Article | Published:

Molecular Diagnostics

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386 (2015).

  2. 2.

    Siegel, R. L., Miller, K. D., Jemal, A. Cancer statistics, 2018. CA: Cancer J. Clin. 68, 7–30 (2018).

  3. 3.

    Jemal, A., Siegel, R., Ward, E., Murray, T., Xu, J., Smigal, C. et al. Cancer statistics, 2006. CA: Cancer J. Clin. 56, 106–130 (2006).

  4. 4.

    Mottet, N., Bellmunt, J., Bolla, M., Briers, E., Cumberbatch, M. G., De Santis, M. et al. EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur. Urol. 71, 618–629 (2017).

  5. 5.

    Gretzer, M. B. & Partin, A. W. PSA levels and the probability of prostate cancer on biopsy. Eur. Urol. Suppl. 1, 21–27 (2002).

  6. 6.

    Roobol, M. J., Kranse, R., Bangma, C. H., van Leenders, A. G., Blijenberg, B. G., van Schaik, R. H. et al. Screening for prostate cancer: results of the Rotterdam section of the European randomized study of screening for prostate cancer. Eur. Urol. 64, 530–539 (2013).

  7. 7.

    Arnold, M., Karim-Kos, H. E., Coebergh, J. W., Byrnes, G., Antilla, A., Ferlay, J. et al. Recent trends in incidence of five common cancers in 26 European countries since 1988: Analysis of the European Cancer Observatory. Eur. J. cancer 51, 1164–1187 (2015).

  8. 8.

    Center, M. M., Jemal, A., Lortet-Tieulent, J., Ward, E., Ferlay, J., Brawley, O. et al. International variation in prostate cancer incidence and mortality rates. Eur. Urol. 61, 1079–1092 (2012).

  9. 9.

    Haas, G. P., Delongchamps, N., Brawley, O. W., Wang, C. Y., de la Roza, G. The worldwide epidemiology of prostate cancer: perspectives from autopsy studies. Can. J. Urol. 15, 3866–3871 (2008).

  10. 10.

    Godtman, R. A., Holmberg, E., Khatami, A., Stranne, J. & Hugosson, J. Outcome following active surveillance of men with screen-detected prostate cancer. Results from the Goteborg randomised population-based prostate cancer screening trial. Eur. Urol. 63, 101–107 (2013).

  11. 11.

    Hayes, J. H., Ollendorf, D. A., Pearson, S. D., Barry, M. J., Kantoff, P. W., Lee, P. A. et al. Observation versus initial treatment for men with localized, low-risk prostate cancer: a cost-effectiveness analysis. Ann. Intern. Med. 158, 853–860 (2013).

  12. 12.

    Lotan, Y. Controlling health care costs for prostate cancer. Eur. Urol. 64, 17–18 (2013).

  13. 13.

    van den Bergh, R. C., Ahmed, H. U., Bangma, C. H., Cooperberg, M. R., Villers, A., Parker, C. C. Novel tools to improve patient selection and monitoring on active surveillance for low-risk prostate cancer: a systematic review. Eur. Urol. 65, 1023–1031 (2014).

  14. 14.

    Tosoian, J. J., Ross, A. E., Sokoll, L. J., Partin, A. W., Pavlovich, C. P. Urinary biomarkers for prostate cancer. Urol. Clin. North Am. 43, 17–38 (2016).

  15. 15.

    Hormaechea-Agulla, D., Gomez-Gomez, E., Ibanez-Costa, A., Carrasco-Valiente, J., Rivero-Cortes, E., LL, F. et al. Ghrelin O-acyltransferase (GOAT) enzyme is overexpressed in prostate cancer, and its levels are associated with patient’s metabolic status: Potential value as a non-invasive biomarker. Cancer Lett. 383, 125–134 (2016).

  16. 16.

    Frantzi, M., van Kessel, K. E., Zwarthoff, E. C., Marquez, M., Rava, M., Malats, N. et al. Development and Validation of Urine-based Peptide Biomarker Panels for Detecting Bladder Cancer in a Multi-center Study. Clin. Cancer Res. 22, 4077–4086 (2016).

  17. 17.

    Frantzi, M., Metzger, J., Banks, R. E., Husi, H., Klein, J., Dakna, M. et al. Discovery and validation of urinary biomarkers for detection of renal cell carcinoma. J. Proteom. 98, 44–58 (2014).

  18. 18.

    Theodorescu, D., Schiffer, E., Bauer, H. W., Douwes, F., Eichhorn, F., Polley, R. et al. Discovery and validation of urinary biomarkers for prostate cancer. Proteom. Clin. Appl. 2, 556–570 (2008).

  19. 19.

    Gomez-Gomez, E., Carrasco-Valiente, J., Blanca-Pedregosa, A., Barco-Sanchez, B., Fernandez-Rueda, J. L., Molina-Abril, H. et al. European randomized study of screening for prostate cancer risk calculator: external validation, variability, and clinical significance. Urology 102, 85–91 (2017).

  20. 20.

    Epstein, J. I., Allsbrook, W. C. Jr., Amin, M. B., Egevad, L. L. & Committee, I. G. The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am. J. Surg. Pathol. 29, 1228–1242 (2005).

  21. 21.

    Mischak, H., Vlahou, A. & Ioannidis, J. P. Technical aspects and inter-laboratory variability in native peptide profiling: the CE-MS experience. Clin. Biochem. 46, 432–443 (2013).

  22. 22.

    Wittke, S., Fliser, D., Haubitz, M., Bartel, S., Krebs, R., Hausadel, F. et al. Determination of peptides and proteins in human urine with capillary electrophoresis-mass spectrometry, a suitable tool for the establishment of new diagnostic markers. J. Chromatogr. A 1013, 173–181 (2003).

  23. 23.

    Kaiser, T., Hermann, A., Kielstein, J. T., Wittke, S., Bartel, S., Krebs, R. et al. Capillary electrophoresis coupled to mass spectrometry to establish polypeptide patterns in dialysis fluids. J. Chromatogr. A 1013, 157–171 (2003).

  24. 24.

    Siwy, J., Mullen, W., Golovko, I., Franke, J. & Zurbig, P. Human urinary peptide database for multiple disease biomarker discovery. Proteom. Clin. Appl 5, 367–374 (2011).

  25. 25.

    Dakna, M., Harris, K., Kalousis, A., Carpentier, S., Kolch, W., Schanstra, J. P. et al. Addressing the challenge of defining valid proteomic biomarkers and classifiers. BMC Bioinforma. 11, 594 (2010).

  26. 26.

    Klein, J., Papadopoulos, T., Mischak, H. & Mullen, W. Comparison of CE-MS/MS and LC-MS/MS sequencing demonstrates significant complementarity in natural peptide identification in human urine. Electrophoresis 35, 1060–1064 (2014).

  27. 27.

    UniProt C. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2017; 45(D1).

  28. 28.

    Zurbig, P., Renfrow, M. B., Schiffer, E., Novak, J., Walden, M., Wittke, S. et al. Biomarker discovery by CE-MS enables sequence analysis via MS/MS with platform-independent separation. Electrophoresis 27, 2111–2125 (2006).

  29. 29.

    Dobbin, K. K. & Simon, R. M. Optimally splitting cases for training and testing high dimensional classifiers. BMC Med. Genom. 4, 31 (2011).

  30. 30.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).

  31. 31.

    Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Mak. 26, 565–574 (2006).

  32. 32.

    Briganti, A., Fossati, N., Catto, J. W. F., Cornford, P., Montorsi, F., Mottet, N. et al. Active surveillance for low-risk prostate cancer: The European Association of Urology Position in 2018. Eur. Urol. 74, 357–368 (2018).

  33. 33.

    Alford, A. V., Brito, J. M., Yadav, K. K., Yadav, S. S., Tewari, A. K. & Renzulli, J. The use of biomarkers in prostate cancer screening and treatment. Rev. Urol. 19, 221–234 (2017).

  34. 34.

    Van Neste, L., Hendriks, R. J., Dijkstra, S., Trooskens, G., Cornel, E. B., Jannink, S. A. et al. Detection of high-grade prostate cancer using a urinary molecular biomarker-based risk score. Eur. Urol. 70, 740–748 (2016).

  35. 35.

    Gronberg, H., Adolfsson, J., Aly, M., Nordstrom, T., Wiklund, P., Brandberg, Y. et al. Prostate cancer screening in men aged 50-69 years (STHLM3): a prospective population-based diagnostic study. Lancet Oncol. 16, 1667–1676 (2015).

  36. 36.

    Kasivisvanathan, V., Rannikko, A. S., Borghi, M., Panebianco, V., Mynderse, L. A., Vaarala, M. H. et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. New Engl. J. Med. 378, 1767–1777 (2018).

  37. 37.

    Ahmed, H. U., El-Shater Bosaily, A., Brown, L. C., Gabe, R., Kaplan, R., Parmar, M. K. et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389, 815–822 (2017).

  38. 38.

    Schiffer, E., Bick, C., Grizelj, B., Pietzker, S. & Schofer, W. Urinary proteome analysis for prostate cancer diagnosis: cost-effective application in routine clinical practice in Germany. Int. J. Urol. 19, 118–125 (2012).

  39. 39.

    Gaggar, A., Jackson, P. L., Noerager, B. D., O’Reilly, P. J., McQuaid, D. B., Rowe, S. M. et al. A novel proteolytic cascade generates an extracellular matrix-derived chemoattractant in chronic neutrophilic inflammation. J. Immunol. 180, 5662–5669 (2008).

  40. 40.

    Takakura, S., Kohno, T., Shimizu, K., Ohwada, S., Okamoto, A. & Yokota, J. Somatic mutations and genetic polymorphisms of the PPP1R3 gene in patients with several types of cancers. Oncogene 19, 836–840 (2000).

Download references

Acknowledgements

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.

Author information

M.F., E.G.G, H.M., and J.C.V. carried out the conception and design of the study; M.F., E.G.G., A.B.P., J.V.R. and J.C.V. contributed to the data acquisition; M.F., E.G.G., A.L., H.M. and J.C.V. carried out the analysis and interpretation of data; M.F. and E.G.G. drafted the paper; A.B.P., J.V.R., A.L., Z.C., A.S.M., R.M.L., M.J.R.T., H.M. and J.C.V. carried out a critical revision of the paper for important intellectual content; M.F. and A.L. performed the statistical analysis; R.M.L., M.J.R.T., H.M. and J.C.V. supervised the work.

Competing interests

H.M. is the founder and co-owner of Mosaiques Diagnostics. M.F. and A.L. are employed by Mosaiques Diagnostics.

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).

Ethics approval and consent to participate

This study was performed as part of the ONCOVER project. Ethical approval was obtained by the Reina Sofia Hospital Research Ethics Committee in accordance with the Declaration of Helsinki and informed consent was obtained from all participants for the project.

Data availability

All data generated or analysed during this study are included in this published article.

Note

This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution 4.0 International (CC BY 4.0).

Correspondence to Julia Carrasco Valiente.

Supplementary information

Supplementary Table 1

Supplementary Table 2

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5