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Connell, Shea, Frantzi, Maria, Latosinska, Agnieszka, Webb, Martyn, Mullen, William, Pejchinovski, Martin, Salji, Mark, Mischak, Harald, Cooper, Colin 
ORCID: https://orcid.org/0000-0003-2013-8042, Clark, Jeremy and Brewer, Daniel ORCID: https://orcid.org/0000-0003-4753-9794
(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 |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Groups > Cancer Studies Faculty of Medicine and Health Sciences > Research Centres > Metabolic Health |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 20 Apr 2022 09:30 |
| Last Modified: | 19 Oct 2023 03:19 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/84697 |
| DOI: | 10.3390/cancers14081995 |
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