Sci Adv. 2026 Jul 10. 12(28):
eady9432
Haichao Wang,
Paulius D Mennea,
Grainne McAndrew,
Ozge Sonmezler,
Dmitry S Shcherbo,
Emma-Jane Ditter,
Sarah Østrup Jensen,
Alessandra I G Buma,
Christopher G Smith,
Zhao Cheng,
Clare Harris,
Rosalind J Cutts,
Sarah Hrebien,
Philip A J Crosbie,
Pippa G Corrie,
Michel M van den Heuvel,
Amit Roshan,
Frank McCaughan,
Robert C Rintoul,
Florian Markowetz,
Tommy Kaplan,
Wendy N Cooper,
Hui Zhao,
Nitzan Rosenfeld.
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.