Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model

Autores
Jones, Christina M.; Monge, Maria Eugenia; Kim, Jaeyeon; Matzuk, Martin M.; Fernández, Facundo M.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Ovarian cancer is a deadly disease killing more than any other gynecologic cancer. Nonspecific symptoms, combined with a lack of early detection methods, contribute to late diagnosis and low five-year survival rates. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype that results in 90% of ovarian cancer deaths. To investigate metabolic patterns for early detection of this deadly ovarian cancer, Dicer- Pten double knockout (DKO) mice that phenocopy many of the features of metastatic HGSC observed in women were studied. Using ultraperformance liquid chromatography−mass spectrometry (UPLC−MS), serum samples from 14 early-stage tumor (ET) DKO mice and 11 controls were analyzed in depth to screen for metabolic signatures capable of differentiating early-stage HGSC from controls. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for classification. Altered metabolic pathways reflected in that panel included those of fatty acids, bile acids, glycerophospholipids, peptides, and some dietary phytochemicals. These alterations revealed impacts to cellular energy storage and membrane stability, as well as changes in defenses against oxidative stress, shedding new light on the metabolic alterations associated with early ovarian cancer stages.
Fil: Jones, Christina M.. Georgia Institute of Technology. School of Chemistry & Biochemistry; Estados Unidos
Fil: Monge, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; Argentina. Georgia Institute of Technology. School of Chemistry & Biochemistry; Estados Unidos
Fil: Kim, Jaeyeon. Baylor College of Medicine. Department of Pathology & Immunology; Estados Unidos. Baylor College of Medicine. Center for Reproductive Medicine; Estados Unidos
Fil: Matzuk, Martin M.. Baylor College of Medicine. Department of Pathology & Immunology; Estados Unidos. Baylor College of Medicine. Department of Molecular and Cellular Biology; Estados Unidos. Baylor College of Medicine. Department of Molecular and Human Genetics; Estados Unidos
Fil: Fernández, Facundo M. . Georgia Institute of Technology. School of Chemistry & Biochemistry; Estados Unidos. Georgia Institute of Technology. Institute of Bioengineering and Biosciences; Estados Unidos
Materia
Ovarian Cancer
Mouse Models
Untargeted Metabolomics
Mass Spectrometry
Liquid Chromatography
Biomarkers
Early Detection
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:11336/4099
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/4099
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse ModelJones, Christina M.Monge, Maria EugeniaKim, JaeyeonMatzuk, Martin M.Fernández, Facundo M. Ovarian CancerMouse ModelsUntargeted MetabolomicsMass SpectrometryLiquid ChromatographyBiomarkersEarly Detectionhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1https://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3Ovarian cancer is a deadly disease killing more than any other gynecologic cancer. Nonspecific symptoms, combined with a lack of early detection methods, contribute to late diagnosis and low five-year survival rates. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype that results in 90% of ovarian cancer deaths. To investigate metabolic patterns for early detection of this deadly ovarian cancer, Dicer- Pten double knockout (DKO) mice that phenocopy many of the features of metastatic HGSC observed in women were studied. Using ultraperformance liquid chromatography−mass spectrometry (UPLC−MS), serum samples from 14 early-stage tumor (ET) DKO mice and 11 controls were analyzed in depth to screen for metabolic signatures capable of differentiating early-stage HGSC from controls. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for classification. Altered metabolic pathways reflected in that panel included those of fatty acids, bile acids, glycerophospholipids, peptides, and some dietary phytochemicals. These alterations revealed impacts to cellular energy storage and membrane stability, as well as changes in defenses against oxidative stress, shedding new light on the metabolic alterations associated with early ovarian cancer stages.Fil: Jones, Christina M.. Georgia Institute of Technology. School of Chemistry & Biochemistry; Estados UnidosFil: Monge, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; Argentina. Georgia Institute of Technology. School of Chemistry & Biochemistry; Estados UnidosFil: Kim, Jaeyeon. Baylor College of Medicine. Department of Pathology & Immunology; Estados Unidos. Baylor College of Medicine. Center for Reproductive Medicine; Estados UnidosFil: Matzuk, Martin M.. Baylor College of Medicine. Department of Pathology & Immunology; Estados Unidos. Baylor College of Medicine. Department of Molecular and Cellular Biology; Estados Unidos. Baylor College of Medicine. Department of Molecular and Human Genetics; Estados UnidosFil: Fernández, Facundo M. . Georgia Institute of Technology. School of Chemistry & Biochemistry; Estados Unidos. Georgia Institute of Technology. Institute of Bioengineering and Biosciences; Estados UnidosAmerican Chemical Society2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/4099Jones, Christina M.; Monge, Maria Eugenia; Kim, Jaeyeon; Matzuk, Martin M.; Fernández, Facundo M. ; Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model; American Chemical Society; Journal of Proteome Research; 14; 2; 1-2015; 917-9271535-3893enginfo:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/pr5009948info:eu-repo/semantics/altIdentifier/doi/DOI:10.1021/pr5009948info:eu-repo/semantics/altIdentifier/issn/1535-3893info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:41:32Zoai:ri.conicet.gov.ar:11336/4099instacron: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:34982025-09-29 09:41:33.113CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model
title Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model
spellingShingle Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model
Jones, Christina M.
Ovarian Cancer
Mouse Models
Untargeted Metabolomics
Mass Spectrometry
Liquid Chromatography
Biomarkers
Early Detection
title_short Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model
title_full Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model
title_fullStr Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model
title_full_unstemmed Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model
title_sort Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model
dc.creator.none.fl_str_mv Jones, Christina M.
Monge, Maria Eugenia
Kim, Jaeyeon
Matzuk, Martin M.
Fernández, Facundo M.
author Jones, Christina M.
author_facet Jones, Christina M.
Monge, Maria Eugenia
Kim, Jaeyeon
Matzuk, Martin M.
Fernández, Facundo M.
author_role author
author2 Monge, Maria Eugenia
Kim, Jaeyeon
Matzuk, Martin M.
Fernández, Facundo M.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ovarian Cancer
Mouse Models
Untargeted Metabolomics
Mass Spectrometry
Liquid Chromatography
Biomarkers
Early Detection
topic Ovarian Cancer
Mouse Models
Untargeted Metabolomics
Mass Spectrometry
Liquid Chromatography
Biomarkers
Early Detection
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Ovarian cancer is a deadly disease killing more than any other gynecologic cancer. Nonspecific symptoms, combined with a lack of early detection methods, contribute to late diagnosis and low five-year survival rates. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype that results in 90% of ovarian cancer deaths. To investigate metabolic patterns for early detection of this deadly ovarian cancer, Dicer- Pten double knockout (DKO) mice that phenocopy many of the features of metastatic HGSC observed in women were studied. Using ultraperformance liquid chromatography−mass spectrometry (UPLC−MS), serum samples from 14 early-stage tumor (ET) DKO mice and 11 controls were analyzed in depth to screen for metabolic signatures capable of differentiating early-stage HGSC from controls. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for classification. Altered metabolic pathways reflected in that panel included those of fatty acids, bile acids, glycerophospholipids, peptides, and some dietary phytochemicals. These alterations revealed impacts to cellular energy storage and membrane stability, as well as changes in defenses against oxidative stress, shedding new light on the metabolic alterations associated with early ovarian cancer stages.
Fil: Jones, Christina M.. Georgia Institute of Technology. School of Chemistry & Biochemistry; Estados Unidos
Fil: Monge, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; Argentina. Georgia Institute of Technology. School of Chemistry & Biochemistry; Estados Unidos
Fil: Kim, Jaeyeon. Baylor College of Medicine. Department of Pathology & Immunology; Estados Unidos. Baylor College of Medicine. Center for Reproductive Medicine; Estados Unidos
Fil: Matzuk, Martin M.. Baylor College of Medicine. Department of Pathology & Immunology; Estados Unidos. Baylor College of Medicine. Department of Molecular and Cellular Biology; Estados Unidos. Baylor College of Medicine. Department of Molecular and Human Genetics; Estados Unidos
Fil: Fernández, Facundo M. . Georgia Institute of Technology. School of Chemistry & Biochemistry; Estados Unidos. Georgia Institute of Technology. Institute of Bioengineering and Biosciences; Estados Unidos
description Ovarian cancer is a deadly disease killing more than any other gynecologic cancer. Nonspecific symptoms, combined with a lack of early detection methods, contribute to late diagnosis and low five-year survival rates. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype that results in 90% of ovarian cancer deaths. To investigate metabolic patterns for early detection of this deadly ovarian cancer, Dicer- Pten double knockout (DKO) mice that phenocopy many of the features of metastatic HGSC observed in women were studied. Using ultraperformance liquid chromatography−mass spectrometry (UPLC−MS), serum samples from 14 early-stage tumor (ET) DKO mice and 11 controls were analyzed in depth to screen for metabolic signatures capable of differentiating early-stage HGSC from controls. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for classification. Altered metabolic pathways reflected in that panel included those of fatty acids, bile acids, glycerophospholipids, peptides, and some dietary phytochemicals. These alterations revealed impacts to cellular energy storage and membrane stability, as well as changes in defenses against oxidative stress, shedding new light on the metabolic alterations associated with early ovarian cancer stages.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
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/11336/4099
Jones, Christina M.; Monge, Maria Eugenia; Kim, Jaeyeon; Matzuk, Martin M.; Fernández, Facundo M. ; Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model; American Chemical Society; Journal of Proteome Research; 14; 2; 1-2015; 917-927
1535-3893
url http://hdl.handle.net/11336/4099
identifier_str_mv Jones, Christina M.; Monge, Maria Eugenia; Kim, Jaeyeon; Matzuk, Martin M.; Fernández, Facundo M. ; Metabolomic Serum Profiling Detects Early-Stage High-Grade Serous Ovarian Cancer in a Mouse Model; American Chemical Society; Journal of Proteome Research; 14; 2; 1-2015; 917-927
1535-3893
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/abs/10.1021/pr5009948
info:eu-repo/semantics/altIdentifier/doi/DOI:10.1021/pr5009948
info:eu-repo/semantics/altIdentifier/issn/1535-3893
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
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
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
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score 13.070432

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