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...
Autores principales: | , , , , , , , , |
---|---|
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 |