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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Related Experiment Video

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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Knowledge-informed multimodal cfDNA analysis improves sensitivity and generalization in cancer detection.

Antonio De Falco1, Piera Grisolia2, Raffaella Giuffrida3

  • 1BIOGEM Institute of Molecular Biology and Genetics, Ariano Irpino, Italy.

Biorxiv : the Preprint Server for Biology
|November 24, 2025
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A new AI-powered liquid biopsy tool, Fate-AI, enhances cancer detection sensitivity and specificity. This approach analyzes cell-free DNA (cfDNA) fragmentomics and methylation patterns for early cancer detection and monitoring.

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Liquid biopsy using cell-free DNA (cfDNA) shows promise for cancer detection but faces sensitivity and specificity challenges.
  • Current tumor-informed and tumor-naive methods have limitations in detecting low tumor fractions and generalizing across patient cohorts.
  • Tumor evolution and heterogeneity further complicate accurate cancer detection via cfDNA analysis.

Purpose of the Study:

  • To develop and validate Fragmentomics Analysis for Tumor Evaluation with AI (Fate-AI), a multimodal liquid biopsy framework.
  • To improve sensitivity and specificity in detecting and monitoring cancers using cfDNA analysis.
  • To enable early cancer detection, minimal residual disease monitoring, and tissue-of-origin classification.

Main Methods:

  • Integrated fragmentomic and methylation features from low-pass whole-genome sequencing (LPWGS) and cfMeDIP-seq.
  • Employed a knowledge-informed strategy to select recurrently altered genomic regions and tissue-specific methylation loci.
  • Validated Fate-AI on 1,219 plasma samples across ten cancer types and healthy controls from multiple laboratories and public datasets.

Main Results:

  • Fate-AI demonstrated superior sensitivity and specificity compared to existing methods, detecting tumor signals at fractions as low as 10-5.
  • The framework achieved robust per-sample normalization, mitigating batch effects and enhancing cross-cohort reproducibility.
  • Fate-AI scores correlated with disease stage, tracked longitudinal progression, and predicted relapse before clinical progression.
  • Tissue-of-origin classification achieved AUCs from 0.84 to 0.97 across six cancer types.

Conclusions:

  • Fate-AI offers a sensitive, generalizable, and clinically actionable liquid biopsy platform.
  • The framework shows potential for early cancer detection, minimal residual disease monitoring, and tissue-of-origin classification in precision oncology.
  • This multimodal AI approach overcomes limitations of current liquid biopsy techniques, paving the way for improved cancer management.