Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics

BACKGROUND: Triple-negative breast cancer (TNBC) accounts for 15-20% of all breast cancers and usually requires the administration of adjuvant chemotherapy after surgery but even with this treatment many patients still suffer from a relapse. The main objective of this study was to identify proteomic...

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Autores: Gámez Pozo, Angelo, Trilla Fuertes, Lucía, Prado Vázquez, Guillermo, Chiva, Cristina, López Vacas, Rocío, Nanni, Paolo, Berges Soria, Julia, Grossmann, Jonas, Díaz Almirón, Mariana, Ciruelos, Eva, Sabidó Aguadé, Eduard, 1981-, Espinosa Arranz, Enrique, Fresno Vara, Juan Ángel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/35042
Acceso en línea:http://hdl.handle.net/10230/35042
http://dx.doi.org/10.1371/journal.pone.0178296
Access Level:acceso abierto
Palabra clave:Breast cancer
Adjuvant chemotherapy
Transcriptome analysis
Proteomics
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spelling Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomicsGámez Pozo, AngeloTrilla Fuertes, LucíaPrado Vázquez, GuillermoChiva, CristinaLópez Vacas, RocíoNanni, PaoloBerges Soria, JuliaGrossmann, JonasDíaz Almirón, MarianaCiruelos, EvaSabidó Aguadé, Eduard, 1981-Espinosa Arranz, EnriqueFresno Vara, Juan ÁngelBreast cancerAdjuvant chemotherapyTranscriptome analysisProteomicsBACKGROUND: Triple-negative breast cancer (TNBC) accounts for 15-20% of all breast cancers and usually requires the administration of adjuvant chemotherapy after surgery but even with this treatment many patients still suffer from a relapse. The main objective of this study was to identify proteomics-based biomarkers that predict the response to standard adjuvant chemotherapy, so that patients at are not going to benefit from it can be offered therapeutic alternatives. METHODS: We analyzed the proteome of a retrospective series of formalin-fixed, paraffin-embedded TNBC tissue applying high-throughput label-free quantitative proteomics. We identified several protein signatures with predictive value, which were validated with quantitative targeted proteomics in an independent cohort of patients and further evaluated in publicly available transcriptomics data. RESULTS: Using univariate Cox analysis, a panel of 18 proteins was significantly associated with distant metastasis-free survival of patients (p<0.01). A reduced 5-protein profile with prognostic value was identified and its prediction performance was assessed in an independent targeted proteomics experiment and a publicly available transcriptomics dataset. Predictor P5 including peptides from proteins RAC2, RAB6A, BIEA and IPYR was the best performance protein combination in predicting relapse after adjuvant chemotherapy in TNBC patients. CONCLUSIONS: This study identified a protein combination signature that complements histopathological prognostic factors in TNBC treated with adjuvant chemotherapy. The protein signature can be used in paraffin-embedded samples, and after a prospective validation in independent series, it could be used as predictive clinical test in order to recommend participation in clinical trials or a more exhaustive follow-up.Public Library of Science (PLoS)201820182017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/35042http://dx.doi.org/10.1371/journal.pone.0178296reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésPLoS One. 2017 Jun 8;12(6):e0178296© 2017 Gámez-Pozo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/350422026-06-12T07:21:37Z
dc.title.none.fl_str_mv Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics
title Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics
spellingShingle Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics
Gámez Pozo, Angelo
Breast cancer
Adjuvant chemotherapy
Transcriptome analysis
Proteomics
title_short Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics
title_full Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics
title_fullStr Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics
title_full_unstemmed Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics
title_sort Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics
dc.creator.none.fl_str_mv Gámez Pozo, Angelo
Trilla Fuertes, Lucía
Prado Vázquez, Guillermo
Chiva, Cristina
López Vacas, Rocío
Nanni, Paolo
Berges Soria, Julia
Grossmann, Jonas
Díaz Almirón, Mariana
Ciruelos, Eva
Sabidó Aguadé, Eduard, 1981-
Espinosa Arranz, Enrique
Fresno Vara, Juan Ángel
author Gámez Pozo, Angelo
author_facet Gámez Pozo, Angelo
Trilla Fuertes, Lucía
Prado Vázquez, Guillermo
Chiva, Cristina
López Vacas, Rocío
Nanni, Paolo
Berges Soria, Julia
Grossmann, Jonas
Díaz Almirón, Mariana
Ciruelos, Eva
Sabidó Aguadé, Eduard, 1981-
Espinosa Arranz, Enrique
Fresno Vara, Juan Ángel
author_role author
author2 Trilla Fuertes, Lucía
Prado Vázquez, Guillermo
Chiva, Cristina
López Vacas, Rocío
Nanni, Paolo
Berges Soria, Julia
Grossmann, Jonas
Díaz Almirón, Mariana
Ciruelos, Eva
Sabidó Aguadé, Eduard, 1981-
Espinosa Arranz, Enrique
Fresno Vara, Juan Ángel
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Breast cancer
Adjuvant chemotherapy
Transcriptome analysis
Proteomics
topic Breast cancer
Adjuvant chemotherapy
Transcriptome analysis
Proteomics
description BACKGROUND: Triple-negative breast cancer (TNBC) accounts for 15-20% of all breast cancers and usually requires the administration of adjuvant chemotherapy after surgery but even with this treatment many patients still suffer from a relapse. The main objective of this study was to identify proteomics-based biomarkers that predict the response to standard adjuvant chemotherapy, so that patients at are not going to benefit from it can be offered therapeutic alternatives. METHODS: We analyzed the proteome of a retrospective series of formalin-fixed, paraffin-embedded TNBC tissue applying high-throughput label-free quantitative proteomics. We identified several protein signatures with predictive value, which were validated with quantitative targeted proteomics in an independent cohort of patients and further evaluated in publicly available transcriptomics data. RESULTS: Using univariate Cox analysis, a panel of 18 proteins was significantly associated with distant metastasis-free survival of patients (p<0.01). A reduced 5-protein profile with prognostic value was identified and its prediction performance was assessed in an independent targeted proteomics experiment and a publicly available transcriptomics dataset. Predictor P5 including peptides from proteins RAC2, RAB6A, BIEA and IPYR was the best performance protein combination in predicting relapse after adjuvant chemotherapy in TNBC patients. CONCLUSIONS: This study identified a protein combination signature that complements histopathological prognostic factors in TNBC treated with adjuvant chemotherapy. The protein signature can be used in paraffin-embedded samples, and after a prospective validation in independent series, it could be used as predictive clinical test in order to recommend participation in clinical trials or a more exhaustive follow-up.
publishDate 2017
dc.date.none.fl_str_mv 2017
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/35042
http://dx.doi.org/10.1371/journal.pone.0178296
url http://hdl.handle.net/10230/35042
http://dx.doi.org/10.1371/journal.pone.0178296
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PLoS One. 2017 Jun 8;12(6):e0178296
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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