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.

中文翻译：

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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。

目的：成人 T 细胞淋巴母细胞淋巴瘤 (T-LBL) 通常受益于急性淋巴细胞白血病 (ALL) 样方案的治疗，但大约 40% 的患者会在这种治疗后复发。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：分析中包括来自 27 个医疗中心的 549 名 T-LBL 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)。我们基于 CpG 甲基化水平与训练队列 (n = 160) 中无复发生存之间的关联，使用 LASSO Cox 回归构建了一个四 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风险高的 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 状态等五个独立预后因素的列线图显示出比每个单一变量显着更高的预测准确性。通过列线图对不同亚组进行分层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大的患者亚组。结论：我们基于四 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。