Development of a multivariable risk model integrating urinary cell DNA methylation and cell‐free RNA data for the detection of significant prostate cancer. Issue 7 (9th March 2020)

• Record Type: Journal Article
• Authors: Connell, Shea P.; O'Reilly, Eve; Tuzova, Alexandra; Webb, Martyn; Hurst, Rachel; Mills , Robert; Zhao, Fang; Bapat, Bharati; Cooper, Colin S.; Perry, Antoinette S.; Clark, Jeremy; Brewer, Daniel S.
• Is Part Of: Prostate. Volume 80:Issue 7(2020)
• Page Start: 547
• Page End: 558
• Publication Date: 2020-03-09
• Subjects: biomarkers -- cell‐free -- liquid biopsy -- machine learning -- methylation -- prostate cancer; Prostate -- Diseases -- Periodicals; 616
• DOI: 10.1002/pros.23968
• Languages: English
• ISSNs: 0270-4137

Abstract

Abstract: Background: Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine‐derived cell‐free messenger RNA (cf‐RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. Methods: Post‐digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf‐RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out‐of‐bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). Results: As ExoMeth risk score (range, 0‐1) increased, the likelihood of high‐grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78‐2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver‐operator characteristic curve (AUC) = 0.89 (95% CI: 0.84‐0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87‐0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. Conclusion: Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed.