Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development

Abstract

In prior studies, we delineated the landscape of neoantigens arising from nonsynonymous point mutations in a murine pancreatic cancer model, Panc02. We developed a peptide vaccine by targeting neoantigens predicted using a pipeline that incorporates the MHC binding algorithm NetMHC. The vaccine, when combined with immune checkpoint modulators, elicited a robust neoepitope-specific antitumor immune response and led to tumor clearance. However, only a small fraction of the predicted neoepitopes induced T cell immunity, similarly to that reported for neoantigen vaccines tested in clinical studies. While these studies have used binding affinities to MHC I as surrogates for T cell immunity, this approach does not include spatial information on the mutated residue that is crucial for TCR activation. Here, we investigate conformational alterations in and around the MHC binding groove induced by selected minimal neoepitopes, and we examine the influence of a given mutated residue as a function of its spatial position. We found that structural parameters, including the solvent-accessible surface area (SASA) of the neoepitope and the position and spatial configuration of the mutated residue within the sequence, can be used to improve the prediction of immunogenic neoepitopes for inclusion in cancer vaccines.

Keywords: Antigen; Immunology; Oncology.

Figure 1. Identification of immunogenic tumor neoantigens in a murine pancreatic cancer model.

(A) Pipeline for identifying nonsynonymous mutations in murine Panc02 cells by whole exome sequencing (WES); examining the transcriptome by RNA-seq; predicting binding affinity and minimal epitopes by NetMHC; generating synthetic long peptides (SLPs) for ELISPOT assays; and performing structural modeling on selected neoepitopes. For further details, please refer to ref. . (B) Inadequate correlation between predicted binding affinity (NetMHC 3.2, 3.4, and pan 2.8) of MEPs and their immunogenicity, as assessed by ELISPOT for IFN-γ–producing T cells in vitro (n = 3 mice per group) (for details on immunogenicity data, please refer to ref. 15). Note the clustering of high-affinity MEPs with poor immunogenicity (upper left quadrant).

Figure 2. Modeling neoepitopes in selected MHC I templates.

(A) Side (left panels) and top views (right panels) of modeled complexes between MEPs and appropriately selected MHC I template (also see Table 3, Table 4, and Table 5). MEP annotation and sequence are shown with the mutated residue in red. The ball represents the mutated residue and displays either an outward or inward orientation. This qualitative data are consistent with corresponding data from GETAREA that annotate the mutant residue and calculate the solvent-accessible surface area (SASA) (Table 3, Table 4, and Table 5). (B) Relationship between SASA (Ų) of the mutated residue of a given MEP and its immunogenicity, as assessed by ELISPOT for IFN-γ (for details on immunogenicity data, please refer to ref. 15).

Figure 3. β-Testing of MEP variants for SASA and immunogenicity.

The position of the Ser residue in MEP 77-5 was shifted by 1 amino acid in 2 variant models, MEP 77-5v1 (FNLSMGKL → LFNLSMGK) and MEP 77-5v2 (FNLSMGKL → SLFNLSMGK). (A) Side (left panels) and top views (right panels) of modeled complexes between MEPs and selected MHC I template. The ball represents the mutated residue. Calculated SASA and other GETAREA parameters are shown in Table 4. (B) Immunogenicity of variant MEPs by ELISPOT for IFN-γ–producing T cells. An H-2Kb–binding OVA (OVA-Kb) peptide is used as control (for details, see Methods) (n = 3 mice per group).