Background Accurate CNS tumor diagnosis can be challenging, and methylation profiling can serve as an adjunct to classify diagnostically difficult cases. Methods An integrated diagnostic approach was employed for a consecutive series of 1258 surgical neuropathology samples obtained primarily in a consultation practice over 2-year period. DNA methylation profiling and classification using the DKFZ/Heidelberg CNS tumor classifier was performed, as well as unsupervised analyses of methylation data. Ancillary testing, where relevant, was performed. Results Among the received cases in consultation, a high-confidence methylation classifier score (>0.84) was reached in 66.4% of cases. The classifier impacted the diagnosis in 46.7% of these high-confidence classifier score cases, including a substantially new diagnosis in 26.9% cases. Among the 289 cases received with only a descriptive diagnosis, methylation was able to resolve approximately half (144, 49.8%) with high-confidence scores. Additional methods were able to resolve diagnostic uncertainty in 41.6% of the low-score cases. Tumor purity was significantly associated with classifier score (P = 1.15e−11). Deconvolution demonstrated that suspected glioblastomas (GBMs) matching as control/inflammatory brain tissue could be resolved into GBM methylation profiles, which provided a proof-of-concept approach to resolve tumor classification in the setting of low tumor purity. Conclusions This work assesses the impact of a methylation classifier and additional methods in a consultative practice by defining the proportions with concordant vs change in diagnosis in a set of diagnostically challenging CNS tumors. We address approaches to low-confidence scores and confounding issues of low tumor purity.

中文翻译：

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甲基化分类器和辅助方法对 CNS 肿瘤诊断的影响

背景准确的中枢神经系统肿瘤诊断可能具有挑战性，甲基化分析可以作为对诊断困难病例进行分类的辅助手段。方法 对主要在 2 年的会诊实践中获得的 1258 个外科神经病理学样本的连续系列采用综合诊断方法。使用 DKFZ/Heidelberg CNS 肿瘤分类器进行 DNA 甲基化分析和分类，以及甲基化数据的无监督分析。进行了相关的辅助测试。结果在收到的会诊病例中，66.4%的病例达到高置信度甲基化分类评分（＞0.84）。在这些高置信度分类器评分案例中，分类器影响了 46.7% 的诊断，其中包括 26.9% 的案例中的实质性新诊断。在仅接受描述性诊断的 289 例病例中，甲基化能够解决大约一半 (144, 49.8%) 的高置信度得分。其他方法能够解决 41.6% 的低分病例的诊断不确定性。肿瘤纯度与分类评分显着相关（P = 1.15e-11）。反卷积表明，与对照/炎症性脑组织匹配的疑似胶质母细胞瘤 (GBM) 可以解析为 GBM 甲基化谱，这为在低肿瘤纯度的情况下解决肿瘤分类提供了一种概念验证方法。结论这项工作通过定义一组具有诊断挑战性的 CNS 肿瘤的诊断一致性与诊断变化的比例来评估甲基化分类器和其他方法在咨询实践中的影响。