Abstract
T cell epitopes bearing tumor-specific mutations discovered using next generation sequencing stimulate T cell-mediated processes that lead to tumor regression. Although neoantigen prediction using computational methods rapidly identifies epitope candidates in the mutanome, a large proportion prove to be non-immunogenic. Innovative computational tools validated for infectious disease can be applied to enhance design of personalized cancer immunotherapies by classification of predicted epitopes according to potential for mounting a tumor-specific response. We developed the JanusMatrix algorithm that parses query sequences into MHC-facing and T cell receptor (TCR)-facing sequences and screens sequence databases to identify MHC ligands that share TCR faces with host-related proteins. A database of human protein sequences is available to identify tumor-specific epitopes that may reduce anti-tumor activity by sequences that activate regulatory T cells (Tregs) trained in the thymus on self-antigens. Similarly, tumor-specific epitope candidates are screened using databases composed of human commensal- or pathogen-derived sequences to identify epitopes that, respectively, may detrimentally or beneficially cross-react with T cells raised over the course of an individual’s immune history. We conducted retrospective analyses of cancer vaccine efficacy studies performed in mice [Kreiter et al. 2015 Nature 520, 692-696] showing that mutanome-directed vaccines effective at preventing tumor growth contained higher numbers of class I and II MHC neo-epitopes and had lower potential to cross-react with other murine proteins, as identified by the MiVax platform. Likewise, an evaluation of mutanomes derived from non-small cell lung cancer patients [Rizvi et al. 2014 Science 348, 124-128] revealed that improved clinical outcomes were observed in patients with mutanomes enriched in class I MHC neo-epitopes with TCR faces distinct from other self-epitopes. While retrospective in nature, the suite of tools used for these analyses have been extensively validated in prospective vaccine studies for infectious disease. Removal of Treg epitopes identified by JanusMatrix has led to the development of a novel H7N9 influenza vaccine scheduled for Phase I clinical trial. These results highlight the benefits of using in silico tools for the selection of high-value neoantigen candidates and how they can improve the design and efficacy of cancer vaccines. Neoantigens with low Treg activation potential may then be used to support development of personalized therapies including vaccination and in vitro expansion of tumor infiltrating lymphocytes for adoptive cell transfer.
Citation Format: Lenny Moise, Guilhem Richard, Frances Terry, William Martin, Ann De Groot. MiVax: an innovative cancer neoantigen prediction system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4540. doi:10.1158/1538-7445.AM2017-4540
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