Genetic risk score to predict biochemical recurrence after radical prostatectomy in prostate cancer: prospective cohort study
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Jong Jin Oh1, Seunghyun Park2,3, Sang Eun Lee1, Sung Kyu Hong1, Sangchul Lee1, Tae Jin Kim1, In Jae Lee1, Jin-Nyoung Ho1,4, Sungroh Yoon2 and Seok-Soo Byun1
1Department of Urology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
2Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
3School of Electrical Engineering, Korea University, Seoul, Korea
4Biomedical Research Institute, Seoul National University Bundang Hospital, Seongnam, Korea
Seok-Soo Byun, email: firstname.lastname@example.org
Keywords: prostate cancer, genetic risk score, recurrence, predictive value
Received: April 12, 2017 Accepted: May 07, 2017 Published: May 26, 2017
Purpose: To investigate the genetic risk score (GRS) from a large-scale exome-wide association study as a tool of prediction for biochemical recurrence (BCR) after radical prostatectomy (RP) in prostate cancer (PCa).
Results: The 16 SNPs were selected as significant predictors of BCR. The GRS in men experiencing BCR was -1.21, significantly higher than in non-BCR patients (–2.43) (p < 0.001). The 10-year BCR-free survival rate was 46.3% vs. 81.8% in the high-versus low GRS group, respectively (p < 0.001). The GRS was a significant factor after adjusting for other variables in Cox proportional hazard models (HR:1.630, p < 0.001). The predictive ability of the multivariate model without GRS was 84.4%, increased significantly to 88.0% when GRS was included (p = 0.0026).
Materials and Methods: Total 912 PCa patients were enrolled who had received RP and genotype analysis using Exome chip (HumanExome BeadChip). Genetic results were obtained by the methods of logistic regression analysis which measured the odds ratio (OR) to BCR. The GRS was calculated by the sum of each weighted-risk allele count multiplied by the natural logarithm of the respective ORs. Survival analyses were performed using the GRS. We compared the accuracy of separate multivariate models incorporating clinicopathological factors that either included or excluded the GRS.
Conclusions: GRS had additional predictive gain of BCR after RP in PCa. The addition of personally calculated GRS significantly increased the BCR prediction rate. After validation of these results, GRS of BCR could be potential biomarker to predict clinical outcomes.
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