Raadpleeg ook de lijst van niet-toxische ondersteuning bij prostaatkanker van arts-bioloog drs. Engelbert Valstar.

En als donateur kunt u ook korting krijgen bij verschillende bedrijven, waaronder bij MEDpro voor o.a. prostasol een veel gebruikt natuurlijk middel bij prostaatkanker als alternatief voor hormoontherapie.

19 april 2024: zie ook dit artikel: https://kanker-actueel.nl/urinetest-met-18-genen-ontdekt-beter-of-prostaatkanker-hooggradig-is-of-zal-worden-dan-de-2-genentest-via-psa-blijkt-uit-nieuwe-studie.html

18 mei 2021: Bron: CANCERS

Niet-invasieve markers uit een urinetest helpen bij de risicobeoordeling van prostaatkanker. Een model dat meting van een EN2-eiwitmarker combineert met waarden van 10 specifieke genen in urine, zou het maken van belastende onnodige biopsieën kunnen verminderen. En daardoor overbehandeling kunnen voorkomen en zouden prostaatkankerbehandelingen specifieker en gerichter kunnen worden gegeven.

Shea P. Connell, van Norwich Medical School aan de Universiteit van East Anglia in het Verenigd Koninkrijk, en collega's ontwikkelden een multivariabel risicomodel (ExoGrail) voor de niet-invasieve detectie van prostaatkanker voorafgaand aan een biopsie dat informatie uit klinisch beschikbare parameters integreert, Engrailed- 2 (EN2) eiwitniveaus in de hele urine en gegevens van celvrij RNA in de urine; In de analyses werden 207 patiënten geïncludeerd.

Prostaatkanker is een ziekte die verantwoordelijk is voor een groot deel van alle sterfgevallen door kanker bij mannen, maar de kans is groot dat een patiënt eerder aan de ziekte zal overlijden dan aan de ziekte zelf. Daarom is er dringend behoefte aan verbeteringen in het diagnosticeren en voorspellen van uitkomsten voor prostaatkankerpatiënten om overdiagnose en overbehandeling te minimaliseren terwijl mannen met een agressieve ziekte op de juiste manier worden behandeld, vooral als dit kan worden gedaan zonder een invasieve biopsie te nemen.

Wetenschappers ontwikkelden een test die uit gegevens verkregen uit een urinemonster voorspelt of een patiënt prostaatkanker heeft en hoe agressief de ziekte is. Dit model combineert de meting van een eiwitmarker genaamd EN2 en de niveaus van 10 genen gemeten in urine en bewijst dat integratie van informatie uit meerdere, niet-invasieve biomarkerbronnen het potentieel heeft om vooraf aan een biopsie te beoordelen of patiënten met een klinisch vermoeden van prostaatkanker een biopt nodig hebben of niet.

Het volledige studierapport is gratis in te zien. Hier het abstract van de studie:

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

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

1,Jeremy Clark
1,

1,5,* andThe Movember GAP1 Urine Biomarker Consortium 1,†

Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK

Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, Norfolk NR4 7UY, UK

Faculty of Health and Medical Sciences, The University of Surrey, Guildford GU2 7XH, UK

School of Pharmacy and Medical Sciences, University of Bradford, Bradford BD7 1DP, UK

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

Simple Summary

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.

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.

4. Discussion

Discriminating disease status in patients before a diagnostic biopsy with higher accuracy than current standards could bring about a sizeable change in treatment pathways and reduce the number of men sent forward for ultimately unnecessary biopsy. Given that up to 75% of patients are negative for prostate cancer when presenting with serum PSA levels ≥ 4 ng/mL [5,43,44], a concentration of research efforts has been made to address this problem. To date, several biomarker panels have been successfully developed to non-invasively detect prostate cancer using urine samples, Gleason ≥ 3 + 4 disease with superior accuracy to current clinically implemented methods, including the PUR model developed by ourselves [20,21,45,46]. However, as only a single aspect of urine, assay method or biological process are assessed by these examples, the heterogeneity of prostate cancer may not be entirely accounted for [47], requiring an approach to be taken that provides a more holistic insight into disease status.

Recent analyses, including those presented here, have demonstrated the added value of integrating multiple prognostic biomarkers within the process of fitting risk models for determining patient risk upon an initial biopsy [23,48]. Urine clearly contains a wealth of useful information concerning the disease status of the prostate through the quantification of cf-RNA transcripts, circulating and cell-free DNA, hypermethylation of DNA, and protein biomarker levels [19,46,49,50,51,52].

Our results show that an improved multivariable risk prediction model can be developed from the careful consideration of information from multiple different urine fractions in men suspected to have prostate cancer. Urinary levels of EN2 protein were quantified by ELISA, whilst the transcript levels of 167 cell-free mRNAs were quantified using NanoString technology. The final model integrating information from those assays with serum PSA levels was deemed ExoGrail. Markers selected for the model include well-known genes associated with prostate cancer and proven in other diagnostic tests, such as PCA3 [45], HOXC6 [20], and the TMPRSS2/ERG gene fusion [53]. An interaction between urinary EN2 protein levels and quantified transcripts of SLC12A1 was observed, further demonstrating the benefit of considering information from multiple biological sources (Figure S4).

ExoGrail was able to accurately predict the presence of significant (Gs ≥ 7) prostate cancer on biopsy with an AUC of 0.89, comparing favourably to other published tests (AUCs for Gs ≥ 7: PUR = 0.77 [46], ExoMeth = 0.89 [23], ExoDX Prostate IntelliScore = 0.77 [21], SelectMDX = 0.78 [20], epiCaPture Gs ≥ 4 + 3 AUC = 0.73 [49]). Furthermore, ExoGrail resulted in accurate predictions even when serum PSA levels alone proved inaccurate; patients with a raised PSA but negative biopsy result possessed ExoGrail scores significantly different from both clinically benign patients and those with low-grade Gleason 6 disease, whilst still able to discriminate between more clinically significant Gleason ≥ 7 cancers (Figure 4). The adoption of ExoGrail into current clinical pathways for reducing unnecessary biopsies was considered, showing the potential for up to 32% of patients to safely forgo an invasive biopsy without incurring excessive risk (Figure 6).

ExoGrail was developed with the explicit goal of being robust to potential overfitting and bias, using strong internal validation methods in bootstrap resampling and out-of-bag predictions. Nonetheless, ExoGrail was developed in a relatively small dataset and so requires external validation in an independent cohort before it can be considered for use as a clinical risk model. To this end, we are currently collecting samples from multiple sites in the UK, EU and Canada using an updated ‘At-Home’ Collection system [54]. The At-Home collection system enables biomarker analysis to be performed on urine samples provided by patients at home, which they send in the post to a centralised laboratory. This collection and analysis system will sidestep the need for a visit to the clinic and lead to a postal screening system for prostate cancer diagnosis and prognosis. In this study, we will also assess the potential utility of supplementing MP-MRI with ExoGrail, as MP-MRI can misrepresent disease status, even with rigorous controls in place [6]. The NanoString expression analysis system used in the ExoGrail signature is a rapid and cost-effective analysis system that is also used in the FDA-approved Prosigna Pam50 test for breast cancer aggressiveness [55], making ExoGrail well-positioned for implementation for patient benefit.

5. Conclusions

ExoGrail was able to accurately predict the presence of significant (Gs ≥ 7) prostate cancer on biopsy and showed the potential for an important number of patients to safely forgo an invasive biopsy. If validated in future studies, ExoGrail has the potential to positively impact the clinical experience of patients being investigated for prostate cancer that ultimately have no disease or indolent prostate cancer.

6. Patents

A patent application has been filed by the authors for the present work and work related to this.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/cancers13092102/s1, Figure S1: Boruta analysis of variables available for the training of the SoC model, Figure S2: Boruta analysis of variables available for the training of the Engrailed model, Figure S3: Boruta analysis of variables available for the training of the ExoRNA model, Figure S4: Partial dependency plots detailing the marginal effects and interactions of SLC12A1 and urinary EN2 on predicted ExoGrail Risk Score, Figure S5: Risk score distributions of the trained models including the EN2 and PSA model, Figure S6: Density plots detailing risk score distributions generated from four trained models. Table S1: List of all features available for selection as input variables for each model prior to bootstrapped Boruta feature selection, Table S2: AUC of all trained models, including a combination of EN2 and PSA, for detecting outcomes of an initial biopsy for varying clinically significant thresholds. Supplementary Methods.

Author Contributions

S.P.C. and D.S.B drafted the manuscript and conceived, designed, and performed the statistical analyses. H.P. and R.M. (Richard Morgan) were involved in sample collection and ELISA analyses at their respective institutes. J.C. and R.M. were involved in sample collection and NanoString analyses, as well as the development of clinical methodologies. D.S.B., J.C., R.M. (Robert Mills), H.P., and C.S.C. had joint and equal contributions to senior authorship and were contributors in writing the manuscript. C.S.C., J.C., H.P. and D.S.B. provided the original idea for this study. All authors have read and agreed to the published version of the manuscript. All authors critiqued the manuscript for intellectual content.

Funding

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.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approval was granted for the collection and processing of samples by the Ethics Committees at the East of England REC.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All data and code required to reproduce these analyses can be found at https://github.com/UEA-Cancer-Genetics-Lab/ExoGrail_paper.

Acknowledgments

The research presented in this paper was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.

Conflicts of Interest

A patent application has been filed by the authors for the present work and work related to this. There are no other conflicts of interest to disclose.

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