Identification of a melanoma marker derived from melanoma-associated endogenous retroviruses.

We previously described the expression of melanoma-associated endogenous retrovirus (MERV) proteins and viral particles in human melanomas and metastases. The objective of the present study was to determine whether a humoral immune response to MERV proteins occurs in melanoma. Candidate B-cell epitopes on MERV proteins were predicted using bioinformatic screening. The reactivity of MERV peptides corresponding to the predicted epitopes with antibodies prevalent in sera of melanoma patients was analyzed. An immunodominant peptide located in the env protein of MERV was identified. Subsequent analyzes using 81 samples from stage I to stage IV melanoma patients and 95 sera from healthy subjects revealed statistically significant differences in seroprevalence of antibodies in melanoma sera samples when compared with reference samples from healthy subjects. The prevalence of anti-MERV antibodies in melanoma patient sera was confirmed by immunofluorescence on env-transfected cells. These data indicate the potential of this candidate peptide as target for diagnosis and immunotherapy.

Azidothymidine inhibits melanoma cell growth in vitro and in vivo.

Azidothymidine (AZT), currently used for HIV treatment, was also shown to induce cell growth inhibition and apoptosis in different human tumors. The objective of this study was to investigate the ability of AZT to inhibit the growth of human melanoma cells in vitro and in vivo. In cytotoxicity assays, treatment of cells with varying concentrations of AZT-induced inhibition of cell growth and apoptosis in three human melanoma cell lines without affecting the growth of nontumorigenic cells. AZT-dependent inhibition of proliferation was accompanied by a significant S-phase arrest of the cell cycle. Coexposure of cells to AZT during cisplatin treatment showed a synergistic effect on cytotoxicity. Moreover, AZT monotreatment of melanoma in a severe combined immunodeficiency-mouse xenotransplantation model resulted in significant tumor reduction. These results demonstrate for the first time the antimelanoma activity of AZT, suggesting its clinical utilization either as a sole agent or in combination with other chemotherapeutic agents.

Analysis and prediction of protective continuous B-cell epitopes on pathogen proteins.

BACKGROUND: The application of peptide based diagnostics and therapeutics mimicking part of protein antigen is experiencing renewed interest. So far selection and design rationale for such peptides is usually driven by T-cell epitope prediction, available experimental and modelled 3D structure, B-cell epitope predictions such as hydrophilicity plots or experience. If no structure is available the rational selection of peptides for the production of functionally altering or neutralizing antibodies is practically impossible. Specifically if many alternative antigens are available the reduction of required synthesized peptides until one successful candidate is found is of central technical interest. We have investigated the integration of B-cell epitope prediction with the variability of antigen and the conservation of patterns for post-translational modification (PTM) prediction to improve over state of the art in the field. In particular the application of machine-learning methods shows promising results. RESULTS: We find that protein regions leading to the production of functionally altering antibodies are often characterized by a distinct increase in the cumulative sum of three presented parameters. Furthermore the concept to maximize antigenicity, minimize variability and minimize the likelihood of post-translational modification for the identification of relevant sites leads to biologically interesting observations. Primarily, for about 50% of antigen the approach works well with individual area under the ROC curve (AROC) values of at least 0.65. On the other hand a significant portion reveals equivalently low AROC values of < or = 0.35 indicating an overall non-Gaussian distribution. While about a third of 57 antigens are seemingly intangible by our approach our results suggest the existence of at least two distinct classes of bioinformatically detectable epitopes which should be predicted separately. As a side effect of our study we present a hand curated dataset for the validation of protectivity classification. Based on this dataset machine-learning methods further improve predictive power to a class separation in an equilibrated dataset of up to 83%. CONCLUSION: We present a computational method to automatically select and rank peptides for the stimulation of potentially protective or otherwise functionally altering antibodies. It can be shown that integration of variability, post-translational modification pattern conservation and B-cell antigenicity improve rational selection over random guessing. Probably more important, we find that for about 50% of antigen the approach works substantially better than for the overall dataset of 57 proteins. Essentially as a side effect our method optimizes for presumably best applicable peptides as they tend to be likely unmodified and as invariable as possible which is answering needs in diagnosis and treatment of pathogen infection. In addition we show the potential for further improvement by the application of machine-learning methods, in particular Random Forests.

Linking the ovarian cancer transcriptome and immunome.

BACKGROUND: Autoantigens have been reported in a variety of tumors, providing insight into the interplay between malignancies and the immune response, and also giving rise to novel diagnostic and therapeutic concepts. Why certain tumor-associated proteins induce an immune response remains largely elusive. RESULTS: This paper analyzes the proposed link between increased abundance of a protein in cancerous tissue and the increased potential of the protein for induction of a humoral immune response, using ovarian cancer as an example. Public domain data sources on differential gene expression and on autoantigens associated with this malignancy were extracted and compared, using bioinformatics analysis, on the levels of individual genes and proteins, transcriptional coregulation, joint functional pathways, and shared protein-protein interaction networks. Finally, a selected list of ovarian cancer-associated, differentially regulated proteins was tested experimentally for reactivity with antibodies prevalent in sera of ovarian cancer patients.Genes reported as showing differential expression in ovarian cancer exhibited only minor overlap with the public domain list of ovarian cancer autoantigens. However, experimental screening for antibodies directed against antigenic determinants from ovarian cancer-associated proteins yielded clear reactions with sera. CONCLUSION: A link between tumor protein abundance and the likelihood of induction of a humoral immune response in ovarian cancer appears evident.

Identification of discontinuous antigenic determinants on proteins based on shape complementarities.

Diverse procedures for identifying antigenic determinants on proteins have been developed, including experimental as well as computational approaches. However, most of these techniques focus on continuous epitopes, whereas fast and reliable identification and verification of discontinuous epitopes remains barely amenable. In this paper, we describe a computational workflow for the detection of discontinuous epitopes on proteins. The workflow uses a given protein 3D structure as input, and combines a per residue solvent accessibility constraint with epitope to paratope shape complementarity measures and binding energies for assigning antigenic determinants in the conformational context. We have developed the procedure on a given set of 26 antigen-antibody complexes with a known structure, and have further expanded the available paratope shapes by generating a virtual paratope library in order to improve the screening for candidate residues constituting discontinuous epitopes. Applying the workflow on the 26 given antigens with known discontinuous epitopes resulted in the correct identification of the spatial proximity of 12 antigen-antibody interaction sites. Combining solvent accessibility, shape complementarity and binding energies towards the identification of discontinuous epitopes clearly outperforms approaches solely considering accessibility and residue distance constraints.

Transforming omics data into context: bioinformatics on genomics and proteomics raw data.

Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables - and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.