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

  • Bert DhondtEmail author
  • Elise De BleserEmail author
  • Tom Claeys
  • Sarah Buelens
  • Nicolaas Lumen
  • Jo Vandesompele
  • Anneleen Beckers
  • Valerie Fonteyne
  • Kim Van der Eecken
  • Aurélie De Bruycker
  • Jérôme Paul
  • Pierre Gramme
  • Piet Ost
Topic Paper

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

Prostate cancer Oligometastasis miRNA Serum Biomarker Machine learning 

Notes

Acknowledgements

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 contributions

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.

Supplementary material

345_2018_2609_MOESM1_ESM.pdf (181 kb)
Supplementary material 1 BRISQ checklist for sample collection and processing (PDF 180 kb)
345_2018_2609_MOESM2_ESM.pdf (1.2 mb)
Supplementary material 2 Discovery sample read distribution and annotation histograms (PDF 1178 kb)
345_2018_2609_MOESM3_ESM.pdf (502 kb)
Supplementary material 3 Comparison validation cohort to discovery cohort (PDF 501 kb)
345_2018_2609_MOESM4_ESM.pdf (1.3 mb)
Supplementary material 4 (PDF 1288 kb)
345_2018_2609_MOESM5_ESM.docx (22 kb)
Supplementary material 5 (DOCX 22 kb)
345_2018_2609_MOESM6_ESM.xlsx (15 kb)
Supplementary material 6 Discovery set annotation table with number of reads and number of mature canonical miRNAs per discovery sample (XLSX 14 kb)
345_2018_2609_MOESM7_ESM.xlsx (435 kb)
Supplementary material 7 Discovery set miRNA read counts (XLSX 435 kb)
345_2018_2609_MOESM8_ESM.xlsx (657 kb)
Supplementary material 8 Discovery set normalized miRNA expression data (XLSX 657 kb)
345_2018_2609_MOESM9_ESM.xlsx (5 mb)
Supplementary material 9 Discovery set isomiR read counts (XLSX 5134 kb)
345_2018_2609_MOESM10_ESM.xlsx (105 kb)
Supplementary material 10 Full results of the differential miRNA expression analysis (XLSX 105 kb)
345_2018_2609_MOESM11_ESM.xlsx (20 kb)
Supplementary material 11 Univariate differences between oligometastatic and polymetastatic patients (XLSX 20 kb)
345_2018_2609_MOESM12_ESM.xlsx (11 kb)
Supplementary material 12 Consensus signature of 100 variables selected by WilcoxE over 200 resamplings (XLSX 10 kb)
345_2018_2609_MOESM13_ESM.xlsx (161 kb)
Supplementary material 13 Discovery set RT-qPCR raw data files—Cq values (XLSX 160 kb)
345_2018_2609_MOESM14_ESM.xlsx (35 kb)
Supplementary material 14 Discovery set RT-qPCR result tables (normalized relative quantities without error values—no logarithm transformation) (XLSX 35 kb)
345_2018_2609_MOESM15_ESM.xlsx (58 kb)
Supplementary material 15 Discovery set RT-qPCR result tables (normalized relative quantities with error values—logarithm transformation) (XLSX 58 kb)
345_2018_2609_MOESM16_ESM.xlsx (13 kb)
Supplementary material 16 RT-qPCR assay annotation (XLSX 13 kb)
345_2018_2609_MOESM17_ESM.xlsx (12 kb)
Supplementary material 17 Univariate differences between oligometastatic and polymetastatic patients (XLSX 11 kb)
345_2018_2609_MOESM18_ESM.xlsx (10 kb)
Supplementary material 18 Predictive models that, tuned to optimize sensitivity, obtained by multivariate analysis (XLSX 10 kb)
345_2018_2609_MOESM19_ESM.xlsx (10 kb)
Supplementary material 19 Consensus variable ranking obtained by averaging the 200 rankings of the resampling procedure (XLSX 10 kb)
345_2018_2609_MOESM20_ESM.xlsx (325 kb)
Supplementary material 20 Validation set RT-qPCR raw data files—Cq values (XLSX 324 kb)
345_2018_2609_MOESM21_ESM.xlsx (65 kb)
Supplementary material 21 Validation set RT-qPCR result tables (normalized relative quantities without error values—no logarithm transformation) (XLSX 64 kb)
345_2018_2609_MOESM22_ESM.xlsx (114 kb)
Supplementary material 22 Validation set RT-qPCR result tables (normalized relative quantities with error values—logarithm transformation) (XLSX 114 kb)
345_2018_2609_MOESM23_ESM.xlsx (18 kb)
Supplementary material 23 Univariate differences between oligometastatic and polymetastatic patients (XLSX 17 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Radiation Oncology and Experimental Cancer Research, Laboratory of Experimental Cancer ResearchGhent UniversityGhentBelgium
  2. 2.Cancer Research Institute GhentGhentBelgium
  3. 3.Department of Radiation Oncology and Experimental Cancer ResearchGhent University HospitalGhentBelgium
  4. 4.Department of UrologyGhent University HospitalGhentBelgium
  5. 5.Center for Medical GeneticsGhent UniversityGhentBelgium
  6. 6.BiogazelleZwijnaardeBelgium
  7. 7.Department of PathologyGhent University HospitalGhentBelgium
  8. 8.DNAlyticsLouvain-La-NeuveBelgium

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