Re: Validation of the Gail et al. model for predicting individual breast cancer risk

Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model

Background

Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35–50.

Methods

In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers.

Results

The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer.

Conclusions

AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35–50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users.

Identifying cost-effective treatment with raloxifene in postmenopausal women using risk algorithms for fractures and invasive breast cancer

INTRODUCTION

The National Osteoporosis Foundation (NOF) recommends considering treatment in women with a 20% or higher 10-year probability of a major fracture. However, raloxifene reduces both the risk of vertebral fractures and invasive breast cancer so that raloxifene treatment may be clinically appropriate and cost-effective in women who do not meet a 20% threshold risk. The aim of this study was to identify cost-effective scenarios of raloxifene treatment compared to no treatment in younger postmenopausal women at increased risk of invasive breast cancer and fracture risks below 20%.

METHOD

A micro-simulation model populated with data specific to American Caucasian women was used to quantify the costs and benefits of 5-year raloxifene treatment. The population evaluated was selected based on 10-year major fracture probability as estimated with FRAX® being below 20% and 5-year invasive breast cancer risk as estimated with the Gail risk model ranging from 1% to 5%.

RESULTS

The cost per QALY gained ranged from US $22,000 in women age 55 with 5% invasive breast cancer risk and 15-19.9% fracture probability, to $110,000 in women age 55 with 1% invasive breast cancer risk and 5-9.9% fracture probability. Raloxifene was progressively cost-effective with increasing fracture risk and invasive breast cancer risk for a given age cohort. At lower fracture risk in combination with lower invasive breast cancer risk or when no preventive raloxifene effect on invasive breast cancer was assumed, the cost-effectiveness of raloxifene worsened markedly and was not cost-effective given a willingness-to-pay of US $50,000. At fracture risk of 15-19.9% raloxifene was cost-effective also in women at lower invasive breast cancer risk.

CONCLUSIONS

Raloxifene is potentially cost-effective in cohorts of young postmenopausal women, who do not meet the suggested NOF 10-year fracture risk threshold. The cost-effectiveness is contingent on their 5-year invasive breast cancer risk. The result highlights the importance of considering a woman's full risk profile when considering anti-osteoporosis treatment.

Clinical application of breast cancer risk assessment models

With the evolving availability of testing for genetic cancer syndromes, oncologists now are increasingly expected to review family histories and to give a genetic risk assessment as part of their care for breast cancer. The most important of these breast cancer genetic syndromes identified to date have been those associated with the BRCA1 and BRCA2 genes. Therefore, the proper identification of potentially affected families and providing risk assessment estimates will be ever more essential. This review outlines several different available breast cancer risk assessment models. Risk models for the development of breast cancer as well as risk models that estimate the chance of having a genetic cancer syndrome are discussed. Their clinical applications are also outlined and clinical situations appropriate for each model are reviewed.

Perceived risk of breast cancer among women at average and increased risk

BACKGROUND

The accuracy of a woman's perception of her risk of developing breast cancer has gained importance as more options for primary prevention have become available for those at increased risk. Conversely, women at average risk who perceive themselves as at increased risk may suffer from avoidable anxiety or unnecessary treatment. This study examined characteristics associated with perception of breast cancer risk among women at average and increased risk.

METHODS

We included 1700 women 40-74 years old without a history of breast cancer. The outcome variable was a woman's perceived lifetime risk of developing breast cancer. The Gail model was used to categorize a woman's actual risk as average or high. Multivariate logistic regression models were used to model a woman's perception that her risk was (1) higher than average for those whose Gail score indicated average risk (<1.67% 5-year risk) and (2) accurate for those whose Gail score indicated increased risk (> or = 1.67% 5-year risk).

RESULTS

Of women at average risk, 72%, but only 43% of those at high risk, accurately perceived their risk. Among women at average risk, those who were younger, had a family history of breast cancer, had no history of childbirth, or had more frequent exposure to lay media information about breast health were more likely than women without these characteristics to overestimate their future risk. Among women at increased risk, younger women and those with a family history of breast cancer were more likely than women without these characteristics to accurately perceive their increased risk. African American women were less likely than white women to accurately perceive their risk.

CONCLUSIONS

A majority of women at high risk of developing breast cancer underestimate their risk, and a substantial proportion of women at average risk perceive they are at increased risk.

Hormonal and genetic risk factors for breast cancer

Breast cancer is a major cause of female morbidity and mortality worldwide. In this review, we discuss the hormonal and genetic risk factors associated with breast cancer development and describe the currently available models for predicting an individual woman's risk. We highlight three more sophisticated surrogate markers of life-time oestrogen exposure (plasma oestradiol, mammographic breast density, bone mineral density) and propose that these may be used to improve estimates of a woman's absolute risk.

Cancer screening in the elderly population

This article reviews the current state of knowledge regarding cancer screening in the geriatric population. Care of the elderly requires knowledge of underlying physiologic changes, comorbidities, quality-of-life factors, and life expectancies. There is always the danger that ageism may prevent elderly cancer patients from receiving the proper treatment. On the other hand, overzealous treatment can lead to adverse results if elderly patients are not properly targeted based on current evidence of the benefits and risks of specific screening practices.