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Evidence suggests that breast cancer hormone receptor status varies by etiologic factors, but studies have been inconsistent. In a population-based case–control study in Poland that included 2,386 cases and 2,502 controls, we assessed ER-α and PR status of tumors based on clinical records according to etiologic exposure data collected via interview. For 842 cancers, we evaluated ER-α, ER-β, PR and HER2 levels by semiquantitative microscopic scoring of immunostained tissue microarrays and a quantitative immunofluorescence method, automated quantitative analysis (AQUA™). We related marker levels in tumors to etiologic factors, using standard regression models and novel statistical methods, permitting adjustment for both correlated tumor features and exposures. Results obtained with different assays were generally consistent. Receptor levels varied most significantly with body mass index (BMI), a factor that was inversely related to risk among premenopausal women and directly related to risk among postmenopausal women with larger tumors. After adjustment for correlated markers, exposures and pathologic characteristics, PR and HER2 AQUA levels were inversely related to BMI among premenopausal women (p-trend = 0.01, both comparisons), whereas among postmenopausal women, PR levels were associated directly with BMI (p-trend = 0.002). Among postmenopausal women, analyses demonstrated that BMI was related to an interaction of PR and HER2: odds ratio (OR) = 0.86 (95% CI = 0.69–1.07) for low PR and HER2 expression vs. OR = 1.78 (95% CI = 1.25–2.55) for high expression (p-heterogeneity = 0.001). PR and HER2 levels in breast cancer vary by BMI, suggesting a heterogeneous etiology for tumors related to these markers. © 2007 Wiley-Liss, Inc.
Amassing data suggest that breast cancers are characterized by “molecular portraits” that are established at inception, remain stable over time and represent critical determinants of tumor biology.1 Hormone receptor status is a key parameter in molecular classifications of breast cancer,2, 3 which serves as a marker of hormone-dependent growth and predictor of responsiveness to hormonal treatments. Consequently, researchers have hypothesized that etiologic factors mediated by hormones might be more strongly associated with breast cancers that express hormone receptors when compared with those that are receptor-negative.4, 5
A recent literature review found evidence that nulliparity, late age at first birth and postmenopausal obesity are associated with greater risk for estrogen receptor-α (ER-α)-positive cancers when compared with ER-α-negative tumors, and that early menarche was more strongly linked to tumors coexpressing ER-α and progesterone receptor (PR).4 Subsequently, a metaanalysis updating this review affirmed the heterogeneous associations for nulliparity and late age at first birth, but not for age at menarche.5 However, results of studies have not been entirely consistent, especially when limited by small sample sizes, missing data and nonstandardized receptor assays (summarized4, 5). Furthermore, analyses have classified receptor status dichotomously, ignoring differences in the percentage of cells expressing receptors, the receptor content per cell and variable criteria for classifying receptor status as positive. Finally, studies have not adjusted for pathologic tumor features, even though risk relationships may vary by tumor size, histologic type, grade and stage.6
To assess associations between marker expression and breast cancer risk associated with established etiologic factors, we analyzed data from a population-based case–control study conducted in Poland. To optimize marker assessments, we analyzed tumor marker status using 3 different methods: (i) clinical reports of ER-α and PR, mainly determined by immunostaining whole tissue sections; (ii) assessment of ER-α, ER-β, PR and HER2 expression by microscopic examination of immunostained tissue microarrays (TMAs); and (iii) determination of ER-α, ER-β, PR and HER2 using a novel quantitative immunofluorescent method, automated quantitative analysis (AQUA).7, 8 To account for patterns of marker coexpression, we employed novel statistical models to analyze multiple markers and exposures simultaneously, which permitted the identification of independent associations between marker levels and exposures.9 These models were also used to adjust for other pathological characteristics (tumor size, histologic type, grade and nodal status), as well as to test a priori hypotheses that risk factor associations for cancers characterized by combinations of markers might demonstrate stronger effects than expected, based on analyses of single markers (i.e. statistical interactions between markers). Specifically, given that coexpression of ER-α and PR predicts a favorable tamoxifen response, whereas coexpression of hormone receptors and HER2 predicts a reduced likelihood of response,10 we speculated that risk associations for hormonally mediated factors might demonstrate interactions for these combinations of markers.
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This analysis supports the view that hormone receptor expression is linearly related to breast cancer risk associations for BMI, and that coexpression of HER2 may modify these relationships.
In addition to analyzing data for ER-α and PR status based on medical records, we attempted to optimize marker assessments by employing 2 additional techniques, which provided semiquantitative and quantitative levels. Results for different assay methods were highly correlated and demonstrated expected patterns of marker coexpression, providing reassurances that misclassification was minimized. Specifically, we found that ER-α and PR expression were correlated and that both markers were positively associated with ER-β and inversely related to HER2, as previously reported in several studies.10, 13, 14
In all analyses, whether based on AQUA, manual interpretation of TMAs or hospital data, we found that PR levels were inversely related to current BMI among premenopausal women and that PR expression was directly associated with BMI among postmenopausal women. In analyses considering each marker separately, ER-α levels were inversely related to BMI among premenopausal women, but the association became weaker and nonsignificant in models that adjusted for both markers. The inverse association between receptor expression and BMI among premenopausal women, and the direct relationship for BMI in postmenopausal women are consistent with most previous reports.4 In our analyses, PR was the strongest determinant of etiologic heterogeneity. PR expression may represent a better marker of hormone-dependent growth than ER-α levels, because transcription of the PR gene requires formation of estradiol–ER-α complexes, implying both the availability of ligand and formation of functional ligand–receptor complexes.15
The observed differences in associations for hormone receptor levels and BMI among pre- and postmenopausal women are biologically plausible. Obesity among premenopausal women has been associated with lower serum hormone levels, which could reduce risks for developing receptor-positive, hormone-dependent cancers.16 In contrast, obesity among postmenopausal women is associated with increased hormone levels, which may enhance growth of receptor-positive tumors.17 We speculate that among premenopausal obese women, low-serum hormone levels may lead to upregulated ER-α and PR levels in benign (“normal”) epithelium, creating conditions conducive to exaggerated hormone responses after menopause as cumulative estrogen exposure rises. This view is supported by limited data linking elevated serum hormone levels and weight gain, specifically to risk for receptor-positive tumors.18, 19, 20
Our analyses also suggested that risk relationships for postmenopausal obesity vary for tumors coexpressing either ER-α or PR with HER2, a pattern that has been designated as molecular subtype “Luminal B.2, 3” In particular, obesity among postmenopausal women was associated with an ∼80% higher risk for strong PR and HER2 coexpressing tumors when compared with tumors with low levels. HER2 overexpression may contribute to carcinogenesis by upregulating aromatase and by activating growth factor pathways, suggesting crosstalk between hormonal and nonhormonal mechanisms of carcinogenesis.21 Furthermore, obesity in postmenopausal women is associated with elevated estrogen levels, which may lead to activation of HER1 and HER2 kinases. Consistent with this view, Arpino et al. suggested that tamoxifen, acting as a weak estrogen, activates HER1 and HER2 kinases, thereby inducing tamoxifen resistance in receptor-positive tumors with HER2 amplification.22 Investigation of other genes included within the “molecular signature” of ER-α-positive breast cancer may reveal additional unrecognized intersections between hormonal and nonhormonal pathways.23 Finally, data suggesting that normal epithelium associated with breast cancer shows inappropriately high hormone receptor expression with respect to serum hormone levels and abnormal coexpression of ER-α and proliferation markers point to exaggerated responses to hormones as a mechanism that may augment effects of excess hormone exposures.24, 25
In analyses considering each tumor marker separately, we found that ER-β was directly related and PR expression was indirectly associated with age at menopause. Late age at menopause has been linked to lower serum estrogen levels,26 which would lead to reduced ER-α-mediated transcription of PR. Furthermore, ER-β may also reduce ER-α-mediated transcription of target genes such as PR.27, 28 However, the associations between marker levels and age at menopause were not significant in modified polytomous regression models that simultaneously adjusted for all markers. Similarly, in some analyses, we reproduced previously reported findings suggesting stronger effects for parity (protective factor) and late age at first full-term birth (risk factor) among ER-α-positive tumors.4 However, these associations were weak and were not found consistently with all assays techniques. Although these associations may reflect chance observations, our ability to detect these relationships with categorical rather continuous classifications of receptor status (i.e. AQUA) may reflect the nonlinear nature of the associations.
Research has shed light on etiologic differences between receptor-positive and -negative tumors, but the genesis of the latter remains poorly understood.29 ER-α-negative tumors may arise from hormonally stimulated precursors that lose receptor expression prior to diagnosis. Alternatively, ER-α-negative and -positive tumors may develop from different progenitor cells at the outset. Furthermore, these proposals are not mutually exclusive; different receptor-negative tumors may develop through different pathways. Focused studies to reveal the etiology of ER-α-negative tumors may be warranted, because these tumors are often clinically aggressive and difficult to treat effectively.
Strengths of the Polish study include its large size, population-based sample, use of standardized and quantitative assays and novel statistical modeling. Although we observed the expected inverse relationship between BMI and cancer risk among premenopausal women, the direct association with risk among postmenopausal women found in most studies was limited to larger tumors in Poland. This may reflect the age truncation of our population at 74 years and the fact that 40% of women were never screened. However, we minimized potential confounding by pathologic factors (size, grade, nodal status) by employing novel statistical methods, enabling us to adjust for these parameters in models that included risk factors and markers. In addition, some etiologic exposures, such as use of birth control pills, HRT and alcohol, were uncommon and therefore not examined. Our study included a high percentage of large, high-grade, and hormone receptor-negative tumors. In fact, small tumors were underrepresented in the case subset prepared as TMAs when compared with the full study, but we attempted to reduce the impact of this concern by statistical adjustment. Finally, some misclassification of markers secondary to sampling, variable fixation, interpretive errors and other factors is unavoidable.
In summary, our data suggest that molecular characteristics of breast cancers vary by BMI. Accordingly, it may be possible in the future to link etiologic exposures to abnormalities in molecular pathways, permitting the identification of molecular targets for risk assessment, screening and prevention.