Evaluation of a genomic classifier in radical prostatectomy patients with lymph node metastasis
Authors Lee H, Yousefi K, Haddad Z, Abdollah F, Lam L, Shin H, Alshalalfa M, Godebu E, Wang S, Shabaik A, Davicioni E, Kane C
Received 6 November 2015
Accepted for publication 18 February 2016
Published 28 June 2016 Volume 2016:8 Pages 77—84
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Jan Colli
Hak J Lee,1 Kasra Yousefi,2 Zaid Haddad,2 Firas Abdollah,3 Lucia LC Lam,2 Heesun Shin,2 Mohammed Alshalalfa,2 Elana Godebu,1 Song Wang,4 Ahmed Shabaik,5 Elai Davicioni,2 Christopher J Kane1
1Department of Urology, University of California, San Diego, San Diego, CA, USA; 2GenomeDx Biosciences Inc., Vancouver, BC, Canada; 3Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, 4UC San Diego Health System, San Diego, CA, 5Department of Pathology, University of California, San Diego, San Diego, CA, USA
Objective: To evaluate the performance of the Decipher test in predicting lymph node invasion (LNI) on radical prostatectomy (RP) specimens.
Methods: We identified 1,987 consecutive patients with RP who received the Decipher test between February and August 2015 (contemporary cohort). In the contemporary cohort, only the Decipher score from RP specimens was available for analysis. In addition, we identified a consecutive cohort of patients treated with RP between 2006 and 2012 at the University of California, San Diego, with LNI upon pathologic examination (retrospective cohort). The retrospective cohort yielded seven, 22, and 18 tissue specimens from prostate biopsy, RP, and lymph nodes (LNs) for individual patients, respectively. Univariable and multivariable logistic regression analyses were used to evaluate the performance of Decipher in the contemporary cohort with LNI as the endpoint. In the retrospective cohort, concordance of risk groups was assessed using validated cut-points for low (<0.45), intermediate (0.45–0.60), and high (>0.60) Decipher scores.
Results: In the contemporary cohort, 51 (2.6%) patients had LNI. Decipher had an odds ratio of 1.73 (95% confidence interval, 1.46–2.05) and 1.42 (95% confidence interval, 1.19–1.7) per 10% increase in score on univariable and multivariable (adjusting for pathologic Gleason score, extraprostatic extension, and seminal vesicle invasion), respectively. No significant difference in the clinical and pathologic characteristics between the LN positive patients of contemporary and retrospective cohorts was observed (all P>0.05). Accordingly, among LN-positive patients in the contemporary cohort and retrospective cohort, 80% and 77% had Decipher high risk scores (P=1). In the retrospective cohort, prostate biopsy cores with the highest Gleason grade and percentage of tumor involvement had 86% Decipher risk concordance with both RP and LN specimens.
Conclusion: Decipher scores were highly concordant between pre- and post-surgical specimens. Further, Decipher scores from RP tissue were predictive of LNI at RP. If validated in a larger cohort of prostate biopsy specimens for prediction of adverse pathology at RP, Decipher may be useful for improved pre-operative staging.
Keywords: prostate, biopsy, lymph node invasion, genomic classifier, radical prostatectomy, decipher, prognosis
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