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Validation of a 10-gene molecular signature for predicting biochemical recurrence and clinical metastasis in localized prostate cancer

  • Hatem Abou-Ouf
  • Mohammed Alshalalfa
  • Mandeep Takhar
  • Nicholas Erho
  • Bryan Donnelly
  • Elai Davicioni
  • R. Jeffrey Karnes
  • Tarek A. BismarEmail author
Original Article – Cancer Research

Abstract

Purpose

To validate a previously characterized 10-gene signature in prostate cancer with implication to distinguish aggressive and indolent disease within low and intermediate patients’ risk groups.

Methods

A case–control study design used to select 545 patients from the Mayo clinic tumor registry who underwent radical prostatectomy. A training set from this cohort (n = 359) was used to build a 10-gene model, based on high-dimensional discriminant analysis (HDDA10) to predict several endpoints of clinical patients’ outcome. An independent set (n = 219) from the same institution was used as validation set.

Results

HDDA10 showed significant performance for predicting metastasis (Mets) (AUC 0.68, p = 6.4E – 6) and biochemical recurrence (BCR) (AUC = 0.65, p = 0.003) in the validation set outperforming Gleason grade grouping (GG) for BCR (AUC 0.57, p = 0.03) and with comparable performance for Mets endpoint (GG AUC 0.66, p = 8.1E – 5). HDDA10 prognostic significance was superior to any clinical–pathological parameter within GG2 + 3 (GS7) patients achieving an AUC of 0.74 (p = 0.0037) for BCR compared to Gleason pattern 4 (AUC 0.64) (p = 0.015) and AUC for Mets of 0.68 versus AUC of 0.65 for Gleason pattern 4 (p = 0.01). HDDA10 remained significant for both BCR and Mets in multivariate analysis, suggesting that it can be used to increase accuracy in stratifying patients eligible for active surveillance.

Conclusion

HDDA10 is of added value to GG and other clinical–pathological parameters in predicting BCR and Mets endpoint, especially in the low to intermediate patients’ risk groups. HDDA10 prognostic value should be further validated prospectively in stratifying patients specifically in low to intermediate GS (GG1-2), such as active surveillance programs.

Keywords

Genetic classifier Grade grouping Gleason score Biomarkers Biochemical recurrence Clinical metastasis Prostate cancer Prognosis 

Notes

Acknowledgements

This work was supported in part by the Prostate Cancer Foundation Young Investigator Award (T.A.B). This work was also supported by Prostate cancer Canada and is proudly funded by the Movember Foundation-Grant #B2013-01. This work was also supported in part by the GAP1 Movember Tissue Biomarker Project.

Author contributions

HAO drafted the paper, MA carried out bioinformatics and computational analysis, TAB is responsible for outline, study design and supervision of work, all authors have approved the submitted and published versions.

Compliance with ethical standards

Conflict of interest

All authors declare no conflict of interest in regards to this manuscript.

Ethical approval

This research was performed in compliance with all ethical standards and approved by the U Calgary ethics board.

Supplementary material

432_2018_2615_MOESM1_ESM.docx (12 kb)
Supplementary material 1 (DOCX 11 KB)

References

  1. Andren O, Fall K, Andersson SO, Rubin MA, Bismar TA, Karlsson M, Johansson JE, Mucci LA (2007) MUC-1 gene is associated with prostate cancer death: a 20-year follow-up of a population-based study in Sweden. Br J Cancer 97(6):730–734. doi:  https://doi.org/10.1038/sj.bjc.6603944 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Attard G, Clark J, Ambroisine L, Fisher G, Kovacs G, Flohr P, Berney D, Foster CS, Fletcher A, Gerald WL, Moller H, Reuter V, De Bono JS, Scardino P, Cuzick J, Cooper CS (2008) Duplication of the fusion of TMPRSS2 to ERG sequences identifies fatal human prostate cancer. Oncogene 27(3):253–263.  https://doi.org/10.1038/sj.onc.1210640 CrossRefPubMedGoogle Scholar
  3. Barros-Silva JD, Ribeiro FR, Rodrigues A, Cruz R, Martins AT, Jeronimo C, Henrique R, Teixeira MR (2011) Relative 8q gain predicts disease-specific survival irrespective of the TMPRSS2-ERG fusion status in diagnostic biopsies of prostate cancer. Genes Chromosom Cancer 50(8):662–671.  https://doi.org/10.1002/gcc.20888 CrossRefPubMedGoogle Scholar
  4. Bergé L, Bouveyron C, Girard S et al (2012) HDclassif: An R package for model-based clustering and discriminant analysis of high-dimensional data. J Stat Softw 46(6):1–29CrossRefGoogle Scholar
  5. Bismar TA, Demichelis F, Riva A, Kim R, Varambally S, He L, Kutok J, Aster JC, Tang J, Kuefer R, Hofer MD, Febbo PG, Chinnaiyan AM, Rubin MA (2006) Defining aggressive prostate cancer using a 12-gene model. Neoplasia 8(1):59–68.  https://doi.org/10.1593/neo.05664 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bismar TA, Dolph M, Teng LH, Liu S, Donnelly B (2012) ERG protein expression reflects hormonal treatment response and is associated with Gleason score and prostate cancer specific mortality. Eur J Cancer 48(4):538–546.  https://doi.org/10.1016/j.ejca.2012.01.001 CrossRefPubMedGoogle Scholar
  7. Bismar TA, Alshalalfa M, Petersen LF, Teng LH, Gerke T, Bakkar A, Al-Mami A, Liu S, Dolph M, Mucci LA, Alhajj R (2013) Interrogation of ERG gene rearrangements in prostate cancer identifies a prognostic 10-gene signature with relevant implication to patients’ clinical outcome. BJU Int.  https://doi.org/10.1111/bju.12262 PubMedGoogle Scholar
  8. Bul M, van den Bergh RCN, Zhu X, Rannikko A, Vasarainen H, Bangma CH, Schröder FH, Roobol MJ (2012) Outcomes of initially expectantly managed patients with low or intermediate risk screen-detected localized prostate cancer. BJU Int 110(11):1672–1677.  https://doi.org/10.1111/j.1464-410X.2012.11434.x CrossRefPubMedGoogle Scholar
  9. Carter HB, Isaacs WB (2004) Improved biomarkers for prostate cancer: a definite need. J Natl Cancer Inst 96(11):813–815CrossRefPubMedGoogle Scholar
  10. Cazares LH, Drake RR, Esquela-Kirscher A, Lance RS, Semmes OJ, Troyer DA (2010) Molecular pathology of prostate cancer. Cancer Biomark Sect A Dis Markers 9(1–6):441–459.  https://doi.org/10.3233/CBM-2011-0181 Google Scholar
  11. Darnel AD, Behmoaram E, Vollmer RT, Corcos J, Bijian K, Sircar K, Su J, Jiao J, Alaoui-Jamali MA, Bismar TA (2009) Fascin regulates prostate cancer cell invasion and is associated with metastasis and biochemical failure in prostate cancer. Clin Cancer Res 15(4):1376–1383.  https://doi.org/10.1158/1078-0432.CCR-08-1789 CrossRefPubMedGoogle Scholar
  12. Demichelis F, Fall K, Perner S, Andren O, Schmidt F, Setlur SR, Hoshida Y, Mosquera JM, Pawitan Y, Lee C, Adami HO, Mucci LA, Kantoff PW, Andersson SO, Chinnaiyan AM, Johansson JE, Rubin MA (2007) TMPRSS2:ERG gene fusion associated with lethal prostate cancer in a watchful waiting cohort. Oncogene 26(31):4596–4599.  https://doi.org/10.1038/sj.onc.1210237 CrossRefPubMedGoogle Scholar
  13. El Sheikh SS, Romanska HM, Abel P, Domin J, Lalani el N (2008) Predictive value of PTEN and AR coexpression of sustained responsiveness to hormonal therapy in prostate cancer—a pilot study. Neoplasia 10(9):949–953CrossRefPubMedPubMedCentralGoogle Scholar
  14. Epstein JI, Feng Z, Trock BJ, Pierorazio PM (2012) Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades. Eur Urol 61(5):1019–1024.  https://doi.org/10.1016/j.eururo.2012.01.050 CrossRefPubMedPubMedCentralGoogle Scholar
  15. Erho N, Crisan A, Vergara IA, Mitra AP, Ghadessi M, Buerki C, Bergstralh EJ, Kollmeyer T, Fink S, Haddad Z, Zimmermann B, Sierocinski T, Ballman KV, Triche TJ, Black PC, Karnes RJ, Klee G, Davicioni E, Jenkins RB (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 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Fall K, Garmo H, Andren O, Bill-Axelson A, Adolfsson J, Adami HO, Johansson JE, Holmberg L (2007) Prostate-specific antigen levels as a predictor of lethal prostate cancer. J Natl Cancer Inst 99(7):526–532.  https://doi.org/10.1093/jnci/djk110 CrossRefPubMedGoogle Scholar
  17. Glinsky GV, Glinskii AB, Stephenson AJ, Hoffman RM, Gerald WL (2004) Gene expression profiling predicts clinical outcome of prostate cancer. J Clin Invest 113(6):913–923.  https://doi.org/10.1172/JCI20032 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Goldenberg A, Mostafavi S, Quon G, Boutros PC, Morris QD (2011) Unsupervised detection of genes of influence in lung cancer using biological networks. Bioinformatics 27(22):3166–3172.  https://doi.org/10.1093/bioinformatics/btr533 CrossRefPubMedGoogle Scholar
  19. Halvorsen OJ, Haukaas SA, Akslen LA (2003) Combined loss of PTEN and p27 expression is associated with tumor cell proliferation by Ki-67 and increased risk of recurrent disease in localized prostate cancer. Clin Cancer Res 9(4):1474–1479PubMedGoogle Scholar
  20. Iremashvili V, Soloway MS, Pelaez L, Rosenberg DL, Manoharan M (2013) Comparative validation of nomograms predicting clinically insignificant prostate cancer. Urology 81(6):1202–1208CrossRefPubMedGoogle Scholar
  21. Karnes RJ, Bergstralh EJ, Davicioni E, Ghadessi M, Buerki C, Mitra AP, Crisan A, Erho N, Vergara IA, Lam LL, Carlson R, Thompson DJS, Haddad Z, Zimmermann B, Sierocinski T, Triche TJ, Kollmeyer T, Ballman KV, Black PC, Klee GG, Jenkins RB (2013) Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. J Urol 190(6):2047–2053CrossRefPubMedPubMedCentralGoogle Scholar
  22. Kibel AS (2007) TMPRSS2:ERG gene fusion associated with lethal prostate cancer in a watchful waiting cohort. Demichelis F, Fall K, Perner S, Andren O, Schmidt F, Setlur SR, Hoshida Y, Mosquera JM, Pawitan Y, Lee C, Adami HO, Mucci LA, Kantoff PW, Andersson SO, Chinnaiyan AM, Johansson JE, Rubin MA, Department of Pathology, Brigham and Women’s Hospital, Boston. Urol Oncol 25 (5):448–449Google Scholar
  23. Li Y, Su J, Dingzhang X, Zhang J, Yoshimoto M, Liu S, Bijian K, Gupta A, Squire JA, Alaoui Jamali MA, Bismar TA (2011) PTEN deletion and heme oxygenase-1 overexpression cooperate in prostate cancer progression and are associated with adverse clinical outcome. J Pathol.  https://doi.org/10.1002/path.2855 Google Scholar
  24. Mucci LA, Pawitan Y, Demichelis F, Fall K, Stark JR, Adami HO, Andersson SO, Andren O, Eisenstein AS, Holmberg L, Huang W, Kantoff PW, Perner S, Stampfer MJ, Johansson JE, Rubin MA (2008) Nine-gene molecular signature is not associated with prostate cancer death in a watchful waiting cohort. Cancer Epidemiol Biomark Prev 17(1):249–251.  https://doi.org/10.1158/1055-9965.EPI-07-0722 CrossRefGoogle Scholar
  25. Odom BD, Mir MC, Hughes S, Senechal C, Santy A, Eyraud R, Stephenson AJ, Ylitalo K, Miocinovic R (2014) Active surveillance for low-risk prostate cancer in African American men: a multi-institutional experience. Urology 83(2):364–368.  https://doi.org/10.1016/j.urology.2013.09.038 CrossRefPubMedGoogle Scholar
  26. Piccolo SR, Sun Y, Campbell JD, Lenburg ME, Bild AH, Johnson WE (2012) A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics 100(6):337–344.  https://doi.org/10.1016/j.ygeno.2012.08.003 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Pierorazio PM, Walsh PC, Partin AW, Epstein JI (2013) Prognostic Gleason grade grouping: data based on the modified Gleason scoring system. BJU Int 111(5):753–760.  https://doi.org/10.1111/j.1464-410X.2012.11611.x CrossRefPubMedPubMedCentralGoogle Scholar
  28. Reid AH, Attard G, Ambroisine L, Fisher G, Kovacs G, Brewer D, Clark J, Flohr P, Edwards S, Berney DM, Foster CS, Fletcher A, Gerald WL, Moller H, Reuter VE, Scardino PT, Cuzick J, de Bono JS, Cooper CS (2010) Molecular characterisation of ERG, ETV1 and PTEN gene loci identifies patients at low and high risk of death from prostate cancer. Br J Cancer 102(4):678–684.  https://doi.org/10.1038/sj.bjc.6605554 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Sboner A, Demichelis F, Calza S, Pawitan Y, Setlur SR, Hoshida Y, Perner S, Adami HO, Fall K, Mucci LA, Kantoff PW, Stampfer M, Andersson SO, Varenhorst E, Johansson JE, Gerstein MB, Golub TR, Rubin MA, Andren O (2010) Molecular sampling of prostate cancer: a dilemma for predicting disease progression. BMC Med Genomics 3:8.  https://doi.org/10.1186/1755-8794-3-8
  30. Sundi D, Kryvenko ON, Carter HB, Ross AE, Epstein JI, Schaeffer EM (2013) Pathological examination of radical prostatectomy specimens in men with very low risk disease at biopsy reveals distinct zonal distribution of cancer in black American men. J UrolGoogle Scholar
  31. Taylor BS, Schultz N, Hieronymus H, Gopalan A, Xiao Y, Carver BS, Arora VK, Kaushik P, Cerami E, Reva B, Antipin Y, Mitsiades N, Landers T, Dolgalev I, Major JE, Wilson M, Socci ND, Lash AE, Heguy A, Eastham JA, Scher HI, Reuter VE, Scardino PT, Sander C, Sawyers CL, Gerald WL (2010) Integrative genomic profiling of human prostate cancer. Cancer Cell 18(1):11–22  https://doi.org/10.1016/j.ccr.2010.05.026 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM (2005) Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310(5748):644–648CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Pathology and Laboratory MedicineUniversity of Calgary, Cumming School of MedicineCalgaryCanada
  2. 2.Calgary Laboratory ServicesCalgaryCanada
  3. 3.Genome DX BiosciencesSan DiegoUSA
  4. 4.Department of UrologyMayo ClinicRochesterUSA
  5. 5.Department of OncologyUniversity of CalgaryCalgaryCanada
  6. 6.Arnie Charbonneau Cancer Institute and Tom Baker Cancer CenterCalgaryCanada
  7. 7.Department of UrologyUniversity of CalgaryCalgaryCanada
  8. 8.Rockyview General Hospital, Department of PathologyCalgary Laboratory ServicesCalgaryCanada

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