Official reprint from UpToDate®
www.uptodate.com ©2017 UpToDate®

Molecular prognostic tests for prostate cancer

Ashley Ross, MD, PhD
Anthony V D'Amico, MD, PhD
Stephen Freedland, MD
Section Editors
Nicholas Vogelzang, MD
Jerome P Richie, MD, FACS
W Robert Lee, MD, MS, MEd
Deputy Editor
Michael E Ross, MD


Prostate cancer represents the most common visceral malignancy in men. While prostate cancer remains a lethal disease (killing roughly 1 in every 36 American males), it represents a disease spectrum, particularly when localized disease is diagnosed, with up to half of men not needing immediate intervention. In addition to this, there are men with higher risk localized disease for whom the extent of treatment remains unclear (ie, surgical only, radiation only, or a combined modality approach that may include surgery, radiation, and/or androgen deprivation). (See "Initial approach to low- and very low-risk clinically localized prostate cancer" and "Initial management of regionally localized intermediate-, high-, and very high-risk prostate cancer".)

In order to better stratify and predict risk of prostate cancer progression, multiple clinicopathologic parameters have been investigated, and nomograms have been developed. While these are powerful tools, they have limitations. Advances over the last decade have dramatically increased both our understanding of prostate cancer biology and our ability to obtain molecular information from small amounts of prostate tissue. Along with these advances have come newly available and emerging clinical molecular tests, which promise to help determine prostate cancer prognosis and guide treatment decisions. (See "Prostate cancer: Risk stratification and choice of initial treatment".)

Current molecular tests that may better determine the aggressiveness of prostate cancer have been developed either based on general features of malignancy (namely proliferation indices) or based on molecular features that are more specific for prostate cancer (table 1). These tests include those that are based on immunohistochemistry (IHC) and those based on ribonucleic acid (RNA) expression. These tests, their possible clinical applications, and the literature supporting their use are discussed here.


Tests based on cell proliferation include the evaluation by immunohistochemistry (IHC) of Ki-67, a nuclear protein that is associated with ribosomal ribonucleic acid (RNA) synthesis, and the cell cycle progression (CCP) score assessed by quantitative reverse transcription polymerase chain reaction (RT-PCR), which incorporates information from 31 cell cycle-related genes and 15 housekeeping genes.

Both Ki-67 IHC and the CCP score act as proxies for tumor proliferation. These tests have been employed in multiple retrospective cohorts, both with and without local treatment. Although they measure similar biological processes, they have not been directly compared; however, data for both tests appear similar.


Subscribers log in here

To continue reading this article, you must log in with your personal, hospital, or group practice subscription. For more information or to purchase a personal subscription, click below on the option that best describes you:
Literature review current through: Apr 2017. | This topic last updated: Apr 20, 2017.
The content on the UpToDate website is not intended nor recommended as a substitute for medical advice, diagnosis, or treatment. Always seek the advice of your own physician or other qualified health care professional regarding any medical questions or conditions. The use of this website is governed by the UpToDate Terms of Use ©2017 UpToDate, Inc.
  1. Polley MY, Leung SC, McShane LM, et al. An international Ki67 reproducibility study. J Natl Cancer Inst 2013; 105:1897.
  2. Pollack A, DeSilvio M, Khor LY, et al. Ki-67 staining is a strong predictor of distant metastasis and mortality for men with prostate cancer treated with radiotherapy plus androgen deprivation: Radiation Therapy Oncology Group Trial 92-02. J Clin Oncol 2004; 22:2133.
  3. Khor LY, Bae K, Paulus R, et al. MDM2 and Ki-67 predict for distant metastasis and mortality in men treated with radiotherapy and androgen deprivation for prostate cancer: RTOG 92-02. J Clin Oncol 2009; 27:3177.
  4. Tollefson MK, Karnes RJ, Kwon ED, et al. Prostate cancer Ki-67 (MIB-1) expression, perineural invasion, and gleason score as biopsy-based predictors of prostate cancer mortality: the Mayo model. Mayo Clin Proc 2014; 89:308.
  5. Fisher G, Yang ZH, Kudahetti S, et al. Prognostic value of Ki-67 for prostate cancer death in a conservatively managed cohort. Br J Cancer 2013; 108:271.
  6. 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; 192:409.
  7. 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; 31:1428.
  8. Cuzick J, Berney DM, Fisher G, et al. Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort. Br J Cancer 2012; 106:1095.
  9. 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; 12:245.
  10. Freedland SJ, Gerber L, Reid J, et al. Prognostic utility of cell cycle progression score in men with prostate cancer after primary external beam radiation therapy. Int J Radiat Oncol Biol Phys 2013; 86:848.
  11. 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; 113:382.
  12. Schoenborn JR, Nelson P, Fang M. Genomic profiling defines subtypes of prostate cancer with the potential for therapeutic stratification. Clin Cancer Res 2013; 19:4058.
  13. Baca SC, Prandi D, Lawrence MS, et al. Punctuated evolution of prostate cancer genomes. Cell 2013; 153:666.
  14. Leslie NR, Downes CP. PTEN function: how normal cells control it and tumour cells lose it. Biochem J 2004; 382:1.
  15. Liu W, Xie CC, Thomas CY, et al. Genetic markers associated with early cancer-specific mortality following prostatectomy. Cancer 2013; 119:2405.
  16. Ding Z, Wu CJ, Chu GC, et al. SMAD4-dependent barrier constrains prostate cancer growth and metastatic progression. Nature 2011; 470:269.
  17. Cuzick J, Yang ZH, Fisher G, et al. Prognostic value of PTEN loss in men with conservatively managed localised prostate cancer. Br J Cancer 2013; 108:2582.
  18. Lotan TL, Gurel B, Sutcliffe S, et al. PTEN protein loss by immunostaining: analytic validation and prognostic indicator for a high risk surgical cohort of prostate cancer patients. Clin Cancer Res 2011; 17:6563.
  19. Antonarakis ES, Keizman D, Zhang Z, et al. An immunohistochemical signature comprising PTEN, MYC, and Ki67 predicts progression in prostate cancer patients receiving adjuvant docetaxel after prostatectomy. Cancer 2012; 118:6063.
  20. Leinonen KA, Saramäki OR, Furusato B, et al. Loss of PTEN is associated with aggressive behavior in ERG-positive prostate cancer. Cancer Epidemiol Biomarkers Prev 2013; 22:2333.
  21. Yoshimoto M, Joshua AM, Cunha IW, et al. Absence of TMPRSS2:ERG fusions and PTEN losses in prostate cancer is associated with a favorable outcome. Mod Pathol 2008; 21:1451.
  22. Ahearn TU, Pettersson A, Ebot EM, et al. A Prospective Investigation of PTEN Loss and ERG Expression in Lethal Prostate Cancer. J Natl Cancer Inst 2016; 108.
  23. 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:e66855.
  24. Klein EA, Yousefi K, Haddad Z, et al. 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. Eur Urol 2015; 67:778.
  25. Karnes RJ, Bergstralh EJ, Davicioni E, et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. J Urol 2013; 190:2047.
  26. Ross AE, Johnson MH, Yousefi K, et al. Tissue-based Genomics Augments Post-prostatectomy Risk Stratification in a Natural History Cohort of Intermediate- and High-Risk Men. Eur Urol 2016; 69:157.
  27. Cooperberg MR, Davicioni E, Crisan A, et al. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. Eur Urol 2015; 67:326.
  28. 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; :JCO2016702811.
  29. Karnes RJ, Choeurng V, Ross AE, et al. Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features. Eur Urol 2017.
  30. Klein EA, Haddad Z, Yousefi K, et al. Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk. Urology 2016; 90:148.
  31. Nguyen PL, Martin NE, Choeurng V, et al. Utilization of biopsy-based genomic classifier to predict distant metastasis after definitive radiation and short-course ADT for intermediate and high-risk prostate cancer. Prostate Cancer Prostatic Dis 2017.
  32. Freedland SJ, Choeurng V, Howard L, et al. Utilization of a Genomic Classifier for Prediction of Metastasis Following Salvage Radiation Therapy after Radical Prostatectomy. Eur Urol 2016; 70:588.
  33. Dalela D, Santiago-Jiménez M, Yousefi K, et al. Genomic Classifier Augments the Role of Pathological Features in Identifying Optimal Candidates for Adjuvant Radiation Therapy in Patients With Prostate Cancer: Development and Internal Validation of a Multivariable Prognostic Model. J Clin Oncol 2017; :JCO2016699918.
  34. Ross AE, Den RB, Yousefi K, et al. Efficacy of post-operative radiation in a prostatectomy cohort adjusted for clinical and genomic risk. Prostate Cancer Prostatic Dis 2016; 19:277.
  35. Den RB, Yousefi K, Trabulsi EJ, et al. Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy. J Clin Oncol 2015; 33:944.
  36. Zhao SG, Chang SL, Spratt DE, et al. Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis. Lancet Oncol 2016; 17:1612.
  37. Ross AE. Multigene Testing in Localized Prostate Cancer. J Natl Compr Canc Netw 2016; 14:659.
  38. 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; 66:550.
  39. 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; 68:123.
  40. Brand TC, Zhang N, Crager MR, et al. Patient-specific Meta-analysis of 2 Clinical Validation Studies to Predict Pathologic Outcomes in Prostate Cancer Using the 17-Gene Genomic Prostate Score. Urology 2016; 89:69.
  41. 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; 21:2591.