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<p>Early detection of prostate cancer is limited by the poor specificity of prostate-specific antigen (PSA)-based screening. Cell-free DNA (cfDNA) fragmentomics offers a promising non-invasive approach to improve screening accuracy and risk stratification. In this study, we enrolled 106 prostate cancer patients and 114 high-risk non-cancer individuals to develop a cfDNA fragmentomics-based screening assay using plasma whole-genome sequencing. Two fragmentomic features—copy number variation and fragment size profile—were incorporated into machine learning models for training and evaluated in an independent validation cohort of 83 cancer patients and 76 non-cancer individuals. The fragmentomics-based model achieved an area under the curve (AUC) of 0.933 in the training cohort (66.0% sensitivity at 95.6% specificity; 51.9% sensitivity at 98.2% specificity) with good calibration (slope: 0.957; intercept: 0.001), and maintained strong performance in the validation cohort (AUC: 0.887; 57.8% sensitivity at 92.1% specificity), showing rising predictive probabilities and sensitivity across advancing stages (Stage I–IV: 27.3% to 77.8%). Importantly, the model performed well in the PSA grey zone (4–10&#xa0;ng/mL) with an AUC of 0.865 (69.0% sensitivity at 81.8% specificity). When integrated with total PSA levels, the combined algorithm achieved an AUC of 0.915 in the validation cohort and improved sensitivity at 98% specificity (Stage I–IV: 30.0% to 87.5%). These findings support the clinical potential of our cfDNA fragmentomic assay, particularly when combined with PSA, as a highly accurate and non-invasive tool for early prostate cancer detection.</p>

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DECIPHER-PRAD: an advanced fragmentomics-based cell-free DNA assay for prostate cancer early detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月29日

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  • Shun Zhang,
  • Guanchen Zhu,
  • Linfeng Xu,
  • Qing Zhang,
  • Xuefeng Qiu,
  • Hua Bao,
  • Min Wu,
  • Xiaotian Zhao,
  • Tao Ding,
  • Fufeng Wang,
  • Shuang Chang,
  • Yang Shao,
  • Junlong Zhuang,
  • Hongqian Guo

摘要

Early detection of prostate cancer is limited by the poor specificity of prostate-specific antigen (PSA)-based screening. Cell-free DNA (cfDNA) fragmentomics offers a promising non-invasive approach to improve screening accuracy and risk stratification. In this study, we enrolled 106 prostate cancer patients and 114 high-risk non-cancer individuals to develop a cfDNA fragmentomics-based screening assay using plasma whole-genome sequencing. Two fragmentomic features—copy number variation and fragment size profile—were incorporated into machine learning models for training and evaluated in an independent validation cohort of 83 cancer patients and 76 non-cancer individuals. The fragmentomics-based model achieved an area under the curve (AUC) of 0.933 in the training cohort (66.0% sensitivity at 95.6% specificity; 51.9% sensitivity at 98.2% specificity) with good calibration (slope: 0.957; intercept: 0.001), and maintained strong performance in the validation cohort (AUC: 0.887; 57.8% sensitivity at 92.1% specificity), showing rising predictive probabilities and sensitivity across advancing stages (Stage I–IV: 27.3% to 77.8%). Importantly, the model performed well in the PSA grey zone (4–10 ng/mL) with an AUC of 0.865 (69.0% sensitivity at 81.8% specificity). When integrated with total PSA levels, the combined algorithm achieved an AUC of 0.915 in the validation cohort and improved sensitivity at 98% specificity (Stage I–IV: 30.0% to 87.5%). These findings support the clinical potential of our cfDNA fragmentomic assay, particularly when combined with PSA, as a highly accurate and non-invasive tool for early prostate cancer detection.