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Background: Polygenic risk scores (PRS) integrate risk information from breast cancer associated SNPs (single nucleotide polymorphism). The risk scores have mostly been developed in populations of European ancestry, and have been shown to improve risk prediction over standard breast cancer risk models in these populations. The ability of the PRS to personalize screening is currently being studied. We included PRS as a component of breast cancer risk assessment in the WISDOM Study, a trial of personalized vs. annual breast cancer screening. In order to account for race/ethnicity in PRS risk assessment, we developed a race/ethnicity calibrated and inclusive PRS risk score that we incorporated here into the Gail model to determine impact on risk stratification. Methods: We constructed two different PRS for each race/ethnicity: For Caucasian populations, we constructed two PRS based on SNPs discovered in European-ancestry populations. One PRS was based on 167 SNPs (PRS-167) and the other based on 313 SNPs (PRS-313) from the Breast Cancer Association Consortium studies as previously published. For each of the Asian-, Hispanic- and African-ancestry populations we added additional ancestry specific SNPs to the PRS-167 or the PRS-313, that were literature curated or our own identified race/ethnicity SNPs that we validated to provide independent risk prediction for their ancestry group: Asian added 10 or 4 additional SNPs, Hispanic 2 SNPs, and African 8 and 12 SNPs, respectively to each model. We tested this approach using datasets from several case-control studies of multiple racial/ethnic populations and compared discrimination of the models using area under the receiver operating characteristic curve (AUROC). Furthermore, we applied our multi-racial/ethnic PRS-313 in a sample of ~3000 multi-racial/ethnic women from the Athena Breast Screening Registry, case-control sampled by Gail score to be at elevated (Gail >1.67) or average (Gail≤1.67) risk, to evaluate the impact of our multi-ethnic adjustment on risk stratification. Results: A multi-race/ethnicity adjusted PRS-313 and PRS-167 plus ethnicity specific SNPs has moderate-high discriminatory power with AUROCs of 0.65 and 0.64, respectively. The specificity of our PRS-167 in the different race/ethnicity ancestries performs relatively well in Asian (AUROC 0.59) and Hispanic (AUROC 0.63) populations, but less so in African-ancestry (AUROC 0.56). Incorporating multi-race/ethnicity PRS into Gail model selected women, resulted in 20% of average-risk women transitioning to risk above 1.67%, and conversely, 38% of elevated risk patients were reclassified to average risk. Conclusion: We constructed a PRS risk score that can be applied to multi-ethnic populations and found moderate-high discrimination. Additional work is needed for the African-ancestry population. The addition of a multi-race/ethnicity SNP model to risk classification based on the Gail model significantly changes risk stratification and clinical care recommendations due to down- or up-reclassification of women at average versus elevated risk. Citation Format: Sarah Theiner, Donglei Hu, Scott Huntsman, Yiwey Shieh, Laura Fejerman, Irene Acerbi, Sarah D Sawyer, Paige Kendall, Wei Zheng, Dezheng Huo, Olufunmilayo I Olopade, Christopher Haiman, Karla Kerlikowske, Steven Cummings, Ester John, Gabriela Torres-Mejia, Lawrence H Kushi, Denise Wolf, Jeffery A Tice, David A Pearce, Laura Esserman, Athena Breast Health Network Investigators and Advocate Partners, Laura J van ‘t Veer, Elad Ziv. A breast cancer multi-racial/ethnic polygenic risk score for improved personalized breast cancer screening [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 P2-10-05.

A 23-year-old male with a history of posterior urethral valves complicated by chronic kidney disease requiring renal transplantation at age 19 was incidentally found to have extensive gingival overgrowth on physical examination ( Figs. 1 and 2). The gingival overgrowth developed several months after the patient's transplant and progressively worsened. His medications had included mycophenolate mofetil, tacrolimus, and amlodipine since his renal transplant 4 years prior.Medications other than anticonvulsants and cyclosporine can cause gingival overgrowth. 1 This patient's gingival overgrowth was attributed to the combination of amlodipine and tacrolimus. Limited studies have demonstrated low rates of gingival overgrowth with tacrolimus, 1 with prevalence ranging from 0% to 29%. However, the incidence increases with concomitant use of calcium channel blockers and in those with poor oral hygiene. 2 As single agents, calcium channel blockers have also rarely been reported to induce gingival overgrowth. 1Proper oral hygiene can help prevent progression, but will not reverse gingival overgrowth. Treatment options include discontinuation of causative medications and gingivectomy, although recurrence rates with this procedure are as high as 40%.

Background: The U.S. Preventive Services Task Force recommends that women with a >3% five-year risk of developing breast cancer consider taking selective estrogen receptor modifiers (SERMs) or aromatase inhibitors (AIs) to reduce their risk. Polygenic risk score (PRS), calculated by adding the individual breast cancer risk association for each common genetic variant (SNP), has been found to predict women at low- to high-risk of breast cancer. We analyze associations between SNP risk alleles and known breast cancer risk factors (ethnicity, family history of breast cancer and number of biopsies); furthermore, we quantify the likely impact on chemoprevention recommendations by adding the PRS to known risk models in a subset of women participating in the University of California 100,000 women Athena Breast Health Network. Methods: Our research cohort included 838 women with no previous diagnosis of breast cancer from the University of California, San Francisco, and was enriched for women determined to be at elevated risk for developing breast cancer by the Gail model. A panel of 75 breast cancer risk SNPs were evaluated on saliva and blood samples (Akesogen Inc; COGS oncochip array). The PRS for each patient was calculated by converting the odds ratio for each SNP into a likelihood ratio (LR) and combining LR's across SNPs. Breast Cancer Surveillance Consortium (BCSC), Gail, BCSC-PRS and Gail-PRS scores (risk models incorporating PRS within a Bayesian framework), were evaluated for each patient. Associations between variables were assessed using t-test or ANOVA. A threshold of p<0.05 was used to assess significance. Results: Women in this study carry an average of 65 risk allele SNPs (of 150, 2 per locus). By ANOVA, there is a statistically significant association between the SNPs risk allele count and ethnicity (p = 0.014), with a trend towards association with a family history of a first-degree relative with breast cancer (p = 0.053). PRS is significantly associated with a family history breast cancer (p = 0.031); neither SNP allele count nor PRS associates with previous biopsy status. We found by adding PRS that 12% (86/707) and 13% (104/776) of patients with a prior BCSC or Gail score <3% five-year risk, respectively, changed classifications and would be eligible for chemoprevention. Conversely, 37% (36/98) and 36% (22/62) of patients with a BCSC or Gail score of >3% five-year risk, respectively, changed classifications by adding PRS and would no longer be eligible for chemoprevention. Conclusion: The addition of SNP based PRS to BCSC and Gail models significantly changes how women are classified and as a result changes whether risk reducing agents are recommended. PRS will be combined with BCSC and genetic test results for 9 breast cancer genes to calculate a women's breast cancer risk in the PCORI-funded Athena WISDOM study of 100,000 women, comparing risk-based vs. annual mammography screening. Citation Format: Sarah Theiner, Sarah D. Sawyer, Paige Kendall, Alexandra S. Perry, Denise Wolf, Scott Huntsman, Bo Pan, Jeffery A. Tice, David A. Pearce, Thomas Cink, Laura Esserman, Elad Ziv, Laura van ‘t Veer. Common genetic variants associated with breast cancer risk used in the Athena study to enhance models identifying women for breast cancer chemoprevention. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2623.

Background: A major area of innovation in breast cancer (BC) is improving risk models for screening efforts. The University of California Athena Breast Health Network uses several risk models, including the Gail model [PMID: 10491430]. The Breast Cancer Surveillance Consortium (BCSC) model [PMID: 18316752] which integrated information on breast density was developed and validated by UCSF investigators and reports Asian-specific risk. The Peking Union Medical College (PUMC) model [PMID: 22662004] is developed from a case-control study in China and reports BC risk in Chinese women. Risk factors and their respective weights included in these models are different. Methods: From December 2012 to April 2014, 2,305 women without previous history of BC and consented to research were enrolled in the UCSF Athena screening cohort. Questions asked included risk factors used in Gail, BCSC and PUMC model, such as age, age of menarche (AOM), age at first live birth, body mass index (BMI), breast biopsy, breast density, hormone replacement therapy (HRT), oral contraceptives (OCP). Women were considered high risk when: Gail > 1.67% 5-year risk, BCSC > 1.67% 5-year risk, PUMC score > 30 (equals >0.20% 1-year risk) respectively. The distribution of the risk factors and the high risk population percentage were compared between Chinese women versus non-Chinese Asian and Caucasian women. Results: 402 Asian women comprise 17.4% of all 2,305 Athena screening cases with 234 Chinese, and 168 non-Chinese Asian (NCA). Differences in risk factor distribution were observed for the following: positive family history was observed 23% for Caucasian, 15% for Chinese and 13% for NCA (p=0.001), and previous breast biopsy was 27%, 17% and 22% respectively (p=0.002). Ever use of HRT was 36% in Caucasian, 17% in Chinese and 11% in NCA (p<0.001). Ever give birth was 68% in Chinese, 65% in NCA, and 57% in Caucasian (p=0.001), while the age at first live birth <30 was 35%, 33%, and 26% correspondingly (p=0.001). Breast density appeared to be higher in Asian women (p=0.095). The high risk proportions by each model are given in Table 1. Table 1 Percentage of high risk population by ethnicity for different risk modelsRisk modelsChinese (n=234)non-Chinese Asian (n=168)Caucasian (n=1,492)P ValueRisklowhighlowhighlowhighGail137 (58.6%)97 (41.4%)107 (63.7%)61 (36.3%)884 (59.2%)608 (40.8%)0.509BCSC211 (90.2%)23 (9.8%)152 (90.5%)16 (9.5%)864 (58.0%)626 (42.0%)0.000PUMC167 (71.7%)66 (28.3%)108 (64.7%)59 (35.3%)781 (52.6%)704 (47.4%)0.000 Conclusion: Chinese and NCA women have a lower proportion of high risk by the BCSC and PUMC model compared to the Caucasian women, whereas by Gail model these proportions appear to be similar. Citation Format: Bo Pan, Jeffrey Tice, Qiang Sun, Celia Kaplan, Zhou Yidong, Yali Xu, Songjie Shen, Changjun Wang, Alexandra Solomon, Lauren Ryan, Paige Kendall, Timothy Henderson, Laura Esserman, Beth Crawford, Athena Breast Health Network, Laura van 't Veer. The comparison of the distribution of breast cancer risk factor between Chinese women, non-Chinese Asian and Caucasian women in the screening cohort of Athena Breast Health Network [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-09-10.