Multimodal Analysis of ctDNA Methylation and Fragmentomic Profiles Enhances Detection of Nonmetastatic Colorectal Cancer
Huu Thinh Nguyen, Le Anh Khoa Huynh, Trieu Vu Nguyen, Duc Huy Tran, Thuy Thi Thu Tran, Nguyen Duy Khang Le, Ngoc-An Trinh Le, Truong-Vinh Ngoc Pham, Minh-Triet Le, Thi Mong Quynh Pham, Trong Hieu Nguyen, Thien Chi Van Nguyen, Thanh Dat Nguyen, Bui Que Tran Nguyen, Minh‐Duy Phan, Hoa Giang, Le Son Tran
Abstract
Aims: Early detection of colorectal cancer (CRC) provides substantially better survival rates. This study aimed to develop a blood-based screening assay named SPOT-MAS (‘screen for the presence of tumor by DNA methylation and size’) for early CRC detection with high accuracy. Methods: Plasma cell-free DNA samples from 159 patients with nonmetastatic CRC and 158 healthy controls were simultaneously analyzed for fragment length and methylation profiles. We then employed a deep neural network with fragment length and methylation signatures to build a classification model. Results: The model achieved an area under the curve of 0.989 and a sensitivity of 96.8% at 97% specificity in detecting CRC. External validation of our model showed comparable performance, with an area under the curve of 0.96. Conclusion: SPOT-MAS based on integration of cancer-specific methylation and fragmentomic signatures could provide high accuracy for early-stage CRC detection.