The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I-Associated Peptides.

Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I-associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.

DIA-MS proteome analysis of formalin-fixed paraffin-embedded glioblastoma tissues.

Developments in quantitative proteomics and data-independent acquisition (DIA) methodology is enabling quantification of proteins in biological samples. Currently, there are a few reports on DIA mass spectrometry (MS) approaches for proteome analysis of formalin-fixed paraffin-embedded (FFPE) tissues. Therefore, to facilitate detection and quantification of immune- and glioblastoma (GBM)-relevant proteins from FFPE patient materials, we established a simple and precise DIA-MS workflow. We first evaluated different lysis buffers for their efficiency in protein extractions from FFPE GBM tissues. Our results showed that more than 1700 proteins were detected and over 1400 proteins were quantified from GBM FFPE tissue microdissections. GBM-relevant proteins (e.g., GFAP, FN1, VIM, and MBP) were quantified with high precision (median coefficient of variation <12%). In addition, immune-related proteins (e.g., ILF2, MIF, and CD38) were consistently detected and quantified. The strategy holds great potential for routinizing protein quantification in FFPE tissue samples.

MicroPOTS Analysis of Barrett's Esophageal Cell Line Models Identifies Proteomic Changes after Physiologic and Radiation Stress.

Moving from macroscale preparative systems in proteomics to micro- and nanotechnologies offers researchers the ability to deeply profile smaller numbers of cells that are more likely to be encountered in clinical settings. Herein a recently developed microscale proteomic method, microdroplet processing in one pot for trace samples (microPOTS), was employed to identify proteomic changes in ∼200 Barrett's esophageal cells following physiologic and radiation stress exposure. From this small population of cells, microPOTS confidently identified >1500 protein groups, and achieved a high reproducibility with a Pearson's correlation coefficient value of R > 0.9 and over 50% protein overlap from replicates. A Barrett's cell line model treated with either lithocholic acid (LCA) or X-ray had 21 (e.g., ASNS, RALY, FAM120A, UBE2M, IDH1, ESD) and 32 (e.g., GLUL, CALU, SH3BGRL3, S100A9, FKBP3, AGR2) overexpressed proteins, respectively, compared to the untreated set. These results demonstrate the ability of microPOTS to routinely identify and quantify differentially expressed proteins from limited numbers of cells.