Gynecologic and breast cancers share several risk factors. Breast cancer risk assessment tools can identify those at elevated risk and allow for enhanced breast surveillance and chemoprevention, however such tools are underutilized. We aim to evaluate the use of routine breast cancer risk assessment in a gynecologic oncology clinic.
A patient-facing web-based tool was used to collect personal and family history and run four validated breast cancer risk assessment models (Tyrer-Cuzick (TC), Gail, BRCAPRO, and Claus) in a gynecologic oncology clinic. We evaluated completion of the tools and identification of patients at elevated risk for breast cancer using the four validated models.
A total of 99 patients were included in this analysis. The BRCAPRO model had the highest completion rate (84.8%), followed by the TC model (74.7%), Gail model (74.7%), and the Claus model (52.1%). The TC model identified 21.6% of patients completing the model as having ≥20% lifetime risk of breast cancer, compared to 6.8% by the Gail model, and 0% for both the BRCAPRO and Claus models. The Gail model identified 52.5% of patients as having ≥1.67% 5-year risk of breast cancer. Among patients identified as high-risk for breast cancer and eligible for screening, 9/9 (100%) were referred to a high-risk breast clinic.
Among patients that completed the TC breast cancer risk assessment in a gynecologic oncology clinic, approximately 1 in 5 were identified to be at significantly elevated lifetime risk for breast cancer. The gynecologic oncologist\'s office might offer a convenient and feasible setting to incorporate this risk assessment into routine patient care, as gynecologic oncologists often have long-term patient relationships and participate in survivorship care.

Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors.

OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: BOADICEA and BRCAPRO outperformed the other tested algorithms in BRCA mutation prediction in Israeli women, but none was valuable in breast cancer risk prediction.

Germline BRCA1 and BRCA2 (BRCA) mutation carriers with pancreatic ductal adenocarcinoma (PDAC) may benefit from precision therapies and their relatives should undergo tailored cancer prevention. In this study, we compared strategies to identify BRCA carriers with PDAC. Incident cases of PDAC were prospectively recruited for BRCA sequencing. Probands were evaluated using the National Comprehensive Cancer Network (NCCN) and the Ontario Ministry of Health and Long-Term Care (MOHLTC) guidelines. The probability of each proband carrying a mutation was estimated by surveying genetic counselors and using BRCAPRO. BRCA mutations were detected in 22/484 (4.5%) probands. 152/484 (31.2%) and 16/484 (3.3%) probands met the NCCN and MOHLTC guidelines, respectively. The NCCN guidelines had higher sensitivity than the MOHLTC guidelines (0.864 versus 0.227, P < 0.001) but lower specificity (0.712 versus 0.976, P < 0.001). One hundred and nineteen genetic counselors completed the survey. Discrimination was similar between genetic counselors and BRCAPRO (area-under-the-curve: 0.755 and 0.775, respectively, P = 0.702). Genetic counselors generally overestimated (P = 0.008), whereas BRCAPRO severely underestimated (P < 0.001), the probability that each proband carried a mutation. Our results indicate that the NCCN guidelines and genetic counselors accurately identify BRCA mutations in PDAC, while the MOHLTC guidelines and BRCAPRO should be updated to account for the association between BRCA and PDAC.

The integrated discrimination improvement (IDI) is commonly used to compare two risk prediction models; it summarizes the extent a new model increases risk in events and decreases risk in non-events. The IDI averages risks across events and non-events and is therefore susceptible to Simpson\'s paradox. In some settings, adding a predictive covariate to a well calibrated model results in an overall negative (positive) IDI. However, if stratified by that same covariate, the strata-specific IDIs are positive (negative). Meanwhile, the calibration (observed to expected ratio and Hosmer-Lemeshow Goodness of Fit Test), area under the receiver operating characteristic curve, and Brier score improve overall and by stratum. We ran extensive simulations to investigate the impact of an imbalanced covariate upon metrics (IDI, area under the receiver operating characteristic curve, Brier score, and R2), provide an analytic explanation for the paradox in the IDI, and use an investigative metric, a Weighted IDI, to better understand the paradox. In simulations, all instances of the paradox occurred under stratum-specific mis-calibration, yet there were mis-calibrated settings in which the paradox did not occur. The paradox is illustrated on Cancer Genomics Network data by calculating predictions based on two versions of BRCAPRO, a Mendelian risk prediction model for breast and ovarian cancer. In both simulations and the Cancer Genomics Network data, overall model calibration did not guarantee stratum-level calibration. We conclude that the IDI should only assess model performance among a clinically relevant subset when stratum-level calibration is strictly met and recommend calculating additional metrics to confirm the direction and conclusions of the IDI. Copyright © 2016 John Wiley & Sons, Ltd.

To establish whether existing mutation prediction models can identify which male breast cancer (MBC) patients should be offered BRCA1 and BRCA2 diagnostic DNA screening, we compared the performance of BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm), BRCAPRO (BRCA probability) and the Myriad prevalence table (\"Myriad\"). These models were evaluated using the family data of 307 Dutch MBC probands tested for BRCA1/2, 58 (19%) of whom were carriers. We compared the numbers of observed vs predicted carriers and assessed the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) for each model. BOADICEA predicted the total number of BRCA1/2 mutation carriers quite accurately (observed/predicted ratio: 0.94). When a cut-off of 10% and 20% prior probability was used, BRCAPRO showed a non-significant better performance (observed/predicted ratio BOADICEA: 0.81, 95% confidence interval [CI]: [0.60-1.09] and 0.79, 95% CI: [0.57-1.09], vs.
UNASSIGNED: 1.02, 95% CI: [0.75-1.38] and 0.94, 95% CI: [0.68-1.31], respectively). Myriad underestimated the number of carriers in up to 69% of the cases. BRCAPRO showed a non-significant, higher AUC than BOADICEA (0.798 vs 0.776). Myriad showed a significantly lower AUC (0.671). BRCAPRO and BOADICEA can efficiently identify MBC patients as BRCA1/2 mutation carriers. Besides their general applicability, these tools will be of particular value in countries with limited healthcare resources.

To evaluate the predictive efficacies including sensitivity and positive predictive value of the genetic risk prediction model BRCAPRO and the Myriad BRCA risk calculator in Korean ovarian cancer patients.
Individuals undergoing genetic testing for BRCA mutations from November 2010-August 2016 were recruited from the Department of Obstetrics and Gynecology at a single institute in Korea. The observed BRCA1 and BRCA2 mutation statuses were compared with the predicted carrier probabilities using BRCAPRO and the Myriad BRCA risk calculator.
Two hundred thirty-two patients were recruited, of whom 99.1% (230/232) were of Korean ethnicity. Of the 232 individuals, 206 and 26 had ovarian and double primary breast/ovarian cancer, respectively. Thirty-six individuals had a family history of breast/ovarian cancer in first-degree relatives. Fifty-seven patients (24.6%) tested positive for BRCA mutation (41 BRCA1, 16 BRCA2). The mean BRCAPRO and Myriad scores for all patients were 6.4% and 7.7%, respectively. The scores were significantly higher for patients with positive BRCA mutation status (29.0% vs. 6.1%, P<0.001, 12.1% vs. 7.7%, P<0.001, respectively). For all patients, the respective areas under the receiver operating characteristics curves were 0.720 and 0.747 for the BRCAPRO and Myriad models to predict the risk of carrying a BRCA mutation. Both models overestimated the mutation probability in patients with a family history of breast/ovarian cancer (1.55-fold and 1.50-fold, respectively) and underestimated the probability in patients without a family history (both, 0.54-fold).
BRCAPRO and Myriad seem to be acceptable risk assessment tools for determining the risk of carrying BRCA mutations in Korean ovarian cancer patients.

The authors retrospectively aimed to determine which of the following three scenarios, related to DCIS entry into BRCAPRO, predicted BRCA mutation status more accurately: (1) DCIS as an invasive breast cancer (IBC) entered using the actual age of diagnosis, (2) DCIS as IBC entered with 10 years added to the actual age of diagnosis, and (3) DCIS entered as no cancer. Of the 85 DCIS patients included in the study, 19% (n = 16) tested positive for a BRCA mutation, and 81% (n = 69) tested negative. DCIS patients who tested positive for a BRCA mutation had a higher BRCAPRO risk estimation (34.61%) than patients who tested negative (11.4%) when DCIS was entered at the actual age of diagnosis. When DCIS was entered with 10 years added to the actual age at diagnosis, the BRCAPRO estimate was still higher amongst BRCA positive patients (25.4%) than BRCA negative patients (7.1%). When DCIS was entered as no cancer, the BRCAPRO estimate remained higher among BRCA positive patients (2.56%) than BRCA negative patents (1.98%). In terms of accuracy of BRCA positivity, there was no statistically significant difference between DCIS at age at diagnosis, DCIS at 10 years later than age at diagnosis, and DCIS entered as no cancer (AUC = 0.77, 0.784, 0.75, respectively: p = 0.60). Our results indicate that regardless of entry approach into BRCAPRO, there were no significant differences in predicting BRCA mutation in patients with DCIS.

Genetic risk prediction models such as BRCAPRO are used routinely in genetic counseling for identification of potential BRCA1 and BRCA2 mutation carriers. They require extensive information on the counselee and her family history, and thus are not practical for primary care. To address this gap, we develop and test a two-stage approach to genetic risk assessment by balancing the tradeoff between the amount of information used and accuracy achieved. The first stage is intended for primary care wherein limited information is collected and analyzed using a simplified version of BRCAPRO. If the assessed risk is sufficiently high, more extensive information is collected and the full BRCAPRO is used (stage two: intended for genetic counseling). We consider three first-stage tools: BRCAPROLYTE, BRCAPROLYTE-Plus, and BRCAPROLYTE-Simple. We evaluate the two-stage approach on independent clinical data on probands with family history of breast and ovarian cancers, and BRCA genetic test results. These include population-based data on 1344 probands from Newton-Wellesley Hospital and mostly high-risk family data on 2713 probands from Cancer Genetics Network and MD Anderson Cancer Center. We use discrimination and calibration measures, appropriately modified to evaluate the overall performance of a two-stage approach. We find that the proposed two-stage approach has very limited loss of discrimination and comparable calibration as BRCAPRO. It identifies a similar number of carriers without requiring a full family history evaluation on all probands. We conclude that the two-stage approach allows for practical large-scale genetic risk assessment in primary care.

The most common result of BRCA1/2 mutation testing when performed in a family without a previously identified mutation is an uninformative negative test result. Women in these families may have an increased risk for breast cancer because of mutations in non-BRCA breast cancer predisposition genes, including moderate- or low-risk genes, or shared environmental factors. Genetic counselors often encourage counselees to share information with family members, however it is unclear how much information counselees share and the impact that shared information may have on accuracy of risk perception in family members. We evaluated 85 sisters and daughters of women who received uninformative negative BRCA1/2 results. We measured accuracy of risk perception using a latent variable model where accuracy was represented as the correlation between perceived risk (indicators = verbal and quantitative measures) and calculated risk (indicators = Claus and BRCAPRO). Participants who reported more information was shared with them by their sister or mother about her genetic counseling session had greater accuracy of risk perception (0.707, p = 0.000) than those who reported little information was shared (0.326, p = 0.003). However, counselees shared very little information; nearly 20 % of family members reported their sister or mother shared nothing with them about her genetic counseling. Family members were generally not aware of the existence of a genetic counseling summary letter. Our findings underscore the need for effective strategies that facilitate counselees to share information about their genetic counseling sessions. Such communication may help their relatives better understand their cancer risks and enhance risk appropriate cancer prevention.