Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.
Genome Med. 2012 Mar 27;4(3):24. doi: 10.1186/gm323.
Recently, it has been proposed that epigenetic variation may contribute to the risk of complex genetic diseases like cancer. We aimed to demonstrate that epigenetic changes in normal cells, collected years in advance of the first signs of morphological transformation, can predict the risk of such transformation.
We analyzed DNA methylation (DNAm) profiles of over 27,000 CpGs in cytologically normal cells of the uterine cervix from 152 women in a prospective nested case-control study. We used statistics based on differential variability to identify CpGs associated with the risk of transformation and a novel statistical algorithm called EVORA (Epigenetic Variable Outliers for Risk prediction Analysis) to make predictions.
We observed many CpGs that were differentially variable between women who developed a non-invasive cervical neoplasia within 3 years of sample collection and those that remained disease-free. These CpGs exhibited heterogeneous outlier methylation profiles and overlapped strongly with CpGs undergoing age-associated DNA methylation changes in normal tissue. Using EVORA, we demonstrate that the risk of cervical neoplasia can be predicted in blind test sets (AUC = 0.66 (0.58 to 0.75)), and that assessment of DNAm variability allows more reliable identification of risk-associated CpGs than statistics based on differences in mean methylation levels. In independent data, EVORA showed high sensitivity and specificity to detect pre-invasive neoplasia and cervical cancer (AUC = 0.93 (0.86 to 1) and AUC = 1, respectively).
We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application.
The ARTISTIC trial is registered with the International Standard Randomised Controlled Trial Number ISRCTN25417821.
最近有人提出,表观遗传变异可能导致癌症等复杂遗传疾病的风险增加。我们旨在证明,在形态学转化的最初迹象出现前多年收集的正常细胞中的表观遗传变化,可以预测这种转化的风险。
我们分析了 152 名女性细胞学正常的子宫颈细胞中超过 27000 个 CpG 的 DNA 甲基化(DNAm)图谱。我们使用基于差异变异性的统计学方法来识别与转化风险相关的 CpG,并使用一种称为 EVORA(用于风险预测分析的表观遗传变量异常值)的新统计算法进行预测。
我们观察到许多 CpG 在 3 年内发展为非侵入性宫颈癌的女性与未患病的女性之间存在差异可变。这些 CpG 表现出异质的异常甲基化谱,与正常组织中与年龄相关的 DNA 甲基化变化的 CpG 强烈重叠。使用 EVORA,我们证明了在盲测试集(AUC = 0.66(0.58 至 0.75))中可以预测宫颈癌的风险,并且 DNAm 变异性的评估允许比基于平均甲基化水平差异的统计学方法更可靠地识别风险相关的 CpG。在独立数据中,EVORA 显示出高灵敏度和特异性来检测癌前病变和宫颈癌(AUC = 0.93(0.86 至 1)和 AUC = 1)。
我们证明,上皮癌起源的形态正常细胞中的 DNA 甲基化谱可以预测肿瘤转化的风险。由于只对人类基因组中的 0.1%的 CpG 进行了分析,因此更广泛覆盖范围的研究可能会产生具有临床应用所需准确性的改进预测和诊断模型。
ARTISTIC 试验在国际标准随机对照试验注册号 ISRCTN25417821 中注册。