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

Breast cancer polygenic risk scores derived in White European populations are not calibrated for women of Ashkenazi Jewish descent

PURPOSE

Polygenic risk scores (PRSs) are a major component of accurate breast cancer (BC) risk prediction but require ethnicity-specific calibration. Ashkenazi Jewish (AJ) population is assumed to be of White European (WE) origin in some commercially available PRSs despite differing effect allele frequencies (EAFs). We conducted a case-control study of WE and AJ women from the Predicting Risk of Cancer at Screening Study. The Breast Cancer in Northern Israel Study provided a separate AJ population-based case-control validation series.

METHODS

All women underwent Illumina OncoArray single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]) analysis. Two PRSs were assessed, SNV142 and SNV78. A total of 221 of 2243 WE women (discovery: cases = 111; controls = 110; validation: cases = 651; controls = 1772) and 221 AJ women (cases = 121; controls = 110) were included from the UK study; the Israeli series consisted of 2045 AJ women (cases = 1331; controls = 714). EAFs were obtained from the Genome Aggregation Database.

RESULTS

In the UK study, the mean SNV142 PRS demonstrated good calibration and discrimination in WE population, with mean PRS of 1.33 (95% CI 1.18-1.48) in cases and 1.01 (95% CI 0.89-1.13) in controls. In AJ women from Manchester, the mean PRS of 1.54 (1.38-1.70) in cases and 1.20 (1.08-1.32) in controls demonstrated good discrimination but overestimation of BC relative risk. After adjusting for EAFs for the AJ population, mean risk was corrected (mean SNV142 PRS cases = 1.30 [95% CI 1.16-1.44] and controls = 1.02 [95% CI 0.92-1.12]). This was recapitulated in the larger Israeli data set with good discrimination (area under the curve = 0.632 [95% CI 0.607-0.657] for SNV142).

CONCLUSION

AJ women should not be given BC relative risk predictions based on PRSs calibrated to EAFs from the WE population. PRSs need to be recalibrated using AJ-derived EAFs. A simple recalibration using the mean PRS adjustment ratio likely performs well.

Breast cancer risk based on adapted IBIS prediction model in Slovenian women aged 40-49 years - could it be better?

Background The aim of the study was to assess the proportion of women that would be classified as at above-average risk of breast cancer based on the 10 year-risk prediction of the Slovenian breast cancer incidence rate (S-IBIS) program in two presumably above-average breast cancer risk populations in age group 40-49 years: (i) women referred for any reason to diagnostic breast centres and (ii) women who were diagnosed with breast cancer aged 40-49 years. Breast cancer is the commonest female cancer in Slovenia, with an incidence rate below European average. The Tyrer-Cuzick breast cancer risk assessment algorithm was recently adapted to S-IBIS. In Slovenia a tailored mammographic screening for women at above average risk in age group 40-49 years is considered in the future. S-IBIS is a possible tool to select population at above-average risk of breast cancer for tailored screening. Patients and methods In 357 healthy women aged 40-49 years referred for any reason to diagnostic breast centres and in 367 female breast cancer patients aged 40-49 years at time of diagnosis 10-years breast cancer risk was calculated using the S-IBIS software. The proportion of women classified as above-average risk of breast cancer was calculated for each subgroup of the study population. Results 48.7% of women in the Breast centre group and 39.2% of patients in the breast cancer group had above-average 10-year breast cancer risk. Positive family history of breast cancer was more prevalent in the Breast centre group (p < 0.05). Conclusions Inclusion of additional risk factors into the S-IBIS is warranted in the populations with breast cancer incidence below European average to reliably stratify women into breast cancer risk groups.

Performance of BRCA1/2 mutation prediction models in male breast cancer patients

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.

BRCAPRO

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.

Validating the IBIS and BOADICEA Models for Predicting Breast Cancer Risk in the Iranian Population

BACKGROUND

Several approaches have been suggested for incorporating risk factors to predict the future risk of breast cancer. The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and International Breast Cancer Intervention Study (IBIS) are among these approaches. We compared the performance of these models in discriminating between cases and noncases in the Iranian population.

PATIENTS AND METHODS

We performed a case-control study in Tehran, from November 2015 to April 2016, and enrolled 1633 women aged 24 to 75 years, including 506 cases of breast cancer, 916 population-based controls, and 211 clinic-based controls. We calculated and compared the risk of breast cancer predicted by the IBIS and BOADICEA models and the logistic regression model. For model discrimination, we computed the area under the receiver operating characteristic (ROC) curve.

RESULTS

The risk of breast cancer predicted by the IBIS model was higher than the BOADICEA model, but lower than the logistic model. The area under the ROC plots indicated that the logistic regression model showed better discrimination between cases and noncases (71.53%) compared with the IBIS model (49.36%) and BOADICEA model (35.84%). Based on the Pierson correlation coefficient, the correlation between IBIS and BOADICEA models was much stronger than the correlation between IBIS and logistic models (0.3884 and 0.1639, respectively).

CONCLUSION

The IBIS model discriminated cases and noncases better than the BOADICEA model in the Iranian population. However, the discrimination of the logistic regression model, which included a larger array of familial, genetic, and personal risk factors, was better than the 2 models.

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

BACKGROUND

The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility SNPs is not known.

METHODS

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.

RESULTS

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.

CONCLUSIONS

By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%.

IMPACT

Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.

Familiärer Brustkrebs – empirische Erkrankungsrisiken und Risikoberechnungsmodelle

BRCA1/2-Anlageträgerinnen haben ein stark erhöhtes Risiko an Brust- und Eierstockkrebs zu erkranken. Die individuelle klinische Betreuung erfordert eine genaue Kenntnis der Erkrankungsrisiken. In diesem Artikel geben wir einen Überblick über die aktuelle Literatur zu empirischen Erkrankungsrisiken, und wir beschreiben Berechnungsmodelle, die derzeit in der klinischen Praxis für die individuelle Risikoeinschätzung genutzt werden.

Die in der Literatur beschriebenen Erkrankungsrisiken zeigen eine große Variabilität zwischen verschiedenen Studien. Das Brustkrebsrisiko beträgt 40–87 % bei BRCA1- und 18–88 % bei BRCA2-Anlageträgerinnen. Für den Eierstockkrebs werden Risiken von 22–65 % bei BRCA1- und 10–35 % bei BRCA2-Anlageträgerinnen angegeben. Auch das kumulative Risiko für kontralateralen Brustkrebs 10 Jahre nach der Ersterkrankung ist mit 27 % (BRCA1) und 19 % (BRCA2) deutlich erhöht. Verschiedene Berechnungsmodelle erlauben eine individuelle Risikovorhersage, indem zusätzlich die jeweilige Familienanamnese, bekannte Hauptgene und ihr Erbgang sowie andere genetische und epidemiologische Risikofaktoren berücksichtigt werden. Durch nutzerfreundliche Programme können diese Modelle direkt in der humangenetischen und klinischen Beratung angewandt werden. Für die genaue Bestimmung von Erkrankungsrisiken sowie für die Validierung der Risikovorhersagemodelle sind prospektive Kohortenstudien von großer Bedeutung. Um Studien dieser Art zu ermöglichen, sollten Frauen mit erhöhtem Risiko im Rahmen von strukturierten Registerstudien betreut und prospektiv nachverfolgt werden.

Breast cancer risks and risk prediction models

BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

Practical problems with clinical guidelines for breast cancer prevention based on remaining lifetime risk

Background:

Clinical guidelines for breast cancer chemoprevention and MRI screening involve estimates of remaining lifetime risk (RLR); in the United States, women with an RLR of 20% or higher meet “high-risk” criteria for MRI screening.

Methods:

We prospectively followed 1764 women without breast cancer to compare the RLRs and 10-year risks assigned by the risk models International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and to compare both sets of model-assigned 10-year risks to subsequent incidence of breast cancer in the cohort. We used chi-square statistics to assess calibration and the area under the receiver operating characteristic curve (AUC) to assess discrimination. All statistical tests are two-sided.

Results:

The models classified different proportions of women as high-risk (IBIS = 59.3% vs BOADICEA = 20.1%) using the RLR threshold of 20%. The difference was smaller (IBIS = 52.9% vs BOADICEA = 43.2%) using a 10-year risk threshold of 3.34%. IBIS risks (mean = 4.9%) were better calibrated to observed breast cancer incidence (5.2%, 95% confidence interval (CI) = 4.2% to 6.4%) than were those of BOADICEA (mean = 3.7%) overall and within quartiles of model risk (P = .20 by IBIS and P = .07 by BOADICEA). Both models gave similar discrimination, with AUCs of 0.67 (95% CI = 0.61 to 0.73) using IBIS and 0.68 (95% CI = 0.62 to 0.74) using BOADICEA. Model sensitivities at thresholds for a 20% false-positive rate were also similar, with 41.8% using IBIS and 38.0% using BOADICEA.

Conclusion:

RLR-based guidelines for high-risk women are limited by discordance between commonly used risk models. Guidelines based on short-term risks would be more useful, as models are generally developed and validated under a short fixed time horizon (≤10 years).

Genetic risk assessment for breast and gynecological malignancies

PURPOSE OF REVIEW

Breast cancer and gynecological cancers impact a significant portion of women each year. Identifying women at high risk is essential for implementation of screening and risk reduction recommendations. Risk assessment for these cancers involves an evaluation of many factors. This review discusses an overview of hereditary breast and gynecological cancers and the process of a cancer genetic risk assessment.

RECENT FINDINGS

Risk assessment models for breast cancer should be used with caution, especially in populations in which they are not validated. Additionally, the BRCAPRO model may underestimate the likelihood of BRCA mutations in certain populations.The utilization of next-generation sequencing panels is increasing. Benefits and limitations of panel testing are described in the literature. There are currently no guidelines for the use of panel testing; however, some reports of institutional experiences and recommendations are available.

SUMMARY

Cancer genetic risk assessment is complex, and models developed to estimate risk may not apply to all populations. Identifying genetic factors related to cancer risk is also becoming increasingly complex with the clinical implementation of panel testing. This testing approach should be critically evaluated by healthcare providers. Further research is needed to create evidence-based guidelines for panel testing and management recommendations for moderately penetrant genes.