A CpG Methylation Classifier to Predict Relapse in Adults with T-Cell Lymphoblastic Lymphoma.
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tian Xiao-Peng, Su Ning, Wang Liang, Huang Wei-Juan, Liu Yan-Hui, Zhang Xi, Huang Hui-Qiang, Lin Tong-Yu, Ma Shu-Yun, Rao Hui-Lan, Li Mei, Liu Fang, Zhang Fen, Zhong Li-Ye, Liang Li, Lan Xiao-Liang, Li Juan, Liao Bing, Li Zhi-Hua, Tang Qiong-Lan, Liang Qiong, Shao Chun-Kui, Zhai Qiong-Li, Cheng Run-Fen, Sun Qi, Ru Kun, Gu Xia, Lin Xi-Na, Yi Kun, Shuang Yue-Rong, Chen Xiao-Dong, Dong Wei, Sun Cai, Sang Wei, Liu Hui, Zhu Zhi-Gang, Rao Jun, Guo Qiao-Nan, Zhou Ying, Meng Xiang-Ling, Zhu Yong, Hu Chang-Lu, Jiang Yi-Rong, Zhang Ying, Gao Hong-Yi, He Wen-Jun, Xia Zhong-Jun, Pan Xue-Yi, Hai Lan, Li Guo-Wei, Song Li-Yan, Kang Tie-Bang, Xie Dan, Cai Qing-Qing
Аннотация
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.