Integrative genomic and epigenomic profiling in plasma and urinary cell-free DNA improves early risk stratification of newly diagnosed prostate cancer

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Abstract
Background and Objective: 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, especially in early-stage PCa. This study aimed at developing a multimodal LBx approach, analyzing genomic and epigenomic cell-free DNA (cfDNA) features in plasma and urine from newly diagnosed PCa patients for early detection, tumor characterization, and risk stratification of aggressive PCa. Methods: Plasma and urine samples were included from 55 localized PCa (lPCa) patients, 18 advanced PCa (aPCa) patients, and 36 cancer-free controls. Low-coverage whole-genome sequencing and methylated DNA immunoprecipitation sequencing were performed to assess fragmentation, chromosomal instability, and methylation in cfDNA. Key findings and Limitations: The complementary (epi)genomic analysis of plasma and urinary cfDNA achieved 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. However, sensitivity in early PCa remains a major limitation. Conclusions and Clinical Implications: This study highlights the potential of multimodal LBx approaches, integrating genomic and epigenomic cfDNA features, for minimally invasive characterization of primary PCa and potential metastasis at initial diagnosis. While promising for risk stratification, sensitivity requires optimization for early detection. Incorporating LBx into clinical workflows could complement diagnostics and support clinical decision-making for personalized treatments tailored to patients’ PCa risk profiles.

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