PreprintCited by 0Prostate Cancer Diagnosis and TreatmentProstate Cancer Treatment and ResearchAdvanced Proteomics Techniques and Applications

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Multi-omics biomarker pipeline reveals elevated levels of protein-glutamine gamma-glutamyltransferase 4 in seminal plasma of prostate cancer patients

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Drabovich, Andrei P., Saraon, Punit, Drabovich, Mikalai, Karakosta, Theano D., Dimitromanolakis, Apostolos, Hyndman, M. Eric, Jarvi, Keith, Diamandis, Eleftherios P.·2017·bioRxiv (Cold Spring Harbor Laboratory)

Preprint — published as 10.1074/mcp.ra119.001612 · Paper page

Abstract

Purpose: Prostate-specific antigen, a blood serum biomarker of prostate cancer, lacks specificity and prognostic significance, so considerable efforts are devoted to developing novel biomarkers. Seminal plasma, due to its proximity to prostate, is a promising fluid for biomarker discovery and non-invasive diagnostics. In this study, we investigated if seminal plasma proteins could increase specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. Experimental Design: To select 148 most promising biomarker candidates, we combined proteins identified through five independent data mining or experimental approaches: tissue transcriptomics, seminal plasma proteomics, cell secretomics, tissue specificity and androgen regulation. A rigorous biomarker development pipeline based on targeted proteomics assays was designed to evaluate the most promising candidates. Results: We qualified 77 and verified 19 proteins in seminal plasma of 67 negative biopsy and 155 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which could predict prostate cancer on biopsy and outperformed age and serum PSA. Machine-learning approaches also revealed improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an inhouse ELISA, TGM4 was up-regulated 3.7-fold ( P =0.006) and revealed AUC 0.66 for detecting prostate cancer on biopsy for patients with serum PSA≥4 ng/mL and age≥50. Low levels of TGM4 (median 120 pg/mL) were detected in blood serum, but could not differentiate between negative biopsy, prostate cancer or prostate inflammation. Conclusions: Performance of TGM4 warrants its further investigation within the distinct genomic subtypes and evaluation for the inclusion into emerging multi-biomarker panels.

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  1. 10.3322/caac.20107 · DOI
  2. 10.1056/NEJMoa1113162 · DOI
  3. 10.1056/NEJMoa0810084 · DOI
  4. 10.1056/NEJMp1111894 · DOI
  5. 10.1097/01.ju.0000177470.84735.55 · DOI
  6. 10.1158/1078-0432.CCR-10-0122 · DOI
  7. 2014, Multicenter European external validation of a prostate health index-based nomogram for predicting prostate cancer at extended biopsy, Eur Urol, 66, 906, 10.1016/j.eururo.2013.12.005 · DOI
  8. 1999, DD3: a new prostate-specific gene, highly overexpressed in prostate cancer, Cancer Res, 59, 5975
  9. 10.1158/1078-0432.CCR-07-0700 · DOI
  10. 10.1016/j.ccr.2008.04.016 · DOI
  11. 10.1016/j.canlet.2010.10.019 · DOI
  12. 10.1111/j.1464-410X.2007.07488.x · DOI
  13. 10.1038/nrurol.2014.74 · DOI
  14. 10.1021/pr100745u · DOI
  15. Batruch I , Smith CR , Mullen BJ , Grober E , Lo KC , Diamandis EP , et al. Analysis of Seminal Plasma from Patients with Non-obstructive Azoospermia and Identification of Candidate Biomarkers of Male Infertility. J Proteome Res 2011:Epub Feb 2 · DOI
  16. 10.1126/scitranslmed.3006260 · DOI
  17. 2011, Verification of male infertility biomarkers in seminal plasma by multiplex selected reaction monitoring assay, Mol Cell Proteomics, 10, 004127
  18. 2017, Preclinical evaluation of a TEX101 protein ELISA test for the differential diagnosis of male infertility, BMC Med, 15, 60, 10.1186/s12916-017-0817-5 · DOI
  19. 2016, XGBoost: A Scalable Tree Boosting System, arXiv, 1603, 02754
  20. 10.1074/mcp.M115.057257 · DOI
  21. 10.1074/mcp.M112.023887 · DOI
  22. 10.1074/mcp.M111.015214 · DOI
  23. 10.1016/j.jprot.2011.05.025 · DOI
  24. 2013, Quantitative proteomic analysis of amniocytes reveals potentially dysregulated molecular networks in Down syndrome, Clin Proteomics, 10, 2, 10.1186/1559-0275-10-2 · DOI
  25. 10.1021/pr401060x · DOI
  26. 10.1074/mcp.M115.057695 · DOI
  27. 2011, Scikit-learn: Machine Learning in Python, The Journal of Machine Learning Research, 12, 2825
  28. 2012, Characterization of the seminal plasma proteome in men with prostatitis by mass spectrometry, Clin Proteomics, 9, 2, 10.1186/1559-0275-9-2 · DOI
  29. 10.1001/jama.293.17.2095 · DOI
  30. 10.1016/j.ccr.2010.05.026 · DOI
  31. 10.1126/science.1260419 · DOI · Paper page
  32. 10.1074/jbc.M112.384438 · DOI
  33. 2014, Mechanisms of Androgen-Independent Prostate Cancer, Ejifcc, 25, 42
  34. 10.1210/er.2002-0032 · DOI · Paper page
  35. 10.1038/msb.2008.61 · DOI
  36. 10.1021/pr900729g · DOI
  37. 2012, Development of a multiplex selected reaction monitoring assay for quantification of biochemical markers of down syndrome in amniotic fluid samples, J Proteome Res, 11, 3880, 10.1021/pr300355a · DOI
  38. 10.1021/pr300014s · DOI
  39. 10.1016/j.cell.2016.06.041 · DOI · Paper page
  40. 10.1111/j.1469-7580.2008.01018.x · DOI
  41. 2016, Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer, Nature communications, 7, 11906, 10.1038/ncomms11906 · DOI
  42. 2013, Overexpression of transglutaminase 4 and prostate cancer progression: a potential predictor of less favourable outcomes, Asian J Androl, 15, 742, 10.1038/aja.2013.79 · DOI
  43. 2017, Targeted proteomics in urinary extracellular vesicles identifies biomarkers for diagnosis and prognosis of prostate cancer, Oncotarget, 8, 4960, 10.18632/oncotarget.13634 · DOI
  44. 10.3858/emm.2010.42.4.031 · DOI
  45. 10.1038/ng0908-1032 · DOI
  46. 10.1126/scitranslmed.3000513 · DOI
  47. 10.1007/s000180050346 · DOI
  48. Mann T , Lutwak-Mann C. Male Reproductive Function and Semen. Berlin: Springer-Verlag; 1981. · DOI
  49. 10.1021/pr100821m · DOI
  50. 2016, Seminal biomarkers for the evaluation of male infertility, Asian J Androl, 18, 426, 10.4103/1008-682X.175781 · DOI
  51. 10.1074/mcp.M114.047571 · DOI
  52. 10.1200/JCO.2014.55.1192 · DOI
  53. 10.1016/j.bbapap.2014.09.006 · DOI
  54. Drabovich AP , Pavlou MP , Batruch I , Diamandis EP. Proteomic and Mass Spectrometry Technologies for Biomarker Discovery. In: Issaq HJ , Veenstra TD , editors. Proteomic and Metabolomic Approaches to Biomarker Discovery. Waltham, MA: Academic Press (Elsevier); 2013. p 17–37. · DOI
  55. 2007, Expression of the prostate transglutaminase (TGase-4) in prostate cancer cells and its impact on the invasiveness of prostate cancer, Journal of experimental therapeutics & oncology, 6, 257
  56. 2013, Prostate transglutaminase (TGase-4, TGaseP) enhances the adhesion of prostate cancer cells to extracellular matrix, the potential role of TGase-core domain, J Transl Med, 11, 269, 10.1186/1479-5876-11-269 · DOI
  57. 2009, The prostate transglutaminase (TGase-4, TGaseP) regulates the interaction of prostate cancer and vascular endothelial cells, a potential role for the ROCK pathway, Microvascular research, 77, 150, 10.1016/j.mvr.2008.09.010 · DOI
  58. 10.1074/mcp.M112.017889 · DOI
  59. Dunning MJ , Vowler SL , Lalonde E , Ross-Adams H , Boutros P , Mills IG , et al. Mining Human Prostate Cancer Datasets: The “camcAPP” Shiny App. EBioMedicine 2017 · DOI
  60. 10.1016/j.cell.2015.10.025 · DOI
  61. 10.1038/nature20788 · DOI

Metadata

Authors
Drabovich, A. P., Saraon, P., Drabovich, M., Karakosta, T. D., Dimitromanolakis, A., Hyndman, M. E., Jarvi, K., & Diamandis, E. P.
Affiliations
  • Mount Sinai Hospital (CA)
  • University of Toronto (CA)
  • University Health Network (CA)
  • Rockyview General Hospital (CA)
Corresponding author
  • Drabovich, Andrei P.
  • Jarvi, Keith
  • Diamandis, Eleftherios P.
Venue
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Published
2017-03-26
OpenAlex
W2609592944
Cited by
0
References
1
Type
posted content
Language
en
Open access
Locations
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
Funders
Prostate Cancer Canada (RS2015–01)
Topics
  • Health Sciences › Medicine › Urology › Prostate Cancer Diagnosis and Treatment
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Prostate Cancer Treatment and Research
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Keywords
prostate cancer, seminal plasma, tumor markers, non-invasive diagnosis, targeted proteomics, multimarker panels, cancer grading, prostate-specific antigen

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