Reproducibility, performance, and clinical utility of a genetic risk prediction model for prostate cancer in Japanese

Abstract

Prostate specific antigen (PSA) is widely used as a diagnostic biomarker for prostate cancer (PC). However, due to its low predictive performance, many patients without PC suffer from the harms of unnecessary prostate needle biopsies. The present study aims to evaluate the reproducibility and performance of a genetic risk prediction model in Japanese and estimate its utility as a diagnostic biomarker in a clinical scenario. We created a logistic regression model incorporating 16 SNPs that were significantly associated with PC in a genome-wide association study of Japanese population using 689 cases and 749 male controls. The model was validated by two independent sets of Japanese samples comprising 3,294 cases and 6,281 male controls. The areas under curve (AUC) of the model were 0.679, 0.655, and 0.661 for the samples used to create the model and those used for validation. The AUCs were not significantly altered in samples with PSA 1-10 ng/ml. 24.2% and 9.7% of the patients had odds ratio <0.5 (low risk) or >2 (high risk) in the model. Assuming the overall positive rate of prostate needle biopsies to be 20%, the positive biopsy rates were 10.7% and 42.4% for the low and high genetic risk groups respectively. Our genetic risk prediction model for PC was highly reproducible, and its predictive performance was not influenced by PSA. The model could have a potential to affect clinical decision when it is applied to patients with gray-zone PSA, which should be confirmed in future clinical studies.

Figure 1. ROC curves for (A) the samples used to create the genetic risk prediction model (AKY), (B)(C) the two validation studies (BBJ and BBJ2), and (D) when all the samples were combined.

The AUCs of the model and 95% confidence intervals are indicated. The AUCs of BBJ and BBJ2 are statistically compared with that of AKY, and P-values are reported. (x: the logarithm of odds ratio at the best cut off, Sens: sensitivity of the model at the best cutoff, Spec: specificity of the model at the best cutoff.)

Figure 2. ROC curves of the genetic risk prediction model when the samples were confined to those with PSA 1–10 ng/ml.

In (A) AKY and (B) BBJ, case samples with PSA 1–10 ng/ml and all the control samples are used for analysis. In (C) BBJ2, samples are confined to those with PSA 1–10 ng/ml in both case and control samples. The AUCs of the model and 95% confidence intervals are indicated. In each of the sample sets, the AUCs are statistically compared to the AUCs when serum PSA level is not confined (as shown in Figure 1), and P-values are reported.