Abstract
Diagnostic biomarkers derived from blood, urine, or prostate tissue provide additional information beyond clinical calculators to determine the risk of detecting high-grade prostate cancer. Once diagnosed, multiple markers leverage prostate cancer biopsy tissue to prognosticate clinical outcomes, including adverse pathology at radical prostatectomy, disease recurrence, and prostate cancer mortality; however the clinical utility of some outcomes to patient decision making is unclear. Markers using tissue from radical prostatectomy specimens provide additional information about the risk of biochemical recurrence, development of metastatic disease, and subsequent mortality beyond existing multivariable clinical calculators (the use of a marker to simply sub-stratify risk groups such as the NCCN groups is of minimal value). No biomarkers currently available for prostate cancer have been prospectively validated to be predict an improved clinical outcome for a specific therapy based on the test result; however, further research and development of these tests may produce a truly predictive biomarker for prostate cancer treatment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Epstein JI, Zelefsky MJ, Sjoberg DD et al (2016) A contemporary prostate cancer grading system: a validated alternative to the Gleason score. Eur Urol 69(3):428–435. https://doi.org/10.1016/j.eururo.2015.06.046
Moschini M, Carroll PR, Eggener SE et al (2017) Low-risk prostate cancer: identification, management, and outcomes. Eur Urol 72(2):238–249. https://doi.org/10.1016/j.eururo.2017.03.009
McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM (2005) Reporting recommendations for tumor marker prognostic studies. J Clin Oncol 23(36):9067–9072. https://doi.org/10.1200/JCO.2004.01.0454
Ballman KV (2015) Biomarker: predictive or prognostic? J Clin Oncol 33(33):3968–3971. https://doi.org/10.1200/JCO.2015.63.3651
Duffy MJ, O’Donovan N, McDermott E, Crown J (2016) Validated biomarkers: the key to precision treatment in patients with breast cancer. Breast 29:192–201. https://doi.org/10.1016/j.breast.2016.07.009
Denduluri N, Somerfield MR, Eisen A et al (2016) Selection of optimal adjuvant chemotherapy regimens for human epidermal growth factor receptor 2 (HER2) -negative and adjuvant targeted therapy for HER2-Positive Breast Cancers. J Clin Oncol (An American Society of Clinical Oncology Guideline Adaptation of the Cancer C) 34(20):2416–2427. https://doi.org/10.1200/JCO.2016.67.0182
Centers for Medicare & Medicaid Services. Clinical Laboratory Improvement Amendments. https://www.cms.gov/Regulations-and-Guidance/Legislation/CLIA/Downloads/LDT-and-CLIA_FAQs.pdf. Published 2013. Accessed 1 Jan 2017
Office of Public Health Strategy and Analysis—Food and Drug Administration (2015) The public health evidence for FDA oversight of laboratory developed tests: 20 case studies. Silver Spring, MD. https://www.fda.gov/AboutFDA/ReportsManualsForms/Reports/ucm472773.htm
Food and Drug Administration (2017) Discussion paper of laboratory developed tests. Silver Spring, MD. https://www.fda.gov/medicaldevices/productsandmedicalprocedures/invitrodiagnostics/laboratorydevelopedtests/default.htm
Palmetto GBA (2018) Molecular diagnostic services program manual. Columbia, SC. https://www.palmettogba.com/Palmetto/moldx.Nsf/files/MolDX_Manual.pdf/$File/MolDX_Manual.pdf?Open&. Accessed 20 Jan 2018
Palmetto GBA (2015) MolDX clinical test evaluation process. Columbia, SC. https://www.palmettogba.com/Palmetto/Moldx.Nsf/files/MolDX_Clinical_Test_Evaluation_Process_(CTEP)_M00096.pdf/$File/MolDX_Clinical_Test_Evaluation_Process_(CTEP)_M00096.pdf. Accessed 20 Jan 2018
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Leapman MS, Nguyen HG, Cooperberg MR (2016) Clinical utility of biomarkers in localized prostate cancer. Curr Oncol Rep 18(5):30. https://doi.org/10.1007/s11912-016-0513-1
Nguyen CT, Kattan MW (2011) How to tell if a new marker improves prediction. Eur Urol 60(2):226–230. https://doi.org/10.1016/j.eururo.2011.04.029
Ankerst DP, Hoefler J, Bock S et al (2014) Prostate cancer prevention trial risk calculator 2.0 for the prediction of low- vs high-grade prostate cancer. Urology 83(6):1362–1367. https://doi.org/10.1016/j.urology.2014.02.035
Cooperberg MR, Broering JM, Carroll PR (2009) Risk assessment for prostate cancer metastasis and mortality at the time of diagnosis. J Natl Cancer Inst 101(12):878–887. https://doi.org/10.1093/jnci/djp122
Cooperberg MR, Hilton JF, Carroll PR (2011) The CAPRA-S score: a straightforward tool for improved prediction of outcomes after radical prostatectomy. Cancer 117(22):5039–5046. https://doi.org/10.1002/cncr.26169
Stephenson AJ, Scardino PT, Eastham JA et al (2005) Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Clin Oncol 23(28):7005–7012. https://doi.org/10.1200/JCO.2005.01.867
Eggener SE, Scardino PT, Walsh PC et al (2011) Predicting 15-year prostate cancer specific mortality after radical prostatectomy. J Urol 185(3):869–875. https://doi.org/10.1016/j.juro.2010.10.057
Sanda MG, Cadeddu JA, Kirkby E et al (2017) Clinically localized prostate cancer: AUA/ASTRO/SUO guideline. Part I: risk stratification, shared decision making, and care options. J Urol. https://doi.org/10.1016/j.juro.2017.11.095
Mohler JL, Antonarakis ES, Armstrong AJ et al (2017) NCCN clinical practice guidelines in oncology—prostate cancer. Version 2. https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf. Published 2017. Accessed 3 Oct 2017
Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak 26(6):565–574. https://doi.org/10.1177/0272989X06295361
Vickers AJ, Cronin AM, Elkin EB, Gonen M (2008) Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak 8:53. https://doi.org/10.1186/1472-6947-8-53
Fitzgerald M, Saville BR, Lewis RJ (2015) Decision curve analysis. JAMA 313(4):409–410. https://doi.org/10.1001/jama.2015.37
Lee DJ, Mallin K, Graves AJ et al (2017) Recent changes in prostate cancer screening practices and epidemiology. J Urol. https://doi.org/10.1016/j.juro.2017.05.074
Catalona WJ, Partin AW, Sanda MG et al (2011) 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 185(5):1650–1655. https://doi.org/10.1016/j.juro.2010.12.032
Loeb S, Sanda MG, Broyles DL et al (2015) The prostate health index selectively identifies clinically significant prostate cancer. J Urol 193(4):1163–1169. https://doi.org/10.1016/j.juro.2014.10.121
Jansen FH, van Schaik RHN, Kurstjens J et al (2010) Prostate-specific antigen (PSA) isoform p2PSA in combination with total PSA and free PSA improves diagnostic accuracy in prostate cancer detection. Eur Urol 57(6):921–927. https://doi.org/10.1016/j.eururo.2010.02.003
de la Calle C, Patil D, Wei JT et al (2015) Multicenter evaluation of the prostate health index to detect aggressive prostate cancer in biopsy naive men. J Urol 194(1):65–72. https://doi.org/10.1016/j.juro.2015.01.091
Boegemann M, Stephan C, Cammann H et al (2016) The percentage of prostate-specific antigen (PSA) isoform [-2]proPSA and the prostate health index improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared with total PSA and percentage free PSA in men. BJU Int 117(1):72–79. https://doi.org/10.1111/bju.13139
Loeb S, Shin SS, Broyles DL et al (2017) Prostate health Index improves multivariable risk prediction of aggressive prostate cancer. BJU Int 120(1):61–68. https://doi.org/10.1111/bju.13676
Foley RW, Gorman L, Sharifi N et al (2016) Improving multivariable prostate cancer risk assessment using the prostate health index. BJU Int 117(3):409–417. https://doi.org/10.1111/bju.13143
Foley RW, Maweni RM, Gorman L et al (2016) European randomised study of screening for prostate cancer (ERSPC) risk calculators significantly outperform the prostate cancer prevention trial (PCPT) 2.0 in the prediction of prostate cancer: a multi-institutional study. BJU Int 118(5):706–713. https://doi.org/10.1111/bju.13437
Hirama H, Sugimoto M, Ito K, Shiraishi T, Kakehi Y (2014) The impact of baseline [-2]proPSA-related indices on the prediction of pathological reclassification at 1 year during active surveillance for low-risk prostate cancer: the Japanese multicenter study cohort. J Cancer Res Clin Oncol 140(2):257–263. https://doi.org/10.1007/s00432-013-1566-2
Tosoian JJ, Loeb S, Feng Z et al (2012) Association of [-2]proPSA with biopsy reclassification during active surveillance for prostate cancer. J Urol 188(4):1131–1136. https://doi.org/10.1016/j.juro.2012.06.009
Guazzoni G, Lazzeri M, Nava L et al (2012) Preoperative prostate-specific antigen isoform p2PSA and its derivatives, %p2PSA and prostate health index, predict pathologic outcomes in patients undergoing radical prostatectomy for prostate cancer. Eur Urol 61(3):455–466. https://doi.org/10.1016/j.eururo.2011.10.038
Fossati N, Buffi NM, Haese A et al (2015) Preoperative prostate-specific antigen isoform p2PSA and its derivatives, %p2PSA and prostate health index, predict pathologic outcomes in patients undergoing radical prostatectomy for prostate cancer: results from a multicentric European prospective Stud. Eur Urol 68(1):132–138. https://doi.org/10.1016/j.eururo.2014.07.034
Vickers AJ, Cronin AM, Aus G et al (2008) 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 6:19. https://doi.org/10.1186/1741-7015-6-19
Vickers A, Cronin A, Roobol M et al (2010) Reducing unnecessary biopsy during prostate cancer screening using a four-kallikrein panel: an independent replication. J Clin Oncol 28(15):2493–2498. https://doi.org/10.1200/JCO.2009.24.1968
Benchikh A, Savage C, Cronin A et al (2010) A panel of kallikrein markers can predict outcome of prostate biopsy following clinical work-up: an independent validation study from the European randomized study of prostate cancer screening, France. BMC Cancer 10:635. https://doi.org/10.1186/1471-2407-10-635
Vickers AJ, Cronin AM, Roobol MJ et al (2010) A four-kallikrein panel predicts prostate cancer in men with recent screening: data from the European randomized study of screening for prostate cancer, Rotterdam. Clin Cancer Res 16(12):3232–3239. https://doi.org/10.1158/1078-0432.CCR-10-0122
Gupta A, Roobol MJ, Savage CJ et al (2010) A four-kallikrein panel for the prediction of repeat prostate biopsy: data from the European randomized study of prostate cancer screening in Rotterdam, Netherlands. Br J Cancer 103(5):708–714. https://doi.org/10.1038/sj.bjc.6605815
Bryant RJ, Sjoberg DD, Vickers AJ et al (2015) Predicting high-grade cancer at ten-core prostate biopsy using four kallikrein markers measured in blood in the ProtecT study. J Natl Cancer Inst 107(7). https://doi.org/10.1093/jnci/djv095
Parekh DJ, Punnen S, Sjoberg DD et al (2015) A multi-institutional prospective trial in the USA confirms that the 4K score accurately identifies men with high-grade prostate cancer. Eur Urol 68(3):464–470. https://doi.org/10.1016/j.eururo.2014.10.021
Nordstrom T, Vickers A, Assel M, Lilja H, Gronberg H, Eklund M (2015) Comparison between the four-kallikrein panel and prostate health index for predicting prostate cancer. Eur Urol 68(1):139–146. https://doi.org/10.1016/j.eururo.2014.08.010
Carlsson S, Maschino A, Schroder F et al (2013) Predictive value of four kallikrein markers for pathologically insignificant compared with aggressive prostate cancer in radical prostatectomy specimens: results from the European randomized study of screening for prostate cancer section Rotterdam. Eur Urol 64(5):693–699. https://doi.org/10.1016/j.eururo.2013.04.040
Lin DW, Newcomb LF, Brown MD et al (2017) Evaluating the four Kallikrein panel of the 4Kscore for prediction of high-grade prostate cancer in men in the canary prostate active surveillance study. Eur Urol 72(3):448–454. https://doi.org/10.1016/j.eururo.2016.11.017
Bussemakers MJ, van Bokhoven A, Verhaegh GW et al (1999) DD3: a new prostate-specific gene, highly overexpressed in prostate cancer. Cancer Res 59(23):5975–5979
Tosoian JJ, Ross AE, Sokoll LJ, Partin AW, Pavlovich CP (2016) Urinary biomarkers for prostate cancer. Urol Clin North Am 43(1):17–38. https://doi.org/10.1016/j.ucl.2015.08.003
Marks LS, Fradet Y, Deras IL et al (2007) PCA3 molecular urine assay for prostate cancer in men undergoing repeat biopsy. Urology 69(3):532–535. https://doi.org/10.1016/j.urology.2006.12.014
Haese A, de la Taille A, van Poppel H et al (2008) Clinical utility of the PCA3 urine assay in European men scheduled for repeat biopsy. Eur Urol 54(5):1081–1088. https://doi.org/10.1016/j.eururo.2008.06.071
Auprich M, Haese A, Walz J et al (2010) External validation of urinary PCA3-based nomograms to individually predict prostate biopsy outcome. Eur Urol 58(5):727–732. https://doi.org/10.1016/j.eururo.2010.06.038
Crawford ED, Rove KO, Trabulsi EJ et al (2012) Diagnostic performance of PCA3 to detect prostate cancer in men with increased prostate specific antigen: a prospective study of 1,962 cases. J Urol 188(5):1726–1731. https://doi.org/10.1016/j.juro.2012.07.023
Wei JT, Feng Z, Partin AW et al (2014) Can urinary PCA3 supplement PSA in the early detection of prostate cancer? J Clin Oncol 32(36):4066–4072. https://doi.org/10.1200/JCO.2013.52.8505
Gittelman MC, Hertzman B, Bailen J et al (2013) PCA3 molecular urine test as a predictor of repeat prostate biopsy outcome in men with previous negative biopsies: a prospective multicenter clinical study. J Urol 190(1):64–69. https://doi.org/10.1016/j.juro.2013.02.018
Scattoni V, Lazzeri M, Lughezzani G et al (2013) Head-to-head comparison of prostate health index and urinary PCA3 for predicting cancer at initial or repeat biopsy. J Urol 190(2):496–501. https://doi.org/10.1016/j.juro.2013.02.3184
Perdona S, Bruzzese D, Ferro M et al (2013) Prostate health index (phi) and prostate cancer antigen 3 (PCA3) significantly improve diagnostic accuracy in patients undergoing prostate biopsy. Prostate 73(3):227–235. https://doi.org/10.1002/pros.22561
Seisen T, Roupret M, Brault D et al (2015) Accuracy of the prostate health index versus the urinary prostate cancer antigen 3 score to predict overall and significant prostate cancer at initial biopsy. Prostate 75(1):103–111. https://doi.org/10.1002/pros.22898
Tomlins SA, Rhodes DR, Perner S et al (2005) Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310(5748):644–648. https://doi.org/10.1126/science.1117679
Tomlins SA, Bjartell A, Chinnaiyan AM et al (2009) ETS gene fusions in prostate cancer: from discovery to daily clinical practice. Eur Urol 56(2):275–286. https://doi.org/10.1016/j.eururo.2009.04.036
Tomlins SA, Day JR, Lonigro RJ et al (2016) Urine TMPRSS2:ERG Plus PCA3 for Individualized prostate cancer risk assessment. Eur Urol 70(1):45–53. https://doi.org/10.1016/j.eururo.2015.04.039
Leyten GHJM, Hessels D, Jannink SA et al (2014) Prospective multicentre evaluation of PCA3 and TMPRSS2-ERG gene fusions as diagnostic and prognostic urinary biomarkers for prostate cancer. Eur Urol 65(3):534–542. https://doi.org/10.1016/j.eururo.2012.11.014
Stephan C, Jung K, Semjonow A et al (2013) Comparative assessment of urinary prostate cancer antigen 3 and TMPRSS2:ERG gene fusion with the serum [-2]proprostate-specific antigen-based prostate health index for detection of prostate cancer. Clin Chem 59(1):280–288. https://doi.org/10.1373/clinchem.2012.195560
Lin DW, Newcomb LF, Brown EC et al (2013) Urinary TMPRSS2:ERG and PCA3 in an active surveillance cohort: results from a baseline analysis in the canary prostate active surveillance study. Clin Cancer Res 19(9):2442–2450. https://doi.org/10.1158/1078-0432.CCR-12-3283
Donovan MJ, Noerholm M, Bentink S et al (2015) A molecular signature of PCA3 and ERG exosomal RNA from non-DRE urine is predictive of initial prostate biopsy result. Prostate Cancer Prostatic Dis 18(4):370–375. https://doi.org/10.1038/pcan.2015.40
McKiernan J, Donovan MJ, O’Neill V et al (2016) A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol 2(7):882–889. https://doi.org/10.1001/jamaoncol.2016.0097
Leyten GHJM, Hessels D, Smit FP et al (2015) Identification of a candidate gene panel for the early diagnosis of prostate cancer. Clin Cancer Res 21(13):3061–3070. https://doi.org/10.1158/1078-0432.CCR-14-3334
Van Neste L, Hendriks RJ, Dijkstra S et al (2016) Detection of high-grade prostate cancer using a urinary molecular biomarker-based risk score. Eur Urol 70(5):740–748. https://doi.org/10.1016/j.eururo.2016.04.012
Stewart GD, Van Neste L, Delvenne P et al (2013) Clinical utility of an epigenetic assay to detect occult prostate cancer in histopathologically negative biopsies: results of the MATLOC study. J Urol 189(3):1110–1116. https://doi.org/10.1016/j.juro.2012.08.219
Chai H, Brown RE (2009) Field effect in cancer-an update. Ann Clin Lab Sci 39(4):331–337
Partin AW, Van Neste L, Klein EA et al (2014) Clinical validation of an epigenetic assay to predict negative histopathological results in repeat prostate biopsies. J Urol 192(4):1081–1087. https://doi.org/10.1016/j.juro.2014.04.013
Van Neste L, Partin AW, Stewart GD, Epstein JI, Harrison DJ, Van Criekinge W (2016) Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies. Prostate 76(12):1078–1087. https://doi.org/10.1002/pros.23191
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. Eur Urol 71(2):183–192. https://doi.org/10.1016/j.eururo.2016.07.008
Loeb S, Ross AE (2017) Genomic testing for localized prostate cancer: where do we go from here? Curr Opin Urol 27(5):495–499. https://doi.org/10.1097/MOU.0000000000000419
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. Eur Urol 66(3):550–560. https://doi.org/10.1016/j.eururo.2014.05.004
Knezevic D, Goddard AD, Natraj N et al (2013) Analytical validation of the oncotype DX prostate cancer assay—a clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genom 14:690. https://doi.org/10.1186/1471-2164-14-690
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
Van Den Eeden SK, Lu R, Zhang N et al (2018) 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 73(1):129–138. https://doi.org/10.1016/j.eururo.2017.09.013
Shipitsin M, Small C, Giladi E 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 12:40. https://doi.org/10.1186/1477-5956-12-40
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(6):1201–1212. https://doi.org/10.1038/bjc.2014.396
Blume-Jensen P, Berman DM, Rimm DL et al (2015) 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
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(3):245–255. https://doi.org/10.1016/S1470-2045(10)70295-3
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(6):1095–1099. https://doi.org/10.1038/bjc.2012.39
Cuzick J, Stone S, Fisher G et al (2015) Validation of an RNA cell cycle progression score for predicting death from prostate cancer in a conservatively managed needle biopsy cohort. Br J Cancer 113(3):382–389. https://doi.org/10.1038/bjc.2015.223
Bishoff JT, Freedland SJ, Gerber L et al (2014) Prognostic utility of the cell cycle progression score generated from biopsy in men treated with prostatectomy. J Urol 192(2):409–414. https://doi.org/10.1016/j.juro.2014.02.003
Tosoian JJ, Chappidi MR, Bishoff JT et al (2017) Prognostic utility of biopsy-derived cell cycle progression score in patients with national comprehensive cancer network low-risk prostate cancer undergoing radical prostatectomy: implications for treatment guidance. BJU Int. https://doi.org/10.1111/bju.13911
Freedland SJ, Gerber L, Reid J 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(5):848–853. https://doi.org/10.1016/j.ijrobp.2013.04.043
Roach M 3rd, Hanks G, Thames HJ et al (2006) Defining biochemical failure following radiotherapy with or without hormonal therapy in men with clinically localized prostate cancer: recommendations of the RTOG-ASTRO Phoenix Consensus conference. Int J Radiat Oncol Biol Phys 65(4):965–974. https://doi.org/10.1016/j.ijrobp.2006.04.029
Oderda M, Cozzi G, Daniele L et al (2017) Cell-cycle progression-score might improve the current risk assessment in newly diagnosed prostate cancer patients. Urology 102:73–78. https://doi.org/10.1016/j.urology.2016.11.038
Crawford ED, Scholz MC, Kar AJ et al (2014) Cell cycle progression score and treatment decisions in prostate cancer: results from an ongoing registry. Curr Med Res Opin 30(6):1025–1031. https://doi.org/10.1185/03007995.2014.899208
Shore ND, Kella N, Moran B et al (2016) Impact of the cell cycle progression test on physician and patient treatment selection for localized prostate cancer. J Urol 195(3):612–618. https://doi.org/10.1016/j.juro.2015.09.072
Shore N, Concepcion R, Saltzstein D et al (2014) Clinical utility of a biopsy-based cell cycle gene expression assay in localized prostate cancer. Curr Med Res Opin 30(4):547–553. https://doi.org/10.1185/03007995.2013.873398
Erho N, Crisan A, Vergara IA et al (2013) Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS ONE 8(6):e66855. https://doi.org/10.1371/journal.pone.0066855
Zhao SG, Chang SL, Spratt DE et al (2016) Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis. Lancet Oncol 17(11):1612–1620. https://doi.org/10.1016/S1470-2045(16)30491-0
Zhao SG, Chang SL, Erho N et al (2017) Associations of luminal and basal subtyping of prostate cancer with prognosis and response to androgen deprivation therapy. JAMA Oncol. https://doi.org/10.1001/jamaoncol.2017.0751
Ross AE, Johnson MH, Yousefi K et al (2016) Tissue-based genomics augments post-prostatectomy risk stratification in a natural history cohort of intermediate- and high-risk men. Eur Urol 69(1):157–165. https://doi.org/10.1016/j.eururo.2015.05.042
Cooperberg MR, Davicioni E, Crisan A, Jenkins RB, Ghadessi M, Karnes RJ (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(2):326–333. https://doi.org/10.1016/j.eururo.2014.05.039
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
Knudsen BS, Kim HL, Erho N et al (2016) Application of a clinical whole-transcriptome assay for staging and prognosis of prostate cancer diagnosed in needle core biopsy specimens. J Mol Diagn 18(3):395–406. https://doi.org/10.1016/j.jmoldx.2015.12.006
Spratt DE, Zhang J, Santiago-Jimenez M, et al (2017) Development and validation of a novel integrated clinical-genomic risk group classification for localized prostate cancer. J Clin Oncol November JCO2017742940. https://doi.org/10.1200/jco.2017.74.2940
Nguyen PL, Martin NE, Choeurng V et al (2017) 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 20(2):186–192. https://doi.org/10.1038/pcan.2016.58
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(11):1428–1434. https://doi.org/10.1200/JCO.2012.46.4396
Ross AE, Feng FY, Ghadessi M et al (2014) A genomic classifier predicting metastatic disease progression in men with biochemical recurrence after prostatectomy. Prostate Cancer Prostatic Dis 17(1):64–69. https://doi.org/10.1038/pcan.2013.49
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. Eur Urol 67(4):778–786. https://doi.org/10.1016/j.eururo.2014.10.036
Karnes RJ, Choeurng V, Ross AE et al (2017) Validation of a genomic risk classifier to predict prostate cancer-specific mortality in men with adverse pathologic features. Eur Urol. https://doi.org/10.1016/j.eururo.2017.03.036
Den RB, Yousefi K, Trabulsi EJ et al (2015) Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy. J Clin Oncol 33(8):944–951. https://doi.org/10.1200/JCO.2014.59.0026
Ross AE, Den RB, Yousefi K et al (2016) Efficacy of post-operative radiation in a prostatectomy cohort adjusted for clinical and genomic risk. Prostate Cancer Prostatic Dis 19(3):277–282. https://doi.org/10.1038/pcan.2016.15
Gore JL, du Plessis M, Santiago-Jimenez M 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(15):2850–2859. https://doi.org/10.1002/cncr.30665
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Gadzinski, A.J., Cooperberg, M.R. (2018). Prostate Cancer Markers. In: Daneshmand, S., Chan, K. (eds) Genitourinary Cancers . Cancer Treatment and Research, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-319-93339-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-93339-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93338-2
Online ISBN: 978-3-319-93339-9
eBook Packages: MedicineMedicine (R0)