# Multimodal plasma and urinary cell-free DNA profiling improves risk stratification in newly diagnosed prostate cancer

## TL;DR

A new liquid biopsy method using plasma and urine DNA improves early prostate cancer detection and risk assessment.

## Contribution

A multimodal liquid biopsy approach combining genomic and epigenomic cfDNA features improves ctDNA detection and risk stratification in prostate cancer.

## Key findings

- A multimodal approach detected ctDNA in 45% of newly diagnosed prostate cancer patients.
- Epigenomic cfDNA features differentiated localized from advanced prostate cancer.
- ctDNA was detected in 46% of patients with prostate-specific antigen <10 ng/mL.

## Abstract

Prostate cancer (PCa) is a heterogeneous disease, impeding early detection and risk stratification. Liquid biopsies (LBx) enable minimally invasive tumor profiling, but circulating tumor-derived DNA (ctDNA) detection remains difficult, particularly in early-stage PCa. We developed a multimodal LBx approach combining genomic and epigenomic cell-free DNA (cfDNA) features in plasma and urine from newly diagnosed PCa patients to improve early characterization of PCa and risk stratification of aggressive disease. Plasma and urine samples from 55 localized PCa (lPCa) patients, 18 advanced PCa (aPCa) patients, and 36 cancer-free controls were subjected to low-coverage whole-genome sequencing and methylated DNA immunoprecipitation sequencing to assess fragmentation, chromosomal instability, and methylation in cfDNA. This complementary approach yielded a 45% ctDNA detection rate in newly diagnosed PCa. Major differences were observed between aPCa and controls, reflecting increasing signals with tumor progression. Epigenomic cfDNA features differentiated lPCa from aPCa, and ctDNA was detected in 46% of PCa patients with prostate-specific antigen <10 ng/mL, suggesting potential for risk stratification. This study highlights the value of multimodal LBx approaches for early characterization of primary PCa and identification of aggressive disease at initial diagnosis. Integration into clinical workflows could complement diagnostics and support personalized decision-making tailored to patients’ PCa risk profiles.