Cargando…

Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response

Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) t...

Descripción completa

Detalles Bibliográficos
Autores principales: He, Jianbo, Whelan, Stephen A., Lu, Ming, Shen, Dejun, Chung, Debra U., Saxton, Romaine E., Faull, Kym F., Whitelegge, Julian P., Chang, Helena R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3202144/
https://www.ncbi.nlm.nih.gov/pubmed/22110952
http://dx.doi.org/10.1155/2011/896476
_version_ 1782214975273566208
author He, Jianbo
Whelan, Stephen A.
Lu, Ming
Shen, Dejun
Chung, Debra U.
Saxton, Romaine E.
Faull, Kym F.
Whitelegge, Julian P.
Chang, Helena R.
author_facet He, Jianbo
Whelan, Stephen A.
Lu, Ming
Shen, Dejun
Chung, Debra U.
Saxton, Romaine E.
Faull, Kym F.
Whitelegge, Julian P.
Chang, Helena R.
author_sort He, Jianbo
collection PubMed
description Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin β4 and β10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment.
format Online
Article
Text
id pubmed-3202144
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-32021442011-11-22 Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response He, Jianbo Whelan, Stephen A. Lu, Ming Shen, Dejun Chung, Debra U. Saxton, Romaine E. Faull, Kym F. Whitelegge, Julian P. Chang, Helena R. Int J Proteomics Research Article Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin β4 and β10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment. Hindawi Publishing Corporation 2011 2011-10-24 /pmc/articles/PMC3202144/ /pubmed/22110952 http://dx.doi.org/10.1155/2011/896476 Text en Copyright © 2011 Jianbo He et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
He, Jianbo
Whelan, Stephen A.
Lu, Ming
Shen, Dejun
Chung, Debra U.
Saxton, Romaine E.
Faull, Kym F.
Whitelegge, Julian P.
Chang, Helena R.
Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response
title Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response
title_full Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response
title_fullStr Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response
title_full_unstemmed Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response
title_short Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response
title_sort proteomic-based biosignatures in breast cancer classification and prediction of therapeutic response
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3202144/
https://www.ncbi.nlm.nih.gov/pubmed/22110952
http://dx.doi.org/10.1155/2011/896476
work_keys_str_mv AT hejianbo proteomicbasedbiosignaturesinbreastcancerclassificationandpredictionoftherapeuticresponse
AT whelanstephena proteomicbasedbiosignaturesinbreastcancerclassificationandpredictionoftherapeuticresponse
AT luming proteomicbasedbiosignaturesinbreastcancerclassificationandpredictionoftherapeuticresponse
AT shendejun proteomicbasedbiosignaturesinbreastcancerclassificationandpredictionoftherapeuticresponse
AT chungdebrau proteomicbasedbiosignaturesinbreastcancerclassificationandpredictionoftherapeuticresponse
AT saxtonromainee proteomicbasedbiosignaturesinbreastcancerclassificationandpredictionoftherapeuticresponse
AT faullkymf proteomicbasedbiosignaturesinbreastcancerclassificationandpredictionoftherapeuticresponse
AT whiteleggejulianp proteomicbasedbiosignaturesinbreastcancerclassificationandpredictionoftherapeuticresponse
AT changhelenar proteomicbasedbiosignaturesinbreastcancerclassificationandpredictionoftherapeuticresponse