecting multiple unrelated traits, is common across cancers, with evidence for genome‐wide significant loci shared across cancer and noncancer traits. This feature is particularly relevant in multiple myeloma (MM) because several susceptibility loci that have been identified to date are pleiotropic. Therefore, the aim of this study was to identify novel pleiotropic variants involved in MM risk using 28 684 independent single nucleotide polymorphisms (SNPs) from GWAS Catalog that reached a significant association (P < 5 × 10−8) with their respective trait. The selected SNPs were analyzed in 2434 MM cases and 3446 controls from the International Lymphoma Epidemiology Consortium (InterLymph). The 10 SNPs showing the strongest associations with MM risk in InterLymph were selected for replication in an independent set of 1955 MM cases and 1549 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and 418 MM cases and 147 282 controls from the FinnGen project. The combined analysis of the three studies identified an association between DNAJB4‐rs34517439‐A and an increased risk of developing MM (OR = 1.22, 95%CI 1.13‐1.32, P = 4.81 × 10−7). rs34517439‐A is associated with a modified expression of the FUBP1 gene, which encodes a multifunctional DNA and RNA‐binding protein that it was observed to influence the regulation of various genes involved in cell cycle regulation, among which various oncogenes and oncosuppressors. In conclusion, with a pleiotropic scan approach we identified DNAJB4‐rs34517439 as a potentially novel MM risk locus.

Abstract Introduction: TP53 aberrations, including mutations and deletion of 17p (del17p), are important adverse prognostic markers in chronic lymphocytic leukemia (CLL). Prevalence of TP53 aberrations ranges from 7-11% in untreated CLL and increases with disease progression and treatment. Among CLL patients with TP53 aberrations, co-occurrence of TP53 mutations with del(17p) is common. CLL patients with TP53 mutations or del(17p) have significantly worse outcomes when compared to wild-type patients. Previous studies, focusing on CLL patients at time of treatment, are mixed as to whether a single or more than one TP53 aberration impacts outcomes. TP53 is less well studied in monoclonal B-cell lymphocytosis (MBL), an asymptomatic pre-malignant state of CLL. Del(17p) occurs in 3-4% of MBL individuals, and a study of 54 MBL individuals reported a 2% mutation frequency in TP53. Here we estimated prevalence and evaluated the impact of TP53 aberrations in a large cohort MBL or untreated CLL individuals. Methods : Patients with CLL or MBL diagnosed between 2000 and 2019 from the Mayo Clinic CLL Resource with pre-treatment peripheral blood mononuclear cells (PBMC) collected within two years of diagnosis were considered. DNA was extracted from PBMCs with purity &gt;80% or with sorted CD5+/CD19+ clonal cells. The entire coding regions of 59 CLL driver genes were paired end sequenced. Median coverage depth was &gt;1000x per nucleotide, allowing for detection of mutations with variant allelic fraction (VAF) as low as 1%. Somatic mutations were called using MuTect2 in tumor-only mode, and high impact mutations (frameshift, nonsense, and splicing variants) and missense mutations in CLL hot spots were selected. Somatic mutations and FISH del(17p) were used to define TP53 state for each patient: 1) wild-type [no TP53 mutations and normal del(17p)], 2) single-hit [one TP53 mutation or del(17p)], or 3) multi-hit [multiple TP53 mutations or TP53 mutation and del(17p)]. Time to first treatment (TTFT) and overall survival (OS) were analyzed by TP53 state. TTFT and OS were defined as time from sample collection to first treatment or death, respectively, or last follow-up date. Median TTFT and OS was estimated by the Kaplan-Meier method. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals for TTFT and OS associations. The models were adjusted for known adverse prognostic factors including clinical diagnosis (CLL or MBL), age at diagnosis, Rai stage, b2 microglobulin, and IGHV mutation status. Results: Individuals with CLL (N=597) or MBL (N=285) were analyzed for prevalence of TP53 mutations and del(17p). We found 58 CLL patients (9.7%) and 15 MBL individuals (5.3%) had TP53 mutations. The median VAF in CLL was 30.9% (&lt;10% VAF, n=21) and in MBL was 13.5% (&lt;10% VAF, n=7). Del(17p) was present in 37 patients with CLL (6.2%) and 7 individuals with MBL (2.7%). In total, 86 CLL/MBL patients had a TP53 mutation and/or del(17p): 56.8% (48 patients) were single-hit and 44.2% (38 patients) were multi-hit (Fig. 1a). Patients with any TP53 aberration had shorter TTFT than wild-type patients (median 2.3 vs 9.4 years). Among patients with TP53 aberrations, median TTFT was shorter in multi-hit patients (20 events, 1.8 years) compared to single-hit patients (24 events, 3.2 years) (Fig. 1b). In Cox regression, single-hit (HR = 1.7 [1.1-2.6]) and multi-hit (HR = 1.8 [1.1-2.9]) patients had shorter TTFT compared to wild-type patients after adjusting for covariates (Fig. 1c). Multi-hit patients also had shorter OS compared to wild-type patients, while OS in single-hit patients did not significantly differ from wild-type patients (Fig. 1d). Median OS was 5.5 years in multi-hit patients (22 deaths) compared to 15.1 years in wild-type patients (196 deaths) and 14.3 years in single-hit patients (15 deaths). In the OS model adjusted for covariates, multi-hit patients had a significant increased risk (HR = 2.6 [1.6-4.1]), but single-hit patients did not (HR = 1.4 [0.8-2.5]) compared with wild-type patients (Fig. 1e). OS HRs remained stable after censoring at time of treatment. Both TTFT and OS HRs were consistent when mutations with VAF &lt; 10% were excluded. Conclusions: This study suggests single versus multi-hit TP53 aberrations may be important for prognostic outcomes in untreated CLL and MBL patients. Prognostic metrics may need to consider single versus multi-hit TP53 aberrations and include TP53 mutations with low VAF. Figure 1 Figure 1. Disclosures Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Parikh: Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding; Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees. Kay: MEI Pharma: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction: Chronic lymphocytic leukemia (CLL) is characterized by multiple copy number alterations (CNA) and mutations that are central to disease pathogenesis, prognosis, risk-stratification, and identification of response or resistance to therapies. Fluorescence in situ hybridization (FISH) is gold standard in the clinical laboratory for detecting prognostic CNAs in CLL (e.g. deletion 17p13 (del(17p), deletion 11q23 (del(11q), deletion 13q14 (del(13q), and trisomy 12). Most clinical FISH assays have high specificity and sensitivity, but the technique can detect a limited number of alterations per assay. Importantly, next-generation sequencing (NGS) techniques have become more readily available for clinical applications and are increasingly being used for screening not only mutations, but also copy number abnormalities in multiple genes and chromosomal regions of interest in hematologic malignancies. Here, we evaluated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using a custom targeted NGS assay for detecting common prognostic chromosomal alterations in CLL and high-count monoclonal B-cell lymphocytosis (MBL), the precursor to CLL. Methods : We designed a SureSelect DNA targeted sequencing panel, covering all exons of 59 recurrently CLL mutated genes and additional amplicons across regions affected by clinically relevant CNAs. All CLL (N=534) and MBL (N=162) patients had pre-treatment peripheral blood mononuclear cells (PBMC) collected within two years of diagnosis. DNA was extracted in cases with purity &gt;80% of CD5+/CD19+ cells. Clinical FISH data was available within 100 days of NGS in all untreated CLL and MBL cases. PatternCNV was used to detect clinically relevant CNAs in chromosomes 11, 12, 13 and 17. We performed a principal component analysis on the CNA calls, excluding chromosomes 11, 12, 13, and 17 to identify clusters of samples. Each cluster was then independently rerun with PatternCNV and the results from chromosomes 11, 12, 13, and 17 were extracted and further analyzed. We excluded samples with low tumor metrics identified by FISH (less than 20% of cells with del(17p), del(11q), trisomy 12 and del(13q)). Results: We sequenced a total of 696 patients of whom 162 were MBL and 534 were untreated CLL. The most commonly mutated genes were NOTCH1 (11.0%), TP53 (8.7%), SF3B1 (7.7%), ATM (4.1%), and CHD2 (3.8%). Based on CNA analyses from the NGS data, we identified 59 (9.1%) individuals with del(17p), 88 (13.4%) individuals with del(11q), 128 (20.0%) individuals with trisomy 12, and 329 (53.0%) individuals with del(13q). All 696 individuals had FISH panels conducted, with 39 (5.6%) individuals with del(17p), 68 (9.8%) individuals with (11q), 119 (17.1%) with trisomy 12, and 295 (42.4%) with del(13q). When we compared our CNA analyses with the FISH data, we found high concordance 95.0% for del(17p), 92.7% del(11p), 94.3% for trisomy 12, and 88.2% for del(13q). For del(17p) we found a sensitivity of 93.9%, specificity of 95.4%, PPV of 52.5%, and NPV of 99.7%. Del(11q) revealed a sensitivity of 88.1%, specificity of 94.0%, PPV of 59.1%, and NPV 98.8%. We found a sensitivity of 93.8%, specificity of 95.6%, PPV 82.0%, and NPV of 98.6% for trisomy 12 and for del(13q) we found a sensitivity of 92.6%, specificity of 90.9%, PPV of 91.7%, and NPV of 93.8%. We found lower PPVs in del(17p) and del(11q) likely due to lower prevalence of these chromosomal abnormalities. Conclusion: Here we show a high sensitivity, specificity, and NPV when comparing targeted sequencing with FISH. FISH panel testing is widely used in clinical practice to characterize highly prognostic chromosomal abnormalities in CLL. Comprehensive genetic profiling with NGS has become increasingly important in the work up of hematologic malignancies and provides additional prognostic and predictive information, including clinically relevant mutations such as TP53, SF3B1, and NOTCH1, tumor mutation load and mutations associated with resistance to chemo-immunotherapy and targeted therapies, such as BTK or BCL2 inhibitors, that FISH cannot offer. We show that NGS can infer clinically relevant CNA in cases without FISH testing while also providing additional clinically relevant information. Figure 1 Figure 1. Disclosures Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Parikh: Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding; Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees. Kay: Genentech: Research Funding; MEI Pharma: Research Funding; Sunesis: Research Funding; Acerta Pharma: Research Funding; 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; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: 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; Tolero Pharmaceuticals: Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Research Funding; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees.

AbstractGene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype‐tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10−7 either in EBV‐transformed B‐lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression‐free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4‐rs1992292, TBRG4‐rs2287535 and ENTPD1‐rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta‐analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4‐rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04‐1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.

Abstract Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10−6), EOMES (P = 6.0 × 10−6) and BTNL2 (P = 2.1 × 10−3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.

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.

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.