A CpG Methylation Classifier to Predict Relapse in Adults with T-Cell Lymphoblastic Lymphoma
Xiao‐Peng Tian, Ning Su, Liang Wang, Wei-Juan Huang, Yan‐Hui Liu, Xi Zhang, Huiqiang Huang, Tongyu Lin, Shu‐Yun Ma, Hui‐Lan Rao, Mei Li, Fang Liu, Fen Zhang, Liye Zhong, Liang Li, Xiao-Liang Lan, Juan Li, Bing Liao, Zhihua Li, Qiong-Lan Tang, Qiong Liang, Chun‐Kui Shao, Qiong-Li Zhai, Run-Fen Cheng, Qi Sun, Kun Ru, Xia Gu, Xi-Na Lin, Kun Yi, Yue-Rong Shuang, Xiao-Dong Chen, Dong Wei, Cai Sun, Wei Sang, Hui Liu, Zhigang Zhu, Jun Rao, Qiao‐Nan Guo, Ying Zhou, Xiang-Ling Meng, Yong Zhu, Chang-Lu Hu, Yirong Jiang, Ying Zhang, Hong-Yi Gao, Wenjun He, Zhongjun Xia, Xue-Yi Pan, Lan Hai, Guo‐Wei Li, Liyan Song, Tiebang Kang, Dan Xie, Qingqing Cai
Abstract
Abstract Purpose: Adults with T-cell lymphoblastic lymphoma (T-LBL) generally benefit from treatment with acute lymphoblastic leukemia (ALL)-like regimens, but approximately 40% will relapse after such treatment. We evaluated the value of CpG methylation in predicting relapse for adults with T-LBL treated with ALL-like regimens. Experimental Design: A total of 549 adults with T-LBL from 27 medical centers were included in the analysis. Using the Illumina Methylation 850K Beadchip, 44 relapse-related CpGs were identified from 49 T-LBL samples by two algorithms: least absolute shrinkage and selector operation (LASSO) and support vector machine–recursive feature elimination (SVM-RFE). We built a four-CpG classifier using LASSO Cox regression based on association between the methylation level of CpGs and relapse-free survival in the training cohort (n = 160). The four-CpG classifier was validated in the internal testing cohort (n = 68) and independent validation cohort (n = 321). Results: The four-CpG–based classifier discriminated patients with T-LBL at high risk of relapse in the training cohort from those at low risk (P < 0.001). This classifier also showed good predictive value in the internal testing cohort (P < 0.001) and the independent validation cohort (P < 0.001). A nomogram incorporating five independent prognostic factors including the CpG-based classifier, lactate dehydrogenase levels, Eastern Cooperative Oncology Group performance status, central nervous system involvement, and NOTCH1/FBXW7 status showed a significantly higher predictive accuracy than each single variable. Stratification into different subgroups by the nomogram helped identify the subset of patients who most benefited from more intensive chemotherapy and/or sequential hematopoietic stem cell transplantation. Conclusions: Our four-CpG–based classifier could predict disease relapse in patients with T-LBL, and could be used to guide treatment decision.