利用福尔马林固定石蜡包埋组织进行DNA甲基化分析，以确定转移性癌症的原发部位。
DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues.
发表日期：2023 Sep 14
作者：
Shirong Zhang, Shutao He, Xin Zhu, Yunfei Wang, Qionghuan Xie, Xianrang Song, Chunwei Xu, Wenxian Wang, Ligang Xing, Chengqing Xia, Qian Wang, Wenfeng Li, Xiaochen Zhang, Jinming Yu, Shenglin Ma, Jiantao Shi, Hongcang Gu
来源：
CLINICAL PHARMACOLOGY & THERAPEUTICS
摘要：
鉴定转移性癌症的原发部位对指导后续治疗至关重要。尽管经过全面的诊断检查，大约3-9%的转移患者仍被诊断为原发部位未知癌症（CUP）。然而，目前仍没有被广泛接受的分子检测方法。在这里，我们报告了一种利用甲醛固定、石蜡包埋组织构建的减少表达位点二硫化物测序文库（FFPE-RRBS）的方法。然后，我们利用来自原发癌症患者的498例新鲜冰冻肿瘤组织的RRBS文库数据集，生成并系统地评估了28个分子分类器，这些分类器基于四个DNA甲基化评分方法和七种机器学习方法。在这些分类器中，基于β值的线性支持向量（BELIVE）表现最佳，使用前K个预测（K=1,2,3）可以实现对215个转移性患者原发部位的总体准确率为81-93%的识别。巧合的是，BELIVE还成功预测了81-93%的CUP患者（n=68）的组织起源。© 2023. Springer Nature Limited.
Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3-9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).© 2023. Springer Nature Limited.