Norman, Aaron D.

Abstract Clonal hematopoiesis of indeterminate potential (CHIP) is defined as somatic mutations in clonal blood cells of individuals without any other hematological abnormalities and have been identified using whole exome sequencing (WES) from pre-specified genes. CHIP is reportedly associated with aging, reduced overall survival, cardiovascular disease, and hematologic disease, mainly myeloid cancers. Additionally, whole genome sequencing and WES studies in CLL cases have identified putative CLL driver genes with recurrent somatic variants or copy number alterations. Here, we investigate the associations of CHIP and mutations in CLL somatic driver genes with risk of CLL and MBL, a precursor to CLL. Using WES, 571 individuals from 132 CLL families were sequenced from two different sites, Mayo Clinic (92 families) and National Cancer Institute (NCI, 40 families), including 251 CLLs, 74 MBLs, and 246 unaffected relatives. DNA was extracted from buccal cells (Mayo Clinic) and peripheral blood (NCI). Allele counts from gVCF files were extracted for specific genes and analyzed. Variants were annotated using the Variant Effect Predictor in Ensembl, genome build GRCh37 (http://useast.ensembl.org/index.html). Highly-sensitive flow cytometry of the blood was used to screen for MBL. CHIP was defined as in Genovese et al., 2014 as the presence of any of the following: A) disruptive mutations in ASXL1, TET2, or PPM1D; B) missense mutation p.617F in JAK2; C) disruptive or missense mutations in DNMT3Alocalized to exons 7 to 23D) any mutations reported at least 7 times in hematological malignancies in the Catalogue of Somatic Mutations in Cancer (COSMIC); and (E) No variant allelic fraction (VAF) threshold. CLL driver genes included disruptive or missense mutations in ATM, BIRC3, MYD88, NOTCH1, SF3B1, TP53, EGR2, POT1, NFKBIE, XPO1, or FBXW7with VAF greater than 0.1. Mutations from COSMIC and CLL driver genes were excluded if the minor allele frequency in 1000 Genomes was greater than 0.5%. Data was analyzed using logistic regression adjusting for age, sex, and site. Odds ratios (OR) and 95% confidence intervals (CI) were estimated. Median age for CLL, MBL, and unaffected was 63, 61.5, and 54, respectively. The unaffected relatives were significantly younger than both the CLL and MBL groups (p<0.0001), with no difference between CLL and MBL (p=0.46). A total of 70 (12%) individuals from 54 different families had CHIP. CHIP was characterized in the majority of samples as mutations in DNMT3A, ASXL1, and TET2with 43, 9 and 8 individuals, respectively. As expected, CHIP was associated with age (p<0.001) but not sex (p=0.87). CHIP was present in 42 (17%) CLLs, 9 (12%) MBLs, and 19 (8%) unaffected relatives. There was a significant association between the presence of CHIP and CLL risk compared to unaffected relatives (OR= 2.19, 95% CI: 1.20 - 4.13, p=0.013) but not with MBL risk (OR=1.34, 95% CI: 0.54 - 3.11, p=0.51). Using the CLL driver genes, we found mutations in 119 (47%) CLL, 20 (27%) MBL, and 70 (29%) unaffected relatives. Mutations in CLL driver genes were associated with CLL risk compared to unaffected relatives (OR = 2.51, 95% CI: 1.64 - 3.87, p<0.0001), but there was no evidence for increased MBL risk (OR = 1.00). These results held when we subset the analysis to the Mayo only data where the DNA was extracted from buccal cells. This study further supports the association between mutations in CHIP related genes and risk of hematologic malignancies, in particular CLL. This is the first report of the prevalence of CHIP in MBLs. Although MBL and CHIP represent clonal population of blood cells, our data suggest the clones are different. Mutations in CLL driver genes are also associated with CLL risk but show little to no evidence with increased MBL risk. Interestingly, our unaffected relatives of CLL cases also had a high frequency of CLL driver mutations. Future studies are needed to understand these acquired mutations (both in CHIP- and CLL driver- related genes) in the unaffected to MBL to CLL continuum. Disclosures Parikh: Abbvie: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Research Funding. Ding:Merck: Research Funding. Cerhan:Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding; Nanostring: Research Funding. Shanafelt:Jansen: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; GlaxoSmithKline: Research Funding. Kay:Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; 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; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.

Abstract Background MBL is a precursor state to CLL, with a prevalence of ~5-8% in the general population and 15-18% in CLL families. To date, 41 single nucleotide polymorphisms (SNPs) have been found to be associated with CLL. We previously showed that a PRS of the weighted average of the number of risk alleles of these SNPs is associated with CLL risk using cases and controls from the International Lymphoma Epidemiology (InterLymph) Consortium. We validated this score in an independent sample of CLL cases and controls from the Genetic Epidemiology of CLL (GEC) Consortium, a cohort of families with ≥2 members with CLL. In the CLL families we also reported an association between the PRS and MBL risk, where 93% of MBLs were low-count MBL. Here we evaluate the PRS in an independent sample ascertained from the Mayo Clinic Biobank. Methods The Mayo Clinic Biobank is a large-scale bio-repository of adult patients assembled to provide a wide array of health-related research studies. Biobank participants (N=2530) were screened for MBL using a highly-sensitive, 8-color (CD38, CD45, Kappa, Lambda, CD19, CD23, CD5 and CD20) flow-cytometry assay on stored cryopreserved peripheral blood mononucleotide cells. Low-count and high-count MBL were defined as those individuals who had <85% and ≥85 clonal B-cells out of total B-cells, respectively. Individuals without MBL ("controls") were frequency matched to MBLs based on age and sex, after excluding non-Caucasian and non-interpretable flow results. Participants were genotyped using the Illumina OmniExpress array. Standard genotyping quality-control metrics were applied. We computed the PRS as previously published and categorized it by quintiles based on values previously used with InterLymph controls. The middle quintile served as the reference category. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CI), adjusting for age at consent, sex, and socioeconomic status. In addition, we compared the median PRS by low-count MBL and high count MBL using the Mann-Whitney U test. Results We identified 276 MBLs overall and 2109 controls; 97% (N=267) of MBLs had low-count MBL and only 3% (N=9) had high-count MBL. We then selected 844 controls frequency matched to the MBL cases on age and sex. Family history of leukemia/lymphoma was 11.3% among MBLs overall and 8.8% among controls. The median PRS was 7.88 and 7.46 (Figure) among MBLs overall and controls, respectively. The continuous PRS had a 1.75-fold increased risk for MBL overall (CI:1.5-2.1, P=2.65x10-12). Compared to the middle quintile, the highest quintile had 2.2-fold increased risk for MBL overall (CI:1.5-3.3, P=1.46x10-4) and the lowest quintile had 0.6-fold decreased risk (CI:0.4-0.96, P=0.03). The median PRS for low-count and high count MBL were 7.86 and 8.04, respectively. The continuous PRS had a 1.75-fold increased risk for low-count MBL (CI:1.5-2.1, P=4.81x10-12) and a 1.89-fold increased risk for high-count MBL (CI:0.8-4.2, P=0.13), however, the effect is not statistically significant between high-count and low-count MBL (P=0.89). Conclusion In this independent sample, we validated our previous findings that the CLL PRS is associated with MBL risk. Although the sample size of high-count MBL was limited (N=9), there is evidence of a weak trend towards a higher PRS compared to low-count MBL and controls Larger sample size of high-count MBL is warranted in order to stratify MBL risk by low-count and high-count MBL. These results may help identify individuals at higher risk of MBL including low-count and high-count MBL, beyond the risk associated with age and family history of CLL. Figure: Polygenic risk score distribution by control and MBL status Figure. Figure. Disclosures Parikh: Janssen: Research Funding; Pharmacyclics: Honoraria, Research Funding; Gilead: Honoraria; Abbvie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; MorphoSys: Research Funding. Cerhan:Nanostring: Research Funding; Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: 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; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees. Shanafelt:Genentech: Research Funding; Pharmacyclics: Research Funding; GlaxoSmithKline: Research Funding; Jansen: Research Funding.

Abstract Abstract 3881 Background: Chronic lymphocytic leukemia (CLL) is largely incurable neoplasm of the blood and has one of the highest familial risks of disease among cancers. Monoclonal B-cell lymphocytosis (MBL) with a CLL immunophenotypic profile (i.e., CLL-like MBL) is a precursor state to CLL. In the general adult population, the prevalence rate of MBL is 5–9% and increases with age. This is in contrast to the 13–18% rate of MBL in first-degree relatives of CLL patients from high-risk families (defined here as families with at least two confirmed living cases of CLL). Few longitudinal studies of MBL incidence exists. One study evaluated MBL status over time in a cohort of individuals with MBL from a rural valley in Northern Italy. Our work herein will be the first to evaluate the status of MBL over time in relatives of CLL patients from high-risk families. Methods: Using a cohort of 41 high-risk CLL families, we evaluated the MBL status and clonal blood B-cell counts in 94 relatives of CLL patients at two time points at least 1 year a part. High sensitivity, multi-color flow cytometry was performed on fresh or previously frozen blood samples in three laboratories: Duke University, the US Food and Drug Administration, and Mayo Clinic. The number of cell events collected for flow cytometry evaluation varied from 1 to 5 x105 cells. To assure concordance across flow cytometry laboratories, 15 samples were evaluated at multiple labs. The concordance of MBL calling was a 100%. We defined three monotypic B-cell subsets as CLL-like MBL (CD5+ CD20dim), atypical MBL (CD5+ CD20+), and non-CLL like MBL (CD5−CD20+). Results: Of the 94 relatives, the median age at consent was 57 years (range 42–93) and 39% were male. At the initial MBL screening, 70 (74%) relatives had normal immunophenotype (NIPT) and 24 (26%) had MBL (1 atypical MBL, 3 non-CLL like MBL, and 20 CLL-like MBL). Seven of the 24 individuals with MBL had monoclonal B-cell counts available at baseline screening; median = 147.4 cells/uL (range 22.7–1663). The 94 relatives underwent repeat evaluation with a median follow up time of 1.8 years (range 1–6). At the second evaluation, we found an increase in the prevalence of MBL, with 11 of the 70 (12%) initially NIPT relatives developing a new MBL clone (10 CLL-like, 1 non-CLL like). These 11 incident MBL cases were from 9 families with 2 families each having 2 relatives changing MBL status. The median number of monoclonal B-cells in these 11 incident MBL cases was 0.58 cells/uL (range 0.38–60.3). The 23 out of the 24 relatives with baseline MBL (20 CLL-like MBL and 3 non-CLL like MBL) had a persistent MBL clone. Only 6 of these 23 individuals had monoclonal B-cell counts available at both time points, and among these 6, the clonal B-cell counts decreased at follow-up screening; median difference −64.1 cells/uL (range −1204.3– 10.5). The second evaluation of the initial atypical MBL relative had too few clonal cells for flow assessment. Conclusion: We found that our CLL-like MBL clones persisted over the period of observation in relatives from high-risk CLL families; this finding is similar to that previously reported using a population-based study conducted in Northern Italy. Notably, 12% of relatives from high-risk CLL families developed incident MBL during just 2 years of follow up. Although these MBL clones are all low count (< 1500 cells/uL), these data provide further evidence that MBL is a marker of inherited predisposition to CLL. It will be important to see how our rate of change of MBL status compares to that of the general population as we continue to follow our cohort of CLL families. Disclosures: No relevant conflicts of interest to declare.

Abstract Background: It is well established that the incidence of skin cancers in patients with CLL are significantly elevated compared to age- and sex- matched controls. However, little is known about the characteristics of CLL patients who develop skin cancer. Herein, we evaluate the associations of CLL clinical and prognostic characteristics, along with UV radiation exposure, with risk of first skin cancer following CLL diagnosis. Methods: Newly diagnosed CLL patients from Minnesota, Iowa, and Wisconsin were enrolled in the Mayo Clinic case-control study from 2002-2015 and systematically followed in the Iowa/Mayo Lymphoma SPORE. Clinical and prognostic CLL data were obtained from the Mayo Clinic CLL database, and skin cancer clinical data were abstracted from medical records using a standard protocol. The CLL international prognostic index (CLL-IPI) was computed using a weighted average of five independent CLL prognostic factors (IGHV mutational status, serum b2-microglobulin, Rai stage, age, and FISH 17p deletion/TP53 status). Self-reported history of midday sun exposure at various ages (birth to age 12; 13 to 21 years; 22 to 40 years, and 41+ years) was obtained from a risk factor questionnaire. For each age, we asked the extent of mid-day sun as: practically no exposure (under 3 hours per week), little exposure (4-7 hours per week), moderate exposure (8 to 14 hours per week) and extensive exposure (15+ hours per week). Midday sun exposure was modeled as an ordinal covariate. To evaluate associations with risk of skin cancer following CLL diagnosis, we calculated time from date of CLL diagnosis to date of first skin cancer, death, or last known follow-up. We used Cox regression analysis to estimate hazard ratios (HRs) and 95% CIs. CLL treatment was considered a time-dependent covariate. Results: Among 846 CLL patients enrolled, the median age at diagnosis was 63 years (range 28-91), 68% were male, 7% had Rai stage III-IV at diagnosis. Based on the CLL-IPI, 42% were categorized as low risk, 33% as intermediate risk, and 25% as high or very-high risk. 109 CLL cases (13%) had one or more reported skin cancers at or prior to CLL diagnosis. Melanoma was observed in 19 (2%) cases and non-melanoma was in 90 (11%) cases. At a median follow-up of 7 years from CLL diagnosis, 165 patients (20%) had one or more skin cancers after CLL diagnosis. Among these patients, 49 had skin cancer before CLL diagnosis. The most frequent skin cancer was squamous cell carcinoma (59%), followed by basal cell (31%), melanoma (5%), and Merkel cell (1%). 552 (65%) of the 846 patients returned a questionnaire. Significant associations of clinical and prognostic characteristics with risk of first skin cancer were observed for age (HR=1.35 per 10 year increase, 95% CI=1.17-1.56, P<0.001), male sex (HR=1.38, 95% CI 0.98-1.96, p=0.07), prior history of skin cancer (HR=4.19, 95% CI=2.98-5.88, P<0.001), and CLL-IPI (HR=1.26, 95% CI= 1.03-1.54, P=0.026, after adjusting for age, sex, and prior skin cancer). Of note, the risk of first skin cancer in those CLL patients categorized as very high via CLL-IPI had 2.28 fold risk (95% CI 1.02-5.11). Midday sun exposures for each of the ages considered showed no evidence of association with risk of first skin cancer (all P>0.05). 50 CLL patients were treated prior to first skin cancer following CLL diagnosis; we observed no evidence of association between treatment and risk of first skin cancer (HR=1.44, 95% CI= 0.93-1.92, P=0.12). Conclusion: CLL patients who are at an increased risk of skin cancer following CLL diagnosis are those who either have had a prior history of skin cancer or a more aggressive CLL disease at diagnosis, according to CLL IPI. Routine skin cancer screening is currently recommended for CLL patients. Our data suggest that more frequent screening would be particularly important among patients with aggressive CLL and who have a prior history of skin cancer. Unexpectedly, we found no evidence of association of skin cancer risk with UV radiation following CLL diagnosis or with CLL treatment. Further investigation is needed to determine whether other factors increase the risk of skin cancer following CLL diagnosis. Disclosures Shanafelt: Genentech: Research Funding; GlaxoSmithkKine: Research Funding; Janssen: Research Funding; Celgene: Research Funding; Cephalon: Research Funding; Pharmacyclics: Research Funding; Hospira: Research Funding.

Abstract Introduction: Several B-cell malignancies, including chronic lymphocytic leukemia (CLL) and multiple myeloma (MM), are known to have a familial component to disease risk. Although several common inherited genetic variants have been found to be associated with risk of these malignancies, a large proportion of the heritability remains unexplained. Here we set out to identify rare high-penetrance susceptibility variants for CLL and MM using the established Mayo Clinic family study of B-cell malignancies. Methods: We performed exome sequencing on germ line DNA of 106 families with either two or more members with CLL (n=93 families) or two or more members with MM or monoclonal gammopathy of undetermined significance (MGUS) (n=13 families) using Aglient capture kits. All samples were sequenced on an Illumina HiSeq2000. Bioinformatics analyses leveraged the following software packages: Novoalign, Picard, The Genome Analysis Toolkit (GATK), and the Biological Reference Repository (bioR). Quality control filters were implemented; subjects with mis-specified relationships were removed as were variants with <95% call rate or <20x coverage, and those variants identified as sequencing artifacts. To identify potentially functional single nucleotide variants, the following was required a) uncommon in public databases, b) cosegregating variants in multiple families, c) variants to be highly conserved and in coding regions, and d) functional prediction status of deleterious (SIFT score), damaging (PolyPhen score), and a moderate, or high variant impact (SNPEffect). The association of these variants with risk was then evaluated in large samples of cases and controls in genome-wide association studies (GWAS). Results: In the CLL familial cohort, there were 443 individuals (160 with CLL, 73 with monoclonal B cell lymphocytosis (MBL), and 210 relatives without CLL or MBL) sequenced; and in the MM cohort, there were 39 individuals (13 with MM, 13 with MGUS and 13 without MM or MGUS) sequenced. A total of 61,992 single nucleotide variants passed quality control filters in the CLL cohort and 39,456 single nucleotide variants in the MM cohort. From these, we identified 32 variants in the CLL cohort and 39 variants in the MM cohort that were cosegregating in multiple CLL or MM families, respectively, and were predicted to affect protein structure or function. Of these, one CLL variant was within 1 Mb of a known GWAS-discovered locus. Moreover, there were seven CLL and four MM variants that were within 1 Mb of single nucleotide polymorphisms (SNPs) that had statistical significance between p<10-4 and p<10-6 in GWAS. Finally, there were 3 variants found to be common between CLL and MM families (see Table). Conclusions: Through whole exome sequencing, we identified a number of rare, highly penetrant predisposition variants for CLL and MM. Several of these variants were common to both B-cell malignancies providing evidence of shared genetic components; one variant may be a functional driver of the GWAS-discovered loci. These identified variants provide a list for future validation studies. Table: Exome variants common in both Chronic Lymphocytic Leukemia and Multiple Myeloma SNP ID rs41314099 rs61754114 rs143025033 Chromosome 17 6 7 Gene WSCD1 BACH2 TNS3 Penetrance (CLL) 0.70 0.75 0.80 Penetrance (MM) 1.00 0.80 0.75 Allele Frequency (CLL) 0.015 0.025 0.017 Allele Frequency (MM) 0.077 0.077 0.058 1000 Genome Frequency (European) 0.010 0.020 0.010 SIFT Term Deleterious Deleterious Deleterious PolyPhen Term Probably Damaging Probably Damaging Probably Damaging SNP: Single Nucleotide Polymorphism; CLL: Chronic Lymphocytic Leukemia; MM: Multiple Myeloma; Penetrance is defined as proportion of individuals carrying the alternate allele that are also affected; SIFT: Sorting Intolerant From Tolerant, predicts whether an amino acid substitution affects protein function; PolyPhen: Polymorphism Phenotyping, predicts possible impact of an amino acid substitution on the structure of a human protein Disclosures No relevant conflicts of interest to declare.

Abstract CLL is a clinically heterogeneous disease with wide ranging disease course. A novel CLL-IPI based on Rai/Binet stage, IGHV-mutation status, TP53 mutation/deletion, B2M level, and age was developed to stratify patients into 4 risk groups, with a c-statistic of 0.75. Next-generation sequencing has identified ~60 genes recurrently mutated in CLL, some of which are associated with poor overall survival, whereas the clinical effect of most genes is still unknown. Herein, we examine whether somatic mutations in these putative driver genes are associated with time to first treatment (TTT), and whether they add prognostic value beyond CLL-IPI. Based on the 2008 International Workshop CLL criteria, we identified 100 CLL and 96 high-count monoclonal B-cell lymphocytosis (MBL) newly diagnosed from the Mayo Clinic CLL biobank. Pre-treatment peripheral blood mononuclear cells were collected &lt;2 years of diagnosis and tumor DNA was extracted from sorted CD5+/CD19+. We sequenced the coding regions of 61 recurrently mutated CLL driver genes using a custom SureSelect panel, with 24 samples per flow cell in Illumina HiSeq 4000. The average coverage depth was &gt;1000X. Somatic mutations were called using MuTect2 in tumor-only mode. To remove germline variants, variants were eliminated based on minor allele frequencies &gt;0.01%, identified in 1000 Genomes Project, ExAC and/or ESP6500 databases, unless present in known mutation hotspots or COSMIC. After filtering, high/moderate impact mutations were analyzed using Cox regression, to estimate hazard ratios (HR) and 95% confidence intervals (CI) to test for associations with TTT. Among 196 patients the most commonly mutated genes were TP53 (11%), ATM (10%), SF3B1 (10%), NOTCH1 (9%), CHD2 (8%), and BIRC3 (7%). The median follow-up was 8.7 years, and 73 patients were subsequently treated. ATM (HR=3.27, CI:1.8-6.1, P=0.0002) and NOTCH1 (HR=2.41, CI:1.3-4.6, P=0.008) were associated with TTT. When evaluating the total number of mutated genes, we found 32%, 29%, and 39% patients had ≥2, 1, or 0 genes mutated, respectively, and this was associated with shorter TTT (HR=1.74, CI:1.3-2.4, P=0.0005) adjusting for sex and CLL-IPI with a c-statistic=0.8 (CI: 0.75-0.84). When stratified by CLL-IPI, the association held for low (N=99, HR=1.88, CI:1.1-3.4, P=0.03) and intermediate risk (N=54, HR=1.87, CI:1.1-3.2, P=0.03) but not high/very high risk (N=35, HR=1.07, CI:0.6-1.9, P=0.83). We demonstrated that the total number of CLL putative driver genes with high or moderate impact mutations provided prognostic information in newly diagnosed CLL/MBL beyond CLL-IPI. Moreover, even among those with low or intermediate CLL-IPI risk, the total number of somatic mutations separated those patients who progressed. Sequencing the CLL driver genes at time of diagnosis could be a potential biomarker for outcome prediction. Citation Format: Geffen Kleinstern, Daniel R. O’Brien, Brian F. Kabat, Kari G. Chaffee, Aaron D. Norman, Timothy G. Call, Sameer A. Parikh, Jose F. Leis, Wei Ding, James R. Cerhan, Neil E. Kay, Susan L. Slager, Esteban Braggio. Somatic mutations within chronic lymphocytic leukemia (CLL) putative driver genes are associated with outcomes beyond the CLL international prognostic index (CLL-IPI) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4466.

Abstract Genome-wide association studies (GWAS) conducted among populations of European ancestry (EA) have identified 23 common single nucleotide polymorphisms (SNPs) associated with multiple myeloma (MM) risk. We hypothesize that the combination of these SNPs in a polygenic risk score (PRS) is likely to be a strong risk factor for MM. However, it is unclear whether the genetic variation associated with MM susceptibility also predisposes to monoclonal gammopathy of undetermined significance (MGUS). Thus, we calculated a PRS and evaluated the association with risk of MM and its precursor, MGUS. We pooled genotype data for 2434 MM and 3446 controls from ten MM GWAS of individuals of EA within the Interlymph Consortium, for 23 MM risk SNPs identified by prior GWAS. An additional 754 MGUS cases were ascertained from Mayo Clinic and MD Anderson clinical practices. To calculate the PRS, we used the risk estimates corresponding to the 23 SNP associations from the largest published MM GWAS. The log of the odds ratio (OR) for each SNP was multiplied by the respective number of risk alleles and summed to generate a PRS for each individual. The PRS was examined continuously, per one standard deviation (SD), and as quintiles, based on the PRS distribution in the controls. Associations of PRS with MM and MGUS risk were examined separately, using multivariable logistic regression assuming an additive model to assess ORs and 95% confidence intervals adjusted for age, sex, and site. We also evaluated age and sex stratified models. The distribution of sex within MM cases, MGUS cases and controls were each ~60% male and ~40% female. The median age was 61, 66, and 66 years for MM cases, MGUS cases and controls, respectively. PRS ranged from 1.52-4.91, with a median PRS of 3.21 for MM cases, 3.19 for MGUS cases, and 3.05 for controls. PRS was significantly associated with MM risk when assessed continuously (OR=1.19 per SD, p=2.2x10-16) and categorically; compared with the middle quintile (Q3), individuals in the highest quintile (Q5) had a 66% increased MM risk (OR=1.66, p=2.3x10-9) and those in the lowest quintile (Q1) had a 38% decreased MM risk (OR=0.62, p=1.3x10-6). PRS was also significantly associated with MGUS risk (OR=1.19 per SD, p=1.7x10-11); individuals with the highest PRS (Q5) had a 77% increased risk (OR=1.77, p=4.0x10-4) and those with lowest PRS (Q1) had 30% decreased risk (OR=0.70, p=0.04), compared with Q3. When stratified by age and sex, similar associations and trends were found. Using an independent sample of MM / MGUS cases and controls, we showed that a PRS constructed from 23 common genetic variants for MM risk is associated with risk of both MM and MGUS, regardless of age or sex. A future direction of this work is testing associations with PRS and clinical characteristics of the MM cases, as well as differences between MGUS cases that progress and those that do not. Our results suggest that common genetic variation may predispose to MGUS as the precursor to MM. Citation Format: Alyssa I. Clay-Gilmour, Michelle A. Hildebrandt, Nicola J. Camp, Elad Ziv, Elizabeth E. Brown, Jonathan N. Hofmann, John J. Spinelli, Graham G. Giles, Parveen Bhatti, Wendy Cozen, Xifeng Wu, Dennis P. Robinson, Aaron D. Norman, Jason P. Sinnwell, Shaji K. Kumar, S Vincent Rajkumar, Susan L. Slager, Celine M. Vachon. Associations between a polygenic risk score and risk of multiple myeloma and its precursor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2686.

Abstract Background: Mammographic breast density declines during menopause. We assessed changes in volumetric breast density across the menopausal transition and factors that influence these changes. Methods: Women without a history of breast cancer, who had full field digital mammograms during both pre- and postmenopausal periods, at least 2 years apart, were sampled from four facilities within the San Francisco Mammography Registry from 2007 to 2013. Dense breast volume (DV) was assessed using Volpara on mammograms across the time period. Annualized change in DV from pre- to postmenopause was estimated using linear mixed models adjusted for covariates and per-woman random effects. Multiplicative interactions were evaluated between premenopausal risk factors and time to determine whether these covariates modified the annualized changes. Results: Among the 2,586 eligible women, 1,802 had one premenopausal and one postmenopausal mammogram, 628 had an additional perimenopausal mammogram, and 156 had two perimenopausal mammograms. Women experienced an annualized decrease in DV [−2.2 cm3 (95% confidence interval, −2.7 to −1.7)] over the menopausal transition. Declines were greater among women with a premenopausal DV above the median (54 cm3) versus below (DV, −3.5 cm3 vs. −1.0 cm3; P &lt; 0.0001). Other breast cancer risk factors, including race, body mass index, family history, alcohol, and postmenopausal hormone therapy, had no effect on change in DV over the menopausal transition. Conclusions: High premenopausal DV was a strong predictor of greater reductions in DV across the menopausal transition. Impact: We found that few factors other than premenopausal density influence changes in DV across the menopausal transition, limiting targeted prevention efforts.

Abstract Purpose: The evidence on associations of mammographic breast density with breast cancer risk by combination of tumor aggressiveness features is limited. We examined associations of breast density phenotypes with risk of aggressive breast tumor features by menopausal status, and current postmenopausal hormone therapy. Methods: This study included 2,635 invasive breast cancer cases and 4,059 controls from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses Health Study, Nurses Health Study II, and San Francisco Mammography Registry. Percent breast density, absolute dense and non-dense areas were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density measures with risk of breast tumor aggressiveness (defined as presence of 2 or more of the following tumor characteristics: size ≥2cm, grade 2 or 3, or positive nodes), stratified by menopausal status and current hormone therapy (i.e., premenopausal, postmenopausal/estrogen therapy, postmenopausal/combined therapy, and postmenopausal/no hormones). We also evaluated differences in the strength of associations across categories. In a secondary analysis, we examined these associations while excluding cases with mammogram date within 2 years of diagnosis. Results: Positive associations of percent density and dense area and inverse associations of non-dense area with breast cancer risk were stronger in aggressive vs. non-aggressive tumors (OR=2.62, 95%CI 2.08-3.31 vs. OR=1.94, 95%CI 1.62-2.33 for percent density≥51% vs. 11-25%, p-heterogeneity=0.001; OR=1.89, 95%CI 1.54-2.31 vs. OR=1.65, 95%CI 1.41-1.93 for dense area 4th vs. 2nd quartile, p-heterogeneity=0.015; OR=0.56, 95%CI 0.44-0.72 vs. OR=0.71, 95%CI 0.59-0.86 for non-dense area 4th vs 2nd quartile, p-heterogeneity=0.007, respectively). These patterns were similar across all menopausal and hormone therapy groups (P-interactions=0.62, 0.76, and 0.23, for percent density, dense area and non-dense area, respectively). Excluding cases diagnosed within 2 years of mammography resulted in similar findings. Conclusion: Mammographic density phenotypes were more strongly associated with aggressive cancer (having two or more of the following: size ≥2cm, grade 2 or 3, or positive nodes) vs. non-aggressive types of breast cancer across categories of menopause and hormone therapy types. Citation Format: Lusine Yaghjyan, Rulla Tamimi, Kimberly Bertrand, Christopher G. Scott, Matthew R. Jensen, Shane Pankratz, Kathleen Brandt, Daniel Visscher, Aaron Norman, Fergus Cough, John Shepherd, Bo Fan, Yunn-Yi Chen, Lin Ma, Andrew H. Beck, Steven R. Cummings, Karla Kerlikowske, Celine Vachon. Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B27.