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No AccessJournal of UrologyAdult Urology1 Jun 2016

Validation of a Genomic Classifier for Predicting Post-Prostatectomy Recurrence in a Community Based Health Care Setting

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    Purpose:

    We determined the value of Decipher®, a genomic classifier, to predict prostate cancer outcomes among patients after prostatectomy in a community health care setting.

    Materials and Methods:

    We examined the experience of 224 men treated with radical prostatectomy from 1997 to 2009 at Kaiser Permanente Northwest, a large prepaid health plan in Portland, Oregon. Study subjects had aggressive prostate cancer with at least 1 of several criteria such as preoperative prostate specific antigen 20 ng/ml or greater, pathological Gleason score 8 or greater, stage pT3 disease or positive surgical margins at prostatectomy. The primary end point was clinical recurrence or metastasis after surgery evaluated using a time dependent c-index. Secondary end points were biochemical recurrence and salvage treatment failure. We compared the performance of Decipher alone to the widely used CAPRA-S (Cancer of the Prostate Risk Assessment Post-Surgical) score, and assessed the independent contributions of Decipher, CAPRA-S and their combination for the prediction of recurrence and treatment failure.

    Results:

    Of the 224 patients treated 12 experienced clinical recurrence, 68 had biochemical recurrence and 34 experienced salvage treatment failure. At 10 years after prostatectomy the recurrence rate was 2.6% among patients with low Decipher scores but 13.6% among those with high Decipher scores (p=0.02). When CAPRA-S and Decipher scores were considered together, the discrimination accuracy of the ROC curve was increased by 0.11 compared to the CAPRA-S score alone (combined c-index 0.84 at 10 years after radical prostatectomy) for clinical recurrence.

    Conclusions:

    Decipher improves our ability to predict clinical recurrence in prostate cancer and adds precision to conventional pathological prognostic measures.

    References

    • 1 : Cancer statistics, 2015. CA Cancer J Clin2015; 65: 5. Google Scholar
    • 2 : Predicting 15-year prostate cancer specific mortality after radical prostatectomy. J Urol2011; 185: 869. LinkGoogle Scholar
    • 3 : Genomic predictors of outcome in prostate cancer. Eur Urol2015; 68: 1033. Google Scholar
    • 4 : Which, when and why? Rational use of tissue-based molecular testing in localized prostate cancer. Prostate Cancer Prostatic Dis2016; 19: 1. Google Scholar
    • 5 : Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. J Urol2013; 190: 2047. LinkGoogle Scholar
    • 6 : Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy. J Clin Oncol2015; 33: 944. Google Scholar
    • 7 : 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 Urol2015; 67: 778. Google Scholar
    • 8 : Tissue-based genomics augments post-prostatectomy risk stratification in a natural history cohort of intermediate- and high-risk men. Eur Urol2016; 69: 157. Google Scholar
    • 9 : The CAPRA-S score: a straightforward tool for improved prediction of outcomes after radical prostatectomy. Cancer2011; 117: 5039. Google Scholar
    • 10 : Utility of risk models in decision making after radical prostatectomy: lessons from a natural history cohort of intermediate- and high-risk men. Eur Urol2016; 69: 496. Google Scholar
    • 11 : Exon array data analysis using Affymetrix power tools and R statistical software. Brief Bioinform2011; 12: 634. Google Scholar
    • 12 : A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics2012; 100: 337. Google Scholar
    • 13 : Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS One2013; 8: e66855. Google Scholar
    • 14 Choeurng V, Luo B, Yousefi K et al: Re-calibration of genomic risk prediction models in prostate cancer to improve individual-level predictions. Presented at 2015 Joint Statistical Meetings, Seattle, Washington, August 8-13, 2015. Google Scholar
    • 15 : Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J Natl Cancer Inst2008; 100: 1432. Google Scholar
    • 16 : REporting recommendations for tumour MARKer prognostic studies (REMARK). Br J Cancer2005; 93: 387. Google Scholar
    • 17 : Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics2000; 56: 337. Google Scholar
    • 18 : Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak2008; 8: 53. Google Scholar
    • 19 : Bias reduction of maximum likelihood estimates. Biometrika1993; 80: 27. Google Scholar
    • 20 : Absolute risk regression for competing risks: interpretation, link functions, and prediction. Stat Med2012; 31: 3921. Google Scholar
    • 21 : Adjuvant and salvage radiotherapy after prostatectomy: American Society of Clinical Oncology clinical practice guideline endorsement. J Clin Oncol2014; 32: 3892. Google Scholar
    • 22 : Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy. Int J Radiat Oncol Biol Phys2014; 89: 1038. Google Scholar
    • 23 : Novel biomarker signature that may predict aggressive disease in African American men with prostate cancer. J Clin Oncol2015; 33: 2789. Google Scholar
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