BOADICEA Model Research Articles

Validation of the BOADICEA model for epithelial tubo-ovarian cancer risk prediction in UK Biobank

BackgroundThe clinical validity of the multifactorial BOADICEA model for epithelial tubo-ovarian cancer (EOC) risk prediction has not been assessed in a large sample size or over a longer term.MethodsWe evaluated the model discrimination and calibration in the UK Biobank cohort comprising 199,429 women (733 incident EOCs) of European ancestry without previous cancer history. We predicted 10-year EOC risk incorporating data on questionnaire-based risk factors (QRFs), family history, a 36-SNP polygenic risk score and pathogenic variants (PV) in six EOC susceptibility genes (BRCA1, BRCA2, RAD51C, RAD51D, BRIP1 and PALB2).ResultsDiscriminative ability was maximised under the multifactorial model that included all risk factors (AUC = 0.68, 95% CI: 0.66–0.70). This model was well calibrated in deciles of predicted risk with calibration slope=0.99 (95% CI: 0.98–1.01). Discriminative ability was similar in women younger or older than 60 years. The AUC was higher when analyses were restricted to PV carriers (0.76, 95% CI: 0.69–0.82). Using relative risk (RR) thresholds, the full model classified 97.7%, 1.7%, 0.4% and 0.2% women in the RR < 2.0, 2.0 ≤ RR < 2.9, 2.9 ≤ RR < 6.0 and RR ≥ 6.0 categories, respectively, identifying 9.1 of incident EOC among those with RR ≥ 2.0.DiscussionBOADICEA, implemented in CanRisk (www.canrisk.org), provides valid 10-year EOC risks and can facilitate clinical decision-making in EOC risk management.

Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers

BackgroundNo validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA...

A study on patients’ selection for BRCA1 and BRCA2 mutations testing by different models in Libyan women with breast cancer

ABSTRACT Introduction The BRCA mutation spectrum of familial breast cancer in Libya remains unknown. Several genetic models developed to predict the probability of BRCA1/2 mutations have not been applied in Libya, where the NCCN criteria are used for highly penetrating breast cancer susceptibility genes. We aimed to predict BRCA1/2 mutation probability in familial breast cancer and genetic testing eligibility using BOADICEA and BRCAPRO models and NCCN criteria. Methods BRCA1/2 mutations were retrospectively predicted in 62 women with familial breast cancer between 2018 and 2021. Logistic regression, ROC analysis, and area under the curve were used to compare NCCN referral criteria with the BRCAPRO and BOADICEA scores. Results Thirty-two out of 62 breast cancer patients (51.6%), with a mean age of 43.5 ± 8 years, were predicted as BRCA mutation carriers by both models. BRCAPRO predicted BRCA1 and BRCA2 mutations in 27.4% and 41.9% of the women, respectively. BOADICEA predicted 8% for BRCA1 and 29% for BRCA2. At least one NCCN criterion was met by 50/62 women (80.6%). Three criteria were statistically significant predictors in BRCAPRO and BOADICEA: breast cancer at age ≤50 years with one or more close blood relatives with breast cancer, breast cancer patient with a close relative of male breast cancer, and triple-negative breast cancer. For the three respective criteria, sensitivity was 0.78, 0.89, and 0.75, specificity was 0.33, 0.39, and 0.22, area under curve was 0.72, 0.75, and 0.76, positive predictive value was 55%, 61%, and 51%, and negative predictive value was 58.5%, 77%, and 45%. Conclusions Our study highlights that certain aspects of the NCCN criteria demonstrate variations in significance when compared to the BRCAPR and BOADICEA models. For the first time, these models were used to predict BRCA mutations in Libyan women, and our finding indicates that these models are promising for improving genetic testing decision-making.

Abstract 4868: Avoiding the pitfalls in developing and validating prediction models for rare outcomes in nested case-control

Abstract Background: Molecular-biomarker-based prediction models, whether based on statistics or artificial intelligence, are becoming increasingly popular in the medical community as an alternative or complement to more traditional prediction tools, such as staging systems. Population-based cohorts are the preferred study design for building and validating such models, but they are generally expensive and frequently unfeasible, particularly, when the outcome of interest is rare, and when the collected data include expensive or difficult-to-obtain molecular biomarkers. As such, studies often develop or validate risk models in cohorts obtained based on data availability, rather than on the representativeness of a target population. Nested case-control (NCC) is an efficient study design for building and validating such models in a rare outcome setting, but it requires a fully enumerated source population, and appropriate methodologies to accommodate the under-sampling of the controls and any matching. Unfortunately, these methodologies are not systematically described in a form easily accessible to clinicians. Methods: We systematically characterized how to correctly develop and evaluate the performance of prediction models in NCC cohorts, by weighing the subjects based on sampling probabilities to account for the sub-cohort sampling. We illustrated the use of weighted metrics for the NCC, with a validation of a model that predicts breast cancer development - BOADICEA version 5 - in the population-based Rotterdam study. We used the C-index, threshold-based metrics, observed-to-expected events ratio (O/E ratio), calibration slope, and decision curve analysis as performance metrics. We compared the metrics obtained in the full cohort with those obtained in NCC cohorts sampled from the Rotterdam study, with and without a matched design. Results: NCC cohorts of 326 women were derived from the full cohort of 4,377 women from the Rotterdam Study. Performance metrics without weight adjustment were biased on the NCC cohorts: the unweighted C-index of the BOADICEA model was 0.61 (0.58-0.63) for the unmatched design. However, with weight adjustment, the C-index in the NCC cohorts (0.65 (0.61-0.69)) corresponds to the C-index in the full cohort (0.65 (0.62-0.69)), despite the NCC cohort being much smaller in size. Similarly, weighted adjustments of O/E ratio, threshold-based metrics, and net benefit for decision curves were unbiased estimates of the corresponding metrics in the full cohort, while the corresponding unweighted metrics were biased. Conclusions: We provided a practical guide for clinicians on how to develop and validate prediction models in NCC cohorts. We showed that this study design is an efficient solution, in case of expensive or difficult-to-obtain biomarkers, and when the outcome is rare; but the performance metrics must be appropriately adjusted to the sampling procedure. Citation Format: Barbara Rentroia-Pacheco, Domenico Bellomo, Inge MM Lakeman, Marlies Wakkee, Loes M. Hollestein, David van Klaveren. Avoiding the pitfalls in developing and validating prediction models for rare outcomes in nested case-control [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 4868.

Abstract IA18: Genetic susceptibility to breast cancer: Recent advances and application to personalised prevention

Abstract The past two decades have seen dramatic progress in our understanding of the inherited basis of breast cancer, driven by large international collaborative epidemiological studies. Genetic factors identified include rare coding variants in susceptibility genes, and more than 200 common polymorphisms, identified through genome-wide association studies (GWAS) and largely in non-coding regions, that confer more moderate risks. The majority of the common variants are more strongly associated with ER-positive disease, but a subset is specifically associated with ER-negative disease. Variants are strongly enriched for regulatory regions and transcription factor binding sites, and the putative target genes are strongly enriched for known breast cancer drivers: however, the underlying target genes and mechanisms remain largely obscure. The associations for common variants appear to be largely independent of each other and can be summarised by a polygenic risk score (PRS) that is strongly predictive of risk. The most extensively validated PRS, comprising 313 SNPs, is associated with a relative risk of approximately 1.6 per standard deviation; women in the top 1% of the risk distribution have an approximately 4-fold risk of ER-positive disease and 3-fold risk of ER-negative disease. The PRS was developed in women of European ancestry but is also predictive of risk in Asian ancestry women, though less so in African ancestry women. In contrast, despite their widespread use, the evidence for association with cancer for coding variants in genes on genetic testing panels is often weak. Recent very large-scale targeted sequencing studies, however, have clarified the position, providing strong evidence for an association between protein truncating or missense variants and breast cancer risk for nine genes: BRCA1, BRCA2, ATM, CHEK2, PALB2, BARD1, RAD51C, RAD51D and TP53. Recent large-scale exome-wide sequencing analysis has shown that rare variants in several additional genes are also risk associated, with the majority being tumour suppressor genes: these include MAP3K1, ATRIP and SAMHD1. These analyses also demonstrate, however, that the contribution of coding variation, beyond the known genes, is likely to be small. The currently identified variants explain about half of the familial risk of breast cancer. Part of the “missing” heritability is due to weaker common variants, many of which should be identified in ongoing larger GWAS. The causes of the remainder are obscure and may include rare non-coding variants, repeat polymorphisms, and epistatic interactions. Risk discrimination is most powerful when all risk factors are considered. The BOADICEA model, implemented in the CanRisk tool, provides a well validated model that incorporates the effects of gene variants, polygenic risk scores, lifestyle factors and breast density to predict breast and ovarian cancer risk. Multiple translational studies are ongoing to evaluate the feasibility and utility of such personalised risk prediction for stratifying screening and prevention. Citation Format: Douglas F Easton. Genetic susceptibility to breast cancer: Recent advances and application to personalised prevention [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr IA18.

Validation of the BOADICEA model for predicting the likelihood of carrying pathogenic variants in eight breast and ovarian cancer susceptibility genes

BOADICEA is a comprehensive risk prediction model for breast and/or ovarian cancer (BC/OC) and for carrying pathogenic variants (PVs) in cancer susceptibility genes. In addition to BRCA1 and BRCA2, BOADICEA version 6 includes PALB2, CHEK2, ATM, BARD1, RAD51C and RAD51D. To validate its predictions for these genes, we conducted a retrospective study including 2033 individuals counselled at clinical genetics departments in Denmark. All counselees underwent comprehensive genetic testing by next generation sequencing on suspicion of hereditary susceptibility to BC/OC. Likelihoods of PVs were predicted from information about diagnosis, family history and tumour pathology. Calibration was examined using the observed-to-expected ratio (O/E) and discrimination using the area under the receiver operating characteristics curve (AUC). The O/E was 1.11 (95% CI 0.97–1.26) for all genes combined. At sub-categories of predicted likelihood, the model performed well with limited misestimation at the extremes of predicted likelihood. Discrimination was acceptable with an AUC of 0.70 (95% CI 0.66–0.74), although discrimination was better for BRCA1 and BRCA2 than for the other genes in the model. This suggests that BOADICEA remains a valid decision-making aid for determining which individuals to offer comprehensive genetic testing for hereditary susceptibility to BC/OC despite suboptimal calibration for individual genes in this population.

Abstract 4174: Incorporating continuous mammographic density into the BOADICEA breast cancer risk prediction model

Abstract BOADICEA, implemented in the CanRisk tool (www.canrisk.org) can be used to calculate future breast cancer risk using data on cancer family history, genetics (rare high- or moderate-risk pathogenic variants; polygenic scores (PGS)), questionnaire-based risk factors and mammographic density (MD) measured using the BI-RADS classification. The BI-RADS categorization requires manual reading which is not feasible at population level. Moreover, BI-RADS captures MD in 4 categories, whereas continuous measures are stronger risk predictors. Here, BOADICEA was extended to incorporate continuous breast density measured by the fully automated Volpara and STRATUS software, working on raw and processed images, respectively. We used data from the KARMA prospective screening cohort (60,276 participants; 1,167 incident breast cancers). The associations between Volpara or STRATUS density measurements and incident breast cancer risk were estimated in a randomly selected training subset consisting of two-thirds of the full dataset. For this, we calculated density residuals after regressing on participant’s age at mammography. Hazard ratios (HR) for the normalized residuals were calculated using a Cox proportional hazards model, adjusting for family history, BMI and all risk factors included in BOADICEA. Multiple Imputation by Chained Equations (MICE) was used to impute missing data. The remaining one-third of the KARMA cohort was used to assess the performance of BOADICEA in predicting 5-year risks, after including the estimated Volpara and STRATUS associations.Under the best fitting multivariable model including PGS, the HRs per SD of residual STRATUS density were estimated to be 1.67 (95%CI: 1.44-1.93) and 1.38 (95%CI: 1.26-1.51) for pre- and post-menopausal women, respectively. The HR estimates per SD of residual Volpara density were 1.38 (95%CI: 1.19-1.59) and 1.39 (95%CI: 1.27-1.52) for pre- and post-menopausal women, respectively. After incorporating these associations in BOADICEA, there was a marked improvement in the model discriminatory ability in the test dataset, compared to using the BI-RADS classification. The largest increase in AUC was observed for STRATUS residual density, with a 3% increase compared to using BI-RADS vs a 1.6% increase in AUC when using Volpara residual density. These increases were consistent across BOADICEA models considering different combinations of risk factors, including PGS.Including continuous breast density in BOADICEA can lead to improved breast cancer risk stratification and could allow for automated measures of MD to be more readily deployed in breast cancer risk prediction. Additional prospective studies are required to further validate the findings. The models will be implemented in the CanRisk tool. Citation Format: Lorenzo Ficorella, Mikael Eriksson, Kamila Czene, Goska Leslie, Xin Yang, Tim J. Carver, Douglas F. Easton, Per F. Hall, Antonis C. Antoniou. Incorporating continuous mammographic density into the BOADICEA breast cancer risk prediction model. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4174.

Incorporating Alternative Polygenic Risk Scores into the BOADICEA Breast Cancer Risk Prediction Model.

The multifactorial risk prediction model BOADICEA enables identification of women at higher or lower risk of developing breast cancer. BOADICEA models genetic susceptibility in terms of the effects of rare variants in breast cancer susceptibility genes and a polygenic component, decomposed into an unmeasured and a measured component - the polygenic risk score (PRS). The current version was developed using a 313 SNP PRS. Here, we evaluated approaches to incorporating this PRS and alternative PRS in BOADICEA. The mean, SD, and proportion of the overall polygenic component explained by the PRS (α2) need to be estimated. $\alpha $ was estimated using logistic regression, where the age-specific log-OR is constrained to be a function of the age-dependent polygenic relative risk in BOADICEA; and using a retrospective likelihood (RL) approach that models, in addition, the unmeasured polygenic component. Parameters were computed for 11 PRS, including 6 variations of the 313 SNP PRS used in clinical trials and implementation studies. The logistic regression approach underestimates $\alpha $, as compared with the RL estimates. The RL $\alpha $ estimates were very close to those obtained by assuming proportionality to the OR per 1 SD, with the constant of proportionality estimated using the 313 SNP PRS. Small variations in the SNPs included in the PRS can lead to large differences in the mean. BOADICEA can be readily adapted to different PRS in a manner that maintains consistency of the model. : The methods described facilitate comprehensive breast cancer risk assessment.

Utilization of breast cancer risk prediction models by cancer genetic counselors in clinical practice predominantly in the United States.

Risk assessment in cancer genetic counseling is essential in identifying individuals at high risk for developing breast cancer to recommend appropriate screening and management options. Historically, many breast cancer risk prediction models were developed to calculate an individual's risk to develop breast cancer or to carry a pathogenic variant in the BRCA1 or BRCA2 genes. However, how or when genetic counselors use these models in clinical settings is currently unknown. We explored genetic counselors' breast cancer risk model usage patterns including frequency of use, reasons for using or not using models, and change in usage since the adoption of multi-gene panel testing. An online survey was developed and sent to members of the National Society of Genetic Counselors; board-certified genetic counselors whose practice included cancer genetic counseling were eligible to participate in the study. The response rate was estimated at 23% (243/1,058), and respondents were predominantly working in the United States. The results showed that 93% of all respondents use at least one breast cancer risk prediction model in their clinical practice. Among the six risk models selected for the study, the Tyrer-Cuzick (IBIS) model was used most frequently (95%), and the BOADICEA model was used least (40%). Determining increased or decreased surveillance and breast MRI eligibility were the two most common reasons for most model usage, while time consumption and difficulty in navigation were the two most common reasons for not using models. This study provides insight into perceived benefits and limitations of risk models in clinical use in the United States, which may be useful information for software developers, genetic counseling program curriculum developers, and currently practicing cancer genetic counselors.

Validation of the BOADICEA model and a 313-variant polygenic risk score for breast cancer risk prediction in a Dutch prospective cohort

PurposeWe evaluated the performance of the recently extended Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in a Dutch prospective cohort, using a polygenic risk score (PRS) based on 313 breast cancer (BC)–associated variants (PRS313) and other, nongenetic risk factors. MethodsSince 1989, 6522 women without BC aged 45 or older of European descent have been included in the Rotterdam Study. The PRS313 was calculated per 1 SD in controls from the Breast Cancer Association Consortium (BCAC). Cox regression analysis was performed to estimate the association between the PRS313 and incident BC risk. Cumulative 10-year risks were calculated with BOADICEA including different sets of variables (age, risk factors and PRS313). C-statistics were used to evaluate discriminative ability. ResultsIn total, 320 women developed BC. The PRS313 was significantly associated with BC (hazard ratio [HR] per SD of 1.56, 95% confidence interval [CI] [1.40–1.73]). Using 10-year risk estimates including age and the PRS313, other risk factors improved the discriminatory ability of the BOADICEA model marginally, from a C-statistic of 0.636 to 0.653. ConclusionsThe effect size of the PRS313 is highly reproducible in the Dutch population. Our results validate the BOADICEA v5 model for BC risk assessment in the Dutch general population.

Abstract 4623: External validation of the BOADICEA model for predicting ovarian cancer risk: The Breast Cancer Family Registry

Abstract Purpose: In the U.S., ovarian cancer is the most fatal of the gynecological cancers with an overall five-year survival rate of 47.6%. While women with a BRCA1 or BRCA2 mutation are at a much higher risk of developing ovarian cancer, mutations in these genes account for less than 50 percent of the familial aggregation of ovarian cancer. Being able to identify women at greatest risk, including those with increased familial risk without a BRCA1 or BRCA2 mutation, is critical for clinical decision-making; however, there is a lack of risk prediction models for ovarian cancer and those that do exist have modest discriminatory power. Therefore, we externally validated the BOADICEA model, a breast and ovarian cancer risk prediction model based on a woman's multigenerational family history and genetic information, for predicting ovarian cancer risk in an independent, prospective cohort of women. Methods: We used data from the Breast Cancer Family Registry (BCFR), a cohort of families with breast and ovarian cancer at baseline that have been prospectively followed. We assessed the 10-year performance of the BOADICEA model (version 3) for ovarian cancer risk overall, and by known BRCA1 or BRCA2 mutation statuses. We included women who did not have an ovarian cancer diagnosis or a bilateral oophorectomy prior to baseline, and who had sufficient data to calculate the 10-year BOADICEA risk score. We assessed model calibration using the ratio of the expected (E) to the observed (O) number of ovarian cancer cases in the cohort (E/O), and model discrimination by the concordance statistics (C-statistic) derived from the receiver operating characteristic curves. Results: There were 125 prospective ovarian cancer cases over a median of 12.3 years of follow-up among 18,534 women eligible for this analysis. For the overall cohort, the BOADICEA model was well calibrated with an E/O of 0.87, 95% confidence interval (CI) (0.70, 1.08). There was a suggestion of model underprediction in the top quartile of assigned risk (1.33% observed risk vs. 1.09% predicted risk), however it was not statistically significant (p=0.15). The C-statistic was 0.77, 95% CI (0.73, 0.82). For known BRCA1 or BRCA2 mutation carriers combined, the E/O was 0.91, 95% CI (0.63, 1.32) and the C-statistic was 0.75, 95% CI (0.67, 0.83). For non-carriers, the E/O was 0.85, 95% CI (0.65, 1.11) and the C-statistic was 0.68, 95% CI (0.59, 0.74). Conclusion: The BOADICEA model is well-calibrated in predicting ovarian cancer risk over 10 years and has good discriminatory power for women at increased familial risk of breast and ovarian cancer, with or without a known mutation in BRCA1 or BRCA2. Therefore, BOADICEA has clinical utility for evaluating ovarian cancer risk based on a woman's family cancer history and genetic information. Citation Format: Jennifer S. Ferris, Jeanine M. Genkinger, Mary Beth Terry, Yuyan Liao, Robert J. MacInnis, Irene L. Andrulis, Saundra S. Buys, Mary B. Daly, Esther M. John, John L. Hopper. External validation of the BOADICEA model for predicting ovarian cancer risk: The Breast Cancer Family Registry [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4623.

Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations

BackgroundThe clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices.MethodsWe used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20–80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol.ResultsThe predictive accuracy of ML-based algorithms (0.843 ≤ AU-ROC ≤ 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as ‘near population’ risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50.ConclusionML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening.

Value of multigene panel retesting of families with BRCA1/2 mutation-negative hereditary breast and ovarian cancer (HBOC).

1582 Background: Despite the use of clinical eligibility criteria and mutation predictive models, a great proportion of families are negative for germline mutations in BRCA1/2 genes. Traditionally, risk assessment of inconclusive results included the recommendation of high-risk surveillance protocol, the update of incident cancer cases in the family and the consideration of additional testing to rule out the possibility of phenocopy. More recently, next generation sequencing multigene panels have become a standard practice in cancer genetics clinics worldwide. We addressed the value of multigene panel retesting of BRCA1/2 negative HBOC families in our institution. Methods: After genetic counseling session and informed consent, a total of 137 individuals (119 probands and 18 extra cancer-affected relatives) from distinct BRCA1/2 negative families were retested using a panel containing 11 breast and ovarian cancer susceptibility genes ( BRCA1/2, PALB2, ATM, CHEK2, PTEN, TP53, STK11, BRIP1, RAD51C, RAD51D). Results: According to the BOADICEA model, the remaining probability of mutation in BRCA1/2 or PALB2 genes in our cohort was 5.5% (0.1-61). The reasons for considering retesting were the addition of any incident cancer diagnosis in 33 cases (24%), a prior study with a low sensitivity screening technique (dHPLC) in 6 families (5%) and the expansion of the study to other putative breast and ovarian susceptibility genes in 98 families (71%). Overall, 3 pathogenic (2 BRCA2, 1 CHEK2) and 8 likely pathogenic variants (1 BRCA2, 4 CHEK2 and 3 ATM) were found. The prevalence was 8%. The detection rate among 19 families with a &gt; 10% remaining probability of mutation in BRCA1/2 and PALB2 genes was 26%. The 3 clinically significant variants in BRCA2 were detected in 2 families and 1 updated cancer family history (BOADICEA remaining probability of 59, 61 and 12%, respectively). Cascade testing was subsequently done in 15 relatives resulting 8 in mutation carriers and 9 true negatives. Conclusions: Our results support the value of updating cancer incident cases and considering expanded panels in selected families.

Evaluating BRCA mutation risk predictive models in a Chinese cohort in Taiwan

Accurate estimation of carrier probabilities of cancer susceptibility gene mutations is an important part of pre-test genetic counselling. Many predictive models are available but their applicability in the Asian population is uncertain. We evaluated the performance of five BRCA mutation risk predictive models in a Chinese cohort of 647 women, who underwent germline DNA sequencing of a cancer susceptibility gene panel. Using areas under the curve (AUCs) on receiver operating characteristics (ROC) curves as performance measures, the models did comparably well as in western cohorts (BOADICEA 0.75, BRCAPRO 0.73, Penn II 0.69, Myriad 0.68). For unaffected women with family history of breast or ovarian cancer (n = 144), BOADICEA, BRCAPRO, and Tyrer-Cuzick models had excellent performance (AUC 0.93, 0.92, and 0.92, respectively). For women with both personal and family history of breast or ovarian cancer (n = 241), all models performed fairly well (BOADICEA 0.79, BRCAPRO 0.79, Penn II 0.75, Myriad 0.70). For women with personal history of breast or ovarian cancer but no family history (n = 262), most models did poorly. Between the two well-performed models, BOADICEA underestimated mutation risks while BRCAPRO overestimated mutation risks (expected/observed ratio 0.67 and 2.34, respectively). Among 424 women with personal history of breast cancer and available tumor ER/PR/HER2 data, the predictive models performed better for women with triple negative breast cancer (AUC 0.74 to 0.80) than for women with luminal or HER2 overexpressed breast cancer (AUC 0.63 to 0.69). However, incorporating ER/PR/HER2 status into the BOADICEA model calculation did not improve its predictive accuracy.

Accuracy of Risk Prediction Models for Breast Cancer and BRCA1/BRCA2 Mutation Carrier Probabilities in Israel.

Several algorithms have been developed to assess the risk of predicting BRCA mutation and breast cancer (BC) risk. The aim of this study was to evaluate the accuracy of these prediction algorithms in the Israeli population. Risk for developing breast cancer and the probability for carrying BRCA1/2 mutations using BOADICEA, BRCAPRO, IBIS, MYRIAD and PENN2 models were computed for individuals counseled and genotyped at the Oncogenetics unit in 2000 and 2005. The predicted mutation carriers and BC risks were compared with actual carrier rates by genotyping and BC diagnoses derived from the Israeli National Cancer Registry database. Overall, 65/648 (10%) study participants were BRCA1/2 mutation carriers. Of 373 cancer-free participants at counseling, 25 had breast cancer by 2016. BOADICEA and BRCAPRO performed best for predicting BRCA mutation (AUC=0.741, 0.738, respectively). No model was clinically useful in predicting breast cancer risk. BOADICEA and BRCAPRO outperformed the other tested algorithms in BRCA mutation prediction in Israeli women, but none was valuable in breast cancer risk prediction.

Abstract IA05: Modeling genetic susceptibility to breast cancer

Abstract Several breast cancer genetic susceptibility variants have been identified to date. These include mutations in the high risk BRCA1 and BRCA2 genes, other rare genetic variants conferring intermediate to high risks (e.g. PALB2, CHEK2, ATM and others) and &amp;gt;150 common alleles (SNPs) conferring low risks. The presentation will provide an overview of the latest developments and challenges in understanding the penetrance of mutations in BRCA1, BRCA2 and PALB2. Genetic counseling of women with BRCA1 and BRCA2 mutations currently relies on average cancer risk estimates obtained from retrospective penetrance studies. The talk will present penetrance estimates from ongoing prospective analyses, based on data from the International BRCA1/2 Carrier Cohort Study, the largest prospective cohort of BRCA1/2 mutation carriers that includes ~10,000 BRCA1/2 mutation carriers with follow-up information. The talk will also review the latest efforts and results from the Consortium of Investigators of Modifiers of BRCA1/2 to identify and characterise genetic modifiers of cancer risks for BRCA1 and BRCA2 mutation carriers and to provide individualized cancer risks for women with BRCA1 and BRCA2 mutations. Finally, the presentation will describe the efforts to develop a comprehensive risk prediction model for breast cancer, specifically the BOADICEA model that includes the explicit effects of mutations in BRCA1, BRCA2, PALB2, CHEK2, ATM, and of the common breast cancer susceptibility variants identified through genome-wide association studies. Citation Format: Antonis C. Antoniou. Modeling genetic susceptibility to breast cancer. [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 IA05.

Incorporating truncating variants in PALB2, CHEK2, and ATM into the BOADICEA breast cancer risk model.

PurposeThe proliferation of gene-panel testing precipitates the need for a breast cancer (BC) risk model that incorporates the effects of mutations in several genes and family history (FH). We extended the BOADICEA model to incorporate the effects of truncating variants in PALB2, CHEK2 and ATM.MethodsThe BC incidence was modelled via the explicit effects of truncating variants in BRCA1/2, PALB2, CHEK2 and ATM and other unobserved genetic effects using segregation analysis methods.ResultsThe predicted average BC risk by age 80 for an ATM mutation carrier is 28%, 30% for CHEK2, 50% for PALB2, 74% for BRCA1 and BRCA2. However, the BC risks are predicted to increase with FH-burden. In families with mutations, predicted risks for mutation-negative members depend on both FH and the specific mutation. The reduction in BC risk after negative predictive-testing is greatest when a BRCA1 mutation is identified in the family, but for women whose relatives carry a CHEK2 or ATM mutation, the risks decrease slightly.ConclusionsThe model may be a valuable tool for counselling women who have undergone gene-panel testing for providing consistent risks and harmonizing their clinical management. A web-application can be used to obtain BC- risks in clinical practice (http://ccge.medschl.cam.ac.uk/boadicea/).

Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry.

The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility SNPs is not known. Using 750 cases and 405 controls from the population-based Australian Breast Cancer Family Registry who were younger than 50 years at diagnosis and recruitment, respectively, Caucasian and not BRCA1 or BRCA2 mutation carriers, we derived absolute 5-year risks of breast cancer using the BOADICEA, BRCAPRO, BCRAT, and IBIS risk prediction models and combined these with a risk score based on 77 independent risk-associated SNPs. We used logistic regression to estimate the OR per adjusted SD for log-transformed age-adjusted 5-year risks. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. We also constructed reclassification tables and calculated the net reclassification improvement. The ORs for BOADICEA, BRCAPRO, BCRAT, and IBIS were 1.80, 1.75, 1.67, and 1.30, respectively. When combined with the SNP-based score, the corresponding ORs were 1.96, 1.89, 1.80, and 1.52. The corresponding AUCs were 0.66, 0.65, 0.64, and 0.57 for the risk prediction models, and 0.70, 0.69, 0.66, and 0.63 when combined with the SNP-based score. By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%. Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.

Abstract MS1-1: Characterising cancer risks for carriers of mutations in BRCA1, BRCA2, PALB2, and RAD51C genes

Abstract The presentation will provide an overview of the latest developments and challenges in understanding the penetrance of mutations in the BRCA1, BRCA2, PALB2 and RAD51C genes. Genetic counselling currently relies on average cancer risk estimates obtained from retrospective penetrance studies involving large numbers of families segregating mutations in these genes. The talk will present penetrance estimates from ongoing prospective analyses, based on data from the International BRCA1/2 Carrier Cohort Study, the largest cohort of BRCA1/2 mutation carriers worldwide that includes &amp;gt;10,000 mutation carriers at baseline with prospective follow-up information. Several common alleles and other risk factors have now been shown to modify breast cancer risk for BRCA1 and BRCA2 mutation carriers. The talk will review the latest efforts and results from the Consortium of Investigators of Modifiers of BRCA1/2 to identify genetic modifiers of risk and provide individualised cancer risks for BRCA1 and BRCA2 mutation carriers on the basis of polygenic risk scores. We will also present penetrance estimates based on analyses of combined data from families segregating mutations in PALB2 and RAD51C, the largest efforts of their kind. Finally the presentation will demonstrate how the effects of common and rare breast cancer susceptibility variants are incorporated into the BOADICEA model to provide comprehensive risks of developing breast cancer in the future. Citation Format: Antonis C. Antoniou. Characterising cancer risks for carriers of mutations in BRCA1, BRCA2, PALB2, and RAD51C genes [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 MS1-1.

Breast cancer risk prediction accuracy in Jewish Israeli high-risk women using the BOADICEA and IBIS risk models

Several breast cancer risk prediction models have been validated in ethnically diverse populations, but none in Israeli high-risk women. To validate the accuracy of the IBIS and BOADICEA risk prediction models in Israeli high-risk women, the 10-year and lifetime risk for developing breast cancer were calculated using both BOADICEA and IBIS models for high-risk, cancer-free women, counselled at the Sheba Medical Center from 1 June 1996-31 May 2000. Women diagnosed with breast cancer by 31 May 2011 were identified from the Israeli National Cancer Registry. The observed to expected breast cancer ratios were calculated to evaluate the predictive value of both algorithms. Overall, 358 mostly (N = 205, 57·2%) Ashkenazi women, were eligible, age range at counselling was 20-75 years (mean 46·76 ± 9·8 years). Over 13·6 ± 1·45 years (range 11-16 years), 15 women (4·19%) were diagnosed with breast cancer, at a mean age of 57 ± 8·6 years. The 10-year risks assigned by BOADICEA and IBIS ranged from 0·2 to 12·6% and 0·89 to 21·7%, respectively. The observed:expected breast cancer ratio was 15/18·6 (0·8-95% CI 0·48-1·33) and 15/28·6 (0·52-95% CI 0·32-0·87), using both models, respectively. In Jewish Israeli high-risk women the BOADICEA model has a better predictive value and accuracy in determining 10-year breast cancer risk than the IBIS model.