Huang R Stephanie, Duan Shiwei, Kistner Emily O, Bleibel Wasim K, Delaney Shannon M, Fackenthal Donna L, Das Soma, Dolan M Eileen
Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA.
Cancer Res. 2008 May 1;68(9):3161-8. doi: 10.1158/0008-5472.CAN-07-6381.
Identifying heritable genetic variants responsible for chemotherapeutic toxicities has been challenging due in part to its multigenic nature. To date, there is a paucity of data on genetic variants associated with patients experiencing severe myelosuppression or cardiac toxicity following treatment with daunorubicin. We present a genome-wide model using International HapMap cell lines that integrate genotype and gene expression to identify genetic variants that contribute to daunorubicin-induced cytotoxicity. A cell growth inhibition assay was used to measure variations in the cytotoxicity of daunorubicin. Gene expression was determined using the Affymetrix GeneChip Human Exon 1.0ST Array. Using sequential analysis, we evaluated the associations between genotype and cytotoxicity, those significant genotypes with gene expression and correlated gene expression of the identified candidates with cytotoxicity. A total of 26, 9, and 18 genetic variants were identified to contribute to daunorubicin-induced cytotoxicity through their effect on 16, 9, and 36 gene expressions in the combined, Centre d' Etude du Polymorphisme Humain (CEPH), and Yoruban populations, respectively. Using 50 non-HapMap CEPH cell lines, single nucleotide polymorphisms generated through our model predicted 29% of the overall variation in daunorubicin sensitivity and the expression of CYP1B1 was significantly correlated with sensitivity to daunorubicin. In the CEPH validation set, rs120525235 and rs3750518 were significant predictors of transformed daunorubicin IC(50) (P = 0.005 and P = 0.0008, respectively), and rs1551315 trends toward significance (P = 0.089). This unbiased method can be used to elucidate genetic variants contributing to a wide range of cellular phenotypes.
由于化疗毒性具有多基因性质,确定导致化疗毒性的可遗传基因变异颇具挑战性。迄今为止,关于柔红霉素治疗后出现严重骨髓抑制或心脏毒性的患者相关基因变异的数据匮乏。我们提出了一种使用国际人类基因组单体型图(HapMap)细胞系的全基因组模型,该模型整合了基因型和基因表达,以识别导致柔红霉素诱导细胞毒性的基因变异。采用细胞生长抑制试验来测量柔红霉素细胞毒性的变化。使用Affymetrix基因芯片人类外显子1.0ST阵列测定基因表达。通过序列分析,我们评估了基因型与细胞毒性之间的关联、那些具有显著意义的基因型与基因表达之间的关联,以及已识别候选基因的相关基因表达与细胞毒性之间的关联。在综合人群、人类多态性研究中心(CEPH)人群和约鲁巴人群中,分别有26个、9个和18个基因变异被确定通过影响16个、9个和36个基因的表达而导致柔红霉素诱导的细胞毒性。使用50个非HapMap CEPH细胞系,通过我们的模型产生的单核苷酸多态性预测了柔红霉素敏感性总体变异的29%,并且CYP1B1的表达与对柔红霉素的敏感性显著相关。在CEPH验证集中,rs120525235和rs3750518是转化后的柔红霉素IC(50)的显著预测因子(P分别为0.005和0.0008),rs1551315有显著趋势(P = 0.089)。这种无偏倚的方法可用于阐明导致广泛细胞表型的基因变异。