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Updated: Jul 12, 2026
Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
Published on: August 24, 2017
A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing
Haichao Wang1,2,3, Paulius D Mennea1,2,3, Grainne McAndrew1,2,3
1Centre for Cancer Cell and Molecular Biology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK.
Abstract:
Cell-free DNA (cfDNA) in body fluids enables noninvasive cancer detection. Multifeature artificial intelligence (AI) can improve sensitivity by integrating diverse biomarkers when cancer signals are sparse. Tumor-informed assays that rely on mutations have limited practicality for early cancer detection. Emerging fragmentomic and epigenetic features underpin tumor-naive approaches to screening for individuals with low tumor burden. Here, we designed UNITE-a universal cfDNA feature ensemble framework that provides scalable cancer detection methods based on "genomic bin-fragment length" matrices derived from shallow whole-genome sequencing (sWGS) data at 0.1× depth. Using sWGS data from 2063 plasma samples (631 controls and 1432 cases from 26 cancer types), we systematically evaluated both XGBoost (UNITE-XGB) and convolutional neural networks (UNITE-CNN) across multiple feature spaces and cancer stages. In stage I-II cancer, UNITE-XGB and UNITE-CNN achieved 31 and 21% sensitivity, respectively, at 95% specificity. These findings provide roadmaps for developing multifeature AI beyond plasma biopsies.
