Polygenic Risk Score Improves Risk Stratification and Prediction of Subsequent Thyroid Cancer after Childhood Cancer

Subsequent thyroid cancer (STC) is one of the most common malignancies in childhood cancer survivors. We aimed to evaluate the polygenic contributions to STC risk and potential utility in improving risk prediction. A polygenic risk score (PRS) was calculated from 12 independent SNPs associated with...

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Published inCancer epidemiology, biomarkers & prevention Vol. 30; no. 11; pp. 2096 - 2104
Main Authors Song, Nan, Liu, Qi, Wilson, Carmen L, Sapkota, Yadav, Ehrhardt, Matthew J, Gibson, Todd M, Morton, Lindsay M, Chanock, Stephen J, Neglia, Joseph P, Arnold, Michael A, Michael, J Robert, Gout, Alexander M, Mulder, Heather L, Easton, John, Bhatia, Smita, Armstrong, Gregory T, Zhang, Jinghui, Delaney, Angela, Hudson, Melissa M, Robison, Leslie L, Yasui, Yutaka, Wang, Zhaoming
Format Journal Article
LanguageEnglish
Published United States 01.11.2021
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Summary:Subsequent thyroid cancer (STC) is one of the most common malignancies in childhood cancer survivors. We aimed to evaluate the polygenic contributions to STC risk and potential utility in improving risk prediction. A polygenic risk score (PRS) was calculated from 12 independent SNPs associated with thyroid cancer risk in the general population. Associations between PRS and STC risk were evaluated among survivors from St. Jude Lifetime Cohort (SJLIFE) and were replicated in survivors from Childhood Cancer Survivor Study (CCSS). A risk prediction model integrating the PRS and clinical factors, initially developed in SJLIFE, and its performance were validated in CCSS. Among 2,370 SJLIFE survivors with a median follow-up of 28.8 [interquartile range (IQR) = 21.9-36.1] years, 65 (2.7%) developed STC. Among them, the standardized PRS was associated with an increased rate of STC [relative rate (RR) = 1.57; 95% confidence interval (CI) = 1.24-1.98; < 0.001]. Similar associations were replicated in 6,416 CCSS survivors, among whom 121 (1.9%) developed STC during median follow-up of 28.9 (IQR = 22.6-34.6) years (RR = 1.52; 95% CI = 1.25-1.83; < 0.001). A risk prediction model integrating the PRS with clinical factors showed better performance than the model considering only clinical factors in SJLIFE ( = 0.004, AUC = 83.2% vs. 82.1%, at age 40), which was further validated in CCSS ( = 0.010, AUC = 72.9% vs. 70.6%). Integration of the PRS with clinical factors provided a statistically significant improvement in risk prediction of STC, although the magnitude of improvement was modest. PRS improves risk stratification and prediction of STC, suggesting its potential utility for optimizing screening strategies in survivorship care.
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Dr. Song and Ms. Liu contributed equally as co-first authors.
Drs. Yasui and Wang contributed equally as senior investigators.
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): L.L. Robison, M.M. Hudson, C.L. Wilson, M.J. Ehrhardt, G.T. Armstrong, T.M. Gibson, L.M. Morton, S.J. Chanock, S. Bhatia, J.P. Neglia, M.A. Arnold, A. Delaney
All authors contributed to data interpretation and writing and approved the final manuscript for publication.
Study supervision: Z. Wang, Y. Yasui, M.M. Hudson, L.L. Robison, J. Zhang
Development of methodology: N. Song, Q. Liu, Y. Yasui, Z. Wang
Writing, review, and/or revision of the manuscript: N. Song, Z. Wang, Q. Liu, and Y. Yasui drafted the initial version of the manuscript; all authors contributed to review and revision of the final manuscript.
Author contributions
Conception and design: Z. Wang, Y. Yasui
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): N. Song, Q. Liu, J.R. Michael, Y. Sapkota, Z. Wang
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): L.L. Robison, M.M. Hudson, J. Zhang, J. Easton, H.L. Mulder, A.M. Gout
ISSN:1055-9965
1538-7755
DOI:10.1158/1055-9965.EPI-21-0448