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

## TL;DR

A new non-invasive blood test using cell-free DNA patterns shows high accuracy for early prostate cancer detection, especially when combined with PSA levels.

## Contribution

Developed a cfDNA fragmentomics-based assay with machine learning that improves prostate cancer screening accuracy and specificity.

## Key findings

- The fragmentomics-based model achieved an AUC of 0.933 in the training cohort with high specificity.
- The model maintained strong performance in the validation cohort with an AUC of 0.887.
- Combining the model with PSA levels improved sensitivity at 98% specificity across cancer stages.

## Abstract

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.

The online version contains supplementary material available at 10.1186/s12964-025-02522-3.