Clinical potential of mass spectrometry-based proteogenomics

Zhang, Bing; Whiteaker, Jeffrey R.; Hoofnagle, Andrew N.; Baird, Geoffrey S.; Rodland, Karin D.; Paulovich, Amanda G.

doi:10.1038/s41571-018-0135-7

Cited by 170 publications

(138 citation statements)

References 168 publications

(171 reference statements)

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“…P roteogenomics has become a routine approach for the detection of protein sequences, resulting from genomic aberrations such as single nucleotide variants (SNVs), insertions and deletions (INDELs), RNA editing, novel junctions, gene fusions, and novel transcription regions [1][2][3] . This is achieved by simultaneously performing whole-exome sequencing (WES), RNA sequencing (RNA-Seq), and tandem mass spectrometry (MS/MS)-based shotgun proteomics analysis on matched samples, producing customized, sample-specific protein databases from DNA, and/or RNA sequencing data, and then searching MS/MS data against the customized protein databases.…”

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confidence: 99%

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

et al. 2020

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Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.

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confidence: 99%

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

et al. 2020

Self Cite

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“…Proteogenomics has emerged as a powerful approach to characterizing expressed protein products within a wide-variety of studies [1][2][3][4][5]. Proteogenomics, a multi-omic approach, involves the integration of genomic and/or transcriptomic data with mass spectrometry (MS)-based proteomics data.…”

Section: Resultsmentioning

confidence: 99%

“…of interest, which may not necessarily be present in reference sequence databases. The value of proteogenomics has been shown in studies of cancer and disease [3][4][5] as well as a means to annotate genomes [7].…”

mentioning

confidence: 99%

Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration

McGowan

Johnson

Kumar

et al. 2019

Preprint

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BackgroundProteogenomics integrates genomics, transcriptomics and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate 'omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation. Findings MVP is built as an HTML Galaxy plugin, primarily based on Javascript. Via the Galaxy API, MVP uses SQlite databases as input --a custom datatype (mzSQlite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript (IGVjs) framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface.Conclusions MVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomics results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization. FindingsProteogenomics has emerged as a powerful approach to characterizing expressed protein products within a wide-variety of studies [1][2][3][4][5]. Proteogenomics, a multi-omic approach, involves the integration of genomic and/or transcriptomic data with mass spectrometry (MS)-based proteomics data. Typically, a proteogenomics-based study starts with a sample (e.g. cells grown in culture, tissue sample etc.) which are analyzed using both next generation sequencing technologies (usually RNA-Seq) and MS-based proteomics. Once assembled from RNA-Seq data, the transcriptome sequence is translated in-silico to generate a database of potentially expressed proteins encoded by the RNA. This protein sequence database contains both proteins of known sequences contained in reference databases, as well as novel protein sequences which are derived from the transcriptome sequence via comparison to reference genome sequence. These novel sequences may include variants arising from single-amino acid substitutions, short insertions/deletions, RNA processing events (truncations, splice variants) or even translation from unexpected genomic regions [2].Parallel to the RNA-Seq analysis, tandem mas...

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“…However, a patient's response to therapy is a complex event related to tumor mutations, as well as genetic and transcriptomic changes. Today, the emerging field of cancer proteogenomics integrates the information obtained from the genomic and proteomic studies toward identifying and validating peptide sequences as potential therapeutic candidates [7].…”

Section: Introduction: Neoantigens In Cancer Immunotherapymentioning

confidence: 99%

Mass Spectrometry-Based Identification of MHC-Associated Peptides

Kote

Piróg

Bedran

et al. 2020

Cancers

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Neoantigen-based immunotherapies promise to improve patient outcomes over the current standard of care. However, detecting these cancer-specific antigens is one of the significant challenges in the field of mass spectrometry. Even though the first sequencing of the immunopeptides was done decades ago, today there is still a diversity of the protocols used for neoantigen isolation from the cell surface. This heterogeneity makes it difficult to compare results between the laboratories and the studies. Isolation of the neoantigens from the cell surface is usually done by mild acid elution (MAE) or immunoprecipitation (IP) protocol. However, limited amounts of the neoantigens present on the cell surface impose a challenge and require instrumentation with enough sensitivity and accuracy for their detection. Detecting these neopeptides from small amounts of available patient tissue limits the scope of most of the studies to cell cultures. Here, we summarize protocols for the extraction and identification of the major histocompatibility complex (MHC) class I and II peptides. We aimed to evaluate existing methods in terms of the appropriateness of the isolation procedure, as well as instrumental parameters used for neoantigen detection. We also focus on the amount of the material used in the protocols as the critical factor to consider when analyzing neoantigens. Beyond experimental aspects, there are numerous readily available proteomics suits/tools applicable for neoantigen discovery; however, experimental validation is still necessary for neoantigen characterization.

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Clinical potential of mass spectrometry-based proteogenomics

Cited by 170 publications

References 168 publications

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration

Mass Spectrometry-Based Identification of MHC-Associated Peptides

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