bioRxiv [Preprint]. 2025 Oct 21:2025.10.20.683167. doi: 10.1101/2025.10.20.683167.

ABSTRACT

Liquid biopsy offers a minimally invasive opportunity to detect and monitor cancers through analysis of cell-free DNA (cfDNA). However, current approaches face challenges of limited sensitivity at low tumor fractions, technical variability, and poor generalization across cohorts. Tumor-informed targeted methods offer high specificity but suffer from low sensitivity due to random sampling, tumor evolution and adaptation (including resistance mechanisms), and other sources of heterogeneity. Conversely, tumor-naive genome-wide methods can increase sensitivity but often sacrifice specificity, particularly at low tumor fractions. We developed Fragmentomics Analysis for Tumor Evaluation with AI (Fate-AI), a multimodal framework that integrates fragmentomic and methylation-derived features from low-pass whole-genome sequencing (LPWGS) and cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). It employs a knowledge-informed strategy to select recurrently altered genomic regions and tissue-specific methylation loci to combine the advantages of tumor-naive approaches with the specificity of tumor-informed approaches. This approach derives robust per-sample normalized features that mitigate batch effects and enhance cross-cohort reproducibility. We evaluated Fate-AI on a total of 1,219 plasma samples spanning ten cancer types and healthy controls from multiple laboratories and sequencing centers, including 432 newly profiled cases (280 with both cfMeDIP-seq and LPWGS) together with 787 samples from four independent public datasets. Fate-AI achieved superior sensitivity and specificity compared to state-of-the-art methods, detecting tumor-derived signals at fractions as low as 10^-5 in experimental dilutions. Fate-AI scores correlated with disease stage and tracked longitudinal progression, anticipating relapse months before clinical progression. Furthermore, Fate-AI enabled tissue-of-origin classification, with AUCs ranging from 0.84 to 0.97 across six cancer types. Collectively, our results demonstrate that Fate-AI provides a sensitive, generalizable, and clinically actionable platform for early detection, minimal residual disease monitoring, and tissue-of-origin classification, supporting its potential as a liquid biopsy framework in precision oncology.

PMID:41278930 | PMC:PMC12633305 | DOI:10.1101/2025.10.20.683167

Eur J Radiol. 2024 Dec;181:111796. doi: 10.1016/j.ejrad.2024.111796. Epub 2024 Oct 21.

ABSTRACT

Liquid biopsy with sequencing of circulating tumor DNA (ctDNA) is a minimally invasive method for sampling body fluids and offers a promising alternative to tissue biopsies that involve greater risks, costs, and time. ctDNA not only identifies actionable targets by revealing unique molecular signatures in cancer, but also may assess treatment response, treatment resistance and progression, and recurrence. Imaging correlates of these applications are already being identified and utilized for various solid tumors. Radiologists have new challenges in interpreting oncologic imaging. Given their integral role in cancer surveillance, they must become familiar with the importance of ctDNA in detecting recurrence and minimal residual disease, measuring treatment response, predicting survival and metastatic patterns, and identifying new molecular therapeutic targets. In this review, we provide an overview of ctDNA testing, and a snapshot of current clinical guidelines from the National Comprehensive Cancer Network and the European Society of Molecular Oncology on the use of ctDNA in lung, breast, colorectal, pancreatic, and hepatobiliary cancers. For each cancer type, we also highlight current research applications of ctDNA that are relevant to the field of diagnostic radiology.

PMID:39461058 | DOI:10.1016/j.ejrad.2024.111796