Accounting for proximal variants improves neoantigen prediction

Hundal, Jasreet; Kiwala, Susanna; Feng, Yangyang; Liu, Connor J.; Govindan, Ramaswamy; Chapman, William C.; Uppaluri, Ravindra; Swamidass, S. Joshua; Griffith, Obi L.; Mardis, Elaine R.; Griffith, Malachi

doi:10.1038/s41588-018-0283-9

Cited by 48 publications

(41 citation statements)

References 41 publications

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“…NetMHCpan (http://www.cbs.dtu.dk/services/NetMHCpan/) is widely used for peptide prediction (38,(42)(43)(44)(45)(46). Based on previous knowledge, peptide prediction using NetMHCpan showed that the P motif of HLA-A * 30:01-binding peptides was biased toward both A1 supertype-preferred residues (Ile and Leu), and A3 supertype-preferred residues (Arg and Lys) ( Figure 7A).…”

Section: Discussionmentioning

confidence: 99%

Divergent Peptide Presentations of HLA-A*30 Alleles Revealed by Structures With Pathogen Peptides

et al. 2019

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Human leukocyte antigen (HLA) alleles have a high degree of polymorphism, which determines their peptide-binding motifs and subsequent T-cell receptor recognition. The simplest way to understand the cross-presentation of peptides by different alleles is to classify these alleles into supertypes. A1 and A3 HLA supertypes are widely distributed in humans. However, direct structural and functional evidence for peptide presentation features of key alleles (e.g., HLA-A * 30:01 and -A * 30:03) are lacking. Herein, the molecular basis of peptide presentation of HLA-A * 30:01 and -A * 30:03 was demonstrated by crystal structure determination and thermostability measurements of complexes with T-cell epitopes from influenza virus (NP44), human immunodeficiency virus (RT313), and Mycobacterium tuberculosis (MTB). When binding to the HIV peptide, RT313, the PΩ-Lys anchoring modes of HLA-A * 30:01, and -A * 30:03 were similar to those of HLA-A * 11:01 in the A3 supertype. However, HLA-A * 30:03, but not -A * 30:01, also showed binding with the HLA * 01:01-favored peptide, NP44, but with a specific structural conformation. Thus, different from our previous understanding, HLA-A * 30:01 and -A * 30:03 have specific peptide-binding characteristics that may lead to their distinct supertype-featured binding peptide motifs. Moreover, we also found that residue 77 in the F pocket was one of the key residues for the divergent peptide presentation characteristics of HLA-A * 30:01 and -A * 30:03. Interchanging residue 77 between HLA-A * 30:01 and HLA-A * 30:03 switched their presented peptide profiles. Our results provide important recommendations for screening virus and tumor-specific peptides among the population with prevalent HLA supertypes for vaccine development and immune interventions.

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Section: Discussionmentioning

confidence: 99%

Divergent Peptide Presentations of HLA-A*30 Alleles Revealed by Structures With Pathogen Peptides

et al. 2019

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“…There are several tools to process genomics and transcriptomics data to identify potential neoantigens. These include, for example, PVacTools [80][81][82] and Neopred Pipe [83] These tools can be used to establish mutations likely present in the immunopeptidome, as well as to narrow down those that have affinity for MHC. The best way to integrate the results of these tools, which are of course limited in their ability to predict correctly, with personalized and directly measured immunopeptidomes remains an open question.…”

Section: Detection and Identification Of Immunopeptides With Mass Spementioning

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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“…In addition, pVACseq now makes use of phasing information taking into account variants proximal to somatic variants of interest. Since proximal variants can change the peptide sequence and affect neoantigen binding predictions, this is important for ensuring that the selected neoantigens correctly represent the individual's genome (9). We have also expanded the supported mutation types for neoantigen predictions to include in-frame indels and frameshift mutations.…”

Section: Figure 1: Overview Of Pvactools Workflowmentioning

confidence: 99%

“…The predicted neoantigens are then filtered and ranked based on defined metrics including sequencing read coverage, variant allele fraction (VAF), gene expression, and differential binding compared to the wild type peptide (agretopicity index score (8)). However, of the small number of such prediction tools (Supp Table 1), most lack key functionality, including predicting neoantigens from gene fusions, aiding optimized vaccine design for DNA cassette vaccines, and incorporating nearby germline or somatic alterations into the candidate neoantigens (9). Furthermore, none of the existing tools offer an intuitive graphical user interface for visualizing and efficiently selecting the most promising candidates; a key feature for facilitating involvement of clinicians and other researchers in the process of neoantigen evaluation.…”

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confidence: 99%

pVACtools: a computational toolkit to identify and visualize cancer neoantigens

Hundal

Kiwala

McMichael

et al. 2018

Preprint

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Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. We have developed an in silico sequence analysis toolkit -pVACtools, to facilitate comprehensive neoantigen characterization. pVACtools supports a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization and selection using a graphical web-based interface (pVACviz) and design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at pvactools.org.

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Accounting for proximal variants improves neoantigen prediction

Cited by 48 publications

References 41 publications

Divergent Peptide Presentations of HLA-A*30 Alleles Revealed by Structures With Pathogen Peptides

Divergent Peptide Presentations of HLA-A*30 Alleles Revealed by Structures With Pathogen Peptides

Mass Spectrometry-Based Identification of MHC-Associated Peptides

pVACtools: a computational toolkit to identify and visualize cancer neoantigens

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