Background: Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). Materials and Method: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy. Results: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer. Conclusion: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

中文翻译：

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其他SNP可改善前列腺癌多基因危险评分的性能

背景：多基因危险评分（PHS）可以识别罹患前列腺癌风险增加的个体。我们估计了其他SNP对先前已验证的PHS（PHS46）的性能的好处。材料和方法：180个SNPs被证明以前与前列腺癌有关，被用于建立欧洲血统男性的PHS模型。使用机器学习方法，即LASSO正规化的Cox回归，选择SNP并估计训练集中（75,596名男性）的SNP系数。在测试/验证集中（6,411名男性）使用两个指标评估了所得模型的性能：（1）危险比（HRs）和（2）前列腺特异性抗原（PSA）测试的阳性预测值（PPV）。在PHS排名前5％的人群与中端40％的人群之间，HR的估算值是（HR95 / 50），最高20％至最低20％（HR80 / 20），最低20％至中等40％（HR20 / 50）。计算PHS的前20％（PPV80）和前5％（PPV95）的PPV，作为在活检中被诊断出具有临床意义的前列腺癌的PSA升高个体的比例。结果：Cox模型（PHS166）中有166个SNP具有非零系数。与PHS46相比，所有HR指标均显示PHS166有了显着改善：HR95 / 50从3.72增加到5.09，HR80 / 20从6.12增加到9.45，HR20 / 50从0.41减少到0.34。相比之下，在具有临床意义的前列腺癌的PSA检测的PPV中未观察到显着差异。结论：合并120个SNP（PHS166与PHS46）可显着改善前列腺癌的HR，而PSA测试的PPV保持不变。计算PHS的前20％（PPV80）和前5％（PPV95）的PPV，作为在活检中被诊断出具有临床意义的前列腺癌的PSA升高个体的比例。结果：Cox模型（PHS166）中有166个SNP具有非零系数。与PHS46相比，所有HR指标均显示PHS166有了显着改善：HR95 / 50从3.72增加到5.09，HR80 / 20从6.12增加到9.45，HR20 / 50从0.41减少到0.34。相比之下，在具有临床意义的前列腺癌的PSA检测的PPV中未观察到显着差异。结论：合并120个SNP（PHS166与PHS46）可显着改善前列腺癌的HR，而PSA测试的PPV保持不变。计算PHS的前20％（PPV80）和前5％（PPV95）的PPV，作为在活检中被诊断出具有临床意义的前列腺癌的PSA升高个体的比例。结果：Cox模型（PHS166）中有166个SNP具有非零系数。与PHS46相比，所有HR指标均显示PHS166有了显着改善：HR95 / 50从3.72增加到5.09，HR80 / 20从6.12增加到9.45，HR20 / 50从0.41减少到0.34。相比之下，在具有临床意义的前列腺癌的PSA检测的PPV中未观察到显着差异。结论：合并120个SNP（PHS166与PHS46）可显着改善前列腺癌的HR，而PSA测试的PPV保持不变。