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- Title
Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization.
- Authors
Van Thien Chi Nguyen; Trong Hieu Nguyen; Nhu Nhat Tan Doan; Thi Mong Quynh Pham; Giang Thi Huong Nguyen; Thanh Dat Nguyen; Thuy Thi Thu Tran; Duy Long Vo; Thanh Hai Phan; Thanh Xuan Jasmine; Van Chu Nguyen; Huu Thinh Nguyen; Trieu Vu Nguyen; Thi Hue Hanh Nguyen; Le Anh Khoa Huynh; Trung Hieu Tran; Quang Thong Dang; Thuy Nguyen Doan; Anh Minh Tran; Viet Hai Nguyen
- Abstract
Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.
- Subjects
CELL-free DNA; IRINOTECAN; CIRCULATING tumor DNA; BREAST; LIVER cancer; MEDICAL screening; MACHINE learning
- Publication
eLife, 2023, p1
- ISSN
2050-084X
- Publication type
Article
- DOI
10.7554/eLife.89083