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How should radiologists incorporate non-imaging prostate cancer biomarkers into daily practice?

  • Special Section: Prostate cancer update
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objective

To review the current body of evidence surrounding non-imaging biomarkers in patients with known or suspected prostate cancer.

Results

Several non-imaging biomarkers have been developed and are available that aim to improve risk estimates at several clinical junctures. For patients with suspicion of prostate cancer who are considering first-time or repeat biopsy, blood- and urine-based assays can improve the prediction of harboring clinically significant disease and may reduce unnecessary biopsy. Blood- and urine-based biomarkers have been evaluated in association with prostate MRI, offering insights that might augment decision-making in the pre and post-MRI setting. Tissue-based genomic and proteomic assays have also been developed that provide independent assessments of prostate cancer aggressiveness that can complement imaging.

Conclusion

A growing number of non-imaging biomarkers are available to assist in clinical decision-making for men with known or suspected prostate cancer. An appreciation for the intersection of imaging and biomarkers may improve clinical care and resource utilization for men with prostate cancer.

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References

  1. Ferlay J, Ervik M, Lam F, et al. Global cancer observatory: cancer today. Lyon, France: International Agency for Research on Cancer. 2018.

    Google Scholar 

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA: A Cancer Journal for Clinicians. 2019;69(1):7-34.

  3. Cooperberg MR, Broering JM, Litwin MS, et al. The contemporary management of prostate cancer in the United States: lessons from the cancer of the prostate strategic urologic research endeavor (CapSURE), a national disease registry. J Urol. 2004;171(4):1393-1401. https://doi.org/10.1097/01.ju.0000107247.81471.06.

    Article  PubMed  Google Scholar 

  4. Welch HG, Gorski DH, Albertsen PC. Trends in Metastatic Breast and Prostate Cancer. The New England journal of medicine. 2016;374(6):596.

    PubMed  Google Scholar 

  5. Schroder FH, Hugosson J, Roobol MJ, et al. Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. Lancet (London, England). 2014;384(9959):2027-2035.

    PubMed Central  Google Scholar 

  6. Kim SP, Karnes RJ, Mwangi R, et al. Contemporary Trends in Magnetic Resonance Imaging at the Time of Prostate Biopsy: Results from a Large Private Insurance Database. Eur Urol Focus. 2019.

  7. Cooperberg MR, Carroll PR. Trends in Management for Patients With Localized Prostate Cancer, 1990-2013. JAMA. 2015;314(1):80-82. https://doi.org/10.1001/jama.2015.6036.

    Article  CAS  PubMed  Google Scholar 

  8. Leapman MS, Cowan JE, Nguyen HG, et al. Active Surveillance in Younger Men With Prostate Cancer. J Clin Oncol. 2017;35(17):1898-1904. https://doi.org/10.1200/JCO.2016.68.0058.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Mohler JL, Antonarakis ES, Armstrong AJ, et al. Prostate Cancer, Version 2.2019, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2019;17(5):479-505. doi: 410.6004/jnccn.2019.0023.

  10. Mottet N. BJ, Briers E., Bolla M., Bourke L., Cornford P., De Santis M., Henry A., Joniau S., Lam T., Mason M.D., Van den Poel H., Van den Kwast T.H., Rouvière O., Wiegel T.; members of the EAU – ESTRO – ESUR –SIOG Prostate Cancer Guidelines Panel.. EAU – ESTRO – ESUR – SIOG Guidelines on Prostate Cancer. 2019.

  11. Eggener SE, Rumble RB, Armstrong AJ, et al. Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline. J Clin Oncol. 2019:JCO1902768.

  12. Thompson IM, Chi C, Ankerst DP, et al. Effect of finasteride on the sensitivity of PSA for detecting prostate cancer. J Natl Cancer Inst. 2006;98(16):1128-1133.

    CAS  PubMed  Google Scholar 

  13. Draisma G, Etzioni R, Tsodikov A, et al. Lead time and overdiagnosis in prostate-specific antigen screening: importance of methods and context. J Natl Cancer Inst. 2009;101(6):374-383.

    PubMed  PubMed Central  Google Scholar 

  14. Postma R, Schroder FH, van Leenders GJ, et al. Cancer detection and cancer characteristics in the European Randomized Study of Screening for Prostate Cancer (ERSPC)--Section Rotterdam. A comparison of two rounds of screening. European urology. 2007;52(1):89-97.

  15. Thompson IM, Pauler DK, Goodman PJ, et al. Prevalence of prostate cancer among men with a prostate-specific antigen level < or = 4.0 ng per milliliter. N Engl J Med. 2004;350(22):2239-2246.

  16. Thompson IM, Ankerst DP, Chi C, et al. Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial. J Natl Cancer Inst. 2006;98(8):529-534.

    PubMed  Google Scholar 

  17. Catalona WJ, Partin AW, Sanda MG, et al. A multicenter study of [-2]pro-prostate specific antigen combined with prostate specific antigen and free prostate specific antigen for prostate cancer detection in the 2.0 to 10.0 ng/ml prostate specific antigen range. J Urol. 2011;185(5):1650-1655.

  18. Wang W, Wang M, Wang L, Adams TS, Tian Y, Xu J. Diagnostic ability of %p2PSA and prostate health index for aggressive prostate cancer: a meta-analysis. Sci Rep. 2014;4:5012.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Loeb S, Sanda MG, Broyles DL, et al. The prostate health index selectively identifies clinically significant prostate cancer. J Urol. 2015;193(4):1163-1169.

    PubMed  Google Scholar 

  20. Network NCC. Prostate Cancer Early Detection (Version 2.2019). 2019; https://www.nccn.org/professionals/physician_gls/PDF/prostate_detection.pdf. Accessed December 27, 2019.

  21. Vickers AJ, Cronin AM, Aus G, et al. A panel of kallikrein markers can reduce unnecessary biopsy for prostate cancer: data from the European Randomized Study of Prostate Cancer Screening in Goteborg, Sweden. BMC Med. 2008;6:19.

    PubMed  Google Scholar 

  22. Parekh DJ, Punnen S, Sjoberg DD, et al. A multi-institutional prospective trial in the USA confirms that the 4Kscore accurately identifies men with high-grade prostate cancer. Eur Urol. 2015;68(3):464-470.

    PubMed  Google Scholar 

  23. Zappala SM, Scardino PT, Okrongly D, Linder V, Dong Y. Clinical performance of the 4Kscore Test to predict high-grade prostate cancer at biopsy: A meta-analysis of us and European clinical validation study results. Rev Urol. 2017;19(3):149-155.

    PubMed  PubMed Central  Google Scholar 

  24. Gronberg H, Adolfsson J, Aly M, et al. Prostate cancer screening in men aged 50-69 years (STHLM3): a prospective population-based diagnostic study. The Lancet. Oncology. 2015;16(16):1667-1676.

    PubMed  Google Scholar 

  25. Strom P, Nordstrom T, Aly M, Egevad L, Gronberg H, Eklund M. The Stockholm-3 Model for Prostate Cancer Detection: Algorithm Update, Biomarker Contribution, and Reflex Test Potential. European urology. 2018;74(2):204-210.

    PubMed  Google Scholar 

  26. Gnanapragasam VJ, Burling K, George A, et al. The Prostate Health Index adds predictive value to multi-parametric MRI in detecting significant prostate cancers in a repeat biopsy population. Scientific Reports. 2016;6(1):35364.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Tosoian JJ, Druskin SC, Andreas D, et al. Use of the Prostate Health Index for detection of prostate cancer: results from a large academic practice. (1476-5608 (Electronic)).

  28. Hsieh P-F, Li W-J, Lin W-C, et al. Combining prostate health index and multiparametric magnetic resonance imaging in the diagnosis of clinically significant prostate cancer in an Asian population. World journal of urology. 2019:1-8.

  29. Falagario UG, Martini A, Wajswol E, et al. Avoiding Unnecessary Magnetic Resonance Imaging (MRI) and Biopsies: Negative and Positive Predictive Value of MRI According to Prostate-specific Antigen Density, 4Kscore and Risk Calculators. European Urology Oncology. 2019.

  30. McKiernan J, Donovan MJ, Margolis E, et al. A Prospective Adaptive Utility Trial to Validate Performance of a Novel Urine Exosome Gene Expression Assay to Predict High-grade Prostate Cancer in Patients with Prostate-specific Antigen 2-10 ng/ml at Initial Biopsy. Eur Urol. 2018;74(6):731-738.

    CAS  PubMed  Google Scholar 

  31. Vlaeminck-Guillem V. Extracellular Vesicles in Prostate Cancer Carcinogenesis, Diagnosis, and Management. Front Oncol. 2018;8:222.

    PubMed  PubMed Central  Google Scholar 

  32. Pan J, Ding M, Xu K, Yang C, Mao LJ. Exosomes in diagnosis and therapy of prostate cancer. Oncotarget. 2017;8(57):97693-97700.

    PubMed  PubMed Central  Google Scholar 

  33. Donovan MJ, Noerholm M, Bentink S, et al. A molecular signature of PCA3 and ERG exosomal RNA from non-DRE urine is predictive of initial prostate biopsy result. Prostate Cancer Prostatic Dis. 2015;18(4):370-375.

    CAS  PubMed  Google Scholar 

  34. Leyten GH, Hessels D, Smit FP, et al. Identification of a Candidate Gene Panel for the Early Diagnosis of Prostate Cancer. Clin Cancer Res. 2015;21(13):3061-3070.

    CAS  PubMed  Google Scholar 

  35. Van Neste L, Hendriks RJ, Dijkstra S, et al. Detection of High-grade Prostate Cancer Using a Urinary Molecular Biomarker-Based Risk Score. Eur Urol. 2016;70(5):740-748.

    PubMed  Google Scholar 

  36. Hendriks RJ, van der Leest MMG, Dijkstra S, et al. A urinary biomarker-based risk score correlates with multiparametric MRI for prostate cancer detection. The Prostate. 2017;77(14):1401-1407.

    CAS  PubMed  Google Scholar 

  37. Wei JT, Feng Z, Partin AW, et al. Can urinary PCA3 supplement PSA in the early detection of prostate cancer? J Clin Oncol. 2014;32(36):4066-4072.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Gadzinski AJ, Cooperberg MR. Prostate Cancer Markers. Cancer Treat Res. 2018;175:55-86.

    PubMed  Google Scholar 

  39. Ploussard G, de la Taille A. The role of prostate cancer antigen 3 (PCA3) in prostate cancer detection. Expert Rev Anticancer Ther. 2018;18(10):1013-1020.

    CAS  PubMed  Google Scholar 

  40. Luo Y, Gou X, Huang P, Mou C. The PCA3 test for guiding repeat biopsy of prostate cancer and its cut-off score: a systematic review and meta-analysis. Asian J Androl. 2014;16(3):487-492.

    PubMed  PubMed Central  Google Scholar 

  41. Esgueva R, Perner S, C JL, et al. Prevalence of TMPRSS2-ERG and SLC45A3-ERG gene fusions in a large prostatectomy cohort. Mod Pathol. 2010;23(4):539-546.

  42. Raja N, Russell CM, George AK. Urinary markers aiding in the detection and risk stratification of prostate cancer. Transl Androl Urol. 2018;7(Suppl 4):S436-S442.

    PubMed  PubMed Central  Google Scholar 

  43. Leyten GH, Hessels D, Jannink SA, et al. Prospective multicentre evaluation of PCA3 and TMPRSS2-ERG gene fusions as diagnostic and prognostic urinary biomarkers for prostate cancer. Eur Urol. 2014;65(3):534-542.

    CAS  PubMed  Google Scholar 

  44. Tomlins SA, Day JR, Lonigro RJ, et al. Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment. Eur Urol. 2016;70(1):45-53.

    CAS  PubMed  Google Scholar 

  45. Sanda MG, Feng Z, Howard DH, et al. Association Between Combined TMPRSS2:ERG and PCA3 RNA Urinary Testing and Detection of Aggressive Prostate Cancer. JAMA Oncol. 2017;3(8):1085-1093.

    PubMed  PubMed Central  Google Scholar 

  46. Pinsky PF, Crawford ED, Kramer BS, et al. Repeat prostate biopsy in the prostate, lung, colorectal and ovarian cancer screening trial. BJU international. 2007;99(4):775-779.

    CAS  PubMed  Google Scholar 

  47. Van Neste L, Partin AW, Stewart GD, Epstein JI, Harrison DJ, Van Criekinge W. Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies. The Prostate. 2016;76(12):1078-1087.

    PubMed  PubMed Central  Google Scholar 

  48. Exterkate L, Wegelin O, Barentsz JO, et al. Is There Still a Need for Repeated Systematic Biopsies in Patients with Previous Negative Biopsies in the Era of Magnetic Resonance Imaging-targeted Biopsies of the Prostate? European urology oncology. 2019.

  49. Cucchiara V, Cooperberg MR, Dall’Era M, et al. Genomic Markers in Prostate Cancer Decision Making. Eur Urol. 2018;73(4):572-582.

    PubMed  Google Scholar 

  50. Cooperberg MR, Carroll PR, Dall’Era MA, et al. The State of the Science on Prostate Cancer Biomarkers: The San Francisco Consensus Statement. Eur Urol. 2019;76(3):268-272.

    PubMed  Google Scholar 

  51. Cuzick J, Swanson GP, Fisher G, et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol. 2011;12(3):245-255.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Cooperberg MR, Simko JP, Cowan JE, et al. Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. J Clin Oncol. 2013;31(11):1428-1434.

    CAS  PubMed  Google Scholar 

  53. Cuzick J, Stone S, Fisher G, et al. Validation of an RNA cell cycle progression score for predicting death from prostate cancer in a conservatively managed needle biopsy cohort. Br J Cancer. 2015;113(3):382-389.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Bishoff JT, Freedland SJ, Gerber L, et al. Prognostic utility of the cell cycle progression score generated from biopsy in men treated with prostatectomy. J Urol. 2014;192(2):409-414.

    PubMed  Google Scholar 

  55. Klein EA, Cooperberg MR, Magi-Galluzzi C, et al. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. (1873-7560 (Electronic)).

  56. Moschini M, Spahn M, Mattei A, Cheville J, Karnes RJ. Incorporation of tissue-based genomic biomarkers into localized prostate cancer clinics. (1741-7015 (Electronic)).

  57. van den Bergh RC, Ahmed HU, Bangma CH, Cooperberg MR, Villers A, Parker CC. Novel tools to improve patient selection and monitoring on active surveillance for low-risk prostate cancer: a systematic review. (1873-7560 (Electronic)).

  58. Klein EA, Cooperberg MR, Magi-Galluzzi C, et al. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur Urol. 2014;66(3):550-560.

    PubMed  Google Scholar 

  59. Cullen J, Rosner IL, Brand TC, et al. A Biopsy-based 17-gene Genomic Prostate Score Predicts Recurrence After Radical Prostatectomy and Adverse Surgical Pathology in a Racially Diverse Population of Men with Clinically Low- and Intermediate-risk Prostate Cancer. Eur Urol. 2015;68(1):123-131.

    PubMed  Google Scholar 

  60. Kornberg Z, Cooperberg MR, Cowan JE, et al. A 17-Gene Genomic Prostate Score as a Predictor of Adverse Pathology in Men on Active Surveillance. J Urol. 2019;202(4):702-709.

    PubMed  Google Scholar 

  61. Kornberg Z, Cowan JE, Westphalen AC, et al. Genomic Prostate Score, PI-RADS version 2 and Progression in Men with Prostate Cancer on Active Surveillance. J Urol. 2019;201(2):300-307.

    PubMed  Google Scholar 

  62. Van Den Eeden SK, Lu R, Zhang N, et al. A Biopsy-based 17-gene Genomic Prostate Score as a Predictor of Metastases and Prostate Cancer Death in Surgically Treated Men with Clinically Localized Disease. Eur Urol. 2018;73(1):129-138.

    Google Scholar 

  63. Karnes RJ, Bergstralh Ej Fau - Davicioni E, Davicioni E Fau - Ghadessi M, et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. Journal of Urology. 2013;1527-3792 (Electronic)(1527-3792 (Electronic)).

  64. Erho N, Crisan A, Vergara IA, et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS One. 2013;8(6):e66855.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Herlemann A, Huang HC, Alam R, et al. Decipher identifies men with otherwise clinically favorable-intermediate risk disease who may not be good candidates for active surveillance. Prostate Cancer Prostatic Dis. 2019.

  66. Kim HL, Li P, Huang HC, et al. Validation of the Decipher Test for predicting adverse pathology in candidates for prostate cancer active surveillance. Prostate Cancer Prostatic Dis. 2019;22(3):399-405.

    PubMed  Google Scholar 

  67. Spratt DE, Yousefi K, Deheshi S, et al. Individual Patient-Level Meta-Analysis of the Performance of the Decipher Genomic Classifier in High-Risk Men After Prostatectomy to Predict Development of Metastatic Disease. J Clin Oncol. 2017;35(18):1991-1998.

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Xu MJ, Kornberg Z, Gadzinski AJ, et al. Genomic Risk Predicts Molecular Imaging-detected Metastatic Nodal Disease in Prostate Cancer. Eur Urol Oncol. 2019;2(6):685-690.

    PubMed  Google Scholar 

  69. Cooperberg MR, Davicioni E, Crisan A, Jenkins RB, Ghadessi M, Karnes RJ. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. European urology. 2015;67(2):326-333.

    PubMed  Google Scholar 

  70. Shipitsin M, Small C, Choudhury S, et al. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error. Br J Cancer. 2014;111(6):1201-1212.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Blume-Jensen P, Berman DM, Rimm DL, et al. Development and clinical validation of an in situ biopsy-based multimarker assay for risk stratification in prostate cancer. Clin Cancer Res. 2015;21(11):2591-2600.

    CAS  PubMed  Google Scholar 

  72. Leapman MS, Westphalen AC, Ameli N, et al. Association between a 17-gene genomic prostate score and multi-parametric prostate MRI in men with low and intermediate risk prostate cancer (PCa). PLoS One. 2017;12(10):e0185535-e0185535.

    PubMed  PubMed Central  Google Scholar 

  73. Stoyanova R, Pollack A, Takhar M, et al. Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget. 2016;7(33):53362.

    PubMed  PubMed Central  Google Scholar 

  74. Martin DT, Ghabili K, Levi A, Humphrey PA, Sprenkle PC. Prostate Cancer Genomic Classifier Relates More Strongly to Gleason Grade Group Than Prostate Imaging Reporting and Data System Score in Multiparametric Prostate Magnetic Resonance Imaging-ultrasound Fusion Targeted Biopsies. Urology. 2019;125:64-72.

    PubMed  Google Scholar 

  75. Purysko AS, Magi-Galluzzi C, Mian OY, et al. Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings. Eur Radiol. 2019;29(9):4861-4870.

    PubMed  PubMed Central  Google Scholar 

  76. Parry MA, Srivastava S, Ali A, et al. Genomic Evaluation of Multiparametric Magnetic Resonance Imaging-visible and -nonvisible Lesions in Clinically Localised Prostate Cancer. European urology oncology. 2019;2(1):1-11.

    PubMed  PubMed Central  Google Scholar 

  77. Press B, Schulster M, Bjurlin MA. Differentiating Molecular Risk Assessments for Prostate Cancer. Rev Urol. 2018;20(1):12-18.

    PubMed  PubMed Central  Google Scholar 

  78. Zapala P, Dybowski B, Poletajew S, Radziszewski P. What Can Be Expected from Prostate Cancer Biomarkers A Clinical Perspective. Urol Int. 2018;100(1):1-12.

    CAS  PubMed  Google Scholar 

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Funding

M.L. National Cancer Institute (NCI) K08CA237872.

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Authors and Affiliations

  1. Department of Urology, Medical University of Silesia, 41-800, Zabrze, Poland

    Pawel Rajwa

  2. Department of Urology, Yale University School of Medicine, 310 Cedar Street BML 238c, PO Box 208058, New Haven, CT, 06520, USA

    Jamil Syed & Michael S. Leapman

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  1. Pawel Rajwa
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Correspondence to Michael S. Leapman.

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Rajwa, P., Syed, J. & Leapman, M.S. How should radiologists incorporate non-imaging prostate cancer biomarkers into daily practice?. Abdom Radiol 45, 4031–4039 (2020). https://doi.org/10.1007/s00261-020-02496-5

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