Prediction of in vitro response to interferon‐α in renal cell carcinoma cell lines

• Published in: Cancer science Vol. 98; no. 4; pp. 529 - 534
• Main Authors: Shimazui, Toru, Ami, Yoshihiro, Yoshikawa, Kazuhiro, Uchida, Kazuhiko, Kojima, Takahiro, Oikawa, Takehiro, Nakamura, Kogenta, Honda, Nobuaki, Hinotsu, Shiro, Miyazaki, Jun, Kunita, Noriko, Akaza, Hideyuki
• Format: Journal Article
• Language: English
• Published: Melbourne, Australia Blackwell Publishing Asia 01.04.2007
• Subjects: Antineoplastic Agents - pharmacology; Carcinoma, Renal Cell - drug therapy; Drug Resistance, Neoplasm; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Interferon alpha-2; Interferon-alpha - pharmacology; Kidney Neoplasms - drug therapy; Models, Genetic; Original; Polymerase Chain Reaction; Recombinant Proteins; RNA, Messenger - analysis; Tumor Cells, Cultured
• ISSN: 1347-9032; 1349-7006
• DOI: 10.1111/j.1349-7006.2007.00421.x

We analyzed the correlation between interferon‐α (IFNα) response and gene expression profiles to predict IFNα sensitivity and identified key molecules regulating the IFNα response in renal cell carcinoma (RCC) cell lines. To classify eight RCC cell lines of the SKRC series into three subgroups according to IFNα sensitivity, that is, sensitive, resistant and intermediate group, responses to IFNα (300–3000 IU/mL) were quantified by WST‐1 assay. Microarray, followed by supervised hierarchical clustering analysis, was applied to selected genes according to IFNα sensitivity. In order to find alteration of expression profiles induced by IFNα, sequential microarray analyses were performed at 3, 6, and 12 h after IFNα treatment of RCC cell lines and mRNA expression level was confirmed using quantitative real time polymerase chain reaction. According to the sequential microarray analysis between IFNα‐sensitive and ‐resistant line, seven genes were selected as candidates for IFNα‐sensitivity‐related genes in RCC cell lines. Among these seven genes, we further developed a model to predict tumor inhibition with four genes, that is, adipose differentiation‐related protein, microphthalmia associated transcription factor, mitochondrial tumor suppressor 1, and troponin T1 using multiple linear regression analysis (coefficient = 0.948, P = 0.0291) and validated the model using other RCC cell lines including six primary cultured RCC cells. The expression levels of the combined selected genes may provide predictive information on the IFNα response in RCC. Furthermore, the IFNα response to RCC might be modulated by regulation of the expression level of these molecules. (Cancer Sci 2007; 98: 529–534)