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Extracapsular extension on MRI indicates a more aggressive cell cycle progression genotype of prostate cancer

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Abstract

Purpose

To explore associations between magnetic resonance imaging (MRI) features of prostate cancer and expression levels of cell cycle genes, as assessed by the Prolaris® test.

Materials and methods

Retrospective analysis of 118 PCa patients with genetic testing of biopsy specimen and prostate MRI from 08/2013 to 11/2015. Associations between the cell cycle risk (CCR) score and MRI features [i.e., PI-RADSv2 score, extracapsular extension (ECE), quantitative metrics] were analyzed with Fisher’s exact test, nonparametric tests, and Spearman’s correlation coefficient. In 41 patients (34.7%), test results were compared to unfavorable features on prostatectomy specimen (i.e., Gleason group ≥ 3, ECE, lymph node metastases).

Results

Fifty-four (45.8%), 60 (50.8%), and 4 (3.4%) patients had low-, intermediate-, and high-risk cancers according to American Urological Association scoring system. Patients with ECE on MRI had significantly higher mean CCR scores (reader 1: 3.9 vs. 3.2, p = 0.015; reader 2: 3.6 vs. 3.2, p = 0.045). PI-RADSv2 scores and quantitative MRI features were not associated with CCR scores. In the prostatectomy subset, ECE on MRI (p = < 0.001–0.001) and CCR scores (p = 0.049) were significantly associated with unfavorable histopathologic features.

Conclusion

The phenotypic trait of ECE on MRI indicates a more aggressive genotype of prostate cancer.

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References

  1. Kane CJ, Eggener SE, Shindel AW, Andriole GL (2017) Variability in Outcomes for Patients with Intermediate-risk Prostate Cancer (Gleason Score 7, International Society of Urological Pathology Gleason Group 2-3) and Implications for Risk Stratification: A Systematic Review. Eur Urol Focus. https://doi.org/10.1016/j.euf.2016.10.010

  2. Cooperberg MR, Pasta DJ, Elkin EP 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 173:1938-1942

    Article  PubMed  PubMed Central  Google Scholar 

  3. Touijer K, Scardino PT (2009) Nomograms for staging, prognosis, and predicting treatment outcomes. Cancer 115:3107-3111

    Article  PubMed  Google Scholar 

  4. Lamy PJ, Allory Y, Gauchez AS et al (2017) Prognostic Biomarkers Used for Localised Prostate Cancer Management: A Systematic Review. Eur Urol Focus. https://doi.org/10.1016/j.euf.2017.02.017

  5. Moschini M, Spahn M, Mattei A, Cheville J, Karnes RJ (2016) Incorporation of tissue-based genomic biomarkers into localized prostate cancer clinics. BMC Med 14:67

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Taghipour M, Ziaei A, Alessandrino F et al (2018) Investigating the role of DCE-MRI, over T2 and DWI, in accurate PI-RADS v2 assessment of clinically significant peripheral zone prostate lesions as defined at radical prostatectomy. Abdom Radiol (NY). https://doi.org/10.1007/s00261-018-1807-6

  7. Mathur S, O’Malley ME, Ghai S et al (2018) Correlation of 3T multiparametric prostate MRI using prostate imaging reporting and data system (PIRADS) version 2 with biopsy as reference standard. Abdom Radiol (NY). https://doi.org/10.1007/s00261-018-1696-8

  8. Campa R, Del Monte M, Barchetti G et al (2018) Improvement of prostate cancer detection combining a computer-aided diagnostic system with TRUS-MRI targeted biopsy. Abdom Radiol (NY). https://doi.org/10.1007/s00261-018-1712-z

  9. Tamada T, Dani H, Taneja SS, Rosenkrantz AB (2017) The role of whole-lesion apparent diffusion coefficient analysis for predicting outcomes of prostate cancer patients on active surveillance. Abdom Radiol (NY) 42:2340-2345

    Article  Google Scholar 

  10. Alessandrino F, Taghipour M, Hassanzadeh E et al (2018) Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer. Abdom Radiol (NY). https://doi.org/10.1007/s00261-018-1718-6

  11. Holtz JN, Silverman RK, Tay KJ et al (2018) New prostate cancer prognostic grade group (PGG): Can multiparametric MRI (mpMRI) accurately separate patients with low-, intermediate-, and high-grade cancer? Abdom Radiol (NY) 43:702-712

    Article  Google Scholar 

  12. Glazer DI, Hassanzadeh E, Fedorov A et al (2017) Diffusion-weighted endorectal MR imaging at 3T for prostate cancer: correlation with tumor cell density and percentage Gleason pattern on whole mount pathology. Abdom Radiol (NY) 42:918-925

    Article  PubMed Central  Google Scholar 

  13. Shaish H, Kang SK, Rosenkrantz AB (2017) The utility of quantitative ADC values for differentiating high-risk from low-risk prostate cancer: a systematic review and meta-analysis. Abdom Radiol (NY) 42:260-270

    Article  Google Scholar 

  14. Renard Penna R, Cancel-Tassin G, Comperat E et al (2016) Apparent diffusion coefficient value is a strong predictor of unsuspected aggressiveness of prostate cancer before radical prostatectomy. World J Urol 34:1389-1395

    Article  PubMed  Google Scholar 

  15. Renard-Penna R, Cancel-Tassin G, Comperat E et al (2015) Multiparametric Magnetic Resonance Imaging Predicts Postoperative Pathology but Misses Aggressive Prostate Cancers as Assessed by Cell Cycle Progression Score. J Urol 194:1617-1623

    Article  PubMed  Google Scholar 

  16. Hassanzadeh E, Glazer DI, Dunne RM, Fennessy FM, Harisinghani MG, Tempany CM (2017) Prostate imaging reporting and data system version 2 (PI-RADS v2): a pictorial review. Abdom Radiol (NY) 42:278-289

    Article  Google Scholar 

  17. Warf M, Reid J, Brown K, Kimbrell H, Kolquist K (2015) Analytical Validation of a Cell Cycle Progression Signature Used as a Prognostic Marker in Prostate Cancer. J Mol Biomark Diagn 5:239

    Google Scholar 

  18. 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 12:245-255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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 106:1095-1099

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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 192:409-414

    Article  PubMed  Google Scholar 

  21. 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 31:1428-1434

    Article  CAS  PubMed  Google Scholar 

  22. 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 86:848-853

    Article  PubMed  PubMed Central  Google Scholar 

  23. 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 113:382-389

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tan N, Shen L, Khoshnoodi P et al (2017) Pathological and 3 Tesla Volumetric Magnetic Resonance Imaging Predictors of Biochemical Recurrence after Robotic Assisted Radical Prostatectomy: Correlation with Whole Mount Histopathology. J Urol. https://doi.org/10.1016/j.juro.2017.10.042

  25. Rosenkrantz AB, Ream JM, Nolan P, Rusinek H, Deng FM, Taneja SS (2015) Prostate Cancer: Utility of Whole-Lesion Apparent Diffusion Coefficient Metrics for Prediction of Biochemical Recurrence After Radical Prostatectomy. AJR Am J Roentgenol 205:1208-1214

    Article  PubMed  PubMed Central  Google Scholar 

  26. Fuchsjager MH, Pucar D, Zelefsky MJ et al (2010) Predicting post-external beam radiation therapy PSA relapse of prostate cancer using pretreatment MRI. Int J Radiat Oncol Biol Phys 78:743-750

    Article  PubMed  PubMed Central  Google Scholar 

  27. 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 71:183-192

    Article  CAS  PubMed  Google Scholar 

  28. Mohammadian Bajgiran A, Afshari Mirak S, Shakeri S et al (2018) Characteristics of missed prostate cancer lesions on 3T multiparametric-MRI in 518 patients: based on PI-RADSv2 and using whole-mount histopathology reference. Abdom Radiol (NY). https://doi.org/10.1007/s00261-018-1823-6

  29. Stocker D, Manoliu A, Becker AS et al (2018) Image Quality and Geometric Distortion of Modern Diffusion-Weighted Imaging Sequences in Magnetic Resonance Imaging of the Prostate. Invest Radiol 53:200-206

    Article  PubMed  Google Scholar 

  30. Sonn GA, Fan RE, Ghanouni P et al (2017) Prostate Magnetic Resonance Imaging Interpretation Varies Substantially Across Radiologists. Eur Urol Focus. https://doi.org/10.1016/j.euf.2017.11.010

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Correspondence to Andreas G. Wibmer.

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Conflict of interest

Steve Stone is an employee of Myriad Genetics, Salt Lake City, UT, USA. Michael K Brawer is a former employee of Myriad Genetics, Salt Lake City, UT, USA. Andreas G Wibmer was supported by the Peter Michael Foundation.

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Wibmer, A.G., Robertson, N.L., Hricak, H. et al. Extracapsular extension on MRI indicates a more aggressive cell cycle progression genotype of prostate cancer. Abdom Radiol 44, 2864–2873 (2019). https://doi.org/10.1007/s00261-019-02023-1

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