Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, Amsterdam Public Health Research Institute, Meibergdreef 9, 1105, AZ, Amsterdam, The Netherlands.
Institute of Mathematics and Statistics, University of Tartu, Narva mnt 18, 51009, Tartu, Estonia.
BMC Cancer. 2021 Dec 20;21(1):1351. doi: 10.1186/s12885-021-08937-8.
Polygenic risk scores (PRS) could potentially improve breast cancer screening recommendations. Before a PRS can be considered for implementation, it needs rigorous evaluation, using performance measures that can inform about its future clinical value.
To evaluate the prognostic performance of a regression model with a previously developed, prevalence-based PRS and age as predictors for breast cancer incidence in women from the Estonian biobank (EstBB) cohort; to compare it to the performance of a model including age only.
We analyzed data on 30,312 women from the EstBB cohort. They entered the cohort between 2002 and 2011, were between 20 and 89 years, without a history of breast cancer, and with full 5-year follow-up by 2015. We examined PRS and other potential risk factors as possible predictors in Cox regression models for breast cancer incidence. With 10-fold cross-validation we estimated 3- and 5-year breast cancer incidence predicted by age alone and by PRS plus age, fitting models on 90% of the data. Calibration, discrimination, and reclassification were calculated on the left-out folds to express prognostic performance.
A total of 101 (3.33‰) and 185 (6.1‰) incident breast cancers were observed within 3 and 5 years, respectively. For women in a defined screening age of 50-62 years, the ratio of observed vs PRS-age modelled 3-year incidence was 0.86 for women in the 75-85% PRS-group, 1.34 for the 85-95% PRS-group, and 1.41 for the top 5% PRS-group. For 5-year incidence, this was respectively 0.94, 1.15, and 1.08. Yet the number of breast cancer events was relatively low in each PRS-subgroup. For all women, the model's AUC was 0.720 (95% CI: 0.675-0.765) for 3-year and 0.704 (95% CI: 0.670-0.737) for 5-year follow-up, respectively, just 0.022 and 0.023 higher than for the model with age alone. Using a 1% risk prediction threshold, the 3-year NRI for the PRS-age model was 0.09, and 0.05 for 5 years.
The model including PRS had modest incremental performance over one based on age only. A larger, independent study is needed to assess whether and how the PRS can meaningfully contribute to age, for developing more efficient screening strategies.
多基因风险评分（PRS）有可能改善乳腺癌筛查建议。在考虑实施 PRS 之前，需要使用能够说明其未来临床价值的性能指标对其进行严格评估。
评估先前开发的基于患病率的 PRS 和年龄作为预测因子的回归模型在爱沙尼亚生物库（EstBB）队列中对女性乳腺癌发病率的预后性能；并将其与仅包含年龄的模型的性能进行比较。
我们分析了来自 EstBB 队列的 30312 名女性的数据。她们在 2002 年至 2011 年之间进入队列，年龄在 20 至 89 岁之间，没有乳腺癌病史，并且在 2015 年之前完成了 5 年的完整随访。我们研究了 PRS 和其他潜在风险因素作为 Cox 回归模型中乳腺癌发病率的可能预测因子。我们使用 10 倍交叉验证，在 90%的数据上拟合模型，估计了仅年龄和 PRS 加年龄预测的 3 年和 5 年乳腺癌发病率。在剩余的折叠中计算校准、区分和重新分类，以表达预后性能。
在 3 年和 5 年内分别观察到 101 例（3.33‰）和 185 例（6.1‰）乳腺癌发病事件。对于处于 50-62 岁规定筛查年龄的女性，在 PRS-年龄模型中观察到的 3 年发病率与预测发病率的比值分别为 PRS 组 75-85%的 0.86、85-95%的 1.34 和前 5%的 1.41。对于 5 年发病率，这分别为 0.94、1.15 和 1.08。然而，每个 PRS 亚组中的乳腺癌事件数量相对较少。对于所有女性，该模型的 AUC 分别为 3 年 0.720（95%CI：0.675-0.765）和 5 年 0.704（95%CI：0.670-0.737），仅比仅年龄模型高 0.022 和 0.023。使用 1%的风险预测阈值，PRS-年龄模型的 3 年 NRI 为 0.09，5 年为 0.05。
包含 PRS 的模型比仅基于年龄的模型具有适度的增量性能。需要进行更大的独立研究，以评估 PRS 是否以及如何能够为制定更有效的筛查策略做出有意义的贡献。
