The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Prediction of Docetaxel Response in Human Breast Cancer by Gene Expression Profiling
Abstract
Purpose: Docetaxel is one of the most effective anticancer drugs available in the treatment of breast cancer. Nearly half of the treated patients, however, do not respond to chemotherapy and suffer from side effects. The ability to reliably predict a patient's response based on tumor gene expression will improve therapeutic decision making and save patients from unnecessary side effects. Patients and Methods: A total of 44 breast tumor tissues were sampled by biopsy before treatment with docetaxel, and the response to therapy was clinically evaluated by the degree of reduction in tumor size. Gene expression profiling of the biopsy samples was performed with 2,453 genes using a high-throughput reverse transcriptase polymerase chain reaction technique. Using genes differentially expressed between responders and nonresponders, a diagnostic system based on the weighted-voting algorithm was constructed. Results: This system predicted the clinical response of 26 previously unanalyzed samples with over 80% accuracy, a level promising for clinical applications. Diagnostic profiles in nonresponders were characterized by elevated expression of genes controlling the cellular redox environment (ie, redox genes, such as thioredoxin, glutathione-S-transferase, and peroxiredoxin). Overexpression of these genes protected cultured mammary tumor cells from docetaxel-induced cell death, suggesting that enhancement of the redox system plays a major role in docetaxel resistance. Conclusion: These results suggest that the clinical response to docetaxel can be predicted by gene expression patterns in biopsy samples. The results also suggest that one of the molecular mechanisms of the resistance is activation of a group of redox genes.
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Cited by
Gene expression profiling in breast cancer: classification, prognostication, and prediction
Reis-Filho, J. S., & Pusztai, L. (2011) · The Lancet · doi:10.1016/s0140-6736(11)61539-0 · Cited by 729
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Mechanisms and strategies to overcome chemotherapy resistance in metastatic breast cancer
Coley, H. M. (2008) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2008.01.007 · Cited by 351
Huang, F., Reeves, K., Han, X., Fairchild, C., Platero, S., Wong, T. W., Lee, F., Shaw, P., & Clark, E. (2007) · Cancer Research · doi:10.1158/0008-5472.can-06-3633 · Cited by 344
Metadata
- Authors
- Iwao-Koizumi, K., Matoba, R., Ueno, N., Kim, S. J., Ando, A., Miyoshi, Y., Maeda, E., Noguchi, S., & Kato, K.
- Venue
- Journal of Clinical Oncology
- Published
- 2005-01-20
- Volume
- 23
- Issue
- 3
- Pages
- 422-431
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- OpenAlex
- W2140486627
- Cited by
- 309
- References
- 28
- FWCI
- 16.32
- Type
- journal article
- Language
- en
- Open access
- closed
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