Gene Expression Patterns that Predict Sensitivity to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Lung Cancer Cell Lines and Human Lung Tumors. Homo sapiens

Global gene expression data were generated from cultured non small cell lung cancer cell lines (NSCLC), normalized using MAS 5.0, filtered and used to predict response of cells to EGFR inhibition Overall design: Gene expression data from additional cell lines and tumors was used to validate the predictive algorithm Total RNA was prepared from NSCLC cell lines and applied to Affymetric U133 2.0 microarrays

本研究的全局基因表达数据源自体外培养的非小细胞肺癌（non-small cell lung cancer, NSCLC）细胞系，经MAS 5.0算法标准化处理与过滤筛选后，用于预测细胞对表皮生长因子受体（epidermal growth factor receptor, EGFR）抑制剂的应答反应。实验整体设计：额外细胞系及肿瘤组织的基因表达数据用于验证该预测算法。本研究从NSCLC细胞系中提取总RNA，并将其应用于Affymetrix U133 2.0微阵列芯片。