Knowledge-informed multimodal cfDNA analysis improves sensitivity and generalization in cancer detection
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Le résumé fourni par la source
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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Knowledge-informed multimodal cfDNA analysis improves sensitivity and generalization in cancer detection
- Date Crossref
- 21/10/2025
- Éditeur
- openRxiv
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Biogem pays non établi dans la noticeOrganisation à but non lucratif
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University of Miami Sylvester Comprehensive Cancer Center pays non établi dans la noticeUniversité ou école supérieure
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Sylvester Comprehensive Cancer Center pays non établi dans la noticeÉtablissement de santé
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University of Campania "Luigi Vanvitelli" Department of Advanced Clinical and Surgical Sciences pays non établi dans la noticeUniversité ou école supérieure
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Link Campus University pays non établi dans la noticeUniversité ou école supérieure
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Cornell University pays non établi dans la noticeUniversité ou école supérieure
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Weill Cornell Medicine pays non établi dans la noticeUniversité ou école supérieure
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University of Siena pays non établi dans la noticeUniversité ou école supérieure
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University of Ferrara pays non établi dans la noticeUniversité ou école supérieure
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University of Miami Health System pays non établi dans la noticeÉtablissement de santé
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Miami Transplant Institute pays non établi dans la noticeÉtablissement de santé
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Memorial Sloan Kettering Cancer Center Division of Hematology and Oncology pays non établi dans la noticeÉtablissement de santé
Biogem, Sylvester Comprehensive Cancer Center — University of Miami et Sylvester Comprehensive Cancer Center, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.