Gene and protein sequence features augment HLA class I ligand predictions

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract The sensitivity of malignant tissues to T cell-based cancer immunotherapies is dependent on the presence of targetable HLA class I ligands on the tumor cell surface. Peptide intrinsic factors, such as HLA class I affinity, likelihood of proteasomal processing, and transport into the ER lumen have all been established as determinants of HLA ligand presentation. However, the role of sequence features at the gene and protein level as determinants of epitope presentation has not been systematically evaluated. To address this, we performed HLA ligandome mass spectrometry on patient-derived melanoma lines and used this data-set to evaluate the contribution of 7,124 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turn-over, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of gene and protein sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as predictive models that include experimental measures of gene expression. Our study highlights the value of gene and protein features to HLA ligand predictions.

Authors 9

1. Kaspar Bresser Aachen Department of Hematology, Leiden University Medical Center

   Leiden University Medical Center · The Netherlands Cancer Institute · Oncode Institute

   Affiliation as printed

   Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands

   Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Oncode Institute, Amsterdam, The Netherlands

2. Benoît P. Nicolet

   Sanquin · Amsterdam University Medical Centers · University of Amsterdam

   Affiliation as printed

   Department of Hematopoiesis, Sanquin Research, Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, Oncode Institute, Amsterdam, The Netherlands

3. Anita Jekő

   Utrecht University

   Affiliation as printed

   Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands

4. Wei Wu

   Utrecht University

   Affiliation as printed

   Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands

5. Fabricio Loayza‐Puch

   German Cancer Research Center

   Affiliation as printed

   Translational Control and Metabolism, German Cancer Research Center (DKFZ), Heidelberg, Germany

6. Reuven Agami

   The Netherlands Cancer Institute · Oncode Institute

   Affiliation as printed

   Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute

7. Albert J. R. Heck

   Utrecht University

   Affiliation as printed

   Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands

8. Monika C. Wolkers

   Sanquin · Amsterdam University Medical Centers · University of Amsterdam

   Affiliation as printed

   Department of Hematopoiesis, Sanquin Research, Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, Oncode Institute, Amsterdam, The Netherlands

9. Ton N. Schumacher corresponding Aachen Department of Hematology, Leiden University Medical Center

   Leiden University Medical Center · The Netherlands Cancer Institute · Oncode Institute

   Affiliation as printed

   Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands

   Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Oncode Institute, Amsterdam, The Netherlands