Prognostic models for predicting overall survival in metastatic castration-resistant prostate cancer: a systematic review

  • M. Pinart
  • F. Kunath
  • V. Lieb
  • I. Tsaur
  • B. Wullich
  • Stefanie Schmidt
  • German Prostate Cancer Consortium (DPKK)
Topic Paper



Prognostic models are developed to estimate the probability of the occurrence of future outcomes incorporating multiple variables. We aimed to identify and summarize existing multivariable prognostic models developed for predicting overall survival in patients with metastatic castration-resistant prostate cancer (mCRPC).


The protocol was prospectively registered (CRD42017064448). We systematically searched Medline and reference lists up to May 2018 and included experimental and observational studies, which developed and/or internally validated prognostic models for mCRPC patients and were further externally validated or updated. The outcome of interest was overall survival. Two authors independently performed literature screening and quality assessment.


We included 12 studies that developed models including 8750 patients aged 42–95 years. Models included 4–11 predictor variables, mostly hemoglobin, baseline PSA, alkaline phosphatase, performance status, and lactate dehydrogenase. Very few incorporated Gleason score. Two models included predictors related to docetaxel and mitoxantrone treatments. Model performance after internal validation showed similar discrimination power ranging from 0.62 to 0.73. Overall survival models were mainly constructed as nomograms or risk groups/score. Two models obtained an overall judgment of low risk of bias.


Most models were not suitable for clinical use due to methodological shortcomings and lack of external validation. Further external validation and/or model updating is required to increase prognostic accuracy and clinical applicability prior to their incorporation in clinical practice as a useful tool in patient management.


Metastasis Castration-resistant prostate cancer Survival Prognostic models Prognosis 



Androgen deprivation therapy


Androgen receptor signaling inhibitors


Concordance index


Checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies


C-reactive protein


Castration-resistant prostate cancer


Dialogue for reverse engineering assessments and methods


Eastern Cooperative Oncology Group


Gonadotrophin-releasing hormone


Lactate dehydrogenase


Metastatic castration-resistant prostate cancer


Prostate cancer


Prostate-specific antigen


Preferred reporting items for systematic reviews and meta-analyses statement


Prediction model study risk of bias assessment tool


Randomized clinical trials


Reporting recommendations for tumor marker prognostic studies


Time-dependent area under the curve



We would like to thank Dr. Robert Wolff for his comments and suggestions to the manuscript. B. Wullich, University Hospital Erlangen, Erlangen, Germany. C. Becker, Deutsche Gesellschaft für Urologie, Düsseldorf, Germany. G. Kristiansen, University Hospital Bonn, Bonn, Germany. G. Seitz, Hospital Bamberg, Bamberg, Germany. J. Linxweiler, University Hospital Saarland, Homburg, Germany. S. Füssel, University Hospital Dresden, Dresden, Germany. S. Wach, University Hospital Erlangen, Erlangen, Germany.

Author contributions

M. Pinart: protocol development, data collection and management, data analysis, manuscript writing and editing. F. Kunath: protocol development, manuscript writing and editing. V. Lieb: manuscript writing and editing. I. Tsaur: manuscript writing and editing. B. Wullich: manuscript writing and editing. S. Schmidt: project development, protocol development, data collection and management, data analysis, manuscript writing and editing.


This research received no specific funding.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Research involving human and/or animal participants

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

For this type of study, formal consent is not required.

Supplementary material

345_2018_2574_MOESM1_ESM.pdf (144 kb)
Supplementary material 1 (PDF 143 kb)
345_2018_2574_MOESM2_ESM.pdf (88 kb)
Supplementary material 2 (PDF 87 kb)
345_2018_2574_MOESM3_ESM.pdf (375 kb)
Supplementary material 3 (PDF 374 kb)
345_2018_2574_MOESM4_ESM.pdf (319 kb)
Supplementary material 4 (PDF 318 kb)
345_2018_2574_MOESM5_ESM.pdf (396 kb)
Supplementary material 5 (PDF 395 kb)


  1. 1.
    Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA 67(1):7–30. CrossRefPubMedGoogle Scholar
  2. 2.
    Kirby M, Hirst C, Crawford ED (2011) Characterising the castration-resistant prostate cancer population: a systematic review. Int J Clin Pract 65(11):1180–1192. CrossRefPubMedGoogle Scholar
  3. 3.
    Vickers AJ (2011) Prediction models in cancer care. CA 61(5):315–326. CrossRefPubMedGoogle Scholar
  4. 4.
    Raymond E, O’Callaghan ME, Campbell J, Vincent AD, Beckmann K, Roder D, Evans S, McNeil J, Millar J, Zalcberg J, Borg M, Moretti K (2017) An appraisal of analytical tools used in predicting clinical outcomes following radiation therapy treatment of men with prostate cancer: a systematic review. Radiat Oncol 12(1):56. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Vickers AJ (2011) Prediction models: revolutionary in principle, but do they do more good than harm? J Clin Oncol 29(22):2951–2952. CrossRefPubMedGoogle Scholar
  6. 6.
    Mallett S, Royston P, Waters R, Dutton S, Altman DG (2010) Reporting performance of prognostic models in cancer: a review. BMC Med 8:21. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Lee YH, Bang H, Kim DJ (2016) How to establish clinical prediction models. Endocrinol Metab (Seoul, Korea) 31(1):38–44. CrossRefGoogle Scholar
  8. 8.
    Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med 6(7):e1000100. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM (2005) REporting recommendations for tumor MARKer prognostic studies (REMARK). Br J Cancer 93(4):387–391. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Pace NLCJ, Eberhart LHJ, Kranke P, Trivella M, Lee A, Bennett MH (2014) Prediction models for the risk of postoperative nausea and vomiting (protocol). Cochrane Database Syst Rev 9:11318. CrossRefGoogle Scholar
  11. 11.
    Geersing GJ, Bouwmeester W, Zuithoff P, Spijker R, Leeflang M, Moons KG (2012) Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews. PLoS One 7(2):e32844. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Steyerberg EW, Harrell FE Jr (2016) Prediction models need appropriate internal, internal–external, and external validation. J Clin Epidemiol 69:245–247. CrossRefPubMedGoogle Scholar
  13. 13.
    Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, Collins GS (2016) External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ 353:i3140. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Mallett S, Royston P, Dutton S, Waters R, Altman DG (2010) Reporting methods in studies developing prognostic models in cancer: a review. BMC Med 8:20. CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, Reitsma JB, Collins GS (2014) Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 11(10):e1001744. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Wolff R, Moons K, Riley R, Whiting P, Westwood M, Collins G, Reitsma J, Kleijnen J, Mallett S (2017) PROBAST—a risk-of-bias tool for prediction-modelling studies. Abstracts of the global evidence summit, Cape Town. Cochrane Database Syst Rev 9(Suppl 1).
  17. 17.
    Armstrong AJ, Garrett-Mayer ES, Yang YC, de Wit R, Tannock IF, Eisenberger M (2007) A contemporary prognostic nomogram for men with hormone-refractory metastatic prostate cancer: a TAX327 study analysis. Clin Cancer Res 13(21):6396–6403. CrossRefPubMedGoogle Scholar
  18. 18.
    Armstrong AJ, Tannock IF, de Wit R, George DJ, Eisenberger M, Halabi S (2010) The development of risk groups in men with metastatic castration-resistant prostate cancer based on risk factors for PSA decline and survival. Eur J Cancer (Oxford, England, 1990) 46(3):517–525. CrossRefGoogle Scholar
  19. 19.
    Chi KN, Kheoh T, Ryan CJ, Molina A, Bellmunt J, Vogelzang NJ, Rathkopf DE, Fizazi K, Kantoff PW, Li J, Azad AA, Eigl BJ, Heng DY, Joshua AM, de Bono JS, Scher HI (2016) A prognostic index model for predicting overall survival in patients with metastatic castration-resistant prostate cancer treated with abiraterone acetate after docetaxel. Ann Oncol 27(3):454–460. CrossRefPubMedGoogle Scholar
  20. 20.
    Halabi S, Small EJ, Kantoff PW, Kattan MW, Kaplan EB, Dawson NA, Levine EG, Blumenstein BA, Vogelzang NJ (2003) Prognostic model for predicting survival in men with hormone-refractory metastatic prostate cancer. J Clin Oncol 21(7):1232–1237. CrossRefPubMedGoogle Scholar
  21. 21.
    Halabi S, Lin CY, Kelly WK, Fizazi KS, Moul JW, Kaplan EB, Morris MJ, Small EJ (2014) Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castration-resistant prostate cancer. J Clin Oncol 32(7):671–677. CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Smaletz O, Scher HI, Small EJ, Verbel DA, McMillan A, Regan K, Kelly WK, Kattan MW (2002) Nomogram for overall survival of patients with progressive metastatic prostate cancer after castration. J Clin Oncol 20(19):3972–3982. CrossRefPubMedGoogle Scholar
  23. 23.
    Deng D, Du Y, Ji Z, Rao K, Wu Z, Zhu Y, Coley RY (2016) Predicting survival time for metastatic castration resistant prostate cancer: an iterative imputation approach. F1000Research 5:2672. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Halabi S, Lin CY, Small EJ, Armstrong AJ, Kaplan EB, Petrylak D, Sternberg CN, Shen L, Oudard S, de Bono J, Sartor O (2013) Prognostic model predicting metastatic castration-resistant prostate cancer survival in men treated with second-line chemotherapy. J Natl Cancer Inst 105(22):1729–1737. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Heller G, Fizazi K, McCormack R, Molina A, MacLean D, Webb IJ, Saad F, de Bono JS, Scher HI (2016) The added value of circulating tumor cell enumeration to standard markers in assessing prognosis in a metastatic castration-resistant prostate cancer population. Clin Cancer Res. CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Petrylak DP, Scher HI, Li Z, Myers CE, Geller NL (1992) Prognostic factors for survival of patients with bidimensionally measurable metastatic hormone-refractory prostatic cancer treated with single-agent chemotherapy. Cancer 70(12):2870–2878CrossRefGoogle Scholar
  27. 27.
    van Hasselt JG, Gupta A, Hussein Z, Beijnen JH, Schellens JH, Huitema AD (2015) Disease progression/clinical outcome model for castration-resistant prostate cancer in patients treated with eribulin. CPT Pharmacomet Syst Pharmacol 4(7):386–395. CrossRefGoogle Scholar
  28. 28.
    Templeton AJ, Pezaro C, Omlin A, McNamara MG, Leibowitz-Amit R, Vera-Badillo FE, Attard G, de Bono JS, Tannock IF, Amir E (2014) Simple prognostic score for metastatic castration-resistant prostate cancer with incorporation of neutrophil-to-lymphocyte ratio. Cancer 120(21):3346–3352. CrossRefPubMedGoogle Scholar
  29. 29.
    Bian XJ, Zhu Y, Shen YJ, Wang JY, Ma CG, Zhang HL, Dai B, Zhang SL, Yao XD, Ye DW (2013) The effectiveness of the TAX 327 nomogram in predicting overall survival in Chinese patients with metastatic castration-resistant prostate cancer. Asian J Androl 15(5):679–684. CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Pond GR, Armstrong AJ, Wood BA, Leopold L, Galsky MD, Sonpavde G (2012) Ability of C-reactive protein to complement multiple prognostic classifiers in men with metastatic castration resistant prostate cancer receiving docetaxel-based chemotherapy. BJU Int 110(11 Pt B):E461–E468. CrossRefPubMedGoogle Scholar
  31. 31.
    Harrell FE (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New York. CrossRefGoogle Scholar
  32. 32.
    Khalaf DJ, Aviles CM, Azad AA, Sunderland K, Todenhofer T, Eigl BJ, Finch D, Le L, Atwell A, Keith B, Kollmannsberger C, Chi KN (2018) A prognostic model for stratifying clinical outcomes in chemotherapy-naive metastatic castration-resistant prostate cancer patients treated with abiraterone acetate. Can Urol Assoc J 12(2):E47–E52. CrossRefPubMedGoogle Scholar
  33. 33.
    Ravi P, Mateo J, Lorente D, Zafeiriou Z, Altavilla A, Ferraldeschi R, Sideris S, Grist E, Smith A, Wong S, Bianchini D, Attard G, de Bono JS (2014) External validation of a prognostic model predicting overall survival in metastatic castrate-resistant prostate cancer patients treated with abiraterone. Eur Urol 66(1):8–11. CrossRefPubMedGoogle Scholar
  34. 34.
    Yang YJ, Lin GW, Li GX, Dai B, Ye DW, Wu JL, Xie HY, Zhu Y (2018) External validation and newly development of a nomogram to predict overall survival of abiraterone-treated, castration-resistant patients with metastatic prostate cancer. Asian J Androl 20(2):184–188. CrossRefPubMedGoogle Scholar
  35. 35.
    Pitcher B, Khoja L, Hamilton RJ, Abdallah K, Pintilie M, Joshua AM (2017) Assessment of a prognostic model, PSA metrics and toxicities in metastatic castrate resistant prostate cancer using data from Project Data Sphere (PDS). PLoS One 12(2):e0170544. CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Singhal U, Wang Y, Henderson J, Niknafs YS, Qiao Y, Gursky A, Zaslavsky A, Chung JS, Smith DC, Karnes RJ, Chang SL, Feng FY, Palapattu GS, Taichman RS, Chinnaiyan AM, Tomlins SA, Morgan TM (2018) Multigene profiling of CTCs in mCRPC identifies a clinically relevant prognostic signature. Mol Cancer Res MCR 16(4):643–654. CrossRefPubMedGoogle Scholar
  37. 37.
    Guinney J, Wang T, Laajala TD, Winner KK, Bare JC, Neto EC, Khan SA, Peddinti G, Airola A, Pahikkala T, Mirtti T, Yu T, Bot BM, Shen L, Abdallah K, Norman T, Friend S, Stolovitzky G, Soule H, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Xie Y, Aittokallio T, Zhou FL, Costello JC (2017) Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol 18(1):132–142. CrossRefPubMedGoogle Scholar
  38. 38.
    Omlin A, Pezaro C, Mukherji D, Mulick Cassidy A, Sandhu S, Bianchini D, Olmos D, Ferraldeschi R, Maier G, Thompson E, Parker C, Attard G, de Bono J (2013) Improved survival in a cohort of trial participants with metastatic castration-resistant prostate cancer demonstrates the need for updated prognostic nomograms. Eur Urol 64(2):300–306. CrossRefPubMedGoogle Scholar
  39. 39.
    Fizazi K, Massard C, Smith M, Rader M, Brown J, Milecki P, Shore N, Oudard S, Karsh L, Carducci M, Damiao R, Wang H, Ying W, Goessl C (2015) Bone-related parameters are the main prognostic factors for overall survival in men with bone metastases from castration-resistant prostate cancer. Eur Urol 68(1):42–50. CrossRefPubMedGoogle Scholar
  40. 40.
    Sullivan S, Northstone K, Gadd C, Walker J, Margelyte R, Richards A, Whiting P (2017) Models to predict relapse in psychosis: a systematic review. PLoS One 12(9):e0183998. CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Wingbermuhle RW, van Trijffel E, Nelissen PM, Koes B, Verhagen AP (2018) Few promising multivariable prognostic models exist for recovery of people with non-specific neck pain in musculoskeletal primary care: a systematic review. J Physiother 64(1):16–23. CrossRefPubMedGoogle Scholar
  42. 42.
    Armstrong AJ, Garrett-Mayer E, de Wit R, Tannock I, Eisenberger M (2010) Prediction of survival following first-line chemotherapy in men with castration-resistant metastatic prostate cancer. Clin Cancer Res 16(1):203–211. CrossRefPubMedGoogle Scholar
  43. 43.
    Moreira DM, Howard LE, Sourbeer KN, Amarasekara HS, Chow LC, Cockrell DC, Pratson CL, Hanyok BT, Aronson WJ, Kane CJ, Terris MK, Amling CL, Cooperberg MR, Freedland SJ (2017) Predicting time from metastasis to overall survival in castration-resistant prostate cancer: results from SEARCH. Clin Genitourin Cancer 15(1):60–66.e62. CrossRefPubMedGoogle Scholar
  44. 44.
    Belderbos BPS, de Wit R, Hoop EO, Nieuweboer A, Hamberg P, van Alphen RJ, Bergman A, van der Meer N, Bins S, Mathijssen RHJ, van Soest RJ (2017) Prognostic factors in men with metastatic castration-resistant prostate cancer treated with cabazitaxel. Oncotarget 8(63):106468–106474. CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Miyake H, Sakai I, Terakawa T, Harada K, Fujisawa M (2013) Oncological outcome of docetaxel-based chemotherapy for Japanese men with metastatic castration-resistant prostate cancer. Urol Oncol 31(6):733–738. CrossRefPubMedGoogle Scholar
  46. 46.
    Olmos D, Brewer D, Clark J, Danila DC, Parker C, Attard G, Fleisher M, Reid AH, Castro E, Sandhu SK, Barwell L, Oommen NB, Carreira S, Drake CG, Jones R, Cooper CS, Scher HI, de Bono JS (2012) Prognostic value of blood mRNA expression signatures in castration-resistant prostate cancer: a prospective, two-stage study. Lancet Oncol 13(11):1114–1124. CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Shiota M, Yokomizo A, Adachi T, Koga H, Yamaguchi A, Imada K, Takeuchi A, Kiyoshima K, Inokuchi J, Tatsugami K, Naito S (2014) The oncological outcomes and risk stratification in docetaxel chemotherapy for castration-resistant prostate cancer. Jpn J Clin Oncol 44(9):860–867. CrossRefPubMedGoogle Scholar
  48. 48.
    Scher HI, Graf RP, Schreiber NA, McLaughlin B, Lu D, Louw J, Danila DC, Dugan L, Johnson A, Heller G, Fleisher M, Dittamore R (2017) Nuclear-specific AR-V7 protein localization is necessary to guide treatment selection in metastatic castration-resistant prostate cancer. Eur Urol 71(6):874–882. CrossRefPubMedGoogle Scholar
  49. 49.
    Kalin M, Cima I, Schiess R, Fankhauser N, Powles T, Wild P, Templeton A, Cerny T, Aebersold R, Krek W, Gillessen S (2011) Novel prognostic markers in the serum of patients with castration-resistant prostate cancer derived from quantitative analysis of the pten conditional knockout mouse proteome. Eur Urol 60(6):1235–1243. CrossRefPubMedGoogle Scholar
  50. 50.
    Ross RW, Galsky MD, Scher HI, Magidson J, Wassmann K, Lee GS, Katz L, Subudhi SK, Anand A, Fleisher M, Kantoff PW, Oh WK (2012) A whole-blood RNA transcript-based prognostic model in men with castration-resistant prostate cancer: a prospective study. Lancet Oncol 13(11):1105–1113. CrossRefPubMedGoogle Scholar
  51. 51.
    Lin HM, Mahon KL, Spielman C, Gurney H, Mallesara G, Stockler MR, Bastick P, Briscoe K, Marx G, Swarbrick A, Horvath LG (2017) Phase 2 study of circulating microRNA biomarkers in castration-resistant prostate cancer. Br J Cancer 116(8):1002–1011. CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Heck MM, Thalgott M, Schmid SC, Oh WK, Gong Y, Wang L, Zhu J, Seitz AK, Porst D, Hoppner M, Retz M, Gschwend JE, Nawroth R (2016) A 2-gene panel derived from prostate cancer-enhanced transcripts in whole blood is prognostic for survival and predicts treatment benefit in metastatic castration-resistant prostate cancer. Prostate 76(13):1160–1168. CrossRefPubMedGoogle Scholar
  53. 53.
    Collette L, van Andel G, Bottomley A, Oosterhof GO, Albrecht W, de Reijke TM, Fossa SD (2004) Is baseline quality of life useful for predicting survival with hormone-refractory prostate cancer? A pooled analysis of three studies of the European Organisation for Research and Treatment of Cancer Genitourinary Group. J Clin Oncol 22(19):3877–3885. CrossRefPubMedGoogle Scholar
  54. 54.
    Podrazil M, Horvath R, Becht E, Rozkova D, Bilkova P, Sochorova K, Hromadkova H, Kayserova J, Vavrova K, Lastovicka J, Vrabcova P, Kubackova K, Gasova Z, Jarolim L, Babjuk M, Spisek R, Bartunkova J, Fucikova J (2015) Phase I/II clinical trial of dendritic-cell based immunotherapy (DCVAC/PCa) combined with chemotherapy in patients with metastatic, castration-resistant prostate cancer. Oncotarget 6(20):18192–18205CrossRefGoogle Scholar
  55. 55.
    Regan MM, O'Donnell EK, Kelly WK, Halabi S, Berry W, Urakami S, Kikuno N, Oh WK (2010) Efficacy of carboplatin-taxane combinations in the management of castration-resistant prostate cancer: a pooled analysis of seven prospective clinical trials. Ann Oncol 21(2):312–318CrossRefGoogle Scholar
  56. 56.
    Oh WK, Miao R, Vekeman F, Sung J, Cheng WY, Gauthier-Loiselle M, Dhawan R, Duh MS (2017) Patient characteristics and overall survival in patients with post-docetaxel metastatic castration-resistant prostate cancer in the community setting. Med Oncol 34(9):160CrossRefGoogle Scholar
  57. 57.
    Oh WK, Miao R, Vekeman F, Sung J, Cheng WY, Gauthier-Loiselle M, Dhawan R, Duh MS (2017) Real-world characteristics and outcomes of patients with metastatic castration-resistant prostate cancer receiving chemotherapy versus Androgen receptor-targeted therapy after failure of first-line Androgen receptor-targeted therapy in the community setting. Clin Genitourin Cancer. CrossRefPubMedGoogle Scholar
  58. 58.
    Meier R, Graw S, Usset J, Raghavan R, Dai J, Chalise P, Ellis S, Fridley B, Koestler D (2016) An ensemble-based Cox proportional hazards regression framework for predicting survival in metastatic castration-resistant prostate cancer (mCRPC) patients. F1000Res 5:2677CrossRefGoogle Scholar
  59. 59.
    Agus DB, Sweeney CJ, Morris MJ, Mendelson DS, McNeel DG, Ahmann FR, Wang J, Derynck MK, Ng K, Lyons B, Allison DE, Kattan MW, Scher HI (2007) Efficacy and safety of single-agent pertuzumab (rhuMAb 2C4), a human epidermal growth factor receptor dimerization inhibitor, in castration-resistant prostate cancer after progression from taxane-based therapy. J Clin Oncol 25(6):675–681CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Urology and Pediatric UrologyUniversity Hospital ErlangenErlangenGermany
  2. 2.UroEvidence@Deutsche Gesellschaft für UrologieBerlinGermany
  3. 3.Department of UrologyUniversity Medicine MainzMainzGermany

Personalised recommendations