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Supplementary Figure S12 from A CpG Methylation Classifier to Predict Relapse in Adults with T-Cell Lymphoblastic Lymphoma

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posted on 2023-03-31, 21:44 authored by Xiao-Peng Tian, Ning Su, Liang Wang, Wei-Juan Huang, Yan-Hui Liu, Xi Zhang, Hui-Qiang Huang, Tong-Yu Lin, Shu-Yun Ma, Hui-Lan Rao, Mei Li, Fang Liu, Fen Zhang, Li-Ye Zhong, Li Liang, Xiao-Liang Lan, Juan Li, Bing Liao, Zhi-Hua 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, Wei Dong, Cai Sun, Wei Sang, Hui Liu, Zhi-Gang Zhu, Jun Rao, Qiao-Nan Guo, Ying Zhou, Xiang-Ling Meng, Yong Zhu, Chang-Lu Hu, Yi-Rong Jiang, Ying Zhang, Hong-Yi Gao, Wen-Jun He, Zhong-Jun Xia, Xue-Yi Pan, Lan Hai, Guo-Wei Li, Li-Yan Song, Tie-Bang Kang, Dan Xie, Qing-Qing Cai

Supplementary Figure S12

Funding

National Key R&D Program of China

National Natural Science Foundation of China

Sun Yat-sen University

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ARTICLE ABSTRACT

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. 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). 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. Our four-CpG–based classifier could predict disease relapse in patients with T-LBL, and could be used to guide treatment decision.

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