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Table 3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Breakdown of case and control classification in the test data (<i>N</i> = 25,369) using 10-year risks from the Gail model with and without PRS<sub>BC</sub>, with a fixed proportion (2.88%) of women considered at “moderate risk”.</p>
Table 3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Breakdown of case and control classification in the test data (<i>N</i> = 25,369) using 10-year risks from the Gail model with and without PRS<sub>BC</sub>, with a fixed proportion (2.88%) of women considered at “moderate risk”.</p>
Figure 3 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Decision curve analysis for Tyrer–Cuzick and Gail 10-year risk scores with and without PRS<sub>BC</sub> in test data (<i>N</i> = 25,359). Plots show net benefit calculated in full available follow-up time. The gray line indicates net benefit if all women were screened, and the black line indicates net benefit if no women were screened.</p>
Table 2 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Breakdown of case and control classification in the test data (<i>N</i> = 25,369) using 10-year risks from the Tyrer-Cuzick model with and without PRS<sub>BC</sub>, with a fixed proportion (6.47%) of women considered at “moderate risk”.</p>
Data from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<div>AbstractBackground:<p>Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorization needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI).</p>Methods:<p>We analyzed data from 126,490 post-menopausal women of “White British” ancestry, aged 40 to 69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer–Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected.</p>Results:<p>The Harrell's C statistic of the 10-year risk from the Tyrer–Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models [0.080 (95% confidence interval (CI), 0.053–0.104) and 0.051 (95% CI, 0.030–0.073), respectively], with negligible impact on controls.</p>Conclusions:<p>The addition of a PRS for breast cancer to the well-established Tyrer–Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification.</p>Impact:<p>These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.</p></div>
Supplementary Figure S1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Figure S1: Analysis population exclusion flowchart. This illustrates the selection of eligible study participants from the UK Biobank.</p>
Supplementary Table S8 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S8: Model performance using Tyrer-Cuzick v8 approach to Genomics plc PRS inclusion, in test data (N=25,369).</p>
Supplementary Table S4 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S4: Hazard ratios (HR) associated with variables in the Tyrer-Cuzick model</p>
Supplementary Table S6 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S6: Reclassification tables for Tyrer-Cuzick model using fixed 5% 10-year risk threshold in test data (N=25,369).</p>
Supplementary Table S4 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S4: Hazard ratios (HR) associated with variables in the Tyrer-Cuzick model</p>
Supplementary Table S1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S1: Derivation of variables for the Tyrer-Cuzick model using UK Biobank data.</p>
Table 1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Model performance with and without PRS<sub>BC</sub> in test data (<i>N</i> = 25,369).</p>
Figure 2 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Time-dependent ROC plots at 10 years of follow up for both the Tyrer–Cuzick (left) and Gail (right) models, with and without PRS<sub>BC</sub>, as well as PRS<sub>BC</sub> alone in test data (<i>N</i> = 25,369).</p>
Figure 1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Calibration plots of 10-year observed versus predicted risk by risk decile, for the Tyrer–Cuzick and Gail models, plotting the observed proportion of women with breast cancer by 10 years of follow-up against the predicted 10-year risk from the models. Top row, predicted risks in training dataset from Tyrer–Cuzick and Gail models. Middle row, predicted risks in test dataset from Tyrer–Cuzick and Gail models, calibrated using the training dataset. Bottom row, predicted risks in test dataset from Cox model containing Tyrer–Cuzick or Gail model and PRS<sub>BC</sub>, developed in training dataset.</p>
Supplementary Figure S5 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Figure S5: Density plot of 10-year predicted risk with and without PRSBC, split by breast cancer cases and controls in test data (N=25,369).</p>
Supplementary Figure S2 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Figure S2: Age-specific breast cancer rates in women in the UK Biobank, overall and within analysis cohort, compared to Office for National Statistics (ONS) 2013 Cancer Registry data.</p>
Table 1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Model performance with and without PRS<sub>BC</sub> in test data (<i>N</i> = 25,369).</p>
Figure 1 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Calibration plots of 10-year observed versus predicted risk by risk decile, for the Tyrer–Cuzick and Gail models, plotting the observed proportion of women with breast cancer by 10 years of follow-up against the predicted 10-year risk from the models. Top row, predicted risks in training dataset from Tyrer–Cuzick and Gail models. Middle row, predicted risks in test dataset from Tyrer–Cuzick and Gail models, calibrated using the training dataset. Bottom row, predicted risks in test dataset from Cox model containing Tyrer–Cuzick or Gail model and PRS<sub>BC</sub>, developed in training dataset.</p>
Supplementary Figure S4 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Figure S4: Schoenfeld residual plots from Cox model containing Gail model 10-year risk and PRSBC in training data (N=101,121).</p>
Supplementary Table S8 from Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank
<p>Supplementary Table S8: Model performance using Tyrer-Cuzick v8 approach to Genomics plc PRS inclusion, in test data (N=25,369).</p>