Epigenetic studies in rhabdomyosarcoma

高级研究员在2011财年转移到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月招募了一名员工科学家，他最初帮助建立了实验室；这位科学家随后于2012年3月开始研究RMS甲基化项目。研究人员将生物信息学策略应用于第一组RMS肿瘤的甲基化数据，以确定融合阳性ARMS和融合阴性ERMS肿瘤之间甲基化差异的基因。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有显著差异的基因。由于有超过27,000个比较，因此对多个比较进行了更正。在ARMS中发现了高甲基化和ERMS中发现了低甲基化的基因，在ARMS中发现了低甲基化和ERMS中发现了高甲基化的基因。由于每个甲基化测量适用于单个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肿瘤中较大的含cpg调控区域的甲基化状态来验证所选基因的差异甲基化。根据一些试验，很明显，我们需要优化实验室的方法，以准确测量肿瘤样本中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种方法，然后我们将在随后的分析中使用RMS标本的DNA。我们的目标是用亚硫酸氢盐处理基因组DNA，扩增选定的含有CpG的区域，然后通过焦磷酸测序表征该区域的甲基化状态。我们确定了几个商业上可用的控制亚硫酸处理基因组DNA的试剂盒之一有效地将所有胞嘧啶转化为尿嘧啶。在之前的研究中，使用体外甲基化基因组DNA对照，我们也证实了这些试剂盒中的亚硫酸氢盐处理不会改变甲基化胞嘧啶。因此，我们可以自信地使用亚硫酸氢盐程序以甲基化特异性的方式修改DNA序列。为了扩增所选择的区域，我们在设计PCR引物和随后的PCR反应时有几个考虑因素。首先，该区域的层序不是原生层序，而是亚硫酸盐处理后的修饰层序。感兴趣的序列是富含CpG的调控区域，包括Illumina阵列中鉴定的感兴趣的CpG。然而，应该强调的是，我们选择的侧翼引物不含CpG二核苷酸，因此不具有依赖于甲基化状态的可变序列。在选择引物的可用算法中，我们确定Methyl Primer Express软件（Applied Biosystems）可用于选择健壮的引物对。除了引物中的基因特异性序列外，我们还确定了在正向和反向引物的5'端添加标签的效用（例如M13正向和反向引物序列）。这些标签允许第二个嵌套步骤，如果第一次聚合酶链反应导致低产量的产品。此外，可以使用单个常见生物素标记的M13正向引物将单链产物与双链PCR产物分离，为焦磷酸测序研究做准备。一旦选择了PCR引物，我们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，我们发现添加BSA和甘油的反应条件允许使用更高的退火温度在循环过程中增加特异性。基于这些不同的参数，我们从一系列融合阴性和阳性的RMS细胞系中扩增了几种差异甲基化基因的CpG-containing区域，现在开始进行焦磷酸测序分析，以确定CpG二核苷酸中胞嘧啶的C:T比例，从而确定这些位点在更大的CpG-containing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.