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Ten years have passed since the emergence of microarray technology. Recent microarray procedures have provided reliable results on all platforms and have enabled highly reproducible gene expression measurements. Thus, nearly all technical matters regarding microarray measurements are thought to have been resolved. Treatment stratification for molecular-targeted drugs can now be achieved based on the presence of somatic mutations, gene amplification, and/or protein overexpression. However, no clinically available biomarkers have been identified for molecular-targeted drugs using microarray analysis. Microarray data as a database for the gene expressions of clinical samples may be a critical issue, especially for the development of molecular-targeted treatments. In addition, microarray analysis during early-phase clinical trials for molecular-targeted drugs is considered to provide critical information, including proof-of-concept and confirmation of the inhibition of the target molecule. Meanwhile, OncotypeDX(®) and MammaPrint(®) assays have been developed to determine the benefits of chemotherapy for breast cancer patients. These multigene-based assays are commercially available and have shown encouraging results for treatment stratification or decision-making for treatment using cytotoxic drugs in clinical settings. During the development of these assays, numerous samples and efforts were required to create a model using multi-center or inter-group investigations. Based on the success of these models, the development of further assays for determining multigene expressions is likely to increase in the future. In the present article, we introduce our data on mutant epidermal growth factor receptor (EGFR) signaling and amplification of fibroblast growth factor receptor 2 (FGFR2) using microarray analysis, and treatment stratification and clinical applications using gene expression profiles for cancer treatments are discussed.

What Can and Cannot Be Done Using a Microarray Analysis? Treatment Stratification and Clinical Applications in Oncology