A model to detect significant prostate cancer integrating urinary peptide and extracellular vesicle RNA data

Connell, Shea, Frantzi, Maria, Latosinska, Agnieszka, Webb, Martyn, Mullen, William, Pejchinovski, Martin, Salji, Mark, Mischak, Harald, Cooper, Colin, Clark, Jeremy and Brewer, Daniel (2022) A model to detect significant prostate cancer integrating urinary peptide and extracellular vesicle RNA data. Cancers, 14 (8). ISSN 2072-6694

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Abstract

There is a clinical need to improve assessment of biopsy-naïve patients for the presence of clinically significant prostate cancer (PCa). In this study, we investigated whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples collected within the Movember GAP1 Urine Biomarker study (n = 192) were analysed by both mass spectrometry-based urine-proteomics and NanoString gene-expression analysis (167 gene-probes). Cross-validated LASSO penalised regression and Random Forests identified a combination of clinical and urinary biomarkers for predictive modelling of significant disease (Gleason Score (Gs) ≥ 3 + 4). Four predictive models were developed: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, and ‘SoC’ (standard of care) clinical data models, alongside a fully integrated omics-model, deemed ‘ExoSpec’. ExoSpec (incorporating four gene transcripts, six peptides, and two clinical variables) is the best model for predicting Gs ≥ 3 + 4 at initial biopsy (AUC = 0.83, 95% CI: 0.77–0.88) and is superior to a standard of care (SoC) model utilising clinical data alone (AUC = 0.71, p < 0.001, 1000 resamples). As the ExoSpec Risk Score increases, the likelihood of higher-grade PCa on biopsy is significantly greater (OR = 2.8, 95% CI: 2.1–3.7). The decision curve analyses reveals that ExoSpec provides a net benefit over SoC and could reduce unnecessary biopsies by 30%.

Item Type: Article
Uncontrolled Keywords: extraceullar vesicles,mass spectrometry,prostate cancer,urinary biomarkers,rna,extracellular vesicles,mass spectrometry,oncology,cancer research,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2700/2730
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 20 Apr 2022 09:30
Last Modified: 25 Jun 2022 00:24
URI: https://ueaeprints.uea.ac.uk/id/eprint/84697
DOI: 10.3390/cancers14081995

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