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Predictive Value of a CpG Methylation Classifier for Relapse in Adults with T-Cell Lymphoblastic Lymphoma: A Multicentre Study

Authors :
Wei Dong
Ying Zhou
Hui Liu
Tong-Yu Lin
Qingqing Cai
Qiao-Nan Guo
Chun-Kui Shao
Wen-Jun He
Zhigang Zhu
Qiong-Lan Tang
Chang-Lu Hu
Li-Ye Zhong
Yan-Hui Liu
Qiong Liang
Xiao-Dong Chen
Fen Zhang
Xi Zhang
Bing Liao
Xia Gu
Guo-Wei Li
Xiao-Liang Lan
Xiang-Ling Meng
Wei Sang
Huiqiang Huang
Hong-Yi Gao
Zhihua Li
Li-Yan Song
Xiao-Peng Tian
Xue-Yi Pan
Hui-Lan Rao
Yong Zhu
Li Liang
Run-Fen Cheng
Mei Li
Yue-Rong Shuang
Wei-Juan Huang
Fang Liu
Zhong-Jun Xia
Lan Hai
Cai Sun
Jun Rao
Ying Zhang
Ning Su
Dan Xie
Qiong-Li Zhai
Juan Li
Kun Ruan
Tie-Bang Kang
Kun Yi
Yi-Rong Jiang
Xi-Na Lin
Kun Ru
Qi Sun
Liang Wang
Source :
SSRN Electronic Journal.
Publication Year :
2019
Publisher :
Elsevier BV, 2019.

Abstract

Background: 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. Methods: 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 (RFS) 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). Findings: The four-CpG-based classifier discriminated T-LBL patients at high risk of relapse in the training cohort from those at low risk (p

Details

ISSN :
15565068
Database :
OpenAIRE
Journal :
SSRN Electronic Journal
Accession number :
edsair.doi...........fa085a86e08a9f100a7ae91ab5394bde
Full Text :
https://doi.org/10.2139/ssrn.3473288