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Annual Review of Medicine

Volume 67, 2016

Molecular Profiles of Prostate Cancer: To Treat or Not to Treat

Abstract

A major dilemma in the selection of treatment for men with prostate cancer is the difficulty in accurately characterizing the risk posed by the cancer. This uncertainty has led physicians to recommend aggressive therapy for most men diagnosed with prostate cancer and has led to concerns about the benefits of screening and the adverse consequences of excessive treatment. Genomic analyses of prostate cancer reveal distinct patterns of alterations in the genomic landscape of the disease that show promise for improved prediction of prognosis and better medical decision making. Several molecular profiles are now commercially available and are being used to inform medical decisions. This article describes the clinical tests available for distinguishing aggressive from nonaggressive prostate cancer, reviews the new genomic tests, and discusses their advantages and limitations and the evidence for their utility in various clinical settings.

Keyword(s): active surveillance, genomic classifier, risk stratification
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2016-01-14
2024-04-30
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Literature Cited

  1. 1. American Cancer Society 2015. Estimated deaths for the four major cancers by sex and age group, 2015 http://www.cancer.org/acs/groups/content/@editorial/documents/document/acspc-044509.pdf
  2. Cooperberg MR, Broering JM, Carroll PR. 2.  2010. Time trends and local variation in primary treatment of localized prostate cancer. J. Clin. Oncol. 28:1117–23 [Google Scholar]
  3. Cooperberg MR, Broering JM, Kantoff PW, Carroll PR. 3.  2007. Contemporary trends in low risk prostate cancer: risk assessment and treatment. J. Urol. 178:S14–S19 [Google Scholar]
  4. Draisma G, Etzioni R, Tsodikov A. 4.  et al. 2009. Lead time and overdiagnosis in prostate-specific antigen screening: importance of methods and context. J. Natl. Cancer Inst. 101:374–83 [Google Scholar]
  5. Thompson I, Thrasher JB, Aus G. 5.  et al. 2007. Guideline for the management of clinically localized prostate cancer: 2007 update. J. Urol. 177:2106–31 [Google Scholar]
  6. Kattan MW, Cuzick J, Fisher G. 6.  et al. 2007. Nomogram incorporating PSA level to predict cancer-specific survival for men with clinically localized prostate cancer managed without curative intent. Cancer 112:69–74 [Google Scholar]
  7. Taylor BS, Schultz N, Hieronymus H. 7.  et al. 2010. Integrative genomic profiling of human prostate cancer. Cancer Cell 18:11–22First large-scale integrated genomic profile of PCa; identified CNAs as an independent prognostic factor. [Google Scholar]
  8. Hieronymus H, Schultz N, Gopalan A. 8.  et al. 2014. Copy number alteration burden predicts prostate cancer relapse. PNAS 111:11139–44 [Google Scholar]
  9. Cuzick J, Swanson GP, Fisher G. 9.  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. 12:245–55First clinical study of CCP score as independent prognostic feature to predict risk of death from cancer in a conservatively managed cohort. [Google Scholar]
  10. Klein EA, Cooperberg MR, Magi-Galluzzi C. 10.  et al. 2014. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur. Urol. 66:550–60First published study of accuracy of GPS to predict risk of unfavorable pathology in RP specimens from biopsy samples. [Google Scholar]
  11. Erho N, Crisan A, Vergara IA. 11.  et al. 2013. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS ONE 8:e66855First study confirming the ability of GC to predict risk of early metastasis after RP. [Google Scholar]
  12. Blume-Jensen P, Berman DM, Rimm DL. 12.  et al. 2015. Development and clinical validation of an in situ biopsy based multi-marker assay for risk stratification in prostate cancer. Clin. Cancer Res. 21:2591–600First study of ProMark immunofluoresence assay to predict aggressiveness of prostate cancer from biopsy specimens. [Google Scholar]
  13. Tosoian JJ, Trock BJ, Landis P. 13.  et al. 2011. Active surveillance program for prostate cancer: an update of the Johns Hopkins experience. J. Clin. Oncol. 29:2185–90 [Google Scholar]
  14. Klotz L, Vesprini D, Sethukavalan P. 14.  et al. 2015. Long-term follow-up of a large active surveillance cohort of patients with prostate cancer. J. Clin. Oncol. 33:272–77Largest and longest prospective study of an AS cohort; includes low- and intermediate-risk patients. [Google Scholar]
  15. Hricak H, Choyke PL, Eberhardt SC. 15.  et al. 2007. Imaging prostate cancer: a multidisciplinary perspective. Radiology 243:28–53 [Google Scholar]
  16. Eifler JB, Feng Z, Lin BM. 16.  et al. 2012. An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011. BJU Int. 11:22–29 [Google Scholar]
  17. Stephenson AJ, Scardino PT, Eastham JA. 17.  et al. 2006. Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J. Natl. Cancer Inst. 98:715–17Most widely used nomogram; combines pretreatment factors to predict risk of recurrence after RP. [Google Scholar]
  18. Stephenson AJ, Kattan MW, Eastham JA. 18.  et al. 2009. Prostate cancer-specific mortality after radical prostatectomy for patients treated in the prostate-specific antigen era. J. Clin. Oncol. 27:4300–5 [Google Scholar]
  19. Mohler JL, Kantoff PW, Armstrong AJ. 19.  et al. 2014. Prostate cancer, version 2.2014. J. Natl. Cancer Netw. 12:686–718 [Google Scholar]
  20. Cooperberg MR, Pasta DJ, Elkin EP. 20.  et al. 2005. The University of California, San Francisco Cancer of the Prostate Risk Assessment Score: a straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy. J. Urol. 173:1938–42First description of CAPRA risk assessment score; uses clinicopathologic features to predict risk of recurrence after RP. [Google Scholar]
  21. Mitchell JA, Cooperberg MR, Elkin EP. 21.  et al. 2005. Ability of 2 pretreatment risk assessment methods to predict prostate cancer recurrence after radical prostatectomy: data from CaPSURE. J. Urol. 173:1126–31 [Google Scholar]
  22. Dinh KT, Mahal BA, Ziehr DR. 22.  et al. 2015. Incidence and predictors of upgrading and upstaging among 10,000 contemporary patients with low-risk prostate cancer. J. Urol. 194343–49
  23. Cuzick J, Fisher G, Kattan MW. 23.  et al. 2006. Long-term outcome among men with conservatively treated localised prostate cancer. Br. J. Cancer 95:1186–94 [Google Scholar]
  24. Wilt TJ, Brawer MK, Jones KM. 24.  et al. 2012. Radical prostatectomy versus observation for localized prostate cancer. N. Engl. J. Med. 367:203–13Second of two randomized clinical trials (RCTs) comparing radical prostatectomy with observation for clinically localized PCa. [Google Scholar]
  25. Welty CJ, Cowan JE, Nguyen H. 25.  et al. 2015. Extended followup and risk factors for disease reclassification in a large active surveillance cohort for localized prostate cancer. J. Urol. 193:807–11 [Google Scholar]
  26. Eggener SE, Mueller A, Berglund RK. 26.  et al. 2009. A multi-institutional evaluation of active surveillance for low risk prostate cancer. J. Urol. 181:1635–41 [Google Scholar]
  27. Fütterer JJ, Briganti A, De Visschere P. 27.  et al. 2015. Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? A systematic review of the literature. Eur. Urol. 681045–53Excellent review of accuracy of MRI in detecting “clinically significant” cancers within the prostate.
  28. Berglund RK, Masterson TA, Vora KC. 28.  et al. 2008. Pathological upgrading and upstaging with immediate repeat biopsy in patients eligible for active surveillance. J. Urol. 180:1964–67 [Google Scholar]
  29. Mullerad M, Hricak H, Wang L. 29.  et al. 2004. Prostate cancer: detection of extracapsular extension by genitourinary and general body radiologists at MR imaging. Radiology 232:140–46 [Google Scholar]
  30. Vargas HA, Akin O, Shukla-Dave A. 30.  et al. 2012. Performance characteristics of MR imaging in the evaluation of clinically low-risk prostate cancer: a prospective study. Radiology 265:478–87 [Google Scholar]
  31. Brown AM, Elbuluk O, Mertan F. 31.  et al. 2015. Recent advances in image-guided targeted prostate biopsy. Abdom. Imaging 401788–99
  32. Pinto PA, Chung PH, Rastinehad AR. 32.  et al. 2011. Magnetic resonance imaging/ultrasound fusion guided prostate biopsy improves cancer detection following transrectal ultrasound biopsy and correlates with multiparametric magnetic resonance imaging. J. Urol 186:1281–85Excellent study of increased accuracy of MRI/ultrasound fusion technology for biopsy detection of high-grade cancer. [Google Scholar]
  33. Roychowdhury S, Chinnaiyan M. 33.  2013. Advancing precision medicine for prostate cancer through genomics. J. Clin. Oncol. 31:1866–73 [Google Scholar]
  34. Fraser M, Berlin A, Bristow RG. 34.  et al. 2015. Genomic, pathological, and clinical heterogeneity as drivers of personalized medicine in prostate cancer. Urol. Oncol. 33:85–94Thorough review of challenge presented by morphologic and molecular heterogeneity of multifocal cancers within the prostate. [Google Scholar]
  35. Knezevic D, Goddard AD, Natraj N. 35.  et al. 2013. Analytical validation of the Oncotype DX prostate cancer assay—a clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genomics 14:690 [Google Scholar]
  36. Shipitsin M, Small C, Choudhury S. 36.  et al. 2014. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error. Br. J. Cancer 111:1201–12 [Google Scholar]
  37. Sattler HP, Rohde V, Bonkhoff H. 37.  et al. 1999. Comparative genomic hybridization reveals DNA copy number gains to frequently occur in human prostate cancer. Prostate 39:79–86 [Google Scholar]
  38. Yano S, Matsuyama H, Matsuda K. 38.  et al. 2004. Accuracy of an array comparative genomic hybridization (CGH) technique in detecting DNA copy number aberrations: comparison with conventional CGH and loss of heterozygosity analysis in prostate cancer. Cancer Genet. Cytogenet. 150:122–27 [Google Scholar]
  39. Liu W, Laitinen S, Khan S. 39.  et al. 2009. Copy number analysis indicates monoclonal origin of lethal metastatic prostate cancer. Nat. Med. 15:559–65 [Google Scholar]
  40. Cheng I, Levin AM, Tai YC. 40.  et al. 2012. Copy number alterations in prostate tumors and disease aggressiveness. Genes Chromosomes Cancer 51:66–76 [Google Scholar]
  41. Feik E, Schweifer N, Baierl A. 41.  et al. 2013. Integrative analysis of prostate cancer aggressiveness. Prostate 73:1413–26 [Google Scholar]
  42. Cuzick J, Berney DM, Fisher G. 42.  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 106:1095–99 [Google Scholar]
  43. Mosley JD, Keri RA. 43.  2008. Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists. BMC Med. Genomics 1:11 [Google Scholar]
  44. Crawford ED, Shore N, Scardino PT. 44.  et al. 2015. Performance of CCP assay in an updated series of biopsy samples obtained from commercial testing Presented at Genitourinary Cancers Symp. (GU ASCO), Feb. 26–28, Orlando, FL
  45. Cooperberg MR, Simko JP, Cowan JE. 45.  et al. 2013. Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. J. Clin. Oncol. 31:1428–34 [Google Scholar]
  46. Bishoff JT, Freedland SJ, Gerber L. 46.  et al. 2014. Prognostic utility of the CCP score generated from biopsy in men treated with prostatectomy. J. Urol. 192:409–14 [Google Scholar]
  47. Freedland SJ, Gerber L, Reid J. 47.  et al. 2013. Prognostic utility of cell cycle progression score in men with prostate cancer after primary external beam radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 86:848–53 [Google Scholar]
  48. Cuzick J, Stone S, Fisher G. 48.  et al. 2015. Validation of an RNA cell cycle progression (CCP) score for predicting prostate cancer death in a conservatively managed needle biopsy cohort. Br. J. Cancer 113382–89
  49. Crawford ED, Scholz MC, Kar AJ. 49.  et al. 2014. Cell cycle progression score and treatment decisions in prostate cancer: results from an ongoing registry. Curr. Med. Res. Opin. 6:1025–31 [Google Scholar]
  50. Simon RM, Paik S, Hayes DF. 50.  2009. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J. Natl. Cancer Inst. 101:1446–52Excellent summary of requirements for establishing a new biomarker for use in clinical practice. [Google Scholar]
  51. Bill-Axelson A, Holmberg L, Garmo H. 51.  et al. 2014. Radical prostatectomy or watchful waiting in early prostate cancer. N. Engl. J. Med. 370:932–42First RCT to establish survival benefit of RP compared with observation. [Google Scholar]
  52. Karnes RJ, Bergstralh EJ, Davicioni E. 52.  et al. 2013. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk population. J. Urol. 190:2047–53 [Google Scholar]
  53. Cooperberg MR, Davicioni E, Crisan A. 53.  et al. 2015. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. Eur. Urol. 67:326–33 [Google Scholar]
  54. Klein EA, Yousefi K, Haddad Z. 54.  et al. 2014. 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. 67:778–86 [Google Scholar]
  55. Bolla M, van Popel H, Tombal B. 55.  et al. 2012. Postoperative radiotherapy after radical prostatectomy for high-risk prostate cancer: long-term results of a randomised controlled trial (EORTC trial 2291). Lancet 830:2018–27 [Google Scholar]
  56. Abrahamsson P-A, Anderson J, Boccon-Gibod L. 56.  et al. 2005. Risks and benefits of hormonal manipulation as monotherapy or adjuvant treatment in localised prostate cancer. Eur. Urol. 48:900–5 [Google Scholar]
  57. Stephenson AJ, Scardino PT, Eastham JA. 57.  et al. 2005. Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J. Clin. Oncol. 23:7005–12 [Google Scholar]
  58. Ross AE, Feng FY, Ghadessi M. 58.  et al. 2014. A genomic classifier predicting metastatic disease progression in men with biochemical recurrence after prostatectomy. Prostate Cancer Prostatic Dis 17:64–69 [Google Scholar]
  59. Den RB, Feng FY, Showalter TN. 59.  et al. 2014. Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy. Int. J. Radiat. Oncol. Bio. Phys. 89:1038–46 [Google Scholar]
  60. Badani KK, Thompson DJ, Brown G. 60.  et al. 2015. Effect of a genomic classifier test on clinical practice decisions for patients with high-risk prostate cancer after surgery. BJU Int. 115:419–29 [Google Scholar]
  61. Eggener SE, Scardino PT, Walsh PC. 61.  et al. 2011. Predicting 15-year prostate cancer specific mortality after radical prostatectomy. J. Urol. 185:869–75 [Google Scholar]
  62. Prensner JR, Rubin MA, Wei JT. 62.  et al. 2012. Beyond PSA: the next generation of prostate cancer biomarkers. Sci. Trans. Med. 4:127rv3 [Google Scholar]
  63. Nguyen HG, Welty CJ, Cooperberg MR. 63.  2015. Diagnostic associations of gene expression signatures in prostate cancer tissue. Curr. Opin. Urol. 1:65–70 [Google Scholar]
  64. Sethi S, Kong D, Land S. 64.  et al. 2013. Comprehensive molecular oncogenomic profiling and miRNA analysis of prostate cancer. Am. J. Trans. Res. 5:200–11 [Google Scholar]
  65. Tomlins SA, Mehra R, Rhodes DR. 65.  et al. 2007. Integrative molecular concept modeling of prostate cancer progression. Nat. Genet. 39:41–51 [Google Scholar]
/content/journals/10.1146/annurev-med-060413-112226
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Molecular Profiles of Prostate Cancer: To Treat or Not to Treat
Annual Review of Medicine 67, 119 (2016); https://doi.org/10.1146/annurev-med-060413-112226
/content/journals/10.1146/annurev-med-060413-112226
/content/journals/10.1146/annurev-med-060413-112226
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  • Article Type: Review Article

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