Dataset Information

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification

ABSTRACT: Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Celina Tretter

LAB HEAD: Angela Krackhardt

PROVIDER: PXD037655 | Pride | 2023-05-16

REPOSITORIES: Pride

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			Action	DRS
	20171007_QX0_MaPe_SA_P509_NEO_1_T1_1.raw	Raw
	20171007_QX0_MaPe_SA_P509_NEO_1_T1_2.raw	Raw
	20171007_QX0_MaPe_SA_P509_NEO_1_T1_3.raw	Raw
	20171007_QX0_MaPe_SA_P509_NEO_2_T1_1.raw	Raw
	20171007_QX0_MaPe_SA_P509_NEO_2_T1_2.raw	Raw

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Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification.

Tretter Celina C de Andrade Krätzig Niklas N Pecoraro Matteo M Lange Sebastian S Seifert Philipp P von Frankenberg Clara C Untch Johannes J Zuleger Gabriela G Wilhelm Mathias M Zolg Daniel P DP Dreyer Florian S FS Bräunlein Eva E Engleitner Thomas T Uhrig Sebastian S Boxberg Melanie M Steiger Katja K Slotta-Huspenina Julia J Ochsenreither Sebastian S von Bubnoff Nikolas N Bauer Sebastian S Boerries Melanie M Jost Philipp J PJ Schenck Kristina K Dresing Iska I Bassermann Florian F Friess Helmut H Reim Daniel D Grützmann Konrad K Pfütze Katrin K Klink Barbara B Schröck Evelin E Haller Bernhard B Kuster Bernhard B Mann Matthias M Weichert Wilko W Fröhling Stefan S Rad Roland R Hiltensperger Michael M Krackhardt Angela M AM

Nature communications 20230802 1

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequen ...[more]

PMID: 37532709

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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.