SummaryThe lbpA gene of Neisseria meningitidis encodes an outer membrane lactoferrin-binding protein and shows homology to the transferrin-binding protein, TbpA. Previously, we have detected part of an open reading frame upstream of lbpA. The putative product of this open reading frame, tentatively designated lbpB, showed homology to the transferrin-binding protein TbpB, suggesting that the lactoferrrin receptor, like the transferrin receptor, consists of two proteins. The complete nucleotide sequence of lbpB was determined. The gene encodes a 77.5 kDa protein, probably a lipoprotein, with homology, 33% identity to the TbpB of N. meningitidis. A unique feature of LbpB is the presence of two stretches of negatively charged residues, which might be involved in lactoferrin binding. Antisera were raised against synthetic peptides corresponding to the C-terminal part of the putative protein and used to demonstrate that the gene is indeed expressed. Consistent with the presence of a putative Fur binding site upstream of the lbpB gene, expression of both LbpA and LbpB was proved to be iron regulated in Western blot experiments. The LbpB protein appeared to be less stable than TbpB in SDScontaining sample buffer. Isogenic mutants lacking either LbpA or LbpB exhibited a reduced ability to bind lactoferrin. In contrast to the lbpB mutant, the lbpA mutant was completely unable to use lactoferrin as a sole source of iron.

BackgroundClinical outcome of patients with triple-negative breast cancer (TNBC) is highly variable. This study aims to identify and validate a prognostic protein signature for TNBC patients to reduce unnecessary adjuvant systemic therapy.MethodsFrozen primary tumors were collected from 126 lymph node–negative and adjuvant therapy–naive TNBC patients. These samples were used for global proteome profiling in two series: an in-house training (n = 63) and a multicenter test (n = 63) set. Patients who remained free of distant metastasis for a minimum of 5 years after surgery were defined as having good prognosis. Cox regression analysis was performed to develop a prognostic signature, which was independently validated. All statistical tests were two-sided.ResultsAn 11-protein signature was developed in the training set (median follow-up for good-prognosis patients = 117 months) and subsequently validated in the test set (median follow-up for good-prognosis patients = 108 months) showing 89.5% sensitivity (95% confidence interval [CI] = 69.2% to 98.1%), 70.5% specificity (95% CI = 61.7% to 74.2%), 56.7% positive predictive value (95% CI = 43.8% to 62.1%), and 93.9% negative predictive value (95% CI = 82.3% to 98.9%) for poor-prognosis patients. The predicted poor-prognosis patients had higher risk to develop distant metastasis than the predicted good-prognosis patients in univariate (hazard ratio [HR] = 13.15; 95% CI = 3.03 to 57.07; P = .001) and multivariable (HR = 12.45; 95% CI = 2.67 to 58.11; P = .001) analysis. Furthermore, the predicted poor-prognosis group had statistically significantly more breast cancer–specific mortality. Using our signature as guidance, more than 60% of patients would have been exempted from unnecessary adjuvant chemotherapy compared with conventional prognostic guidelines.ConclusionsWe report the first validated proteomic signature to assess the natural course of clinical TNBC.

Tamoxifen resistance is a major cause of death in patients with recurrent breast cancer. Current clinical factors can correctly predict therapy response in only half of the treated patients. Identification of proteins that are associated with tamoxifen resistance is a first step toward better response prediction and tailored treatment of patients. In the present study we intended to identify putative protein biomarkers indicative of tamoxifen therapy resistance in breast cancer using nano-LC coupled with FTICR MS. Comparative proteome analysis was performed on ϳ5,500 pooled tumor cells (corresponding to ϳ550 ng of protein lysate/analysis) obtained through laser capture microdissection (LCM) from two independently processed data sets (n ‫؍‬ 24 and n ‫؍‬ 27) containing both tamoxifen therapy-sensitive and therapy-resistant tumors. Peptides and proteins were identified by matching mass and elution time of newly acquired LC-MS features to information in previously generated accurate mass and time tag reference databases. A total of 17,263 unique peptides were identified that corresponded to 2,556 nonredundant proteins identified with >2 peptides. 1,713 overlapping proteins between the two data sets were used for further analysis. Comparative proteome analysis revealed 100 putatively differentially abundant proteins between tamoxifen-sensitive and tamoxifen-resistant tumors. The presence and relative abundance for 47 differentially abundant proteins were verified by targeted nano-LC-MS/MS in a selection of unpooled, non-microdissected discovery set tumor tissue extracts. ENPP1, EIF3E, and GNB4 were significantly associated with progression-free survival upon tamoxifen treatment for recurrent disease. Differential abundance of our top discriminating protein, extracellular matrix metalloproteinase inducer, was validated by tissue microarray in an independent patient cohort (n ‫؍‬ 156). Extracellular matrix metalloproteinase inducer levels were higher in therapy-resistant tumors and significantly associated with an earlier tumor progression following first line tamoxifen treatment (hazard ratio, 1.87; 95% confidence interval, 1.25-2.80; p ‫؍‬ 0.002). In summary, comparative proteomics performed on laser capture microdissection-derived breast tumor cells using nano-LC-FTICR MS technology revealed a set of putative biomarkers associated with tamoxifen therapy resistance in recurrent breast cancer.

Mass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while inappropriate statistical methods will lead to false positive hits. All these issues have hampered the identification of reliable protein markers. A workflow, which integrates reproducible and robust sample preparation and data handling methods, is highly desirable in clinical proteomics investigations. Here we describe a label-free tissue proteomics pipeline, which encompasses laser capture microdissection (LCM) followed by nanoscale liquid chromatography and high resolution MS. This pipeline routinely identifies on average ∼10,000 peptides corresponding to ∼1,800 proteins from sub-microgram amounts of protein extracted from ∼4,000 LCM breast cancer epithelial cells. Highly reproducible abundance data were generated from different technical and biological replicates. As a proof-of-principle, comparative proteome analysis was performed on estrogen receptor α positive or negative (ER+/−) samples, and commonly known differentially expressed proteins related to ER expression in breast cancer were identified. Therefore, we show that our tissue proteomics pipeline is robust and applicable for the identification of breast cancer specific protein markers.Electronic supplementary materialThe online version of this article (doi:10.1007/s10911-012-9252-6) contains supplementary material, which is available to authorized users.

Quantitative proteomics plays an important role in validation of breast-cancer-related biomarkers. In this study, we systematically compared the performance of label-free quantification (LFQ) and SILAC with shotgun and directed methods for quantifying breast-cancer-related markers in microdissected tissues. We show that LFQ leads to slightly higher coefficient of variation (CV) for protein quantification (median CV = 16.3%) than SILAC quantification (median CV = 13.7%) (P < 0.0001), but LFQ method enables ∼60% more protein quantification and is also more reproducible (∼20% more proteins were quantified in all replicate samples). Furthermore, we describe a method to accurately quantify multiple proteins within one pathway, that is, "focal adhesion pathway", in trace amounts of breast cancer tissues using a SILAC-based SRM assay. Using this SILAC-based SRM assay, we precisely quantified five "focal adhesion" proteins with good quantitative precision (CV range: 2.4-5.9%) in replicate whole tissue lysate samples and replicate microdissected samples (CV range: 5.8-16.1%). Our results show that in microdissected breast cancer tissues LFQ in combination with shotgun proteomics performed the best overall and is therefore suitable for both biomarker discovery and validation in these types of specimens. The SILAC-based SRM method can be used for the development of clinically relevant protein assays in tumor biopsies.

Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in‐depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in‐house training and a multi‐center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano‐LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step‐down to develop a predictor for tamoxifen treatment outcome. The 4‐protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin‐fixed paraffin‐embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER‐positive breast cancer. IHC further showed that PDCD4 is an independent marker.

Proteomics assays hold great promise for unraveling molecular events that underlie human diseases. Effective analysis of clinical samples is essential, but this task is considerably complicated by tissue heterogeneity. Laser capture microdissection (LCM) can be used to selectively isolate target cells from their native tissue environment. However, the small number of cells that is typically procured by LCM severely limits proteome coverage and biomarker discovery potential achievable by conventional proteomics platforms. Herein, we describe the use of nanoLC-FT-ICR MS for analyzing protein digests of 3000 LCM-derived tumor cells from breast carcinoma tissue, corresponding to 300 ng of total protein. A total of 2282 peptides were identified by matching LC-MS data to accurate mass and time (AMT) tag databases that were previously established for human breast (cancer) cell lines. One thousand and three unique proteins were confidently identified with two or more peptides. Based on gene ontology categorization, identified proteins appear to cover a wide variety of biological functions and cellular compartments. This work demonstrates that a substantial number of proteins can be detected and identified from limited number of cells using the AMT tag approach, and opens doors for high-throughput in-depth proteomics analysis of clinical samples.

Antiandrogens are widely used agents in the treatment of prostate cancer, as inhibitors of AR (androgen receptor) action. Although the precise mechanism of antiandrogen action is not yet elucidated, recent studies indicate the involvement of nuclear receptor co-repressors. In the present study, the regulation of AR transcriptional activity by N-CoR (nuclear receptor co-repressor), in the presence of different ligands, has been investigated. Increasing levels of N-CoR differentially affected the transcriptional activity of AR occupied with either agonistic or antagonistic ligands. Small amounts of co-transfected N-CoR repressed CPA (cyproterone acetate)-and mifepristone (RU486)-mediated AR activity, but did not affect agonist (R1881)-induced AR activity. Larger amounts of co-transfected N-CoR repressed AR activity for all ligands, and converted the partial agonists CPA and RU486 into strong AR antagonists. In the presence of the agonist R1881, co-expression of the p160 co-activator TIF2 (transcriptional intermediary factor 2) relieved N-CoR repression up to control levels. However, in the presence of RU486 and CPA, TIF2 did not functionally compete with N-CoR, suggesting that antagonistbound AR has a preference for N-CoR. The AR mutation T877A (Thr 877 → Ala), which is frequently found in prostate cancer and affects the ligand-induced conformational change of the AR, considerably reduced the repressive action of NCoR. The agonistic activities of CPA-and hydroxyflutamideoccupied T877A-AR were hardly affected by N-CoR, whereas TIF2 strongly enhanced their activities. These results indicate that lack of N-CoR action allows these antiandrogens to act as strong agonists on the mutant AR.