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Identification of neoantigens for individualized therapeutic cancer vaccines.

ABSTRACT: Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.

SUBMITTER: Lang F

PROVIDER: S-EPMC7612664 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNeoantigens, new immunogenic sequences arising from tumor mutations, have been associated with response to immunotherapy and are considered potential targets for vaccination. Hepatocellular carcinoma (HCC) is a moderately mutated tumor, where the neoantigen repertoire has not been investigated. Our aim was to analyze whether tumors in HCC patients contain immunogenic neoantigens suitable for future use in therapeutic vaccination.MethodsWhole-exome sequencing and RNAseq were performed in a cohort of fourteen HCC patients submitted to surgery or liver transplant. To identify mutations, single-nucleotide variants (SNV) originating non-synonymous changes that were confirmed at the RNA level were analyzed. Immunogenicity of putative neoAgs predicted by HLA binding algorithms was confirmed by using in vitro HLA binding assays and T-cell stimulation experiments, the latter in vivo, by immunizing HLA-A*02.01/HLA-DRB1*01 (HHD-DR1) transgenic mice, and in in vitro, using human lymphocytes.ResultsSequencing led to the identification of a median of 1217 missense somatic SNV per patient, narrowed to 30 when filtering by using RNAseq data. A median of 13 and 5 peptides per patient were predicted as potential binders to HLA class I and class II molecules, respectively. Considering only HLA-A*02.01- and HLA-DRB1*01-predicted binders, 70% demonstrated HLA-binding capacity and about 50% were immunogenic when tested in HHD-DR1 mice. These peptides induced polyfunctional T cells that specifically recognized the mutated but not the wild-type sequence as well as neoantigen-expressing cells. Moreover, coimmunization experiments combining CD8 and CD4 neoantigen epitopes resulted in stronger CD8 T cell responses. Finally, responses against neoantigens were also induced in vitro using human cells.ConclusionThese results show that mutations in HCC tumors may generate immunogenic neoantigens with potential applicability for future combinatorial therapeutic strategies.

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Identification of neoantigens for individualized therapeutic cancer vaccines.

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