NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors

Autores
DeVette, Christa I.; Andreatta, Massimo; Bardet, Wilfried; Cate, Steven J.; Jurtz, Vanessa I.; Jackson, Kenneth W.; Welm, Alana L.; Nielsen, Morten; Hildebrand, William H.
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
With the advancement of personalized cancer immunotherapies, new tools are needed to identify tumor antigens and evaluate T-cell responses in model systems, specifically those that exhibit clinically relevant tumor progression. Key transgenic mouse models of breast cancer are generated and maintained on the FVB genetic background, and one such model is the mouse mammary tumor virus-polyomavirus middle T antigen (MMTV-PyMT) mouse-an immunocompetent transgenic mouse that exhibits spontaneous mammary tumor development and metastasis with high penetrance. Backcrossing the MMTV-PyMT mouse from the FVB strain onto a C57BL/6 genetic background, in order to leverage well-developed C57BL/6 immunologic tools, results in delayed tumor development and variable metastatic phenotypes.Therefore, we initiated characterization of the FVB MHC class I H-2q haplotype to establish useful immunologic tools for evaluating antigen specificity in the murine FVB strain. Our study provides the first detailed molecular and immunoproteomic characterization of the FVB H-2q MHC class I alleles, including >8,500 unique peptide ligands, a multiallele murine MHC peptide prediction tool, and in vivo validation of these data using MMTV-PyMT primary tumors. This work allows researchers to rapidly predict H-2 peptide ligands for immune testing, including, but not limited to, the MMTV-PyMT model for metastatic breast cancer.
Fil: DeVette, Christa I.. Oklahoma Medical Research Foundation; Estados Unidos
Fil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentina
Fil: Bardet, Wilfried. Oklahoma Medical Research Foundation; Estados Unidos
Fil: Cate, Steven J.. Oklahoma Medical Research Foundation; Estados Unidos
Fil: Jurtz, Vanessa I.. Technical University of Denmark; Dinamarca
Fil: Jackson, Kenneth W.. Oklahoma Medical Research Foundation; Estados Unidos
Fil: Welm, Alana L.. University of Utah; Estados Unidos
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca
Fil: Hildebrand, William H.. Universidad Nacional de San Martín; Argentina
Materia
MHC
Mouse models
Breast cancer
T cell epitopes
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/100066
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/100066
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling NetH2pan: A computational tool to guide MHC peptide prediction on murine tumorsDeVette, Christa I.Andreatta, MassimoBardet, WilfriedCate, Steven J.Jurtz, Vanessa I.Jackson, Kenneth W.Welm, Alana L.Nielsen, MortenHildebrand, William H.MHCMouse modelsBreast cancerT cell epitopeshttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3With the advancement of personalized cancer immunotherapies, new tools are needed to identify tumor antigens and evaluate T-cell responses in model systems, specifically those that exhibit clinically relevant tumor progression. Key transgenic mouse models of breast cancer are generated and maintained on the FVB genetic background, and one such model is the mouse mammary tumor virus-polyomavirus middle T antigen (MMTV-PyMT) mouse-an immunocompetent transgenic mouse that exhibits spontaneous mammary tumor development and metastasis with high penetrance. Backcrossing the MMTV-PyMT mouse from the FVB strain onto a C57BL/6 genetic background, in order to leverage well-developed C57BL/6 immunologic tools, results in delayed tumor development and variable metastatic phenotypes.Therefore, we initiated characterization of the FVB MHC class I H-2q haplotype to establish useful immunologic tools for evaluating antigen specificity in the murine FVB strain. Our study provides the first detailed molecular and immunoproteomic characterization of the FVB H-2q MHC class I alleles, including >8,500 unique peptide ligands, a multiallele murine MHC peptide prediction tool, and in vivo validation of these data using MMTV-PyMT primary tumors. This work allows researchers to rapidly predict H-2 peptide ligands for immune testing, including, but not limited to, the MMTV-PyMT model for metastatic breast cancer.Fil: DeVette, Christa I.. Oklahoma Medical Research Foundation; Estados UnidosFil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Bardet, Wilfried. Oklahoma Medical Research Foundation; Estados UnidosFil: Cate, Steven J.. Oklahoma Medical Research Foundation; Estados UnidosFil: Jurtz, Vanessa I.. Technical University of Denmark; DinamarcaFil: Jackson, Kenneth W.. Oklahoma Medical Research Foundation; Estados UnidosFil: Welm, Alana L.. University of Utah; Estados UnidosFil: Nielsen, Morten. Technical University of Denmark; DinamarcaFil: Hildebrand, William H.. Universidad Nacional de San Martín; ArgentinaAmerican Association for Cancer Research2018-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/100066DeVette, Christa I.; Andreatta, Massimo; Bardet, Wilfried; Cate, Steven J.; Jurtz, Vanessa I.; et al.; NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors; American Association for Cancer Research; Cancer Immunology Research; 6; 6; 6-2018; 636-6442326-60662326-6074CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://cancerimmunolres.aacrjournals.org/lookup/doi/10.1158/2326-6066.CIR-17-0298info:eu-repo/semantics/altIdentifier/doi/10.1158/2326-6066.CIR-17-0298info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:20:38Zoai:ri.conicet.gov.ar:11336/100066instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:20:38.932CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors
title NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors
spellingShingle NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors
DeVette, Christa I.
MHC
Mouse models
Breast cancer
T cell epitopes
title_short NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors
title_full NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors
title_fullStr NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors
title_full_unstemmed NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors
title_sort NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors
dc.creator.none.fl_str_mv DeVette, Christa I.
Andreatta, Massimo
Bardet, Wilfried
Cate, Steven J.
Jurtz, Vanessa I.
Jackson, Kenneth W.
Welm, Alana L.
Nielsen, Morten
Hildebrand, William H.
author DeVette, Christa I.
author_facet DeVette, Christa I.
Andreatta, Massimo
Bardet, Wilfried
Cate, Steven J.
Jurtz, Vanessa I.
Jackson, Kenneth W.
Welm, Alana L.
Nielsen, Morten
Hildebrand, William H.
author_role author
author2 Andreatta, Massimo
Bardet, Wilfried
Cate, Steven J.
Jurtz, Vanessa I.
Jackson, Kenneth W.
Welm, Alana L.
Nielsen, Morten
Hildebrand, William H.
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv MHC
Mouse models
Breast cancer
T cell epitopes
topic MHC
Mouse models
Breast cancer
T cell epitopes
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv With the advancement of personalized cancer immunotherapies, new tools are needed to identify tumor antigens and evaluate T-cell responses in model systems, specifically those that exhibit clinically relevant tumor progression. Key transgenic mouse models of breast cancer are generated and maintained on the FVB genetic background, and one such model is the mouse mammary tumor virus-polyomavirus middle T antigen (MMTV-PyMT) mouse-an immunocompetent transgenic mouse that exhibits spontaneous mammary tumor development and metastasis with high penetrance. Backcrossing the MMTV-PyMT mouse from the FVB strain onto a C57BL/6 genetic background, in order to leverage well-developed C57BL/6 immunologic tools, results in delayed tumor development and variable metastatic phenotypes.Therefore, we initiated characterization of the FVB MHC class I H-2q haplotype to establish useful immunologic tools for evaluating antigen specificity in the murine FVB strain. Our study provides the first detailed molecular and immunoproteomic characterization of the FVB H-2q MHC class I alleles, including >8,500 unique peptide ligands, a multiallele murine MHC peptide prediction tool, and in vivo validation of these data using MMTV-PyMT primary tumors. This work allows researchers to rapidly predict H-2 peptide ligands for immune testing, including, but not limited to, the MMTV-PyMT model for metastatic breast cancer.
Fil: DeVette, Christa I.. Oklahoma Medical Research Foundation; Estados Unidos
Fil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentina
Fil: Bardet, Wilfried. Oklahoma Medical Research Foundation; Estados Unidos
Fil: Cate, Steven J.. Oklahoma Medical Research Foundation; Estados Unidos
Fil: Jurtz, Vanessa I.. Technical University of Denmark; Dinamarca
Fil: Jackson, Kenneth W.. Oklahoma Medical Research Foundation; Estados Unidos
Fil: Welm, Alana L.. University of Utah; Estados Unidos
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca
Fil: Hildebrand, William H.. Universidad Nacional de San Martín; Argentina
description With the advancement of personalized cancer immunotherapies, new tools are needed to identify tumor antigens and evaluate T-cell responses in model systems, specifically those that exhibit clinically relevant tumor progression. Key transgenic mouse models of breast cancer are generated and maintained on the FVB genetic background, and one such model is the mouse mammary tumor virus-polyomavirus middle T antigen (MMTV-PyMT) mouse-an immunocompetent transgenic mouse that exhibits spontaneous mammary tumor development and metastasis with high penetrance. Backcrossing the MMTV-PyMT mouse from the FVB strain onto a C57BL/6 genetic background, in order to leverage well-developed C57BL/6 immunologic tools, results in delayed tumor development and variable metastatic phenotypes.Therefore, we initiated characterization of the FVB MHC class I H-2q haplotype to establish useful immunologic tools for evaluating antigen specificity in the murine FVB strain. Our study provides the first detailed molecular and immunoproteomic characterization of the FVB H-2q MHC class I alleles, including >8,500 unique peptide ligands, a multiallele murine MHC peptide prediction tool, and in vivo validation of these data using MMTV-PyMT primary tumors. This work allows researchers to rapidly predict H-2 peptide ligands for immune testing, including, but not limited to, the MMTV-PyMT model for metastatic breast cancer.
publishDate 2018
dc.date.none.fl_str_mv 2018-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/100066
DeVette, Christa I.; Andreatta, Massimo; Bardet, Wilfried; Cate, Steven J.; Jurtz, Vanessa I.; et al.; NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors; American Association for Cancer Research; Cancer Immunology Research; 6; 6; 6-2018; 636-644
2326-6066
2326-6074
CONICET Digital
CONICET
url http://hdl.handle.net/11336/100066
identifier_str_mv DeVette, Christa I.; Andreatta, Massimo; Bardet, Wilfried; Cate, Steven J.; Jurtz, Vanessa I.; et al.; NetH2pan: A computational tool to guide MHC peptide prediction on murine tumors; American Association for Cancer Research; Cancer Immunology Research; 6; 6; 6-2018; 636-644
2326-6066
2326-6074
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://cancerimmunolres.aacrjournals.org/lookup/doi/10.1158/2326-6066.CIR-17-0298
info:eu-repo/semantics/altIdentifier/doi/10.1158/2326-6066.CIR-17-0298
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Association for Cancer Research
publisher.none.fl_str_mv American Association for Cancer Research
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082584819269632
score 13.22299

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