Authors
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
Published in
Science advances. Volume 12. Issue 28. Pages eady9432. Jul 10, 2026. Epub Jul 10, 2026.
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.
PMID:
42430497
Bibliographic data and abstract were imported from PubMed on 11 Jul 2026.
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