Aiming to improve the predictability of HLA peptide ligands, Bresser et al. began with immunopeptidomics data from three melanoma cell lines, and investigated ligand-extrinsic hard-coded features of the encoding mRNA, including sequence composition/codon usage, translation-impacting RNA modifications (e.g., methylation), and contextual protein degradation signals (ubiquitination). Inclusion of these features produced a series of predictors that outperformed conventional binding affinity, equivalent to inclusion of “wet lab” RNA expression and translation data. The results were validated with external datasets.
Contributed by Ed Fritsch
ABSTRACT: The sensitivity of malignant tissues to T cell-based immunotherapies depends on the presence of targetable human leukocyte antigen (HLA) class I ligands. Peptide-intrinsic factors, such as HLA class I affinity and proteasomal processing, have been established as determinants of HLA ligand presentation. However, the role of gene and protein sequence features as determinants of epitope presentation has not been systematically evaluated. We perform HLA ligandome mass spectrometry to evaluate the contribution of 7,135 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turnover, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of such "hard-coded" sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as experimental measures of gene expression. Our study highlights the value of gene and protein features for HLA ligand predictions.
Author Info: (1) Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Oncode Institute, Amsterdam, the Netherlands; Department of Hematology, Leiden University Medical
Author Info: (1) Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Oncode Institute, Amsterdam, the Netherlands; Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands. (2) Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab, Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam, The Netherlands; Oncode Institute, Utrecht, The Netherlands. (3) Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands. (4) Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands. (5) Translational Control and Metabolism, German Cancer Research Center (DKFZ), Heidelberg, Germany. (6) Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands. (7) Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands. (8) Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab, Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam, The Netherlands; Oncode Institute, Utrecht, The Netherlands. (9) Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Oncode Institute, Amsterdam, the Netherlands; Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: t.schumacher@nki.nl.
