The immunoinformatics of cancer immunotherapy

DeLuca, David S.; Blasczyk, Rainer

doi:10.1111/j.1399-0039.2007.00914.x

Cited by 19 publications

(8 citation statements)

References 75 publications

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“…Recently, there has been tremendous progress in both epitope discovery and the development of predictive algorithms to identify antigenic peptides that can participate in protective T cell responses toward both pathogenic organisms and neoplastic tissue (reviewed in (1–6)). With the steadily increasing number of known pathogen and cancer-derived epitopes comes great potential for the use of synthetic peptides for vaccination and immunotherapy.…”

Section: Introductionmentioning

confidence: 99%

Orchestration of CD4 T Cell Epitope Preferences after Multipeptide Immunization

Tung

Sant

2013

The Journal of Immunology

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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, that limits the diversity of CD4 T cell recognition, is generally attributed to intracellular antigen 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 multi-peptide 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 indoleamine 2,3-dioxygenase (IDO), and that regulatory T cell (Treg) activities may also regulate preferences in CD4 T cell specificity. We propose that after multi-peptide 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.

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Section: Introductionmentioning

confidence: 99%

Orchestration of CD4 T Cell Epitope Preferences after Multipeptide Immunization

Tung

Sant

2013

The Journal of Immunology

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“…These technologies allow construction of DNA vaccines encoding selected tumor antigens together with molecules to direct and amplify the desired effector pathways, as well as highly targeted vaccines aimed at specific epitopes. Reliable predictions of immunogenic T cell epitope peptides are crucial for rational vaccine design and represent a key problem in immunoinformatics [23, 24]. …”

Section: Epitope Focused Immunoinformaticsmentioning

confidence: 99%

Cancer Vaccines: State of the Art of the Computational Modeling Approaches

Pappalardo

Chiacchio

Motta

2013

BioMed Research International

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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.

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“…This is closely examined in a plethora of recent reviews. [12][13][14][15][16][17] Immunoinformatics is the application of standard bioinformatic techniques, such as comparative genomics, to immunology. Yet there are also bioinformatics problems exclusive to immunology, principally immunogenicity prediction.…”

Section: Predicting Immmunogenicity For Biopharmaceuticalsmentioning

confidence: 99%

Advances in Predicting and Manipulating the Immunogenicity of Biotherapeutics and Vaccines

Flower

2009

BioDrugs

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Therapeutic proteins are vital to the future of human health provision and the survival and profitability of the global pharmaceutical industry. Returns from protein therapeutics are experiencing unprecedented growth: both their number and their economic dividend have increased by an order of magnitude in the last 10 years. The potential immunogenicity of protein therapeutics raises many clinical and safety concerns. Many poorly understood factors relating to both product and host affect immune responses. Available laboratory measurement of immunogenicity is of little utility for predicting the clinical properties of biotherapeutics. Coupled with assay variability and standardization issues, this precludes adequate prediction of the biological or clinical responses of therapeutic proteins, arguing for the utilization of informatic strategies in the analysis and prediction of protein immunogenicity. Currently, many unresolved issues must be addressed and thus circumvented before effective prediction can become routine.

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The immunoinformatics of cancer immunotherapy

Cited by 19 publications

References 75 publications

Orchestration of CD4 T Cell Epitope Preferences after Multipeptide Immunization

Orchestration of CD4 T Cell Epitope Preferences after Multipeptide Immunization

Cancer Vaccines: State of the Art of the Computational Modeling Approaches

Advances in Predicting and Manipulating the Immunogenicity of Biotherapeutics and Vaccines

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