Dataset Information

Deep motif deconvolution of HLA-II peptidomes

ABSTRACT: CD4 T cells are key for priming and regulating immune recognition of infected and cancer cells, but predictions of class II epitopes have limited accuracy. We profiled over 100’000 HLA-II ligands by Mass Spectrometry and developed a novel motif deconvolution algorithm to analyze these data. Our work demonstrates substantial improvements in the definition of HLA-II binding motifs and enhanced accuracy in HLA-II ligand and class II epitope predictions.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Michal Bassani-Sternberg

LAB HEAD: Michal Bassani-Sternberg

PROVIDER: PXD012308 | Pride | 2019-10-16

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20140927_QEp3_MiBa_SA_JY_HLA-DR-HB298-1-1.raw	Raw
	20140927_QEp3_MiBa_SA_JY_HLA-DR-HB298-1.raw	Raw
	20140927_QEp3_MiBa_SA_JY_HLA-II-HB145_1.raw	Raw
	20140927_QEp3_MiBa_SA_JY_HLA-II-HB145_2.raw	Raw
	20160520_GD149_145_1_R1.raw	Raw

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Publications

Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.

Racle Julien J Michaux Justine J Rockinger Georg Alexander GA Arnaud Marion M Bobisse Sara S Chong Chloe C Guillaume Philippe P Coukos George G Harari Alexandre A Jandus Camilla C Bassani-Sternberg Michal M Gfeller David D

Nature biotechnology 20191014 11

Predictions of epitopes presented by class II human leukocyte antigen molecules (HLA-II) have limited accuracy, restricting vaccine and therapy design. Here we combined unbiased mass spectrometry with a motif deconvolution algorithm to profile and analyze a total of 99,265 unique peptides eluted from HLA-II molecules. We then trained an epitope prediction algorithm with these data and improved prediction of pathogen and tumor-associated class II neoepitopes. ...[more]

PMID: 31611696

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Project description:Background: Patient derived organoids (PDOs) can be established from colorectal cancers as in vitro models to interrogate cancer biology and its clinical relevance. We applied mass spectrometry (MS) immunopeptidomics to investigate neoantigen presentation and whether this can be augmented through interferon gamma (IFN) or MEK-inhibitors. Methods: Four PDOs from chemotherapy refractory and one from a treatment naïve CRC were expanded to replicates with 100 million cells each, and HLA class I and class II peptide ligands were analysed by MS. Results: We identified an average of 9,936 unique peptides per PDO which compares favourably against published immunopeptidomics studies, suggesting high sensitivity. Loss of heterozygosity of the HLA locus was associated with low peptide diversity in one PDO. Peptides from genes without detectable expression by RNA-sequencing were rarely identified by MS. Only 3 out of 612 non-silent mutations encoded for neoantigens that were detected by MS. Treatment of four PDOs with IFN upregulated HLA class I expression and qualitatively changed the immunopeptidome, with increased presentation of IFN-inducible genes. HLA class II presented peptides increased on average 16-fold with IFN treatment. MEK-inhibitor treatment showed no consistent effect on class I or II HLA expression or the peptidome. Importantly, no additional HLA class I or II presented neoantigens became detectable with any treatment. Conclusions: Only 3 out of 612 non-synonymous mutations encoded for neoantigens that were detectable by MS. Although MS has sensitivity limits and biases, and likely underestimated the true neoantigen burden, this established a lower bound of the percentage of non-silent mutations that encode for presented neoantigens, which may be as low as 0.5%. This could be a reason for the poor responses of non-hypermutated CRCs to immune checkpoint inhibitors. MEK-inhibitors recently failed to improve checkpoint-inhibitor efficacy in CRC and the observed lack of HLA upregulation or improved peptide presentation may explain this.