Olson, Janet E.

Abstract Background: Pathogenic variants detected in multi-gene cancer predisposition panels are increasingly used to counsel women regarding their risk for breast cancer. However, the clinical implications of moderate penetrance genes (e.g. CHEK2, ATM) remain unclear. Breast MRI is indicated for women with a lifetime risk of breast cancer of >20%, and polygenic risk scores (PRS) based on common variants discovered in recent genome-wide association studies (GWAS) may identify women with pathogenic variants in CHEK2 and ATM who are above or below this level of risk. PRS may also stratify carriers with substantially elevated lifetime risks of breast cancer. Previous work found that PRS modified breast cancer risk in carriers of pathogenic variant in BRCA1 or BRCA2, but the joint effects of pathogenic variants and PRS have not been studied in samples drawn from the general population. There is also no published study evaluating the effect of PRS among women with pathogenic variants in genes other than BRCA1/2 and CHEK2. Here, we evaluated the joint effects of PRS and pathogenic variants in ten established breast cancer predisposition genes in a population-based case-control sample. Method: We analyzed 43,144 non-Hispanic European-ancestry individuals (20,988 controls and 22,120 cases) drawn from 7 cohorts and 2 population-based case-control studies in the CAnceR RIsk Estimates Related to Susceptibility” (CARRIERS) consortium. Targeted sequencing was performed using a dual bar-coded QIAseq panel of breast cancer predisposition genes. Variant calling was conducted with Haplotype Caller and Vardict. The PRS was calculated based on 106 common variants using effect estimates from the largest published breast cancer GWAS. Genetic status was evaluated for 10 breast cancer predisposition genes: ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, NF1, PALB2 and TP53. Lasso Penalized regression was performed to assess the combined effect on overall breast cancer of pathogenic variants and common variants (including main and interaction effects between age, family history, carrier status and PRS). The fraction of women at >2-fold increased risk (which is associated with approximately 20% lifetime risk of breast cancer) was calculated for pathogenic variant carriers and non-carriers of different family history and age groups (<40, 40-50, 50-60, 60-70, >70yrs old). Results: Among non-carriers, one standard deviation change in the PRS was associated with a 1.63x change in odds of breast cancer. The best-fitting risk model did not include any PRS-by-carrier interaction terms, implying the relative risk gradient associated with PRS among women with pathogenic variants is similar to that among non-carriers. For carriers of pathogenic variants in moderate penetrance genes with no family history of breast cancer, the PRS can identify 60.6% of ATM pathogenic variant carriers and 30% of CHEK2 pathogenic variant carriers that have less than 2-fold risk of breast cancer; when carriers have a family history of breast cancer, the PRS can identify 30% of ATM pathogenic variant carriers and 11.8% of CHEK2 pathogenic variant carriers with less than 2-fold risk of breast cancer. Conclusion: In our large, population-based case-control study, the effect of the PRS among pathogenic variant carriers was not significantly different than among non-carriers. However, the PRS may be particularly important for estimating breast cancer risk among carriers of pathogenic variants in moderate penetrance genes such as CHEK2 and ATM, enabling a more precise approach for MRI screening strategy and breast cancer risk management. Citation Format: Chi Gao, Eric C. Polley, Chunling Hu, Steven N. Hart, Rohan Gnanaolivu, Kun Y. Lee, Jie Na, Nicholas J. Boddicker, Raed Samara, Paul Auer, Leslie Bernstein, Mia Gaudet, Amy Trentham-Dietz, Song Yao, Christopher Haiman, Janet E. Olson, Susan Neuhausen, Jeffrey N. Weitzel, David E. Goldgar, Susan Domchek, Katherine L. Nathanson, Fergus J. Couch, Peter Kraft, on behalf of the CARRIERS consortium. Polygenic risk scores provide clinically meaningful risk stratification among women carrying moderate penetrance pathogenic variants in breast cancer predisposition genes: Results from the CARRIERS study [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 PD3-02.

Abstract Germline mutations in several cancer predisposition genes included in hereditary multigene testing panels have been associated with increased breast cancer risk. However, estimates of breast cancer risks for each gene have in large part been derived from studies of high-risk populations enriched for family history of breast and ovarian cancer, young age of breast cancer diagnosis, or founder mutations. The breast cancer risks associated with mutations in many of these genes in the general population remain to be clearly defined. As clinical hereditary cancer genetic testing is already provided to many women at increased risk and may soon be offered to all breast cancer patients, population-based risk estimates associated with predisposition gene mutations will be needed for appropriate clinical management of mutation carriers. The “CAnceR RIsk Estimates Related to Susceptibility” (CARRIERS) consortium of 17 cohort-based case-control, population-based case-control, and family studies focused on estimation of population-based breast cancer risks and penetrance analysis for mutations in known and candidate breast cancer predisposition genes. Germline DNA from 39486 breast cancer cases and 35868 matched controls was subjected to dual bar-coded QIAseq multiplex amplification of 1733 target regions in 37 predisposition genes. Products from sets of 768 samples were pooled and sequenced in each lane of a HiSeq 4000 system. Mutations were called by GATK Haplotype Caller and Verdict. High quality sequence data was obtained for 99.3% of target regions. Here we report on results from 34741 population-based breast cancer cases and 32728 matched unaffected controls. The mean age of breast cancer diagnosis for cases was 61.2 years and mean age for controls was 61.1 years. Overall, 20.9% of cases and 14.3% of controls had a family history of breast cancer. Furthermore, 74.3% of cases and 75.8% of controls were non-Hispanic white, whereas 13.3% of cases and 15.3% of controls were African American. Among cases with tumor information available, 81.7% were ER-positive and 7.7% were triple negative breast cancers (TNBC). We assessed mutation frequencies in 24 genes including 12 established breast cancer predisposition genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, NF1, PALB2, PTEN, RAD51C, RAD51D, andTP53). The overall mutation frequency was 6.2% in cases and 2.7% in controls. Case-control association analysis after adjusting for study, age, family history of breast cancer, and race/ethnicity showed that BRCA1(OR=7.45; 95%CI:5.24-10.95) and BRCA2(OR=5.31; 95%CI:4.15-6.88) were associated with high risks of breast cancer in the general population. However, PALB2mutations were only associated with moderate risks (OR=3.63; 95%CI:2.57-5.26). ATMmutations conferred attenuated risks (OR=1.83; 95%CI:1.47-2.28) relative to studies of high-risk families or young onset breast cancers. NBNmutations were not associated with increased breast cancer risk. Mutations in BARD1, RAD51C, RAD51Dand XRCC2were specifically significantly associated with moderate risks of ER-negative breast cancer and TNBC, whereas mutations in ATM, CDH1and CHEK2were specifically significantly associated with ER-positive disease. On the basis of these findings age-related absolute breast cancer risks for commonly mutated genes were estimated. Overall, results from the CARRIERS study establish that mutations in predisposition genes are associated with lower risks of breast cancer in the general population than in enriched populations including high-risk families and young onset, bilateral, or multiple primary cases. We anticipate that the results from this study will inform cancer screening and other risk management strategies for women in the general population with mutations in predisposition genes. Citation Format: Fergus J Couch, Chunling Hu, Steven N Hart, Rohan Gnanaolivu, Kun Y Lee, Jie Na, Chi Gao, Nicholas J Boddicker, Bruce Eckloff, Raed Samara, Josh Klebba, Christine B Ambrosone, Hoda Anton-Culver, Paul Auer, Leslie Bernstein, Mia M Gaudet, Christopher Haiman, Esther M John, Susan Neuhausen, Janet E Olson, Tuya Pal, Julie R Palmer, Dale P Sandler, Jack A Taylor, Amy Trentham-Dietz, Celine M Vachon, Clarice Weinberg, Song Yao, Jeffrey N Weitzel, David E Goldgar, Susan M Domchek, Katherine L Nathanson, Peter Kraft, Eric C Polley. Population-based breast cancer risk estimates for predisposition gene mutations: Results from the CARRIERS study [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 PD3-01.

Abstract Purpose: To determine the sensitivity of germline hereditary cancer genetic testing recommendations for detection of pathogenic mutations in breast cancer predisposition genes in an unselected cohort of breast cancer patients. Patients and Methods: The study population included patients enrolled in a prospective breast cancer registry after being seen at Mayo Clinic for newly diagnosed breast cancer between 2004 and 2016. All patients were subjected to germline mutation testing for 12 known breast cancer predisposition genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, NF1, PALB2, PTEN, RAD51C, RAD51D and TP53). Results from testing of all breast cancer patients according to American Society of Breast Surgeons (ASBrS) genetic testing recommendations and patients meeting National Comprehensive Cancer Network (NCCN) criteria for BRCA1/2 testing, based on age at diagnosis and personal and family history of cancer, were compared. Results: Among the 4516 patients in the cohort, the median age at diagnosis of breast cancer was 57 years, 80.5% had estrogen receptor positive tumors, and 43% had a family history of breast cancer. All patients in the cohort met ASBrS criteria for hereditary cancergenetic testing, 2050 (45.4%) met NCCN criteria, 1892 (41.9%) did not meet NCCN criteria, and 574 (12.7%) patients could not be fully evaluated for NCCN testing criteria due to missing data. The frequencies of pathogenic variants in the cohort are shown in Table 1. Patients meeting NCCN criteria were more likely to carry a pathogenic variant compared to those who did not (9.8% vs. 3.8%, p<0.001, Table 1). However, 72 (26.5%) out of the 272 patients with mutations in 12 breast cancer predisposition genes and 15 (12.4%) out of 121 with mutations in BRCA1/2 were overlooked when NCCN selection criteria alone were applied. When testing was expanded to include all patients with breast cancer diagnosed at age ≤65 and/or meeting NCCN testing criteria, only 20 (7.4%) out of 272 mutation carriers in 12 breast cancer genes and 2 (1.6%) out of 121 BRCA1/2 mutation carriers were overlooked. However, this increased the number tested to detect one mutation carrier on average from 17 to 25 for BRCA1/2 and from 9 to 12 for the 12 breast cancer predisposition genes. In total, this age cutoff led to an increase in the number of patients tested by 28% while the number of patients in the cohort that need not undergo germline genetic testing was 20%. Conclusions: Although NCCN criteria for BRCA1/2 germline genetic testing detect the majority of predisposition gene mutation carriers, a significant number of carriers are missed in an unselected cohort of breast cancer patients using these criteria. Expanding the current NCCN criteria to include all women with breast cancer diagnosed ≤ 65 years of age, will substantially improve the sensitivity of mutation testing, without the need for evaluation of all breast cancer patients. Table 1: Comparison of frequency of germline mutations between patients who met and did not meet NCCN guidelines for germline genetic testing#Total, Meets ASBrS guidelines N=3942Meets NCCN guidelines, N=2050Did not meet NCCN guidelines, N=1892P-value€ NCCN vs non-NCCNPercent mutation carriers missed by NCCN criteria* BRCA1/2121 (3.1%)106 (5.2%)15 (0.8%)<0.00112.4%6 High risk genes$150 (3.8%)120 (5.8%)30 (1.6%)<0.00120.0%12 Breast cancer genes:272 (6.9%)200 (9.8%)72 (3.8%)<0.00126.5%ATM45 (1.1%)32 (1.56%)13 (0.69%)28.9%BARD16 (0.2%)5 (0.24%)1 (0.05%)16.7%BRCA158 (1.5%)53 (2.59%)5 (0.26%)8.6%BRCA263 (1.6%)53 (2.59%)10 (0.53%)15.9%CDH16 (0.2%)3 (0.15%)3 (0.16%)50.0%CHEK267 (1.7%)41 (2.00%)26 (1.36%)38.8%NF10 (0.0%)0 (0.00%)0 (0.00%)0.0%PALB216 (0.4%)7 (0.34%)9 (0.48%)56.3%PTEN1 (0.0%)1 (0.05%)0 (0.00%)0.0%RAD51C6 (0.2%)6 (0.29%)0 (0.00%)0.0%RAD51D4 (0.1%)2 (0.10%)2 (0.11%)50.0%TP537 (0.2%)3 (0.15%)4 (0.21%)57.1%#: Patients who did not meet NCCN criteria excluded from analysis$: BRCA1, BRCA2, CDH1, PALB2, PTEN and TP53;€: Fisher exact test comparing frequency of germline mutations between those who met NCCN guidelines and those who did not;*: Denominators for percentages are the total mutation carriers in respective categories. Citation Format: Siddhartha Yadav, Chunling Hu, Steven N. Hart, Nicholas Boddicker, Eric C. Polley, Jie Na, Rohan Gnanaolivu, Kun Y. Lee, Janet E. Olson, Kathryn J. Ruddy, Fergus J. Couch. Comparison of recommendations for germline genetic testing in an unselected cohort of patients with 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 P6-08-01.

Abstract Background: Polygenic risk scores (PRS) based on a large number of SNPs associated with breast cancer have recently been made available in combination with multigene panel testing for clinical management of breast cancer risk. Breast cancer risk assessment has historically been performed using various non-genetic risk models, which predict lifetime risk of breast cancer in unaffected women based on personal and family history information. While clinical use of PRS in combination with these models continues to increase, the predictive performance of a PRS combined with clinical information in real-world patient populations has not been thoroughly investigated. Methods: We assessed a 100-SNP breast cancer PRS in combination with the Tyrer-Cuzick model-estimated lifetime breast cancer risk in 5,374 Caucasian women from the case-control Mayo Clinic Breast Cancer Study (MCBCS), comprised of 3,373 breast cancer cases and 2,001 unaffected controls seen at Mayo Clinic from 2004 to 2016. Clinical, family history, and demographic information was collected on all MCBCS participants via questionnaires and medical record review. SNPs were selected for the PRS if they exceeded genome-wide significance in ≥2 large-scale breast cancer genome wide association studies (GWAS) in women of European descent. Selected SNPs were then used to construct a population standardized PRS, accounting for per-allele odds ratios reported in the largest GWAS to date and population-specific allele frequencies. PRS and Tyrer-Cuzick model associations with breast cancer and related clinical features were tested using multivariate logistic regression. Results: Among cases and controls, mean±SD age at diagnosis and study enrollment was 56.4±12.4 and 56.2±14.1, respectively. Approximately 36.9% of cases and 16.9% of controls had ≥1 first- or second-degree relative with breast or ovarian cancer, respectively. Among unaffected women, the median (IQR) estimated lifetime risk of breast cancer to age 85 based on Tyrer-Cuzick alone was 8.3% (6.9%), and 2.6% had a clinically actionable lifetime risk of ≥20%. The mean±SD sum of the risk alleles across 100 SNPs was significantly higher for cases vs. controls (94.8±6.3 vs. 92.7±6.2, p<0.001). Compared to women in the lowest quartile of PRS, those in the 2nd, 3rd and 4th quartiles were 1.47 (95% CI: 1.26-1.72), 1.87 (95% CI: 1.60-2.19), and 2.22 (95% CI: 1.89-2.61) times more likely to have breast cancer after adjustment for age and family history. The AUC was 0.59 (95% CI: 0.58-0.61) for PRS and 0.60 (95% CI: 0.58-0.61) for Tyrer-Cuzick model estimated risk alone; but when combined, the model achieved an AUC of 0.63 (95% CI: 0.62-0.65). Among unaffected women, 6.7% had ≥20% lifetime risk of breast cancer based on the combined PRS-clinical model. Conclusions: These findings suggest that the 100-SNP PRS significantly improves estimation of breast cancer risk based on non-genetic models, and can be used to further identify women at increased lifetime risk who would otherwise not be identified by clinical assessment alone. Citation Format: Mary Helen Black, Shuwei Li, Holly LaDuca, Sebastian Armasu, Eric C. Polley, Patrick Fitz-Gibbon, Chris G. Scott, Stacey J. Winham, Janet E. Olson, Kathryn J. Ruddy, Celine M. Vachon, Fergus J. Couch. Performance of a polygenic risk score combined with clinical assessment for breast cancer risk [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 P5-07-06.

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.

PURPOSE To determine the sensitivity and specificity of genetic testing criteria for the detection of germline pathogenic variants in women with breast cancer. MATERIALS AND METHODS Women with breast cancer enrolled in a breast cancer registry at a tertiary cancer center between 2000 and 2016 were evaluated for germline pathogenic variants in 9 breast cancer predisposition genes ( ATM , BRCA1, BRCA2, CDH1, CHEK2, NF1, PALB2, PTEN, and TP53). The performance of the National Comprehensive Cancer Network (NCCN) hereditary cancer testing criteria was evaluated relative to testing of all women as recommended by the American Society of Breast Surgeons. RESULTS Of 3,907 women, 1,872 (47.9%) meeting NCCN criteria were more likely to carry a pathogenic variant in 9 predisposition genes compared with women not meeting criteria (9.0% v 3.5%; P < .001). Of those not meeting criteria (n = 2,035), 14 (0.7%) had pathogenic variants in BRCA1 or BRCA2. The sensitivity of NCCN criteria was 70% for 9 predisposition genes and 87% for BRCA1 and BRCA2, with a specificity of 53%. Expansion of the NCCN criteria to include all women diagnosed with breast cancer at ≤ 65 years of age achieved > 90% sensitivity for the 9 predisposition genes and > 98% sensitivity for BRCA1 and BRCA2. CONCLUSION A substantial proportion of women with breast cancer carrying germline pathogenic variants in predisposition genes do not qualify for testing by NCCN criteria. Expansion of NCCN criteria to include all women diagnosed at ≤ 65 years of age improves the sensitivity of the selection criteria without requiring testing of all women with breast cancer.

Background: Current cardiovascular disease (CVD) risk scores are derived from research cohorts and are particularly inaccurate in women, older adults, and those with missing data. To overcome these limitations, we aimed to develop a cohort to capitalize on the depth and breadth of clinical data within electronic health record (EHR) systems in order to develop next-generation sex-specific risk prediction scores for incident CVD. Methods: All individuals 30 years of age or older residing in Olmsted County, Minnesota on 1/1/2006 were identified. We developed and validated algorithms to define a variety of risk factors, thus building a comprehensive risk profile for each patient. Outcomes including myocardial infarction (MI), percutaneous intervention (PCI), coronary artery bypass graft (CABG), and CVD death were ascertained through 9/30/2017. Results: We identified 73,069 individuals without CVD (Table). We retrieved a total of 14,962,762 lab results; 14,534,466 diagnoses; 17,062,601 services/procedures; 1,236,998 outpatient prescriptions; 1,079,065 heart rate measurements; and 1,320,115 blood pressure measurements. The median number of blood pressure and heart rate measurements ascertained per individuals were 11 and 9, respectively. The five most prevalent conditions were: hypertension, hyperlipidemia, arthritis, depression, and cardiac arrhythmias. During follow-up 1,455 MIs, 1,581 PCI, 652 CABG, and 2,161 CVD-related deaths occurred. Conclusions: We developed a cohort with comprehensive risk profiles and follow-up for each patient. Using sophisticated machine learning approaches, this electronic cohort will be utilized to develop next-generation sex-specific CVD risk prediction scores. These approaches will allow us to address several challenges with use of EHR data including the ability to 1) deal with missing values, 2) assess and utilize a large number of variables without over-fitting, 3) allow non-linear relationships, and 4) use time-to-event data.

Introduction Population-level screening has been shown to reduce the incidence and mortality of colorectal cancer (CRC). Unfortunately, adherence to screening recommendations among eligible US adults remains below national goals. A relatively new non-invasive screening modality, the Food and Drug Administration–approved multi-target stool DNA (mt-sDNA) assay (commercialised as Cologuard), which combines the detection of haemoglobin and DNA abnormalities, has been completed by more than 3 million individuals. Given mt-sDNA’s recent availability, the effectiveness of mt-sDNA screening with respect to CRC incidence and mortality reduction has not yet been established. Methods and analysis Through an academic–industry collaboration, a prospective cohort study (Voyage) was designed with an initial enrolment target of 150 000 individuals with mt-sDNA ordered by their healthcare provider for CRC screening. Consented participants will be asked to complete a baseline questionnaire to collect sociodemographic and health information. Additional questionnaires will be provided after 1 year, and every 3 years thereafter, to collect data regarding CRC screening follow-up in order to estimate rates of CRC incidence and other health outcomes. Linkage to the National Death Index will be used to estimate mortality rates. Ethics and dissemination The Voyage study will be conducted in accordance with international guidelines and local regulatory requirements and laws. Data will be stored and retained at Mayo Clinic. Only limited data elements required for research purposes will be transmitted between Mayo Clinic and Exact Sciences Laboratories. Results of the Voyage study will be disseminated through scientific presentations and publications. Trial registration number NCT04124406.

Purpose The Mayo Clinic Biobank was established to provide a large group of patients from which comparison groups (ie, controls) could be selected for case–control studies, to create a prospective cohort with sufficient power for common outcomes and to support electronic health record (EHR) studies. Participants A total of 56 862 participants enrolled (21% response rate) into the Mayo Clinic Biobank from Rochester, Minnesota (77%, n=43 836), Jacksonville, Florida (18%, n=10 368) and La Crosse, Wisconsin (5%, n=2658). Participants were all Mayo Clinic patients, 18 years of age or older and US residents. Findings to date Overall, 43% of participants were 65 years of age or older and female participants were more frequent (59%) than males at all sites. Most participants resided in the Upper Midwest regions of the USA (Minnesota, Iowa, Illinois or Wisconsin), Florida or Georgia. Self-reported race among Biobank participants was 90% white. Here we provide examples of the types of studies that have successfully utilised the resource, including (1) investigations of the population itself, (2) provision of controls for case–control studies, (3) genotype-driven research, (4) EHR-based research and (5) prospective recruitment to other studies. Over 270 projects have been approved to date to access Biobank data and/or samples; over 200 000 sample aliquots have been approved for distribution. Future plans The data and samples in the Mayo Clinic Biobank can be used for various types of epidemiological and clinical studies, especially in the setting of case–control studies for which the Biobank samples serve as control samples. We are planning cohort studies with additional follow-up and acquisition of genetic information on a large scale.

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.