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1.  Autoantibody signatures involving glycolysis and splicesome proteins precede a diagnosis of breast cancer among post-menopausal women 
Cancer research  2012;73(5):1502-1513.
We assessed the autoantibody repertoire of a mouse model engineered to develop breast cancer and the repertoire of autoantibodies in plasmas collected at a pre-clinical time point and at the time of clinical diagnosis of breast cancer. In seeking to identify common pathways, networks and protein families associated with the humoral response, we elucidated the dynamic nature of tumor antigens and autoantibody interactions. Lysate proteins from an immortalized cell line from an MMTV-neu mouse model and from MCF7 human breast cancers were spotted onto nitrocellulose microarrays and hybridized with mouse and human plasma samples, respectively. Ig-based plasma immunoreactivity against glycolysis and spliceosome proteins was a predominant feature observed both in tumor bearing mice and in pre-diagnostic human samples. Interestingly, autoantibody reactivity was more pronounced further away than closer to diagnosis. We provide evidence for dynamic changes in autoantibody reactivity with tumor development and progression that may depend in part on the extent of antigen-antibody interactions.
PMCID: PMC4085738  PMID: 23269276
Breast cancer; Autoantibody; Glycolysis; Spliceosome; Pre-diagnostic
2.  Concordant release of glycolysis proteins into the plasma preceding a diagnosis of ER+ breast cancer 
Cancer research  2012;72(8):1935-1942.
Although the identification of peripheral blood biomarkers would enhance early detection strategies for breast cancer, the discovery of protein markers has been challenging. In this study, we sought to identify coordinated changes in plasma proteins associated with breast cancer based on large scale quantitative mass spectrometry. We analyzed plasma samples collected up to 74 weeks prior to diagnosis from 420 ER+ cases and matched controls enrolled in the Women's Health Initiative cohort. A gene set enrichment analysis was applied to 467 quantified proteins linking their corresponding genes to particular biological pathways. Based upon differences in the concentration of individual proteins, glycolysis pathway proteins exhibited a statistically significant difference between cases and controls. In particular, the enrichment was observed among cases in which blood was drawn closer to diagnosis (effect size for the 0–38 weeks pre-diagnostic group: 1.91; p-value: 8.3E-05). Analysis of plasmas collected at the time of diagnosis from an independent set of cases and controls confirmed upregulated levels of glycolysis proteins among cases relative to controls. Together, our findings indicate that the concomitant release of glycolysis proteins into the plasma is a pathophysiological event that precedes a diagnosis of ER+ breast cancer.
PMCID: PMC4066614  PMID: 22367215
3.  Proteome and Transcriptome Profiles of a Her2/Neu-driven Mouse Model of Breast Cancer 
Proteomics. Clinical applications  2011;5(3-4):179-188.
We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens.
Experimental design
Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias.
In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue.
Conclusions and clinical relevance
These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.
PMCID: PMC3069718  PMID: 21448875
Breast cancer; Her2; mouse; proteome; transcriptome
4.  A targeted proteomics–based pipeline for verification of biomarkers in plasma 
Nature biotechnology  2011;29(7):625-634.
High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.
PMCID: PMC3232032  PMID: 21685906
5.  Detection of Elevated Plasma Levels of EGF Receptor Prior to Breast Cancer Diagnosis among Hormone Therapy Users 
Cancer research  2010;70(21):8598-8606.
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.
PMCID: PMC2970770  PMID: 20959476
Breast cancer; epidermal growth factor receptor; menopausal hormone therapy
7.  Novel proteins associated with risk for coronary heart disease or stroke among postmenopausal women identified by in-depth plasma proteome profiling 
Genome Medicine  2010;2(7):48.
Coronary heart disease (CHD) and stroke were key outcomes in the Women's Health Initiative (WHI) randomized trials of postmenopausal estrogen and estrogen plus progestin therapy. We recently reported a large number of changes in blood protein concentrations in the first year following randomization in these trials using an in-depth quantitative proteomics approach. However, even though many affected proteins are in pathways relevant to the observed clinical effects, the relationships of these proteins to CHD and stroke risk among postmenopausal women remains substantially unknown.
The same in-depth proteomics platform was applied to plasma samples, obtained at enrollment in the WHI Observational Study, from 800 women who developed CHD and 800 women who developed stroke during cohort follow-up, and from 1-1 matched controls. A plasma pooling strategy, followed by extensive fractionation prior to mass spectrometry, was used to identify proteins related to disease incidence, and the overlap of these proteins with those affected by hormone therapy was examined. Replication studies, using enzyme-linked-immunosorbent assay (ELISA), were carried out in the WHI hormone therapy trial cohorts.
Case versus control concentration differences were suggested for 37 proteins (nominal P < 0.05) for CHD, with three proteins, beta-2 microglobulin (B2M), alpha-1-acid glycoprotein 1 (ORM1), and insulin-like growth factor binding protein acid labile subunit (IGFALS) having a false discovery rate < 0.05. Corresponding numbers for stroke were 47 proteins with nominal P < 0.05, three of which, apolipoprotein A-II precursor (APOA2), peptidyl-prolyl isomerase A (PPIA), and insulin-like growth factor binding protein 4 (IGFBP4), have a false discovery rate < 0.05. Other proteins involved in insulin-like growth factor signaling were also highly ranked. The associations of B2M with CHD (P < 0.001) and IGFBP4 with stroke (P = 0.005) were confirmed using ELISA in replication studies, and changes in these proteins following the initiation of hormone therapy use were shown to have potential to help explain hormone therapy effects on those diseases.
In-depth proteomic discovery analysis of prediagnostic plasma samples identified B2M and IGFBP4 as risk markers for CHD and stroke respectively, and provided a number of candidate markers of disease risk and candidate mediators of hormone therapy effects on CHD and stroke.
Clinical Trials Registration identifier: NCT00000611
PMCID: PMC2923740  PMID: 20667078
8.  Integrative Proteomic Analysis of Serum and Peritoneal Fluids Helps Identify Proteins that Are Up-Regulated in Serum of Women with Ovarian Cancer 
PLoS ONE  2010;5(6):e11137.
We used intensive modern proteomics approaches to identify predictive proteins in ovary cancer. We identify up-regulated proteins in both serum and peritoneal fluid. To evaluate the overall performance of the approach we track the behavior of 20 validated markers across these experiments.
Mass spectrometry based quantitative proteomics following extensive protein fractionation was used to compare serum of women with serous ovarian cancer to healthy women and women with benign ovarian tumors. Quantitation was achieved by isotopically labeling cysteine amino acids. Label-free mass spectrometry was used to compare peritoneal fluid taken from women with serous ovarian cancer and those with benign tumors. All data were integrated and annotated based on whether the proteins have been previously validated using antibody-based assays.
We selected 54 quantified serum proteins and 358 peritoneal fluid proteins whose case-control differences exceeded a predefined threshold. Seventeen proteins were quantified in both materials and 14 are extracellular. Of 19 validated markers that were identified all were found in cancer peritoneal fluid and a subset of 7 were quantified in serum, with one of these proteins, IGFBP1, newly validated here.
Proteome profiling applied to symptomatic ovarian cancer cases identifies a large number of up-regulated serum proteins, many of which are or have been confirmed by immunoassays. The number of currently known validated markers is highest in peritoneal fluid, but they make up a higher percentage of the proteins observed in both serum and peritoneal fluid, suggesting that the 10 additional markers in this group may be high quality candidates.
PMCID: PMC2886122  PMID: 20559444
9.  Postmenopausal estrogen and progestin effects on the serum proteome 
Genome Medicine  2009;1(12):121.
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.
PMCID: PMC2808737  PMID: 20034393
10.  Mapping Genes Responsible for Strain-specific Iron Phenotypes in Murine Chromosome Substitution Strains 
Blood cells, molecules & diseases  2007;39(2):199-205.
The highly variable clinical phenotype observed in patients homozygous for the C282Y mutation of the hereditary hemochromatosis gene (HFE) is likely due to the influence of non-HFE modifier genes. The primary functional abnormality causing iron overload in hemochromatosis is hyper-absorption of dietary iron. We found that iron absorption in inbred mice varies in a strain-specific manner, as does the pattern of iron distribution to the liver and spleen. A/J mice absorbed approximately twice the amount of 59Fe delivered by gavage compared to the C57BL/6 strain. Genetic comparisons between A/J and C57BL/6 were facilitated by the availability of consomic chromosome substitution strains (CSS). Each CSS has an individual chromosome pair from A/J on an otherwise C57BL/6J background. We found that iron absorption and iron content in liver and in spleen were continuous variables suggesting that each trait is under multigenic control. No trait co-segregated among the CSS. Chromosome 5 from A/J, however, imparted the highest iron absorption phenotype and multiple CSS had absorption levels equivalent to A/J. Chromosomes 9 and X were associated with high spleen iron content. These data suggest that multiple genes contribute to the regulation of iron absorption and that individual organ iron phenotypes are independently regulated.
PMCID: PMC2703004  PMID: 17493847
Iron; Phenotype; Mapping

Results 1-10 (10)