Predicting Risk of Breast Cancer in Postmenopausal Women by Hormone Receptor Status

Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA 90502, USA.
Journal of the National Cancer Institute (Impact Factor: 12.58). 11/2007; 99(22):1695-705. DOI: 10.1093/jnci/djm224
Source: PubMed


Strategies for estrogen receptor (ER)-positive breast cancer risk reduction in postmenopausal women require screening of large populations to identify those with potential benefit. We evaluated and attempted to improve the performance of the Breast Cancer Risk Assessment Tool (i.e., the Gail model) for estimating invasive breast cancer risk by receptor status in postmenopausal women.
In The Women's Health Initiative cohort, breast cancer risk estimates from the Gail model and models incorporating additional or fewer risk factors and 5-year incidence of ER-positive and ER-negative invasive breast cancers were determined and compared by use of receiver operating characteristics and area under the curve (AUC) statistics. All statistical tests were two-sided.
Among 147,916 eligible women, 3236 were diagnosed with invasive breast cancer. The overall AUC for the Gail model was 0.58 (95% confidence interval [CI]=0.56 to 0.60). The Gail model underestimated 5-year invasive breast cancer incidence by approximately 20% (P<.001), mostly among those with a low estimated risk. Discriminatory performance was better for the risk of ER-positive cancer (AUC = 0.60, 95% CI = 0.58 to 0.62) than for the risk of ER-negative cancer (AUC = 0.50, 95% CI = 0.45 to 0.54). Age and age at menopause were statistically significantly associated with ER-positive but not ER-negative cancers (P=.05 and P=.04 for heterogeneity, respectively). For ER-positive cancers, no additional risk factors substantially improved the Gail model prediction. However, a simpler model that included only age, breast cancer in first-degree relatives, and previous breast biopsy examination performed similarly for ER-positive breast cancer prediction (AUC=0.58, 95% CI= 0.56 to 0.60); postmenopausal women who were 55 years or older with either a previous breast biopsy examination or a family history of breast cancer had a 5-year breast cancer risk of 1.8% or higher.
In postmenopausal women, the Gail model identified populations at increased risk for ER-positive but not ER-negative breast cancers. A model with fewer variables appears to provide a simpler approach for screening for breast cancer risk.

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Available from: Dorothy S Lane, Apr 20, 2015
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    • "There is compelling evidence that reproductive [36] and familial factors [37] have stronger effects on the risk of early-onset than late-onset breast tumors. In fact, these risk factors showed consistently weaker associations in three large US cohorts of postmenopausal women [4, 5] than in the Gail model. "
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    ABSTRACT: The Gail model for predicting the absolute risk of invasive breast cancer has been validated extensively in US populations, but its performance in the international setting remains uncertain. We evaluated the predictive accuracy of the Gail model in 54,649 Spanish women aged 45–68 years who were free of breast cancer at the 1996–1998 baseline mammographic examination in the population-based Navarre Breast Cancer Screening Program. Incident cases of invasive breast cancer and competing deaths were ascertained until the end of 2005 (average follow-up of 7.7 years) through linkage with population-based cancer and mortality registries. The Gail model was tested for calibration and discrimination in its original form and after recalibration to the lower breast cancer incidence and risk factor prevalence in the study cohort, and compared through cross-validation with a Navarre model fully developed from this cohort. The original Gail model overpredicted significantly the 835 cases of invasive breast cancer observed in the cohort (ratio of expected to observed cases 1.46, 95 % CI 1.36–1.56). The recalibrated Gail model was well calibrated overall (expected-to-observed ratio 1.00, 95 % CI 0.94–1.07), but it tended to underestimate risk for women in low-risk quintiles and to overestimate risk in high-risk quintiles (P = 0.01). The Navarre model showed good cross-validated calibration overall (expected-to-observed ratio 0.98, 95 % CI 0.92–1.05) and in different cohort subsets. The Navarre and Gail models had modest cross-validated discrimination indexes of 0.542 (95 % CI 0.521–0.564) and 0.544 (95 % CI 0.523–0.565), respectively. Although the original Gail model cannot be applied directly to populations with different underlying rates of invasive breast cancer, it can readily be recalibrated to provide unbiased estimates of absolute risk in such populations. Nevertheless, its limited discrimination ability at the individual level highlights the need to develop extended models with additional strong risk factors. Electronic supplementary material The online version of this article (doi:10.1007/s10549-013-2428-y) contains supplementary material, which is available to authorized users.
    Breast Cancer Research and Treatment 02/2013; 138(1). DOI:10.1007/s10549-013-2428-y · 3.94 Impact Factor
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    • "It was not surprising that ‘ever having previous benign breast biopsy’ was not included in this revised prediction for Singaporean women as this reflects a specific health care delivery system in which biopsies were not common as is the case for the majority of Asian women (including Chinese-Occidental migrants) [20,21] although an increasing proportion of these women now receive mammographic screening [22,23]. Other evidence for the use of simpler, but more targeted, models has been provided by predictions of estrogen receptor-positive breast cancer in postmenopausal women in the USA [24]. However, the concordance from GAIL-SBSP (AUC = 0.61) is relatively low. "
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    ABSTRACT: Background Gail and others developed a model (GAIL) using age-at-menarche, age-at-birth of first live child, number of previous benign breast biopsy examinations, and number of first-degree-relatives with breast cancer as well as baseline age-specific breast cancer risks for predicting the 5-year risk of invasive breast cancer for Caucasian women. However, the validity of the model for projecting risk in South-East Asian women is uncertain. We evaluated GAIL and attempted to improve its performance for Singapore women of Chinese, Malay and Indian origins. Methods Data from the Singapore Breast Screening Programme (SBSP) are used. Motivated by lower breast cancer incidence in many Asian countries, we utilised race-specific invasive breast cancer and other cause mortality rates for Singapore women to produce GAIL-SBSP. By using risk factor information from a nested case-control study within SBSP, alternative models incorporating fewer then additional risk factors were determined. Their accuracy was assessed by comparing the expected cases (E) with the observed (O) by the ratio (E/O) and 95% confidence interval (CI) and the respective concordance statistics estimated. Results From 28,883 women, GAIL-SBSP predicted 241.83 cases during the 5-year follow-up while 241 were reported (E/O=1.00, CI=0.88 to 1.14). Except for women who had two or more first-degree-relatives with breast cancer, satisfactory prediction was present in almost all risk categories. This agreement was reflected in Chinese and Malay, but not in Indian women. We also found that a simplified model (S-GAIL-SBSP) including only age-at-menarche, age-at-birth of first live child and number of first-degree-relatives performed similarly with associated concordance statistics of 0.5997. Taking account of body mass index and parity did not improve the calibration of S-GAIL-SBSP. Conclusions GAIL can be refined by using national race-specific invasive breast cancer rates and mortality rates for causes other than breast cancer. A revised model containing only three variables (S-GAIL-SBSP) provides a simpler approach for projecting absolute risk of invasive breast cancer in South-East Asia women. Nevertheless its role in counseling the individual women regarding their risk of breast cancer remains problematical and needs to be validated in independent data.
    BMC Cancer 11/2012; 12(1):529. DOI:10.1186/1471-2407-12-529 · 3.36 Impact Factor
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    • "Unlike previous results (McTiernan et al., 2003) (Lee et al., 2001), we found no inverse trend among postmenopausal women. For ER+ PR+ tumors, however, a substantial inverse trend was found, in line with some (Chlebowski et al., 2007; Peters et al., 2009; Schmidt et al., 2008) but not all previous studies (Lee et al., 2001) (Leitzmann et al., 2008). A protective effect of PA on both ER+PR+ and ER+PR− tumors has also reported (Bardia et al., 2006). "
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    ABSTRACT: Physical activity may decrease breast cancer risk. However, it is unclear what intensity of exercise and during which life periods this effect on decreasing risk is efficiently expressed, and whether the associations differ by the estrogen-/progesterone- receptor (ER/PR) status of tumors. We investigated associations between age- and intensity-specific leisure-time physical activity and ER/PR-defined breast cancer risk. We conducted a hospital-based case-control study in Nagano, Japan. Subjects were 405 cases newly diagnosed (>99% known ER/PR) from 2001 to 2005, who were age-/area-matched with 405 controls. Activity was assessed with a self-reported questionnaire which considered intensity level (moderate and/or strenuous) at different ages (at 12 and 20 years, and in the previous 5 years). Odds ratios (ORs) and 95% confidence intervals were calculated using logistic regression. Strenuous but not moderate physical activity at age 12 was inversely associated with pre- and postmenopausal breast cancer risk across ER/PR subtypes [overall OR(≥ 5 days/week vs. none) = 0.24 (0.14-0.43)]. Moderate physical activity in the previous 5 years was significantly associated with a decrease in risk for postmenopausal ER + PR + tumors only [OR(≥ 1 day/week vs. none) = 0.35 (0.18-0.67)]. Strenuous activity in teens and moderate activity after menopause may contribute to a reduction in breast cancer risk.
    Cancer Causes and Control 11/2010; 21(11):1787-98. DOI:10.1007/s10552-010-9605-7 · 2.74 Impact Factor
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