This study analyzed breast cancer risk estimates from three models (Gail, Claus, Tyrer-Cuzick) in a sample of 1795 Brazilian women. Risk was higher on average for women with a family history of breast cancer across all three models. Correlations were found between the models, highest between Gail and Tyrer-Cuzick. Considering 10% lifetime risk as high-risk, 20.1%, 17.5%, 17.8% of women had elevated risk by Gail, Claus and Tyrer-Cuzick models respectively, indicating a substantial proportion with potentially increased screening needs.

1. A
Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
Associação Hospitalar Moinhos de Vento, Porto Alegre, Brazil.
Funding: CNPq, FAPERGS, FIPE/HCPA CAPES, Susan G. Komen for the Cure.
BACKGROUNDBACKGROUND
Nowadays, breast cancer (BC) is the most prevalent type of cancer among women.
Specifically in Brazil, BC is a public health issue due to its morbidity and high incidence
and mortality rates. Mathematical models were developed to predict a woman’s lifetime
risk for BC considering her family history (FH) and personal risk factors for breast cancer
(PF). Between these models, the Gail and Tyrer-Cuzick models and the Claus tables are
the most used. There are no references to application of these risk models in a large
sample from Brazil.
OBJECTIVESOBJECTIVES
 To describe the risk estimatives, according the three different models, in a sample of
women undergoing mammographic screening in Sothern Brazil, a geographic area with
high breast cancer incidence and mortality rates.
 To find correlations between the risk estimatives derived from the models in the sample.
SCHMIDT AV, GIACOMAZZI J, ASHTON-PROLLA P, CAMEY SA,SCHMIDT AV, GIACOMAZZI J, ASHTON-PROLLA P, CAMEY SA, CALLEFI MCALLEFI M
METHODOLOGYMETHODOLOGY
Study Participants: We used data from women in the population-based cohort study
NMPOA (n=9218), which represents a large sample from an underserved community in
southern Brazil. For this study, a sample of 1795 women was analysed, 885 of whom
answered positively to at least one of seven questions about FH of breast, ovarian and
colorectal cancer. The remaining 910 participants answered negatively to all questions
on the presence of a positive family history for any of these tumors. Patients were
referred to genetic cancer risk assessment (GCRA) where interviews with clinical
geneticists and detailed pedigree analyses were performed. Ethical approval was
obtained from the institutional ethics committees and patient inclusion required
signature of informed consent.
Statistical Analysis: To characterize the models, the average and the respective
confidence intervals of 95% were used. To obtain the population mean and prevalence
data we used weight estimation, weighting the group with (1285/885) and without
(7933/910) positive family history. The Pearson’s Correlation Coefficient was used to
verify the existence of correlation between models.
Table 1: Variables used in each BC prediction model
Variable Gail Claus Tyrer-Cuzick
Personal Information
Age
BMI
Reproductive History
Menarche
Age at firsl live-birth
Menopause
Breast Disease
Breast biopsy
Atypical hyperplasia
Ductal carcinoma in situ
Family History of BC
1st
degree
2nd
degree
Age at diagnosis
BC in relatives
Ovarian Cancer
Based on Lalloo et al. 2005
RESULTSRESULTS
All women included in the study were cancer unaffected. Average age at risk
estimation was 47.24 years (minimum = 17, maximum = 84, SD =11.67). The number of
women who confirmed FH in the GRCA interview was 800. The number of valid cases to
estimate risk by each model was 1755, 635 and 1750 for Gail, Claus and Tyrer-Cuzick
models, respectively.
For the entire sample, the following correlation values were encountered: between the
Claus tables and the Tyrer-Cuzick model (0.44, p<0.01), between the Gail and Tyrer-
Cuzick models (0.69, p<0.01) and between the Claus tables and the Gail model (0.40,
p<0.01). Table 2 represents these correlations for both groups, with and without family
history. Averages and confidence intervals (CI95%) of the estimated risk of developing
breast cancer according to the Gail and Tyrer-Cuzick models and the Claus tables are
shown in table 3.
Considering the average estimated lifetime breast cancer risk in Brazilian women of
10% and using this value as cutoff for each model, 1882 (20.1%), 1616 (17.5%) and
1390 (17.8%) of the women studied had lifetime risk estimates above 10% using the Gail
Model, Claus tables and the Tyrer-Cuzick model, respectively.
Table 2 : Correlations between the models
DISCUSSIONDISCUSSION
Overall, the average estimated lifetime risk of developing breast cancer in this sample of
cancer-unaffected women from an underserved community in Southern Brazil was within the
expected interval for average risk women of the general population in this country. As expexted,,
mean risk estimates were higher in women with a family history of the disease, using all three
models. In addition, we observed a correlation between the risks given by the three models;
additional analyses are necessary to determine which one fits better to the study’s community.
The highest correlation was observed between the Gail and Tyrer-Cuzick models, and this is
likely explained by their general similar structure and high number of common variables, which
are possibly more expressive in the sample than the main variables (family history) used by the
Claus Tables.
OBS: The values of the superior and inferior diagonals represent the correlations in the group with
and without family history, respectively.
Table 3 : Mean risk estimates and confidence intervals
for each model
Gail Claus Tyrer-Cuzick
Gail - 0.184 (p=0.96) 0.637 (p<0.01)
Claus 0.369 (p<0.01) - 0.281 (p<0.01)
Tyrer-Cuzick 0.599 (p<0.01) 0.43 (p<0.01) -
Mean CI95%
Gail
All women 8.14 8.07-8.21
With FH 11.57 11.28-11.87
Without FH 7.53 7.48-7.59
Claus
All women 8.91 8.85-8.98
With FH 11.88 11.59-12.17
Without FH 8.39 8.34-8.44
Tyrer-Cuzick
All women 7.02 6.94-7.09
With FH 11.26 10.99-11.53
Without FH 6.26 6.20-6.33
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Correlation between Claus Tables, and Gail and Tyrer-Correlation between Claus Tables, and Gail and Tyrer-
Cuzick models in a population-based cohort studyCuzick models in a population-based cohort study
Contact: aishameriane.schmidt@ufrgs.br
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