A comparison of methods currently used in clinical practice to estimate familial breast cancer risks

Assessing Risk of Breast Cancer: A Review of Risk Prediction Models

Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman's breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Background

In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.

Objectives

To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.

Design

Two cohort studies assessing breast cancer incidence.

Setting

High-risk FHC and the NHSBSP Greater Manchester, UK.

Participants

A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].

Interventions

Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.

Main outcome measures

Breast cancer incidence.

Data sources

The NHSBSP; cancer registration.

Results

A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.

Conclusions

Risk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.

Future work

A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

Assessing women at high risk of breast cancer: a review of risk assessment models

Women who are at high risk of breast cancer can be offered more intensive surveillance or prophylactic measures, such as surgery or chemoprevention. Central to decisions regarding the level of prevention is accurate and individualized risk assessment. This review aims to distill the diverse literature and provide practicing clinicians with an overview of the available risk assessment methods. Risk assessments fall into two groups: the risk of carrying a mutation in a high-risk gene such as BRCA1 or BRCA2 and the risk of developing breast cancer with or without such a mutation. Knowledge of breast cancer risks, taken together with the risks and benefits of the intervention, is needed to choose an appropriate disease management strategy. A number of models have been developed for assessing these risks, but independent validation of such models has produced variable results. Some models are able to predict both mutation carriage risks and breast cancer risk; however, to date, all are limited by only moderate discriminatory accuracy. Further improvements in the knowledge of how to best integrate both new risk factors and newly discovered genetic variants into these models will allow clinicians to more accurately determine which women are most likely to develop breast cancer. These steady and incremental improvements in models will need to undergo revalidation.

The current state of cancer family history collection tools in primary care: a systematic review

Systematic collection of family history is a prerequisite for identifying genetic risk. This study reviewed tools applicable to the primary care assessment of family history of breast, colorectal, ovarian, and prostate cancer. MEDLINE, EMBASE, CINAHL, and Cochrane Central were searched for publications. All primary study designs were included. Characteristics of the studies, the family history collection tools, and the setting were evaluated. Of 40 eligible studies, 18 relevant family history tools were identified, with 11 developed for use in primary care. Most collected information on more than one cancer and on affected relatives used self-administered questionnaires and paper-based formats. Eleven tools had been evaluated relative to current practice, demonstrating 46-78% improvement in data recording over family history recording in patient charts and 75-100% agreement with structured genetic interviews. Few tools have been developed specifically for primary care settings. The few that have been evaluated performed well. The very limited evidence, which depends in part on extrapolation from studies in settings other than primary care, suggests that systematic tools may add significant family health information compared with current primary care practice. The effect of their use on health outcomes has not been evaluated.

Breast cancer risk-assessment models

There are two main questions when assessing a woman for interventions to reduce her risks of developing or dying from breast cancer, the answers of which will determine her access: What are her chances of carrying a mutation in a high-risk gene such as BRCA1 or BRCA2? What are her risks of developing breast cancer with or without such a mutation? These risks taken together with the risks and benefits of the intervention will then determine whether an intervention is appropriate. A number of models have been developed for assessing these risks with varying degrees of validation. With further improvements in our knowledge of how to integrate risk factors and to eventually integrate further genetic variants into these models, we are confident we will be able to discriminate with far greater accuracy which women are most likely to develop breast cancer.

Morphology of Breast Cancer as a Means of Triage of Patients for BRCA1 Genetic Testing

BACKGROUND

Women who have germline mutations in the BRCA1 gene are at substantially increased lifetime risk of developing breast and ovarian cancer but are otherwise normal. Currently, early age of onset of cancer and a strong family history are relied upon as the chief clues as to who should be offered genetic testing. Certain morphologic and immunohistochemical features are overrepresented in BRCA1-associated breast cancers but these differences have not been incorporated into the current selection criteria for genetic testing.

DESIGN

Each of the 4 pathologists studied 30 known cases of BRCA1- and BRCA2-associated breast cancer from kConFab families. After reviewing the literature, we agreed on a semiquantitative scoring system for estimating the chances of presence of an underlying BRCA1 mutation, based on the number of the reported prototypic features present. After a time lag of 12 months, we each examined a series of 62 deidentified cases of breast cancer, inclusive of cases of BRCA1-associated breast cancer and controls. The controls included cases of BRCA2-associated breast cancer and sporadic cases.

RESULTS

Our predictions had a sensitivity of 92%, specificity of 86%, positive predictive value of 61%, and negative predictive value of 98%. For comparison the sensitivity of currently used selection criteria are in the range of 25% to 30%.

CONCLUSION

The inclusion of morphologic and immunohistochemical features of breast cancers in algorithms to predict the likelihood of presence of germline mutations in the BRCA1 gene improves the accuracy of the selection process.

Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme

INTRODUCTION

Accurate individualised breast cancer risk assessment is essential to provide risk-benefit analysis prior to initiating interventions designed to lower breast cancer risk. Several mathematical models for the estimation of individual breast cancer risk have been proposed. However, no single model integrates family history, hormonal factors, and benign breast disease in a comprehensive fashion. A new model by Tyrer and Cuzick has addressed these deficiencies. Therefore, this study has assessed the goodness of fit and discriminatory value of the Tyrer-Cuzick model against established models namely Gail, Claus, and Ford.

METHODS

The goodness of fit and discriminatory accuracy of the models was assessed using data from 1933 women attending the Family History Evaluation and Screening Programme, of whom 52 developed cancer. All models were applied to these women over a mean follow up of 5.27 years to estimate risk of breast cancer.

RESULTS

The ratios (95% confidence intervals) of expected to observed numbers of breast cancers were 0.48 (0.37 to 0.64) for Gail, 0.56 (0.43 to 0.75) for Claus, 0.49 (0.37 to 0.65) for Ford, and 0.81 (0.62 to 1.08) for Tyrer-Cuzick. The accuracy of the models for individual cases was evaluated using ROC curves. These showed that the area under the curve was 0.735 for Gail, 0.716 for Claus, 0.737 for Ford, and 0.762 for Tyrer-Cuzick.

CONCLUSION

The Tyrer-Cuzick model is the most consistently accurate model for prediction of breast cancer. The Gail, Claus, and Ford models all significantly underestimate risk, although the accuracy of the Claus model may be improved by adjustments for other risk factors.

Risk Estimation for Healthy Women from Breast Cancer Families: New Insights and New Strategies

Risk estimation in breast cancer families is often estimated by use of the Claus tables. We analyzed the family histories of 196 counselees; compared the Claus tables with the Claus, the BRCA1/2, the BRCA1/2/ models; and performed linear regression analysis to extend the Claus tables with characteristics of hereditary breast cancer. Finally, we compared the Claus extended method with the Claus, the BRCA1/2, and the BRCA1/2/u models. We found 47% agreement for Claus table versus Claus model; 39% agreement for Claus table versus BRCA1/2 model; 48% agreement for Claus table versus BRCA1/2/u model; 37% agreement for Claus extended method versus Claus model; 44% agreement for Claus extended model versus BRCA1/2 model; and 66% agreement for Claus extended method versus BRCA1/2/u model. The regression formula (Claus extended method) for the lifetime risk for breast cancer was 0.08 + 0.40 (*) Claus Table + 0.07 (*) ovarian cancer + 0.08 (*) bilateral breast cancer + 0.07 (*) multiple cases. This new method for risk estimation, which is an extension of the Claus tables, incorporates information on the presence of ovarian cancer, bilateral breast cancer, and whether there are more than two affected relatives with breast cancer. This extension might offer a good alternative for breast cancer risk estimation in clinical practice.

Effects of genetic consultation on perception of a family risk of breast/ovarian cancer and determinants of inaccurate perception after the consultation

The aim of this study was to assess the effects of cancer genetic consultations on women's perception of their family risk of breast/ovarian cancer, and to determine which factors were associated with an inaccurate perception after the consultation. A multicenter prospective survey was carried out on women (n = 397) attending cancer genetic clinics in France for the first time, in which the perceived family risk was measured both before and after the consultation, using self-administered questionnaires. The effects of the consultation on risk perception were significant among low (P <.001) and moderate risk women (P <.05). However, after the consultation, 76.3% of the "low"-risk women did not perceive their family as "low"-risk families, and 21.9% of the moderate-risk women were still definitely sure there was a genetic risk running in their family. The consultation did not affect the family risk perception of the high risk women (n = 171): the risk was thought to be very high both before (87.7%) and after (89.5%) the consultation (NS); however 10.5% of this group still perceived their family as being unlikely to be at risk after the consultation. In the low- and moderate-risk groups after multivariate adjustment, the inaccurate perceptions varied, depending on the clinics and on the psychosocial context of the consultation: they increased when the consultee was personally affected by cancer, and decreased when the consultee had a health occupation. Cancer genetic consultations had only marginal effects on the perception of family risk on the whole, although they were significant in the case of low- and moderate-risk women. The question arises as to whether a more comprehensive approach should be implemented and how to go about providing efficient cancer risk information in the context of health care systems.