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Prediction of sensitivity of rectal cancer cells in response to preoperative radiotherapy by DNA microarray analysis of gene expression profiles.

Watanabe T, Komuro Y, Kiyomatsu T, Kanazawa T, Kazama Y, Tanaka J, Tanaka T, Yamamoto Y, Shirane M, Muto T, Nagawa H

Department of Surgical Oncology, University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. toshwatanabe@yahoo.co.jp

Preoperative radiotherapy has been widely used to improve local control of disease and to improve survival in the treatment of rectal cancer. However, the response to radiotherapy differs among individual tumors. Our objective here was to identify a set of discriminating genes that can be used for characterization and prediction of response to radiotherapy in rectal cancer. Fifty-two rectal cancer patients who underwent preoperative radiotherapy were studied. Biopsy specimens were obtained from rectal cancer before preoperative radiotherapy. Response to radiotherapy was determined by histopathologic examination of surgically resected specimens and classified as responders or nonresponders. By determining gene expression profiles using human U95Av2 Gene Chip, we identified 33 novel discriminating genes of which the expression differed significantly between responders and nonresponders. Using this gene set, we were able to establish a new model to predict response to radiotherapy in rectal cancer with an accuracy of 82.4%. The list of discriminating genes included growth factor, apoptosis, cell proliferation, signal transduction, or cell adhesion-related genes. Among 33 discriminating genes, apoptosis inducers (lumican, thrombospondin 2, and galectin-1) showed higher expression in responders whereas apoptosis inhibitors (cyclophilin 40 and glutathione peroxidase) showed higher expression in nonresponders. The present study suggested the possibility that gene expression profiling may be useful in predicting response to radiotherapy to establish an individualized tailored therapy for rectal cancer. Global expression profiles of responders and nonresponders may provide insights into the development of novel therapeutic targets.

Published 4 April 2006 in Cancer Res, 66(7): 3370-4.
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Microarrays Research Today Archive:

Volume 1 (2004)
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Microarrays Books

The Analysis of Gene Expression Data

The Analysis of Gene Expression Data