Epigenetic studies in rhabdomyosarcoma

这位高级调查员在2011财年期间转到NCI，并在2012财年上半年雇用人员建立了一个研究实验室。2011年11月招聘了一名工作人员科学家，这名科学家最初帮助建立了实验室；这名科学家随后于2012年3月开始从事RMS甲基化项目。这位科学家将生物信息学策略应用于第一组RMS肿瘤的甲基化数据，以识别融合阳性手臂肿瘤和融合阴性ERMS肿瘤之间差异甲基化的基因。使用监督分析算法比较融合阴性和融合阳性肿瘤中每个基因的平均甲基化水平，然后确定两组肿瘤中平均甲基化水平显著不同的基因。由于比较次数超过27,000次，因此对多次比较进行了更正。发现了同时存在ARM高甲基化和ERMS低甲基化的基因，以及存在ARM低甲基化和ERMS高甲基化的其他基因。由于每个甲基化测量都适用于一个CpG位点，当在同一基因中发现多个CpG的关系时，信心和兴趣增加。我们的下一个目标是通过确定融合阴性和阳性RMS肿瘤中较大的包含CpG的调节区的甲基化状态来验证选定基因的差异甲基化。根据一些试验性实验，很明显，我们需要优化我们实验室的方法学，以准确地测量肿瘤样本中的基因特异性甲基化水平。我们最初使用RMS肿瘤细胞系来制定这一方法学，然后我们将在随后的分析中使用RMS样本的DNA。我们的目标是用亚硫酸氢盐处理基因组DNA，扩增选定的CpG区域，然后通过焦磷酸测序来表征该区域的甲基化状态。我们确定了几种可用于控制亚硫酸氢盐处理基因组DNA的商用试剂盒中的一种，可以有效地将所有胞嘧啶转化为尿嘧啶。在之前的研究中，使用体外甲基化的基因组DNA对照，我们也证实了来自这些试剂盒的亚硫酸氢盐处理不会改变甲基化的胞嘧啶。因此，我们可以自信地使用这种亚硫酸氢盐程序以甲基化特异性的方式修改DNA序列。为了扩增选定的区域，我们在设计聚合酶链式反应引物和随后的聚合酶链式反应时有几个考虑因素。首先，该区域的序列不是天然序列，而是亚硫酸氢盐处理后的修饰序列。感兴趣的序列是富含CpG的调节区，包括在Illumina阵列中确定的感兴趣的CpG。然而，应该强调的是，我们选择的侧翼引物不包含CpG二核苷酸，因此没有依赖于甲基化状态的可变序列。在现有的选择引物的算法中，我们确定了甲基底物快速软件(应用生物系统)在选择稳健的引物对方面是有用的。此外，我们还确定了在正向和反向引物(如M13正向和反向引物序列)的5‘端添加标签的有效性。如果第一次聚合酶链式反应结果产物产量低，则这些标签允许第二嵌套步骤。此外，一个普通的生物素标记的M13正向引物可用于从双链聚合酶链式反应产物中分离出单链产物，为焦磷酸测序研究做准备。一旦选择了PCR引物，我们就寻找最佳的反应条件，特别是对于这些高CpG含量的区域。特别是，我们发现，在反应条件中添加牛血清白蛋白和甘油，可以通过在循环过程中使用更高的热处理温度来提高特异性。基于这些不同的参数，我们已经从一系列融合阴性和阳性的RMS细胞系中扩增了几个差异甲基化基因的CpG区域，现在正在开始焦磷酸测序分析，以确定CpG二核苷酸中胞嘧啶的C：T比率，从而确定这些位置在更大的CpG区域中的甲基化状态。

The Senior Investigator moved to the NCI during FY2011 and hired personnel to set up a research laboratory during the first half of FY2012. A Staff Scientist was recruited in November 2011, and this individual initially helped to set up the laboratory; this scientist then began working on this RMS methylation project in March 2012. The staff scientist applied bioinformatic strategies to the methylation data from the first set of RMS tumors to identify genes that are differentially methylated between fusion-positive ARMS and fusion-negative ERMS tumors. Supervised analysis algorithms were used to compare mean methylation levels for each gene in the fusion-negative and fusion-positive tumors, and then to determine those genes whose mean methylation levels were significantly different between the two groups of tumors. As there were greater than 27,000 comparisons, corrections were made for multiple comparisons. Genes were found for which there was both evidence of hypermethylation in ARMS and hypomethylation in ERMS, and other genes were found for which there was evidence of hypomethylation in ARMS and hypermethylation in ERMS. As each methylation measurement applies to a single CpG site, confidence and interest increased when the relationship was found for more than one CpG in the same gene. Our next goal was to validate the differential methylation of selected genes by determining the methylation status of the larger CpG-containing regulatory region in fusion-negative and positive RMS tumors. Based on some trial experiments, it was clear that we needed to optimize methodology in our laboratory to accurately measure gene-specific methylation levels in tumor samples. We initially used RMS tumor cell lines to work out this methodology and then we will subsequently use DNA from RMS specimens in our later analyses. Our goal was to treat genomic DNA with bisulfite, amplify selected CpG containing regions, and then characterize the methylation status of this region by pyrosequencing. We determined that one of several commercially available kits for controlled bisulfite treatment of genomic DNA efficiently converts all cytosines to uracil. In previous studies, using in vitro methylated genomic DNA controls, we also confirmed that the bisulfite treatment from these kits does not modify methylated cytosines. Therefore, we can confidently use this bisulfite procedure to modify the sequence of DNA in a methylation-specific fashion. To amplify the selected regions, there were several considerations in our design of PCR primers and the subsequent PCR reaction. First, the sequence of the region was not the native sequence but the modified sequence after bisulfite treatment. The sequences of interest were the CpG-rich regulatory regions including the CpG's of interest identified in the Illumina array. However, it should be emphasized that we select flanking primers that do not contain CpG dinucleotides and thus do not have a variable sequence dependent on methylation status. Of the available algorithms for selecting primers, we determined that Methyl Primer Express software (Applied Biosystems) was useful in selecting robust primer pairs. In addition to the gene specific sequences in the primer, we also determined the utility of adding tags on the 5' ends of the forward and reverse primers (such as M13 forward and reverse primer sequences). These tags permit a second nested step if the first PCR results in a low yield of product. In addition, a single common biotin-labeled M13 forward primer can be used to isolate the single stranded product from the double stranded PCR products in preparation for the pyrosequencing studies. Once the PCR primers were selected, we looked for optimal reaction conditions, particularly for these high-CpG-containing regions. In particular, we found that addition of BSA and glycerol to the reaction conditions permitted increased specificity by allowing use of higher annealing temperatures during cycling. Based on these various parameters, we have amplified CpG-containing regions for several differentially methylated genes from a series of fusion-negative and positive RMS cells lines, and are now commencing pyrosequencing analysis to determine the C:T ration at cytosines within CpG dinucleotides and thereby determining the methylation status of these sites within the larger CpG-containing region.