Tryptophan metabolism is inversely regulated in the tumor and blood of patients with glioblastoma.

Tryptophan (Trp)-catabolic enzymes (TCEs) produce metabolites that activate the aryl hydrocarbon receptor (AHR) and promote tumor progression and immunosuppression in glioblastoma. As therapies targeting TCEs or AHR become available, a better understanding of Trp metabolism is required. Methods: The combination of LC-MS/MS with chemical isobaric labeling enabled the simultaneous quantitative comparison of Trp and its amino group-bearing metabolites in multiple samples. We applied this method to the sera of a cohort of 43 recurrent glioblastoma patients and 43 age- and sex-matched healthy controls. Tumor volumes were measured in MRI data using an artificial neural network-based approach. MALDI MSI visualized Trp and its direct metabolite N-formylkynurenine (FK) in glioblastoma tissue. Analysis of scRNA-seq data was used to detect the presence of Trp metabolism and AHR activity in different cell types in glioblastoma. Results: Compared to healthy controls, glioblastoma patients showed decreased serum Trp levels. Surprisingly, the levels of Trp metabolites were also reduced. The decrease became smaller with more enzymatic steps between Trp and its metabolites, suggesting that Trp availability controls the levels of its systemic metabolites. High tumor volume associated with low systemic metabolite levels and low systemic kynurenine levels associated with worse overall survival. MALDI MSI demonstrated heterogeneity of Trp catabolism across glioblastoma tissues. Analysis of scRNA-seq data revealed that genes involved in Trp metabolism were expressed in almost all the cell types in glioblastoma and that most cell types, in particular macrophages and T cells, exhibited AHR activation. Moreover, high AHR activity associated with reduced overall survival in the glioblastoma TCGA dataset. Conclusion: The novel techniques we developed could support the identification of patients that may benefit from therapies targeting TCEs or AHR activation.

Profiling of IgG antibodies targeting unmodified and corresponding citrullinated autoantigens in a multicenter national cohort of early arthritis in Germany.

OBJECTIVE: To assess the diagnostic potential of IgG antibodies to citrullinated and corresponding native autoantigens in early arthritis. METHODS: IgG autoantibodies to 390 distinct unmodified and corresponding in vitro citrullinated recombinant proteins were measured by a multiplex assay in baseline blood samples from a German multicenter national cohort of 411 early arthritis patients (56.5 ± 14.6 years, 62.8% female). The cohort was randomly split into a training cohort (n = 329, 28.6% ACPA positive) and a validation cohort (n = 82, 32.9% ACPA pos.). The diagnostic properties of candidate antibodies to predict a subsequent diagnosis of rheumatoid arthritis (RA) as opposed to a non-RA diagnosis were assessed by receiver operating characteristics analysis and generalized linear modeling (GLM) with Bonferroni correction in comparison to clinically determined IgM rheumatoid factor (RF) and citrullinated peptide antibody (ACPA) status. RESULTS: Of 411 patients, 309 (75.2%) were classified as RA. Detection rates of antibody responses to citrullinated and uncitrullinated forms of the proteins were weakly correlated (Spearman's r = 0.13 (95% CI 0.029-0.22), p = 0.01). The concentration of 34 autoantibodies (32 to citrullinated and 2 to uncitrullinated antigens) was increased at least 2-fold in RA patients and further assessed. In the training cohort, a significant association of citrullinated "transformer 2 beta homolog" (cTRA2B)-IgG with RA was observed (OR 5.3 × 103, 95% CI 0.8 × 103-3.0 × 106, p = 0.047). Sensitivity and specificity of cTRA2B-IgG (51.0%/82.9%) were comparable to RF (30.8%/91.6%) or ACPA (32.1%/94.7%). Similar results were obtained in the validation cohort. The addition of cTRA2B-IgG to ACPA improved the diagnostic performance over ACPA alone (p = 0.026 by likelihood ratio test). CONCLUSIONS: cTRA2B-IgG has the potential to improve RA diagnosis in conjunction with RF and ACPA in early arthritis.

Proteomic analysis to define predictors of treatment response to adalimumab or methotrexate in rheumatoid arthritis patients.

Seropositivity for anti-citrullinated peptide antibodies (ACPA) in patients with rheumatoid arthritis (RA), a chronic autoimmune arthritis, is associated with worse long-term disease outcomes. ACPA is ubiquitously tested in RA patients, but other autoantibodies exist (in both citrullinated and non-citrullinated form) which may provide additional information on RA subtypes and/or treatment response. We used a multiplex bead-based assay of 376 autoantibodies to test associations between these autoantibodies and treatment response in RA patients. Clusters of patients with similar autoantibody expression were defined and cluster membership was associated with treatment response. Thirty-four autoantibodies were differentially expressed in RA patients compared with healthy controls; citrullinated vimentin was associated with treatment response. A selection of citrullinated autoantibodies was found to be associated with treatment response in a subanalysis of ACPA-negative RA patients. Finer ACPA specificities in ACPA-negative RA patients may be predictive of treatment response and could represent a rich vein of future study.

Comprehensive Longitudinal Surveillance of the IgG Autoantibody Repertoire in Established Systemic Lupus Erythematosus.

OBJECTIVE: To investigate the role of epitope spreading in established systemic lupus erythematosus (SLE). METHODS: IgG autoantibody reactivity with 398 distinct recombinant proteins was measured over a period of 6 years in 69 SLE patients and compared to that in 45 controls. Changes in mean fluorescence intensity (MFI), number of autoantibodies to distinct antigens, and reactivity with distinct clones of established antigenic targets (e.g., U1 RNP, Sm, and ribosomal P) representing epitope fine mapping were assessed. Linear mixed modeling, adjusted with Bonferroni correction for age and sex, was applied. RESULTS: The total number of autoantibodies, mean MFI, and number of autoantibodies in epitope fine mapping were higher in SLE patients compared to controls (P < 0.0001). The total number of antibodies to distinct autoantigens remained stable over time, while the mean MFI decreased over time in SLE (P < 0.021). SLE patients showed variable recognition of epitopes in fine mapping over time. In particular, in SLE patients, more clones of the U1 RNP complex were recognized at the time of new organ involvement (+0.65) (P = 0.007). Mean MFI was higher in patients with lupus nephritis (P = 0.047). The time-averaged MFIs of 22 individual autoantibodies (including double-stranded DNA [dsDNA]) were higher, after Bonferroni correction, in SLE (P < 0.0001). The MFIs of dsDNA and histone cluster 2 H3c were associated with scores on the Systemic Lupus Activity Measure (P < 0.0001). CONCLUSION: Longitudinal surveillance of the IgG autoantibody repertoire in established SLE reveals evidence of sustained breadth of autoantibody repertoire without significant expansion. Associations of disease activity with dsDNA and with histone H3 autoantibodies were confirmed.

Label-free microarray-based detection of autoantibodies in human serum.

Multiplex assays for autoantibodies have shown utility both in research towards understanding the basic biology of autoimmune disease, and as tools for clinical diagnosis. New label-free multiplex analysis methods have the potential to streamline both the process of assay development and assay workflow. We report fabrication and testing of a 5-plex autoantigen microarray using the Arrayed Imaging Reflectometry (AIR) platform. This label-free technology provides rapid, sensitive, and quantitative detection of an arbitrary number of analytes in a standard multiwell format. In this work, we demonstrate that AIR is able to detect antibodies to Ro60, La/SSB, Scl-70, BicD2, and Ro52 in single-donor human serum samples with multiplex results comparable to singleplex ELISA or Luminex assays.

High diagnostic accuracy of histone H4-IgG autoantibodies in systemic lupus erythematosus.

Objective: Diagnosis of SLE relies on the detection of autoantibodies. We aimed to assess the diagnostic potential of histone H4 and H2A variant antibodies in SLE. Methods: IgG-autoantibodies to histones H4 (HIST1H4A), H2A type 2-A (HIST2H2AA3) and H2A type 2-C (HIST2H2AC) were measured along with a standard antibody (SA) set including SSA, SSB, Sm, U1-RNP and RPLP2 in a multiplex magnetic microsphere-based assay in 153 SLE patients [85% female, 41 (13.5) years] and 81 healthy controls [77% female, 43.3 (12.4) years]. Receiver operating characteristic analysis was performed to assess diagnostic performance of individual markers. Logistic regression analysis was performed on a random split of samples to determine the additional value of histone antibodies in comparison with SA by likelihood ratio test and determination of diagnostic accuracy in the remaining validation samples. Results: Microsphere-based assay showed good interclass correlation (mean 0.85, range 0.73-0.99) and diagnostic performance in receiver operating characteristic analysis (area under the curve (AUC) range 84.8-93.2) compared with routine assay for SA parameters. HIST1H4A-IgG was the marker with the best individual diagnostic performance for SLE vs healthy (AUC 0.97, sensitivity 95% at 90% specificity). HIST1H4A-IgG was an independent significant predictor for the diagnosis of SLE in multivariate modelling (P < 0.0001), and significantly improved prediction of SLE over SA parameters alone (residual deviance 45.9 vs 97.1, P = 4.3 × 10-11). Diagnostic accuracy in the training and validation samples was 89 and 86% for SA, and 95 and 89% with the addition of HIST1H4A-IgG. Conclusion: HIST1H4A-IgG antibodies improve diagnostic accuracy for SLE vs healthy.

The RA-MAP Consortium: a working model for academia-industry collaboration.

Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world.

Multivariate binary classification of imbalanced datasets-A case study based on high-dimensional multiplex autoimmune assay data.

The classification of a population by a specific trait is a major task in medicine, for example when in a diagnostic setting groups of patients with specific diseases are identified, but also when in predictive medicine a group of patients is classified into specific disease severity classes that might profit from different treatments. When the sizes of those subgroups become small, for example in rare diseases, imbalances between the classes are more the rule than the exception and make statistical classification problematic when the error rate of the minority class is high. Many observations are classified as belonging to the majority class, while the error rate of the majority class is low. This case study aims to investigate class imbalance for Random Forests and Powered Partial Least Squares Discriminant Analysis (PPLS-DA) and to evaluate the performance of these classifiers when they are combined with methods to compensate imbalance (sampling methods, cost-sensitive learning approaches). We evaluate all approaches with a scoring system taking the classification results into consideration. This case study is based on one high-dimensional multiplex autoimmune assay dataset describing immune response to antigens and consisting of two classes of patients: Rheumatoid Arthritis (RA) and Systemic Lupus Erythemathodes (SLE). Datasets with varying degrees of imbalance are created by successively reducing the class of RA patients. Our results indicate possible benefit of cost-sensitive learning approaches for Random Forests. Although further research is needed to verify our findings by investigating other datasets or large-scale simulation studies, we claim that this work has the potential to increase awareness of practitioners to this problem of class imbalance and stresses the importance of considering methods to compensate class imbalance.

Sequential high-content profiling of the IgG-autoantibody repertoire reveals novel antigens in rheumatoid arthritis.

BACKGROUND: The aim was to identify novel diagnostic autoantibody candidates for rheumatoid arthritis (RA) by comprehensive screening for autoreactivity. METHOD: We incubated 5892 recombinant proteins coupled to fluorescent beads, with patients' sera for the detection of IgG-autoantibodies in three independent patient cohorts: A (n = 72 patients with established RA); B/B- (n = 116 patients with early RA (B) and n = 51 CCP-negative patients with early RA from B (B-)); and C (n = 184 patients with early seronegative RA), in comparison to matched healthy controls. Intersects of significantly increased autoantibodies as determined by the Mann-Whitney test were sought. RESULT: Screening of 5892 antigens in RA cohorts A and B, or the seronegative cohorts B- and C revealed intersects of 23 and 13 significantly increased autoantibodies, respectively. Reactivity to three antigens was increased in all cohorts tested: N-acetylglucosamine-1-phosphate transferase, gamma subunit (GNPTG), heterogeneous nuclear ribonucleoprotein A1-like 2 (HNRNPA1), and insulin-like growth factor binding protein 2 (IGFBP2). CONCLUSIONS: Comprehensive sequential screening for autoantibodies reveals novel candidates for diagnostic markers in both seropositive and seronegative RA and suggests new fields of research into the pathogenesis of RA.

Comparison of pre-processing methods for multiplex bead-based immunoassays.

BACKGROUND: High throughput protein expression studies can be performed using bead-based protein immunoassays, such as the Luminex® xMAP® technology. Technical variability is inherent to these experiments and may lead to systematic bias and reduced power. To reduce technical variability, data pre-processing is performed. However, no recommendations exist for the pre-processing of Luminex® xMAP® data. RESULTS: We compared 37 different data pre-processing combinations of transformation and normalization methods in 42 samples on 384 analytes obtained from a multiplex immunoassay based on the Luminex® xMAP® technology. We evaluated the performance of each pre-processing approach with 6 different performance criteria. Three performance criteria were plots. All plots were evaluated by 15 independent and blinded readers. Four different combinations of transformation and normalization methods performed well as pre-processing procedure for this bead-based protein immunoassay. CONCLUSIONS: The following combinations of transformation and normalization were suitable for pre-processing Luminex® xMAP® data in this study: weighted Box-Cox followed by quantile or robust spline normalization (rsn), asinh transformation followed by loess normalization and Box-Cox followed by rsn.

Serum Autoantibodies in Chronic Prostate Inflammation in Prostate Cancer Patients.

BACKGROUND: Chronic inflammation is frequently observed on histological analysis of malignant and non-malignant prostate specimens. It is a suspected supporting factor for prostate diseases and their progression and a main cause of false positive PSA tests in cancer screening. We hypothesized that inflammation induces autoantibodies, which may be useful biomarkers. We aimed to identify and validate prostate inflammation associated serum autoantibodies in prostate cancer patients and evaluate the expression of corresponding autoantigens. METHODS: Radical prostatectomy specimens of prostate cancer patients (N = 70) were classified into high and low inflammation groups according to the amount of tissue infiltrating lymphocytes. The corresponding pre-surgery blood serum samples were scrutinized for autoantibodies using a low-density protein array. Selected autoantigens were identified in prostate tissue and their expression pattern analyzed by immunohistochemistry and qPCR. The identified autoantibody profile was cross-checked in an independent sample set (N = 63) using the Luminex-bead protein array technology. RESULTS: Protein array screening identified 165 autoantibodies differentially abundant in the serum of high compared to low inflammation patients. The expression pattern of three corresponding antigens were established in benign and cancer tissue by immunohistochemistry and qPCR: SPAST (Spastin), STX18 (Syntaxin 18) and SPOP (speckle-type POZ protein). Of these, SPAST was significantly increased in prostate tissue with high inflammation. All three autoantigens were differentially expressed in primary and/or castration resistant prostate tumors when analyzed in an inflammation-independent tissue microarray. Cross-validation of the inflammation autoantibody profile on an independent sample set using a Luminex-bead protein array, retrieved 51 of the significantly discriminating autoantibodies. Three autoantibodies were significantly upregulated in both screens, MUT, RAB11B and CSRP2 (p>0.05), two, SPOP and ZNF671, close to statistical significance (p = 0.051 and 0.076). CONCLUSIONS: We provide evidence of an inflammation-specific autoantibody profile and confirm the expression of corresponding autoantigens in prostate tissue. This supports evaluation of autoantibodies as non-invasive markers for prostate inflammation.

Serum-autoantibodies for discovery of prostate cancer specific biomarkers.

BACKGROUND: The currently used prostate cancer serum marker has a low cancer specificity and improved diagnostics are needed. Here we evaluated whether autoantibodies are present in sera of prostate cancer patients and whether they are useful diagnostic markers for prostate cancer. METHODS: Sera from 20 prostate cancer patients and 20 healthy controls were incubated on expression clone arrays containing more than 37,000 recombinant human proteins. Functional annotation clustering of the identified autoantigens was performed using the DAVID database. Autoantigens identified in the prostate cancer group were validated on microarrays using sera of 40 prostate cancer patients, 40 patients with elevated PSA levels but prostate cancer negative biopsies (benign disease), and 40 healthy controls. RESULTS: We detected autoantibodies against 408 different antigens in sera of prostate cancer patients. One hundred seventy-four of these were exclusively detected in the cancer group compared to the healthy control group. Functional annotation clustering revealed an enrichment of RNA-associated, cytoskeleton, and nuclear proteins. The autoantibody panel was validated in serum samples of independent prostate cancer patients. Autoantibody profiles discriminated between prostate cancer patients and benign disease patients with an ROC curve AUC of 0.71. TTLL12, a protein recently described to be over-expressed in prostate cancer, was the highest ranked discrimination autoantigen. CONCLUSION: A variety of autoantibodies were identified in sera of prostate cancer patients and provide a first step towards autoantibody diagnostics. Serum autoantibodies reflect the disease and represent valuable tools not only for prostate cancer, but also for other diseases affecting the immune response.

Isobaric tagging-based selection and quantitation of cerebrospinal fluid tryptic peptides with reporter calibration curves.

In the past few years, mass spectrometry (MS) has emerged as an efficient tool for the multiplexed peptide and protein concentration determination by isotope dilution. Despite the growing use of isobaric tagging to perform relative quantitation for the discovery of potential biomarkers in biological fluids, no real application has so far been presented for their absolute quantitation. Isobaric tandem mass tags (TMTs) were used herein for the selection and quantitation of tryptic peptides derived from brain damage related proteins in cerebrospinal fluid (CSF). Proteotypic tryptic peptide analogues were synthesized, prepared in four reference amounts, differentially labeled with four isobaric TMTs with reporter-ions at m/z = 128.1, 129.1, 130.1, and 131.1, and mixed with CSF sample previously labeled with TMT 126.1. Off-gel electrophoresis (OGE) was used as first-dimension separation of the pooled sample. The resulting fractions were analyzed with reversed-phase liquid chromatography (RP-LC) tandem mass spectrometry (MS/MS), using tandem time-of-flight (TOF/TOF) and hybrid linear ion trap-orbitrap (LTQ-OT) instruments. Under collision-induced dissociation (CID) or higher-energy C-trap dissociation (HCD), the release of the reporter fragments from the TMT-labeled peptide standards provided an internal calibration curve to assess the concentration of these peptides in the CSF. This tool also allowed identifying selectively these peptides in CSF as only the targeted peptides showed specific fragmentation pattern in the TMT reporter-ion zone of the tandem mass spectra. Assays for the concentration measurements of peptides from PARK7, GSTP1, NDKA, and S100B proteins in CSF were further characterized using this novel, efficient, and straightforward approach.

Breast Cancer Proteomics - Differences in Protein Expression between Estrogen Receptor-Positive and -Negative Tumors Identified by Tandem Mass Tag Technology.

BACKGROUND: Proteomic analysis has become an effective tool in breast cancer research. In this study, we applied the new gel-free tandem mass tag (TMT) reference method for the first time in breast cancer. MATERIALS AND METHODS: Proteomic analysis was used to compare 10 estrogen receptor (ER)-positive and 10 ER-negative samples. The results of the proteomic approach were validated by Western blot, immunohistochemistry and gene expression analysis. RESULTS: 17 proteins with significant differences in expression were identified. 13 proteins were overexpressed in ER-negative tumors and 4 were overexpressed in ER-positive samples. All these proteins were characterized by relatively high cellular abundance. CONCLUSIONS: Our results demonstrate that the gel-free TMT approach allows the quantification of differences in protein expression levels. Further improvement of the sensitivity by subfractionation of the tissue should allow also the identification of low-abundance proteins and might lead to the use of this method in breast cancer research.

Candidate verification of iron-regulated Neisseria meningitidis proteins using isotopic versions of tandem mass tags (TMT) and single reaction monitoring.

Tandem Mass Tags (TMT) are suited to both global and targeted quantitation approaches of proteins and peptides. Different versions of these tags allow for the generation of both isobaric and isotopic sets of reagents sharing the same common structure. This feature allows for a straightforward transfer of data obtained during discovery studies into targeted investigations. In prior discovery studies, an isobaric set of these reagents was used to identify Neisseria meningitidis proteins expressed under iron-limitation. Here, we apply isotopic versions of those reagents in combination with single reaction monitoring to verify selected candidates found to be differentially regulated in these discovery studies, representing both well-known and novel iron-regulated proteins, such as the MtrCDE drug efflux pump. In this targeted approach (TMT-SRM), the selectivity of SRM is maintained while allowing the incorporation of an internal reference standard into the experiment. By monitoring 184 transitions, TMT-SRM resulted in the quantitation of 33 peptides representing 12 proteins. The acquired data corroborated the results obtained during the discovery phase. Furthermore, these data obtained by MS-based quantitation of peptides were independently confirmed by western blotting results, an orthogonal approach based on quantitation at the protein level.

In vivo profiling of DPP4 inhibitors reveals alterations in collagen metabolism and accumulation of an amyloid peptide in rat plasma.

Dipeptidyl peptidase 4 (DPP4) inhibitors represent a novel class of oral anti-hyperglycemic agents. The complete pharmacological profile of these protease inhibitors remains unclear. In order to gain deeper insight into the in vivo effects caused by DPP4 inhibition, two different DPP4 inhibitors (vildagliptin and AB192) were analyzed using differential peptide display. Wistar rats were treated with the DPP4 inhibitors (0.3mgkg(-1); 1mgkg(-1) or 3mgkg(-1) body weight) and DPP4 activity was measured before and at the end of the experiment. One hour after compound administration, blood plasma samples were collected to generate peptide displays and to subsequently identify differentially regulated peptides. A dose-dependent decrease in blood plasma DPP4 activity was measured for both inhibitors. DPP4 inhibition influenced collagen metabolism leading to depletion of collagen derived peptides (e.g. collagen alpha 1 (III) 521-554) and accumulation of related N-terminally extended collagen derived peptides (e.g. collagen alpha 1 (III) 519-554). Furthermore, the intact amyloid rat BRI (1-23) peptide was detected in plasma following in vivo DPP4 inhibition. DPP4 catalyzed cleavage kinetics of the BRI peptide were determined in vitro. The k(cat) and K(m) for cleavage by DPP4 were 5.2s(-1) and 14microM, respectively, resulting in a specificity constant k(cat)/K(m) of 0.36 x 10(6)s(-1)M(-1). Our results demonstrate that differential peptide analysis can be applied to monitor action of DPP4 inhibition in blood plasma. For the first time effects on basal collagen metabolism following DPP4 inhibition in vivo were demonstrated and the BRI amyloid peptide was identified as a novel DPP4 substrate.

Peptidomic analysis of human peripheral monocytes persistently infected by Chlamydia trachomatis.

Peptidomic analysis using Differential Peptide Display (DPD) of human peripheral blood mononuclear cells (PBMC) mock-infected or persistently infected by Chlamydia trachomatis (CT) revealed 10 peptides, expressed upon CT infection. Analysis of these 10 candidates by tandem mass spectrometry enabled the determination of seven candidates as fragments from the precursors (I) ferritin heavy chain subunit, (II) HLA class II histocompatibility antigen, (III) vimentin, (IV) indoleamine 2,3-dioxygenase, (V and VI) pre-B cell enhancing factor (PBEF), and (VII) Interleukin-8 (CXCL8). The identified candidates proved the presence of anti-bacterial and immunologically active monocytic proteins after CT infection.

Metrological sharp shooting for plasma proteins and peptides: The need for reference materials for accurate measurements in clinical proteomics and in vitro diagnostics to generate reliable results.

Reliable study results are necessary for the assessment of discoveries, including those from proteomics. Reliable study results are also crucial to increase the likelihood of making a successful choice of biomarker candidates for verification and subsequent validation studies, a current bottleneck for the transition to in vitro diagnostic (IVD). In this respect, a major need for improvement in proteomics appears to be accuracy of measurements, including both trueness and precision of measurement. Standardization and total quality management systems (TQMS) help to provide accurate measurements and reliable results. Reference materials are an essential part of standardization and TQMS in IVD and are crucial to provide metrological correct measurements and for the overall quality assurance process. In this article we give an overview on how reference materials are defined, prepared and what role they play in standardization and TQMS to support the generation of reliable results. We discuss how proteomics can support the establishment of reference materials and biomarker tests for IVD applications, how current reference materials used in IVD may be beneficially applied in proteomics, and we provide considerations on the establishment of reference materials specific for proteomics. For clarity, we solely focus on reference materials related to serum and plasma.

Peptidomic analysis of breast cancer reveals a putative surrogate marker for estrogen receptor-negative carcinomas.

Estrogen-receptor status provides a major biomarker in breast cancer classification and has an important impact on prognosis and treatment options. The aim of this study was to investigate peptide profiles of invasive breast cancer with positive (n=39) and negative receptor status (n=41). Peptide profiles were generated by 'Differential Peptide Display', which is an offline-coupled combination of reversed-phase-HPLC and MALDI mass spectrometry. Mass spectrometric data were correlated with the immunohistochemically determined receptor state. Identification of peptides of interest was carried out by additional mass spectrometric methods (eg MALDI-TOF-TOF-MS-MS). Approximately 3000-7000 signals were detected per sample and thymosin alpha-1, an asparaginyl endopeptidase generated cleavage product of the ubiquitous acidic protein prothymosin-alpha, was found to differentiate the tumor samples according to their receptor status with the highest specificity. The concentration of Thymosin alpha-1 was found to be upregulated (n=37) in estrogen-negative cancer samples and downregulated (n=32) in estrogen-positive breast cancer samples. The expression of the precursor protein (Prothymosin-alpha) has been discussed previously as a prognostic factor in breast cancer. It is involved in the ER signal transduction pathway as an anti-coactivator-inhibitor. From our findings we conclude that Thymosin alpha-1 could serve as a surrogate marker in breast cancers and may indicate ER functionality.