Skip to main content
SpringerLink
Log in

A review on the role of tissue-based molecular biomarkers for active surveillance

  • Topic Paper
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

Over the last decade, we have seen the emergence of tissue-based genomic prognostic markers that can be used for decision-making regarding the need for treatment. This review provides an up-to-date summary of the relevant literature surrounding these markers with a discussion of the relevant strength and limitations.

Methods

We performed a literature search of tissue-based genomic prognostic markers and selected those that were currently available for clinical use. We selected the following markers for further review: Decipher (Decipher Bioscience), Polaris (Myriad), Genome Prostate Score (Oncotype Dx), and Promark. We selected the initial validation study for each marker along with other validation studies in independent cohorts. Furthermore, we selected available clinical utility studies or studies combining multi parametric MRI.

Results

In this article, we provide an in-depth review of four commercially available biomarkers and discuss the current literature surrounding these markers, including the benefits and limitations of their use. We found that each of these markers has evidence supporting their role as an independent predictor of relevant prostate cancer endpoints, which can be helpful for clinical decision-making. However, issues related to heterogeneity and a lack of prospective randomized studies supporting their utility are limitations. Evidence appears to suggest that MRI and genomic risk assessment maybe complementary.

Conclusion

Although these markers can help in improved risk stratification of patients eligible for AS, more prospective studies with head to head comparison between markers are needed to elucidate the true potential of these markers in AS.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

AS:

Active surveillance

RP:

Radical prostatectomy

mpMRI:

Multi-parametric MRI

NCCN:

National Comprehensive Cancer Network

ASCO:

American Society of Clinical Oncology

F-IR:

Favorable intermediate risk prostate cancer

GPS:

Genomic Prostate Score

GG:

Gleason grade group

CCP:

Cell cycle progression

AA:

African American

RT-PCR:

Reverse transcription polymerase chain reaction

UCSF:

University of California, San Francisco

CAPRA:

Cancer of prostate risk assessment

BCR:

Biochemical recurrence

References

  1. National Comprehensive Cancer Network. Prostate cancer (Version 2.2020). https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf Accessed 15 Sep 2020.

  2. Mottet N, Bellmunt J, Bolla M et al (2017) EAU-ESTRO-SIOG Guidelines on Prostate cancer. Part 1: screening, diagnosis and local treatment with curative intent. EurUrol 71:618–629. https://doi.org/10.1016/j.eururo.2016.08.003

    Article  Google Scholar 

  3. Justin EB, Bryan RR, Chen RC et al (2018) Clinically Localized Prostate Cancer: ASCO Clinical Practice Guideline Endorsement of an American Urological Association/American Society for Radiation Oncology/Society of Urologic Oncology Guideline. J Clin Oncol 36(32):3251–3258. https://doi.org/10.1200/JCO.18.00606

    Article  Google Scholar 

  4. Sandra MG, Chen RC, Crispini T et al. Clinically Localized Prostate Cancer: AUA/ASTRO/SUO Guideline. 2017 Update. https://www.auanet.org/guidelines/prostate-cancer-clinically-localized-guideline. Accessed Oct 2020

  5. Morash C, Tey R, Agbassi C, Klotz L, McGowan T, Srigley J et al (2015) Active surveillance for the management of localized prostate cancer: Guideline recommendations. Can UrolAssoc J 9(5–6):171–178. https://doi.org/10.5489/cuaj.2806

    Article  Google Scholar 

  6. NICE guidance—prostate cancer: diagnosis and management. (c) NICE (2019) Prostate cancer: diagnosis and management. BJU Int 124(2019):9–26. https://doi.org/10.1111/bju.14809

    Article  Google Scholar 

  7. Hamdy FC, Donovan JL, Lane JA et al (2016) 10-Year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. N Engl J Med 375:1415–1424. https://doi.org/10.1056/NEJMoa1606220

    Article  PubMed  Google Scholar 

  8. Wilt TJ, Brawer MK, Jones KM et al (2012) Radical prostatectomy versus observation for localized prostate cancer. N Engl J Med 367:203–213. https://doi.org/10.1056/NEJMoa1113162

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Dall’Era MA, Albertsen PC, Bangma C et al (2012) Active surveillance for prostate cancer: a systematic review of the literature. Eur Urol. 62(6):976–983. https://doi.org/10.1016/j.eururo.2012.05.072

    Article  PubMed  Google Scholar 

  10. Cooperberg MR, Zheng Y, Faino AV et al (2020) Tailoring intensity of active surveillance for low-risk prostate cancer based on individualized prediction of risk stability. JAMA Oncol 6(10):e203187. https://doi.org/10.1001/jamaoncol.2020.3187

    Article  PubMed  PubMed Central  Google Scholar 

  11. Mahal BA, Butler S, Franco I et al (2019) Use of active surveillance or watchful waiting for low-risk prostate cancer and management trends across risk groups in the United States 2010–2015. JAMA 321(7):704–706. https://doi.org/10.1001/jama.2018.19941

    Article  PubMed  PubMed Central  Google Scholar 

  12. Yamamoto T, Musunuru B, Vesprini D et al (2016) Metastatic prostate cancer in men initially treated with active surveillance. J Urol 195:1409–1414. https://doi.org/10.1016/j.juro.2015.11.075

    Article  PubMed  Google Scholar 

  13. Patel HD, Tosoian JJ, Carter HB et al (2018) Adverse pathologic findings for men electing immediate radical prostatectomy. JAMA Oncol 4:89–92. https://doi.org/10.1001/jamaoncol.2017.1879

    Article  PubMed  Google Scholar 

  14. Kasivisvanathan V, Rannikko AS, Borghi M et al (2018) MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 378:1767–1777. https://doi.org/10.1056/NEJMoa1801993

    Article  PubMed  Google Scholar 

  15. Ahmed HU, El-ShaterBosaily A, Brown LC et al (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389:815–822. https://doi.org/10.1016/S0140-6736(16)32401-1

    Article  PubMed  Google Scholar 

  16. Klotz L, Loblaw A, Sugar L, Moussa M, Berman DM et al (2019) Active surveillance magnetic resonance imaging study (ASIST): results of a randomized multicenter prospective trial. EurUrol 75(2):300–309. https://doi.org/10.1016/j.eururo.2018.06.025

    Article  Google Scholar 

  17. Klotz L, Pond G, Loblaw A, Sugar L et al (2020) Randomized study of systematic biopsy versus magnetic resonance imaging and targeted and systematic biopsy in men on active surveillance (ASIST): 2-year post biopsy follow-up. EurUrol 77(3):311–317. https://doi.org/10.1016/j.eururo.2019.10.007

    Article  Google Scholar 

  18. Klein EA, Cooperberg MR, Magi-Galluzzi C et al (2014) A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. EurUrol 66:550–560. https://doi.org/10.1016/j.eururo.2014.05.004

    Article  Google Scholar 

  19. Cullen J, Rosner IL, Brand TC et al (2015) 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. 68(1):123–131. https://doi.org/10.1016/j.eururo.2014.11.030

    Article  PubMed  Google Scholar 

  20. Kornberg Z, Cooperberg MR, Cowan JE et al (2019) A 17-gene genomic prostate score as a predictor of adverse pathology in men on active surveillance. J Urol 202(4):702–709. https://doi.org/10.1097/JU.0000000000000290

    Article  PubMed  Google Scholar 

  21. Lin DW, Zheng Y, McKenney JK, Brown MD (2020) 17-gene genomic prostate score test results in the canary prostate active surveillance study (pass) cohort. J Clin Oncol. 38(14):1549–1557. https://doi.org/10.1200/JCO.19.02267

    Article  PubMed  PubMed Central  Google Scholar 

  22. Albala D, Kemeter MJ, Febbo PG, Lu R, John V, Stoy D, Denes B, McCall M, Shindel AW, Dubeck F (2016) Health economic impact and prospective clinical utility of Oncotype DX® Genomic Prostate Score. Rev Urol 18(3):123–132. https://doi.org/10.3909/riu0725

    Article  PubMed  PubMed Central  Google Scholar 

  23. Eggener S, Karsh LI, Richardson T et al (2019) A 17-gene panel for prediction of adverse prostate cancer pathologic features: prospective clinical validation and utility. Urology 126:76–82. https://doi.org/10.1016/j.urology.2018.11.050

    Article  PubMed  Google Scholar 

  24. Cuzick J, Swanson GP, Fisher G et al (2011) Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol 12:245–255. https://doi.org/10.1016/S1470-2045(10)70295-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Cuzick J, Berney DM, Fisher G et al (2012) Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort. Br J Cancer 106:1095–1099. https://doi.org/10.1038/bjc.2012.39

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Cooperberg MR, Simko JP, Cowan JE et al (2013) Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. J Clin Oncol 31:1428–1434. https://doi.org/10.1200/JCO.2012.46.4396

    Article  CAS  PubMed  Google Scholar 

  27. Canter DJ, Reid J, Latsis M et al (2019) Comparison of the prognostic utility of the cell cycle progression score for predicting clinical outcomes in African American and Non-African American Men with Localized Prostate Cancer. EurUrol 75(3):515–522. https://doi.org/10.1016/j.eururo.2018.10.028

    Article  Google Scholar 

  28. de Pouvourville G (2015) Cost-effectiveness analysis for the use of the CCP score in the management of early low-risk prostate cancer in the French context. Value Health 18(7):A358. https://doi.org/10.1016/j.jval.2015.09.680

    Article  Google Scholar 

  29. Klein EA, Yousefi K, Haddad Z et al (2015) A genomic classifier improves prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate cancer patients managed by radical prostatectomy without adjuvant therapy. EurUrol 67:778–786. https://doi.org/10.1016/j.eururo.2014.10.036

    Article  Google Scholar 

  30. Cooperberg MR, Davicioni E, Crisan A et al (2015) Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. EurUrol 67:326–333. https://doi.org/10.1016/j.eururo.2014.05.039

    Article  Google Scholar 

  31. Gore JL, du Plessis M, Santiago-Jimenez M, Yousefi K et al (2017) Decipher test impacts decision making among patients considering adjuvant and salvage treatment after radical prostatectomy: interim results from the Multicenter Prospective PRO-IMPACT study. Cancer 123:2850–2859. https://doi.org/10.1002/cncr.30665

    Article  PubMed  Google Scholar 

  32. Klein EA, Haddad Z, Yousefi K et al (2016) Decipher genomic classifier measured on prostate biopsy predicts metastasis risk. Urology 90:148–152. https://doi.org/10.1016/j.urology.2016.01.012

    Article  PubMed  Google Scholar 

  33. Nguyen PL, Haddad Z, Ross AE, Martin A et al (2017) Ability of a genomic classifier to predict metastasis and prostate cancer-specific mortality after radiation or surgery based on needle biopsy specimens. Eur Urol. 72(5):845–852. https://doi.org/10.1016/j.eururo.2017.05.009

    Article  PubMed  Google Scholar 

  34. Herlemann A, Huang HC, Alam R et al (2020) Decipher identifies men with otherwise clinically favorable-intermediate risk disease who may not be good candidates for active surveillance. Prostate Cancer Prostatic Dis 23(1):136–143. https://doi.org/10.1038/s41391-019-0167-9

    Article  PubMed  Google Scholar 

  35. Shipitsin M, Small C, Choudhury S et al (2014) Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error. Br J Cancer 111:1201–1212. https://doi.org/10.1038/bjc.2014.396

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Shipitsin M, Small C, Giladi E, Siddiqui S, Choudhury S, Hussain S et al (2014) Automated quantitative multiplex immunofluorescence in situ imaging identifies phospho-S6 and phospho-PRAS40 as predictive protein biomarkers for prostate cancer lethality. Proteome Sci. 12(1):1–13. https://doi.org/10.1186/1477-5956-12-40

    Article  CAS  Google Scholar 

  37. Blume-Jensen P, Berman DM, Rimm DL, Shipitsin M, Putzi M, Nifong TP et al (2015) Biology of human tumors development and clinical validation of an in situ biopsy-based multimarker assay for risk stratification in prostate cancer. Clin Cancer Res 21(11):2591–2600. https://doi.org/10.1158/1078-0432.CCR-14-2603

    Article  CAS  PubMed  Google Scholar 

  38. Saad F, Latour M, Lattouf JB, Widmer H, Zorn KC, Mes-Masson AM et al (2017) Biopsy based proteomic assay predicts risk of biochemical recurrence after radical prostatectomy. J Urol 197:1034–1040. https://doi.org/10.1016/j.juro.2016.09.116

    Article  PubMed  Google Scholar 

  39. Peabody JW, DeMaria LM, Tamondong-Lachica D, Florentino J, Czarina Acelajado M, Ouenes O et al (2017) Impact of a protein-based assay that predicts prostate cancer aggressiveness on urologists’ recommendations for active treatment or active surveillance: a randomized clinical utility trial. BMC Urol 17:51. https://doi.org/10.1186/s12894-017-0243-1

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wei L, Wang J, Lampert E et al (2017) Intratumoral and intertumoral genomic heterogeneity of multifocal localized prostate cancer impacts molecular classifications and genomic prognosticators. EurUrol 71:183–192. https://doi.org/10.1016/j.jmoldx.2015.12.006

    Article  CAS  Google Scholar 

  41. Salami SS, Hovelson DH, Kaplan JB et al (2018) Transcriptomic heterogeneity in multifocal prostate cancer. JCI Insight 3(21):e123468. https://doi.org/10.1172/jci.insight.123468

    Article  PubMed Central  Google Scholar 

  42. Cedars BE, Washington SL 3rd, Cowan JE et al (2019) Stability of a 17-gene genomic prostate score in serial testing of men on active surveillance for early stage prostate cancer. J Urol 202(4):696–701. https://doi.org/10.1097/JU.0000000000000271

    Article  PubMed  Google Scholar 

  43. Radtke JP, Takhar M, Bonekamp D et al (2018) Transcriptome wide analysis of magnetic resonance imaging-targeted biopsy and matching surgical specimens from high-risk prostate cancer patients treated with radical prostatectomy: the target must be hit. EurUrol Focus 2017 4(4):540–546. https://doi.org/10.1016/j.euf.2017.01.005

    Article  Google Scholar 

  44. Kornberg Z, Cowan JE, Westphalen AC et al (2019) Genomic Prostate Score, PI-RADSTM version 2 and progression in men with prostate cancer on active surveillance. J Urol 201(2):300–307. https://doi.org/10.1016/j.juro.2018.08.047

    Article  PubMed  Google Scholar 

  45. Renard-Penna R, Cancel-Tassin G, Comperat E et al (2015) Multiparametric magnetic resonance imaging predicts postoperative pathology but misses aggressive prostate cancers as assessed by cell cycle progression score. J Urol 194:1617–1623. https://doi.org/10.1016/j.juro.2015.06.107

    Article  PubMed  Google Scholar 

  46. Wibmer AG, Robertson NL, Hricak H et al (2019) Extracapsular extension on MRI indicates a more aggressive cell cycle progression genotype of prostate cancer. AbdomRadiol (NY) 44(8):2864–2873. https://doi.org/10.1007/s00261-019-02023-1

    Article  Google Scholar 

  47. Martin DT, Ghabili K, Levi A, Humphrey PA, Sprenkle PC (2019) 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 125:64–72. https://doi.org/10.1016/j.urology.2018.12.001

    Article  PubMed  Google Scholar 

  48. Soodana-Prakash N, Stoyanova R, Bhat A et al (2018) Entering an era of radiogenomics in prostate cancer risk stratification. TranslAndrolUrol 7(Suppl 4):S443–S452. https://doi.org/10.21037/tau.2018.07.04

    Article  Google Scholar 

Download references

Funding

The authors have no relevant financial or non-financial interests to disclose.

Author information

Authors and Affiliations

  1. Miller School of Medicine and Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, United States

    Banerjee & Sanoj Punnen

Authors
  1. Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanoj Punnen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

IB: project development, data collection, data analysis, manuscript writing. SP: project development, data collection, data analysis, manuscript writing and editing.

Corresponding author

Correspondence to Sanoj Punnen.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Human and animal participants

As this is an invited review article the manuscript does not involve research involving Human Participants and/or Animals and hence no informed consent was necessary.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Banerjee, Punnen, S. A review on the role of tissue-based molecular biomarkers for active surveillance. World J Urol 40, 27–34 (2022). https://doi.org/10.1007/s00345-021-03610-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00345-021-03610-y

Keywords

Search

Navigation