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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Cancer Res. Author manuscript; available in PMC 2011 November 1.
Published in final edited form as:
PMCID: PMC2970770

Detection of Elevated Plasma Levels of EGF Receptor Prior to Breast Cancer Diagnosis among Hormone Therapy Users


Applying advanced proteomic technologies to prospectively collected specimens from large studies is one means of identifying preclinical changes in plasma proteins that are potentially relevant to the early detection of diseases like breast cancer. We conducted fourteen independent quantitative proteomics experiments comparing pooled plasma samples collected from 420 estrogen receptor positive (ER+) breast cancer patients ≤17 months prior to their diagnosis and matched controls. Based on the over 3.4 million tandem mass spectra collected in the discovery set, 503 proteins were quantified of which 57 differentiated cases from controls with a p-value<0.1. Seven of these proteins, for which quantitative ELISA assays were available, were assessed in an independent validation set. Of these candidates, epidermal growth factor receptor (EGFR) was validated as a predictor of breast cancer risk in an independent set of preclinical plasma samples for women overall [odds ratio (OR)=1.44, p-value=0.0008], and particularly for current users of estrogen plus progestin (E+P) menopausal hormone therapy (OR=2.49, p-value=0.0001). Among current E+P users EGFR's sensitivity for breast cancer risk was 31% with 90% specificity. While EGFR's sensitivity and specificity are insufficient for a clinically useful early detection biomarker, this study suggests that proteins that are elevated preclinically in women who go on to develop breast cancer can be discovered and validated using current proteomic technologies. Further studies are warranted to both examine the role of EGFR and to discover and validate other proteins that could potentially be used for breast cancer early detection.

Keywords: Breast cancer, epidermal growth factor receptor, menopausal hormone therapy


Breast cancer is a disease of considerable public health importance given that it is the most commonly diagnosed cancer in women worldwide. At present, the best available tool for breast cancer early detection is mammography as it has been shown to reduce mortality in randomized trials (1). Despite improvements in technology and the widespread use of mammography, breast cancer still remains the second leading cause of cancer mortality in women in the United States (2), and the leading cause of cancer mortality in women worldwide (3).

Considerable effort has been invested in trying to interrogate changes related to breast cancer in biological specimens. Numerous challenges have been faced including use of discovery platforms with poor reproducibility, and interrogation of specimens collected either at or after a breast cancer diagnosis (when the specimens of greatest clinical interest are those ascertained prior to diagnosis). We utilized advanced technologies for proteome profiling to systematically examine changes in the plasma proteome of pre-clinical samples from a case-control study nested in the Women's Health Initiative (WHI) Observational Study (OS) prospective cohort. The purpose of this study was to identify proteins that may be elevated preclinically in estrogen receptor positive (ER+) breast cancer cases, and to validate promising candidates in an independent sample set.

Materials and Methods

We conducted a nested case-control study within the WHI OS prospective cohort that was approved by the Fred Hutchinson Cancer Research Center Institutional Review Board. The WHI OS is a prospective cohort study, and details on the study's scientific rationale, eligibility criteria, and design have been published previously (4, 5). Briefly, 93,676 postmenopausal women 50-79 years of age were enrolled between October 1, 1993 and December 31, 1998 through 40 clinical centers in the United States. Epidemiologic data and biologic specimens were collected from participants uniformly according to a standardized IRB approved procedures and protocols by trained study staff. Blood specimens were collected at two time points, at enrollment (baseline) and at year 3 of follow-up. All participants provided written informed consent for this research study at the time of enrollment.

Case and Control Identification

Cohort members completed self-administered demographic and risk factor questionnaires annually, and the medical records of all women reporting a breast cancer diagnosis were reviewed by a study adjudicator to verify the diagnosis. The medical records of verified cases were then forwarded to the WHI coordinating center for coding of breast cancer stage, estrogen receptor (ER) status, progesterone receptor (PR) status, and histology. A total of 943 women clinically diagnosed with invasive breast cancer within 17 months of either their baseline or year 3 blood draw without a prior history of breast cancer were identified. Given the heterogeneity of breast cancer and the likelihood that breast cancer early detection biomarkers may be specific to certain breast cancer types, the work described herein focused exclusively on ER+ breast cancer cases.

Potential controls were selected from the pool of women never diagnosed with any type of cancer through September 15, 2005. Controls were individually matched 1:1 to cases on age at enrollment (±1 year), race/ethnicity (white, black, Hispanic, Asian/Pacific Islander, or other), blood draw date (±1 year), and clinical center of enrollment. Matching was done in a time forward manner to ensure that each control had at least as much follow-up time following her blood draw as the time from blood draw to breast cancer diagnosis of the case to which she was matched.

Discovery Studies

Fourteen independent large scale quantitative proteomics experiments were performed using a previously described method (6) which employed immunodepletion, isotopic labeling with acrylamide (7), extensive fractionation (8), and high resolution tandem mass spectrometry. Each experiment compared a pool of 300 uL of plasma from 35 pre-diagnostic breast cancer cases (equal volume) with a pool of 300 uL of 35 matched controls (equal volume). Pools of case and control plasma were immunodepleted of the top six most abundant proteins (albumin, IgG, IgA, transferrin, haptoglobin, and antitrypsin) using a Hu-6 column (4.6 × 250 mm, Agilent). Columns were equilibrated with buffer A at 0.5 mL/min for 13 minutes, and pooled plasma were injected after filtration through a 0.22 μm syringe filter. Flow-through fractions were collected for 10 minutes at a flow rate of 0.5 mL/min buffer A, and stored at -80 °C until further use. Immunodepleted samples were concentrated using a Centricon YM-3 device (Millipore) and re-diluted in 8 M urea, 30 mM Tris, pH 8.5, 0.5% OG (octyl-beta-d-glucopyranoside, Roche Diagnostics). Samples were reduced with DTT and isotopic labeling of cysteine residues of intact proteins was performed. Pools received either the light acrylamide isotope (C12, Sigma-Aldrich) or the heavy 1,2,3-C13-acrylamide isotope (C13, Cambridge Isotope Laboratories). Alkylation with acrylamide was performed for 1 hour at room temperature. The pools were then mixed together for further analysis.

The combined isotopically labeled samples were separated by an automated online 2D HPLC system (Shimadzu). The combined labeled plasma samples were separated in the first dimension by anion exchange chromatography (Poros HQ/10, 10 mm i.d. × 100 mm l, Applied Biosystems) using an 8 step-elution (0 to 1000 mM NaCl) at 0.8 mL/min. Fractions from each of the 8 anion-exchange separation elution steps were automatically transferred onto a reversed-phase column (PorosR2/10, 4.6 mm i.d. × 100 mm l, Applied Biosystems) for a second dimension of separation. A 25 min gradient elution (5 to 95% mobile phase B) was used at 2.4 mL/min. Mobile phase A for anion-exchange chromatography consisted of 20 mM Tris (Sigma-Aldrich), 6% isopropanol (Fisher Scientific, and 4 M urea, pH 8.5, and mobile phase B was the same composition and pH as mobile phase A with 1 M NaCl (Fisher Scientific) added. Mobile phase A for reversed-phase chromatography consisted of 95% water, 5% acetonitrile, and 0.1% TFA (Supelco), and mobile phase B consisted of 90% acetonitrile, 10% water, and 0.1% TFA.

In-solution digestion was performed with lyophilized aliquots from the reversed-phase (second dimension) fractionation step. Proteins were resuspended in 0.25 M urea containing 50 mM ammonium bicarbonate and 4% acetonitirle and then digested overnight at 37 °C with modified trypsin (Promega). The digestion was stopped by addition of 10% formic acid. Aliquots were subjected to mass spectrometry shotgun analysis. Ninety-six fractions were analyzed for each experiment using a LTQ-Orbitrap (Thermo) mass spectrometer coupled with a NanoLC-1D (Eksigent). Liquid chromatography separation was performed on a 25 cm column (Picofrit 75 μm i.d., New Objectives, packed in-house with Magic C18 resin) using a 90 min linear gradient from 5 to 40% of acetonitirle in 0.1% formic acid at 300 nL/min for shotgun analysis. Spectra were acquired in data-dependent mode over m/z range 400-1800, and included selection of the 5 most abundant doubly or triply charged ions of each MS spectrum for MS/MS analysis. Mass spectrometer parameters included capillary voltage of 2.0 kV, capillary temperature of 200 °C, resolution of 60,000, and target value of 1,000,000.

Each experiment compared a set of pooled plasma samples collected from 35 breast cancer cases to pooled plasma samples from their 35 matched controls, so a total of 490 cases and 490 controls contributed to these discovery experiments. The characteristics of our 14 experiments are summarized in Table 1. Eight experiments were performed comparing plasma from ER+/PR+ ductal breast cancer patients to controls with four experiments focusing on cases whose blood was drawn 0-38 weeks prior to their breast cancer diagnosis, and four on cases whose blood was drawn between 38 weeks to 17 months prior to diagnosis. Two experiments compared ER+/PR- ductal breast cancer patients to controls, one containing specimens collected 0-38 weeks pre-diagnosis, and the other containing cases collected 38 weeks to 17 months pre-diagnosis. Two experiments compared ER+/PR+ lobular breast cancer patients to controls, one containing cases collected 0-38 weeks pre-diagnosis, and the other containing cases collected 38 weeks to 17 months pre-diagnosis. Lastly, two experiments compared ER-/PR- ductal breast cancer patients to controls with one experiment comparing cases whose blood was drawn 0-38 weeks prior to their breast cancer diagnosis, and one experiment comparing cases whose blood was drawn between 38 weeks and 17 months prior to diagnosis. Seven of the experiments were run with the C13 (heavy) acrylamide on the cases and C12 (light) on the controls, and the other seven experiments had the labels reversed to account for potential artifacts of this labeling.

Table 1
Composition of case and control pools interrogated in our discovery experiments

This pooling strategy and sample size was selected because it is effectively equivalent to a sample size of 255 individual cases and 255 individual controls for our overall analysis, and 145 individual cases and 145 individual controls for analyses restricted to ER+ cases. With these sample sizes we had >80% to discover markers with an area under the curve (AUC) of 0.6 and a sensitivity of only 20%.

Analysis of Discovery Data

Mass spectra were searched using Mascot against the human International Protein Index (IPI) database (v. 3.13). Quantitative information was extracted from acrylamide labeled peptides using an in-house script (Q3) for peptides with minimum PeptideProphet = 0.75, expect score < 0.10, and maximum fractional delta mass = 20 ppm (7). Also, because the label orientation was reversed in some experiments, we additionally required that any quantified peptide had to be observed in both isotopic states at least once in at least one experiment. This criterion removes spurious ratios that arise due to misidentification. Proteins with ProteinProphet scores > 0.90 were aligned by their protein group number in order to identify master groups of indistinguishable proteins. The master group ratio for each experiment was set to the geometric mean of the corresponding peptide ratios, logarithmically transformed and median centered. Proteins that had become defunct in IPI or were immunodepleted were removed from the analysis. Mean log2 ratios and moderated p-values for groups of experiments were computed using the LIMMA package from and weighted using the number of quantitated events (peptides) (9).

The validation work described here focuses on ER+ breast cancer so we selected candidate proteins from the following subsets of experiments: all cases (n=14 experiments), ER+ (n=12 experiments), ER+/PR+ (n=10 experiments), and ER+, blood collected 0-38 weeks pre-diagnosis (n=6 experiments). Candidate proteins that were associated with a fold change ≥1.15, p-value<0.10 and were quantified in at least two experiments in any one of the subsets were chosen for validation. Of the 503 total proteins quantified in our discovery experiments, 57 met these criteria and are listed in Supplementary Table S1. More stringent statistical criteria were not applied to our discovery data in order to make our candidate lists more inclusive given our plan to employ a rigorous validation to identify the false positives while not discarding potential true positives that use of more stringent criteria could miss.

Validation Assays

Of the 57 top ranked candidates, we selected 7 for which an ELISA assay was readily available for validation. Assays were performed according to directions provided by the various manufacturers. ELISA reagents for EGFR, IGFBP1, and NOV were obtained from R&D Systems. ELISA kits for FN1, LTF, and VWF were purchased from Bendermed Systems, Calbiochem, and Assaypro, respectively. TFF3 assays were performed using commercial reagents based on a previously described procedure (10). All samples were assayed in duplicate blinded to case/control status. We conducted two rounds of ELISA validation assays. The first round included pre-clinical plasma samples from 105 women who were later diagnosed with invasive ER+ breast cancer and their matched controls not used in our discovery experiments and involved assessment of all seven candidates. The second round included pre-clinical plasma samples from 93 women who were later diagnosed with invasive ER+ breast cancer and their matched controls not used in either our discovery experiments or the first round validation. This second round validation focused only on EGFR and TFF3 based on the promising data generated from the first round validation as an effort to strengthen our validation conclusions in an additional independent set. A sample size of ~100 cases and ~100 controls was selected for each round of validation because it provided 70% power to characterize a marker with an AUC of 0.60 and a sensitivity of 20%, and 99% power to characterize a marker with an AUC of 0.65 and a sensitivity of 30%.

Statistical Methods

ELISA measurements above and below the detection limit for assays were imputed by the maximum and minimum computable values for the assay. All statistical analyses were done on a log2 scale. Values were Winsorized by 99%/1% percentile to compensate for the influence of extreme values, and were standardized such that the mean value and standard deviation of the control group of each set of assays were set to zero and one respectively. Multivariate logistic regression was used to compute odds ratios (OR), p-values, and 95% confidence intervals (CI) comparing cases to controls. All ORs were adjusted for the following potential confounders: age, time between blood draw and cancer diagnosis for cases/matched reference date for controls, race/ethnicity, body mass index (BMI) at baseline, and first degree family history of breast cancer. Analyses stratified by recency of menopausal hormone therapy use at the time of the blood draw [never/former user, current unopposed estrogen (E) user, current estrogen plus progestin (E+P) user] were conducted. Of these covariates, BMI was measured and family history of breast cancer and use of menopausal hormone therapy were self-reported. In analyses where samples are paired, the time to diagnosis for a healthy control was set to that of her matched case.


In both the discovery and validation sets cases and controls had similar frequencies of first degree family history of breast cancer, but cases were somewhat more likely to be current E+P users and to be overweight/obese (body mass index ≥25.0 kg/m2) and less likely to have had a hysterectomy compared to controls (Table 2).

Table 2
Characteristics of breast cancer cases and controls used for biomarker discovery and validation

The discovery experiments acquired 3,412,733 tandem mass spectra from which 3,154 proteins were identified and assigned to 1,491 groups of indistinguishable proteins. Acrylamide labels cysteine-containing peptides, so only 603 of the protein groups had measurable ratios. After removing proteins that were either immunodepleted or had defunct IPI entries, 503 protein groups remained for statistical analysis. While the odds ratios and p-values for EGFR, FN1, IGFBP1, LTF, NOV, TFF3, and VWF did not necessarily place them near the top of our overall candidate list, they were selected for validation based on the fact that these were the only candidates with commercially available ELISA assays.

The overall results of our first round of ELISA validations on 105 cases and 105 controls are shown in Table 3. Overall, levels of EGFR significantly differentiated cases from controls (OR=1.64, p=0.002), while levels of FN1, IGFBP1, LTF, TFF3, NOV, and VWF did not. Though the multivariate adjusted odds ratios estimated from our ELISA validation are not directly comparable to the fold changes calculated from our discovery data using pooled samples, the EGFR discovery and validation data are consistent in both observing that EGFR levels are on average higher in cases compared to controls. Menopausal hormone therapy has been shown to affect a significant portion of the serum proteome (6). For this reason, we stratified our validation results according to use of menopausal hormone therapy (never/former users, current E users, and current E+P users). The first round validation data suggested that EGFR levels were elevated in both current E users and current E+P users (Table 4). In addition, the data suggested that while TFF3 levels did not distinguish cases from controls, TFF3 levels were higher in current menopausal hormone therapy users compared to never/former users regardless of case/control status. E users had an odds ratio of 2.15 (p-value < 0.001) compared to never/former users, while E+P users had an odds ratio of 1.90 (p-value < 0.001) compared to never/former users. Associations with the other proteins assessed in our first round of ELISA validations were not influenced by use of menopausal hormones.

Table 3
Results from the first round of ELISA based validations on 105 case/control pairs
Table 4
First and second round validation results for EGFR

To further validate these findings a second round validation was conducted where the finding that EGFR levels were elevated in cases compared to controls who were E+P users was replicated (Table 4). Combining data from both validation rounds, EGFR levels were elevated in cases vs. controls among all women (OR=1.44, 95% confidence interval [CI]: 1.16-1.79), and particularly among E+P users (OR=2.49, 95% CI: 1.56-3.99). We used logistic regression modeling to test whether differences in the odds ratios across menopausal hormone therapy use subgroups were statistically different. The p-value comparing the odds ratios among current E+P users to never/former users was 0.0019, and the p-value comparing current E+P users to current E users was 0.12. We also evaluated risk of breast cancer by quartile of EGFR levels. Overall, women in the highest EGFR quartile had a 2.90-fold (95% CI: 1.60-5.32) increased risk of developing breast cancer compared to those in the lowest quartile, but among current E+P users those in the highest EGFR quartile had a 9.04-fold (95% CI: 2.78-33.21) increased risk (Table 5). The performance characteristics of EGFR in women taking E+P hormone therapy are shown in Figure 1. The ROC curve has an area under the curve of 0.7. At 80% specificity, EGFR's sensitivity as a single marker is 56%, and at 90% specificity its sensitivity is 31%. Of note, these risk estimates did not vary by breast cancer stage. Combining data from the two rounds of ELISA assays, EGFR was elevated among women overall who went on to be diagnosed with both localized (OR=1.39, 95% CI: 1.09-1.78) and regional/distant disease (OR=1.75, 95% CI: 1.04-2.92), and among current E+P users who went on to be diagnosed with both localized (OR=2.52, 95% CI: 1.37-4.64) and regional/distant disease (OR=2.84, 95% CI: 1.03-7.80).

Figure 1
EGFR receiver operator curve for case versus control among all current E+P users.
Table 5
Quartile distributions of EGFR validation results among all case/control sets and among current estrogen and progestin users

The peptide sequence coverage for EGFR in the discovery experiments is shown in Figure 2a. EGFR is a transmembrane protein and the peptides identified by mass spectrometry are all located on the extracellular region. The TFF3 peptides observed in the discovery work are shown in Figure 2b. TFF3 is a small secreted protein and sequence coverage suggests that we are observing the intact form of the secreted protein.

Figure 2
Sequence coverage for (A) EGFR (extracellular domain) and (B) TFF3. The signal peptide is underlined and identified peptides are highlighted in blue. Representative MS/MS spectra for quantified peptides for (C) TFF3 and (D) EGFR.

In our second round validation we also replicated our TFF3 results. When we combined data from both validation rounds TFF3 levels were elevated among both unopposed estrogen users (OR=2.17, p-value<0.001) and E+P users (OR=1.93, p-value<0.001) compared to never/former users.


There is paucity of breast cancer proteomic studies that have used pre-diagnostic blood samples. From a list of candidates identified by a high dimensional mass spectrometry based discovery approach, we attempted to validate a handful of proteins that were not particularly highly ranked, but met particular statistical criteria and had an available ELISA assay that could readily be used for validation. Of the seven proteins assessed, EGFR may have some utility as an indicator of breast cancer risk prior to diagnosis, particularly among current E+P users, based on its high statistical significance in our validation set. However, its performance with respect to its sensitivity and specificity is insufficient for it to serve as a single early detection marker.

We found that EGFR had an AUC of 0.70, at 80% specificity its sensitivity is 56%, and at 90% specificity its sensitivity is 31%. In comparison, PSA, which is clinically used to screen men for prostate cancer, has an AUC of 0.68 and at 81.1% specificity its sensitivity is 40.5%, and at 93.8% specificity its sensitivity is 20.5% (11). While EGFR is insufficient as a stand-alone early detection marker, its utility as a risk factor in conjunction with mammography has yet to be determined. For example, more frequent mammography among E+P users with elevated EGFR levels could potentially be useful for detecting breast cancer earlier among these women, but this needs to be formally evaluated.

EGFR is a cell surface tyrosine kinase receptor and is a member of the ERBB protoncogene family which also includes HER2. The identification of extracellular peptides, and no transmembrane or intracellular peptides by mass spectrometry in our discovery IPAS experiments, suggests shedding of the EGFR extracellular domain by cells. EGFR is involved in numerous cancer relevant pathways involving cell proliferation, survival, differentiation, and migration and binding of EGFR by various ligands can result in increased uncontrolled proliferation of cancer cells (12, 13). Further, EGFR is over-expressed in 20-81% of breast tumors (14-16). Several studies have measured blood levels of EGFR in relation to breast cancer, though overall the results are quite inconsistent. It is difficult to directly compare the results of these studies to ours since none involved measurements of EGFR in the preclinical period prior to a breast cancer patient's diagnosis. Among women with hormone receptor positive disease, serum EGFR levels decreased significantly after 1 month and 3 months of letrozole therapy versus pre-treatment conditions (17). Reduction in serum levels of EGFR in post-operative breast cancer cases compared to pre-operative cases has been shown to correlate with disease-free survival (18). While EGFR is involved in hormonal pathways relevant to breast cancer, there is no clear explanation at this point for why EGFR may be useful for the early detection of breast cancer primarily among E+P users, but not among either E users or never/former users. Thus, further investigations of the potential utility of EGFR as an indication of increased risk of breast cancer are warranted.

TFF3 is a small stable secreted protein, predominantly expressed in the gastrointestinal tract as well as a variety of other normal tissues and tumors. TFF3 has been shown to be estrogen responsive at both the gene and protein level. The gene encoding TFF3 contains a palindromic estrogen response element (ERE) that is conserved between human and mouse (19). We have recently demonstrated an association between proteins containing EREs in the upstream region of their gene sequence, such as TFF3, with increased serum levels when comparing women taking oral estrogen treatment after one year compared to baseline (6). Our observation that TFF3 levels are elevated in current E and current E+P users compared to never/former users is consistent with this work. While TFF3 was up-regulated in our discovery work, this was likely due to the imbalance in hormone therapy use in cases compared to controls (58.9% vs. 48.8% were current users) in our discovery experiments.

Of the seven proteins we attempted to validate, EGFR was the only one that showed statistically significant differences in overall levels for cases compared to controls. The likely primary reason why we failed to validate the other candidates is that all of them had a relatively high false discovery rate as we used fairly broad statistical criteria when including candidates on our list of those warranting further follow-up. The primary limitation of this study is the lack of readily available means to validate the numerous other candidates we discovered, most of which were much more compelling candidates based on their discovery odds ratios, p-values, and false discovery rates.

While EGFR alone cannot be viewed as clinically useful for breast cancer early detection, our validation of increased EGFR levels as an indication of increased risk for ER+ breast cancer specific to E+P users is important in two respects. First, no prior studies have validated even a single breast cancer early detection biomarker in preclinical specimens to the degree we have here, validating increased EGFR levels in two completely independent validation sets. Second, consideration of other exposures in biomarker discovery studies is likely critical given that while overall EGFR was not found to distinguish cases from controls among E+P users it is highly statistically significant. So incorporation of factors like use of hormone therapy, which has been shown to have a major impact on the plasma proteome (6), is critical in this type of work. Future work aimed at discovering and validating preclinical changes related to breast cancer is needed, and our EGFR finding warrants further replication and study.

Supplementary Material


WHI Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller.

WHI Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Ross Prentice, Garnet Anderson, Andrea LaCroix, Charles L. Kooperberg; (Medical Research Labs, Highland Heights, KY) Evan Stein; (University of California at San Francisco, San Francisco, CA) Steven Cummings.

WHI Clinical Centers: (Albert Einstein College of Medicine, Bronx, NY) Sylvia Wassertheil-Smoller; (Baylor College of Medicine, Houston, TX) Haleh Sangi-Haghpeykar; (Brigham and Women's Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (Brown University, Providence, RI) Charles B. Eaton; (Emory University, Atlanta, GA) Lawrence S. Phillips; (Fred Hutchinson Cancer Research Center, Seattle, WA) Shirley Beresford; (George Washington University Medical Center, Washington, DC) Lisa Martin; (Los Angeles Biomedical Research Institute at Harbor- UCLA Medical Center, Torrance, CA) Rowan Chlebowski; (Kaiser Permanente Center for Health Research, Portland, OR) Erin LeBlanc; (Kaiser Permanente Division of Research, Oakland, CA) Bette Caan; (Medical College of Wisconsin, Milwaukee, WI) Jane Morley Kotchen; (MedStar Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Northwestern University, Chicago/Evanston, IL) Linda Van Horn; (Rush Medical Center, Chicago, IL) Henry Black; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (State University of New York at Stony Brook, Stony Brook, NY) Dorothy Lane; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Alabama at Birmingham, Birmingham, AL) Cora E. Lewis; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of California at Davis, Sacramento, CA) John Robbins; (University of California at Irvine, CA) F. Allan Hubbell; (University of California at Los Angeles, Los Angeles, CA) Lauren Nathan; (University of California at San Diego, LaJolla/Chula Vista, CA) Robert D. Langer; (University of Cincinnati, Cincinnati, OH) Margery Gass; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Hawaii, Honolulu, HI) J. David Curb; (University of Iowa, Iowa City/Davenport, IA) Robert Wallace; (University of Massachusetts/Fallon Clinic, Worcester, MA) Judith Ockene; (University of Medicine and Dentistry of New Jersey, Newark, NJ) Norman Lasser; (University of Miami, Miami, FL) Mary Jo O'Sullivan; (University of Minnesota, Minneapolis, MN) Karen Margolis; (University of Nevada, Reno, NV) Robert Brunner; (University of North Carolina, Chapel Hill, NC) Gerardo Heiss; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (University of Tennessee Health Science Center, Memphis, TN) Karen C. Johnson; (University of Texas Health Science Center, San Antonio, TX) Robert Brzyski; (University of Wisconsin, Madison, WI) Gloria E. Sarto; (Wake Forest University School of Medicine, Winston-Salem, NC) Mara Vitolins; (Wayne State University School of Medicine/Hutzel Hospital, Detroit, MI) Michael S. Simon.

Financial support:

The Women's Health Initiative (WHI) program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221


Potential conflicts of interest: None


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