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

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