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Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture

Xiang-Yu Meng1,2, Xiong-Hui Zhou3, Shuo Li4,5

  • 1Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.

Clinical Chemistry
|October 21, 2024
PubMed
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Liquid biopsy using cell-free DNA (cfDNA) fragmentomics shows promise for noninvasive bladder cancer (BLCA) detection. This cfDNA hotspot-based model achieved 87% sensitivity at 100% specificity for BLCA diagnosis.

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Noninvasive bladder cancer (BLCA) detection is a significant unmet clinical need.
  • Cell-free DNA (cfDNA) fragmentomics offers a potential noninvasive diagnostic approach.

Purpose of the Study:

  • To evaluate the diagnostic performance of machine-learning models based on cfDNA hotspots for BLCA detection.
  • To explore the biological underpinnings of cfDNA hotspots in BLCA.

Main Methods:

  • Assessed diagnostic performance of cfDNA hotspot-driven machine-learning models in a cohort of 55 BLCA patients, 51 benign controls, and 11 healthy volunteers.
  • Performed functional bioinformatics analysis to understand diagnostic capabilities.

Main Results:

  • The cfDNA hotspots model achieved high diagnostic performance (AUC 0.96), with 87% sensitivity at 100% specificity for BLCA detection.
  • The model demonstrated superior performance compared to other cfDNA-derived features.
  • Stage-stratified analysis showed 71% sensitivity for early-stage and 92% for advanced-stage BLCA at 100% specificity.

Conclusions:

  • Urine cfDNA fragmentation hotspots are applicable for noninvasive BLCA diagnosis.
  • Findings support further translational studies on BLCA molecular pathology and heterogeneity.