Multidimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer

作者：Yuepeng Cao, Nannan Wang, Xuxiaochen Wu, Wanxiangfu Tang, Hua Bao, Chengshuai Si, Peng Shao, Dongzheng Li, Xin Zhou, Dongqin Zhu, Shanshan Yang, Fufeng Wang, Guoqing Su, Ke Wang, Qifan Wang, Yao Zhang, Qiangcheng Wang, Dongsheng Yu, Qian Jiang, 俊一 久保, Yang Liu · 发表于：Cancer Research · 年份：2024 · DOI：10.1158/0008-5472.can-23-3486 · 被引用次数：33 · 研究领域：Cancer Genomics and Diagnostics、Genetic factors in colorectal cancer、Colorectal Cancer Screening and Detection

Colorectal cancer is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage colorectal cancer detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 patients with colorectal cancer and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish patients with colorectal cancer from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 patients with colorectal cancer and 119 controls) and a prospective cohort of 242 participants (129 patients with colorectal cancer and 113 controls). The ensemble stacked model showed remarkable discriminatory power between patients with colorectal cancer and controls, outperforming all base models and achieving a high area under the receiver operating characteristic curve of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting colorectal cancer in the validation cohort, with sensitivity increasing as the cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run t...