Exploration of multi-omics liquid biopsy approaches for multi-cancer early detection: The PROMISE study

作者：Jianchun Duan, Qiang Gao, Zhijie Wang, Jiachen Xu, Yuzi Zhang, Yanan Wang, Xi Yang, Lei Zhang, Yu Xu, Qiaoxia Zhou, Bo Yang, Xingyu Yang, Guoqiang Wang, Jing Zhao, Xuefei Wang, Di Ge, Lijie Tan, Wenju Chang, Jianmin Xu, Ping‐Hong Zhou, Meng‐Jiang He, Yuanlin Song, Jie Hu, Wenhui Lou, Wenchuan Wu, Tingyan Shi, Rongyu Zang, Yuan Ji, Sunfang Jiang, Youpei Lin, Liangqing Dong, Shangli Cai, Fan Jia, Jie Wang · 发表于：The Innovation · 年份：2025 · DOI：10.1016/j.xinn.2025.101076 · 被引用次数：5 · 研究领域：Cancer Genomics and Diagnostics、Molecular Biology Techniques and Applications、Radiomics and Machine Learning in Medical Imaging

Although circulating cell-free DNA (cfDNA) methylation has emerged as the mainstream approach in multi-cancer detection blood tests (MCDBTs), the potential of integrating proteins and mutations, to enhance its performance remains unclear. The PROMISE study (NCT04972201) was conducted to investigate the feasibility of a multi-omics integration strategy in MCDBTs across nine types of cancers in head and neck (excluding nasopharynx), esophagus, lung, stomach, liver, biliary tract, pancreas, colorectum, and ovary. Blood samples were prospectively collected from 1,706 participants (840 non-cancer; 866 cancer) and then randomly divided into training and validation sets. The complementarity between various omics were investigated, and specific omics features were carefully selected for further multimodal model construction. The methylation-based classifier outperformed both the mutation-based and protein-based classifiers. As 95.0% of cancer cases detected by the mutation-based classifier were simultaneously identified by the methylation-based classifier, while 14.0% of the protein-positive samples were missed, protein markers may provide complementary value to the methylation-based classifier. Compared with the methylation-based classifier, the multimodal classifier combining methylation and protein features exhibited an improved sensitivity of 75.1% (95% confidence interval [CI], 69.3%-80.3%) at the same specificity of 98.8% with the accuracy of top predicted origin (TPO1) of 73.1...