Connell, Shea, Mills, Robert, Pandha, Hardev, Morgan, Richard ORCID: https://orcid.org/0000-0002-8721-4479, Cooper, Colin, Clark, Jeremy and Brewer, Daniel (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 (9). p. 2102.

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Abstract

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.

Item Type:	Article
Identifier:	10.3390/cancers13092102
Additional Information:	This research was funded by Movember Foundation GAP1 Urine Biomarker project, The Masonic Charitable Foundation, The Bob Champion Cancer Trust, the King family, The Andy Ripley Memorial Fund and the Stephen Hargrave Trust.
Keywords:	prostate cancer; biomarker; urine; machine learning; TRIPOD; liquid biopsy
Subjects:	Natural sciences > Cell and molecular biology
Related URLs:	Publisher
Depositing User:	Richard Morgan
Date Deposited:	04 May 2021 11:29
Last Modified:	28 Aug 2024 11:45
URI:	https://repository.uwl.ac.uk/id/eprint/7842

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