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Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Abstract

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

Key points

  • Standard-of-care clinical tools for the management of localized prostate cancer result in substantial overdiagnosis and overtreatment.

  • Fluid-based protein biomarkers have the potential to complement clinical decision-making.

  • Advances in mass spectrometry, such as increased scan speeds and mass resolution, have enabled the systematic discovery and validation of protein biomarkers in prostate-associated fluids.

  • Appropriate sample selection for biomarker discovery and validation can improve detection of prostate-derived proteins in fluids.

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Fig. 1: The role of biomarkers in prostate cancer management.
Fig. 2: Biomarker discovery to translation into the clinic.
Fig. 3: Clinically relevant fluids in prostate cancer.

References

  1. 1.

    Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    PubMed  Google Scholar 

  2. 2.

    Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA Cancer J. Clin. 70, 7–30 (2020).

    Google Scholar 

  3. 3.

    Musunuru, H. B. et al. Active surveillance for intermediate risk prostate cancer: survival outcomes in the sunnybrook experience. J. Urol. 196, 1651–1658 (2016).

    PubMed  Google Scholar 

  4. 4.

    Ku, S.-Y. Y., Gleave, M. E. & Beltran, H. Towards precision oncology in advanced prostate cancer. Nat. Rev. Urol. 16, 645–654 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Naji, L. et al. Digital rectal examination for prostate cancer screening in primary care: a systematic review and meta-analysis. Ann. Fam. Med. 16, 149–154 (2018).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Thompson, I. M. et al. Operating characteristics of prostate-specific antigen in men with an initial PSA level of 3.0 ng/mL or lower. J. Am. Med. Assoc. 294, 66–70 (2005).

    CAS  Google Scholar 

  7. 7.

    Serefoglu, E. C. et al. How reliable is 12-core prostate biopsy procedure in the detection of prostate cancer? J. Can. Urol. Assoc. 7, E293–E298 (2013).

    Google Scholar 

  8. 8.

    Freedland, S. J. et al. Upgrading and downgrading of prostate needle biopsy specimens: risk factors and clinical implications. Urology 69, 495–499 (2007).

    PubMed  Google Scholar 

  9. 9.

    Epstein, J. I., Feng, Z., Trock, B. J. & Pierorazio, P. M. Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades. Eur. Urol. 61, 1019–1024 (2012).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Loeb, S. et al. Overdiagnosis and overtreatment of prostate cancer. Eur. Urol. 65, 1046–1055 (2014).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Rider, J. R. et al. Long-term outcomes among noncuratively treated men according to prostate cancer risk category in a nationwide, population-based study. Eur. Urol. 63, 88–96 (2013).

    PubMed  Google Scholar 

  12. 12.

    Philipson, R. G. et al. Patterns of clinical progression in radiorecurrent high-risk prostate cancer. Eur. Urol. https://doi.org/10.1016/j.eururo.2021.04.035 (2021).

    Article  PubMed  Google Scholar 

  13. 13.

    Mottet, N. et al. EAU-ESTRO-SIOG guidelines on prostate cancer. Part I: screening, diagnosis, and local treatment with curative intent. Eur. Urol. 71, 618–629 (2017).

    PubMed  Google Scholar 

  14. 14.

    Mohler, J. L. et al. Prostate cancer, version 2.2019. JNCCN 17, 479–505 (2019).

    CAS  PubMed  Google Scholar 

  15. 15.

    Zimmerman, L. J., Li, M., Yarbrough, W. G., Slebos, R. J. C. & Liebler, D. C. Global stability of plasma proteomes for mass spectrometry-based analyses. Mol. Cell. Proteom. 11, M111.014340 (2012).

    Google Scholar 

  16. 16.

    Decramer, S. et al. Urine in clinical proteomics. Mol. Cell. Proteom. 7, 1850–1862 (2008).

    CAS  Google Scholar 

  17. 17.

    Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Google Scholar 

  18. 18.

    Ellis, M. J. et al. Connecting genomic alterations to cancer biology with proteomics: the NCI clinical proteomic tumor analysis consortium. Cancer Discov. 3, 1108–1112 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

    CAS  PubMed  Google Scholar 

  20. 20.

    Stabile, A. et al. Multiparametric MRI for prostate cancer diagnosis: current status and future directions. Nat. Rev. Urol. 17, 41–61 (2019).

    PubMed  Google Scholar 

  21. 21.

    Koo, K. M., Mainwaring, P. N., Tomlins, S. A. & Trau, M. Merging new-age biomarkers and nanodiagnostics for precision prostate cancer management. Nat. Rev. Urol. 16, 302–317 (2019).

    PubMed  Google Scholar 

  22. 22.

    Jeon, J. et al. Temporal stability and prognostic biomarker potential of the prostate cancer urine miRNA transcriptome. J. Natl Cancer Inst. 112, 247–255 (2020).

    PubMed  Google Scholar 

  23. 23.

    Balk, S. P., Ko, Y. J. & Bubley, G. J. Biology of prostate-specific antigen. J. Clin. Oncol. 21, 383–391 (2003).

    CAS  PubMed  Google Scholar 

  24. 24.

    Hossack, T. et al. Location and pathological characteristics of cancers in radical prostatectomy specimens identified by transperineal biopsy compared to transrectal biopsy. J. Urol. 188, 781–785 (2012).

    PubMed  Google Scholar 

  25. 25.

    Loeb, S. et al. Systematic review of complications of prostate biopsy. Eur. Urol. 64, 876–892 (2013).

    PubMed  Google Scholar 

  26. 26.

    Stefanova, V. et al. Transperineal prostate biopsies using local anesthesia: experience with 1,287 patients. prostate cancer detection rate, complications and patient tolerability. J. Urol. 201, 1121–1125 (2019).

    PubMed  Google Scholar 

  27. 27.

    Lomas, D. J. & Ahmed, H. U. All change in the prostate cancer diagnostic pathway. Nat. Rev. Clin. Oncol. 17, 372–381 (2020).

    PubMed  Google Scholar 

  28. 28.

    Capitanio, U. et al. Biopsy core number represents one of foremost predictors of clinically significant gleason sum upgrading in patients with low-risk prostate cancer. Urology 73, 1087–1091 (2009).

    PubMed  Google Scholar 

  29. 29.

    Fütterer, J. J. et al. Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? A systematic review of the literature. Eur. Urol. 68, 1045–1053 (2015).

    PubMed  Google Scholar 

  30. 30.

    Ahdoot, M. et al. MRI-targeted, systematic, and combined biopsy for prostate cancer diagnosis. N. Engl. J. Med. 382, 917–928 (2020).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Johnson, D. C. et al. Detection of individual prostate cancer foci via multiparametric magnetic resonance imaging. Eur. Urol. 75, 712–720 (2019).

    CAS  PubMed  Google Scholar 

  32. 32.

    Filson, C. P. et al. Prostate cancer detection with magnetic resonance-ultrasound fusion biopsy: the role of systematic and targeted biopsies. Cancer 122, 884–892 (2016).

    PubMed  Google Scholar 

  33. 33.

    Schoots, I. G. et al. Magnetic resonance imaging in active surveillance of prostate cancer: a systematic review. Eur. Urol. 67, 627–636 (2015).

    PubMed  Google Scholar 

  34. 34.

    Hsiang, W. et al. Outcomes of serial multiparametric magnetic resonance imaging and subsequent biopsy in men with low-risk prostate cancer managed with active surveillance. Eur. Urol. Focus. 7, 47–54 (2019).

    PubMed  Google Scholar 

  35. 35.

    Padhani, A. R., Haider, M. A., Villers, A. & Barentsz, J. O. Multiparametric magnetic resonance imaging for prostate cancer detection: what we see and what we miss. Eur. Urol. 75, 721–722 (2019).

    PubMed  Google Scholar 

  36. 36.

    Sonn, G. A. et al. Prostate magnetic resonance imaging interpretation varies substantially across radiologists. Eur. Urol. Focus. 5, 592–599 (2019).

    PubMed  Google Scholar 

  37. 37.

    Noguchi, M., Stamey, T. A., McNeal, J. E. & Yemoto, C. M. Relationship between systematic biopsies and histological features of 222 radical prostatectomy specimens: lack of prediction of tumor significance for men with nonpalpable prostate cancer. J. Urol. 166, 104–110 (2001).

    CAS  PubMed  Google Scholar 

  38. 38.

    Boutros, P. C. et al. Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat. Genet. 47, 736–745 (2015).

    CAS  PubMed  Google Scholar 

  39. 39.

    Chua, M. L. K. et al. A prostate cancer “nimbosus”: genomic instability and SChLAP1 dysregulation underpin aggression of intraductal and cribriform subpathologies. Eur. Urol. 72, 665–674 (2017).

    CAS  PubMed  Google Scholar 

  40. 40.

    Boorjian, S. A. et al. Long-term risk of clinical progression after biochemical recurrence following radical prostatectomy: the impact of time from surgery to recurrence. Eur. Urol. 59, 893–899 (2011).

    PubMed  Google Scholar 

  41. 41.

    Addona, T. A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol. 27, 633–641 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Leth-Larsen, R., Lund, R. R. & Ditzel, H. J. Plasma membrane proteomics and its application in clinical cancer biomarker discovery. Mol. Cell. Proteom. 9, 1369–1382 (2010).

    CAS  Google Scholar 

  43. 43.

    Petricoin, E. F., Belluco, C., Araujo, R. P. & Liotta, L. A. The blood peptidome: a higher dimension of information content for cancer biomarker discovery. Nat. Rev. Cancer 6, 961–967 (2006).

    CAS  PubMed  Google Scholar 

  44. 44.

    Niu, Y.-N. & Xia, S.-J. Stroma-epithelium crosstalk in prostate cancer. Asian J. Androl. 11, 28–35 (2009).

    CAS  PubMed  Google Scholar 

  45. 45.

    Fraser, M. et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature 541, 359–364 (2017).

    CAS  PubMed  Google Scholar 

  46. 46.

    Espiritu, S. M. G. et al. The evolutionary landscape of localized prostate cancers drives clinical aggression. Cell 173, 1003–1013.e15 (2018).

    CAS  PubMed  Google Scholar 

  47. 47.

    Sinha, A. et al. The proteogenomic landscape of curable prostate cancer. Cancer Cell 35, 414–427.e6 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Lindberg, J., Kristiansen, A., Wiklund, P., Grönberg, H. & Egevad, L. Tracking the origin of metastatic prostate cancer. Eur. Urol. 67, 819–822 (2015).

    PubMed  Google Scholar 

  49. 49.

    Shipitsin, M. et al. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error. Br. J. Cancer 111, 1201–1212 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Bruderer, R. et al. Analysis of 1508 plasma samples by capillary-flow data-independent acquisition profiles proteomics of weight loss and maintenance. Mol. Cell. Proteom. 18, 1242–1254 (2019).

    CAS  Google Scholar 

  51. 51.

    Halabi, S. et al. Overall survival of black and white men with metastatic castration-resistant prostate cancer treated with docetaxel. J. Clin. Oncol. 37, 403–410 (2019).

    CAS  PubMed  Google Scholar 

  52. 52.

    Taylor, R. A. et al. Germline BRCA2 mutations drive prostate cancers with distinct evolutionary trajectories. Nat. Commun. 8, 13671 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Steen, H. & Mann, M. The ABC’s (and XYZ’s) of peptide sequencing. Nat. Rev. Mol. Cell Biol. 5, 699–711 (2004).

    CAS  PubMed  Google Scholar 

  54. 54.

    Laskay, Ü. A., Lobas, A. A., Srzentić, K., Gorshkov, M. V. & Tsybin, Y. O. Proteome digestion specificity analysis for rational design of extended bottom-up and middle-down proteomics experiments. J. Proteome Res. 12, 5558–5569 (2013).

    CAS  PubMed  Google Scholar 

  55. 55.

    Batth, T. S., Francavilla, C. & Olsen, J. V. Off-line high-pH reversed-phase fractionation for in-depth phosphoproteomics. J. Proteome Res. 13, 6176–6186 (2014).

    CAS  PubMed  Google Scholar 

  56. 56.

    Wolters, D. A., Washburn, M. P. & Yates, J. R. An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 73, 5683–5690 (2001).

    CAS  PubMed  Google Scholar 

  57. 57.

    Batth, T. S. & Olsen, J. V. Offline high pH reversed-phase peptide fractionation for deep phosphoproteome coverage. in Methods in Molecular Biology Vol. 1355 179–192 (Humana Press Inc., 2016).

  58. 58.

    Gomes, F. P. & Yates, J. R. Recent trends of capillary electrophoresis-mass spectrometry in proteomics research. Mass. Spectrom. Rev. 38, 445–460 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Vasilopoulou, C. G. et al. Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts. Nat. Commun. 11, 331 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Bekker-Jensen, D. B. et al. A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients. Mol. Cell. Proteom. 19, 716–729 (2020).

    CAS  Google Scholar 

  61. 61.

    Mitchell Wells, J. & McLuckey, S. A. Collision-induced dissociation (CID) of peptides and proteins. Methods Enzymol. 402, 148–185 (2005).

    PubMed  Google Scholar 

  62. 62.

    Kislinger, T. et al. PRISM, a generic large scale proteomic investigation strategy for mammals. Mol. Cell. Proteom. 2, 96–106 (2003).

    CAS  Google Scholar 

  63. 63.

    Elias, J. E., Haas, W., Faherty, B. K. & Gygi, S. P. Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations. Nat. Methods 2, 667–675 (2005).

    CAS  PubMed  Google Scholar 

  64. 64.

    Wong, J. W. H. & Cagney, G. An overview of label-free quantitation methods in proteomics by mass spectrometry. Methods Mol. Biol. 604, 273–283 (2010).

    CAS  PubMed  Google Scholar 

  65. 65.

    Thompson, A. et al. Tandem mass tags: A novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003).

    CAS  PubMed  Google Scholar 

  66. 66.

    Hogrebe, A. et al. Benchmarking common quantification strategies for large-scale phosphoproteomics. Nat. Commun. 9, 1045 (2018).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Altelaar, A. F. M. et al. Benchmarking stable isotope labeling based quantitative proteomics. J. Proteom. 88, 14–26 (2013).

    CAS  Google Scholar 

  68. 68.

    Li, Z. et al. Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ orbitrap velos. J. Proteome Res. 11, 1582–1590 (2012).

    CAS  PubMed  Google Scholar 

  69. 69.

    Venable, J. D., Dong, M. Q., Wohlschlegel, J., Dillin, A. & Yates, J. R. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).

    CAS  PubMed  Google Scholar 

  70. 70.

    Xuan, Y. et al. Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies. Nat. Commun. 11, 5248 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).

    PubMed  Google Scholar 

  72. 72.

    Principe, S. et al. In-depth proteomic analyses of exosomes isolated from expressed prostatic secretions in urine. Proteomics 13, 1667–1671 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Rontogianni, S. et al. Proteomic profiling of extracellular vesicles allows for human breast cancer subtyping. Commun. Biol. 2, 325 (2019).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Yang, W., Freeman, M. R. & Kyprianou, N. Personalization of prostate cancer therapy through phosphoproteomics. Nat. Rev. Urol. 15, 483–497 (2018).

    PubMed  Google Scholar 

  75. 75.

    Drake, J. M. et al. Phosphoproteome integration reveals patient-specific networks in prostate cancer. Cell 166, 1041–1054 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Gahmberg, C.G. & Tolvanen, M. Why mammalian cell surface proteins are glycoproteins. Trends Biochem. Sci. 21, 308–311 (1996).

    CAS  PubMed  Google Scholar 

  77. 77.

    Leitner, A. Enrichment strategies in phosphoproteomics. in Methods in Molecular Biology Vol. 1355 105–121 (Humana Press Inc., 2016).

  78. 78.

    Riley, N. M., Bertozzi, C. R. & Pitteri, S. J. A pragmatic guide to enrichment strategies for mass spectrometry-based glycoproteomics. Mol. Cell. Proteom. 20, 100029 (2020).

    Google Scholar 

  79. 79.

    Mertins, P. et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat. Protoc. 13, 1632–1661 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Huang, P., Li, H., Gao, W., Cai, Z. & Tian, R. A fully integrated spintip-based approach for sensitive and quantitative profiling of region-resolved in vivo brain glycoproteome. Anal. Chem. 91, 9181–9189 (2019).

    CAS  PubMed  Google Scholar 

  81. 81.

    Leutert, M., Rodríguez-Mias, R. A., Fukuda, N. K. & Villén, J. R2-P2 rapid-robotic phosphoproteomics enables multidimensional cell signaling studies. Mol. Syst. Biol. 15, e9021 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Zhu, Y. et al. Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells. Nat. Commun. 9, 882 (2018).

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Moggridge, S., Sorensen, P. H., Morin, G. B. & Hughes, C. S. Extending the compatibility of the SP3 paramagnetic bead processing approach for proteomics. J. Proteome Res. 17, 1730–1740 (2018).

    CAS  PubMed  Google Scholar 

  84. 84.

    Frantzi, M., Latosinska, A., Merseburger, A. S. & Mischak, H. Recent progress in urinary proteome analysis for prostate cancer diagnosis and management. Expert Rev. Mol. Diagn. 15, 1539–1554 (2015).

    CAS  PubMed  Google Scholar 

  85. 85.

    Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).

    CAS  PubMed  Google Scholar 

  86. 86.

    Rifai, N., Gillette, M. A. & Carr, S. A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24, 971–983 (2006).

    CAS  PubMed  Google Scholar 

  87. 87.

    Hoofnagle, A. N. & Wener, M. H. The fundamental flaws of immunoassays and potential solutions using tandem mass spectrometry. J. Immunol. Methods 347, 3–11 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Joshi, A. & Mayr, M. In aptamers they trust: caveats of the SOMAscan biomarker discovery Platform from SomaLogic. Circulation 138, 2482–2485 (2018).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Wang, P., Whiteaker, J. R. & Paulovich, A. G. The evolving role of mass spectrometry in cancer biomarker discovery. Cancer Biol. Ther. 8, 1083–1094 (2009).

    CAS  PubMed  Google Scholar 

  90. 90.

    Lange, V., Picotti, P., Domon, B. & Aebersold, R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4, 222 (2008).

    PubMed  PubMed Central  Google Scholar 

  91. 91.

    Carr, S. A. & Anderson, L. Protein quantitation through targeted mass spectrometry: the way out of biomarker purgatory? Clin. Chem. 54, 1749–1752 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Schiess, R., Wollscheid, B. & Aebersold, R. Targeted proteomic strategy for clinical biomarker discovery. Mol. Oncol. 3, 33–44 (2009).

    CAS  PubMed  Google Scholar 

  93. 93.

    Anderson, N. L. et al. A human proteome detection and quantitation project. Mol. Cell. Proteom. 8, 883–886 (2009).

    CAS  Google Scholar 

  94. 94.

    Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25, 125–131 (2007).

    CAS  PubMed  Google Scholar 

  95. 95.

    Faserl, K., Sarg, B., Maurer, V. & Lindner, H. H. Exploiting charge differences for the analysis of challenging post-translational modifications by capillary electrophoresis-mass spectrometry. J. Chromatogr. A 1498, 215–223 (2017).

    CAS  PubMed  Google Scholar 

  96. 96.

    Lombard-Banek, C., Choi, S. B. & Nemes, P. Single-cell proteomics in complex tissues using microprobe capillary electrophoresis mass spectrometry. in Methods in Enzymology Vol. 628 263–292 (Academic Press Inc., 2019).

  97. 97.

    Carr, S. A. et al. Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Mol. Cell. Proteom. 13, 907–917 (2014).

    CAS  Google Scholar 

  98. 98.

    Virreira Winter, S. et al. Urinary proteome profiling for stratifying patients with familial Parkinson’s disease. EMBO Mol Med. 13, e13257 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Geyer, P. E. et al. Plasma proteome profiling to detect and avoid sample-related biases in biomarker studies. EMBO Mol. Med. 11, e10427 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Makarov, A., Denisov, E., Lange, O. & Horning, S. Dynamic range of mass accuracy in LTQ orbitrap hybrid mass spectrometer. J. Am. Soc. Mass. Spectrom. 17, 977–982 (2006).

    CAS  PubMed  Google Scholar 

  101. 101.

    Geyer, P. E., Holdt, L. M., Teupser, D. & Mann, M. Revisiting biomarker discovery by plasma proteomics. Mol. Syst. Biol. 13, 942 (2017).

    PubMed  PubMed Central  Google Scholar 

  102. 102.

    Annesley, T. M. Ion suppression in mass spectrometry. Clin. Chem. 49, 1041–1044 (2003).

    CAS  PubMed  Google Scholar 

  103. 103.

    Hanash, S. M., Pitteri, S. J. & Faca, V. M. Mining the plasma proteome for cancer biomarkers. Nature 452, 571–579 (2008).

    CAS  PubMed  Google Scholar 

  104. 104.

    Tu, C. et al. Depletion of abundant plasma proteins and limitations of plasma proteomics. J. Proteome Res. 9, 4982–4991 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Drake, R. R. et al. In-depth proteomic analyses of direct expressed prostatic secretions. J. Proteome Res. 9, 2109–2116 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Kim, Y. et al. Identification of differentially expressed proteins in direct expressed prostatic secretions of men with organ-confined versus extracapsular prostate cancer. Mol. Cell. Proteom. 11, 1870–1884 (2012).

    Google Scholar 

  107. 107.

    Drabovich, A. P., Saraon, P., Jarvi, K. & Diamandis, E. P. Seminal plasma as a diagnostic fluid for male reproductive system disorders. Nat. Rev. Urol. 11, 278–288 (2014).

    CAS  PubMed  Google Scholar 

  108. 108.

    Drake, R. R. et al. Clinical collection and protein properties of expressed prostatic secretions as a source for biomarkers of prostatic disease. J. Proteom. 72, 907–917 (2009).

    CAS  Google Scholar 

  109. 109.

    McNaughton Collins, M., Fowler, F. J., Elliott, D. B., Albertsen, P. C. & Barry, M. J. Diagnosing and treating chronic prostatitis: do urologists use the four-glass test? Urology 55, 403–407 (2000).

    CAS  PubMed  Google Scholar 

  110. 110.

    Theodorescu, D. et al. Discovery and validation of urinary biomarkers for prostate cancer. Proteomics Clin. Appl. 2, 556–570 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Harpole, M., Davis, J. & Espina, V. Current state of the art for enhancing urine biomarker discovery. Expert Rev. Proteom. 13, 609–626 (2016).

    CAS  Google Scholar 

  112. 112.

    Principe, S. et al. Identification of prostate-enriched proteins by in-depth proteomic analyses of expressed prostatic secretions in urine. J. Proteome Res. 11, 2386–2396 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Pellegrini, K. L. et al. Detection of prostate cancer-specific transcripts in extracellular vesicles isolated from post-DRE urine. Prostate 77, 990–999 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Kim, Y. et al. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat. Commun. 7, 11906 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Bussemakers, M. J. et al. DD3: a new prostate-specific gene, highly overexpressed in prostate cancer 1. Cancer Res. 59, 5975–5979 (1999).

    CAS  PubMed  Google Scholar 

  116. 116.

    van Niel, G., D’Angelo, G. & Raposo, G. Shedding light on the cell biology of extracellular vesicles. Nat. Rev. Mol. Cell Biol. 19, 213–228 (2018).

    PubMed  Google Scholar 

  117. 117.

    Becker, A. et al. Extracellular vesicles in cancer: cell-to-cell mediators of metastasis. Cancer Cell 30, 836–848 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Overbye, A. et al. Identification of prostate cancer biomarkers in urinary exosomes. Oncotarget 6, 30357–30376 (2015).

    PubMed  PubMed Central  Google Scholar 

  119. 119.

    Merchant, M. L., Rood, I. M., Deegens, J. K. J. & Klein, J. B. Isolation and characterization of urinary extracellular vesicles: implications for biomarker discovery. Nat. Rev. Nephrol. 13, 731–749 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Füzéry, A. K., Levin, J., Chan, M. M. & Chan, D. W. Translation of proteomic biomarkers into FDA approved cancer diagnostics: Issues and challenges. Clin. Proteom. 10, 13 (2013).

    Google Scholar 

  121. 121.

    Uhlén, M. et al. The human secretome. Sci. Signal. 12, eaaz0274 (2019).

    PubMed  Google Scholar 

  122. 122.

    Sequeiros, T. et al. Targeted proteomics in urinary extracellular vesicles identifies biomarkers for diagnosis and prognosis of prostate cancer. Oncotarget 8, 4960–4976 (2017).

    PubMed  Google Scholar 

  123. 123.

    Jedinak, A. et al. Novel non-invasive biomarkers that distinguish between benign prostate hyperplasia and prostate cancer. BMC Cancer 15, 259 (2015).

    PubMed  PubMed Central  Google Scholar 

  124. 124.

    Zhang, M. et al. Combined serum and EPS-urine proteomic analysis using iTRAQ technology for discovery of potential prostate cancer biomarkers. Discov. Med. 22, 281–295 (2016).

    PubMed  Google Scholar 

  125. 125.

    Grupp, K. et al. Cysteine-rich secretory protein 3 overexpression is linked to a subset of PTEN-deleted ERG fusion-positive prostate cancers with early biochemical recurrence. Mod. Pathol. 26, 733–742 (2013).

    CAS  PubMed  Google Scholar 

  126. 126.

    Al Bashir, S. et al. Cysteine-rich secretory protein 3 (CRISP3), ERG and PTEN define a molecular subtype of prostate cancer with implication to patients’ prognosis. J. Hematol. Oncol. 7, 21 (2014).

    PubMed  PubMed Central  Google Scholar 

  127. 127.

    Macagno, A. et al. Analytical performance of thrombospondin-1 and cathepsin D immunoassays part of a novel CE-IVD marked test as an aid in the diagnosis of prostate cancer. PLoS One 15, e0233442 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. 128.

    Cima, I. et al. Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proc. Natl Acad. Sci. USA 108, 3342–3347 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Pye, H. et al. Evaluation of Proclarix, a prostate cancer risk score, used together with magnetic resonance imaging for the diagnosis of clinically significant prostate cancer. J. Clin. Oncol. 38, 278–278 (2020).

    Google Scholar 

  130. 130.

    Fujita, K. et al. Proteomic analysis of urinary extracellular vesicles from high Gleason score prostate cancer. Sci. Rep. 7, 1–9 (2017).

    Google Scholar 

  131. 131.

    Gandham, S. et al. Technologies and standardization in research on extracellular vesicles. Trends Biotechnol. 38, 1066–1098 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. 132.

    Yan, B. et al. iTRAQ-based comparative serum proteomic analysis of prostate cancer patients with or without bone metastasis. J. Cancer 10, 4165–4177 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Kohli, M. et al. Serum proteomics on the basis of discovery of predictive biomarkers of response to androgen deprivation therapy in advanced prostate cancer. Clin. Genitourin. Cancer 17, 248–253.e7 (2019).

    PubMed  PubMed Central  Google Scholar 

  134. 134.

    Ishizuya, Y. et al. The role of actinin-4 (ACTN4) in exosomes as a potential novel therapeutic target in castration-resistant prostate cancer. Biochem. Biophys. Res. Commun. 523, 588–594 (2020).

    CAS  PubMed  Google Scholar 

  135. 135.

    Abbatiello, S. E., Mani, D. R., Keshishian, H. & Carr, S. A. Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry. Clin. Chem. 56, 291–305 (2010).

    CAS  PubMed  Google Scholar 

  136. 136.

    Müller, T. et al. Automated sample preparation with SP 3 for low-input clinical proteomics. Mol. Syst. Biol. 16, e911 (2020).

    Google Scholar 

  137. 137.

    Stadlmann, J. et al. Improved sensitivity in low-input proteomics using micropillar array-based chromatography. Anal. Chem. 91, 14203–14207 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  138. 138.

    Zhang, Z. & Chan, D. W. The road from discovery to clinical diagnostics: lessons learned from the first FDA-cleared in vitro diagnostic multivariate index assay of proteomic. Biomarkers. 19, 2995–2999 (2010).

    CAS  Google Scholar 

  139. 139.

    Kraus, V. B. Biomarkers as drug development tools: discovery, validation, qualification and use. Nat. Rev. Rheumatol. 14, 354–362 (2018).

    CAS  PubMed  Google Scholar 

  140. 140.

    Raab, S. S. The cost-effectiveness of immunohistochemistry. Arch. Pathol. Lab. Med. 124, 1185–1191 (2000).

    CAS  PubMed  Google Scholar 

  141. 141.

    Drake, J. M. et al. Metastatic castration-resistant prostate cancer reveals intrapatient similarity and interpatient heterogeneity of therapeutic kinase targets. Proc. Natl Acad. Sci. USA 110, E4762–E4769 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Myers, J. S. et al. Proteomic characterization of paired non-malignant and malignant African-American prostate epithelial cell lines distinguishes them by structural proteins. BMC Cancer 17, 480 (2017).

    PubMed  PubMed Central  Google Scholar 

  143. 143.

    Jarnuczak, A. F. et al. An integrated landscape of protein expression in human cancer. Sci. Data 8, 115 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. 144.

    Kishan, A. U. et al. Transcriptomic heterogeneity of gleason grade group 5 prostate cancer. Eur. Urol. 78, 327–332 (2020).

    CAS  PubMed  Google Scholar 

  145. 145.

    Guo, T. et al. Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers. Life Sci. Alliance 1, e201800042 (2018).

    PubMed  PubMed Central  Google Scholar 

  146. 146.

    Keerthikumar, S. et al. ExoCarta: a web-based compendium of exosomal cargo. J. Mol. Biol. 428, 688–692 (2016).

    CAS  PubMed  Google Scholar 

  147. 147.

    Käll, L., Krogh, A. & Sonnhammer, E. L. L. Advantages of combined transmembrane topology and signal peptide prediction — the Phobius web server. Nucleic Acids Res. 35, W429–W432 (2007).

    PubMed  PubMed Central  Google Scholar 

  148. 148.

    Nielsen, H. Predicting secretory proteins with signaIP. in Methods in Molecular Biology Vol. 1611 59–73 (Humana Press Inc., 2017).

  149. 149.

    Namekawa, T., Ikeda, K., Horie-Inoue, K. & Inoue, S. Application of prostate cancer models for preclinical study: advantages and limitations of cell lines, patient-derived xenografts, and three-dimensional culture of patient-derived cell. Cells 8, 74 (2019).

    CAS  PubMed Central  Google Scholar 

  150. 150.

    Huang, X. et al. miRNA-95 mediates radioresistance in tumors by targeting the sphingolipid phosphatase SGPP1. Cancer Res. 73, 6972–6986 (2013).

    CAS  PubMed  Google Scholar 

  151. 151.

    Ghiam, A. F. et al. Long non-coding RNA urothelial carcinoma associated 1 (UCA1) mediates radiation response in prostate cancer. Oncotarget 8, 4668–4689 (2017).

    Google Scholar 

  152. 152.

    Höti, N., Shah, P., Hu, Y., Yang, S. & Zhang, H. Proteomics analyses of prostate cancer cells reveal cellular pathways associated with androgen resistance. Proteomics 17, https://doi.org/10.1002/pmic.201600228 (2017).

  153. 153.

    Katsogiannou, M. et al. Integrative proteomic and phosphoproteomic profiling of prostate cell lines. PLoS One 14, e0224148 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. 154.

    Cunningham, D. & You, Z. In vitro and in vivo model systems used in prostate cancer research. J. Biol. Methods 2, e17 (2015).

    PubMed  Google Scholar 

  155. 155.

    Ben-David, U. et al. Genetic and transcriptional evolution alters cancer cell line drug response. Nature 560, 325–330 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. 156.

    Davies, A. H., Wang, Y. & Zoubeidi, A. Patient-derived xenografts: a platform for accelerating translational research in prostate cancer. Mol. Cell. Endocrinol. 462, 17–24 (2018).

    CAS  PubMed  Google Scholar 

  157. 157.

    Ben-David, U. et al. Patient-derived xenografts undergo mouse-specific tumor evolution. Nat. Genet. 49, 1567–1575 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. 158.

    Gao, D. et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 159, 176–187 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. 159.

    Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).

    CAS  PubMed  Google Scholar 

  160. 160.

    Gingrich, J. R. & Greenberg, N. M. A transgenic mouse prostate cancer model. Toxicol. Pathol. 24, 502–504 (1996).

    CAS  PubMed  Google Scholar 

  161. 161.

    Gingrich, J. R. et al. Metastatic prostate cancer in a transgenic mouse. Cancer Res. 56, 4096–102 (1996).

    CAS  PubMed  Google Scholar 

  162. 162.

    Gelman, I. H. How the tramp model revolutionized the study of prostate cancer progression. Cancer Res. 76, 6137–6139 (2016).

    CAS  PubMed  Google Scholar 

  163. 163.

    Pencik, J. et al. STAT3 regulated ARF expression suppresses prostate cancer metastasis. Nat. Commun. 6, 7736 (2015).

    CAS  PubMed  Google Scholar 

  164. 164.

    Wang, S. et al. Prostate-specific deletion of the murine Pten tumor suppressor gene leads to metastatic prostate cancer. Cancer Cell 4, 209–221 (2003).

    CAS  PubMed  Google Scholar 

  165. 165.

    Zhang, Y. et al. Quantitative proteomics of TRAMP mice combined with bioinformatics analysis reveals that PDGF-B regulatory network plays a key role in prostate cancer progression. J. Proteome Res. 17, 2401–2411 (2018).

    CAS  PubMed  Google Scholar 

  166. 166.

    Zhang, J. et al. Proteomic and transcriptomic profiling of Pten gene-knockout mouse model of prostate cancer. Prostate 80, 588–605 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  167. 167.

    Hensleym, P. J. & Kyprianou, N. Modeling prostate cancer in mice: review limitations and opportunities. J. Androl. 33, 133–144 (2012).

    Google Scholar 

  168. 168.

    Terp, M. G. & Ditzel, H. J. Application of proteomics in the study of rodent models of cancer. Proteom. Clin. Appl. 8, 640–652 (2014).

    CAS  Google Scholar 

  169. 169.

    Gong, I. Y., Fox, N. S., Huang, V. & Boutros, P. C. Prediction of early breast cancer patient survival using ensembles of hypoxia signatures. PLoS One 13, e0204123 (2018).

    PubMed  PubMed Central  Google Scholar 

  170. 170.

    Bayani, J. et al. Molecular stratification of early breast cancer identifies drug targets to drive stratified medicine. NPJ Breast Cancer 3, 3 (2017).

    PubMed  PubMed Central  Google Scholar 

  171. 171.

    Bhandari, V. & Boutros, P. C. Comparing continuous and discrete analyses of breast cancer survival information. Genomics 108, 78–83 (2016).

    CAS  PubMed  Google Scholar 

  172. 172.

    Lalonde, E. et al. Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lancet Oncol. 15, 1521–1532 (2014).

    PubMed  Google Scholar 

  173. 173.

    Lalonde, E. et al. Translating a prognostic DNA genomic classifier into the clinic: retrospective validation in 563 localized prostate tumors. Eur. Urol. 72, 22–31 (2017).

    CAS  PubMed  Google Scholar 

  174. 174.

    Andor, N., Maley, C. C. & Ji, H. P. Genomic instability in cancer: teetering on the limit of tolerance. Cancer Res. 77, 2179–2185 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. 175.

    Tarabichi, M. et al. A practical guide to cancer subclonal reconstruction from DNA sequencing. Nat. Methods 18, 144–155 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  176. 176.

    Bhandari, V. et al. Molecular landmarks of tumor hypoxia across cancer types. Nat. Genet. 51, 308–318 (2019).

    CAS  PubMed  Google Scholar 

  177. 177.

    Bhandari, V., Li, C. H., Bristow, R. G. & Boutros, P. C. Divergent mutational processes distinguish hypoxic and normoxic tumours. Nat. Commun. 11, 737 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  178. 178.

    Hopkins, J. F. et al. Mitochondrial mutations drive prostate cancer aggression. Nat. Commun. 8, 656 (2017).

    PubMed  PubMed Central  Google Scholar 

  179. 179.

    Haider, S. et al. Pathway-based subnetworks enable cross-disease biomarker discovery. Nat. Commun. 9, 4746 (2018).

    PubMed  PubMed Central  Google Scholar 

  180. 180.

    Fox, N. S., Haider, S., Harris, A. L. & Boutros, P. C. Landscape of transcriptomic interactions between breast cancer and its microenvironment. Nat. Commun. 10, 3116 (2019).

    PubMed  PubMed Central  Google Scholar 

  181. 181.

    Endt, K. et al. Development and clinical testing of individual immunoassays for the quantification of serum glycoproteins to diagnose prostate cancer. PLoS ONE 12, e0181557 (2017).

    PubMed  PubMed Central  Google Scholar 

  182. 182.

    Steuber, T. et al. Thrombospondin 1 and cathepsin D improve prostate cancer diagnosis by avoiding potentially unnecessary prostate biopsies. BJU Int. 123, 826–833 (2019).

    CAS  PubMed  Google Scholar 

  183. 183.

    Klocker, H. et al. Development and validation of a novel multivariate risk score to guide biopsy decision for the diagnosis of clinically significant prostate cancer. BJUI Compass 1, 15–20 (2020).

    Google Scholar 

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Acknowledgements

This work was partially funded by the National Cancer Institute Early Detection Research Network (1U01CA214194-01), a Prostate Cancer Canada Discovery Grant (400398) and a Canadian Cancer Society Impact Grant (705649). A.K. was supported by an Ontario Graduate Scholarship and a Paul STARITA Graduate Student Fellowship. L.Y.L. was supported by a CIHR Vanier Award. This work was supported by the NIH/NCI under award number P30CA016042.

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All authors made substantial contributions to discussions of content, wrote the article and reviewed and edited the manuscript before submission.

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Correspondence to Paul C. Boutros, Stanley K. Liu or Thomas Kislinger.

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Related links

Clinical Proteomic Tumour Analysis Consortium: https://proteomics.cancer.gov/programs/cptac

The Cancer Genome Atlas: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga

The Human Protein Atlas: https://www.proteinatlas.org/

Glossary

Protein abundance

A measure of the number of proteins in a sample, estimated from direct measurements of peptide abundance.

Mass spectrometry

(MS). An analytical technique that separates ions by mass:charge ratio in a mass analyser.

Proteomics

The large-scale study of proteomes, which are sets of proteins produced in a biological context such as by a cell, tissue or organism.

Shotgun proteomics

An untargeted workflow for identifying and quantifying proteins by mass spectrometry via proteolytic digestion of proteins into peptides.

Dynamic range

In mass spectrometry, this term is the range of protein abundances in a sample.

Precursor ion

Intact ions that later dissociate into smaller fragment ions.

Fragment ion

An ion that is the product of fragmentation. For peptides, fragment ions are produced from fragmentation at the peptide backbone.

Data-dependent acquisition

(DDA). A mass-spectrometry acquisition method in which the top N most intense peptides are selected for fragmentation.

Data-independent acquisition

(DIA). A mass-spectrometry acquisition method in which all peptides within a given mass window (such as 15–50 m/z) are selected for fragmentation. Peptides in a selected mass range are fragmented using sequential windows.

Selected reaction monitoring/multiple reaction monitoring

(SRM/MRM). A targeted mass spectrometry method that sequentially isolates and records pre-selected fragment ion masses from a peptide.

Triple-quadrupole mass spectrometer

A tandem mass spectrometer consisting of two quadrupole mass analysers arranged sequentially for mass isolation with an additional quadrupole in the middle that is used for collision-induced dissociation.

Parallel reaction monitoring

(PRM). A targeted mass spectrometry method that isolates and records all fragment ion masses from a peptide.

Orbitrap mass analyser

An ion-trap mass analyser that detects m/z signals by oscillating ions around a cylindrical electrode with tapered ends.

Glycoproteomics

The large-scale study of the glycoproteome, the set of glycosylated proteins, by selective enrichment of N-glycosylated or O-glycosylated peptides.

Patient-derived xenografts

(PDX). Tumours grown from the implantation of a patient’s tumour cells into immunodeficient or immunocompromised mice.

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Khoo, A., Liu, L.Y., Nyalwidhe, J.O. et al. Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry. Nat Rev Urol 18, 707–724 (2021). https://doi.org/10.1038/s41585-021-00500-1

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