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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC 2012 March 1.
Published in final edited form as:
PMCID: PMC3051011

Protein Microarray Analysis of Mammary Epithelial Cells from Obese and Non-Obese Women at High Risk for Breast Cancer: Feasibility Data



Obesity is a well-established risk factor for cancer, accounting for up to 20% of cancer deaths in women. Studies of women with breast cancer have shown obesity to be associated with an increased risk of dying from breast cancer and increased risk of distant metastasis. While previous studies have focused on differences in circulating hormone levels as a cause for increased breast cancer incidence in post-menopausal women, few studies have focused on potential differences in the protein expression patterns of mammary epithelial cells obtained from obese versus non-obese women.


Protein expression was assessed by reverse phase protein microarray in mammary epithelial cells from 31 random periareolar fine needle aspirations performed on 26 high-risk women.


In this pilot and exploratory study, vimentin (unadjusted p=0.028) expression was significantly different between obese and non-obese women.


Vimentin is integral to adipocyte structure and function as well as to the epithelial-to-mesenchymal transition needed for cancer cell metastasis. Further research is needed to confirm this finding and determine the possible effects of the adipocyte microenvironment on the initiation and progression of breast cancer in high-risk women.


Differential protein expression patterns obtained from a future expanded study may serve to elaborate the underlying pathology of breast cancer initiation and progression in obese women and identify potential biomarkers of response to preventative interventions, such as dietary changes and exercise.

Keywords: protein microarray, random periareolar fine needle aspiration, obesity, breast cancer risk assessment


Obesity is one of the most important known preventable causes of cancer, accounting for up to 20% of cancer deaths in women, with highest body mass index (BMI) category (BMI > 40 kg/m2) conferring higher risk (1). Previous studies have shown an association between postmenopausal breast cancer risk and excessive body weight, and this association is increased in women with a positive family history of breast cancer (23). In addition, women with breast cancer have an increased risk of dying from breast cancer as well as an increased risk of distant metastasis in obese women (45).

Studies investigating the molecular basis for the association between breast cancer and increased adiposity have focused on differences in circulating hormones, such as estrogen and insulin, in normal weight versus obese individuals, or on how mammary adipocytes and their secreted adipokines may influence tumor cells growth and invasion (67). However, few studies have focused on potential differences in the protein expression patterns of mammary epithelial cells obtained from obese versus non-obese women.

Random Periareolar Fine Needle Aspiration (RPFNA) is a research technique used to sample mammary cells from the whole breast of asymptomatic women at high risk for developing breast cancer. RPFNA has been shown to successfully predict short-term breast cancer risk in high-risk women (8) and is highly reproducible across institutions (9). The presence of atypia in a breast RPFNA confers a 5.6-fold increased short-term breast cancer risk (8).

Reverse-Phase Protein Microarray (RPPM) is a high-throughput proteomic tool, developed to test for dysregulation of protein signaling networks in human biopsy specimens. RPPM uses denatured lysates so antigen retrieval, which is a limitation for tissue arrays, is not a problem. RPPM’s sensitivity and analytical robustness allow for very small amounts of mammary epithelial cells to be selected from RPFNA samples via laser capture microdissection (LCM) and analyzed for differential protein expression patterns (1012).

The purpose of this brief communication is to relay findings obtained from a pilot and exploratory study aimed at investigating whether mammary epithelial cells obtained from obese (BMI ≥ 30 kg/m2) and non-obese (BMI < 30 kg/m2) women at high risk for developing breast cancer display differential expression of proteins involved in cancer cell growth, survival, and metastasis.

Materials & Methods

Informed Consent

This study was conducted among 26 women who underwent RPFNA under Institutional Review Board-approved protocol at the Duke University Medical Center (June 2008-February 2010) and who presented sufficient cells for analysis.


Women were required to have at least one of the following major risk factors for breast cancer: (a) 5-year Gail risk calculation >1.7%, (b) prior biopsy exhibiting atypical hyperplasia, lobular carcinoma in situ, or ductal carcinoma in situ, or (c) known BRCA1/2 mutation carrier. Sociodemographic variables, family history of breast cancer, and menopausal status were collected. Women were defined as postmenopausal if they had no menses for >12 months in the absence of pregnancy and/or status post-bilateral oophorectomy.

BMI Calculation

Body weights and heights were clinically measured on a platform scale with a fixed stadiometer. BMIs were calculated, and the standard cutoff point of 30 kg/m2 was used to dichotomize obese from non-obese women (13).


RPFNA was performed as published previously, in accordance with methods established and validated by Fabian et al (14). Cells were classified qualitatively as nonproliferative, hyperplasia, or hyperplasia with atypia. The most atypical cell cluster was examined and scored by the Masood cytology index (Table 1A). Morphologic assessment, Masood cytology index scores, and cell count were assigned by a single dedicated pathologist who was blinded to BMI.

Table 1A
Masood Cytology Index

Fixation and Microdissection of RPFNA Cytology

RPFNA samples were immediately placed and fixed in modified CytoLytR (Hologic Inc., Marlborough, MA) containing 1% formalin for 24 hours at room temperature. Samples were washed with CytoLytR until the majority of red blood cells were removed, followed by storage at 4°C. Cytospin slides were made for each of the samples using a Shandon CytospinR 4 Cytocentrifuge (Thermo Scientific, Waltham, MA). RPFNA samples were spun onto slides, using Shandon Single CytofunnelsR at 420 rpm for 1 minute, which were then immediately placed in 75% ethanol for 30 seconds. The slides were then stained with hematoxylin (Sigma-Aldrich, St. Louis, MO) for 20 seconds; dehydrated in 75, 95 and 100% ethanol for 30 seconds each, followed by xylene for 5 minutes and allowed to air dry. The 75% ethanol and hematoxylin staining solutions were supplemented with Complete protease inhibitor tablets (Roche Applied Science, Indianapolis, IN). Approximately 5,000 epithelial cells were microdissected with a PixCell II Laser Capture Microdissection system (MDS Analytical Technologies, Sunnyvale, CA) and stored onto microdissection caps which were maintained at −80°C until lysed.


As previously described (15), cellular lysates were printed in triplicate onto nitrocellulose-coated slides, using an Aushon 2470 arrayer equipped with 350 μm pins. The following cellular lysates served as positive controls for antibody staining: A431 ± EGF, HeLa ± pervanadate, or Jurkat ± calyculin. Immunostaining was performed as previously described (15). Polyclonal and monoclonal antibodies were purchased from Cell Signaling Technologies (Danvers, MA), Biosource/Invitrogen (Carlsbad, CA), BD Biosciences (San Jose, CA), and Upstate/Millipore (Billerica, MA). Each antibody-stained array was scanned and normalized to total protein, as determined by SYPROR Ruby staining. Image analysis was performed as previously described (15).

Immunohistochemical Staining and Scoring

Cytology specimens were mounted on Superfrost® Excell slides (Fisher, Pittsburgh, PA) and preserved in 95% ethanol. These slides were dehydrated in 95% and 100% ethanol for 30 seconds each and air-dried for 10 minutes. Specimens were fixed in ice-cold acetone for 20 minutes and then air-dried. Rehydration was achieved in TBS for 5 minutes, followed by immersion in 3% hydrogen peroxide for 5 minutes, deionized water, and TBS.

All immunostaining was carried out on Dako Autostainer (DAKO, Carpinteria, CA). Specimens were placed in Background Buster (Innovex Biosciences, Richmond, CA) for 30 minutes, followed by incubation with mouse monoclonal anti-human vimentin antibody (DAKO, M7020) at 1:300 dilution for 45 minutes and single wash in TBS. Detection and visualization were performed with Envision+ and DAB+ (DAKO) as per manufacturer’s recommendations. Specimens were counterstained in hematoxylin for 30 seconds, dipped in ammonium water four times, dehydrated in graded series of ethanol and xylene, and coverslipped. Frozen human tonsil tissue and inflammatory cells served as positive external and internal controls, respectively.

Immunostained specimens were reviewed and scored by a pathologist (J.G.) who was blinded to BMI and patient identity. Samples were evaluated for average staining intensity (0–3) and percentage of positively stained cells (0%–100%). As previously described (16), a HistoScore was calculated for each stained specimen (product of the two aforementioned parameters).


Mann-Whitney test was used to compare the relative intensity of each proteomic marker between non-obese and obese samples, given the lack of normal distribution of these variables. To compare difference in the median age between non-obese and obese women, Mann-Whitney U-test was performed (Table 1B). Fisher’s exact test was used to determine the relationships between the two groups and the following variables: race, menopausal status, and Masood cytology. The Ward method for hierarchical clustering analysis was performed with JMP v5.0 software (SAS Institute, Cary, NC). P-values < 0.05 were considered statistically significant.

Table 1B
Patient Characteristics


Subject Characteristics

This study was performed on 31 RPFNA samples obtained from 26 high-risk women. Of these samples, 10 were bilateral and 21 were unilateral. Table 1B lists the characteristics of study subjects according to two BMI categories, non-obese (BMI < 30 kg/m2) and obese (BMI ≥ 30 kg/m2). We have chosen the BMI cutoff of 30 because of well-established associations between poor prognosis and survival of women in the highest BMI categories (4). In the present study, the median BMI of obese women is 31.3 kg/m2, while the median BMI of non-obese women is 22.3 kg/m2. These two groups of women are not significantly different with respect to age, race, and menopausal status.

RPFNA samples were stratified according to Masood cytology index scores (8, 14) (Table 1A) with the following distribution: 3% were nonproliferative, 16% were hyperplastic, 74% were atypical, and 7% were suspicious of cancer. These scores were further classified into High and Low Masood groups (Table 1B), with a Masood score cutoff of 15 for cytologic atypia. The presence of atypia has been used as a surrogate marker of short-term breast cancer risk in high-risk women (8). Here, the short-term breast cancer risk in obese and non-obese women is not significantly different (p=0.67).

Protein Expression by RPPM

Figure 1A depicts the expression pattern of log 2-transformed intensity values of 52 out of the original 60 total, phosphorylated and cleaved proteins obtained from all 31 RPFNA samples. Eight proteins did not yield sufficient intensity signal; these were excluded from statistical analysis.

Figure 1
Protein expression of 52 endpoints in 31 RPFNA samples. A, Unsupervised hierarchical clustering analysis of log 2-transformed intensity values of each protein endpoint (rows) are broadly divided by obese and non-obese samples (columns). Non-obese (BMI ...

In unadjusted analysis, the expression of vimentin (p=0.028) and phosphorylated Bad (p-Bad) S136 (p=0.049) are statistically different between samples of non-obese and obese women (Fig. 1B and 1C). However after adjusting for false discovery rate via Benjamini-Hockberg method, vimentin and p-Bad S136 expression are no longer statistically significant.

Vimentin IHC

We performed retrospective analysis of vimentin expression by immunohistochemistry (IHC) on a limited number of archived RPFNA cytologic samples. A majority of RPFNA samples had been expended in other experiments, and only 20 of 31 samples were evaluable for vimentin expression by IHC. Representative photomicrographs of epithelial cell clusters from obese and non-obese women are shown on Fig. 2A and 2B. Box-plot representation of the distribution of HistoScores of non-obese and obese samples is shown on Fig. 2C. Due to the limited samples available for retrospective IHC analysis, we were unable to test for correlation between vimentin expression by RPPM and by IHC.

Figure 2
Photomicrographs of representative cytologic samples from obese and non-obese women. Brown cytoplasmic staining indicates vimentin expression. Cytologic sample from obese subject (A) received a HistoScore of 150, with 50% of positive-staining cells and ...


In this exploratory study, RPPM analysis allowed us to investigate a wide array of proteins that play a role in cell survival, growth, differentiation, and death. The preliminary results presented herein is a first report suggesting that mammary epithelial cells obtained from high-risk obese women may differ in vimentin expression compared to non-obese women. Vimentin is often used as a general marker of mesenchymal cells and has been shown to play a critical role in epithelial-to-mesenchymal transition (EMT) necessary for cancer cell invasion and metastasis (17). Additionally, vimentin is an integral structural component in fat droplet organization in adipocytes, as well as key for lipid metabolism and adipogenesis (1820). Treatment of MCF10A cells with arachidonic acid, a metabolite obtained from a high-fat diet, induced EMT and increased vimentin expression with concomitant decrease in E-cadherin expression (21). Considering the established associations between adiposity, adipokines, and vimentin, further investigation into how adipocyte microenvironment influences mammary epithelial cell protein expression in high-risk obese women is warranted.

Few members of the apoptotic pathway have been implicated in adipocyte apoptosis induced by leptin, a hormone that regulates food intake and energy metabolism (22). Animal model studies have shown no difference in protein expression of pro-apoptotic Bax in mammary tumors of obese and non-obese mice that were fed with high-fat diet and low-fat diet, respectively (23). In the present study, Bax expression is not significantly different (p=0.055) in obese and non-obese women (Fig. 1D), while pro-survival p-Bad S136 expression is higher in non-obese women (Fig. 1C). In the absence of dietary information at the time of recruitment and RPFNA collection, we cannot attribute the differential expression of these proteins to a recent change in caloric intake. But, it is interesting to speculate that diet or exercise may impact apoptotic signaling proteins independently of BMI.

An important limitation of this study is small sample size that does not allow for statistical significance of associations between protein expression and risk factors for breast cancer, such as age, menopausal status and cytologic atypia. Thus, it is not possible to draw definitive conclusion on causal relationship between differential protein expression and breast cancer risk in obese versus non-obese women.

In this study, we did not archive adequate RPFNA cytologic samples for a statistically meaningful comparison of RPPM and IHC data. However, our limited data demonstrates that it is possible to prospectively compare RPPM and IHC analysis of specific proteins. The direct comparison of RPPM data with western blot analysis and/or IHC is clearly important for the future use of RPPM as a tool for multiplexed cell signaling analysis of human samples. Recent studies by Accordi et al. demonstrate concordance between RPPM results and western blot analysis of proteins, such as Bcl-2 S70, that are activated in primary leukemia samples with MML translocations (24). In the present study, we lack statistical power to draw definitive conclusion about the correlation between RPPM and IHC data, given the insufficient or small number of samples. Moreover, the IHC analysis that we employed has obvious limitations, such as variable cellularity, heterogeneity of staining, and subjective nature of scoring the cytologic samples. In the future, we will address these limitations by using comparable number of cells for IHC and employing image analysis of stained cells to quantify protein expression.

Our future proteomic studies will include a larger cohort with equal number of obese and non-obese women of Caucasian and African American descent and analysis of adipokines to better understand the underlying pathology of breast cancer initiation and progression in obese women. African American women have higher rates of obesity and more aggressive forms of breast cancer with greater likelihood of dying from breast cancer in pre- and post-menopausal women (25). Future proteomic studies will better delineate whether these two factors are causally related as well as identify potential biomarkers of response to preventative interventions, such as dietary changes and exercise.


Grant Support: Primary Support was provided by Susan G. Komen Promise Grant KG091020 (D. Yu and V.L. Seewaldt), NCI/AVON Partners in Progress Grant 3 P30 CA014236-32S1 (V.L. Seewaldt and D. Yu), NIH/NCI grants R01CA88799 and R01CA114068 (V.L. Seewaldt), and Susan G. Komen Career Catalyst in Disparities Research KG090730 (C. Ibarra-Drendall).


random periareolar fine needle aspiration
reverse phase protein microarray
body mass index
epithelial-to-mesenchymal transition
laser capture microdissection
epidermal growth factor
Tris-buffered saline
mixed lineage leukemia


1. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med. 2003;348:1625–38. [PubMed]
2. Carpenter CL, Ross RK, Paganini-Hill A, Bernstein L. Effect of family history, obesity and exercise on breast cancer risk among postmenopausal women. Int J Cancer. 2003;106:96–102. [PubMed]
3. Seewaldt VL, Goldenberg V, Jones LW, et al. Overweight and obese perimenopausal and postmenopausal women exhibit increased abnormal mammary epithelial cytology. Cancer Epidemiol Biomarkers Prev. 2007;16:613–6. [PubMed]
4. Ewertz M, editor. AACR San Antonio Breast Cancer Symposium. San Antonio, TX: AACR; 2009. Dec 10–13, Effect of Obesity on Prognosis after Early Breast Cancer.
5. Berclaz G, Li S, Price KN, et al. Body mass index as a prognostic feature in operable breast cancer: the International Breast Cancer Study Group experience. Ann Oncol. 2004;15:875–84. [PubMed]
6. Lorincz AM, Sukumar S. Molecular links between obesity and breast cancer. Endocr Relat Cancer. 2006;13:279–92. [PubMed]
7. Iyengar P, Combs TP, Shah SJ, et al. Adipocyte-secreted factors synergistically promote mammary tumorigenesis through induction of anti-apoptotic transcriptional programs and proto-oncogene stabilization. Oncogene. 2003;22:6408–23. [PubMed]
8. Fabian CJ, Kimler BF, Zalles CM, et al. Short-term breast cancer prediction by random periareolar fine-needle aspiration cytology and the Gail risk model. J Natl Cancer Inst. 2000;92:1217–27. [PubMed]
9. Ibarra-Drendall C, Wilke LG, Zalles C, et al. Reproducibility of random periareolar fine needle aspiration in a multi-institutional Cancer and Leukemia Group B (CALGB) cross-sectional study. Cancer Epidemiol Biomarkers Prev. 2009;18:1379–85. [PMC free article] [PubMed]
10. Wulfkuhle JD, Aquino JA, Calvert VS, et al. Signal pathway profiling of ovarian cancer from human tissue specimens using reverse-phase protein microarrays. Proteomics. 2003;3:2085–90. [PubMed]
11. Espina V, Mehta AI, Winters ME, et al. Protein microarrays: molecular profiling technologies for clinical specimens. Proteomics. 2003;3:2091–100. [PubMed]
12. Liotta LA, Espina V, Mehta AI, et al. Protein microarrays: meeting analytical challenges for clinical applications. Cancer Cell. 2003;3:317–25. [PubMed]
13. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults--The Evidence Report. National Institutes of Health. Obes Res. 1998;6 (Suppl 2):51S–209S. [PubMed]
14. Vasilatos SN, Broadwater G, Barry WT, et al. CpG island tumor suppressor promoter methylation in non-BRCA-associated early mammary carcinogenesis. Cancer Epidemiol Biomarkers Prev. 2009;18:901–14. [PMC free article] [PubMed]
15. Petricoin EF, 3rd, Espina V, Araujo RP, et al. Phosphoprotein pathway mapping: Akt/mammalian target of rapamycin activation is negatively associated with childhood rhabdomyosarcoma survival. Cancer Res. 2007;67:3431–40. [PubMed]
16. Flowers JL, Burton GV, Cox EB, et al. Use of monoclonal antiestrogen receptor antibody to evaluate estrogen receptor content in fine needle aspiration breast biopsies. Ann Surg. 1986;203:250–4. [PubMed]
17. Mendez MG, Kojima S, Goldman RD. Vimentin induces changes in cell shape, motility, and adhesion during the epithelial to mesenchymal transition. FASEB J. 2010;24:1838–51. [PubMed]
18. Shen WJ, Patel S, Eriksson JE, Kraemer FB. Vimentin is a functional partner of hormone sensitive lipase and facilitates lipolysis. J Proteome Res. 2010;9:1786–94. [PMC free article] [PubMed]
19. Franke WW, Hergt M, Grund C. Rearrangement of the vimentin cytoskeleton during adipose conversion: formation of an intermediate filament cage around lipid globules. Cell. 1987;49:131–41. [PubMed]
20. Lieber JG, Evans RM. Disruption of the vimentin intermediate filament system during adipose conversion of 3T3-L1 cells inhibits lipid droplet accumulation. J Cell Sci. 1996;109:3047–58. [PubMed]
21. Martinez-Orozco R, Navarro-Tito N, Soto-Guzman A, Castro-Sanchez L, Perez Salazar E. Arachidonic acid promotes epithelial-to-mesenchymal-like transition in mammary epithelial cells MCF10A. Eur J Cell Biol. 2010;89:476–88. [PubMed]
22. Gullicksen PS, Della-Fera MA, Baile CA. Leptin-induced adipose apoptosis: Implications for body weight regulation. Apoptosis. 2003;8:327–35. [PubMed]
23. Dogan S, Hu X, Zhang Y, Maihle NJ, Grande JP, Cleary MP. Effects of high-fat diet and/or body weight on mammary tumor leptin and apoptosis signaling pathways in MMTV-TGF-alpha mice. Breast Cancer Res. 2007;9:R91. [PMC free article] [PubMed]
24. Accordi B, Espina V, Giordan M, et al. Functional Protein Network Activation Mapping Reveals New Potential Molecular Drug Targets for Poor Prognosis Pediatric BCP-ALL. PLoS One. 2010;5:e13552. [PMC free article] [PubMed]
25. Rose DP, Haffner SM, Baillargeon J. Adiposity, the metabolic syndrome, and breast cancer in African-American and white American women. Endocr Rev. 2007;28:763–77. [PubMed]