Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Badalona, Barcelona, Spain.
PLoS One. 2012;7(8):e42682. doi: 10.1371/journal.pone.0042682. Epub 2012 Aug 15.
The study of somatic genetic alterations in tumors contributes to the understanding and management of cancer. Genetic alterations, such us copy number or copy neutral changes, generate allelic imbalances (AIs) that can be determined using polymorphic markers. Here we report the development of a simple set of calculations for analyzing microsatellite multiplex PCR data from control-tumor pairs that allows us to obtain accurate information not only regarding the AI status of tumors, but also the percentage of tumor-infiltrating normal cells, the locus copy-number status and the mechanism involved in AI. We validated this new approach by re-analyzing a set of Neurofibromatosis type 1-associated dermal neurofibromas and comparing newly generated data with results obtained for the same tumors in a previous study using MLPA, Paralog Ratio Analysis and SNP-array techniques.Microsatellite multiplex PCR analysis (MMPA) should be particularly useful for analyzing specific regions of the genome containing tumor suppressor genes and also for determining the percentage of infiltrating normal cells within tumors allowing them to be sorted before they are analyzed by more expensive techniques.
研究肿瘤中的体基因改变有助于理解和管理癌症。遗传改变，如拷贝数或拷贝中性改变，会产生等位基因不平衡（AIs），可以使用多态性标记来确定。在这里，我们报告了一组用于分析对照-肿瘤对中微卫星多重 PCR 数据的简单计算方法的开发，该方法不仅可以获得有关肿瘤 AI 状态的准确信息，还可以获得肿瘤浸润正常细胞的百分比、基因座拷贝数状态以及 AI 涉及的机制。我们通过重新分析一组神经纤维瘤病 1 相关的皮肤神经纤维瘤，并将新生成的数据与使用 MLPA、等位基因比例分析和 SNP 芯片技术对同一肿瘤进行的先前研究的结果进行比较，验证了这种新方法。微卫星多重 PCR 分析 (MMPA) 特别有助于分析包含肿瘤抑制基因的基因组的特定区域，还可用于确定肿瘤内浸润正常细胞的百分比，从而在使用更昂贵的技术对其进行分析之前对其进行分类。
