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Supplemental tables and figures from NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors

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posted on 2023-04-03, 23:23 authored by Christa I. DeVette, Massimo Andreatta, Wilfried Bardet, Steven J. Cate, Vanessa I. Jurtz, Kenneth W. Jackson, Alana L. Welm, Morten Nielsen, William H. Hildebrand
<p>Supplemental Table S1: AUC and PPV for NetH2pan network ensemble Supplemental Table S2: AUC and PPV for prediction of ligands by NetH2pan, trained with or without sequence context of five residues flanking the peptide. Supplemental Table S3. Yield (# of peptides) from soluble MHC transfectants and MMTV-PyMT tumors. Supplemental Table S4. Cancer-associated peptides eluted from MMTV-PyMT tumors. Supplemental Figure S1. MHC Class I peptide tissue extraction approach. Supplemental Figure S2. Peptide overlap with soluble MHC peptide elution and primary tumor cell elution. Supplemental Figure S3. MMTV-PyMT tumors express Class I MHC.</p>

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Susan G. Komen

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ARTICLE ABSTRACT

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-2q 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-2q 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. ©2018 AACR.