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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

  作者 Yiu, CCP; Sasano, H; Ono, K; Chow, LWC  
  选自 期刊  Expert opinion on investigational drugs;  卷期  2010年19-S;  页码  S79-S89  
  关联知识点  
 

[摘要]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.

 
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