Hang-Yu Chen1, Wei-Long Zhang2, Lei Zhang3, Ping Yang2, Fang Li2, Ze-Ruo Yang3, Jing Wang2, Meng Pang2, Yun Hong2, Changjian Yan2, Wei Li2, Jia Liu2, Nuo Xu1, Long Chen1, Xiu-Bing Xiao4, Yan Qin5, Xiao-Hui He5, Hui Liu6, Hai-Chuan Zhu7, Chuan He8, Jian Lin9, Hong-Mei Jing10. 1. Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Innovation Center for Genomics, Peking University, Beijing, 100871, People's Republic of China. 2. Department of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, 100191, People's Republic of China. 3. Yang Sheng Tang Natural Medicine Research Institute, Hangzhou, 310024, People's Republic of China. 4. Lymphoma Head and Neck Oncology, Fifth Medical Center of PLA General Hospital, Beijing, 100039, People's Republic of China. 5. Department of Medical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China. 6. Department of Hematology, Beijing Hospital, National Center of Gerontology, Beijing, 1000730, People's Republic of China. 7. Institute of Biology and Medicine, College of Life and Health 20 Sciences, Wuhan University of Science and Technology, Hubei, 430081, People's Republic of China. 8. Department of Chemistry, University of Chicago, Chicago, IL, 60637, USA. 9. Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Innovation Center for Genomics, Peking University, Beijing, 100871, People's Republic of China. linjian@pku.edu.cn. 10. Department of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, 100191, People's Republic of China. hongmeijing@bjmu.edu.cn.
Abstract
BACKGROUND: Although R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard chemotherapy regimen for diffuse large B cell lymphoma (DLBCL) patients, not all patients are responsive to the scheme, and there is no effective method to predict treatment response. METHODS: We utilized 5hmC-Seal to generate genome-wide 5hmC profiles in plasma cell-free DNA (cfDNA) from 86 DLBCL patients before they received R-CHOP chemotherapy. To investigate the correlation between 5hmC modifications and curative effectiveness, we separated patients into training (n = 56) and validation (n = 30) cohorts and developed a 5hmC-based logistic regression model from the training cohort to predict the treatment response in the validation cohort. RESULTS: In this study, we identified thirteen 5hmC markers associated with treatment response. The prediction performance of the logistic regression model, achieving 0.82 sensitivity and 0.75 specificity (AUC = 0.78), was superior to existing clinical indicators, such as LDH and stage. CONCLUSIONS: Our findings suggest that the 5hmC modifications in cfDNA at the time before R-CHOP treatment are associated with treatment response and that 5hmC-Seal may potentially serve as a clinical-applicable, minimally invasive approach to predict R-CHOP treatment response for DLBCL patients.
BACKGROUND: Although R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard chemotherapy regimen for diffuse large B cell lymphoma (DLBCL) patients, not all patients are responsive to the scheme, and there is no effective method to predict treatment response. METHODS: We utilized 5hmC-Seal to generate genome-wide 5hmC profiles in plasma cell-free DNA (cfDNA) from 86 DLBCL patients before they received R-CHOP chemotherapy. To investigate the correlation between 5hmC modifications and curative effectiveness, we separated patients into training (n = 56) and validation (n = 30) cohorts and developed a 5hmC-based logistic regression model from the training cohort to predict the treatment response in the validation cohort. RESULTS: In this study, we identified thirteen 5hmC markers associated with treatment response. The prediction performance of the logistic regression model, achieving 0.82 sensitivity and 0.75 specificity (AUC = 0.78), was superior to existing clinical indicators, such as LDH and stage. CONCLUSIONS: Our findings suggest that the 5hmC modifications in cfDNA at the time before R-CHOP treatment are associated with treatment response and that 5hmC-Seal may potentially serve as a clinical-applicable, minimally invasive approach to predict R-CHOP treatment response for DLBCL patients.
Entities:
Keywords:
5-Hydroxymethylcytosine (5hmC); Diffuse large B cell lymphoma; Epigenetics; Logistic regression modeling; R-CHOP