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Article

Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy

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Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
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Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk NR4 7UY, UK
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Faculty of Health and Medical Sciences, The University of Surrey, Guildford GU2 7XH, UK
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School of Pharmacy and Medical Sciences, University of Bradford, Bradford BD7 1DP, UK
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The Earlham Institute, Norwich Research Park, Norwich, Norfolk NR4 7UZ, UK
*
Author to whom correspondence should be addressed.
The Movember GAP1 Urine Biomarker Consortium: Bharati Bapat, Rob Bristow, Andreas Doll, Jeremy Clark, Colin Cooper, Hing Leung, Ian Mills, David Neal, Mireia Olivan, Hardev Pandha, Antoinette Perry, Chris Parker, Martin Sanda, Jack Schalken, Hayley Whitaker.
Academic Editor: Jonas Cicenas
Cancers 2021, 13(9), 2102; https://doi.org/10.3390/cancers13092102
Received: 15 February 2021 / Revised: 13 April 2021 / Accepted: 14 April 2021 / Published: 27 April 2021
(This article belongs to the Special Issue Urological Cancer 2021)
Prostate cancer is a disease responsible for a large proportion of all male cancer deaths but there is a high chance that a patient will die with the disease rather than from. Therefore, there is a desperate need for improvements in diagnosing and predicting outcomes for prostate cancer patients to minimise overdiagnosis and overtreatment whilst appropriately treating men with aggressive disease, especially if this can be done without taking an invasive biopsy. In this work we develop a test that predicts whether a patient has prostate cancer and how aggressive the disease is from a urine sample. This model combines the measurement of a protein-marker called EN2 and the levels of 10 genes measured in urine and proves that integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.
The objective is to develop a multivariable risk model for the non-invasive detection of prostate cancer prior to biopsy by integrating information from clinically available parameters, Engrailed-2 (EN2) whole-urine protein levels and data from urinary cell-free RNA. Post-digital-rectal examination urine samples collected as part of the Movember Global Action Plan 1 study which has been analysed for both cell-free-RNA and EN2 protein levels were chosen to be integrated with clinical parameters (n = 207). A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. ExoGrail risk (range 0–1) was able to determine the outcome of an initial trans-rectal ultrasound guided (TRUS) biopsy more accurately than clinical standards of care, predicting the presence of any cancer with an area under the receiver operator curve (AUC) = 0.89 (95% confidence interval(CI): 0.85–0.94), and discriminating more aggressive Gleason ≥ 3 + 4 disease returning an AUC = 0.84 (95% CI: 0.78–0.89). The likelihood of more aggressive disease being detected significantly increased as ExoGrail risk score increased (Odds Ratio (OR) = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59). Decision curve analysis of the net benefit of ExoGrail showed the potential to reduce the numbers of unnecessary biopsies by 35% when compared to current standards of care. Integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy. View Full-Text
Keywords: prostate cancer; biomarker; urine; machine learning; TRIPOD; liquid biopsy prostate cancer; biomarker; urine; machine learning; TRIPOD; liquid biopsy
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MDPI and ACS Style

Connell, S.P.; Mills, R.; Pandha, H.; Morgan, R.; Cooper, C.S.; Clark, J.; Brewer, D.S.; The Movember GAP1 Urine Biomarker Consortium. Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy. Cancers 2021, 13, 2102. https://doi.org/10.3390/cancers13092102

AMA Style

Connell SP, Mills R, Pandha H, Morgan R, Cooper CS, Clark J, Brewer DS, The Movember GAP1 Urine Biomarker Consortium. Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy. Cancers. 2021; 13(9):2102. https://doi.org/10.3390/cancers13092102

Chicago/Turabian Style

Connell, Shea P., Robert Mills, Hardev Pandha, Richard Morgan, Colin S. Cooper, Jeremy Clark, Daniel S. Brewer, and The Movember GAP1 Urine Biomarker Consortium. 2021. "Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy" Cancers 13, no. 9: 2102. https://doi.org/10.3390/cancers13092102

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