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Cell-Free DNA Integrity Analysis in Urine Samples

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Published on: January 5, 2017

Cell-free DNA fragmentomics in cancer

W H Adrian Tsui¹, Peiyong Jiang², Y M Dennis Lo²

¹Centre for Novostics, Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China; Department of Chemical Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China.

|October 3, 2025

Abstract:

The analysis of cell-free DNA (cfDNA) fragmentation patterns, known as "fragmentomics," has opened new opportunities in noninvasive cancer diagnostics. Due to its close relationships with genomic organization and cell death, cfDNA fragmentomics lies at the intersection of many aspects of cancer biology, including epigenetic dysregulation, transcriptomic alterations, and aberrant cellular turnover patterns. Recent advances in library preparation, sequencing technologies, and integrative epigenomic-fragmentomic analyses have uncovered novel fragmentomic features that reveal specific cellular dysfunctions in cancer. Additionally, cutting-edge artificial intelligence algorithms now harness high-dimensional fragmentomic features, boosting the precision and power of cancer detection. Promising results from recent clinical trials evaluating the utility of fragmentomic analyses in real-world settings support its potential. In this review, we explore the exciting frontiers of cfDNA fragmentomics, discuss critical unanswered questions, and highlight future directions to unlock the promise of fragmentomics-based liquid biopsies in cancer care.

Keywords:

artificial intelligence cancer detection cancer risk prediction cancer treatment monitoring cell-free DNA fragmentomics machine learning

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