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Vol.46 No.3 contents | Japanese/English |
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- The 20th Lung Cancer Workshop <Session II> -
Prediction of Sensitivity of Non-small Cell Lung Cancers to Gefitinib by Gene Expression Profiling
Soji Kakiuchi1, Seiji Yano1, Saburo Sone11Department of Internal Medicine and Molecular Therapeutics, Institute of Health Biosciences, University of Tokushima Graduate School
Objective. Gefitinib (Iressa), an inhibitor of epidermal growth factor receptor-tyrosine kinase (EGFR-TK), has shown favorable anti-tumor activity to a subset of patients with advanced non-small cell lung cancer (NSCLC). However, gefitinib failed to significantly prolong survival in comparison to placebo in an unselected population. Consequently, selection of patients who would benefit from this treatment is clinically important. EGFR-activating mutations are potent markers of dramatic response, but do not seem to be associated with stable disease. Because stable disease seems to contribute to the overall survival benefit derived from EGFR-TKI treatment, EGFR mutations do not qualify to be used as a predictive biomarkers for selection of NSCLC patients for treatment with EGFR inhibitors. The goal of this study was to identify gene expression that correlated with gefitinib sensitivity, and to establish a prediction system of clinical outcome of NSCLC patients treated by gefitinib. Methods. To establish a scoring system to predict the response of NSCLC patients to gefitinib, we used a genome-wide DNA microarray to analyze 33 biopsy samples of advanced NSCLC from patients who had been treated with gefitinib monotherapy. We carried out a random permutation test to identify a set of genes expressed differentially responders and non-responders. The gefitinib response score (GRS) reflecting gene expression was calculated by a weighted vote method and this numerical scoring system was validated by the leave-one-out approach and independent test cases. Results. We identified 51 genes the expression of which differed significantly between responders and non-responders to the drug. We selected the 12 genes that showed the most significant differences to establish a numerical scoring system for predicting response to gefitinib treatment. This system clearly separated the responders and non-responders without any overlap, and accurately predicted responses to the drug in additional NSCLC cases. Moreover, this system enabled separation of intermediate tumor responses (SD) into two groups, one representing patients who succeeded in maintaining the tumor-static effect for a long period and the other representing patients who failed to do so; the former group was a good target for this treatment and the latter was not. Conclusion. Our results suggest that the clinical outcome of NSCLC patients treated with gefitinib could be predicted by gene expression profile.
key words: Gefitinib, Personalized medicine, Gene expression, DNA microarray, Non-small cell lung cancer
JJLC 46 (3): 245-251, 2006
