Discovery and validation of a serum microRNA signature to characterize oligo- and polymetastatic prostate cancer: not ready for prime time



Patients with oligometastatic prostate cancer (PC) may benefit from metastasis-directed therapy (MDT), delaying disease progression and the start of palliative systemic treatment. However, a significant proportion of oligometastatic PC patients progress to polymetastatic PC within a year following MDT, suggesting an underestimation of the metastatic load by current staging modalities. Molecular markers could help to identify true oligometastatic patients eligible for MDT.


Patients with asymptomatic biochemical recurrence following primary PC treatment were classified as oligo- or polymetastatic based on 18F-choline PET/CT imaging. Oligometastatic patients had up to three metastases at baseline and did not progress to more than three lesions following MDT or surveillance within 1 year of diagnosis of metastases. Polymetastatic patients had > 3 metastases at baseline or developed > 3 metastases within 1 year following imaging. A model aiming to prospectively distinguish oligo- and polymetastatic PC patients was trained using clinicopathological parameters and serum-derived microRNA expression profiles from a discovery cohort of 20 oligometastatic and 20 polymetastatic PC patients. To confirm the models predictive performance, it was applied on biomarker data obtained from an independent validation cohort of 44 patients with oligometastatic and 39 patients with polymetastatic disease.


Oligometastatic PC patients had a more favorable prognosis compared to polymetastatic ones, as defined by a significantly longer median CRPC-free survival (not reached versus 38 months; 95% confidence interval 31–45 months with P < 0.001). Despite the good performance of a predictive model trained on the discovery cohort, with an AUC of 0.833 (0.693–0.973; 95% CI) and a sensitivity of 0.894 (0.714–1.000; 95% CI) for oligometastatic disease, none of the miRNA targets were found to be differentially expressed between oligo- and polymetastatic PC patients in the signature validation cohort. The multivariate model had an AUC of 0.393 (0.534 after cross-validation) and therefore, no predictive ability.


Although PC patients with oligometastatic disease had a more favorable prognosis, no serum-derived biomarkers allowing for prospective discrimination of oligo- and polymetastatic prostate cancer patients could be identified.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2


  1. 1.

    Hellman S, Weichselbaum RR (1995) Oligometastases. J Clin Oncol 13(1):8–10

    CAS  Article  Google Scholar 

  2. 2.

    De Bleser E, Tran PT, Ost P (2017) Radiotherapy as metastasis-directed therapy for oligometastatic prostate cancer. Curr Opin Urol 27(6):587–595

    Article  Google Scholar 

  3. 3.

    Schweizer MT, Zhou XC, Wang H, Yang T, Shaukat F, Partin AW et al (2013) Metastasis-free survival is associated with overall survival in men with PSA-recurrent prostate cancer treated with deferred androgen deprivation therapy. Ann Oncol 24(11):2881–2886

    CAS  Article  Google Scholar 

  4. 4.

    Ost P, Decaestecker K, Lambert B, Fonteyne V, Delrue L, Lumen N et al (2014) Prognostic factors influencing prostate cancer-specific survival in non-castrate patients with metastatic prostate cancer. Prostate 74(3):297–305

    CAS  Article  Google Scholar 

  5. 5.

    Sridharan S, Steigler A, Spry NA, Joseph D, Lamb DS, Matthews JH et al (2016) Oligometastatic bone disease in prostate cancer patients treated on the TROG 03.04 RADAR trial. Radiother Oncol J Eur Soc Ther Radiol Oncol 121(1):98–102

    Article  Google Scholar 

  6. 6.

    Sweeney CJ, Chen YH, Carducci M, Liu G, Jarrard DF, Eisenberger M et al (2015) Chemohormonal therapy in metastatic hormone-sensitive prostate cancer. N Engl J Med 373(8):737–746

    CAS  Article  Google Scholar 

  7. 7.

    De Bruycker A, Lambert B, Claeys T, Delrue L, Mbah C, De Meerleer G et al (2017) Prevalence and prognosis of low-volume, oligorecurrent, hormone-sensitive prostate cancer amenable to lesion ablative therapy. BJU Int 120(6):815–821

    Article  Google Scholar 

  8. 8.

    Hong MK, Macintyre G, Wedge DC, Van Loo P, Patel K, Lunke S et al (2015) Tracking the origins and drivers of subclonal metastatic expansion in prostate cancer. Nat Commun 6:6605

    CAS  Article  Google Scholar 

  9. 9.

    Gundem G, Van Loo P, Kremeyer B, Alexandrov LB, Tubio JMC, Papaemmanuil E et al (2015) The evolutionary history of lethal metastatic prostate cancer. Nature 520(7547):353–357

    CAS  Article  Google Scholar 

  10. 10.

    Weichselbaum RR, Hellman S (2011) Oligometastases revisited. Nat Rev Clin Oncol 8(6):378–382

    CAS  Article  Google Scholar 

  11. 11.

    Ost P, Bossi A, Decaestecker K, De Meerleer G, Giannarini G, Karnes RJ et al (2015) Metastasis-directed therapy of regional and distant recurrences after curative treatment of prostate cancer: a systematic review of the literature. Eur Urol 67(5):852–863

    Article  Google Scholar 

  12. 12.

    Ost P, Reynders D, Decaestecker K, Fonteyne V, Lumen N, De Bruycker A et al (2018) Surveillance or metastasis-directed therapy for oligometastatic prostate cancer recurrence: a prospective, randomized, multicenter phase II trial. J Clin Oncol 36(5):446–453

    CAS  Article  Google Scholar 

  13. 13.

    Wei J, Zhu H, Liao X (2018) Trigger pSA predicting recurrence from positive choline PET/CT with prostate cancer after initial treatment. Oncotarget 9(18):14630–14641

    Article  Google Scholar 

  14. 14.

    Perera M, Papa N, Christidis D, Wetherell D, Hofman MS, Murphy DG et al (2016) Sensitivity, specificity, and predictors of positive (68)Ga-prostate-specific membrane antigen positron emission tomography in advanced prostate cancer: a systematic review and meta-analysis. Eur Urol 70(6):926–937

    Article  Google Scholar 

  15. 15.

    van Leeuwen PJ, Stricker P, Hruby G, Kneebone A, Ting F, Thompson B et al (2016) (68) Ga-PSMA has a high detection rate of prostate cancer recurrence outside the prostatic fossa in patients being considered for salvage radiation treatment. BJU Int 117(5):732–739

    Article  Google Scholar 

  16. 16.

    Murphy DG, Sweeney CJ, Tombal B (2017) “Gotta Catch ‘em All”, or Do We? Pokemet approach to metastatic prostate cancer. Eur Urol 72(1):1–3

    Article  Google Scholar 

  17. 17.

    Joice GA, Rowe SP, Pienta KJ, Gorin MA (2017) Oligometastatic prostate cancer: shaping the definition with molecular imaging and an improved understanding of tumor biology. Curr Opin Urol 27(6):533–541

    Article  Google Scholar 

  18. 18.

    Dhondt B, Rousseau Q, De Wever O, Hendrix A (2016) Function of extracellular vesicle-associated miRNAs in metastasis. Cell Tissue Res 365:621–641

    CAS  Article  Google Scholar 

  19. 19.

    Cornford P, Bellmunt J, Bolla M, Briers E, De Santis M, Gross T et al (2017) EAU-ESTRO-SIOG guidelines on prostate cancer. Part II: treatment of relapsing, metastatic, and castration-resistant prostate cancer. Eur Urol. 71(4):630–642

    Article  Google Scholar 

  20. 20.

    Moore HM, Kelly AB, Jewell SD, McShane LM, Clark DP, Greenspan R et al (2011) Biospecimen reporting for improved study quality (BRISQ). Cancer Cytopathol 119(2):92–102

    Article  Google Scholar 

  21. 21.

    Lussier YA, Xing HR, Salama JK, Khodarev NN, Huang Y, Zhang Q et al (2011) MicroRNA expression characterizes oligometastasis(es). PLoS One 6(12):e28650

    CAS  Article  Google Scholar 

  22. 22.

    Lussier YA, Khodarev NN, Regan K, Corbin K, Li H, Ganai S et al (2012) Oligo- and polymetastatic progression in lung metastasis(es) patients is associated with specific microRNAs. PLoS One 7(12):e50141

    CAS  Article  Google Scholar 

  23. 23.

    Pitroda SP, Khodarev NN, Huang L, Uppal A, Wightman SC, Ganai S et al (2018) Integrated molecular subtyping defines a curable oligometastatic state in colorectal liver metastasis. Nat Commun 9(1):1793

    Article  Google Scholar 

  24. 24.

    Banks RE, Stanley AJ, Cairns DA, Barrett JH, Clarke P, Thompson D et al (2005) Influences of blood sample processing on low-molecular-weight proteome identified by surface-enhanced laser desorption/ionization mass spectrometry. Clin Chem 51(9):1637–1649

    CAS  Article  Google Scholar 

  25. 25.

    McLerran D, Grizzle WE, Feng Z, Bigbee WL, Banez LL, Cazares LH et al (2008) Analytical validation of serum proteomic profiling for diagnosis of prostate cancer: sources of sample bias. Clin Chem 54(1):44–52

    CAS  Article  Google Scholar 

  26. 26.

    Wang K, Yuan Y, Cho J-H, McClarty S, Baxter D, Galas DJ (2012) Comparing the MicroRNA spectrum between serum and plasma. PLoS One 7(7):e41561 (Ahuja SK, editor)

    CAS  Article  Google Scholar 

  27. 27.

    Radwan N, Phillips R, Ross A, Rowe SP, Gorin MA, Antonarakis ES et al (2017) A phase II randomized trial of Observation versus stereotactic ablative RadiatIon for OLigometastatic prostate CancEr (ORIOLE). BMC Cancer 17(1):453

    Article  Google Scholar 

Download references


This work was supported by “Kom op tegen Kanker (Stand up to Cancer), the Flemisch cancer society” (Bert Dhondt: Emmanuel Vander Schueren Research Grant). Piet Ost is a senior clinical investigator of the Research Foundation—Flanders, Belgium.

Author information




BD sample collection, data analysis and reporting, data management, manuscript writing. EDB data management, manuscript writing. TC sample collection, data collection and management. SB sample collection, manuscript editing. NL manuscript editing. JV project development, manuscript editing. AB project development. VF data collection and management, manuscript editing. JP data analysis and reporting. PG data analysis and reporting, manuscript editing. PO project development, data collection and management, manuscript editing. All authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Bert Dhondt or Elise De Bleser.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 BRISQ checklist for sample collection and processing (PDF 180 kb)

Supplementary material 2 Discovery sample read distribution and annotation histograms (PDF 1178 kb)

Supplementary material 3 Comparison validation cohort to discovery cohort (PDF 501 kb)

Supplementary material 4 (PDF 1288 kb)

Supplementary material 5 (DOCX 22 kb)

Supplementary material 6 Discovery set annotation table with number of reads and number of mature canonical miRNAs per discovery sample (XLSX 14 kb)

Supplementary material 7 Discovery set miRNA read counts (XLSX 435 kb)

Supplementary material 8 Discovery set normalized miRNA expression data (XLSX 657 kb)

Supplementary material 9 Discovery set isomiR read counts (XLSX 5134 kb)

Supplementary material 10 Full results of the differential miRNA expression analysis (XLSX 105 kb)

Supplementary material 11 Univariate differences between oligometastatic and polymetastatic patients (XLSX 20 kb)

Supplementary material 12 Consensus signature of 100 variables selected by WilcoxE over 200 resamplings (XLSX 10 kb)

Supplementary material 13 Discovery set RT-qPCR raw data files—Cq values (XLSX 160 kb)

Supplementary material 14 Discovery set RT-qPCR result tables (normalized relative quantities without error values—no logarithm transformation) (XLSX 35 kb)

Supplementary material 15 Discovery set RT-qPCR result tables (normalized relative quantities with error values—logarithm transformation) (XLSX 58 kb)

Supplementary material 16 RT-qPCR assay annotation (XLSX 13 kb)

Supplementary material 17 Univariate differences between oligometastatic and polymetastatic patients (XLSX 11 kb)

Supplementary material 18 Predictive models that, tuned to optimize sensitivity, obtained by multivariate analysis (XLSX 10 kb)

Supplementary material 19 Consensus variable ranking obtained by averaging the 200 rankings of the resampling procedure (XLSX 10 kb)

Supplementary material 20 Validation set RT-qPCR raw data files—Cq values (XLSX 324 kb)

Supplementary material 21 Validation set RT-qPCR result tables (normalized relative quantities without error values—no logarithm transformation) (XLSX 64 kb)

Supplementary material 22 Validation set RT-qPCR result tables (normalized relative quantities with error values—logarithm transformation) (XLSX 114 kb)

Supplementary material 23 Univariate differences between oligometastatic and polymetastatic patients (XLSX 17 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dhondt, B., De Bleser, E., Claeys, T. et al. Discovery and validation of a serum microRNA signature to characterize oligo- and polymetastatic prostate cancer: not ready for prime time. World J Urol 37, 2557–2564 (2019).

Download citation


  • Prostate cancer
  • Oligometastasis
  • miRNA
  • Serum
  • Biomarker
  • Machine learning