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Impact of Decipher Biopsy testing on clinical outcomes in localized prostate cancer in a prospective statewide collaborative

Prostate Cancer and Prostatic Diseases volume 25, pages 677–683 (2022)Cite this article

Subjects

Abstract

Background

Decipher Biopsy is a commercially available gene expression classifier used in risk stratification of newly diagnosed prostate cancer (PCa). Currently, there are no prospective data evaluating its clinical utility. We seek to assess the clinical utility of Decipher Biopsy in localized PCa patients.

Methods

A multi-institutional study of 855 men who underwent Decipher Biopsy testing between February 2015 and October 2019. All patients were tracked through the prospective Michigan Urological Surgery Improvement Collaborative and linked to the Decipher Genomics Resource Information Database (GRID®; NCT02609269). Patient matching was performed by an independent third-party (ArborMetrix Inc.) using two or more unique identifiers. Cumulative incidence curves for time to treatment (TTT) and time to failure (TTF) were constructed using Kaplan–Meier estimates. Multivariable Cox proportional hazard models were used to evaluate the independent association of high-risk Decipher scores with the conversion from AS to radical therapy and treatment failure (biochemical failure or receipt of salvage therapy).

Results and limitations

Eight hundred fifty-five patients underwent Decipher Biopsy testing during the study period. Of the 855 men, 264 proceeded to AS (31%), and 454 (53%) received radical therapy. In men electing AS, after adjusting for NCCN risk group, age, PSA, prostate volume, body mass index, and percent positive cores, a high-risk Decipher score was independently associated with shorter TTT (HR 2.51, 95% CI 1.52–4.13 p < 0.001). Similarly, in patients that underwent radical therapy, a high-risk Decipher score was independently associated with TTF (HR 2.98, 95% CI 1.22–7.29, p = 0.01) on multivariable analysis. Follow-up time was a limitation.

Conclusion

In a prospective statewide registry, high-risk Decipher Biopsy score was strongly and independently associated with conversion from AS to definitive treatment and treatment failure. These real-world data support the clinical utility of Decipher Biopsy. An ongoing phase 3 randomized trial (NCT04396808) will provide level 1 evidence of the clinical impact of Decipher biopsy testing.

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Fig. 1: Correlation of decipher biopsy with clinical variables.
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References

  1. Tosoian JJ, Mamawala M, Epstein JI, Landis P, Macura KJ, Simopoulos DN, et al. Active surveillance of grade group 1 prostate cancer: long-term outcomes from a large prospective cohort. Eur Urol. 2020;77:675–82.

    Article  Google Scholar 

  2. Klotz L, Vesprini D, Sethukavalan P, Vibhuti J, Liying Z, Suneil J, et al. Long-term follow-up of a large active surveillance cohort of patients with prostate cancer. J Clin Oncol. 2015;33:272–7.

    Article  Google Scholar 

  3. Bul M, Zhu X, Valdagni R, Pickles T, Kakehi Y, Rannikko A, et al. Active surveillance for low-risk prostate cancer worldwide: the PRIAS study. Eur Urol. 2013;63:597–603.

    Article  Google Scholar 

  4. Hamdy FC, Donovan JL, Lane JA, Mason M, Metcalfe C, Holding P, et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. N Engl J Med. 2016;375:1415–24.

    Article  Google Scholar 

  5. Spratt DE, Zhang J, Santiago-Jimenez M, Dess RT, Davis JW, Den RB, et al. Development and validation of a novel integrated clinical-genomic risk group classification for localized prostate cancer. J Clin Oncol. 2018;36:581–90.

    Article  Google Scholar 

  6. Cooperberg MR, Erho N, Chan JM, Feng FY, Fishbane N, Shuang G, et al. The diverse genomic landscape of clinically low-risk prostate cancer. Eur Urol. 2018;74:444–52.

    Article  CAS  Google Scholar 

  7. Felker ER, Margolis DJ, Nassiri N, Marks LS. Prostate cancer risk stratification with magnetic resonance imaging. Urol Oncol. 2016;34:311–9.

    Article  Google Scholar 

  8. Cooperberg MR, Broering JM, Carroll PR. Risk assessment for prostate cancer metastasis and mortality at the time of diagnosis. J Natl Cancer Inst. 2009;101:878–87.

    Article  Google Scholar 

  9. Cuzick J, Swanson GP, Fisher G, Brothman AR, Berney DM, Reid JR, 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;12:245–55.

    Article  CAS  Google Scholar 

  10. Knezevic D, Goddard AD, Natraj N, Cherbavaz DB, Clark KM, Clark-Langone KC, et al. Analytical validation of the Oncotype DX prostate cancer assay—a clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genomics. 2013;14:690.

    Article  CAS  Google Scholar 

  11. Erho N, Crisan A, Vergara IA, Mitra AP, Ghadessi M, Buerki C, et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS ONE. 2013;8:e66855.

    Article  CAS  Google Scholar 

  12. Spratt DE, Yousefi K, Deheshi S, Ross AE, Den RB, Schaeffer EM, et al. Individual patient-level meta-analysis of the performance of the decipher genomic classifier in high-risk men after prostatectomy to predict development of metastatic disease. J Clin Oncol. 2017;35:1991–8.

    Article  CAS  Google Scholar 

  13. Cooperberg MR, Davicioni E, Crisan A, Jenkins RB, Ghadessi M, Karnes RJ. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. Eur Urol. 2015;67:326–33.

    Article  Google Scholar 

  14. Berlin A, Murgic J, Hosni A, Jenkins RB, Ghadessi M, Karnes RJ, et al. Genomic classifier for guiding treatment of intermediate-risk prostate cancers to dose-escalated image guided radiation therapy without hormone therapy. Int J Radiat Oncol Biol Phys. 2019;103:84–91.

    Article  Google Scholar 

  15. Nguyen PL, Martin NE, Choeurng V, Palmer-Aronsten B, Kolisnik T, Beard CJ, et al. 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. 2017;20:186–92.

    Article  CAS  Google Scholar 

  16. Jairath NK, Dal Pra A, Vince R, Jr., Dess RT, Jackson WC, Tosoian JJ, et al. A systematic review of the evidence for the decipher genomic classifier in prostate cancer. Eur Urol. 2020;79:374–83.

    Article  Google Scholar 

  17. Kaye DR, Qi J, Morgan TM, Linsell S, Lane BR, Montie JE, et al. Association between early confirmatory testing and the adoption of active surveillance for men with favorable-risk prostate cancer. Urology. 2018;118:127–33.

    Article  Google Scholar 

  18. Singhal U, Tosoian JJ, Qi J, Miller DC, Linsell SM, Cher M, et al. Overtreatment and underutilization of watchful waiting in men with limited life expectancy: an analysis of the Michigan urological surgery improvement collaborative registry. Urology. 2020;145:190–6.

    Article  Google Scholar 

  19. Ginsburg KB, Cher ML, Montie JE. Defining quality metrics for active surveillance: the Michigan Urological Surgery Improvement Collaborative Experience. J Urol. 2020;204:1119–21.

    Article  Google Scholar 

  20. Klein EA, Haddad Z, Yousefi K, Lam LLC, Wang O, Choeurng V, et al. Decipher genomic classifier measured on prostate biopsy predicts metastasis risk. Urology. 2016;90:148–52.

    Article  Google Scholar 

  21. Roach M 3rd, Hanks G, Thames H Jr., Schellhammer P, Shipley WU, Sokol GH, et al. 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. 2006;65:965–74.

    Article  Google Scholar 

  22. Lonergan PE, Washington SL 3rd, Cowan JE, Zhao S, Nguyen HG, Shinohara K, et al. Risk factors for biopsy reclassification over time in men on active surveillance for early stage prostate cancer. J Urol. 2020;204:1216–21.

    Article  Google Scholar 

  23. Cooperberg MR, Zheng Y, Faino AV, Newcomb LF, Zhu K, Cowan JE, et al. Tailoring intensity of active surveillance for low-risk prostate cancer based on individualized prediction of risk stability. JAMA Oncol. 2020;6:e203187.

    Article  Google Scholar 

  24. Chan BKC. Data analysis using R programming. Adv Exp Med Biol. 2018;1082:47–122.

    Article  Google Scholar 

  25. Herlemann A, Huang HC, Alam R, Tosoian JJ, Kim HL, Klein EA, et al. Decipher identifies men with otherwise clinically favorable-intermediate risk disease who may not be good candidates for active surveillance. Prostate Cancer Prostatic Dis. 2020;23:136–43.

    Article  Google Scholar 

  26. Kim HL, Li P, Huang HC, Deheshi S, Marti T, Knudsen B, et al. Validation of the Decipher test for predicting adverse pathology in candidates for prostate cancer active surveillance. Prostate Cancer Prostatic Dis. 2019;22:399–405.

    Article  Google Scholar 

  27. Hu JC, Tosoian JJ, Qi J, Kaye D, Johnson A, Linsell S, et al. Clinical utility of gene expression classifiers in men with newly diagnosed prostate cancer. JCO Precis Oncol. 2018;2. https://doi.org/10.1200/PO.18.00163.

  28. Lin DW, Zheng Y, McKenney JK, Brown MD, Lu R, Crager M, et al. 17-gene genomic prostate score test results in the canary prostate active surveillance study (PASS) cohort. J Clin Oncol. 2020;38:1549–57.

    Article  Google Scholar 

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Acknowledgements

Support for this project is provided by Blue Cross Blue Shield of Michigan; the Department of Defense Physician Research Training Award No. W81XWH-14-1-0287 (TMM); Alfred A. Taubman Institute; Prostate Cancer Foundation (TMM and DES); and National Cancer Institute Grant No. R01CA240991-01 (TMM and DES). The authors acknowledge the significant contribution of the clinical champions, urologists, and data abstractors in each participating Michigan Urological Surgery Improvement Collabora tive (MUSIC) practice.

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Author notes

  1. These authors contributed equally: Randy A. Vince Jr., Ralph Jiang, Daniel E. Spratt, Todd M. Morgan.

Authors and Affiliations

  1. Department of Urology, University of Michigan, Ann Arbor, MI, USA

    Randy A. Vince Jr., Ji Qi, Jeffrey J. Tosoian, Susan Linsell, Anna Johnson, David C. Miller, Arvin George, Khurshid Ghani, Mariana Seymore & Todd M. Morgan

  2. Department of Biostatics, University of Michigan, Ann Arbor, MI, USA

    Ralph Jiang & Matthew Schipper

  3. Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA

    Rebecca Takele

  4. Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA

    Felix Y. Feng

  5. Oakland University William Beaumont School of Medicine, Auburn Hills, MI, USA

    Sughand Shetty, Patrick Hurley & Fionna Sun

  6. Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA

    Robert T. Dess, William C. Jackson, Matthew Schipper & Daniel E. Spratt

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  1. Randy A. Vince Jr.
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Contributions

RV: Project design, data acquistion, data interpretation, drafting manuscript, revising manuscript; RJ: project design, data acquistion, data interpretation; JQ: data acquistion, data interpretation; JT: drafting manuscript, revising manuscript; RT: drafting manuscript, revising manuscript; FF: drafting manuscript, revising manuscript; SL: data acquistion; AJ: data acquistion; SS: data acquistion; PH: data acquistion; AG: data acquistion; KG: data acquistion; FS: drafting manuscript, revising manuscript; MS: drafting manuscript, revising manuscript; RTD: drafting manuscript, revising manuscript; WJ: drafting manuscript, revising manuscript; MS: project design, data acquistion, data interpretation; DS: project design, data acquistion, data interpretation, drafting manuscript, revising manuscript; TM: project design, data acquistion, data interpretation, drafting manuscript, revising manuscript.

Corresponding author

Correspondence to Randy A. Vince Jr..

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Competing interests

DES: Personal Fees from Janssen, AstraZeneca, and Blue Earth. Funding from Janssen. TMM: Research funding: Decipher Biosciences and Myriad Genetics. Advisory Board: Blue Earth. JJT: Co-founder with equity interest, consulting fees: LynxDx, Inc.

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Vince, R.A., Jiang, R., Qi, J. et al. Impact of Decipher Biopsy testing on clinical outcomes in localized prostate cancer in a prospective statewide collaborative. Prostate Cancer Prostatic Dis 25, 677–683 (2022). https://doi.org/10.1038/s41391-021-00428-y

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