Breast Cancer Polygenic Risk Scores Research Articles

Using polygenic risk modification to improve breast cancer prevention: study protocol for the PRiMo multicentre randomised controlled trial

IntroductionEstablished personal and familial risk factors contribute collectively to a woman’s risk of breast or ovarian cancer. Existing clinical services offer genetic testing for pathogenic variants in high-risk genes to...

Assessing the Risk Stratification of Breast Cancer Polygenic Risk Scores in a Brazilian Cohort

Polygenic risk scores (PRSs) for breast cancer have a clear clinical utility in risk prediction. PRS transferability across populations and ancestry groups is hampered by population-specific factors, ultimately leading to differences in variant effects, such as linkage disequilibrium and differences in variant frequency (allele frequency differences). Thus, locally sourced population-based phenotypic and genomic data sets are essential to assess the validity of PRSs derived from signals detected across populations. This study assesses the transferability of a breast cancer PRS composed of 313 risk variants (313-PRS) in a Brazilian trihybrid admixed ancestries (European, African, and Native American) whole-genome sequenced cohort, the Rare Genomes Project. 313-PRS was computed in the Rare Genomes Project (n=853) using the UK Biobank (UKBB; n=264,307) as reference. The Brazilian cohorts have a high European ancestry (EA) component, with allele frequency differences and to a lesser extent linkage disequilibrium patterns similar to those found in EA populations. The 313-PRS distribution was found to be inflated when compared with that of the UKBB, leading to potential overestimation of PRS-based risk if EA is taken as a standard. However, case controls lead to equivalent predictive power when compared with UKBB-EA samples with area under the receiver operating characteristic curve values of 0.66 to 0.62 compared with 0.63 for UKBB.

Limitations in next-generation sequencing-based genotyping of breast cancer polygenic risk score loci

Considering polygenic risk scores (PRSs) in individual risk prediction is increasingly implemented in genetic testing for hereditary breast cancer (BC) based on next-generation sequencing (NGS). To calculate individual BC risks, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) with the inclusion of the BCAC 313 or the BRIDGES 306 BC PRS is commonly used. The PRS calculation depends on accurately reproducing the variant allele frequencies (AFs) and, consequently, the distribution of PRS values anticipated by the algorithm. Here, the 324 loci of the BCAC 313 and the BRIDGES 306 BC PRS were examined in population-specific database gnomAD and in real-world data sets of five centers of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), to determine whether these expected AFs can be reproduced by NGS-based genotyping. Four PRS loci were non-existent in gnomAD v3.1.2 non-Finnish Europeans, further 24 loci showed noticeably deviating AFs. In real-world data, between 11 and 23 loci were reported with noticeably deviating AFs, and were shown to have effects on final risk prediction. Deviations depended on the sequencing approach, variant caller and calling mode (forced versus unforced) employed. Therefore, this study demonstrates the necessity to apply quality assurance not only in terms of sequencing coverage but also observed AFs in a sufficiently large cohort, when implementing PRSs in a routine diagnostic setting. Furthermore, future PRS design should be guided by the technical reproducibility of expected AFs across commonly used genotyping methods, especially NGS, in addition to the observed effect sizes.

Breast cancer polygenic risk score and patient survival outcomes in the Pathways study.

10502 Background: Many common, low-penetrance variants have been identified for breast cancer risk through genome-wide association studies, leading to the development of polygenic scores (PGS) to aggregate the effects of individual variants for risk prediction. Much less is known about genetic determinants of breast cancer-related outcomes. We hypothesized that breast cancer patients with high breast cancer PGS are at risk of a second breast-related adverse event, such as recurrence, contralateral breast cancer, or death. Methods: The Pathways Study is a prospective cohort study of breast cancer survivors enrolled shortly after diagnosis in 2006-2013 at Kaiser Permanente Northern California, with ongoing follow-up. Genome-wide genotype data were available from 3,995 patients for calculation of 4 PGS, including PGS313, PGS4k, PGS5k, and PGS6m. Scores were categorized into tertiles to investigate their associations with 7 survival outcomes including recurrence, contralateral second primary breast cancer, other second primary cancer, and death. Hazard ratios (HR) and 95% confidence intervals (CI) were derived from multivariable Cox proportional hazards regression models. Results: The median age at diagnosis was 60 (23.6-94.8) years, and most (68%) self-identified as White. Many women had estrogen receptor (ER)+ (83.4%), stage I and II tumors (89%), with 13.3% HER2+ tumors. The majority (60%) received lumpectomy and some type of adjuvant therapy (44.3% radiotherapy, 47.0% chemotherapy, and 74.6% received hormonal therapy). By December 31, 2021, the median follow-up time was 10.5 (0.2-14.2) years, with 504 recurrences, 419 total second primary cancers, 146 contralateral second primary breast cancers, 237 non-breast second primary cancers, and 762 deaths (352 breast cancer-specific deaths) observed. In Table 1, patients with medium (T2) and high (T3) PGS313 had higher risk of recurrence, but not contralateral breast cancer or other second primary cancer. They also had higher risk of all-cause death, breast cancer-specific death, total breast cancer event, and invasive disease. The other 3 PGS were not associated with survival outcomes examined. Conclusions: Our study reveals that breast cancer patients with higher PGS313 had higher risk of recurrence, total breast cancer and invasive breast cancer events, and death. These findings highlight a potential role of breast cancer PGS for prognosis, warranting further exploration. [Table: see text]

Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants.

To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer. Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated with gene expression and DNA methylation levels. A new SMR-based PRS was computed from the identified variants (functional PRS) and compared to an established 313-variant breast cancer PRS (GWAS PRS). The two scores were evaluated in 3560 breast cancer cases and 3383 non-cancer controls and also in a prospective study (n = 10,213) comprising 418 cases. We identified 149 variants showing pleiotropic association with breast cancer risk (eQTLHEIDI > 0.05 = 9, mQTLHEIDI > 0.05 = 165). The discriminatory ability of the functional PRS (AUCcontinuous [95% CI]: 0.540 [0.526 to 0.553]) was found to be lower than that of the GWAS PRS (AUCcontinuous [95% CI]: 0.609 [0.596 to 0.622]). Even when utilizing 457 distinct variants from both the functional and GWAS PRS, the combined discriminatory performance remained below that of the GWAS PRS (AUCcontinuous, combined [95% CI]: 0.561 [0.548 to 0.575]). A binary high/low-risk classification based on the 80th centile PRS in controls revealed a 6% increase in cases using the GWAS PRS compared to the functional PRS. The functional PRS identified an additional 12% of high-risk cases but also led to a 13% increase in high-risk classification among controls. Similar findings were observed in the SCHS prospective cohort, where the GWAS PRS outperformed the functional PRS, and the highest-performing PRS, a combined model, did not significantly improve over the GWAS PRS. While this study identified potentially functional variants associated with breast cancer risk, their inclusion did not substantially enhance the predictive accuracy of the GWAS PRS.

Abstract PS10-06: Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction

Abstract Artificial intelligence (AI) models based on deep learning of mammogram images have been developed for lesion detection and diagnosis as well as breast cancer (BC) risk. We and others have shown that these AI models, coupled with breast density, predict long-term breast cancer risk. Common germline genetic variation, in particular a polygenic risk score (PRS) that captures the cumulative impact of hundreds of common risk variants, has been shown associated with 3-fold increased BC risk. The BC-PRS may complement imaging risk factors to improve identification of women at high BC risk and better target prevention strategies. We evaluated the contribution of a BC-PRS to two imaging-based AI models (Transpara AI cancer detection system and Mirai 5-year risk) and breast density measures to long-term BC risk. We hypothesized that the BC-PRS will improve long-term risk prediction above AI and density measures. We examined this hypothesis within a cohort of 10,271 women enrolled to the Mayo Clinic Biobank between 2009 and 2015. Eligible women had no prior cancer, were ages 35 to 85, had a full-field digital screening mammogram within two years of enrollment and genotyping by Regeneron. Incident invasive and DCIS BC was identified through the Mayo tumor registry and follow-up questionnaires, and person years were calculated from date of mammogram through date of BC, last mammogram at Mayo or last questionnaire. We estimated Transpara AI malignancy score (1-10), 5-year Mirai risk and Volpara volumetric density measures (volumetric percent density, dense volume) on screening mammograms at enrollment and obtained clinical BI-RADS density. We calculated the BC-PRS using 268 SNPs (of the 313-SNP score) and the weighted sum of the SNPs using odds ratios from the largest BC GWAS of European ancestry to date. We performed cox proportional hazards regression adjusting for age and BMI to estimate hazard ratios (HRs), 95% confidence intervals (CI), and C-statistics (AUC) to describe the contribution of BC-PRS (per standard deviation (SD)) to the association of AI scores and breast density with BC risk. Likelihood ratio tests (LRT) and bootstrapping were used to compare model performance with vs. without inclusion of BC-PRS, at 10-years follow-up. Comparisons to the Tyrer-Cuzick model are ongoing. Over a median of 9.1 years (Interquartile range, 6.8 to 10.6), 446 incident BC were identified of which 285 invasive and 103 DCIS were confirmed by pathology report (60 were self-reported); 399 (256 invasive) occurred within 10 years and were used in primary analyses. Transpara score [HR per 1 unit=1.18 (1.14, 1.23), AUC=0.655 (0.628, 0.682)] and Mirai risk [HR per SD log risk=1.54 (1.43, 1.65), AUC=0.681 (0.655, 0.706)] were both associated with BC. All three breast density measures were also significantly associated with BC, including in models with AI scores (Table). Addition of the BC-PRS to models with AI scores and breast density improved both model fit (All PLRT < 0.001; Table) and discrimination, with increases in AUC ranging from 0.033 to 0.034 for models with Transpara and density and 0.025 to 0.026 for models with Mirai and density (all P-values < 0.001; Table). Analyses restricted to invasive BC showed identical results. But, the BC-PRS contribution at 5-year follow-up was attenuated. In this cohort study, we found BC-PRS contributed to long-term risk prediction of BC above imaging AI models and breast density. Imaging and genetic risk factors are complementary and may outperform existing risk models and provide more accurate risk assessment to aid in precision prevention strategies. Citation Format: Celine Vachon, Christopher Scott, Imon Banerjee, Ramon Correa Moderno, Dan Hursh, Matthew Jensen, Yvonne Wang, Kathleen Brandt, Karla Kerlikowske, Sandhya Pruthi, Fergus Couch, Stacey Winham. Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-06.

Breast Cancer Polygenic Risk Score Validation and Effects of Variable Imputation.

Breast cancer (BC) is a complex disease affecting one in eight women in the USA. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to augment current risk models, but replication is often limited. We evaluated 2 robust PRSs with 313 and 3820 SNPs and the effects of multiple genotype imputation replications in BC cases and control populations. Biological samples from BC cases and cancer-free controls were drawn from three European ancestry cohorts. Genotyping on the Illumina Global Screening Array was followed by stringent quality control measures and 20 genotype imputation replications. A total of 468 unrelated cases and 4337 controls were scored, revealing significant differences in mean PRS percentiles between cases and controls (p < 0.001) for both SNP sets (313-SNP PRS: 52.81 and 48.07; 3820-SNP PRS: 55.45 and 49.81), with receiver operating characteristic curve analysis showing area under the curve values of 0.596 and 0.603 for the 313-SNP and 3820-SNP PRS, respectively. PRS fluctuations (from ~2-3% up to 9%) emerged across imputation iterations. Our study robustly reaffirms the predictive capacity of PRSs for BC by replicating their performance in an independent BC population and showcases the need to average imputed scores for reliable outcomes.

Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank.

Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorization needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI). We analyzed data from 126,490 post-menopausal women of "White British" ancestry, aged 40 to 69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer-Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected. The Harrell's C statistic of the 10-year risk from the Tyrer-Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models [0.080 (95% confidence interval (CI), 0.053-0.104) and 0.051 (95% CI, 0.030-0.073), respectively], with negligible impact on controls. The addition of a PRS for breast cancer to the well-established Tyrer-Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification. These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.

Abstract 7010: A pan-tissue investigation of epigenetic aging in association with genetic risk for cancer among European descendants

Abstract Epigenetic age (EA) is recognized as a valuable biomarker of aging. The discrepancy between epigenetic and chronological age (CA), termed epigenetic age acceleration (EAA), has been associated with increased mortality from various age-related diseases, including cancer. However, previous studies primarily focused on the EA in blood cells, resulting in a notable gap in understanding the EA in cancer-relevant tissues and its relationship to cancer development. To examine if genetic risks for seven distinct cancers are associated with EAA in cancer-relevant tissues and blood cells, we utilized Illumina EPIC-array-based DNA methylation data and whole genome sequencing (WGS)-derived genotype data of 659 normal tissue and 42 whole blood samples from 315 European-ancestry subjects in the Genotype-Tissue Expression (GTEx) project. EA was estimated across 30 female breast, 167 colon, 41 kidney, 163 lung, 118 ovary, 96 prostate, 44 testis, and 42 whole blood samples via seven established epigenetic clocks: HorvathAge, GrimAge, GrimAge2, HannumAge, PhenoAge, Zhang-BLUP-Age, and Zhang-EN-Age. EAA was calculated as the residual of EA regressed on CA. For each participant, a polygenic risk score (PRS) was computed to assess the genetic risk for each cancer, using validated cancer-specific PRSs for European populations from the Polygenic Score Catalogue. Linear regression analysis was performed to evaluate the association between cancer PRS and EAA, adjusting for body mass index, sex (where applicable), cigarette smoking, and WGS platform and library construction protocol. All EA estimates of cancer-relevant tissues and blood cells exhibited significant correlations with CA (maximum P=2.44 × 10−3). Among them, GrimAge, known for its strong predictive capacity for mortality, had the most pronounced correlation with CA, with Spearman coefficients ranging from 0.80 in blood cells to 0.98 in breast tissues. A higher prostate cancer PRS was significantly associated with increased EAA in normal prostate tissues measured by PhenoAge (β=2.49; P=0.01), Zhang-BLUP-Age (β=1.71; P=0.02), HannumAge (β=1.36; P=0.04), and GrimAge (β=0.73; P=0.04). In addition, a higher PRS for renal cell carcinoma showed a significant association with increased GrimAge acceleration in normal kidney tissues (β=0.82; P=0.02). Moreover, breast and lung cancer PRSs were significantly associated with increased EAA in blood cells. Specifically, breast cancer PRS was associated with Zhang-BLUP-Age acceleration (β=0.99; P=0.04), while lung cancer PRS showed associations with HannumAge (β=1.78; P=0.01) and Zhang-EN-Age acceleration (β=0.94; P=0.02). Our results suggest a potential connection between genetic susceptibility to certain cancers and accelerated aging in relevant tissues and/or blood cells before cancer onset. Future large-scale studies are needed to substantiate and expand upon our findings. Citation Format: Yaxin Chen, Yawen Qi, Gang Wang, Yufang Xie, Yaohua Yang, Weimin Li. A pan-tissue investigation of epigenetic aging in association with genetic risk for cancer among European descendants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7010.

Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses.

A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that(a) for breast cancer diagnosed at a young age, for example,<50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS-disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first-degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.

Comprehensive Inherited Risk Estimation for Risk-Based Breast Cancer Screening in Women.

Family history (FH) and pathogenic variants (PVs) are used for guiding risk surveillance in selected high-risk women but little is known about their impact for breast cancer screening on population level. In addition, polygenic risk scores (PRSs) have been shown to efficiently stratify breast cancer risk through combining information about common genetic factors into one measure. In longitudinal real-life data, we evaluate PRS, FH, and PVs for stratified screening. Using FinnGen (N = 117,252), linked to the Mass Screening Registry for breast cancer (1992-2019; nationwide organized biennial screening for age 50-69 years), we assessed the screening performance of a breast cancer PRS and compared its performance with FH of breast cancer and PVs in moderate- (CHEK2)- to high-risk (PALB2) susceptibility genes. Effect sizes for FH, PVs, and high PRS (>90th percentile) were comparable in screening-aged women, with similar implications for shifting age at screening onset. A high PRS identified women more likely to be diagnosed with breast cancer after a positive screening finding (positive predictive value [PPV], 39.5% [95% CI, 37.6 to 41.5]). Combinations of risk factors increased the PPVs up to 45% to 50%. A high PRS conferred an elevated risk of interval breast cancer (hazard ratio [HR], 2.78 [95% CI, 2.00 to 3.86] at age 50 years; HR, 2.48 [95% CI, 1.67 to 3.70] at age 60 years), and women with a low PRS (<10th percentile) had a low risk for both interval- and screen-detected breast cancers. Using real-life screening data, this study demonstrates the effectiveness of a breast cancer PRS for risk stratification, alone and combined with FH and PVs. Further research is required to evaluate their impact in a prospective risk-stratified screening program, including cost-effectiveness.

Cancer risks among first-degree relatives of women with a genetic predisposition to breast cancer.

Associations between germline alterations in women and cancer risks among their relatives are largely unknown. We used women from two Swedish cohorts (KARMA and pKARMA), including 28,362 women with genotyping data and 13,226 with sequencing data. Using Swedish Multi-Generation Register, we linked these women to 133,389 first-degree relatives. Associations between protein-truncating variants (PTVs) in 8 risk genes and breast cancer polygenic risk score (PRS) in index women and cancer risks among their relatives were modeled via Cox regression. Female relatives of index women who were PTV carriers in any of the 8 risk genes had an increased breast cancer risk compared to those of non-carriers (HR1.85, 95% CI: 1.52-2.27), with the strongest association found for PTVs in BRCA1/2. These relatives had a statistically higher risk of early-onset than late-onset breast cancer (P = .001). Elevated breast cancer risk was also observed in female relatives of index women with higher PRS (HR per SD: 1.28, 95% CI: 1.23-1.32). The estimated lifetime risk was 22.3% for female relatives of PTV carriers and 14.4% for those related to women in the top PRS quartile. Moreover, relatives of index women with PTV presence (HR: 1.30, 95% CI: 1.06-1.59) or higher PRS (HR per SD: 1.04, 95% CI: 1.01-1.07) were also at higher risk of non-breast-HBOC cancers, including prostate, ovarian, pancreatic cancer, and melanoma. Both PTVs of risk genes and higher PRS in index women are associated with an increased risk of breast and other HBOC-related cancers among relatives.

Breast Cancer Polygenic-Risk Score Influence on Risk-Reducing Endocrine Therapy Use: Genetic Risk Estimate (GENRE) Trial 1-Year and 2-Year Follow-Up.

Risk-reducing medications for breast cancer are considerably underused. Informing women at risk with precise and individualized risk assessment tools may substantially affect the incidence of breast cancer. In our study, a risk assessment tool (IBIS-polygenic-risk score) yielded promising results, with 39% of women at highest risk starting preventive medication.

Association between allostatic load and breast cancer risk: a cohort study

BackgroundAllostatic load (AL) reflects the collective load of chronic stress during lifetime. Previous studies have shown that higher AL is associated with poor clinical outcomes among breast cancer patients. However, the relationship between AL and breast cancer risk is still unclear.MethodsTo fill the gap, we analyzed the association between AL and the development of breast cancer in 181,455 women identified from the UK Biobank.ResultsDuring the follow-up from 2006 to 2020, 5,701 women were diagnosed with incident breast cancer. Significantly higher AL was observed among incident breast cancer cases than all study participants (mean: 2.77 vs. 2.63, P < 0.01). Univariate Cox regression analysis indicated the risk of breast cancer was increased by 5% per one AL unit increase (hazard ratio (HR) = 1.05, 95% confidence interval (CI) 1.04, 1.07). In multivariate analyses, after adjusting demographics, family history of breast cancer, reproductive factors, socioeconomic status, lifestyle factors, and breast cancer polygenic risk score (PRS), the significant association remained (HR = 1.05, 95%CI 1.03, 1.07). The significant relationship was further confirmed in the categorical analysis. Compared with women in the low AL group (AL: 0 ~ 2), those in the high AL group (AL: 3 ~ 11) had a 1.17-fold increased risk of breast cancer (HR = 1.17, 95%CI 1.11, 1.24). Finally, in the stratified analysis, joint effects on the risk of breast cancer were observed between the AL and selected known breast cancer risk factors, including age, family history of breast cancer, PRS, income, physical activity, and alcohol consumption.ConclusionIn summary, those findings have demonstrated that higher AL was associated with an increased breast cancer risk in women. This association is likely independent of known breast cancer risk factors. Thus, the AL could be a valuable biomarker to help breast cancer risk prediction and stratification.

A Breast Cancer Polygenic Risk Score Is Feasible for Risk Stratification in the Norwegian Population.

Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population. To evaluate the performance of PRS models for BC in a Norwegian dataset. We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool. The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406-1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples. The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway.

Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank

PurposeThe benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening. MethodsWe studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk. ResultsIn total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail2-year > 0.5%: 47%, PRS2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability. ConclusionRisk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors.

Validation of a breast cancer polygenic risk score in 15,490 Brazilians using exome sequencing.

10631 Background: Brazil has a highly admixed population with significant Southern European, African and Native American genetic ancestries. Approximately 75% of the population has two or more continental ancestries. Therefore, breast cancer polygenic risk scores (PRS) developed in predominantly European populations requires validation in the Brazilian population. Compared with genotyping arrays and whole genome sequencing, affordable and scalable whole exome sequencing (WES) allows genotyping of both rare and common variants in the targeted regions and imputation of common variants not directly captured. In this study we validate a previously developed 3820-variant PRS using WES in 15,490 Brazilians. Methods: The cohort consisted of 6,362 women with a history of breast cancer (mean age 49.6, stddev 11.6) and 9,128 unphenotyped adults as controls (mean age 41.6, stddev 13.3). WES were performed on germline DNA and variants were called and curated. Imputation was done using the 1000 Genomes Phase 3 reference panel for variants falling outside the WES captured regions. After excluding subjects with known pathogenic or likely pathogenic variants in BRCA1, BRCA2, PALB2, PTEN, or TP53, and first or second-degree relatives of the probands, 14,577 individuals remained in the final analysis (5,730 cases and 8,847 controls). We restricted PRS calculation to 2,893 of the original 3,820 variants with an estimated global imputation accuracy of 96%. Odds ratios (OR) were adjusted using the top 10 principal components. Results: The breast cancer PRS showed a highly significant correlation with cancer risk (p-value = 7.1e-81, OR = 1.43, 95% confidence interval (CI) = 1.38-1.48 per PRS standard deviation). Compared with women in the middle deciles, those in the highest 10% had an OR of 1.93, while those in the lowest 10% had an OR of 0.52 (Table). Conclusions: In this study we show that WES can capture 76% of the variants included in a previously developed and validated breast cancer PRS, and the calculated PRS is correlated with breast cancer risk in the admixed Brazilian population. This opens the door for clinical studies into the application of PRS in Brazil, particularly in women without mutations in breast cancer high-penetrance genes. In addition, our WES-based PRS strategy can be quickly, affordably, and seamlessly adopted by labs that already offer Hereditary Breast Cancer panels that use WES as a backbone. [Table: see text]

Prediction of breast cancer risk for sisters of women attending screening.

Risk assessment is important for breast cancer prevention and early detection. We aimed to examine whether common risk factors, mammographic features, and breast cancer risk prediction scores of a woman were associated with breast cancer risk for her sisters. We included 53 051 women from the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Established risk factors were derived using self-reported questionnaires, mammograms, and single nucleotide polymorphism genotyping. Using the Swedish Multi-Generation Register, we identified 32 198 sisters of the KARMA women (including 5352 KARMA participants and 26 846 nonparticipants). Cox models were used to estimate the hazard ratios of breast cancer for both women and their sisters, respectively. A higher breast cancer polygenic risk score, a history of benign breast disease, and higher breast density in women were associated with an increased risk of breast cancer for both women and their sisters. No statistically significant association was observed between breast microcalcifications and masses in women and breast cancer risk for their sisters. Furthermore, higher breast cancer risk scores in women were associated with an increased risk of breast cancer for their sisters. Specifically, the hazard ratios for breast cancer per 1 standard deviation increase in age-adjusted KARMA, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), and Tyrer-Cuzick risk scores were 1.16 (95% confidence interval [CI] = 1.07 to 1.27), 1.23 (95% CI = 1.12 to 1.35), and 1.21 (95% CI = 1.11 to 1.32), respectively. A woman's breast cancer risk factors are associated with her sister's breast cancer risk. However, the clinical utility of these findings requires further investigation.

Development and evaluation of a novel educational program for providers on the use of polygenic risk scores

PurposeThis study aimed to develop an online educational program for using polygenic risk score (PRS) for breast and ovarian cancer risk assessments and to evaluate the impact on the attitudes, confidence, knowledge, and preparedness of genetic health care providers (GHPs). MethodsThe educational program comprises an online module that covers the theoretical aspects of PRS and a facilitated virtual workshop with prerecorded role-plays and case discussions. Data were collected in pre- and posteducation surveys. Eligible participants were GHPs working in Australian familial cancer clinics registered to recruit patients for a breast and ovarian cancer PRS clinical trial (n = 12). ResultsA total of 124 GHPs completed the PRS education, of whom 80 (64%) and 67 (41%) completed the pre- and posteducation surveys, respectively. Before education, GHPs reported limited experience, confidence, and preparedness using PRS, but they recognized its potential benefits. After education, GHPs indicated improved attitudes (P ≤ .001), confidence (P ≤ .001), knowledge (P ≤ .001), and preparedness (P ≤ .001) to use PRS. Most GHPs thought that the program entirely met their learning needs (73%) and was completely relevant to their clinical practice (88%). GHPs identified PRS implementation barriers, including limited funding models, diversity issues, and need for clinical guidelines. ConclusionOur education program improved GHP attitudes, confidence, knowledge, and preparedness for using PRS/personalized risk and provides a framework for the development of future programs.

184P PGS000508 as a prediction for breast cancer risk in Taiwanese populations

Breast cancer has been the most common cancer diagnosed among women in Taiwan since 2003. Genetic variants account for high risks of developing breast cancer was less studied in Asian population. Many researches had discovered variant polygenic risk score (PRS) to evaluate the risk of developing breast cancer. This study aims to determine a predictive breast cancer PRS for Taiwanese populations and assess the heterogeneity in the relationships with breast cancer risk by biomarker testing, age at diagnosis, clinical and pathological stages. The development dataset comprised 28,443 control subjects and 1,501 case subjects from Taiwan Precision Medicine Initiative (TPMI) array and breast cancer registry lists in Taichung Veterans General Hospital (TCVGH). Samples were analyzed by using breast cancer associated PGS Catalog, and significant PGSs were selected by stepwise logistic regression. The best performing PGS was yielded and 1,501 breast cancer patients were evaluated for further heterogeneity of breast cancer risk analysis. Logistic regression results showed PGS000508 were significantly associated with breast cancer risk after multiple testing (odds ratio, 3.57; P=1.0850 E-53). Dose-response association was observed. Women in the highest quartile had a significantly increased risk compared to women in the lower quartile( odds ratio, 1.95; 95% confidence interval 1.75-2.17; P=3.54 E-34). Over 1,501 breast cancer diagnosed women stratified by PRS distribution, the highest quartile women had more advanced pathological stage (p=0.03); younger age while first diagnosis (p=0.01) and positive hormone status (p=0.03) compared to women in the lower quartile. Our study identified PGS000508 as a risk predictor of breast cancer among Taiwanese women as well as advanced pathological stage, positive hormone status and younger age at first diagnosis.