Prediction of radiation sensitivity using a gene expression classifier
Prediction of radiation sensitivity using a gene expression classifier
复制标题
DOI：
10.1158/0008-5472.can-05-0656
复制
发表时间：
2005-08-15
期刊：
影响因子：
11.2
通讯作者：
Yeatman, T
中科院分区：
文献类型：
--
作者：
Torres-Roca, JF;Eschrich, S;Yeatman, T
几十年来，成功的辐射敏感性预测分析的发展一直是辐射生物学的主要目标。我们已经开发了一种辐射分类器，预测固有的放射敏感性的肿瘤细胞系的生存分数在2戈伊（SF 2），从文献中获得的基因表达谱的基础上测量。我们的分类器正确预测了来自国家癌症研究所60人小组的35种细胞系中的22种的SF 2值，结果与偶然性显著不同（P = 0.0002）。在我们的方法中，我们把辐射敏感性作为一个连续变量，显着性分析的微阵列用于基因选择，和一个多元线性回归模型用于辐射敏感性预测。基因选择步骤鉴定了三个新基因（RbAp 48、RGS 19和R5 PIA），其表达值与辐射敏感性相关。通过定量实时PCR确认基因表达。为了生物学验证我们的分类器，我们将RbAp 48转染到三种癌细胞系（HS-578 T、MALME-3 M和MDA-MB-231）中。RbAp 48过表达诱导放射增敏（1.5至2倍）相比，模拟转染的细胞系。此外，我们发现HS-578 T-RbAp 48过表达者在G（2）-M（细胞周期的放射敏感期）中的细胞比例更高（27%对5%）。最后，RbAp 48过表达与Akt的去磷酸化相关，表明RbAp 48可能通过拮抗Ras途径发挥其作用。我们的研究结果具有重大意义。我们建立了辐射敏感性可以预测基因表达谱的基础上，我们介绍了一种基因组的方法来识别新的分子标记的辐射敏感性。
The development of a successful radiation sensitivity predictive assay has been a major goal of radiation biology for several decades. We have developed a radiation classifier that predicts the inherent radiosensitivity of tumor cell lines as measured by survival fraction at 2 Gy (SF2), based on gene expression profiles obtained from the literature. Our classifier correctly predicts the SF2 value in 22 of 35 cell lines from the National Cancer Institute panel of 60, a result significantly different from chance (P = 0.0002). In our approach, we treat radiation sensitivity as a continuous variable, significance analysis of microarrays is used for gene selection, and a multivariate linear regression model is used for radiosensitivity prediction. The gene selection step identified three novel genes (RbAp48, RGS19, and R5PIA) of which expression values are correlated with radiation sensitivity. Gene expression was confirmed by quantitative real-time PCR. To biologically validate our classifier, we transfected RbAp48 into three cancer cell lines (HS-578T, MALME-3M, and MDA-MB-231). RbAp48 overexpression induced radiosensitization (1.5- to 2-fold) when compared with mock-transfected cell lines. Furthermore, we show that HS-578T-RbAp48 overexpressors have a higher proportion of cells in G(2)-M (27% versus 5%), the radiosensitive phase of the cell cycle. Finally, RbAp48 overexpression is correlated with dephosphorylation of Akt, suggesting that RbAp48 may be exerting its effect by antagonizing the Ras pathway. The implications of our findings are significant. We establish that radiation sensitivity can be predicted based on gene expression profiles and we introduce a genomic approach to the identification of novel molecular markers of radiation sensitivity.