Supplementary Table S2 from Plasma Metabolic Profiles-Based Prediction of Induction Chemotherapy Efficacy in Nasopharyngeal Carcinoma: Results of a Bidirectional Clinical Trial

Tang, Tingxi; Zhou, Zhenhua; Chen, Min; Li, Nan; Sun, Jianda; Chen, Zekai; Xiao, Ting; Wang, Xiaoqing; Zhang, Longshan; Wang, Yingqiao; Zhang, Hanbin; Zheng, Xiuting; Chen, Bei; Ye, Feng; Guan, Jian

doi:10.1158/1078-0432.26302566.v1

Supplementary Table S2 from Plasma Metabolic Profiles-Based Prediction of Induction Chemotherapy Efficacy in Nasopharyngeal Carcinoma: Results of a Bidirectional Clinical Trial

journal contribution

posted on 2024-07-15, 07:21 authored by Tingxi Tang, Zhenhua Zhou, Min Chen, Nan Li, Jianda Sun, Zekai Chen, Ting Xiao, Xiaoqing Wang, Longshan Zhang, Yingqiao Wang, Hanbin Zhang, Xiuting Zheng, Bei Chen, Feng Ye, Jian Guan

Supplementary Table S2 List of lipoproteins measured by 1H-NMR

Funding

Clinical Research Program of Nanfang Hospital, Southern Medical University

Medical Scientific Research Foundation of Guangdong Province

China Postdoctoral Science Foundation (China Postdoctoral Foundation Project)

College Students’ Innovative Entrepreneurial Training Plan Program

The Southern Hospital President’s Fund

The National Natural Science Foundation of China

Guangdong Basic and Applied Basic Research Foundation

Clinical Research Startup Program of Southern Medical University by High-level University Constuction Funding of Guangdong Provincial Department of Education

History

ARTICLE ABSTRACT

The efficacy of induction chemotherapy (IC) as a primary treatment for advanced nasopharyngeal carcinoma (NPC) remains a topic of debate, with a lack of dependable biomarkers for predicting its efficacy. This study seeks to establish a predictive classifier using plasma metabolomics profiles. A total of 166 NPC patients enrolled in the clinical trial NCT05682703 who were undergoing IC were included in the study. Plasma lipoprotein profiles were obtained using 1H-nuclear magnetic resonance before and after IC treatment. An artificial intelligence-assisted radiomics method was developed to effectively evaluate its efficacy. Metabolic biomarkers were identified through a machine learning approach based on a discovery cohort and subsequently validated in a validation cohort that mimicked the most unfavorable real-world scenario. Our research findings indicate that the effectiveness of IC varies among individual patients, with a correlation observed between efficacy and changes in metabolite profiles. Using machine learning techniques, it was determined that the extreme gradient boosting model exhibited notable efficacy, attaining an area under the curve (AUC) value of 0.792 (95% CI, 0.668–0.913). In the validation cohort, the model exhibited strong stability and generalizability, with an AUC of 0.786 (95% CI, 0.533–0.922). In this study, we found that dysregulation of plasma lipoprotein may result in resistance to IC in NPC patients. The prediction model constructed based on the plasma metabolites’ profile has good predictive capabilities and potential for real-world generalization. This discovery has implications for the development of treatment strategies and may offer insight into potential targets for enhancing the effectiveness of IC.