Dataset Information

High-Throughput Stability Screening of Neoantigen/HLA Complexes Improves Immunogenicity Predictions.

ABSTRACT: Mutated peptides (neoantigens) from a patient's cancer genome can serve as targets for T-cell immunity, but identifying which peptides can be presented by an MHC molecule and elicit T cells has been difficult. Although algorithms that predict MHC binding exist, they are not yet able to distinguish experimental differences in half-lives of the complexes (an immunologically relevant parameter, referred to here as kinetic stability). Improvement in determining actual neoantigen peptide/MHC stability could be important, as only a small fraction of peptides in most current vaccines are capable of eliciting CD8+ T-cell responses. Here, we used a rapid, high-throughput method to experimentally determine peptide/HLA thermal stability on a scale that will be necessary for analysis of neoantigens from thousands of patients. The method combined the use of UV-cleavable peptide/HLA class I complexes and differential scanning fluorimetry to determine the Tm values of neoantigen complexes. Measured Tm values were accurate and reproducible and were directly proportional to the half-lives of the complexes. Analysis of known HLA-A2-restricted immunogenic peptides showed that Tm values better correlated with immunogenicity than algorithm-predicted binding affinities. We propose that temperature stability information can be used as a guide for the selection of neoantigens in cancer vaccines in order to focus attention on those mutated peptides with the highest probability of being expressed on the cell surface.

SUBMITTER: Blaha DT

PROVIDER: S-EPMC6324732 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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High-Throughput Stability Screening of Neoantigen/HLA Complexes Improves Immunogenicity Predictions.

Blaha Dylan T DT Anderson Scott D SD Yoakum Daniel M DM Hager Marlies V MV Zha Yuanyuan Y Gajewski Thomas F TF Kranz David M DM

Cancer immunology research 20181113 1

Mutated peptides (neoantigens) from a patient's cancer genome can serve as targets for T-cell immunity, but identifying which peptides can be presented by an MHC molecule and elicit T cells has been difficult. Although algorithms that predict MHC binding exist, they are not yet able to distinguish experimental differences in half-lives of the complexes (an immunologically relevant parameter, referred to here as kinetic stability). Improvement in determining actual neoantigen peptide/MHC stabilit ...[more]

PMID: 30425106

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Project description:Adverse Outcome Pathways (AOPs) establish a connection between a molecular initiating event (MIE) and an adverse outcome. Detailed understanding of the MIE provides the ideal data for determining chemical properties required to elicit the MIE. This study utilized high-throughput screening data from the ToxCast program, coupled with chemical structural information, to generate chemical clusters using three similarity methods pertaining to nine MIEs within an AOP network for hepatic steatosis. Three case studies demonstrate the utility of the mechanistic information held by the MIE for integrating biological and chemical data. Evaluation of the chemical clusters activating the glucocorticoid receptor identified activity differences in chemicals within a cluster. Comparison of the estrogen receptor results with previous work showed that bioactivity data and structural alerts can be combined to improve predictions in a customizable way where bioactivity data are limited. The aryl hydrocarbon receptor (AHR) highlighted that while structural data can be used to offset limited data for new screening efforts, not all ToxCast targets have sufficient data to define robust chemical clusters. In this context, an alternative to additional receptor assays is proposed where assays for proximal key events downstream of AHR activation could be used to enhance confidence in active calls. These case studies illustrate how the AOP framework can support an iterative process whereby in vitro toxicity testing and chemical structure can be combined to improve toxicity predictions. In vitro assays can inform the development of structural alerts linking chemical structure to toxicity. Consequently, structurally related chemical groups can facilitate identification of assays that would be informative for a specific MIE. Together, these activities form a virtuous cycle where the mechanistic basis for the in vitro results and the breadth of the structural alerts continually improve over time to better predict activity of chemicals for which limited toxicity data exist.

Dataset Information

High-Throughput Stability Screening of Neoantigen/HLA Complexes Improves Immunogenicity Predictions.

Publications

High-Throughput Stability Screening of Neoantigen/HLA Complexes Improves Immunogenicity Predictions.

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