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208. Optimal subsampling for the Cox proportional hazards model with massive survival data.

Author

Qiao, Nan, Li, Wangcheng, Xiao, Feng, and Lin, Cunjie

Subjects
*SURVIVAL analysis (Biometry), *ASYMPTOTIC normality, *PROPORTIONAL hazards models
Abstract

Massive survival data has become common in survival analysis. In this study, a subsampling algorithm is proposed for Cox proportional hazards model with time-dependent covariates when the sample size is extraordinarily large but the computing resources are relatively limited. A subsample estimator is developed by maximizing a weighted partial likelihood, and shown to have consistency and asymptotic normality. By minimizing the asymptotic mean squared error of the subsample estimator, the optimal subsampling probabilities are formulated with explicit expression. Simulation studies show that the proposed method has satisfactory performances in approximating the full data estimator. The proposed method is applied to the corporate loan data and breast cancer data, with different censoring rates, and the outcome also confirms the practical advantages. • A subsampling algorithm is proposed for Cox proportional hazards model when the sample size is extraordinarily large but the computing resources are relatively limited. • A subsample estimator is developed by maximizing a weighted partial likelihood, and shown to have consistency and asymptotic normality. • The numerical studies suggest that the proposed subsampling method achieves the same estimation accuracy as the full data estimator but with much less computation time. [ABSTRACT FROM AUTHOR]

Published
2024
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209. Whole‐Cell‐Mimicking Carrier‐Free Nanovaccines Amplify Immune Responses Against Cancer and Bacterial Infection.

Author

Qin, Ming, Du, Guangsheng, Qiao, Nan, Guo, Zhaofei, Jiang, Min, He, Chunting, Bai, Shuting, He, Penghui, Xu, Yanhua, Wang, Hairui, Gong, Tao, Zhang, Zhirong, and Sun, Xun

Subjects
BACTERIAL diseases, IMMUNE response, BACTERIAL cell membranes, MEMBRANE proteins, ANTIGEN presentation
Abstract

Whole‐cell vaccines that provide broad‐spectrum antigens have been explored for recent decades. But so far, they have revealed limited success in clinical trials, possibly owing to their inefficiency in targeting immune organs and antigen‐presenting cells (APCs). Herein, a facile strategy is developed to convert the whole‐cell vaccines into the nanoscale size to promote their lymphatic migration and subsequent intracellular antigen presentation to maximize immune responses. Briefly, the study designs a multiple antigen delivery platform based on alum‐induced and cancer/bacterial cell membrane coated protein antigen nanoparticles. Through a simple two‐step vortex‐sonification method, a universal carrier‐free nano‐vaccine without additional excipients could be quickly fabricated. After subcutaneous vaccination, the developed nanovaccines rapidly migrate to lymph nodes, leading to their effective uptake by lymph node‐resident APCs, and subsequent antigen presentation and activation of downstream immune processes. Overall, the described promising work offers a safe, effective, facile, and widely applicable vaccine strategy, which has great potential for clinical transformation. [ABSTRACT FROM AUTHOR]

Published
2022
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210. Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches.

Author

Xiong, Zhaoping, Cheng, Ziqiang, Lin, Xinyuan, Xu, Chi, Liu, Xiaohong, Wang, Dingyan, Luo, Xiaomin, Zhang, Yong, Jiang, Hualiang, Qiao, Nan, and Zheng, Mingyue

Abstract

Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has been proposed to utilize distributed data from different sources without leaking sensitive information of the data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success rate of AI-powered drug discovery. Here, we simulated the federated learning process with different property and activity datasets from different sources, among which overlapping molecules with high or low biases exist in the recorded values. Beyond the benefit of gaining more data, we also demonstrated that federated training has a regularization effect superior to centralized training on the pooled datasets with high biases. Moreover, different network architectures for clients and aggregation algorithms for coordinators have been compared on the performance of federated learning, where personalized federated learning shows promising results. Our work demonstrates the applicability of federated learning in predicting drug-related properties and highlights its promising role in addressing the small and biased data dilemma in drug discovery. [ABSTRACT FROM AUTHOR]

Published
2022
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214. Fast-Bonito: A Faster Basecaller for Nanopore Sequencing

Author

Xu, Zhimeng, primary, Mai, Yuting, additional, Liu, Denghui, additional, He, Wenjun, additional, Lin, Xinyuan, additional, Xu, Chi, additional, Zhang, Lei, additional, Meng, Xin, additional, Mafofo, Joseph, additional, Zaher, Walid Abbas, additional, Li, Yi, additional, and Qiao, Nan, additional

Published
2020
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216. Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle

Author

Xia, Xian, primary, Chen, Xingwei, additional, Wu, Gang, additional, Li, Fang, additional, Wang, Yiyang, additional, Chen, Yang, additional, Chen, Mingxu, additional, Wang, Xinyu, additional, Chen, Weiyang, additional, Xian, Bo, additional, Chen, Weizhong, additional, Cao, Yaqiang, additional, Xu, Chi, additional, Gong, Wenxuan, additional, Chen, Guoyu, additional, Cai, Donghong, additional, Wei, Wenxin, additional, Yan, Yizhen, additional, Liu, Kangping, additional, Qiao, Nan, additional, Zhao, Xiaohui, additional, Jia, Jin, additional, Wang, Wei, additional, Kennedy, Brian K., additional, Zhang, Kang, additional, Cannistraci, Carlo V., additional, Zhou, Yong, additional, and Han, Jing-Dong J., additional

Published
2020
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219. Systemic In Silico Screening in Drug Discovery for Coronavirus Disease (COVID-19) with an Online Interactive Web Server

Author

Xu, Chi, primary, Ke, Zunhui, additional, Liu, Chuandong, additional, Wang, Zhihao, additional, Liu, Denghui, additional, Zhang, Lei, additional, Wang, Jingning, additional, He, Wenjun, additional, Xu, Zhimeng, additional, Li, Yanqing, additional, Yang, Yanan, additional, Huang, Zhaowei, additional, Lv, Panjing, additional, Wang, Xin, additional, Han, Dali, additional, Li, Yan, additional, Qiao, Nan, additional, and Liu, Bing, additional

Published
2020
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220. Hangzhou criteria as downstaging criteria in hepatocellular carcinoma before liver transplantation: A multicenter study from China

Author

Zhan, Qi-Fan, primary, Ling, Sun-Bin, additional, Deng, Yi-Nan, additional, Shan, Qiao-Nan, additional, Ye, Qian-Wei, additional, Xu, Sheng-Jun, additional, Jiang, Guang-Jiang, additional, Lu, Di, additional, Wei, Xu-Yong, additional, Zhuang, Li, additional, Zhang, Wu, additional, Shen, Tian, additional, Cen, Bei-Ni, additional, Xie, Hai-Yang, additional, Liu, Ji-Min, additional, Wu, Jian, additional, Zheng, Shu-Sen, additional, Yang, Yang, additional, and Xu, Xiao, additional

Published
2020
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222. A CpG Methylation Classifier to Predict Relapse in Adults with T-Cell Lymphoblastic Lymphoma

Author

Tian, Xiao-Peng, primary, Su, Ning, additional, Wang, Liang, additional, Huang, Wei-Juan, additional, Liu, Yan-Hui, additional, Zhang, Xi, additional, Huang, Hui-Qiang, additional, Lin, Tong-Yu, additional, Ma, Shu-Yun, additional, Rao, Hui-Lan, additional, Li, Mei, additional, Liu, Fang, additional, Zhang, Fen, additional, Zhong, Li-Ye, additional, Liang, Li, additional, Lan, Xiao-Liang, additional, Li, Juan, additional, Liao, Bing, additional, Li, Zhi-Hua, additional, Tang, Qiong-Lan, additional, Liang, Qiong, additional, Shao, Chun-Kui, additional, Zhai, Qiong-Li, additional, Cheng, Run-Fen, additional, Sun, Qi, additional, Ru, Kun, additional, Gu, Xia, additional, Lin, Xi-Na, additional, Yi, Kun, additional, Shuang, Yue-Rong, additional, Chen, Xiao-Dong, additional, Dong, Wei, additional, Sun, Cai, additional, Sang, Wei, additional, Liu, Hui, additional, Zhu, Zhi-Gang, additional, Rao, Jun, additional, Guo, Qiao-Nan, additional, Zhou, Ying, additional, Meng, Xiang-Ling, additional, Zhu, Yong, additional, Hu, Chang-Lu, additional, Jiang, Yi-Rong, additional, Zhang, Ying, additional, Gao, Hong-Yi, additional, He, Wen-Jun, additional, Xia, Zhong-Jun, additional, Pan, Xue-Yi, additional, Hai, Lan, additional, Li, Guo-Wei, additional, Song, Li-Yan, additional, Kang, Tie-Bang, additional, Xie, Dan, additional, and Cai, Qing-Qing, additional

Published
2020
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232. A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study

Author

Tian, Xiao-Peng, primary, Xie, Dan, additional, Huang, Wei-Juan, additional, Ma, Shu-Yun, additional, Wang, Liang, additional, Liu, Yan-Hui, additional, Zhang, Xi, additional, Huang, Hui-Qiang, additional, Lin, Tong-Yu, additional, Rao, Hui-Lan, additional, Li, Mei, additional, Liu, Fang, additional, Zhang, Fen, additional, Zhong, Li-Ye, additional, Liang, Li, additional, Lan, Xiao-Liang, additional, Li, Juan, additional, Liao, Bing, additional, Li, Zhi-Hua, additional, Tang, Qiong-Lan, additional, Liang, Qiong, additional, Shao, Chun-Kui, additional, Zhai, Qiong-Li, additional, Cheng, Run-Fen, additional, Sun, Qi, additional, Ru, Kun, additional, Gu, Xia, additional, Lin, Xi-Na, additional, Yi, Kun, additional, Shuang, Yue-Rong, additional, Chen, Xiao-Dong, additional, Dong, Wei, additional, Sang, Wei, additional, Sun, Cai, additional, Liu, Hui, additional, Zhu, Zhi-Gang, additional, Rao, Jun, additional, Guo, Qiao-Nan, additional, Zhou, Ying, additional, Meng, Xiang-Ling, additional, Zhu, Yong, additional, Hu, Chang-Lu, additional, Jiang, Yi-Rong, additional, Zhang, Ying, additional, Gao, Hong-Yi, additional, He, Wen-Jun, additional, Xia, Zhong-Jun, additional, Pan, Xue-Yi, additional, Lan, Hai, additional, Li, Guo-Wei, additional, Liu, Lu, additional, Bao, Hui-Zheng, additional, Song, Li-Yan, additional, Kang, Tie-Bang, additional, and Cai, Qing-Qing, additional

Published
2020
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234. Genomic Epidemiology of SARS-CoV-2 in the United Arab Emirates Reveals Novel Insights into the Virus Mutation, Co-Infection and Host-Dependent RNA Editing

Author

Liu, Rong, primary, Wu, Pei, additional, Ogrodzki, Pauline, additional, Mahmoud, Sally, additional, Liang, Ke, additional, Liu, Pengjuan, additional, Francis, Stephen S., additional, Khalak, Hanif, additional, Liu, Denghui, additional, Li, Junhua, additional, Ma, Tao, additional, Chen, Fang, additional, Liu, Weibin, additional, Huang, Xinyu, additional, He, Wenjun, additional, Yuan, Zhaorong, additional, Qiao, Nan, additional, Meng, Xin, additional, Alqarni, Budoor, additional, Quilez, Javier, additional, Kusuma, Vinay, additional, Lin, Long, additional, Jin, Xin, additional, Anton, Xavier, additional, Koshy, Ashish, additional, Yang, Huanming, additional, Xu, Xun, additional, Wang, Jian, additional, Xiao, Peng, additional, Kaabi, Nawal Ahmed Mohamed Al, additional, Fasihuddin, Mohammed Saifuddin, additional, Hosani, Farida Ismail Al, additional, Liu, Siyang, additional, and Zaher, Walid Abbas, additional

Published
2020
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240. AutoGenome: An AutoML Tool for Genomic Research

Author

Liu, Denghui, primary, Xu, Chi, additional, He, Wenjun, additional, Xu, Zhimeng, additional, Fu, Wenqi, additional, Zhang, Lei, additional, Yang, Jie, additional, Peng, Guangdun, additional, Han, Dali, additional, Bai, Xiaolong, additional, and Qiao, Nan, additional

Published
2019
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243. Rapid and non-destructive measurement of spinach pigments content during storage using hyperspectral imaging with chemometrics

Author

Qiao-Nan Wang, Xiao Yuzhao, Yong He, Chu Zhang, and Fei Liu

Subjects
Pixel, Correlation coefficient, Applied Mathematics, 010401 analytical chemistry, Hyperspectral imaging, 04 agricultural and veterinary sciences, Condensed Matter Physics, 040401 food science, 01 natural sciences, Sample (graphics), 0104 chemical sciences, Chemometrics, chemistry.chemical_compound, 0404 agricultural biotechnology, chemistry, Chlorophyll, Content (measure theory), Partial least squares regression, Electrical and Electronic Engineering, Instrumentation, Remote sensing, Mathematics
Abstract

Hyperspectral imaging covering spectral range of 874–1734 nm was used to measure spinach leaf pigments content (chlorophyll-a (Chla), chlorophyll-b (Chlb), total chlorophyll (tChl), carotenoids (Car)) under storage of 20 °C (Sample set 1) and 4 °C (Sample set 2). A sample set combining the two sample sets was formed, partial least squares (PLS) models on full spectra obtained acceptable results for all sample sets with correlation coefficient of prediction (rp) near or over 0.8. Random frog was used to select 20, 20, 20, 22 optimal wavelengths for Chla, Chlb, tChl and Car from the combined sample set, respectively. PLS models on optimal wavelengths obtained better results than corresponding full spectra PLS models. The visualization of the distribution map of pigments content were acquired by applying the PLS models on pixels within the hyperspectral image. The overall results indicated that hyperspectral imaging could be used for spinach leaf pigments content measurement, providing an alternative for real-world on-line vegetables quality monitor.

Published
2017

244. The sliding mode controller with improved reaching law for harvesting robots.

Author

Qiao, Nan, Wang, Lihui, Liu, Mingjie, and Wang, Zhuoxuan

Abstract

The chattering problem is caused by the switching function of sliding mode controller, which makes the accuracy of autonomous driving limited. To solve this problem, a steering sliding mode controller with improved nonlinear reaching law is proposed. The exponential function is used in the new reaching law to eliminate chattering problem, and the initial value of switching function is employed to avoid output saturation in the case of large initial errors. In the new reaching law, the large slope of sign function coefficient ensures the anti-disturbance ability of the controller after the system converges. A disturbance observer is constructed for real-time observation of the disturbance signals, which makes the controller get rid of the dependence on the prior knowledge of the disturbance. This paper introduces the tuning principle of controller parameters in detail and constructs the simulations and experiments of autonomous driving system. The experiments demonstrate that the proposed controller restrains the path-tracking mean error by 38% and restrains the standard error by 18%, the accuracy and efficiency of autonomous driving system are improved. Furthermore, the proposed reaching law can be used in other systems besides autonomous harvesting robots. [ABSTRACT FROM AUTHOR]

Published
2022
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246. A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study

Author

Xiao-Peng, Tian, Dan, Xie, Wei-Juan, Huang, Shu-Yun, Ma, Liang, Wang, Yan-Hui, Liu, Xi, Zhang, Hui-Qiang, Huang, Tong-Yu, Lin, 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, Wei, Sang, Cai, Sun, 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, Hai, Lan, Guo-Wei, Li, Lu, Liu, Hui-Zheng, Bao, Li-Yan, Song, Tie-Bang, Kang, and Qing-Qing, Cai

Subjects
Adult, Male, Nomograms, Humans, Female, Middle Aged, Precursor T-Cell Lymphoblastic Leukemia-Lymphoma, Transcriptome, Disease-Free Survival, Retrospective Studies
Abstract

We aimed to establish a discriminative gene-expression-based classifier to predict survival outcomes of T-cell lymphoblastic lymphoma (T-LBL) patients. After exploring global gene-expression profiles of progressive (n = 22) vs. progression-free (n = 28) T-LBL patients, 43 differentially expressed mRNAs were identified. Then an eleven-gene-based classifier was established using LASSO Cox regression based on NanoString quantification. In the training cohort (n = 169), high-risk patients stratified using the classifier had significantly lower progression-free survival (PFS: hazards ratio 4.123, 95% CI 2.565-6.628; p 0.001), disease-free survival (DFS: HR 3.148, 95% CI 1.857-5.339; p 0.001), and overall survival (OS: HR 3.790, 95% CI 2.237-6.423; p 0.001) compared with low-risk patients. The prognostic accuracy of the classifier was validated in the internal testing (n = 84) and independent validation cohorts (n = 360). A prognostic nomogram consisting of five independent variables including the classifier, lactate dehydrogenase levels, ECOG-PS, central nervous system involvement, and NOTCH1/FBXW7 status showed significantly greater prognostic accuracy than each single variable alone. The addition of a five-miRNA-based signature further enhanced the accuracy of this nomogram. Furthermore, patients with a nomogram score ≥154.2 significantly benefited from the BFM protocol. In conclusion, our nomogram comprising the 11-gene-based classifier may make contributions to individual prognosis prediction and treatment decision-making.

Published
2019

247. High expression of ID1 facilitates metastasis in human osteosarcoma by regulating the sensitivity of anoikis via PI3K/AKT depended suppression of the intrinsic apoptotic signaling pathway

Author

Guo-Sheng, Zhao, Qiao, Zhang, Ya, Cao, Yang, Wang, Yang-Fan, Lv, Zhao-Si, Zhang, Yuan, Zhang, Qiu-Lin, Tan, Yu, Chang, Zheng-Xue, Quan, Dian-Ming, Jiang, and Qiao-Nan, Guo

Subjects
musculoskeletal diseases, Original Article, neoplasms
Abstract

A lack of understanding of the molecular basis underlying the regulation of metastatic disease and its effective therapy are the primary causes of high mortality in osteosarcoma. Thus, new insights into metastases and novel effective targets for metastatic osteosarcoma are urgently required. Anoikis resistance is considered a hallmark of cancer cells with metastatic ability. However, the molecular mechanism of anoikis is poorly understood in osteosarcoma. We applied immunohistochemistry to investigate the correlation between inhibitor of differentiation or DNA binding 1 (ID1) and clinicopathological features, and investigated the correlation between ID1 and the metastatic behavior of osteosarcoma cells, in vitro and in vivo. The results revealed that ID1 is overexpressed in human osteosarcoma tissues, is positively associated with lung metastases, and is a potential biomarker of poor prognosis. Overexpression of ID1 could increase anoikis insensitivity of osteosarcoma cells to facilitate metastasis through the PI3K/AKT-dependent mitochondrial apoptosis pathway. Knockdown of ID1 partly reversed the high potential of metastasis in anoikis-resistant osteosarcoma cells. Our findings revealed, that ID1 is a candidate molecular target for metastatic potential osteosarcoma by highlighting the role of anoikis resistance. In addition ID1 might be a potential predictor of poor prognosis in patients with osteosarcoma.

Published
2019

248. Three Healthcare Topics: Adult Children's Informal Care to Aging Parents, Working Age Population's Marijuana Use, and Indigenous Adolescents' Suicidal Behaviors

Author

Qiao, Nan

Subjects
Employment, Aging, Suicidal behavior, Indigenous adolescent, Informal Care, Marijuana
Abstract

Indiana University-Purdue University Indianapolis (IUPUI), This dissertation examines three vulnerable groups��� health and healthcare access. The first research uses the 2002���2011 Health and Retirement Study data to estimate the effects of adult children���s employment on their caregiving to aging parents. State monthly unemployment rates are used as an instrument for employment. Results show that being employed affects neither male nor female adult children���s caregiving to aging parents significantly. The findings imply that the total amount of informal care provided by adult children might not be affected by changes in labor market participation trends of the two genders. The second research studies the labor impact of Colorado and Washington���s passage of recreational marijuana laws in December 2012. The difference-in-differences method is applied on the 2010���2013 National Survey on Drug Use and Health state estimates and the 2008���2013 Survey of Income and Program Participation data to estimate legalization���s effects on employment. The results show that legalizing recreational marijuana increases marijuana use and reduces the number of weeks employed in a given month by 0.090 among those aged 21 to 25. The laws��� labor effects are not significant on those aged 26 and above. To reduce legalization���s negative effects on employment, states may consider raising the minimum legal age for recreational marijuana use. The third research examines disparities in suicidal behaviors between indigenous and non-indigenous adolescents. The study analyzes the 2001���2013 Youth Risk Behavior Survey data. Oaxaca decomposition is applied to detect sources of disparities in suicide consideration, planning, and attempts. The study finds that the disparities in suicidal behaviors can be explained by differences in suicidal factors��� prevalence and effect sizes between the two groups. Suicidal behavior disparities might be reduced by protecting male indigenous adolescents from sexual abuse and depression, reducing female indigenous adolescents��� substance use, as well as involving male indigenous adolescents in sports teams.

Published
2019
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249. Additional file 1: of Additive interaction of snoring and body mass index on the prevalence of metabolic syndrome among Chinese coal mine employees: a cross-sectional study

Author

Yanyan Li, Gao, Qian, Li, Lu, Yanan Shen, Lu, Qing, Jianjun Huang, Chenming Sun, Wang, Hui, Qiao, Nan, Wang, Cong, Haixia Zhang, and Wang, Tong

Subjects
Statistics::Applications, Statistics::Methodology, Quantitative Biology::Genomics
Abstract

Table S1. Characteristics of the study population (before and after multiple imputation). Table S2. Additive interactions evaluated by multiple imputated data set. (DOCX 28 kb)

Published
2019
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250. Predictive Value of a CpG Methylation Classifier for Relapse in Adults with T-Cell Lymphoblastic Lymphoma: A Multicentre Study

Author

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, and Liang Wang

Subjects
Oncology, medicine.medical_specialty, business.industry, Proportional hazards model, medicine.medical_treatment, Lymphoblastic lymphoma, Cancer, Hematopoietic stem cell transplantation, Nomogram, medicine.disease, Institutional review board, Internal medicine, Cohort, DNA methylation, medicine, business
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

Published
2019

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