A pipeline for identification and validation of tumor-specific antigens in a mouse model of metastatic breast cancer

Abstract

Cancer immunotherapy continues to make headway as a treatment for advanced stage tumors, revealing an urgent need to understand the fundamentals of anti-tumor immune responses. Noteworthy is a scarcity of data pertaining to the breadth and specificity of tumor-specific T cell responses in metastatic breast cancer. Autochthonous transgenic models of breast cancer display spontaneous metastasis in the FVB/NJ mouse strain, yet a lack of knowledge regarding tumor-bound MHC/peptide immune epitopes in this mouse model limits the characterization of tumor-specific T cell responses, and the mechanisms that regulate T cell responses in the metastatic setting. We recently generated the NetH2pan prediction tool for murine class I MHC ligands by building an FVB/NJ H-2q ligand database and combining it with public information from six other murine MHC alleles. Here, we deployed NetH2pan in combination with an advanced proteomics workflow to identify immunogenic T cell epitopes in the MMTV-PyMT transgenic model for metastatic breast cancer. Five unique MHC I/PyMT epitopes were identified. These tumor-specific epitopes were confirmed to be presented by the class I MHC of primary MMTV-PyMT tumors and their T cell immunogenicity was validated. Vaccination using a DNA construct encoding a truncated PyMT protein generated CD8 + T cell responses to these MHC class I/peptide complexes and prevented tumor development. In sum, we have established an MHC-ligand discovery pipeline in FVB/NJ mice, identified and tracked H-2Dq/PyMT neoantigen-specific T cells, and developed a vaccine that prevents tumor development in this metastatic model of breast cancer.

Keywords

• tumor immunology
• breast cancer
• mmtv-pymt
• mhc class i
• peptide prediction
• neth2pan