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The use of exome genotyping to predict pathological Gleason score upgrade after radical prostatectomy in low-risk prostate cancer patients.
Oh JJ, Park S, Lee SE, Hong SK, Lee S, Choe G, Yoon S, Byun SS.
PLoS One. 2014 Aug 5;9(8):e104146.
Active surveillance (AS) is a promising option for patients with low-risk prostate cancer (PCa), however current criteria could not select the patients correctly, many patients who fulfilled recent AS criteria experienced pathological Gleason score upgrade (PGU) after radical prostatectomy (RP). In this study, we aimed to develop an accurate model for predicting PGU among low-risk PCa patients by using exome genotyping.
We genotyped 242,221 single nucleotide polymorphisms (SNP)s on a custom HumanExome BeadChip v1.0 (Illuminam Inc.) in blood DNA from 257 low risk PCa patients (PSA <10 ng/ml, biopsy Gleason score (GS) ≤6 and clinical stage ≤T2a) who underwent radical prostatectomy. Genetic data were analyzed using an unconditional logistic regression to calculate an odds ratio as an estimate of relative risk of PGU, which defined pathologic GS above 7. Among them, we selected persistent SNPs after multiple testing using FDR method, and we compared accuracies from the multivariate logistic model incorporating clinical factors between included and excluded selected SNP information.
After analysis of exome genotyping, 15 SNPs were significant to predict PGU in low risk PCa patients. Among them, one SNP - rs33999879 remained significant after multiple testing. When a multivariate model incorporating factors in Epstein definition - PSA density, biopsy GS, positive core number, tumor per core ratio and age was devised for the prediction of PGU, the predictive accuracy of the multivariate model was 78.4% (95%CI: 0.726-0.834). By addition the factor of rs33999879 in aforementioned multivariate model, the predictive accuracy was 82.9%, which was significantly increased (p = 0.0196).
The rs33999879 SNP is a predictor for PGU. The addition of genetic information from the exome sequencing effectively enhanced the predictive accuracy of the multivariate model to establish suitable active surveillance criteria.