Quantum dots-based molecular classification of breast cancer by quantitative spectroanalysis of hormone receptors and HER2
| Autor: | Shan Zhu, Jun Peng, Chun-Wei Peng, Chu-Bo Qi, Xue-Qin Yang, Ming-Bai Hu, Yi-Ping Gong, Dai-Wen Pang, Shao-Ping Liu, Chuang Chen, Shengrong Sun, Yan Li |
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| Rok vydání: | 2011 |
| Předmět: |
Prognosis prediction
Receptor ErbB-2 Biophysics Bioengineering Breast Neoplasms Bioinformatics Biomaterials Breast cancer Molecular classification Quantum Dots medicine Humans Spectral analysis Human Epidermal Growth Factor Receptor 2 business.industry medicine.disease Quantitative determination Microscopy Fluorescence Receptors Estrogen Mechanics of Materials Hormone receptor Tissue Array Analysis Ceramics and Composites Female Personalized medicine business Receptors Progesterone |
| Zdroj: | Biomaterials. 32(30) |
| ISSN: | 1878-5905 |
| Popis: | The emerging molecular breast cancer (BC) classification based on key molecules, including hormone receptors (HRs), and human epidermal growth factor receptor 2 (HER2) has been playing an important part of clinical practice guideline. The current molecular classification mainly based on their fingerprints, however, could not provide enough essential information for treatment decision making. The molecular information on both patterns and quantities could be more helpful to heterogeneities understanding for BC personalized medicine. Here we conduct quantitative determination of HRs and HER2 by quantum dots (QDs)-based quantitative spectral analysis, which had excellent consistence with traditional method. Moreover, we establish a new molecular classification system of BC by integrating the quantitative information of HER2 and HRs, which could better reveal BC heterogeneity and identify 5 molecular subtypes with different 5-year prognosis. Furthermore, the emerging 5 molecular subtypes based on simple quantitative molecules information could be as informative as multi-genes analysis in routine practice, and might help formulate a more personalized comprehensive therapy strategy and prognosis prediction. |
| Databáze: | OpenAIRE |
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