Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class I neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment

Abstract

Background: Patient derived organoids (PDOs) can be established from colorectal cancers (CRCs) 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 (IFNγ) or MEK-inhibitor treatment.

Methods: Four microsatellite stable 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 analyzed by MS.

Results: We identified an average of 9936 unique peptides per PDO which compares favorably 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. In contrast, computational HLA binding prediction estimated that 304 mutations could generate neoantigens. One hundred ninety-six of these were located in expressed genes, still exceeding the number of MS-detected neoantigens 65-fold. Treatment of four PDOs with IFNγ upregulated HLA class I expression and qualitatively changed the immunopeptidome, with increased presentation of IFNγ-inducible genes. HLA class II presented peptides increased dramatically with IFNγ treatment. MEK-inhibitor treatment showed no consistent effect on HLA class I or II 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-silent 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.

Keywords: Antigen presentation; Colorectal cancer; Human leukocyte antigen; Immunogenicity; Immunotherapy; Mass spectrometry; Neoantigens; Patient derived organoids.

Fig. 1

HLA-I immunopeptidome in five PDOs. a Number of unique peptides detected per PDO. b Number of source proteins to which peptides from A uniquely mapped. c Correlation of HLA-I molecules per cell (assessed by flow cytometry) against the number of unique peptides for all five PDOs. The Pearson correlation coefficient is shown. d RNA expression of genes involved in antigen processing and presentation on HLA-I. e DNA copy number data generated from exome sequencing of PDO CRC-05. f Venn diagrams showing the concordance and discordance of all peptides between pairs of PDOs which share the indicated HLA-I allele. Venn diagrams were re-scaled so the area represents the peptide numbers in each segment. g Comparison of the normalized peptide intensity of PDOs that share HLA-I alleles. h Violin plot of percentile ranks predicted by NetMHCpan4.0 for all MS identified peptides from panel A to the autologous HLA molecules per PDO. Dashed lines show the median for each PDO (red) and the overall median (black). i Number of MS detected peptides expressed at or below the indicated RNA expression value

Fig. 2

HLA-II immunopeptidome in five PDOs. a Number of unique peptides detected per PDO. b Number of source proteins to which peptides from A uniquely mapped. c RNA expression of genes involved in antigen processing and presentation on HLA-II

Fig. 3

MS-detected and predicted neoantigens in five PDOs. a log2 gene expression of all genes harboring a mutation that encodes for an amino acid alteration. The three genes from which neoantigens were identified by MS are highlighted in red. b Number of mutations that encode for amino acid changes (missense, frame-shift and stop-loss mutations), genes predicted to generate strong binders predicted by NetMHCpan4.0 (defined as percentile rank below 0.5%), and strong binder-generating genes that are expressed, compared to MS-detected neoantigens. c HLA percentile rank from NetMHCpan4.0 for all predicted strong and weak HLA-binding neoantigen peptides in the two PDOs harboring MS-detected neoantigens. Predicted neoantigens were ordered from lowest to highest rank, with the predicted ranks of MS-detected neoantigens highlighted in red

Fig. 4

Changes of the immunopeptidome through IFNγ treatment (600 ng/ml for 48 h) in four PDOs. a Flow cytometric quantification of HLA-I molecules per cell with and without IFNγ treatment. b Number of unique peptides detected per PDO with and without IFNγ treatment. c Change in peptide diversity and HLA-I abundance with and without IFNγ treatment. d Venn diagram comparing the specific peptides detected in untreated and IFNγ-treated PDOs. Venn diagrams were re-scaled so the area represents the peptide numbers in each segment. e Volcano plots showing the fold change of normalized peptide abundance with IFNγ treatment. Known IFNγ-inducible genes which show a statistically significant (q < 0.05) fold change above +/− 2 are drawn in red. f MS intensities of neoantigens between untreated and IFNγ-treated conditions. g Number of unique peptides detected by MS on HLA-II molecules with and without IFNγ treatment. h Flow cytometric quantification of HLA-II molecules per cell with and without IFNγ treatment

Fig. 5

Changes of the immunopeptidome through trametinib treatment (30 nM for 48 h) in four PDOs. a Western blot showing inhibition of ERK phosphorylation (pERK) through trametinib. b Number of HLA-I molecules per cell with and without trametinib treatment. c Number of unique peptides presented on HLA-I with and without trametinib treatment. d Change in peptide diversity and HLA-I abundance with and without trametinib treatment. e Volcano plots showing the fold change of normalized peptide abundance with trametinib treatment. The dashed red lines indicate a q-value of 0.05 and vertical dashed lines fold changes exceeding +/− 2. f Number of unique peptides detected by MS on HLA-II molecules with and without trametinib treatment