A five-microRNA model (pCaP) for predicting prostate cancer aggressiveness using cell-free urine

Jacob Fredsøe; Anne K I Rasmussen; Peter Mouritzen; Michael Borre; Torben Ørntoft; Karina D Sørensen

doi:10.1002/ijc.32296

A five-microRNA model (pCaP) for predicting prostate cancer aggressiveness using cell-free urine

Jacob Fredsøe, Anne K I Rasmussen, Peter Mouritzen, Michael Borre, Torben Ørntoft, Karina D Sørensen

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Abstract

Improved biomarkers for prostate cancer (PC) risk stratification are urgently needed. Here, we aimed to develop a novel multi-marker model for prediction of biochemical recurrence (BCR) after curatively intended radical prostatectomy (RP), based on minimally-invasive sampling of blood and urine. We initially measured the levels of 45 selected miRNAs by RT-qPCR in exosome enriched cell-free urine samples collected prior to RP from 215 PC patients (cohort 1, training). We trained a novel logistic regression model (pCaP), comprising five urine miRNAs (miR-151a-5p, miR-204-5p, miR-222-3p, miR-23b-3p, and miR-331-3p) and serum prostate specific antigen (PSA), which significantly predicted time to BCR in cohort 1 (univariate cox regression analysis: HR=3.12, p<0.001). Next, using the same exact numeric cutoff for dichotomization as trained in cohort 1, we tested and successfully validated the prognostic potential of pCaP in two additional cohorts, including 199 (cohort 2, HR=2.24, p=0.002) and 205 (cohort 3, HR=2.15, p=0.004) RP patients, respectively. pCaP remained a significant predictor of BCR, also after adjustment for pathological T-stage, surgical margin status and Gleason grade group (p<0.05 in multivariate cox regression analysis: HR=2.72, 1.94, and 1.83 for cohorts 1, 2, and 3, respectively). Additionally, pCaP scores correlated positively with the established clinical risk stratification nomogram CAPRA in all three PC cohorts (Pearson's rho: 0.45, 0.39, and 0.44). Together, our results suggest that the minimally-invasive pCaP model could potentially be used in the future to improve PC risk stratification and to guide more personalized treatment decisions. Further clinical validation studies are warranted. This article is protected by copyright. All rights reserved.

Original language	English
Journal	International Journal of Cancer
Volume	145
Issue	9
Pages (from-to)	2558-2567
Number of pages	10
ISSN	0020-7136
DOIs	https://doi.org/10.1002/ijc.32296
Publication status	Published - 1 Nov 2019

Keywords

biomarker
microRNA
pCaP
prognosis
prostate cancer
urine

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@article{43bc569112e646bd9c6fc4b45b4d9bf5,

title = "A five-microRNA model (pCaP) for predicting prostate cancer aggressiveness using cell-free urine",

abstract = "Improved biomarkers for prostate cancer (PC) risk stratification are urgently needed. Here, we aimed to develop a novel multi-marker model for prediction of biochemical recurrence (BCR) after curatively intended radical prostatectomy (RP), based on minimally-invasive sampling of blood and urine. We initially measured the levels of 45 selected miRNAs by RT-qPCR in exosome enriched cell-free urine samples collected prior to RP from 215 PC patients (cohort 1, training). We trained a novel logistic regression model (pCaP), comprising five urine miRNAs (miR-151a-5p, miR-204-5p, miR-222-3p, miR-23b-3p, and miR-331-3p) and serum prostate specific antigen (PSA), which significantly predicted time to BCR in cohort 1 (univariate cox regression analysis: HR=3.12, p<0.001). Next, using the same exact numeric cutoff for dichotomization as trained in cohort 1, we tested and successfully validated the prognostic potential of pCaP in two additional cohorts, including 199 (cohort 2, HR=2.24, p=0.002) and 205 (cohort 3, HR=2.15, p=0.004) RP patients, respectively. pCaP remained a significant predictor of BCR, also after adjustment for pathological T-stage, surgical margin status and Gleason grade group (p<0.05 in multivariate cox regression analysis: HR=2.72, 1.94, and 1.83 for cohorts 1, 2, and 3, respectively). Additionally, pCaP scores correlated positively with the established clinical risk stratification nomogram CAPRA in all three PC cohorts (Pearson's rho: 0.45, 0.39, and 0.44). Together, our results suggest that the minimally-invasive pCaP model could potentially be used in the future to improve PC risk stratification and to guide more personalized treatment decisions. Further clinical validation studies are warranted. This article is protected by copyright. All rights reserved.",

keywords = "biomarker, microRNA, pCaP, prognosis, prostate cancer, urine",

author = "Jacob Freds{\o}e and Rasmussen, {Anne K I} and Peter Mouritzen and Michael Borre and Torben {\O}rntoft and S{\o}rensen, {Karina D}",

year = "2019",

month = nov,

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doi = "10.1002/ijc.32296",

language = "English",

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A five-microRNA model (pCaP) for predicting prostate cancer aggressiveness using cell-free urine. / Fredsøe, Jacob; Rasmussen, Anne K I; Mouritzen, Peter et al.
In: International Journal of Cancer, Vol. 145, No. 9, 01.11.2019, p. 2558-2567.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - A five-microRNA model (pCaP) for predicting prostate cancer aggressiveness using cell-free urine

AU - Fredsøe, Jacob

AU - Rasmussen, Anne K I

AU - Mouritzen, Peter

AU - Borre, Michael

AU - Ørntoft, Torben

AU - Sørensen, Karina D

PY - 2019/11/1

Y1 - 2019/11/1

N2 - Improved biomarkers for prostate cancer (PC) risk stratification are urgently needed. Here, we aimed to develop a novel multi-marker model for prediction of biochemical recurrence (BCR) after curatively intended radical prostatectomy (RP), based on minimally-invasive sampling of blood and urine. We initially measured the levels of 45 selected miRNAs by RT-qPCR in exosome enriched cell-free urine samples collected prior to RP from 215 PC patients (cohort 1, training). We trained a novel logistic regression model (pCaP), comprising five urine miRNAs (miR-151a-5p, miR-204-5p, miR-222-3p, miR-23b-3p, and miR-331-3p) and serum prostate specific antigen (PSA), which significantly predicted time to BCR in cohort 1 (univariate cox regression analysis: HR=3.12, p<0.001). Next, using the same exact numeric cutoff for dichotomization as trained in cohort 1, we tested and successfully validated the prognostic potential of pCaP in two additional cohorts, including 199 (cohort 2, HR=2.24, p=0.002) and 205 (cohort 3, HR=2.15, p=0.004) RP patients, respectively. pCaP remained a significant predictor of BCR, also after adjustment for pathological T-stage, surgical margin status and Gleason grade group (p<0.05 in multivariate cox regression analysis: HR=2.72, 1.94, and 1.83 for cohorts 1, 2, and 3, respectively). Additionally, pCaP scores correlated positively with the established clinical risk stratification nomogram CAPRA in all three PC cohorts (Pearson's rho: 0.45, 0.39, and 0.44). Together, our results suggest that the minimally-invasive pCaP model could potentially be used in the future to improve PC risk stratification and to guide more personalized treatment decisions. Further clinical validation studies are warranted. This article is protected by copyright. All rights reserved.

AB - Improved biomarkers for prostate cancer (PC) risk stratification are urgently needed. Here, we aimed to develop a novel multi-marker model for prediction of biochemical recurrence (BCR) after curatively intended radical prostatectomy (RP), based on minimally-invasive sampling of blood and urine. We initially measured the levels of 45 selected miRNAs by RT-qPCR in exosome enriched cell-free urine samples collected prior to RP from 215 PC patients (cohort 1, training). We trained a novel logistic regression model (pCaP), comprising five urine miRNAs (miR-151a-5p, miR-204-5p, miR-222-3p, miR-23b-3p, and miR-331-3p) and serum prostate specific antigen (PSA), which significantly predicted time to BCR in cohort 1 (univariate cox regression analysis: HR=3.12, p<0.001). Next, using the same exact numeric cutoff for dichotomization as trained in cohort 1, we tested and successfully validated the prognostic potential of pCaP in two additional cohorts, including 199 (cohort 2, HR=2.24, p=0.002) and 205 (cohort 3, HR=2.15, p=0.004) RP patients, respectively. pCaP remained a significant predictor of BCR, also after adjustment for pathological T-stage, surgical margin status and Gleason grade group (p<0.05 in multivariate cox regression analysis: HR=2.72, 1.94, and 1.83 for cohorts 1, 2, and 3, respectively). Additionally, pCaP scores correlated positively with the established clinical risk stratification nomogram CAPRA in all three PC cohorts (Pearson's rho: 0.45, 0.39, and 0.44). Together, our results suggest that the minimally-invasive pCaP model could potentially be used in the future to improve PC risk stratification and to guide more personalized treatment decisions. Further clinical validation studies are warranted. This article is protected by copyright. All rights reserved.

KW - biomarker

KW - microRNA

KW - pCaP

KW - prognosis

KW - prostate cancer

KW - urine

U2 - 10.1002/ijc.32296

DO - 10.1002/ijc.32296

M3 - Journal article

C2 - 30903800

SN - 0020-7136

VL - 145

SP - 2558

EP - 2567

JO - International Journal of Cancer

JF - International Journal of Cancer

IS - 9

ER -

A five-microRNA model (pCaP) for predicting prostate cancer aggressiveness using cell-free urine

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Keywords

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