Proteogenomic analysis unveils the HLA Class I presented immunopeptidome in melanoma and EGFR mutant lung adenocarcinoma

Qi, Yue; Maity, Tapan K.; Cultraro, Constance M.; Misra, Vikram; Zhang, Xu; Ade, Catherine M.; Gao, Shaojian; Milewski, David; Nguyen, Khoa Dang P.; Ebrahimabadi, Mohammad Haghir; Hanada, Ken‐ichi; Khan, Javed; Sahinalp, S. Cenk; Yang, James Chih‐Hsin; Guha, Udayan

doi:10.1101/2020.08.04.236331

Cited by 3 publications

(3 citation statements)

References 78 publications

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“…For example, Catalogue of Somatic Mutations in Cancer (COSMIC), containing somatic mutations from variety of cancer types, has been widely used for generating customized reference and identifying cancer-specific mutations (Zhu et al, 2018). Qi and colleagues utilized LNCipedia to predict lncRNAs regions and discovered lncRNA-coded neoantigens in lung adenocarcinoma (Qi et al, 2021). A key consideration in developing a proteogenomic database search strategy is the determination of an appropriate false-discovery rate (FDR).…”

Section: Biomarker Discovery Workflow Using Proteogenomics 41 Genomic...mentioning

confidence: 99%

Variant biomarker discovery using mass spectrometry-based proteogenomics

et al. 2023

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Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants—including single nucleotide variants, frameshift variants, and mis-splicing isoforms—are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases.

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Section: Biomarker Discovery Workflow Using Proteogenomics 41 Genomic...mentioning

confidence: 99%

Variant biomarker discovery using mass spectrometry-based proteogenomics

et al. 2023

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“…This has led to a few promising applications using de novo peptide sequencing including complete de novo sequencing of antibody sequences and discovery of new the human leukocyte antigen (HLA) antigens. [10,87,90] However, there is still a huge gap between de novo peptide sequencing and database search based peptide identification methods in terms of accuracy of peptide identification, thus resulting in its limited applications in proteomics studies. Further improvement could be achieved from, but not limited to, the following aspects.…”

Section: Deep Learning For De Novo Peptide Sequencingmentioning

confidence: 99%

Deep Learning in Proteomics

et al. 2020

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Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.

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“…MS-based peptide sequencing has been widely used for high throughput MHC-associated peptidome discovery [ 12 , 13 , 14 , 15 ]. To systematically and accurately quantify the HLA associated immunopeptides presented on the tumor cell surface, we leveraged stable isotope labeling by amino acids in cell culture (SILAC) and mass spectrometry (MS)-based proteomics.…”

Section: Introductionmentioning

confidence: 99%

Alterations in HLA Class I-Presented Immunopeptidome and Class I-Interactome upon Osimertinib Resistance in EGFR Mutant Lung Adenocarcinoma

Maity

Gao

et al. 2021

Cancers

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Immune checkpoint inhibitor (ICI) therapy has been a paradigm shift in the treatment of cancer. ICI therapy results in durable responses and survival benefit for a large number of tumor types. Osimertinib, a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) has shown great efficacy treating EGFR mutant lung cancers; however, all patients eventually develop resistance. ICI therapy has not benefitted EGFR mutant lung cancer. Herein, we employed stable isotope labeling by amino acids in cell culture (SILAC) quantitative mass spectrometry-based proteomics to investigate potential immune escape molecular mechanisms in osimertinib resistant EGFR mutant lung adenocarcinoma by interrogating the alterations in the human leukocyte antigen (HLA) Class I-presented immunopeptidome, Class I-interactome, and the whole cell proteome between isogenic osimertinib-sensitive and -resistant human lung adenocarcinoma cells. Our study demonstrates an overall reduction in HLA class I-presented immunopeptidome and downregulation of antigen presentation core complex (e.g., TAP1 and ERAP1/2) and immunoproteasome in osimertinib resistant lung adenocarcinoma cells. Several key components in autophagy pathway are differentially altered. S100 proteins and SLC3A2 may play critical roles in reduced antigen presentation. Our dataset also includes ~1000 novel HLA class I interaction partners and hundreds of Class I-presented immunopeptides in EGFR mutant lung adenocarcinoma. This large-scale unbiased proteomics study provides novel insights and potential mechanisms of immune evasion of EGFR mutant lung adenocarcinoma.

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Proteogenomic analysis unveils the HLA Class I presented immunopeptidome in melanoma and EGFR mutant lung adenocarcinoma

Cited by 3 publications

References 78 publications

Variant biomarker discovery using mass spectrometry-based proteogenomics

Variant biomarker discovery using mass spectrometry-based proteogenomics

Deep Learning in Proteomics

Alterations in HLA Class I-Presented Immunopeptidome and Class I-Interactome upon Osimertinib Resistance in EGFR Mutant Lung Adenocarcinoma

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