Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

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 by combining proteogenomics with phenotypic and functional analyses. By using an optimized computational approach, we discovered a large number of novel tumor-specific and tumor-associated antigens including shared common target candidates. To create a pipeline for the identification of neoantigens in our cohort, we combined deep DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity. In fact, we could detect a broad variety of non-wild type HLA-binding peptides in the majority of patients and confirmed the immunogenicity of 24 neoantigens. Most interestingly, the majority of total and immunogenic neoantigens originated from variants identified in the RNA dataset, illustrating the importance of RNA as a still understudied source of cancer antigens. Moreover, the amount of these mainly RNA-based immunogenic neoantigens correlated positively with overall CD8+ tumor-infiltrating T cells. This study therefore underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

Improvement in Neoantigen Prediction via Integration of RNA Sequencing Data for Variant Calling

“…Our analysis of tumor variants using DNAseq and RNAseq data obtained from 25 CRC patients identified a moderate proportion (22.4%) of shared somatic variants (Figure 2A). This finding is consistent with a previous study that reported a similar trend in two datasets (58).…”

Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling

“…Our analysis of tumor variants using DNAseq and RNAseq data obtained from 25 CRC patients identified a moderate proportion (22.4%) of shared somatic variants ( Figure 2A ). This finding is consistent with a previous study that reported a similar trend in two datasets ( 59 ). The differences in variants identified by DNAseq and RNAseq could be attributed to variations in sequencing technologies or variant calling tools, as reported in previous studies ( 60 ).…”

Discovery of Species-Specific Proteotypic Peptides To Establish a Spectral Library Platform for Identification of Nontuberculosis Mycobacteria from Mass Spectrometry-Based Proteomics

“…This rescoring approach has been already found useful in large-scale proteomic experiments as well as in confirming neoantigen (genometranslated or variant database) identifications. 42,55,56 Finally, confidence levels for SSPPs were established by assessing their species-level specificity within UniProt and RefSeq proteomes. Later, these peptides were categorized into high, medium, low, and poor confidence based on stringent SA and Abs.…”

“…Later, the experimental and predicted peptide spectra for each peptide precursor of a species were subjected to pairwise spectral comparison for calculation of normalized spectral contrast angle (SA). 40,42 Orbitrap (FTMS) and Ion Trap (ITMS) derived fragment spectra (MS/MS) were matched to HCD and collision-induced dissociation (CID) model derived predicted spectra with 20 ppm and 0.5 Da match tolerance using in-house Python script (https://github.com/chinmayaNK22/ SpecAngle), respectively. The retention time difference between the experimental and predicted spectra of each peptide precursor was calculated as a further substantiation factor.…”

“…The retention time difference between the experimental and predicted spectra of each peptide precursor was calculated as a further substantiation factor. This approach was employed as described in Tretter et al 42 In brief, the Prosit predicted indexed retention time (iRT) of each peptide precursor was aligned with its corresponding experimental retention time (RT) based on LOESS-regression. This was done on a set of peptide precursors identified within each raw file.…”