154
Views
7
CrossRef citations to date
0
Altmetric
Original Research

Changes in protein expression after neoadjuvant use of aromatase inhibitors in primary breast cancer: a proteomic approach to search for potential biomarkers to predict response or resistance

, , &
Pages S79-S89 | Published online: 07 Apr 2010
 

Abstract

Objective: Aromatase inhibitors (AI) have been established as a useful hormonal therapy in hormone receptor-expressing breast carcinoma. However, changes in tumor protein expression after exposure to AIs are not necessarily well understood. These changes may provide insight into how breast carcinomas respond or develop into a state of resistance towards AIs, and lead to the discovery of potential biomarkers to predict treatment responses. Methods: Post-menopausal breast cancer patients were recruited to receive 3 months treatment with neoadjuvant AI. Carcinoma tissues were collected before and after the use of AIs and protein expression profiles were compared using two-dimensional gel electrophoresis. Protein spots with different levels of expression were identified using mass spectrometry. Results: A total of 14 matched pairs of tumor tissues were collected. Both up-regulated and down-regulated proteins were selected and identified as follows: heat shock protein 70 protein 2; Cyclin M3, alpha 1 antichymotrypsin precursor; carbonic anhydrase I, cancer antigen 1; SOBP protein; Rho GDP dissociation inhibitor alpha; glyoxalase I with benzyl glutathione inhibitor; lipid-free human apolipoprotein A-I and RAB4A member RAS oncogene family. Among these proteins, heat shock protein 70 demonstrated the most significant positive correlation with clinical response of the patients. Conclusion: After neoadjuvant use of AI, heat shock protein 70 demonstrated the most consistent phenotypic consistency in both up-regulated and down-regulated protein expression levels among the 14 studied pairs of tumor tissue. Other proteins worthwhile exploring were also identified in this study. These proteins could serve as potential predictors for AI response.

Acknowledgements

This paper is published as part of a supplement forming the Proceedings of the 5th Annual Conference of the Organisation for Oncology and Translational Research (OOTR). Publication of this supplement is supported by an educational grant from GlaxoSmithKline Ltd. The 5th Annual Conference of OOTR was supported by the following sponsors: GlaxoSmithKline; Pfizer; Novartis; Sanofi-aventis; Roche; AstraZeneca; Genomic Health; Wyeth; Orient Europharma; Medicom; Tin Hang Technology; MacKay Medical Group; Macau Tourism Board.

Notes

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.