Imatinib (imatinib mesylate, STI-571, Gleevec) is a selective BCR-ABL tyrosine kinase inhibitor that has been used as a highly effective chemoagent for treating chronic myelogenous leukemia. However, the initial response to imatinib is often followed by the recurrence of a resistant form of the disease, which is major obstacle to many therapeutic modalities. The aim of this study was to identify the gene expression signatures that confer resistance to imatinib. A series of four resistant K562 sublines was established with different imatinib dosage (200, 400, 600 and 800 nM) and analyzed using microarray technology. The transcripts of the genes showing universal or dose-dependent expression changes across the resistant sublines were identified. The gene sets associated with the imatinib-resistance were also identified using gene set enrichment analysis. In the resistant K562 sublines, the transcriptionand apoptosis-related expression signatures were upregulated, whereas those related to the protein and energy metabolism were downregulated. Several genes identified in this study such as IGF1 and RAB11A have the potential to become surrogate markers useful in a clinical evaluation of imatinibresistant patients without BCR-ABL mutation. The expression signatures identified in this study provide insights into the mechanism of imatinib-resistance and are expected to facilitate the development of an effective diagnostic and therapeutic strategy.

Objectives: To evaluate axial cervical vertebral (ACV) shape quantitatively and to build a prediction model for skeletal maturation level using statistical shape analysis for Japanese individuals. Methods: The sample included 24 female and 19 male patients with hand-wrist radiographs and CBCT images. Through generalized Procrustes analysis and principal components (PCs) analysis, the meaningful PCs were extracted from each ACV shape and analysed for the estimation regression model. Results: Each ACV shape had meaningful PCs, except for the second axial cervical vertebra. Based on these models, the smallest prediction intervals (PIs) were from the combination of the shape space PCs, age and gender. Overall, the PIs of the male group were smaller than those of the female group. There was no significant correlation between centroid size as a size factor and skeletal maturation level. Conclusions: Our findings suggest that the ACV maturation method, which was applied by statistical shape analysis, could confirm information about skeletal maturation in Japanese individuals as an available quantifier of skeletal maturation and could be as useful a quantitative method as the skeletal maturation index.