Postmenopausal estrogen and progestin effects on the serum proteome
- Sharon J Pitteri1,
- Samir M Hanash1,
- Aaron Aragaki1,
- Lynn M Amon1,
- Lin Chen1,
- Tina Busald Buson1,
- Sophie Paczesny1, 2,
- Hiroyuki Katayama1, 3,
- Hong Wang1,
- Melissa M Johnson1,
- Qing Zhang1,
- Martin McIntosh1,
- Pei Wang1,
- Charles Kooperberg1,
- Jacques E Rossouw4,
- Rebecca D Jackson5,
- JoAnn E Manson6,
- Judith Hsia7,
- Simin Liu8,
- Lisa Martin9 and
- Ross L Prentice1Email author
© Prentice et al.; licensee BioMed Central Ltd. 2009
Received: 9 September 2009
Accepted: 24 December 2009
Published: 24 December 2009
Women's Health Initiative randomized trials of postmenopausal hormone therapy reported intervention effects on several clinical outcomes, with some important differences between estrogen alone and estrogen plus progestin. The biologic mechanisms underlying these effects, and these differences, have yet to be fully elucidated.
Baseline serum samples were compared with samples drawn 1 year later for 50 women assigned to active hormone therapy in both the estrogen-plus-progestin and estrogen-alone randomized trials, by applying an in-depth proteomic discovery platform to serum pools from 10 women per pool.
In total, 378 proteins were quantified in two or more of the 10 pooled serum comparisons, by using strict identification criteria. Of these, 169 (44.7%) showed evidence (nominal P < 0.05) of change in concentration between baseline and 1 year for one or both of estrogen-plus-progestin and estrogen-alone groups. Quantitative changes were highly correlated between the two hormone-therapy preparations. A total of 98 proteins had false discovery rates < 0.05 for change with estrogen plus progestin, compared with 94 for estrogen alone. Of these, 84 had false discovery rates <0.05 for both preparations. The observed changes included multiple proteins relevant to coagulation, inflammation, immune response, metabolism, cell adhesion, growth factors, and osteogenesis. Evidence of differential changes also was noted between the hormone preparations, with the strongest evidence in growth factor and inflammation pathways.
Serum proteomic analyses yielded a large number of proteins similarly affected by estrogen plus progestin and by estrogen alone and identified some proteins and pathways that appear to be differentially affected between the two hormone preparations; this may explain their distinct clinical effects.
Postmenopausal hormone therapy was shown to have multiple effects of public-health importance in the Women's Health Initiative (WHI) randomized, placebo-controlled hormone-therapy trials of 0.625 mg/day conjugated equine estrogen (E-alone)  or of this same estrogenic preparation plus 2.5 mg/day medroxyprogesterone acetate (E+P) , over respective average intervention periods of 7.1 and 5.6 years. The observed effects were similar for the two preparations for some outcomes, including stroke [3, 4] and hip fracture [5, 6]; whereas E+P effects were unfavorable (P < 0.05) compared with those for E-alone for other outcomes, including coronary heart disease (CHD) [7, 8], breast cancer [9, 10], and venous thromboembolism (VT) [11, 12], and a global index [1, 2] that was designed to summarize major health benefits versus risks .
Several of the articles just cited formally examined whether interactions occurred between the hormone-therapy hazard ratios and baseline study-subject characteristics. Although some moderate variations were detected (for example, for E-alone and breast cancer ), these tended to provide limited insight into the biologic mechanisms and pathways involved in the observed clinical effects. A cardiovascular disease nested case-control study also was conducted to relate baseline values of candidate biomarkers and post-randomization biomarker changes to observed hormone-therapy effects. This study confirmed baseline biomarker disease associations and identified some pertinent biomarker changes after hormone-therapy initiation, but identified few interactive or explanatory biomarkers for either CHD  or stroke , although the E+P hazard ratio elevation for CHD appeared to be smaller among women having relatively low baseline low-density lipoprotein cholesterol .
It follows that much remains to be explained about the pattern of biologic changes induced by these hormone-therapy preparations in relation to the outcome effects mentioned earlier. Proteomic discovery work has the potential to identify biomarkers that may help to explain E+P or E-alone clinical effects or differences in effects between the two preparations. Hence, we applied a comprehensive quantitative proteomic approach designated Intact Protein Analysis System (IPAS) [16–19] to compare the serum proteome at 1 year after randomization to baseline for 50 women assigned to E+P and for 50 women assigned to E-alone, in the WHI hormone-therapy trials. These women were selected to be free of major disease outcomes through the WHI clinical trial intervention phase and were selected to be adherent to their assigned hormone regimen over the first year of treatment, but were otherwise randomly selected from women assigned to active treatment in the trial cohorts. The IPAS approach involves extensive fractionation followed by tandem mass spectrometry and is capable of identifying proteins over seven orders of abundance. For reasons of throughput, serum pools were formed from 10 E+P women (five baseline and five 1-year pools), or from 10 E-alone women, before proteomic analysis.
We recently reported  proteomic changes from the E-alone component of this project. An impressive 10.5% of proteins had false discovery rates, for a change, of <0.05. The affected proteins had relevance to multiple pathways, including coagulation, metabolism, osteogenesis, and inflammation, among others. Ten of 14 protein changes tested were confirmed with enzyme-linked immunosorbent assays (ELISAs) in the original samples, and in serum samples from 50 nonoverlapping randomly chosen women, selected by using the same criteria, from the E-alone trial treatment group.
Here, we sought to uncover proteins and pathways that are differentially affected by E+P therapy relative to E-alone that would provide leads for the comparatively unfavorable effects with E+P observed in these trials.
The use of human samples was approved by the Fred Hutchinson Cancer Research Center Institutional Review Board. Fifty study subjects were randomly selected from the 8,506 women assigned to active E+P in the WHI clinical trial, which also included 8,102 women assigned to placebo. All women were postmenopausal, with a uterus, and in the age range from 50 to 79 years, at recruitment during 1993 through 1998. The selected women were required to have been adherent to study medication (80% or more of pills taken) over the first year after randomization, and without a major clinical event (CHD, stroke, VT, breast or colorectal cancer, or hip fracture) over the intervention and follow-up period (through March 2005). A second nonoverlapping subset of E+P women was selected, by using the same criteria, for replication studies with ELISA. As previously reported , the same selection criteria were used for the E-alone discovery and replication phases of the study. Women enrolled in the E-alone trial (10,739) satisfied the same eligibility criteria as E+P enrollees, but were posthysterectomy at randomization. Women who used hormone therapy before trial enrollment had mostly stopped such treatment, months or years before enrollment, and were otherwise required to undergo a 3-month washout before randomization. Serum samples, collected at baseline and 1 year, were stored at -80°C until proteomic analyses.
Sample preparation, protein fractionation, and mass spectrometry analysis
These methods were previously described  in detail and are only briefly summarized here. As in the E-alone project component, pools formed from 30 μl of serum for 10 randomly selected women from the 50 E+P group women were formed from baseline and 1-year specimens.
After immunodepletion of the six most abundant proteins (albumin, IgG, IgA, transferrin, haptoglobin, and antitrypsin), pools were concentrated, and intact proteins having cysteine residues were isotopically labeled with acrylamide (baseline pools received the 'light' C12 acrylamide; 1-year pools the 'heavy' C13 acrylamide). The baseline and 1-year pools were than mixed together for further analysis.
The combined sample was diluted, and each sample was separated into 12 subsamples by using anion exchange chromatography, and each subsample was further separated into 60 fractions by using reversed-phase chromatography, giving a total of 720 fractions for each original mixed sample. Aliquots of 200 μl from each fraction, corresponding to about 200 μg of protein, were separated for mass spectrometry 'shotgun' analysis.
Lyophilized aliquots from the 720 individual fractions were subjected to in-solution trypsin digestion, and individual digested fractions, four to 60 from each reversed-phase run, were combined into 11 pools, giving a total of 132 (12 × 11) fractions for analysis from each original mixed baseline and 1-year pool. Tryptic peptides were analyzed with an LTQ-FT mass spectrometer. Spectra were acquired in a data-dependent mode in a mass/charge range of 400 to 1,800, and the five most abundant +2 or +3 ions were selected from each spectrum for tandem mass spectrometry (MS/MS) analysis.
Protein identification and baseline versus 1-year concentration assessment
The acquired LC-MS/MS data were automatically processed by the Computational Proteomics Analysis System . Database searches were performed by using X!Tandem against the human International Protein Index (IPI) by using tryptic search. Database search results were analyzed by using PeptideProphet  and ProteinProphet .
The relative quantitation of 1-year to baseline concentration for cysteine-containing peptides identified by MS/MS was extracted by using a script designated Q3 ProteinRatioParser , which calculates the relative peak areas of heavy to light acrylamide-labeled peptides. Peaks with zero area were reset to a background value to avoid singularities. Peptides having PeptideProphet ≥ 0.75, Tandem expect score <0.10, and mass deviation <20 ppm were considered for quantification. Proteins were identified as those having ProteinProphet scores ≥ 0.90, and their ratios were calculated by taking the geometric mean of all the associated peptide ratios. Proteins from all 10 IPAS experiments were aligned by their protein group number, assigned by ProteinProphet, to identify master groups of indistinguishable proteins across experiments. Ratios for these protein groups were logarithmically transformed and median-centered at zero. The following protein groups were removed in this analysis: groups that had fewer than five peptide ratios across all 10 experiments; groups that contained proteins that were targeted for depletion; and groups in which all proteins had been annotated as 'defunct' by IPI.
Statistical analysis of 1-year versus baseline protein concentrations
Protein log-(concentration) ratios were analyzed by first normalizing further, so that the median of the log-ratios is zero for all the proteins identified from a mixed baseline and 1-year sample. Concentration changes after E+P use were identified by testing the hypothesis that the mean of the log-ratios across the (up to 5) mixed samples is zero, by using a weighted moderated t statistic  implemented in the R package LIMMA : the log-ratios were weighted by the number of quantified peptides for each protein, and a matrix of weights was included in the linear model. The variance was estimated by using the sum of the sample variances from the E+P and E-alone data, with the requirement of at least one degree of freedom for variance estimation. Benjamini and Hochberg's method  was used to accommodate multiple testing for the large number of proteins quantified, through the calculation of estimated false discovery rates (FDRs).
The same method was used to identify proteins for which the 1-year to baseline change in concentration differed between E+P and E-alone. Specifically, a moderated t statistic was used to test for a difference in means between the log-ratios for E+P and those for E-alone, with common log-ratio variance for the two preparations.
Biologic pathway analysis
We developed a regularized Hotelling T2 procedure (Chen LS, Prentice RL, and Wang P, submitted for publication, 2009) to identify sets of proteins, defined by biologic pathways, that change concentration with E+P, or that change differentially in the E+P and E-alone project components. This testing procedure takes advantage of the correlation structure among the log-ratios for proteins in a given set. Protein sets were defined by using the KEGG database [27, 28].
To accommodate multiple hypotheses testing issues, the significance for individual proteins or for biologic pathways, is based on a 5% FDR criterion.
ELISAs are commercially available for some of the proteins for which evidence emerged of change after E+P use, or of differential change between E+P and E-alone. ELISA tests were applied according to manufacturer's protocols for individual baseline and 1-year serum samples from an additional randomly selected nonoverlapping 50 E+P and 50 E-alone women, for independent validation of leads from the proteomic discovery work. P values were obtained by applying t tests to log-transformed 1-year-to-baseline concentration ratios. Log-ratios from ELISA and IPAS were compared to assess discovery platform signals.
Baseline characteristics among women included in hormone-therapy proteomics project (n = 50 for E+P and for E-alone trials)
Age group at screening, years
Postmenopausal hormone therapy use
Current user (3-month 'wash out' before enrollment)
Never pregnant/no term pregnancy
≥ 1term pregnancy
Age at first birth, years
Treated for hypertension or BP ≥ 140/90
History of high cholesterol requiring pills
Statin use at baseline
Aspirin (≥ 80 mg) use at baseline
History of MI
History of angina
History of CABG/PTCA
History of DVT or PE
Family history of breast cancer (female)
History of fracture on or after age 55
Gail Model Five Year Risk of Breast Cancer
1 - <2
2 - <5
Number of falls in last 12 months
3 or more times
P value b
Age at screening, years
Body-mass index (BMI), kg/m2
Serum protein concentration ratios
Protein concentration ratios were further filtered and curated by using stringent standards (see Methods) for protein identification, including a requirement that a protein is quantified in at least two of the 10 IPAS experiments leading to a focus on 378 proteins (IPIs), all but 10 of which were quantified for both E+P and E-alone. A remarkable 169 (44.7%) of these showed evidence (nominal P < 0.05) of change from baseline to 1-year with E+P or E-alone, or with both. For E+P, 371 proteins were quantified under these quality standards, of which 132 (35.6%) had P < 0.05 as compared with 18.6 expected by chance, and 98 (26.4%) had FDRs <0.05 compared with 94 for E-alone. Of these, 84 had FDR <0.05 for both preparations. Table S1 in Additional file 1 shows estimated 1-year-to-baseline concentration log ratios for all 378 proteins ranked according to the minimum of P values for change with E+P or change with E-alone. Significance levels (P values) are also given for a test of equality of the E+P and E-alone ratios.
Gene ontology classification of proteins with statistically significant changes (FDR < 0.01) for E+P or E-alone
Log2 ratio year 1 relative to baseline
Log2 ratio year 1 relative to baseline
Blood coagulation and inflammation
Antithrombin III variant
Coagulation factor XII
β2- Glycoprotein 1
Coagulation factor IX
Tissue factor pathway inhibitor
Vitamin D-binding protein
Plasma retinol-binding protein
Ectonucleotide Pyrophosphatase/phosphodiesterase family member 2
Collagen α-1(I) chain
Neurogenic locus notch homologue protein 2
Complement and immune response
α1- acid glycoprotein 2
C4B-binding protein α chain
Complement factor H-related protein 1
Complement factor B
Complement component C8 α chain
C4B-binding protein β chain
Peptidoglycan recognition protein
Complement factor H-related 5
Mannan-binding lectin serine protease 2
Complement factor H-related protein 2
Complement component C8 β chain
Inter-α-trypsin inhibitor heavy chain H4
complement component C6
Mannose-binding protein C
Leukocyte immunoglobulin-like receptor subfamily A member 3
Intercellular adhesion molecule 1
Multiple epidermal growth factor-like domains 10
Growth factor activity
Insulin-like growth factor-binding protein 7
Protein NOV homologue
Insulin-like growth factor IA
Insulin-like growth factor-binding protein 1
Insulin-like growth factor-binding protein 4
Sex hormone-binding globulin
Trefoil factor 3
Out at first protein homologue
Target of NESH-SH3
Fibrinogen α chain
SPARC-like protein 1
Carboxypeptidase N subunit 2
Platelet endothelial aggregation receptor 1
Difference in Year 1 from baseline concentration ratios (E+P minus E-Alone) for all proteins with difference of P < 0.05
Difference of log2 ratios (year 1 relative to baseline): E+P minus E-alone
Insulin-like growth factor-binding protein 1
Peptidoglycan recognition protein
Neurogenic locus notch homologue protein 2
Actin cytoplasmic 1
Lymphatic vessel endothelial hyaluronic acid receptor 1
α2-Glycoprotein 1 zinc
Intercellular adhesion molecule 1
SPARC-like protein 1
Collagen α-1(I) chain
Coagulation factor V
Similar to protein C6ORF115
72-kDa type IV collagenase
ATP-binding cassette subfamily A member 9
Cell-surface glycoprotein MUC18
Protein NOV Homologue
Trefoil factor 2
Extracellular matrix protein 1
Insulin-like growth factor-binding protein 4
Complement factor B
KEGG pathways having two or more quantitated proteins for which evidence of differential change between baseline to 1-year concentration with E+P and E-alone was significant, with FDR < 0.05
Number quantified protein
Porphyrin and chlorophyll metabolism
Dorsoventral axis formation
Motor signaling pathway
Renal cell carcinoma
Notch signaling pathway
Ether lipid metabolism
Long term depression
Regulation of actin cytoskeleton
Cytokine-cytokine receptor interaction
Porphyrin and chlorophyll metabolism
GNRH signaling pathway
Ether lipid metabolism
KEGG pathways having two or more quantitated proteins for which evidence of differential change between E+P and E-alone was significant, with FDR < 0.05
GNRH signaling pathway
Number of proteins
Proteins in the pathway
MMP2, THBS1, VEGFC
ELISA-based protein assays in an independent set of subjects
These analyses show that 1 year of use of E+P has a profound effect on the serum proteome, with more than a fourth (26.4%) of quantified proteins having FDR < 0.05 for change. Eight proteins with altered levels were further tested in an independent set of samples. ELISA assays of six of the eight proteins showed changes concordant with the mass spectrometry data. The correlation of initial concentration ratios by mass spectrometry with ELISA ratios from an independent set of samples supports the reliability of the protein changes observed. Our previous report on E-alone  provided a detailed discussion of the proteins that changed after treatment with conjugated equine estrogens, in which 19% of proteins were changed after 1 year of treatment. Findings for 10 proteins were confirmed and validated by ELISA assays. Proteins altered with E-alone therapy had relevance to processes such as coagulation, inflammation, growth factors, osteogenesis, metabolism, and cell adhesion, among others. The most striking feature of the present E+P analysis is the similarity in these quantitative proteomic changes when medroxyprogesterone acetate is added to the daily conjugated equine estrogen. For changes with E+P, 98 proteins had FDR < 0.05 compared with 94 proteins for E-alone. Of these, 84 proteins had FDR < 0.05 for both preparations, and corresponding intensity ratios tended to be quite similar between the two regimens for most of these proteins. Hence, our prior discussion  of proteins and pathways that were changed after E-alone is largely applicable to the E+P hormone preparation as well. The 1 year of aging between the baseline and 1-year blood-sample collection could have some influence on the serum proteome, but any such influence should be absent for the comparison of E+P versus E-alone changes, because age-related changes would apply equally for the two regimens.
First, consider proteins involved in the insulin growth factor-signaling pathway. The overall pattern (Table 3) is a greater increase in insulin growth factor-binding proteins (IGFBP1, IGFBP4) with E alone compared with E+P, whereas the decrease in NOV was relatively greater with E+P. ELISA testing produced trends in these same directions for all three proteins, but only that for IGFBP1 approached statistical significance in the independent set. Collectively, these analyses suggest that progestin may attenuate some of the estrogen-induced increases in IGF-binding proteins.
It has been previously suggested that medroxyprogesterone acetate has only a weak degree of opposition to the estrogen-induced decrease of total IGF-1 (which is primarily of hepatic origin), in agreement with our study findings for IGF-1 levels . However, given reduced levels of IGF-binding proteins, it would be expected that less IGF is bound, possibly increasing the availability of free IGF. The IGF-signaling pathway plays a role in cell proliferation, tissue development, and tumorigenesis. The IGF pathway has been linked to colorectal malignancy [32, 33], and serum levels of IGF1 have been associated with colon cancer risk . Changes in the IGF pathway with E+P compared with E-alone could potentially explain some of the differences in the clinical outcomes. In particular, IGF-1 is a strong mitogen, and varying levels of free IGF-1 between E+P and E-alone treatment could help explain the increased risk of breast cancer with E+P.
In addition to proteins related to the IGF pathway, several other proteins of biologic interest in the context of E+P versus E-alone effects on the serum proteome are presented in Table 3. Expression of the α2-glycoprotein 1 zinc (AZGP1) gene is regulated predominantly androgens and progestins [35, 36]. Our data suggest an increase in AZGP1 protein levels with E+P and a decrease with E-alone. AZGP1 has been identified as a potential prognostic marker for early-stage breast cancer and a useful immunohistochemical marker of apocrine cell differentiation in human breast tissue . Increased levels of circulating AZGP1 in E+P compared with E-alone may be associated with increased risk of breast cancer in the former group. Circulating levels of extracellular matrix proteins (collagen α-1 chain (COL1A1), lumican (LUM), and extracellular matrix protein 1 (ECM1) may also be differentially affected by E+P compared with E-alone, whereas MMP2, a metalloproteinase that breaks down COL1A1, may be increased with E+P compared with E. The extracellular matrix plays a variety of physiological roles, many of which are related to cancer, including tumor invasion. Changes in the extracellular matrix with E+P compared with E-alone could also help explain the differences in cancer risks associated with these treatments.
Several proteins listed in Table 3 have been linked to atherogenesis, and thus may suggest avenues for exploring mechanisms underlying a more-substantial early increase in CHD risk with E+P than with E-alone in the randomized trials. For example, matrix metalloproteinases (for example, MMP2) are thought to participate in atherogenic inflammation . The role of innate immunity in atherogenesis is less well established; PGLYRP1 participates in recognition of bacteria by neutrophils, but is independently associated with coronary artery calcification and abdominal aortic plaque . The difference in PGLYRP1 concentration among women taking E+P versus E-alone suggests a possible mechanistic link.
Other extracellular matrix proteins (Table 3) may also shed light on the relation between E+P and CHD events. One such protein, lumican (LUM), contributes to variation in proteoglycan composition of arterial intima by location within the human vasculature. Enhanced deposition of lumican has been observed in the intima of the atherosclerosis-prone internal carotid artery compared with the internal thoracic artery, a relatively atherosclerosis-resistant vessel . The relation between serum and tissue proteoglycan levels, the impact of proteoglycan composition on plaque stability, and the clinical significance of lumican all remain to be determined.
Our proteomic comparisons (Table 3) may also provide insight into the greater elevation in venous thromboembolic event risk when progestin was added to conjugated estrogens. For example, coagulation factor V (F5) binds to multimerin 1 (MMFN1), with high affinity for storage in human platelet granules, and may modulate thrombosis .
Some important considerations exist in assessing the effects of estrogens and progestins broadly on the serum proteome. The preparations considered here are conjugated equine estrogen and medroxyprogesterone acetate. Further studies would be needed to determine whether the changes reported here also arise for the other estrogen (for example, 17β-estradiol) and progesterone (norethisterone acetate or levonorgestrel) treatments. Related to this, these substances are taken orally, and the first-pass hepatic metabolism of oral estrogens is known to stimulate a wide variety of proteins, synthesized in the liver. Of the 378 proteins reported here, 73 are included in the liver-specific gene set listed in Hsiao and colleagues  (Table S1 in Additional file 1). For example, of 66 significantly upregulated proteins (FDR < 0.05), 35 are in the liver-specific gene path, as were 28 of 71 for E-alone.
Of the proteins emphasized in the preceding discussion, IGFBP1, AZGP1, and F5, but not others, are part of the liver-specific list. Given that transdermal estrogen, which is being increasingly used in clinical practice to treat menopausal symptoms, bypasses the liver, these proteins may not be affected when estrogen is administered transdermally.
In summary, E+P, like E-alone, has a profound effect on the serum proteome and affects multiple pathways that are relevant to observed clinical effects on cancer, cardiovascular disease, and fractures, among others. The addition of 2.5 mg/d medroxyprogesterone acetate to 0.625 mg/d conjugated equine estrogen may have an impact on the IGF pathway proteins and may affect circulating levels of extracellular matrix proteins (for example, MMP2) of potential relevance to the less-favorable E+P effects, compared with those for E-alone, on breast cancer, and CHD. Similarly the addition of medroxyprogesterone acetate may also augment the effects of conjugated estrogens on coagulation factors (for example, factor V), of potential relevance to a relatively greater elevation in venous thromboembolism with E+P. These and other leads from our proteomic study will benefit from further testing in women who experienced major clinical outcomes and in matched controls from the WHI hormone therapy trials, to evaluate more directly the potential of these protein-concentration changes to contribute to a biologic explanation for observed trial-outcome patterns.
The following additional files for this article are available online: Additional file 1 contains Table S1, which shows year 1 to baseline log-transformed concentration ratios after estrogen plus progestin (E+P) or estrogen (E-Alone) exposure for all 378 quantified proteins.
- AZGP1 :
α2-glycoprotein 1 zinc
coronary heart disease
- COL1A1 :
collagen α-1 chain
- CP :
conjugated equine estrogen
conjugated equine estrogen plus medoxyprogesterone acetate
- ECM1 :
extracellular matrix protein 1
enzyme-linked immunosorbent assay
- F5 :
coagulation factor V
false discovery rate
- ICAM1 :
intercellular adhesion molecule 1
insulin-like growth factor
insulin-like growth factor-binding protein
Intact Protein Analysis System
International Protein Index
liquid chromatography tandem mass spectrometry
- LUM :
- MMFN1 :
- MMP2 :
matrix metalloproteinase 2
- NOV :
protein NOV homologue
- PGLYRP1 :
peptidoglycan recognition protein
- PLA2G1B :
- THBS1 :
- THY1 :
THY-1 membrane glycoprotein
- VCAM1 :
vascular cell adhesion protein 1
- VEGFC :
vascular endothelial growth factor C
Women's Health Initiative.
Funding/Support: This work was supported by the National Heart, Lung, and Blood Institute, National Institutes of Health, U. S. Department of Health and Human Services [contracts HHSN268200764314C, N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-19, 32122, 42107-26, 42129-32, and 44221]. Clinical Trials Registration: ClinicalTrials.gov identifier: NCT00000611. The work of Dr. Prentice was partially supported by grant CA53996 from the National Cancer Institute.
Role of the sponsor: Decisions concerning study design, data collection and analysis, interpretation of the results, the preparation of the manuscript, or the decision to submit the manuscript for publication resided with committees comprising WHI investigators that included NHLBI representatives.
Program office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller.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.
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, Irvine, CA) F. Allan Hubbell; (University of California at Los Angeles, Los Angeles, CA) Lauren Nathan; (University of California at San Diego, La Jolla/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. Women's Health Initiative Memory Study: (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker.
- Women's Health Initiative Steering Committee: Effects of conjugated equine estrogen in postmenopausal women with hysterectomy: the Women's Health Initiative randomized controlled trial. JAMA. 2004, 291: 1701-1712. 10.1001/jama.291.14.1701.View ArticleGoogle Scholar
- Writing Group for the Women's Health Initiative Investigators: Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women's Health Initiative randomized controlled trial. JAMA. 2002, 288: 321-333. 10.1001/jama.288.3.321.View ArticleGoogle Scholar
- Hendrix SL, Wassertheil-Smoller S, Johnson KC, Howard BV, Kooperberg C, Rossouw JE, Trevisan M, Aragaki A, Baird AE, Bray PF, Buring JE, Criqui MH, Herrington D, Lynch JK, Rapp SR, Torner J, WHI Investigators: Effects of conjugated equine estrogen on stroke in the Women's Health Initiative. Circulation. 2006, 113: 2425-2434. 10.1161/CIRCULATIONAHA.105.594077.PubMedView ArticleGoogle Scholar
- Wassertheil-Smoller S, Hendrix SL, Limacher M, Heiss G, Kooperberg C, Baird A, Kotchen T, Curb JD, Black H, Rossouw JE, Aragaki A, Safford M, Stein E, Laowattana S, Mysiw WJ, WHI Investigators: Effect of estrogen plus progestin on stroke in postmenopausal women: the Women's Health Initiative: a randomized trial. Jama. 2003, 289: 2673-2684. 10.1001/jama.289.20.2673.PubMedView ArticleGoogle Scholar
- Cauley JA, Robbins J, Chen Z, Cummings SR, Jackson RD, LaCroix AZ, LeBoff M, Lewis CE, McGowan J, Neuner J, Pettinger M, Stefanick ML, Wactawski-Wende J, Watts NB, Women's Health Initiative Investigators: Effects of estrogen plus progestin on risk of fracture and bone mineral density: the Women's Health Initiative randomized trial. JAMA. 2003, 290: 1729-1738. 10.1001/jama.290.13.1729.PubMedView ArticleGoogle Scholar
- Jackson RD, Wactawski-Wende J, LaCroix AZ, Pettinger M, Yood RA, Watts NB, Robbins JA, Lewis CE, Beresford SA, Ko MG, Naughton MJ, Satterfield S, Bassford T, Women's Health Initiative Investigators: Effects of conjugated equine estrogen on risk of fractures and BMD in postmenopausal women with hysterectomy: results from the Women's Health Initiative randomized trial. J Bone Miner Res. 2006, 21: 817-828. 10.1359/jbmr.060312.PubMedView ArticleGoogle Scholar
- Hsia J, Langer RD, Manson JE, Kuller L, Johnson KC, Hendrix SL, Pettinger M, Heckbert SR, Greep N, Crawford S, Eaton CB, Kostis JB, Caralis P, Prentice R, Women's Health Initiative Investigators: Conjugated equine estrogens and coronary heart disease: the Women's Health Initiative. Arch Intern Med. 2006, 166: 357-365. 10.1001/archinte.166.3.357.PubMedView ArticleGoogle Scholar
- Manson JE, Hsia J, Johnson KC, Rossouw JE, Assaf AR, Lasser NL, Trevisan M, Black HR, Heckbert SR, Detrano R, Strickland OL, Wong ND, Crouse JR, Stein E, Cushman M, Women's Health Initiative Investigators: Estrogen plus progestin and the risk of coronary heart disease. N Engl J Med. 2003, 349: 523-534. 10.1056/NEJMoa030808.PubMedView ArticleGoogle Scholar
- Chlebowski RT, Hendrix SL, Langer RD, Stefanick ML, Gass M, Lane D, Rodabough RJ, Gilligan MA, Cyr MG, Thomson CA, Khandekar J, Petrovitch H, McTiernan A, WHI Investigators: Influence of estrogen plus progestin on breast cancer and mammography in healthy postmenopausal women: the Women's Health Initiative Randomized Trial. JAMA. 2003, 289: 3243-3253. 10.1001/jama.289.24.3243.PubMedView ArticleGoogle Scholar
- Stefanick ML, Anderson GL, Margolis KL, Hendrix SL, Rodabough RJ, Paskett ED, Lane DS, Hubbell FA, Assaf AR, Sarto GE, Schenken RS, Yasmeen S, Lessin L, Chlebowski RT, WHI Investigators: Effects of conjugated equine estrogens on breast cancer and mammography screening in postmenopausal women with hysterectomy. JAMA. 2006, 295: 1647-1657. 10.1001/jama.295.14.1647.PubMedView ArticleGoogle Scholar
- Curb JD, Prentice RL, Bray PF, Langer RD, Van Horn L, Barnabei VM, Bloch MJ, Cyr MG, Gass M, Lepine L, Rodabough RJ, Sidney S, Uwaifo GI, Rosendaal FR: Venous thrombosis and conjugated equine estrogen in women without a uterus. Arch Intern Med. 2006, 166: 772-780. 10.1001/archinte.166.7.772.PubMedView ArticleGoogle Scholar
- Cushman M, Kuller LH, Prentice R, Rodabough RJ, Psaty BM, Stafford RS, Sidney S, Rosendaal FR: Estrogen plus progestin and risk of venous thrombosis. JAMA. 2004, 292: 1573-1580. 10.1001/jama.292.13.1573.PubMedView ArticleGoogle Scholar
- Anderson GL, Kooperberg C, Geller N, Rossouw JE, Pettinger M, Prentice RL: Monitoring and reporting of the Women's Health Initiative Randomized Hormone Therapy Trials. Clin Trials. 2007, 4: 207-217. 10.1177/1740774507079252.PubMedView ArticleGoogle Scholar
- Rossouw JE, Cushman M, Greenland P, Lloyd-Jones DM, Bray P, Kooperberg C, Pettinger M, Robinson J, Hendrix S, Hsia J: Inflammatory, lipid, thrombotic, and genetic markers of coronary heart disease risk in the Women's Health Initiative Trials of Hormone Therapy. Arch Intern Med. 2008, 168: 2245-2253. 10.1001/archinte.168.20.2245.PubMedPubMed CentralView ArticleGoogle Scholar
- Kooperberg C, Cushman M, Hsia J, Robinson JG, Aragaki AK, Lynch JK, Baird AE, Johnson KC, Kuller LH, Beresford SA, Rodriguez B: Can biomarkers identify women at increased stroke risk? The Women's Health Initiative Hormone Trials. PLoS Clin Trials. 2007, 2: e28-10.1371/journal.pctr.0020028.PubMedPubMed CentralView ArticleGoogle Scholar
- Faca V, Coram M, Phanstiel D, Glukhova V, Zhang Q, Fitzgibbon M, McIntosh M, Hanash S: Quantitative analysis of acrylamide labeled serum proteins by LC-MS/MS. J Proteome Res. 2006, 5: 2009-2018. 10.1021/pr060102+.PubMedView ArticleGoogle Scholar
- Faca V, Pitteri SJ, Newcomb L, Glukhova V, Phanstiel D, Krasnoselsky A, Zhang Q, Struthers J, Wang H, Eng J, Fitzgibbon M, McIntosh M, Hanash S: Contribution of protein fractionation to depth of analysis of the serum and plasma proteomes. J Proteome Res. 2007, 6: 3558-3565. 10.1021/pr070233q.PubMedView ArticleGoogle Scholar
- Faca VM, Song KS, Wang H, Zhang Q, Krasnoselsky AL, Newcomb LF, Plentz RR, Gurumurthy S, Redston MS, Pitteri SJ, Pereira-Faca SR, Ireton RC, Katayama H, Glukhova V, Phanstiel D, Brenner DE, Anderson MA, Misek D, Scholler N, Urban ND, Barnett MJ, Edelstein C, Goodman GE, Thornquist MD, McIntosh MW, DePinho RA, Bardeesy N, Hanash SM: A mouse to human search for plasma proteome changes associated with pancreatic tumor development. PLoS Med. 2008, 5: e123-10.1371/journal.pmed.0050123.PubMedPubMed CentralView ArticleGoogle Scholar
- Hanash SM, Pitteri SJ, Faca VM: Mining the plasma proteome for cancer biomarkers. Nature. 2008, 452: 571-579. 10.1038/nature06916.PubMedView ArticleGoogle Scholar
- Katayama H, Paczesny S, Prentice R, Aragaki A, Faca VM, Pitteri SJ, Zhang Q, Wang H, Silva M, Kennedy J, Rossouw J, Jackson R, Hsia J, Chlebowski R, Manson J, Hanash S: Application of serum proteomics to the Women's Health Initiative conjugated equine estrogens trial reveals a multitude of effects relevant to clinical findings. Genome Medicine. 2009, 1: 47-10.1186/gm47.PubMedPubMed CentralView ArticleGoogle Scholar
- Rauch A, Bellew M, Eng J, Fitzgibbon M, Holzman T, Hussey P, Igra M, Maclean B, Lin CW, Detter A: Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments. J Proteome Res. 2006, 5: 112-121. 10.1021/pr0503533.PubMedView ArticleGoogle Scholar
- Keller A, Nesvizhskii AI, Kolker E, Aebersold R: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem. 2002, 74: 5383-5392. 10.1021/ac025747h.PubMedView ArticleGoogle Scholar
- Nesvizhskii AI, Keller A, Kolker E, Aebersold R: A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem. 2003, 75: 4646-4658. 10.1021/ac0341261.PubMedView ArticleGoogle Scholar
- Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004, 3: Article 3-Google Scholar
- Smyth GK: Limma: linear models for microarray data. Bioinformatics and computational biology solutions using R and bioconductor. Edited by: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. 2005, New York: Springer, 397-420. full_text.View ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B (Methodological). 1995, 57: 289-300.Google Scholar
- Kanehisa M: The KEGG database. Novartis Found Symp. 2002, 247: 91-101. full_text. discussion 101-103, 119-128, 144-152PubMedView ArticleGoogle Scholar
- KEGG PATHWAY Database. [http://www.genome.jp/kegg/pathway.html]
- Shi W, Harris AL: Notch signaling in breast cancer and tumor angiogenesis: cross-talk and therapeutic potentials. J Mammary Gland Biol Neoplasia. 2006, 11: 41-52. 10.1007/s10911-006-9011-7.PubMedView ArticleGoogle Scholar
- Wu F, Stutzman A, Mo YY: Notch signaling and its role in breast cancer. Front Biosci. 2007, 12: 4370-4383. 10.2741/2394.PubMedView ArticleGoogle Scholar
- Campagnoli C, Abba C, Ambroggio S, Peris C: Differential effects of progestins on the circulating IGF-I system. Maturitas. 2003, 46 (suppl 1): S39-S44. 10.1016/j.maturitas.2003.09.017.PubMedView ArticleGoogle Scholar
- Davies M, Gupta S, Goldspink G, Winslet M: The insulin-like growth factor system and colorectal cancer: clinical and experimental evidence. Int J Colorectal Dis. 2006, 21: 201-208. 10.1007/s00384-005-0776-8.PubMedView ArticleGoogle Scholar
- Durai R, Yang W, Gupta S, Seifalian AM, Winslet MC: The role of the insulin-like growth factor system in colorectal cancer: review of current knowledge. Int J Colorectal Dis. 2005, 20: 203-220. 10.1007/s00384-004-0675-4.PubMedView ArticleGoogle Scholar
- Rinaldi S, Cleveland R, Norat T, Biessy C, Rohrmann S, Linseisen J, Boeing H, Pischon T, Panico S, Agnoli C, Palli D, Tumino R, Vineis P, Peeters PH, van Gils CH, Bueno-de-Mesquita BH, Vrieling A, Allen NE, Roddam A, Bingham S, Khaw KT, Manjer J, Borgquist S, Dumeaux V, Gram IT, Lund E, Trichopoulou A, Makrygiannis G, Benetou V, Molina E, et al: Serum levels of IGF-I, IGFBP-3 and colorectal cancer risk: results from the EPIC cohort, plus a meta-analysis of prospective studies. Int J Cancer. 2009.Google Scholar
- Chalbos D, Haagensen D, Parish T, Rochefort H: Identification and androgen regulation of two proteins released by T47D human breast cancer cells. Cancer Res. 1987, 47: 2787-2792.PubMedGoogle Scholar
- Lopez-Boado YS, Diez-Itza I, Tolivia J, Lopez-Otin C: Glucocorticoids and androgens up-regulate the Zn-alpha 2-glycoprotein messenger RNA in human breast cancer cells. Breast Cancer Res Treat. 1994, 29: 247-258. 10.1007/BF00666478.PubMedView ArticleGoogle Scholar
- Hassan MI, Waheed A, Yadav S, Singh TP, Ahmad F: Zinc alpha 2-glycoprotein: a multidisciplinary protein. Mol Cancer Res. 2008, 6: 892-906. 10.1158/1541-7786.MCR-07-2195.PubMedView ArticleGoogle Scholar
- Deguchi JO, Aikawa M, Tung CH, Aikawa E, Kim DE, Ntziachristos V, Weissleder R, Libby P: Inflammation in atherosclerosis: visualizing matrix metalloproteinase action in macrophages in vivo. Circulation. 2006, 114: 55-62. 10.1161/CIRCULATIONAHA.106.619056.PubMedView ArticleGoogle Scholar
- Rohatgi A, Ayers CR, Khera A, McGuire DK, Das SR, Matulevicius S, Timaran CH, Rosero EB, de Lemos JA: The association between peptidoglycan recognition protein-1 and coronary and peripheral atherosclerosis: observations from the Dallas Heart Study. Atherosclerosis. 2009, 203: 569-575. 10.1016/j.atherosclerosis.2008.07.015.PubMedView ArticleGoogle Scholar
- Talusan P, Bedri S, Yang S, Kattapuram T, Silva N, Roughley PJ, Stone JR: Analysis of intimal proteoglycans in atherosclerosis-prone and atherosclerosis-resistant human arteries by mass spectrometry. Mol Cell Proteomics. 2005, 4: 1350-1357. 10.1074/mcp.M500088-MCP200.PubMedPubMed CentralView ArticleGoogle Scholar
- Jeimy SB, Fuller N, Tasneem S, Segers K, Stafford AR, Weitz JI, Camire RM, Nicolaes GA, Hayward CP: Multimerin 1 binds factor V and activated factor V with high affinity and inhibits thrombin generation. Thromb Haemost. 2008, 100: 1058-1067.PubMedGoogle Scholar
- Hsiao LL, Dangond F, Yoshida T, Hong R, Jensen RV, Misra J, Dillon W, Lee KF, Clark KE, Haverty P, Haverty P, Weng Z, Mutter GL, Frosch MP, Macdonald ME, Milford EL, Crum CP, Bueno R, Pratt RE, Mahadevappa M, Warrington JA, Stephanopoulos G, Stephanopoulos G, Gullans SR: A compendium of gene expression in normal human tissues. Physiol Genomics. 2001, 7: 97-104.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.