Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, PR China.
National Center for Neurological Disorders, Shanghai, PR China.
J Pathol Clin Res. 2024 Nov;10(6):e70005. doi: 10.1002/2056-4538.70005.
Molecular features are incorporated into the integrated diagnostic system for adult diffuse gliomas. Of these, copy number variation (CNV) markers, including both arm-level (1p/19q codeletion, +7/-10 signature) and gene-level (EGFR gene amplification, CDKN2A/B homozygous deletion) changes, have revolutionized the diagnostic paradigm by updating the subtyping and grading schemes. Shallow whole genome sequencing (sWGS) has been widely used for CNV detection due to its cost-effectiveness and versatility. However, the parallel detection of glioma-associated CNV markers using sWGS has not been optimized in a clinical setting. Herein, we established a model-based approach to classify the CNV status of glioma-associated diagnostic markers with a single test. To enhance its clinical utility, we carried out hypothesis testing model-based analysis through the estimation of copy ratio fluctuation level, which was implemented individually and independently and, thus, avoided the necessity for normal controls. Besides, the customization of required minimal tumor fraction (TF) was evaluated and recommended for each glioma-associated marker to ensure robust classification. As a result, with 1× sequencing depth and 0.05 TF, arm-level CNVs could be reliably detected with at least 99.5% sensitivity and specificity. For EGFR gene amplification and CDKN2A/B homozygous deletion, the corresponding TF limits were 0.15 and 0.45 to ensure the evaluation metrics were both higher than 97%. Furthermore, we applied the algorithm to an independent glioma cohort and observed the expected sample distribution and prognostic stratification patterns. In conclusion, we provide a clinically applicable algorithm to classify the CNV status of glioma-associated markers in parallel.
分子特征被纳入成人弥漫性神经胶质瘤的综合诊断系统。其中，拷贝数变异 (CNV) 标志物，包括臂级（1p/19q 缺失、+7/-10 特征）和基因级（EGFR 基因扩增、CDKN2A/B 纯合缺失）改变，通过更新亚分型和分级方案，彻底改变了诊断范式。由于成本效益高、用途广泛，浅层全基因组测序 (sWGS) 已广泛用于 CNV 检测。然而，在临床环境中，尚未优化使用 sWGS 平行检测与胶质瘤相关的 CNV 标志物。在此，我们建立了一种基于模型的方法，通过单次测试来分类与胶质瘤相关的诊断标志物的 CNV 状态。为了增强其临床实用性，我们通过拷贝比波动水平的估计，对基于假设检验的模型分析进行了检验，这种方法单独且独立进行，因此避免了对正常对照的需求。此外，还对每个与胶质瘤相关的标志物进行了所需最小肿瘤分数 (TF) 的定制评估，并建议了定制化的最小 TF，以确保稳健的分类。结果显示，在 1×测序深度和 0.05 TF 下，臂级 CNV 可以可靠地检测到，其敏感性和特异性均至少为 99.5%。对于 EGFR 基因扩增和 CDKN2A/B 纯合缺失，相应的 TF 限制分别为 0.15 和 0.45，以确保评估指标均高于 97%。此外，我们将该算法应用于独立的胶质瘤队列中，观察到了预期的样本分布和预后分层模式。总之，我们提供了一种临床适用的算法，可用于并行分类与胶质瘤相关的标志物的 CNV 状态。
