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Usage Information

Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development
Neeha Zaidi, … , Shozeb Haider, Elizabeth M. Jaffee
Neeha Zaidi, … , Shozeb Haider, Elizabeth M. Jaffee
Published September 3, 2020
Citation Information: JCI Insight. 2020;5(17):e136991. https://doi.org/10.1172/jci.insight.136991.
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Resource and Technical Advance Immunology Oncology

Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development

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Abstract

In prior studies, we delineated the landscape of neoantigens arising from nonsynonymous point mutations in a murine pancreatic cancer model, Panc02. We developed a peptide vaccine by targeting neoantigens predicted using a pipeline that incorporates the MHC binding algorithm NetMHC. The vaccine, when combined with immune checkpoint modulators, elicited a robust neoepitope-specific antitumor immune response and led to tumor clearance. However, only a small fraction of the predicted neoepitopes induced T cell immunity, similarly to that reported for neoantigen vaccines tested in clinical studies. While these studies have used binding affinities to MHC I as surrogates for T cell immunity, this approach does not include spatial information on the mutated residue that is crucial for TCR activation. Here, we investigate conformational alterations in and around the MHC binding groove induced by selected minimal neoepitopes, and we examine the influence of a given mutated residue as a function of its spatial position. We found that structural parameters, including the solvent-accessible surface area (SASA) of the neoepitope and the position and spatial configuration of the mutated residue within the sequence, can be used to improve the prediction of immunogenic neoepitopes for inclusion in cancer vaccines.

Authors

Neeha Zaidi, Mariya Soban, Fangluo Chen, Heather Kinkead, Jocelyn Mathew, Mark Yarchoan, Todd D. Armstrong, Shozeb Haider, Elizabeth M. Jaffee

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Usage data is cumulative from August 2021 through August 2022.

Usage JCI PMC
Text version 1,608 383
PDF 290 103
Figure 148 11
Table 101 0
Citation downloads 56 0
Totals 2,203 497
Total Views 2,700

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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