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

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, whe...
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Autor*in:	Neeha Zaidi [verfasserIn] Mariya Soban [verfasserIn] Fangluo Chen [verfasserIn] Heather Kinkead [verfasserIn] Jocelyn Mathew [verfasserIn] Mark Yarchoan [verfasserIn] Todd D. Armstrong [verfasserIn] Shozeb Haider [verfasserIn] Elizabeth M. Jaffee [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Immunology Oncology

Übergeordnetes Werk:	In: JCI Insight - American Society for Clinical investigation, 2020, 5(2020), 17
Übergeordnetes Werk:	volume:5 ; year:2020 ; number:17

Links:	Link aufrufen Link aufrufen Journal toc

Katalog-ID:	DOAJ02008725X

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allfields	(DE-627)DOAJ02008725X (DE-599)DOAJecdd87c059d54eb1a318c0283e12f92b DE-627 ger DE-627 rakwb eng Neeha Zaidi verfasserin aut Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Immunology Oncology Medicine R Mariya Soban verfasserin aut Fangluo Chen verfasserin aut Heather Kinkead verfasserin aut Jocelyn Mathew verfasserin aut Mark Yarchoan verfasserin aut Todd D. Armstrong verfasserin aut Shozeb Haider verfasserin aut Elizabeth M. Jaffee verfasserin aut In JCI Insight American Society for Clinical investigation, 2020 5(2020), 17 (DE-627)872610594 (DE-600)2874757-4 23793708 nnns volume:5 year:2020 number:17 https://doaj.org/article/ecdd87c059d54eb1a318c0283e12f92b kostenfrei https://doi.org/10.1172/jci.insight.136991 kostenfrei https://doaj.org/toc/2379-3708 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 17
spelling	(DE-627)DOAJ02008725X (DE-599)DOAJecdd87c059d54eb1a318c0283e12f92b DE-627 ger DE-627 rakwb eng Neeha Zaidi verfasserin aut Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Immunology Oncology Medicine R Mariya Soban verfasserin aut Fangluo Chen verfasserin aut Heather Kinkead verfasserin aut Jocelyn Mathew verfasserin aut Mark Yarchoan verfasserin aut Todd D. Armstrong verfasserin aut Shozeb Haider verfasserin aut Elizabeth M. Jaffee verfasserin aut In JCI Insight American Society for Clinical investigation, 2020 5(2020), 17 (DE-627)872610594 (DE-600)2874757-4 23793708 nnns volume:5 year:2020 number:17 https://doaj.org/article/ecdd87c059d54eb1a318c0283e12f92b kostenfrei https://doi.org/10.1172/jci.insight.136991 kostenfrei https://doaj.org/toc/2379-3708 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 17
allfields_unstemmed	(DE-627)DOAJ02008725X (DE-599)DOAJecdd87c059d54eb1a318c0283e12f92b DE-627 ger DE-627 rakwb eng Neeha Zaidi verfasserin aut Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Immunology Oncology Medicine R Mariya Soban verfasserin aut Fangluo Chen verfasserin aut Heather Kinkead verfasserin aut Jocelyn Mathew verfasserin aut Mark Yarchoan verfasserin aut Todd D. Armstrong verfasserin aut Shozeb Haider verfasserin aut Elizabeth M. Jaffee verfasserin aut In JCI Insight American Society for Clinical investigation, 2020 5(2020), 17 (DE-627)872610594 (DE-600)2874757-4 23793708 nnns volume:5 year:2020 number:17 https://doaj.org/article/ecdd87c059d54eb1a318c0283e12f92b kostenfrei https://doi.org/10.1172/jci.insight.136991 kostenfrei https://doaj.org/toc/2379-3708 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 17
allfieldsGer	(DE-627)DOAJ02008725X (DE-599)DOAJecdd87c059d54eb1a318c0283e12f92b DE-627 ger DE-627 rakwb eng Neeha Zaidi verfasserin aut Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Immunology Oncology Medicine R Mariya Soban verfasserin aut Fangluo Chen verfasserin aut Heather Kinkead verfasserin aut Jocelyn Mathew verfasserin aut Mark Yarchoan verfasserin aut Todd D. Armstrong verfasserin aut Shozeb Haider verfasserin aut Elizabeth M. Jaffee verfasserin aut In JCI Insight American Society for Clinical investigation, 2020 5(2020), 17 (DE-627)872610594 (DE-600)2874757-4 23793708 nnns volume:5 year:2020 number:17 https://doaj.org/article/ecdd87c059d54eb1a318c0283e12f92b kostenfrei https://doi.org/10.1172/jci.insight.136991 kostenfrei https://doaj.org/toc/2379-3708 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 17
allfieldsSound	(DE-627)DOAJ02008725X (DE-599)DOAJecdd87c059d54eb1a318c0283e12f92b DE-627 ger DE-627 rakwb eng Neeha Zaidi verfasserin aut Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Immunology Oncology Medicine R Mariya Soban verfasserin aut Fangluo Chen verfasserin aut Heather Kinkead verfasserin aut Jocelyn Mathew verfasserin aut Mark Yarchoan verfasserin aut Todd D. Armstrong verfasserin aut Shozeb Haider verfasserin aut Elizabeth M. Jaffee verfasserin aut In JCI Insight American Society for Clinical investigation, 2020 5(2020), 17 (DE-627)872610594 (DE-600)2874757-4 23793708 nnns volume:5 year:2020 number:17 https://doaj.org/article/ecdd87c059d54eb1a318c0283e12f92b kostenfrei https://doi.org/10.1172/jci.insight.136991 kostenfrei https://doaj.org/toc/2379-3708 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 17
language	English
source	In JCI Insight 5(2020), 17 volume:5 year:2020 number:17
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container_title	JCI Insight
authorswithroles_txt_mv	Neeha Zaidi @@aut@@ Mariya Soban @@aut@@ Fangluo Chen @@aut@@ Heather Kinkead @@aut@@ Jocelyn Mathew @@aut@@ Mark Yarchoan @@aut@@ Todd D. Armstrong @@aut@@ Shozeb Haider @@aut@@ Elizabeth M. Jaffee @@aut@@
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title	Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development
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title_full	Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development
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author_browse	Neeha Zaidi Mariya Soban Fangluo Chen Heather Kinkead Jocelyn Mathew Mark Yarchoan Todd D. Armstrong Shozeb Haider Elizabeth M. Jaffee
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title_sort	role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development
title_auth	Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development
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Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development

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