Gail model risk factors: impact of adding an extended family history for breast cancer

Gail model utilization in predicting breast cancer risk in Egyptian women: a cross-sectional study

PURPOSE

Herein, our purpose was to calculate the 5-year and lifetime risk of breast cancer and to assess new breast cancer potential contributors among Egyptian women utilizing the modified Gail model, while presenting a global comparison of risk assessment.

METHODS

This study included 7009 women from both urban and rural areas scattered across 40% of the Egyptian provinces. The 5-year risk categories were defined as low risk (≤ 1.66%) or high risk (> 1.66%), whereas the lifetime risk categories were defined as low risk (≤ 20%) or high risk (> 20%). Pearson's Chi-squared test was performed to determine the association between participants' characteristics and distinct risk categories. Binary logistic regression was carried out for correlation analysis.

RESULTS

The mean estimated risk for developing invasive breast cancer over 5 years was 0.86 (± 0.67), whereas the mean lifetime breast cancer risk score was 11.26 (± 5.7). Accordingly, only 614 (8.75%) and 470 (6.7%) women were categorized as individuals with high risk of breast cancer incidence in 5-year and lifetime, respectively. Only 192 participants (2.7%) conferred both high 5-year and high lifetime risk scores. Marital status, method of feeding, physical activity behavior, contraceptive use, menopause and number of children were found to have a statistically significant association with both 5-year and lifetime breast cancer risk categories.

CONCLUSION

We revealed that modified Gail model had a well-fitting and discrimination accuracy in Egyptian women when compared with other countries.

Discovery of breast cancer risk genes and establishment of a prediction model based on estrogen metabolism regulation

Background

Multiple common variants identified by genome-wide association studies have shown limited evidence of the risk of breast cancer in Chinese individuals. In this study, we aimed to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes in breast cancer (BC) and establish a risk prediction model composed of estrogen-metabolizing enzyme genes and GWAS-identified breast cancer-related genes based on a polygenic risk score.

Methods

Unrelated BC patients and healthy subjects were recruited for analysis of estrogen levels and single nucleotide polymorphisms (SNPs) in genes encoding estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes, which was calculated using a Bayesian approach. An independent sample t-test was used to evaluate the differences between PRS scores of BC and healthy subjects. The discriminatory accuracy of the models was compared using the area under the receiver operating characteristic (ROC) curve.

Results

The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulating in the serum of BC patients. We then established a PRS model to evaluate the role of SNPs in multiple genes. PRS model 1 (M1) was established from SNPs in 6 GWAS-identified high risk genes. On the basis of M1, we added SNPs from 7 estrogen metabolism enzyme genes to establish PRS model 2 (M2). The independent sample t-test results showed that there was no difference between BC and healthy subjects in M1 (P = 0.17); however, there was a significant difference between BC and healthy subjects in M2 (P = 4.9*10^− 5). The ROC curve results showed that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than that of M1 (AUC = 54.56%).

Conclusion

Estrogen and related metabolic enzyme gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme gene SNPs has a good predictive ability for breast cancer risk, and the accuracy is greatly improved compared with that of the PRS model that only includes GWAS-identified gene SNPs.

Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review

Introduction

Currently, the Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model (GM) has been widely recognized and adapted for to study disparity in racial and ethnic groups in America including Asian and Pacific Islander American females. However, its applicability outside America remains uncertain due to diversity in epidemiology and risk factors of breast cancer in populations especially in Asian females. We sought to evaluate the performance of the GM to predict breast cancer risk in Asian countries.

Material and methods

This study identified articles published from 2010 by searching PubMed, MEDLINE, Scopus, Web of Science, Google Scholar and gray literature. The initial search terms were breast cancer, mammary, carcinoma, tumor, neoplasm, risk assessment tool, BCRAT, breast cancer prediction, Gail model, Asia, and Asian.

Results

The search yielded 20 articles, with 7 articles addressing the AUC and/or the expected (E) to observed (O) ratio of predicted breast cancer risk, representing the accuracy of the GM in the Asian population. One publication reported the sensitivity and specificity but no AUC. None of the studies were accepted as the standard for reporting prognostic models. Several studies reported good prognostic testing and likely developed a new model modifying the items in the instrument.

Conclusion

The results are not strong enough to develop breast cancer risk in the setting of Asian countries. Involving the breast cancer risk of the Asian population in developing a prognostic model with good statistical understanding is particularly important and can reduce flawed or biased models. Identifying the best methods to achieve well-suited prognostic models in the Asian population should be a priority.

Diagnostic and Prognostic Significance of Carboxypeptidase A4 (CPA4) in Breast Cancer

Recent research focused on prolonged survival has suggested that carboxypeptidase A4 (CPA4) plays a role in both tumor microenvironment formation and distant metastasis in cancer. In some patients, serum and expression (mRNA) levels of CPA4 have been found to be correlated with the aggressiveness and progression of the disease. Accordingly, we conducted a first study to investigate the diagnostic and prognostic significance of CPA4 in the case of breast cancer (BC), the most common form of malignancy in women. The study included a total of 50 patients with BC and 20 healthy women as the control group. The participants’ serum CPA4 levels were determined by the ELISA test, and, for assessment of CPA4 mRNA, we used the PCR method. The serum CPA4 (p = 0.001) and CPA4 mRNA (p = 0.015) levels were found to be statistically significantly higher in the controls, compared to the patient group. When the results of patient group were statistically analyzed based on subgrouping by tumor characteristics, the measured CPA4 mRNA levels showed significant difference with respect to the molecular subtype (p = 0.006), pN status (p = 0.023), and pathological stage (p = 0.039), while the serum CPA4 measurements differed significantly in terms of pathological type only (p = 0.024). We conclude that CPA4 is diagnostically and prognostically not futile when used in combination with the other considerations and measurements in breast cancer.

Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women

Background

There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population.

Methods

We used data from 40,158 women aged 50–69 years (via the lifepool cohort) participating in Australia’s BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance.

Results

Over a median of 4.3 years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O) = 1.09, 95% confidence interval (CI) 1.00–1.18). There was good agreement across decile groups of Gail scores (χ² = 7.1, p = 0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O = 1.65, 95% CI 1.33–2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73–3.02, p < 0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06–1.70, p = 0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44–0.79, p < 0.0001). Similar patterns were observed separately for women aged 50–59 and 60–69 years. The model’s overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56–0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups.

Conclusions

This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50–69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1084-x) contains supplementary material, which is available to authorized users.

Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis

BACKGROUND

The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA).

METHODS

Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive.

RESULTS

Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively.

CONCLUSIONS

The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses.

TRIAL REGISTRATION

PROSPERO CRD42016047215 .