CE–MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer

Maria Frantzi; Enrique Gomez Gomez; Ana Blanca Pedregosa; José Valero Rosa; Agnieszka Latosinska; Zoran Culig; Axel S. Merseburger; Raul M. Luque; María José Requena Tapia; Harald Mischak; Julia Carrasco Valiente

doi:10.1038/s41416-019-0472-z

CE–MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer

Maria Frantzi, Enrique Gomez Gomez, Ana Blanca Pedregosa, José Valero Rosa, Agnieszka Latosinska, Zoran Culig, Axel S. Merseburger, Raul M. Luque, María José Requena Tapia, Harald Mischak, Julia Carrasco Valiente^*

^*Corresponding author for this work

Department of Urology

33 Citations (Scopus)

Abstract

Background: Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management. Methods: Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (n_Sig = 98; n_non-Sig = 445) and a validation set of 280 patients (n_Sig = 48, n_non-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm. Results: Independent validation of the 19-biomarker model in 280 patients resulted in a 90% sensitivity and 59% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set. Conclusions: This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics.

Original language	English
Journal	British Journal of Cancer
Volume	120
Issue number	12
Pages (from-to)	1120-1128
Number of pages	9
ISSN	0007-0920
DOIs	https://doi.org/10.1038/s41416-019-0472-z
Publication status	Published - 11.06.2019

Funding

E. Gómez-Gómez thanks The Carlos III Health Institute (ISCIII) and the European Social Funds (FSE) which funds his Rio Hortega research grant contract (CM16/00180). The biological samples repository node (Córdoba, Spain) is gratefully acknowledged for coordination tasks in the selection of urine samples. Ipek Guler from the methodology department of the Maimonides Institute of Biomedical Research of Cordoba is also gratefully acknowledged for collaboration in the statistical analysis. Funding: This work was supported by The Spanish Ministerio de Economía y Competitividad (MINECO) and FEDER programme which are gratefully acknowledged for the financial support (Projects “Development of methods for early cancer detection; ONCOVER—detection system of volatile compounds for early diagnosis of lung, colon and prostate cancer”, CCB.030PM). This research was also supported in part by the BioGuidePCa (E! 11023, Eurostars) funded by BMBF (Germany) and PCaProTreat (H2020-MSCA-IF-2017-800048).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

DFG Research Classification Scheme

2.22-23 Reproductive Medicine, Urology

Access to Document

10.1038/s41416-019-0472-z

Cite this

Frantzi, M., Gomez Gomez, E., Blanca Pedregosa, A., Valero Rosa, J., Latosinska, A., Culig, Z., Merseburger, A. S., Luque, R. M., Requena Tapia, M. J., Mischak, H., & Carrasco Valiente, J. (2019). CE–MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer. British Journal of Cancer, 120(12), 1120-1128. https://doi.org/10.1038/s41416-019-0472-z

@article{dc21fa27e4724410b16084c14e87eba5,

title = "CE–MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer",

abstract = "Background: Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management. Methods: Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (nSig = 98; nnon-Sig = 445) and a validation set of 280 patients (nSig = 48, nnon-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm. Results: Independent validation of the 19-biomarker model in 280 patients resulted in a 90\% sensitivity and 59\% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set. Conclusions: This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics.",

author = "Maria Frantzi and \{Gomez Gomez\}, Enrique and \{Blanca Pedregosa\}, Ana and \{Valero Rosa\}, Jos{\'e} and Agnieszka Latosinska and Zoran Culig and Merseburger, \{Axel S.\} and Luque, \{Raul M.\} and \{Requena Tapia\}, \{Mar{\'i}a Jos{\'e}\} and Harald Mischak and \{Carrasco Valiente\}, Julia",

note = "Funding Information: E. G{\'o}mez-G{\'o}mez thanks The Carlos III Health Institute (ISCIII) and the European Social Funds (FSE) which funds his Rio Hortega research grant contract (CM16/00180). The biological samples repository node (C{\'o}rdoba, Spain) is gratefully acknowledged for coordination tasks in the selection of urine samples. Ipek Guler from the methodology department of the Maimonides Institute of Biomedical Research of Cordoba is also gratefully acknowledged for collaboration in the statistical analysis. Funding Information: Funding: This work was supported by The Spanish Ministerio de Econom{\'i}a y Competitividad (MINECO) and FEDER programme which are gratefully acknowledged for the financial support (Projects “Development of methods for early cancer detection; ONCOVER—detection system of volatile compounds for early diagnosis of lung, colon and prostate cancer”, CCB.030PM). This research was also supported in part by the BioGuidePCa (E! 11023, Eurostars) funded by BMBF (Germany) and PCaProTreat (H2020-MSCA-IF-2017-800048). Publisher Copyright: {\textcopyright} 2019, Cancer Research UK. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.",

year = "2019",

month = jun,

day = "11",

doi = "10.1038/s41416-019-0472-z",

language = "English",

volume = "120",

pages = "1120--1128",

journal = "British Journal of Cancer",

issn = "0007-0920",

publisher = "Springer Nature",

number = "12",

}

Frantzi, M, Gomez Gomez, E, Blanca Pedregosa, A, Valero Rosa, J, Latosinska, A, Culig, Z, Merseburger, AS, Luque, RM, Requena Tapia, MJ, Mischak, H & Carrasco Valiente, J 2019, 'CE–MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer', British Journal of Cancer, vol. 120, no. 12, pp. 1120-1128. https://doi.org/10.1038/s41416-019-0472-z

TY - JOUR

T1 - CE–MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer

AU - Frantzi, Maria

AU - Gomez Gomez, Enrique

AU - Blanca Pedregosa, Ana

AU - Valero Rosa, José

AU - Latosinska, Agnieszka

AU - Culig, Zoran

AU - Merseburger, Axel S.

AU - Luque, Raul M.

AU - Requena Tapia, María José

AU - Mischak, Harald

AU - Carrasco Valiente, Julia

N1 - Funding Information: E. Gómez-Gómez thanks The Carlos III Health Institute (ISCIII) and the European Social Funds (FSE) which funds his Rio Hortega research grant contract (CM16/00180). The biological samples repository node (Córdoba, Spain) is gratefully acknowledged for coordination tasks in the selection of urine samples. Ipek Guler from the methodology department of the Maimonides Institute of Biomedical Research of Cordoba is also gratefully acknowledged for collaboration in the statistical analysis. Funding Information: Funding: This work was supported by The Spanish Ministerio de Economía y Competitividad (MINECO) and FEDER programme which are gratefully acknowledged for the financial support (Projects “Development of methods for early cancer detection; ONCOVER—detection system of volatile compounds for early diagnosis of lung, colon and prostate cancer”, CCB.030PM). This research was also supported in part by the BioGuidePCa (E! 11023, Eurostars) funded by BMBF (Germany) and PCaProTreat (H2020-MSCA-IF-2017-800048). Publisher Copyright: © 2019, Cancer Research UK. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 2019/6/11

Y1 - 2019/6/11

N2 - Background: Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management. Methods: Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (nSig = 98; nnon-Sig = 445) and a validation set of 280 patients (nSig = 48, nnon-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm. Results: Independent validation of the 19-biomarker model in 280 patients resulted in a 90% sensitivity and 59% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set. Conclusions: This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics.

AB - Background: Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management. Methods: Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (nSig = 98; nnon-Sig = 445) and a validation set of 280 patients (nSig = 48, nnon-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm. Results: Independent validation of the 19-biomarker model in 280 patients resulted in a 90% sensitivity and 59% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set. Conclusions: This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics.

UR - https://www.scopus.com/pages/publications/85065962976

U2 - 10.1038/s41416-019-0472-z

DO - 10.1038/s41416-019-0472-z

M3 - Journal articles

C2 - 31092909

AN - SCOPUS:85065962976

SN - 0007-0920

VL - 120

SP - 1120

EP - 1128

JO - British Journal of Cancer

JF - British Journal of Cancer

IS - 12

ER -

CE–MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer

Abstract

Funding

UN SDGs

DFG Research Classification Scheme

Access to Document

Other files and links

Fingerprint

Cite this