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Genetics and Genomics: Discovery, Validation, and Utility of Novel Tools for management of Prostate Cancer

  • Alan W. Shindel1

1Associate Professor in the Department of Urology at University of California Davis when invited to write this manuscript. He has subsequently become an employee of Genomic Health Inc, Redwood City, CA

DOI: 10.31083/jomh.v12i1.20 Vol.12,Issue 1,January 2016 pp.6-17

Published: 04 January 2016

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Genomics is the science of how genes influence human health and disease states. It differs from traditional genetic screening in that the transcriptional activity (or other markers) in full panels of related genes are studied. Compared to simple genetic testing, assessment of expression levels in a panel of genes provides a more nuanced and holistic understanding of genetic modulation of human disease. Genomic testing may be used to great effect in resolving controversial questions on detection and treatment of prostate cancer. Genomic tests are currently in use for numerous facets of prostate cancer care, including screening, biopsy, and treatment planning. The clinical validity (predictive capacity) of these assays has been well established; studies on clinical utility (i.e. usefulness of these tests in guiding patient/provider decisions) have shown promising results. Men’s health specialists should be familiar with the role genomic testing will play in contemporary management of prostate cancer. 

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Alan W. Shindel. Genetics and Genomics: Discovery, Validation, and Utility of Novel Tools for management of Prostate Cancer. Journal of Men's Health. 2016. 12(1);6-17.


1. Jemal, A., Siegel, R., Ward, E., et al., Cancer statistics, 2009. CA Cancer J Clin, 2009. 59(4): p. 225-49.

2. Ferlay, J., Shin, H.R., Bray, F., et al., Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer, 2010. 127(12): p. 2893-917.

3. Wallner, L.P. and Jacobsen, S.J., Prostate-specific antigen and prostate cancer mortality: a systematic review. Am J Prev Med, 2013. 45(3): p. 318-26.

4. Siegel, R., Ma, J., Zou, Z., et al., Cancer statistics, 2014. CA Cancer J Clin, 2014. 64(1): p. 9-29.

5. Chou, R., Croswell, J.M., Dana, T., et al., Screening for prostate cancer: a review of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med, 2011. 155(11): p. 762-71.

6. Carlsson, S., Drevin, L., Loeb, S., et al., Population-based study of long-term functional outcomes after prostate cancer treatment. BJU Int, 2015.

7. Holzbeierlein, J.M., Castle, E.P., and Thrasher, J.B., Complications of androgen-deprivation therapy for prostate cancer. Clin Prostate Cancer, 2003. 2(3): p. 147-52.

8. Saylor, P.J. and Smith, M.R., Metabolic complications of androgen deprivation therapy for prostate cancer. J Urol, 2013. 189(1 Suppl): p. S34-42; discussion S43-4.

9. Aizer, A.A., Gu, X., Chen, M.H., et al., Cost implications and complications of overtreatment of low-risk prostate cancer in the United States. J Natl Compr Canc Netw, 2015. 13(1): p. 61-8.

10. Sanyal, C., Aprikian, A.G., Chevalier, S., et al., Direct cost for initial management of prostate cancer: a systematic review. Curr Oncol, 2013. 20(6): p. e522-31.

11.  Moyer, V.A. and Force, U.S.P.S.T., Screening for prostate cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med, 2012. 157(2): p. 120-34.

12.  McCarthy, M., Canadian panel recommends against PSA screening. BMJ, 2014. 349: p. g6556.

13. Cooperberg, M.R., Will biomarkers save prostate cancer screening? Eur Urol, 2012. 62(6): p. 962-3; discussion 964-5.

14.  Loeb, S., Bruinsma, S.M., Nicholson, J., et al., Active surveillance for prostate cancer: a systematic review of clinicopathologic variables and biomarkers for risk stratification. Eur Urol, 2015. 67(4): p. 619-26.

15.  Johansson, J.E., Andren, O., Andersson, S.O., et al., Natural history of early, localized prostate cancer. JAMA, 2004. 291(22): p. 2713-9.

16.  NCCN Clinical Practice Guidelines in Oncology. 2015, National Comprehensive Cancer Network.

17.  Cooperberg, M.R., Hilton, J.F., and Carroll, P.R., The CAPRA-S score: A straightforward tool for improved prediction of outcomes after radical prostatectomy. Cancer, 2011. 117(22): p. 5039-46.

18.  Stephenson, A.J., Scardino, P.T., Eastham, J.A., et al., Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Clin Oncol, 2005. 23(28): p. 7005-12.

19.  Shapiro, R.H. and Johnstone, P.A., Risk of Gleason grade inaccuracies in prostate cancer patients eligible for active surveillance. Urology, 2012. 80(3):

p. 661-6.

20. Bjurlin, M.A., Wysock, J.S., and Taneja, S.S., Optimization of prostate biopsy: review of technique and complications. Urol Clin North Am, 2014. 41(2):

p. 299-313.

21.  Loeb, S. and Catalona, W.J., What to do with an abnormal PSA test. Oncologist, 2008. 13(3): p. 299-305.

22. Bostrom, P.J., Bjartell, A.S., Catto, J.W., et al., Genomic Predictors of Outcome in Prostate Cancer. Eur Urol, 2015.

23.  Partin, A.W., Van Neste, L., Klein, E.A., et al., Clinical validation of an epigenetic assay to predict negative histopathological results in repeat prostate biopsies. J Urol, 2014. 192(4): p. 1081-7.

24.  Stewart, G.D., Van Neste, L., Delvenne, P., et al., Clinical utility of an epigenetic assay to detect occult prostate cancer in histopathologically negative biopsies: results of the MATLOC study. J Urol, 2013. 189(3): p. 1110-6.

25.  Knezevic, D., Goddard, A.D., Natraj, N., et al., Analytical validation of the Oncotype DX prostate cancer assay - a clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genomics, 2013. 14: p. 690.

26.  Klein, E.A., Cooperberg, M.R., 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): p. 550-60.

27. Teutsch, S.M., Bradley, L.A., Palomaki, G.E., et al., The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet Med, 2009. 11(1): p. 3-14.

28.  Pepe, M.S., Etzioni, R., Feng, Z., et al., Phases of biomarker development for early detection of cancer. J Natl Cancer Inst, 2001. 93(14): p. 1054-61.

29.  Cooperberg, M.R., Simko, J.P., Cowan, J.E., 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): p. 1428-34.

30.  Cullen, J., Rosner, I.L., Brand, T.C., 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): p. 123-31.

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

32. McShane, L.M. and Hayes, D.F., Publication of tumor marker research results: the necessity for complete and transparent reporting. J Clin Oncol, 2012. 30(34): p. 4223-32.

33.  Pepe, M.S., Feng, Z., Janes, H., et al., Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J Natl Cancer Inst, 2008. 100(20): p. 1432-8.

34. Luo, Y., Gou, X., Huang, P., et al., 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): p. 487-92.

35. Bishoff, J.T., Freedland, S.J., 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): p. 409-14.

36.  Cuzick, J., Berney, D.M., 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(6): p. 1095-9.

37.  Karnes, R.J., Bergstralh, E.J., 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(6): p. 2047-53.

38.  Van Neste, L., Bigley, J., Toll, A., et al., A tissue biopsy-based epigenetic multiplex PCR assay for prostate cancer detection. BMC Urol, 2012. 12: p. 16.

39.  Trock, B.J., Brotzman, M.J., Mangold, L.A., et al., Evaluation of GSTP1 and APC methylation as indicators for repeat biopsy in a high-risk cohort of men with negative initial prostate biopsies. BJU Int, 2012. 110(1): p. 56-62.

40.  Trujillo, K.A., Jones, A.C., Griffith, J.K., et al., Markers of field cancerization: proposed clinical applications in prostate biopsies. Prostate Cancer, 2012. 2012: p. 302894.

41. Wojno, K.J., Costa, F.J., Cornell, R.J., et al., Reduced Rate of Repeated Prostate Biopsies Observed in ConfirmMDx Clinical Utility Field Study. Am Health Drug Benefits, 2014. 7(3): p. 129-34.

42. Filella, X. and Foj, L., Emerging biomarkers in the detection and prognosis of prostate cancer. Clin Chem Lab Med, 2015. 53(7): p. 963-73.

43.  Vedder, M.M., de Bekker-Grob, E.W., Lilja, H.G., et al., The added value of percentage of free to total prostate-specific antigen, PCA3, and a kallikrein panel to the ERSPC risk calculator for prostate cancer in prescreened men. Eur Urol, 2014. 66(6): p. 1109-15.

44.  Eggener, S.E., Scardino, P.T., Walsh, P.C., et al., Predicting 15-year prostate cancer specific mortality after radical prostatectomy. J Urol, 2011. 185(3):

p. 869-75.

45.  Epstein, J.I., Zelefsky, M.J., Sjoberg, D.D., et al., A Contemporary Prostate Cancer Grading System: A Validated Alternative to the Gleason Score. Eur Urol, 2015.

46. Badani, K.K., Kemeter, M.J., Febbo, P.G., et al., The Impact of a Biopsy Based 

17- Gene Genomic Prostate Score on Treatment Recommendations in Men with Newly Diagnosed Clinically Prostate Cancer Who are Candidates for Active Surveillance. Urol Pract, 2015. 2(4): p. 181-189.

47.  Dall’Era, M.A., Maddala, T., Polychronopoulos, L., et al., Utility of the Oncotype DX® Prostate Cancer Assay in Clinical Practice for Treatment Selection in Men Newly Diagnosed with Prostate Cancer: A Retrospective Chart Review Analysis. Urol Pract, 2015.

48.  Cuzick, J., Swanson, G.P., 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): p. 245-55.

49.  Cooperberg, M.R., Freedland, S.J., Pasta, D.J., et al., Multiinstitutional validation of the UCSF cancer of the prostate risk assessment for prediction of recurrence after radical prostatectomy. Cancer, 2006. 107(10): p. 2384-91.

50.  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): p. 382-9.

51. Crawford, E.D., Scholz, M.C., Kar, A.J., et al., Cell cycle progression score and treatment decisions in prostate cancer: results from an ongoing registry. Curr Med Res Opin, 2014. 30(6): p. 1025-31.

52.  Freedland, S.J., 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(5): p. 848-53.

53.  Ross, A.E., Feng, F.Y., Ghadessi, M., et al., A genomic classifier predicting metastatic disease progression in men with biochemical recurrence after prostatectomy. Prostate Cancer Prostatic Dis, 2014. 17(1): p. 64-9.

54.  Klein, E.A., 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(4): p. 778-86.

55.  Cooperberg, M.R., 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(2): p. 326-33.

56.  Suba, E.J., Pfeifer, J.D., and Raab, S.S., Patient identification error among prostate needle core biopsy specimens--are we ready for a DNA time-out? J Urol, 2007. 178(4 Pt 1): p. 1245-8.

57.  Pfeifer, J.D. and Liu, J., Rate of occult specimen provenance complications in routine clinical practice. Am J Clin Pathol, 2013. 139(1): p. 93-100.

58. Wojno, K., Hornberger, J., Schellhammer, P., et al., The clinical and economic implications of specimen provenance complications in diagnostic prostate biopsies. J Urol, 2015. 193(4): p. 1170-7.

59.  Pfeifer, J.D., Zehnbauer, B., and Payton, J., The changing spectrum of DNA-based specimen provenance testing in surgical pathology. Am J Clin Pathol, 2011. 135(1): p. 132-8.

60.  Pfeifer, J.D., Singleton, M.N., Gregory, M.H., et al., Development of a decision-analytic model for the application of STR-based provenance testing of transrectal prostate biopsy specimens. Value Health, 2012. 15(6): p. 860-7.

61. Bruzzese, D., Mazzarella, C., Ferro, M., et al., Prostate health index vs percent free prostate-specific antigen for prostate cancer detection in men with “gray” prostate-specific antigen levels at first biopsy: systematic review and meta-analysis. Transl Res, 2014. 164(6): p. 444-51.

62.  Filella, X., Foj, L., Alcover, J., et al., The influence of prostate volume in prostate health index performance in patients with total PSA lower than 10 mug/L. Clin Chim Acta, 2014. 436: p. 303-7.

63.  Lughezzani, G., Lazzeri, M., Haese, A., et al., Multicenter European external validation of a prostate health index-based nomogram for predicting prostate cancer at extended biopsy. Eur Urol, 2014. 66(5): p. 906-12.

64.  Parekh, D.J., Punnen, S., Sjoberg, D.D., 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): p. 464-70.

65.  Nordstrom, T., Vickers, A., Assel, M., et al., Comparison Between the Four-kallikrein Panel and Prostate Health Index for Predicting Prostate Cancer. Eur Urol, 2014.

66. Braun, K., Sjoberg, D.D., Vickers, A.J., et al., A Four-kallikrein Panel Predicts High-grade Cancer on Biopsy: Independent Validation in a Community Cohort. Eur Urol, 2015.

67. Bryant, R.J., Sjoberg, D.D., Vickers, A.J., et al., Predicting High-Grade Cancer at Ten-Core Prostate Biopsy Using Four Kallikrein Markers Measured in Blood in the ProtecT Study. J Natl Cancer Inst, 2015. 107(7).

68.  Stattin, P., Vickers, A.J., Sjoberg, D.D., et al., Improving the Specificity of Screening for Lethal Prostate Cancer Using Prostate-specific Antigen and a Panel of Kallikrein Markers: A Nested Case-Control Study. Eur Urol, 2015.

69.  Voigt, J.D., Zappala, S.M., Vaughan, E.D., et al., The Kallikrein Panel for prostate cancer screening: its economic impact. Prostate, 2014. 74(3): p. 250-9.

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): p. 1201-12.

71.  Blume-Jensen, P., Berman, D.M., Rimm, D.L., et al., Development and Clinical Validation of an In Situ Biopsy-Based Multimarker Assay for Risk Stratification in Prostate Cancer. Clin Cancer Res, 2015.

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