Integrated Multi-Omics Analysis Reveals the Role of Digital Twin Technology in Precision Oncology: A Prospective Cohort Study
Authors: ZHANG Wei, LI Ming, WANG Fang, CHEN Yu, LIU Yang, ZHAO Lei, SUN Jing, ZHOU Kai, WU Hao, XU Dan
Background: Digital twin technology has emerged as a promising tool in precision oncology, yet its clinical utility remains underexplored. Methods: We conducted a prospective cohort study integrating multi-omics data (genomics, transcriptomics, proteomics, and metabolomics) from 1,200 cancer patients to construct digital twin models. Results: The digital twin models accurately predicted treatment responses (AUC=0.89) and identified novel biomarkers for early detection. Integration of multi-omics improved prognostic accuracy by 23% compared to single-omics approaches. Conclusions: Digital twin technology, when integrated with multi-omics data, significantly enhances precision oncology by enabling personalized treatment strategies and improving patient outcomes.