A CpG Methylation Classifier to Predict Relapse in Adults with T-Cell Lymphoblastic Lymphoma
A CpG Methylation Classifier to Predict Relapse in Adults with T-Cell Lymphoblastic Lymphoma
复制标题
用于预测成人 T 细胞淋巴母细胞淋巴瘤复发的 CpG 甲基化分类器
DOI：
10.1158/1078-0432.ccr-19-4207
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发表时间：
2020-07-15
影响因子：
11.5
通讯作者：
Cai, Qing-Qing
中科院分区：
文献类型：
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作者：
Tian, Xiao-Peng;Su, Ning;Cai, Qing-Qing
目的：患有 T 细胞淋巴母细胞淋巴瘤 (T-LBL) 的成人通常受益于急性淋巴细胞白血病 (ALL) 类疗法的治疗，但大约 40% 的患者会在此类治疗后复发。我们评估了 CpG 甲基化在预测接受 ALL 样方案治疗的 T-LBL 成人患者复发方面的价值。实验设计：分析中纳入了来自 27 个医疗中心的总共 549 名患有 T-LBL 的成人。使用 Illumina 甲基化 850K Beadchip，通过两种算法从 49 个 T-LBL 样本中识别出 44 个与复发相关的 CpG：最小绝对收缩和选择器操作 (LASSO) 和支持向量机递归特征消除 (SVM-RFE)。我们根据训练队列 (n = 160) 中 CpG 甲基化水平与无复发生存率之间的关联，使用 LASSO Cox 回归构建了四 CpG 分类器。四 CpG 分类器在内部测试队列 (n = 68) 和独立验证队列 (n = 321) 中得到验证。结果：基于四 CpG 的分类器将训练队列中复发高风险的 T-LBL 患者与低风险患者区分开来（P < 0.001）。该分类器在内部测试队列 (P < 0.001) 和独立验证队列 (P < 0.001) 中也显示出良好的预测价值。包含五个独立预后因素（包括基于 CpG 的分类器、乳酸脱氢酶水平、东部肿瘤合作组表现状态、中枢神经系统受累和 NOTCH1/FBXW7 状态）的列线图显示出比每个单一变量显着更高的预测准确性。通过列线图分层到不同的亚组有助于确定从更强化的化疗和/或序贯造血干细胞移植中获益最多的患者亚组。结论：我们的基于四 CpG 的分类器可以预测 T-LBL 患者的疾病复发，并可用于指导治疗决策。
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.