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A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines.

ABSTRACT: Epitope-based vaccines (EVs) have a wide range of applications: from therapeutic to prophylactic approaches, from infectious diseases to cancer. The development of an EV is based on the knowledge of target-specific antigens from which immunogenic peptides, so-called epitopes, are derived. Such epitopes form the key components of the EV. Due to regulatory, economic, and practical concerns the number of epitopes that can be included in an EV is limited. Furthermore, as the major histocompatibility complex (MHC) binding these epitopes is highly polymorphic, every patient possesses a set of MHC class I and class II molecules of differing specificities. A peptide combination effective for one person can thus be completely ineffective for another. This renders the optimal selection of these epitopes an important and interesting optimization problem. In this work we present a mathematical framework based on integer linear programming (ILP) that allows the formulation of various flavors of the vaccine design problem and the efficient identification of optimal sets of epitopes. Out of a user-defined set of predicted or experimentally determined epitopes, the framework selects the set with the maximum likelihood of eliciting a broad and potent immune response. Our ILP approach allows an elegant and flexible formulation of numerous variants of the EV design problem. In order to demonstrate this, we show how common immunological requirements for a good EV (e.g., coverage of epitopes from each antigen, coverage of all MHC alleles in a set, or avoidance of epitopes with high mutation rates) can be translated into constraints or modifications of the objective function within the ILP framework. An implementation of the algorithm outperforms a simple greedy strategy as well as a previously suggested evolutionary algorithm and has runtimes on the order of seconds for typical problem sizes.

SUBMITTER: Toussaint NC

PROVIDER: S-EPMC2588662 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines.

Toussaint Nora C NC Dönnes Pierre P Kohlbacher Oliver O

PLoS computational biology 20081226 12

Epitope-based vaccines (EVs) have a wide range of applications: from therapeutic to prophylactic approaches, from infectious diseases to cancer. The development of an EV is based on the knowledge of target-specific antigens from which immunogenic peptides, so-called epitopes, are derived. Such epitopes form the key components of the EV. Due to regulatory, economic, and practical concerns the number of epitopes that can be included in an EV is limited. Furthermore, as the major histocompatibility ...[more]

PMID: 19112482

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Project description:BackgroundOverexpression of nonmutated proteins involved in oncogenesis is a mechanism by which such proteins become immunogenic. We questioned whether overexpressed colorectal cancer associated proteins found at higher incidence and associated with poor prognosis could be effective vaccine antigens. We explored whether vaccines targeting these proteins could inhibit the development of intestinal tumors in the azoxymethane (AOM)-induced colon model and APC Min mice.MethodsHumoral immunity was evaluated by ELISA. Web-based algorithms identified putative Class II binding epitopes of the antigens. Peptide and protein specific T-cells were identified from human peripheral blood mononuclear cells using IFN-gamma ELISPOT. Peptides highly homologous between mouse and man were formulated into vaccines and tested for immunogenicity in mice and in vivo tumor challenge. Mice treated with AOM and APC Min transgenic mice were vaccinated and monitored for tumors.ResultsSerum IgG for CDC25B, COX2, RCAS1, and FASCIN1 was significantly elevated in colorectal cancer patient sera compared to volunteers (CDC25B p=0.002, COX-2 p=0.001, FASCIN1 and RCAS1 p<0.0001). Epitopes predicted to bind to human class II MHC were identified for each protein and T-cells specific for both the peptides and corresponding recombinant protein were generated from human lymphocytes validating these proteins as human antigens. Some peptides were highly homologous between mouse and humans and after immunization, mice developed both peptide and protein specific IFN-γ-secreting cell responses to CDC25B, COX2 and RCAS1, but not FASCIN1. FVB/nJ mice immunized with CDC25B or COX2 peptides showed significant inhibition of growth of the syngeneic MC38 tumor compared to control (p<0.0001). RCAS1 peptide vaccination showed no anti-tumor effect. In the prophylactic setting, after immunization with CDC25B or COX2 peptides mice treated with AOM developed significantly fewer tumors as compared to controls (p<0.0002) with 50% of mice remaining tumor free in each antigen group. APC Min mice immunized with CDC25B or COX2 peptides developed fewer small bowel tumors as compared to controls (p=0.01 and p=0.02 respectively).ConclusionsImmunization with CDC25B and COX2 epitopes consistently suppressed tumor development in each model evaluated. These data lay the foundation for the development of multi-antigen vaccines for the treatment and prevention of colorectal cancer.

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A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines.

Publications

A mathematical framework for the selection of an optimal set of peptides for epitope-based vaccines.

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