Identification of B and T cell epitope based peptide vaccine from IGF-1 receptor in breast cancer

The insulin-like growth factor-1 receptor (IGF-1R) plays a key role in proliferation, growth, differentiation, and development of several human malignancies including breast and pancreatic adenocarcinoma. IGF-1R targeted immunotherapeutic approaches are particularly attractive, as they may potentially elicit even stronger antitumor responses than traditional targeted approaches. Cancer peptide vaccines can produce immunologic responses against cancer cells by triggering helper T cell (Th) or cytotoxic T cells (CTL) in association with Major Histocompatibility Complex (MHC) class I or II molecules on the cell surface of antigen presenting cells. In our previous study, we set a technique based on molecular docking in order to find the best MHC class I and II binder peptides using GOLD. In the present work, molecular docking analyses on a library consisting of 30 peptides mimicking discontinuous epitopes from IGF-1R extracellular domain identified peptides 249 and 86, as the best MHC binder peptides to both MHC class I and II molecules. The receptors most often targeted by peptide 249 are HLA-DR4, HLA-DR3 and HLA-DR2 and those most often targeted by peptide 86 are HLA-DR4, HLA-DP2 and HLA-DR3. These findings, based on bioinformatics analyses, can be conducted in further experimental analyses in cancer therapy and vaccine design.     

Structure-based prediction of MHC-peptide association: algorithm comparison and application to cancer vaccine design

Peptide vaccination for cancer immunotherapy requires identification of peptide epitopes derived from antigenic proteins associated with the tumor. Such peptides can bind to MHC proteins (MHC molecules) on the tumor-cell surface, with the potential to initiate a host immune response against the tumor. Computer prediction of peptide epitopes can be based on known motifs for peptide sequences that bind to a certain MHC molecule, on algorithms using experimental data as a training set, or on structure-based approaches. We have developed an algorithm, which we refer to as PePSSI, for flexible structural prediction of peptide binding to MHC molecules. Here, we have applied this algorithm to identify peptide epitopes (of nine amino acids, the common length) from the sequence of the cancer-testis antigen KU-CT-1, based on the potential of these peptides to bind to the human MHC molecule HLA-A2. We compared the PePSSI predictions with those of other algorithms and found that several peptides predicted to be strong HLA-A2 binders by PePSSI were similarly predicted by another structure-based algorithm, PREDEP. The results show how structure-based prediction can identify potential peptide epitopes without known binding motifs and suggest that side chain orientation in binding peptides may be obtained using PePSSI.     

In silico design of knowledge-based Plasmodium falciparum epitope ensemble vaccines

Malaria is a global health burden, and a major cause of mortality and morbidity in Africa. Here we designed a putative malaria epitope ensemble vaccine by selecting an optimal set of pathogen epitopes. From the IEDB database, 584 experimentally-verified CD8+ epitopes and 483 experimentally-verified CD4+ epitopes were collected; 89% of which were found in 8 proteins. Using the PVS server, highly conserved epitopes were identified from variability analysis of multiple alignments of Plasmodium falciparum protein sequences. The allele-dependent binding of epitopes was then assessed using IEDB analysis tools, from which the population protection coverage of single and combined epitopes was estimated. Ten conserved epitopes from four well-studied antigens were found to have a coverage of 97.9% of the world population: 7 CD8+ T cell epitopes (LLMDCSGSI, FLIFFDLFLV, LLACAGLAYK, TPYAGEPAPF, LLACAGLAY, SLKKNSRSL, and NEVVVKEEY) and 3 CD4+ T cell epitopes (MRKLAILSVSSFLFV, KSKYKLATSVLAGLL and GLAYKFVVPGAATPYE). The addition of four heteroclitic peptides − single point mutated epitopes − increased HLA binding affinity and raised the predicted world population coverage above 99%.     

Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Peptides that induce and recall T-cell responses are called T-cell epitopes. T-cell epitopes may be useful in a subunit vaccine against malaria. Computer models that simulate peptide binding to MHC are useful for selecting candidate T-cell epitopes since they minimize the number of experiments required for their identification. We applied a combination of computational and immunological strategies to select candidate T-cell epitopes. A total of 86 experimental binding assays were performed in three rounds of identification of HLA-A11 binding peptides from the six preerythrocytic malaria antigens. Thirty-six peptides were experimentally confirmed as binders. We show that the cyclical refinement of the ANN models results in a significant improvement of the efficiency of identifying potential T-cell epitopes.     

用于T细胞表位预测的分类器集成方法*

T细胞表位预测技术对于减少实验合成重叠肽,理解T细胞介导的免疫特异性和研制亚单位多肽及基因疫苗均有重要意义.为弥补已有基于机器学习方法的T细胞表位预测模型的可理解性的不足并进一步提高模型的预测精度,首先通过肽的预处理构建出了存储等长肽段的决策表,而后提出了基于粗糙集的分类器集成算法.该算法不但综合利用了基于信息熵的属性约简完备算法和其他属性约简算法的优势,而且将T细胞表位预测领域中的锚点知识融入到了属性值约简过程中.最后利用该算法来预测MHC Ⅱ类分子HLA-DR4(B1·0401)的结合肽,首次提取出了预测精度高且能帮助专家理解MHC分子与抗原肽的结合机理的产生式规则,为下一步的分子建模工作奠定了基础.     

Towards defining the whole salivary peptidome

We report the identification of 2294 peptides/proteins in whole saliva in the mass range between 700 and 16 000 Da by LC‐MS and MS/MS using a MALDI‐TOF/TOF mass spectrometer. Most of the identified peptides/proteins are originated from cellular debris or plasma components while only 26% (n = 607) correspond to salivary peptides/proteins species. In spite of the presence of the major salivary peptides in all samples from the six subjects analyzed, each individual presents a different pattern of fragments, many deriving from the same protein sequence. All our data, in particular the large number of fragments found, suggest high proteolytic activity insight the oral cavity. The analysis of samples by gelatin zymography showed that all saliva donors displayed multiple proteolytic bands, two identified as cathepsin D and G by MS. Analysis of the cleavage site distribution on the main peptide sequences based on contingency tables shows that the predominant cleavages occur between Gln‐Gly or Tyr‐Gly. These cleavages are largely associated with proline‐rich proteins peptides and with histatin 1 and P‐B peptide, respectively. However, depending on the peptide class, different cleavage hits were observed suggesting the presence of a set of proteases acting in different ways according to different peptide sequences. Comparing the number of cleavages involving all residues, it is possible to observe that 44% (±10%) of the observed cleavages in histatin, statherin and P‐B peptide in all individuals may be explained by cathepsin D, suggesting a major role for this enzyme in oral cavity proteolysis.     

Protein profiling of oral brush biopsies: S100A8 and S100A9 can differentiate between normal, premalignant, and tumor cells

In oral mucosa lesions it is frequently difficult to differentiate between precursor lesions and already manifest oral squamous cell carcinoma. Therefore, multiple scalpel biopsies are necessary to detect tumor cells already in early stages and to guarantee an accurate follow‐up. We analyzed oral brush biopsies (n = 49) of normal mucosa, inflammatory and hyperproliferative lesions, and oral squamous cell carcinoma with ProteinChip Arrays (SELDI) as a non‐invasive method to characterize putative tumor cells. Three proteins were found that differentiated between these three stages. These three proteins are able to distinguish between normal cells and tumor cells with a sensitivity of 100% and specificity of 91% and can distinguish inflammatory/hyperproliferative lesions from tumor cells with a sensitivity of up to 91% and specificity of up to 90%. Two of these proteins have been identified by immunodepletion as S100A8 and S100A9 and this identification was confirmed by immunocytochemistry. For the first time, brush biopsies have been successfully used for proteomic biomarker discovery. The identified protein markers are highly specific for the distinction of the three analyzed stages and therewith reflect the progression from normal to premalignant non‐dysplastic and finally to tumor tissue. This knowledge could be used as a first diagnostic step in the monitoring of mucosal lesions.     

MS analysis of rheumatoid arthritic synovial tissue identifies specific citrullination sites on fibrinogen

Purpose : Citrullination is a post‐translational modification of arginine residues to citrulline catalyzed by peptidyl arginine deiminases. Induced expression of citrullinated proteins are frequently detected in various inflammatory states including arthritis; however, direct detection of citrullination in arthritic samples has not been successfully performed in the past. Experimental design : Citrullination of human fibrinogen, a candidate autoantigen in arthritis, was studied. Accurate identification of citrullinated fibrinogen peptides from rheumatoid arthritis synovial tissue specimens was performed using accurate mass and retention time analysis. Results : A peptide with the sequence ESSSHHPGIAEFPSRGK corresponding to amino acids 559–575 of fibrinogen α‐chain was identified to be citrullinated with an occupancy rate between 1.4 and 2.5%. Citrullination of the peptide KREEAPSLRPAPPPISGGGYRARPAK corresponding to amino acids 52–77 of the fibrinogen β‐chain was identified with an occupancy rate of 1.2%. Conclusions and clinical relevance : We report a proof of principle study for the identification of citrullinated proteins and within them, identification of citrullination sites and quantification of their occupancies in synovial tissue from rheumatoid arthritis patients using high‐resolution MS. Detailed studies on which molecules are citrullinated in arthritis can provide information about their role in immune regulation and serve as novel biomarkers and potentially even as therapeutic targets.     

SEREX, Proteomex, AMIDA, and beyond: Serological screening technologies for target identification

Despite the great body of knowledge about the aetiology, pathogenesis, risk factors, and associated molecular processes, cancer remains a prime health concern. Over the past decades scientific and medical research focused on the identification of biomarkers and target molecules for the diagnosis and therapy of cancer. Such markers may allow for improved and early diagnosis, as well as for immunotherapeutic approaches for cancer treatment. A plethora of technologies dedicated to the identification of target molecules was developed including those relying on a humoral response against tumour-associated antigens (TAA) in diseased individuals. As for other diseases, cancers elicit immune responses that result in the induction of T and B lymphocytes specific for tumour-associated proteins, largely self-antigens, but also those comprising viral and bacterial proteins. Cancer-specific serum antibodies are of great use for the isolation and subsequent identification of their cognate antigens. The present review will concentrate on three major serological target identification methods, i.e. SEREX, Proteomex, and AMIDA, concluding with a summary of the milestones in the clinical advancement and applications of serological TAA.     

Modelling of HLA-DQ2 and its interaction with gluten peptides to explain molecular recognition in celiac disease

Celiac disease (CD) is sustained by abnormal intestinal mucosal T-cell response to gluten and it is strongly associated with HLA class II molecules encoded by DQA1*0501/DQB1*02 (DQ2) or DQA1*03/DQB1*0302 (DQ8). The in vitro stimulatory activity of gliadin increases after treatment with tissue transglutaminase (tTG) which catalyses the deamidation of specific residues of glutamine to glutamate that can serve as anchors for binding to DQ2 as well as to DQ8 molecules. We modelled the three-dimensional structure of the DQ2 dimer protein, the most frequent in celiac patients, by using a homology modelling strategy, and deposited the model in the Protein Data Bank (PDB). Then, we simulated the interactions of DQ2 with different gluten peptides and the deamidation of specific peptide glutamines in the known p4, p6, p7 and p9 anchor positions, as well as in p1 and p5 positions, and other substitutions for which experimental effects on binding are available by previous experimental studies. By evaluating the energy of interaction and the H-bond interactions, we were able to distinguish what substitutions improve the interaction peptide-DQ2, in agreement with previously published experimental data. By analysing the peptide-DQ2 complex at the atom level, we observed that these glutamate side chains can interact with specific positively charged amino acids of DQ2, absent in other HLA alleles not related to celiac disease. The simulation was also extended to other peptides, related to celiac disease but for which no experimental data exists about the effects of glutamine deamidation. Our results give an interpretation at the molecular level of previously reported binding experimental data and open a new window to gain further insights about peptide recognition in celiac disease.     

Quantitative online prediction of peptide binding to the major histocompatibility complex

With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501.     

A novel tumor-related protein, C7orf24, identified by proteome differential display of bladder urothelial carcinoma

Proteome analysis of bladder cancer with narrow-range pH 2-DE has identified a novel protein on chromosome 7 encoded by ORF 24 (C7orf24) as one of the highly expressed proteins in cancer cells. C7orf24 is currently registered in the protein database as a hypothetical protein with unknown function. The homologs of C7orf24 in other animals have also been registered as putative protein genes. Western blot analysis using a mAb against C7orf24 confirmed its higher expression in bladder cancer compared with normal tissue. Several other cancer cell lines were also found to express C7orf24. However, the introduction of C7orf24 into Rat-1 or NIH3T3 cells did not cause malignant transformation. A stable transfectant of NIH3T3 cells with recombinant retrovirus vector was produced for a growth rate assay, and a higher growth rate was observed in C7orf24-expressing cells compared with the controls. Six kinds of small interfering RNAs (siRNAs) were then produced, and C7orf24-siRNA#5 showed a strong knockdown effect on protein expression and significant antiproliferative effects on cancer cell lines were demonstrated by the MTT assay. Therefore, C7orf24 may have an important role in cancer cell proliferation, and may be an appropriate therapeutic target molecule against cancer.     

Proteomics-based identification of HSP60 as a tumor-associated antigen in colorectal cancer

Patients with cancer frequently develop autoantibodies. The identification of tumor autoantigens may have utility in early cancer diagnosis and immunotherapy. In this study, we used serological proteomics analysis (SERPA) to identify tumor proteins that elicit humoral response in colorectal cancer (CRC). The CRC cell line HCT116 was used as a source of proteins for 2-DE and subsequent Western blot analysis in which individual serum from patients with CRC was analyzed for autoantibodies. An autoantibody against HSP60 identified by MS was detected in 13 out of 25 patients with CRC and 1 out of 15 healthy subjects. In addition, the HSP60 expressions in tumor tissues collected from 40 patients with CRC were assessed by immunohistochemistry, and serum specimens from 100 patients with cancer and 30 healthy controls were screened for antibody titer to HSP60 by ELISA. The results showed that expressions of HSP60 in tumor tissue and serum antibody titer to HSP60 were significantly higher in patients with CRC than in healthy subjects. Thus, we conclude that the SERPA is an excellent assay for the identification of tumor-associated antigens and tumor markers. The detection of HSP60 may have clinical utility in CRC screening, diagnosis, and immunotherapy.     

Development of an immuno tandem mass spectrometry (iMALDI) assay for EGFR diagnosis

The epidermal growth factor receptor (EGFR) is highly expressed in a variety of tumors, and is therefore an important biomarker for cancer diagnosis and a target for cancer therapy. We have developed a novel peptide-based immuno tandem mass spectrometry (iMALDI) diagnostic assay for highly sensitive, highly specific, and quantitative analysis of EGFR, which we have applied to the detection of the EGFR peptide in three cell lines and in a tumor biopsy sample. This assay is capable of detecting the EGFR target peptide bound to the antibody beads at attomole levels. The ability to directly obtain amino acid sequence data by MS/MS on any affinity-captured peptides provides specificity to this diagnostic technique. This avoids the problem of "false positives" which can result from the nonspecific binding that can occur with any affinity-based technique. The addition of stable-labeled versions of the target peptide (synthesized from stable-isotope coded amino acids) as internal standards allows absolute quantitation of the target protein.     

An experimental strategy for quantitative analysis of the humoral immune response to prostate cancer antigens using natural protein microarrays

The identification of human tumor antigens has potential utility in the diagnosis and treatment of cancers. We demonstrate here a complete strategy to profile immunoreactivity and identify tumor antigens from proteins derived from tumor cell lines. Microarrays of proteins produced from 2-D LC fractionation of prostate tumor cell-line lysates were used to profile immunoreactivity in the sera of prostate cancer patients and control subjects. Cancer-associated immunoreactivity to distinct groups of chromatography fractions was present in about 50% of the patients, with greater immunoreactivity present in patients with non-organ-confined cancer than in patients with organ-confined cancer. We grouped the immunoreactive fractions by similarities in elution order and patterns of immunoreactivity to guide and interpret the MS analysis of selected fractions, which was used to identify the proteins that may be responsible for the immunoreactivity. As a complementary method to further characterize and validate the immunoreactivity of the proteins identified by mass spectrometry, we demonstrate the use of focused microarrays of recombinant proteins. Disease-associated immunoreactivity was confirmed for one of the identified proteins, human Kallikrein 11. These results demonstrate a practical approach to screening, identifying, and validating immunoreactive proteins that could be applied to diverse studies on humoral immune responses.     

Bioimaging of geographically adjacent proteins in a single cell by quantum dot-based fluorescent resonance energy transfer

Thousands of proteins are simultaneously involved in the maintenance of a single cancer cell. Fluorescent resonance energy transfer (FRET) is one of the most general techniques for imaging biologically interacting molecules in a cell. Here, we applied FRET to image the co‐localization of two proteins that do not interact biologically (nucleolin and integrin α_vβ_3), both of which are highly expressed in the plasma membrane of cancer cells. AS1411 aptamer, which targets nucleolin, was labeled by Cy3 (Cy3‐AS1411) and arginine–glycine–aspartic acid (RGD) peptide, which targets integrin α_vβ₃, was conjugated with quantum dot (525 nm, Qd) Qd arginine–glycine–aspartic acid (Qd‐RGD). FRET activities between Cy3‐AS1411 and Qd‐RGD were measured in HeLa cells, a human cervical cancer cell line. FRET phenomena between Qd and Cy3 showed good compatibility according to proximity. The fluorescence signature using Qd‐RGD and Cy3‐AS1411 showed that nucleolin and integrin α_vβ₃ proteins were highly expressed in HeLa cells. Co‐incubation of Qd‐RGD and Cy3‐AS1411 in a single HeLa cell demonstrated that the fluorescence overlay by FRET was quantitatively and geographically quite different from that of individual confocal images. These results suggest that Qd‐based FRET analysis can provide information on geographical co‐localization of proteins in naïve cells, which is very important for determining the molecular and cellular functions of genes involved in cancers and other clinical diseases.     

The proteomics of cancer stem cells: potential clinical applications for innovative research in oncology

Cancer stem cells (CSCs) or tumour-maintaining cells are becoming an important new reality in oncology. The intriguing molecular pathophysiology of CSCs may justify some of the obscure pathogenetic, diagnostic, prognostic, and above all, therapeutic aspects of cancer and, eventually, lead to new solutions in oncology. CSC is a cell within the tumour that possesses the capacity to self-renew and, in doing so, gives rise to the heterogeneous lineages that comprise the tumour. The precise identification of this peculiar subpopulation of cancer cells, which has some intriguing similarities to normal stem cells, is becoming an important and urgent topic in oncology. In fact, some debated CSC markers have been already adopted by pharmacological research as targets of new and/or old anticancer drugs, showing an intriguing therapeutic index. These discussed identification markers include cell surface proteins, different activated signalling pathways, several molecules of the stem cell niche, various drug resistance mechanisms (ABCG2 and ALDH), telomerase, oncogenes and oncosuppressors (p16INK4 - Rb) and lastly, various microRNAs. In this new promising area of cancer research, proteomics, in general, and oncoproteomics, in particular, can and must play a significant role if the methodological approaches and the experimental protocols are correctly designed and interpreted.     

Investigation of an albumin‐enriched fraction of human serum and its albuminome

The removal of albumin and other high abundance proteins is a routine first step in the analysis of serum and plasma proteomes. However, as albumin can bind proteins and peptides, there is a universal concern as to how the serum proteome is changed by the removal of albumin. To address this concern, the current study was designed to identify proteins and peptides removed from the serum during albumin depletion; to determine which of these are bound to albumin (rather than copurified) and whether the bound proteins are intact proteins or peptide fragments. Sequential, independent analyses including both anti‐albumin antibody (anti‐HSA) affinity chromatography and SEC were used to isolate albumin‐bound proteins. RP‐HPLC and 1‐D SDS‐PAGE were then used to further separate the proteins prior to identification by MS/MS. Finally, whole protein molecular weight (MW) MS measurements coupled with protein coverage obtained by MS were combined to assess whether the bound proteins were intact or peptide fragments. Combining the results from multiple approaches, 35 proteins, of which 24 are intact, were found to be associated with albumin, and they include both known high and low abundance proteins.