The immunoinformatics of cancer immunotherapy

Structural immunoinformatics approach for rational design of a multi-epitope vaccine against triple negative breast cancer

TNBC is a highly malignant breast cancer known for its aggressive behavior affecting young female adults. The standard treatment for TNBC includes surgery, chemotherapy, and radiotherapy, which often have significant side effects. Therefore, novel preventive methods are required to combat TNBC effectively. In this study, we utilized immunoinformatics to construct an in-silico vaccine against TNBC using the TRIM25 molecule via the reverse vaccinology method. Four vaccines were designed by generating T and B-cell epitopes linked with four different linkers. The modeled vaccine was docked and the results showed that vaccine-3 exhibited the highest affinity with the immune receptors. The molecular dynamics results revealed that the binding affinity and stability of Vaccine-3 were greater than those of Vaccine 2 complexes. This study has great potential preventive measures for TNBC, and further research is warranted to evaluate its efficacy in preclinical settings. This study presents an innovative preventive strategy for triple-negative breast cancer (TNBC) through immunoinformatics and reverse vaccinology to develop an in-silico vaccine. Leveraging these innovative techniques offers a novel avenue for combating the complex challenges associated with TNBC. This approach demonstrates considerable potential as a significant breakthrough in preventive measures for this particularly aggressive and malignant form of breast cancer.

Epitope-specific anti-PrP antibody toxicity: a comparative in-silico study of human and mouse prion proteins

Despite having therapeutic potential, anti-PrP antibodies caused a major controversy due to their neurotoxic effects. For instance, treating mice with ICSM antibodies delayed prion disease onset, but both were found to be either toxic or innocuous to neurons by researchers following cross-linking PrP^C. In order to elucidate and understand the reasons that led to these contradictory outcomes, we conducted a comprehensive in silico study to assess the antibody-specific toxicity. Since most therapeutic anti-PrP antibodies were generated against human truncated recombinant PrP^91-231 or full-length mouse PrP^23-231, we reasoned that host specificity (human vs murine) of PrP^C might influence the nature of the specific epitopes recognized by these antibodies at the structural level possibly explaining the 'toxicity' discrepancies reported previously. Initially, molecular dynamics simulation and pro-motif analysis of full-length human (hu)PrP and mouse (mo)PrP 3D structure displayed conspicuous structural differences between huPrP and moPrP. We identified 10 huPrP and 6 moPrP linear B-cell epitopes from the prion protein 3D structure where 5 out of 10 huPrP and 3 out of 6 moPrP B-cell epitopes were predicted to be potentially toxic in immunoinformatics approaches. Herein, we demonstrate that some of the predicted potentially 'toxic' epitopes identified by the in silico analysis were similar to the epitopes recognized by the toxic antibodies such as ICSM18 (146-159), POM1 (138-147), D18 (133-157), ICSM35 (91-110), D13 (95-103) and POM3 (95-100). This in silico study reveals the role of host specificity of PrP^C in epitope-specific anti-PrP antibody toxicity.

Associations of cigarette smoking and alcohol drinking with stomach cancer survival: A prospective patient cohort study in Japan

Cigarette smoking and alcohol drinking may affect the prognosis of stomach cancer, but evidence has been inconsistent. We investigated the associations between pretreatment smoking and alcohol drinking and the risk of all-cause and stomach cancer death among 1,576 patients with histologically confirmed stomach cancer diagnosed during 1997-2010 at a single hospital in Japan. Histories of smoking and alcohol drinking were assessed using a self-administered questionnaire. The patients were followed until December 31, 2013. The Cox proportional hazards model was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). During 9,625.1 person-years, 670 all-cause and 419 stomach cancer deaths were documented. Among the patients overall, ever-drinking was significantly associated with an increased risk of all-cause death (HR: 1.25; 95% CI: 1.03-1.51), but not stomach cancer death. Positive linear associations with the frequency of drinking (p_trend  = 0.02) and the amount of alcohol consumed per day (p_trend  = 0.03) were observed for the risk of all-cause death. Ever-smoking was not related to either the risk of all-cause or stomach cancer death. Conversely, among the patients who underwent curative resection, a significant positive association was found between ever-smoking and the risk of stomach cancer death (HR: 2.44; 95% CI: 1.17-5.08). A positive association was also found for earlier age at start of smoking (p_trend  = 0.0046). Pretreatment smoking and alcohol drinking have significant effects on stomach cancer survival. Lifestyle adjustments throughout life may improve survival.

Associations between the cytotoxic T lymphocyte antigen 4 polymorphisms and risk of bone sarcomas

Cytotoxic T lymphocyte antigen 4 (CTLA-4) genetic polymorphisms are implicated to be associated with susceptibility to bone sarcomas, but published studies have reported inconclusive results. The objective of our study was to conduct a meta-analysis investigating the associations between CTLA-4 gene polymorphisms and risk of bone sarcomas. PubMed and Embase databases were searched for all articles published up to June 2, 2013. Odds ratio (OR) with a 95 % confidence interval (95 % CI) was used to assess the association. Finally, 11 individual studies with a total of 2,951 cases with bone sarcomas and 3,396 controls were included in the meta-analysis. There were four studies on the CTLA-4 49G/A polymorphism, three studies on CTLA-4 318C/T polymorphism, two studies on CTLA-4 1661A/G polymorphism, and two studies on CTLA-4 60A/G polymorphism. Overall, CTLA-4 49G/A polymorphism was obviously associated with risk of bone sarcomas (A vs. G: OR = 1.36, 95 % CI = 1.20-1.54; AA vs. GG: OR = 2.24, 95 % CI = 1.67-2.99; AA vs.

AG/GG

OR = 2.00, 95 % CI = 1.53-2.62; AA/GA vs. GG: OR = 1.35, 95 % CI = 1.14-1.61). However, CTLA-4 318C/T, 1661A/G, and 60A/G polymorphisms were not associated with risk of bone sarcomas. The current meta-analysis suggests that CTLA-4 49G/A polymorphism is obviously associated with risk of bone sarcomas. More studies are needed to further evaluate the associations between CTLA-4 polymorphisms and risk of bone sarcomas.

Immunoinformatics: a brief review

A large volume of data relevant to immunology research has accumulated due to sequencing of genomes of the human and other model organisms. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records. This accumulation of the information is like a goldmine for researchers looking for mechanisms of immune function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics. Immunoinformatics, otherwise known as computational immunology, is the interface between computer science and experimental immunology. It represents the use of computational methods and resources for the understanding of immunological information. It not only helps in dealing with huge amount of data but also plays a great role in defining new hypotheses related to immune responses. This chapter reviews classical immunology, different databases, and prediction tool. Further, it briefly describes applications of immunoinformatics in reverse vaccinology, immune system modeling, and cancer diagnosis and therapy. It also explores the idea of integrating immunoinformatics with systems biology for the development of personalized medicine. All these efforts save time and cost to a great extent.

Orchestration of CD4 T cell epitope preferences after multipeptide immunization

A detailed understanding of the molecular and cellular mechanisms that underlie epitope preferences in T cell priming is important for vaccines designed to elicit a broad T cell response. Protein vaccinations generally elicit CD4 T cell responses that are skewed toward a small fraction of epitopes, a phenomenon known as immunodominance. This characteristic of T cell responses, which limits the diversity of CD4 T cell recognition, is generally attributed to intracellular Ag processing. However, we recently discovered that immunodominance hierarchies persist even after vaccination with synthetic peptides. In this study, we probed the regulatory mechanisms that cause diminished CD4 T cell responses to subdominant peptides after such multipeptide immunization in mice. We have found that the delivery of subdominant and dominant epitopes on separate dendritic cells rescues expansion of less favored CD4 T cells. Furthermore, through the use of genetic models and inhibitors, we have found that selective losses in CD4 T cell responses are mediated by an IFN-γ-induced pathway, involving IDO, and that regulatory T cell activities may also regulate preferences in CD4 T cell specificity. We propose that after multipeptide immunization, the expansion and differentiation of dominant T cells initiate complex regulatory events that determine the final peptide specificity of the elicited CD4 T cell response.

Cancer vaccines: state of the art of the computational modeling approaches

Cancer vaccines are a real application of the extensive knowledge of immunology to the field of oncology. Tumors are dynamic complex systems in which several entities, events, and conditions interact among them resulting in growth, invasion, and metastases. The immune system includes many cells and molecules that cooperatively act to protect the host organism from foreign agents. Interactions between the immune system and the tumor mass include a huge number of biological factors. Testing of some cancer vaccine features, such as the best conditions for vaccine administration or the identification of candidate antigenic stimuli, can be very difficult or even impossible only through experiments with biological models simply because a high number of variables need to be considered at the same time. This is where computational models, and, to this extent, immunoinformatics, can prove handy as they have shown to be able to reproduce enough biological complexity to be of use in suggesting new experiments. Indeed, computational models can be used in addition to biological models. We now experience that biologists and medical doctors are progressively convinced that modeling can be of great help in understanding experimental results and planning new experiments. This will boost this research in the future.

FRED--a framework for T-cell epitope detection

Over the last decade, immunoinformatics has made significant progress. Computational approaches, in particular the prediction of T-cell epitopes using machine learning methods, are at the core of modern vaccine design. Large-scale analyses and the integration or comparison of different methods become increasingly important. We have developed FRED, an extendable, open source software framework for key tasks in immunoinformatics. In this, its first version, FRED offers easily accessible prediction methods for MHC binding and antigen processing as well as general infrastructure for the handling of antigen sequence data and epitopes. FRED is implemented in Python in a modular way and allows the integration of external methods.

AVAILABILITY

FRED is freely available for download at http://www-bs.informatik.uni-tuebingen.de/Software/FRED.

EpiToolKit--a web server for computational immunomics

Predicting the T-cell-mediated immune response is an important task in vaccine design and thus one of the key problems in computational immunomics. Various methods have been developed during the last decade and are available online. We present EpiToolKit, a web server that has been specifically designed to offer a problem-solving environment for computational immunomics. EpiToolKit offers a variety of different prediction methods for major histocompatibility complex class I and II ligands as well as minor histocompatibility antigens. These predictions are embedded in a user-friendly interface allowing refining, editing and constraining the searches conveniently. We illustrate the value of the approach with a set of novel tumor-associated peptides. EpiToolKit is available online at www.epitoolkit.org.