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Predicting Response to Methotrexate, Vinblastine, Doxorubicin, and Cisplatin Neoadjuvant Chemotherapy for Bladder Cancers through Genome-Wide Gene Expression Profiling

Authors' Affiliations: ¹ Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; ² SNP Research Center, RIKEN (Institute of Physical and Chemical Research), Yokohama, Japan; ³ Department of Urology, Kochi Medical School, Nankoku, Japan; ⁴ Department of Urology, Kyoto Prefectural University of Medicine, Kyoto, Japan; ⁵ Department of Urology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan; ⁶ Department of Nephro-urology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan; and ⁷ Department of Urology, Iwate Medical University, Morioka, Japan

Requests for reprints: Yusuke Nakamura, Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. Phone: 81-3-5449-5372; Fax: 81-3-5449-5433; E-mail: yusuke{at}ims.u-tokyo.ac.jp.

Purpose: Neoadjuvant chemotherapy for invasive bladder cancer, involving a regimen of methotrexate, vinblastine, doxorubicin, and cisplatin (M-VAC), can improve the resectability of larger neoplasms for some patients and offer a better prognosis. However, some suffer severe adverse drug reactions without any effect, and no method yet exists for predicting the response of an individual patient to chemotherapy. Our purpose in this study is to establish a method for predicting response to the M-VAC therapy.

Experimental Design: We analyzed gene expression profiles of biopsy materials from 27 invasive bladder cancers using a cDNA microarray consisting of 27,648 genes, after populations of cancer cells had been purified by laser microbeam microdissection.

Results: We identified dozens of genes that were expressed differently between nine "responder" and nine "nonresponder" tumors; from that list we selected the 14 "predictive" genes that showed the most significant differences and devised a numerical prediction scoring system that clearly separated the responder group from the nonresponder group. This system accurately predicted the drug responses of 8 of 9 test cases that were reserved from the original 27 cases. Because real-time reverse transcription–PCR data were highly concordant with the cDNA microarray data for those 14 genes, we developed a quantitative reverse transcription–PCR–based prediction system that could be feasible for routine clinical use.

Conclusions: Our results suggest that the sensitivity of an invasive bladder cancer to the M-VAC neoadjuvant chemotherapy can be predicted by expression patterns in this set of genes, a step toward achievement of "personalized therapy" for treatment of this disease.

Key Words: prediction system • cDNA microarray • quantitative RT-PCR • personalized therapy

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