Body fatness and breast cancer risk in relation to phosphorylated mTOR expression in a sample of predominately Black women

This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Study participants were women recruited between 2001 and 2015 for the Women’s Circle of Health Study (WCHS), a multisite case-control study conducted in New York City and 10 counties in eastern New Jersey. Details on study recruitment have been described elsewhere [16, 17]. All participants provided written informed consent. The protocol was approved by all relevant institutional review boards. In brief, the cases included patients who self-identified as Black or as White women, were 20–75 years of age with no previous history of cancer other than nonmelanoma skin cancer, and were within 9 months of having received a diagnosis of primary, histologically confirmed, invasive breast cancer or ductal carcinoma in situ (DCIS). Controls had the same inclusion criteria but no history of cancer and were identified during the same period. Controls were obtained by random digit dialing and frequency matched with patients by 5-year age groups, race, and telephone exchanges (area code plus 3-digit prefixes for cases from New York City) or county of residence (cases from New Jersey). In New Jersey, controls for Black patients were also recruited through outreach sources, such as health events [17].

Of patients eligible for inclusion, more than 95% signed a release form for their tumor tissue as part of the informed consent. Clinical and tumor characteristics, including the expression status of ER, progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), were obtained from pathology reports. Formalin-fixed paraffin-embedded tissue specimens were used for tissue microarray construction that was guided by a specialized breast pathologist (T. K.) and included at least 2 cores, ranging in size from 0.6 to 1.2 mm, per patient. In total, samples from 770 cases included in tissue microarrays (TMAs) were available for laboratory assays. After immunostaining, patients with tumor tissue cores with insufficient cells (< 25 cells) for scoring were excluded, and 1 case and 1 control were excluded for missing BMI data, resulting in a final data set of 715 cases (574 [80%] Black and 141 [20%] White patients) and 1983 controls (1280 [64%] Black and 713 [36%] White women). The percentage of Black women in cases was higher than that in controls because WCHS focused on collecting tumor specimens in Black women in the later stage of the study. Among the cases, 644 had invasive breast cancer, and 71 had DCIS.

We used p-mTOR to indicate mTOR pathway activation as the protein expression was positively correlated with BMI as well as key phosphorylated proteins in the upstream and downstream of the mTOR pathway, including p-AKT, p-p70S6K, and p-4EBP1 [14, 15]. Sections from TMAs were cut at 5 μm, placed on charged slides, and dried at 60 °C for 1 h. Slides were cooled to room temperature, placed in the Dako Omnis autostainer, where they were deparaffinized with Clearify, and rehydrated using graded alcohols. Flex Target Retrieval Solution, High pH (Agilent; catalog No. GV80411) was used for 30 min. Slides were incubated with p-mTOR antibody (Ser2448, clone 49F9, Cell Signaling; catalog No. 2976) for 40 min at a dilution of 1:75. Envision FLEX HRP-labeled polymer (Agilent; catalog No. DM842) was applied for 30 min. Diaminobenzidine (DAB; Agilent; catalog No. K3468), applied for 5 min, was used for chromogen visualization. Slides were counterstained with hematoxylin for 8 min and then placed in distilled water. After being removed from the autostainer, the sections were cleared and coverslipped.

Stained slides were digitally imaged at magnification × 20 using the Aperio ScanScope XT (Leica Biosystems) digital slide scanner system. Regions were identified and manually annotated to appropriately represent the heterogeneity of staining of each TMA core and to reduce irrelevant regions from image analysis calculations. The Aperio image analysis platform was used to develop quantitative image analysis algorithm macros for the quantification of IHC slides. A cytoplasmic algorithm was tailored to fine tune the cell feature detection using cellular, nuclear, and stain parameters, creating a specific algorithm macro for p-mTOR based on the cell compartment location of the target protein. The cytoplasmic algorithm analyzed DAB staining intensity and the percentage of cells containing stain within the cytoplasm compartment. The algorithm results provided total number of cells and percentage per scoring class (0, none; 1+, partial or weak; 2+, moderate; or 3+, strong). A histologic score (H-score) at the core level was calculated by the formula [1 × (% cells 1+) + 2 × (% cells 2+) + 3 × (% cells 3+)] × 100 [18]. The core-level data were collapsed into case-level data using a cellularity-weighted approach [19]. Figure 1 shows representative images of p-mTOR IHC staining.

Data on demographic characteristics, history of benign breast disease, reproductive and menstrual histories, family history of breast cancer in a first-degree relative, oral contraceptive use, and history of diabetes and its treatment were obtained by in-person interviews [20]. Anthropometric measurements were obtained by trained staff during the in-person interviews using a standardized protocol previously described [20]. WC was measured at the umbilicus, and hip circumference was measured at the maximum extension of the buttocks. Fat mass and percent body fat were measured by bioelectrical impedance analysis using a Tanita TBF-300A total body composition analyzer. Fat mass index was calculated as fat mass in kilograms divided by the square of height in meters.

Breast cancer cases with p-mTOR overexpressed tumors were defined as having p-mTOR expression levels above the 75th percentile level (i.e., approximate H-score = 80), and cases with p-mTOR negative/low tumors were those having expression levels below the 75th percentile. Because there is no standard definition for p-mTOR overexpression, we examined 2 additional cutoff points of p-mTOR expression levels: 25th percentile (H-score = 1) and 50th percentile (H-score = 20) for defining p-mTOR overexpressed tumors. Demographics and body fatness measurements were compared among p-mTOR overexpressed cases vs. controls and p-mTOR negative/low cases vs. controls using t tests for continuous variables and χ² tests for categorical variables. We performed polytomous (as known as multinomial) logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for p-mTOR overexpressed and for p-mTOR negative/low breast cancer in relation to the body fatness variables [21]. The models were adjusted for age, race, family history of breast cancer, age at menarche, parity, breastfeeding, menopausal status, oral contraceptive use, and history of diabetes (defined as using any medications or injections for diabetes). BMI was classified as < 25.0 (normal weight), 25.0–29.9 (overweight), 30.0–34.9 (class I obesity), and ≥ 35.0 kg/m² (class II/III obesity) [22]. WC, WHR, and percent body fat were classified into quartiles based on their distributions in controls. Because body fatness variables were positively correlated with each other at various levels, for example, BMI was highly associated with WC, percent body fat, and fat mass index (r > 0.80), and WHR was modestly correlated with BMI, percent body fat, and fat mass index (r = 0.35 to 0.40; Supplemental Table 1), these variables were not mutually adjusted to prevent multicollinearity. Trend tests were performed by using the median values of each category of the body fatness variables as a continuous variable in the regression models. The differences in the associations of body fatness with breast cancer risk by the p-mTOR expression status were assessed by tests for heterogeneity with a null hypothesis that the relationship between body fatness and p-mTOR overexpressed tumors was not different from that for p-mTOR negative/low tumors, i.e., disease subtype heterogeneity hypothesis [23]. For stratified analyses, we performed separate regression models for ER+ and ER– tumors. We also stratified the associations by menopausal status and by race as secondary analyses because breast cancer etiology related to obesity may differ by these factors [24]. Two sets of sensitivity analyses were performed to evaluate the associations for invasive breast cancer cases vs. controls and for ER+/PR+ and ER–/PR– tumor subtypes vs. controls. All tests were two-sided, and P < .05 was considered statistically significant. Statistical analyses were performed using SAS, version 9.4 (SAS Institute Inc).