NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors.
DeVette, C. I., Andreatta, M., Bardet, W., Cate, S. J., Jurtz, V. I., Jackson, K. W., Welm, A. L., Nielsen, M. and Hildebrand, W. H.
University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma.
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, Buenos Aires, Argentina.
Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.
University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma.
With the advancement of personalized cancer immunotherapies, new tools are needed to identify tumor antigens and evaluate T-cell responses in model systems, specifically those that exhibit clinically relevant tumor progression. Key transgenic mouse models of breast cancer are generated and maintained on the FVB genetic background, and one such model is the mouse mammary tumor virus-polyomavirus middle T antigen (MMTV-PyMT) mouse-an immunocompetent transgenic mouse that exhibits spontaneous mammary tumor development and metastasis with high penetrance. Backcrossing the MMTV-PyMT mouse from the FVB strain onto a C57BL/6 genetic background, in order to leverage well-developed C57BL/6 immunologic tools, results in delayed tumor development and variable metastatic phenotypes. Therefore, we initiated characterization of the FVB MHC class I H-2(q) haplotype to establish useful immunologic tools for evaluating antigen specificity in the murine FVB strain. Our study provides the first detailed molecular and immunoproteomic characterization of the FVB H-2(q) MHC class I alleles, including >8,500 unique peptide ligands, a multiallele murine MHC peptide prediction tool, and in vivo validation of these data using MMTV-PyMT primary tumors. This work allows researchers to rapidly predict H-2 peptide ligands for immune testing, including, but not limited to, the MMTV-PyMT model for metastatic breast cancer. Cancer Immunol Res; 6(6); 636-44. (c)2018 AACR.
Cancer Immunol Res 6(6): 636-644 (2018)