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Risk Prediction of Primary Ovarian Insufficiency in Childhood Cancer Survivors Using Polygenic Risk Scores and Clinical Risk Score
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- Author / Creator
- Yu, Lin
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Thanks to the advancement in cancer treatments, most children diagnosed with cancer are long-term survivors. However, it is estimated that two-thirds of childhood cancer survivors will develop chronic diseases later in life due to their cancer treatments. A reproductive late effect in female cancer survivors is primary ovarian insufficiency (POI), defined as menopause occurring naturally before age 40. Approximately 10% of female survivors of childhood cancer experience POI, far more frequent than the general population (about 1%). Women experiencing POI often face physical and mental health problems and have a decreased quality of life.
A consequence of POI is not able to have biological children. Fertility preservation options are available, but these procedures are invasive and expensive. It is difficult to evaluate the need for fertility preservation without knowing the risk of POI. Currently, treatment-related risk factors have been established and used for risk prediction. In recent decades, genome-wide association (GWA) studies have identified genetic variants associated with menopause-related phenotypes. In this study, I evaluated the genetic risk for POI in the form of a polygenic risk score (PRS) and investigated the incremental value of the PRS in the clinical risk prediction model.
A total of 1985 participants in the Childhood Cancer Survivor Study (CCSS) original cohort were used in this study. The published GWA studies for age at natural menopause conducted in the general population were used to construct a general population-based PRS (gPRS), while top genetic risk associations (P<10-5) from a published GWA study of POI among female cancer survivors participating in the St. Jude Lifetime Cohort Study was used to evaluate a cancer survivor-based PRS (cPRS). The clumping and thresholding method was also applied to the cancer survivor GWA study to construct additional cPRS (named ctPRS) for the CCSS samples under more liberal linkage disequilibrium and p-value thresholds. Time-specific logistic regression models were developed for risk prediction. A clinical risk score (CRS), modified from a previous study, was included in the models as an offset term to account for the clinical risk factors. The interaction between PRS and ovarian radiation therapy (RT: yes/no) [PRSRT] and CRS [PRSCRS] were also evaluated. The area under the ROC curve (AUC), average precision (AP), scaled Brier Score (sBrS), Spiegelhalter-z statistic, and the calibration curve were computed using a 5-fold cross-validation framework to assess the model performance. Finally, these metrics were compared with those of the baseline clinical prediction model – CRS model.
Risk prediction of POI provides quantitative evidence in the discussion of fertility preservation for childhood cancer survivors. Incorporating the genetic information in the predictive model did not improve the discrimination but sometimes improved the model calibration. In summary, the gPRS main effect model and ctPRS*RT interaction models had similar performance and were the best models among all: the overall performance improved compared to the CRS model, where the improvement came from better model calibration. The generalizability of the models should be assessed in external validation in the future.
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- Subjects / Keywords
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- Graduation date
- Spring 2022
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.