DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues
Shirong Zhang, Shutao He, Xin Zhu, Yunfei Wang, Qionghuan Xie, Xianrang Song, C. Xu, Wenxian Wang, Ligang Xing, Cheng-qing Xia, Qian Wang, Wenfeng Li, Xiaochen Zhang, Jinming Yu, Shenglin Ma, Jiantao Shi, Hongcang Gu
Abstract
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).