Department of Pathology, Technical University of Munich, Trogerstr. 18, 81675, Munich, Germany.
Department of Medical Informatics, Statistics and Epidemiology, Technical University of Munich, Ismaningerstr. 22, 81675, Munich, Germany.
Sci Rep. 2018 Aug 22;8(1):12551. doi: 10.1038/s41598-018-30971-z.
Chromosomal instability (CIN) is a hallmark of distinct subclasses of tumours with potential clinical relevance. The aim of our study was to establish a time and cost effective method for the determination of CIN in gastric carcinomas (GC). We developed a microsatellite based multiplex PCR assay for the detection of allelic imbalances (AI) using experimentally defined marker specific threshold values for AI. The assay was tested in 90 formalin-fixed paraffin-embedded GC and results were compared in a subset of 30 carcinomas with the Affymetrix OncoScan assay, which detects copy number variations on genome wide level. The ratios of alterations detected by the two methods demonstrated a significant correlation (r = 0.88). Based on the results of the OncoScan assay, tumours were classified in CIN-High and CIN-Low and a threshold of the AI ratio determined with the PCR assay was defined. Accordingly, 20 of the 90 GC (22%) were CIN-Low and 70 (78%) CIN-High. A significant association of CIN-High was found with intestinal type tumours and proximal tumour localization. In conclusion, we established a PCR based method to categorize AI as surrogate for CIN, which is easy to perform and useful for the clarification of the clinical relevance of CIN in large GC cohorts.
染色体不稳定性 (CIN) 是具有潜在临床相关性的不同肿瘤亚类的标志。我们研究的目的是建立一种时间和成本效益高的方法，用于确定胃腺癌 (GC) 中的 CIN。我们开发了一种基于微卫星的多重 PCR 检测方法，用于检测等位基因失衡 (AI)，使用实验定义的标记特异性 AI 阈值。该检测方法在 90 例福尔马林固定石蜡包埋的 GC 中进行了测试，并在 30 例癌症亚组中与 Affymetrix OncoScan 检测进行了比较，后者检测全基因组水平的拷贝数变化。两种方法检测到的改变比例显示出显著的相关性 (r = 0.88)。根据 OncoScan 检测的结果，肿瘤被分类为 CIN-High 和 CIN-Low，并确定了 PCR 检测的 AI 比值的阈值。因此，90 例 GC 中有 20 例 (22%) 为 CIN-Low，70 例 (78%) 为 CIN-High。CIN-High 与肠型肿瘤和近端肿瘤定位显著相关。总之，我们建立了一种基于 PCR 的方法，将 AI 分类为 CIN 的替代物，该方法易于操作，对于阐明 CIN 在大型 GC 队列中的临床相关性非常有用。
