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Universal newborn genetic screening for pediatric cancer predisposition syndromes: model-based insights

Abstract

Purpose

Genetic testing for pediatric cancer predisposition syndromes (CPS) could augment newborn screening programs, but with uncertain benefits and costs.

Methods

We developed a simulation model to evaluate universal screening for a CPS panel. Cohorts of US newborns were simulated under universal screening versus usual care. Using data from clinical studies, ClinVar, and gnomAD, the presence of pathogenic/likely pathogenic (P/LP) variants in RET, RB1, TP53, DICER1, SUFU, PTCH1, SMARCB1, WT1, APC, ALK, and PHOX2B were assigned at birth. Newborns with identified variants underwent guideline surveillance. Survival benefit was modeled via reductions in advanced disease, cancer deaths, and treatment-related late mortality, assuming 100% adherence.

Results

Among 3.7 million newborns, under usual care, 1,803 developed a CPS malignancy before age 20. With universal screening, 13.3% were identified at birth as at-risk due to P/LP variant detection and underwent surveillance, resulting in a 53.5% decrease in cancer deaths in P/LP heterozygotes and a 7.8% decrease among the entire cohort before age 20. Given a test cost of $55, universal screening cost $244,860 per life-year gained; with a $20 test, the cost fell to $99,430 per life-year gained.

Conclusion

Population-based genetic testing of newborns may reduce mortality associated with pediatric cancers and could be cost-effective as sequencing costs decline.

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Fig. 1: Modeled clinical outcomes for targeted next-generation sequencing (t-NGS) vs. usual care.

Data availability

Additional details about the Precision Medicine Policy and Treatment (PreEMPT) model, data used as model input parameters, and output data from the model are available by request from the corresponding author. A list of variants included in the modeling study is also provided in Supplemental Table 2.

References

  1. 1.

    Centers for Disease Control and Prevention. Impact of expanded newborn screening—United States, 2006. MMWR Morb. Mortal. Wkly Rep. 57, 1012–1015 (2008).

    Google Scholar 

  2. 2.

    Holm, I. A. et al. The BabySeq project: implementing genomic sequencing in newborns. BMC Pediatr. 18, 225 (2018).

    Article  Google Scholar 

  3. 3.

    Berg, J. S. et al. Newborn sequencing in genomic medicine and public health. Pediatrics 139, e20162252 (2017).

  4. 4.

    Brodeur, G. M., Nichols, K. E., Plon, S. E., Schiffman, J. D. & Malkin, D. Pediatric cancer predisposition and surveillance: an overview, and a tribute to Alfred G. Knudson Jr. Clin. Cancer Res. 23, e1–e5 (2017).

    Article  Google Scholar 

  5. 5.

    Owens, D. K. et al. Use of decision models in the development of evidence-based clinical preventive services recommendations: methods of the U.S. Preventive Services Task Force. Ann. Intern. Med. 165, 501–508 (2016).

    Article  Google Scholar 

  6. 6.

    Payne, K., Gavan, S. P., Wright, S. J. & Thompson, A. J. Cost-effectiveness analyses of genetic and genomic diagnostic tests. Nat. Rev. Genet. 19, 235–246 (2018).

    CAS  Article  Google Scholar 

  7. 7.

    Prosser, L. A., Grosse, S. D., Kemper, A. R., Tarini, B. A. & Perrin, J. M. Decision analysis, economic evaluation, and newborn screening: challenges and opportunities. Genet. Med. 14, 703–712 (2013).

    Article  Google Scholar 

  8. 8.

    Wasserman, J. D. et al. Multiple endocrine neoplasia and hyperparathyroid-jaw tumor syndromes: clinical features, genetics, and surveillance recommendations in childhood. Clin. Cancer Res. 23, e123–e132 (2017).

    CAS  Article  Google Scholar 

  9. 9.

    Kamihara, J. et al. Retinoblastoma and neuroblastoma predisposition and surveillance. Clin. Cancer Res. 23, e98–e106 (2017).

    CAS  Article  Google Scholar 

  10. 10.

    Kratz, C. P. et al. Cancer screening recommendations for individuals with Li-Fraumeni syndrome. Clin. Cancer Res. 23, e38–e45 (2017).

    CAS  Article  Google Scholar 

  11. 11.

    Foulkes, W. D. et al. Cancer surveillance in Gorlin syndrome and rhabdoid tumor predisposition syndrome. Clin. Cancer Res. 23, e62–e67 (2017).

    CAS  Article  Google Scholar 

  12. 12.

    Schultz, K. A. P. et al. DICER1 and associated conditions: identification of at-risk individuals and recommended surveillance strategies. Clin. Cancer Res. 24, 2251–2261 (2018).

    CAS  Article  Google Scholar 

  13. 13.

    Kalish, J. M. et al. Surveillance recommendations for children with overgrowth syndromes and predisposition to Wilms tumors and hepatoblastoma. Clin. Cancer Res. 23, e115–e122 (2017).

    CAS  Article  Google Scholar 

  14. 14.

    Achatz, M. I. et al. Cancer screening recommendations and clinical management of inherited gastrointestinal cancer syndromes in childhood. Clin. Cancer Res. 23, e107–e114 (2017).

    Article  Google Scholar 

  15. 15.

    Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018).

    CAS  Article  Google Scholar 

  16. 16.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  Article  Google Scholar 

  17. 17.

    National Cancer Institute. Surveillance, Epidemiology and End Results (SEER) Program. SEER*Stat Database. Incidence-based mortality SEER 18 regs (excl Louisiana) research data, Nov 2016 sub (2000-2014) —linked to county attributes—total U.S., 1969-2016 counties. https://seer.cancer.gov/datasoftware/documentation/seerstat/nov2016/ (2016).

  18. 18.

    Revised American Thyroid Association guidelines for the management of medullary thyroid carcinoma. Pediatrics 142, e20183062 (2018).

  19. 19.

    Yeh, J. M. et al. Life expectancy of adult survivors of childhood cancer over 3 decades. JAMA Oncol. 6, 350–357 (2020).

  20. 20.

    Armstrong, G. T. et al. Reduction in late mortality among 5-year survivors of childhood cancer. N. Engl. J. Med. 374, 833–842 (2016).

    CAS  Article  Google Scholar 

  21. 21.

    Invitae. Invitae pediatric solid tumors panel. https://www.invitae.com/en/physician/tests/01104/ (2020).

  22. 22.

    Prosser, L. A. Defining the value of treatments of rare pediatric conditions. JAMA Pediatr. 172, 1123–1124 (2018).

    Article  Google Scholar 

  23. 23.

    Neumann, P. J., Cohen, J. T. & Weinstein, M. C. Updating cost-effectiveness-the curious resilience of the $50,000-per-QALY threshold. N. Engl. J. Med. 371, 796–797 (2014).

    CAS  Article  Google Scholar 

  24. 24.

    Duffy, K. A., Grand, K. L., Zelley, K. & Kalish, J. M. Tumor screening in Beckwith-Wiedemann syndrome: parental perspectives. J. Genet. Couns. 27, 844–853 (2018).

    Article  Google Scholar 

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Acknowledgements

We thank Matt Lebo, Sami Amr, Lorena Lazo de la Vega, Lisa Marie Mahanta, and Natascia Anastasio for curating the variant list.

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Affiliations

Authors

Contributions

Conceptualization: J.M.Y., N.K.S., K.D.C., P.M.M., C.L.B.Z., R.C.G., C.Y.L., H.L.R., M.S.W., L.D., A.C.W. Data curation: J.M.Y., N.K.S., A.C., K.D.C., P.M.M., M.G., C.L.B.Z., L.D., A.C.W. Formal analysis: J.M.Y., N.K.S., K.D.C., P.M.M., G.O., M.G., N.R., L.D., A.C.W. Funding acquisition: J.M.Y., N.K.S., R.C.G., A.C.W. Investigation: J.M.Y., N.K.S., A.C., K.D.C., P.M.M., G.O., N.R., C.L.B.Z., R.C.G., H.L.R., M.S.W., L.D., A.C.W. Methodology: J.M.Y., N.K.S., K.D.C., P.M.M., M.G., A.C.W. Visualization: J.Y., G.O., N.R. Writing—original draft: J.M.Y., N.K.S., K.D.C., P.M.M., G.O., C.L.B.Z., R.C.G., H.L.R., M.S.W., L.D., A.C.W. Writing—review & editing: J.M.Y., N.K.S., A.C., K.D.C., M.G., P.M.M., G.O., N.R., C.L.B.Z., R.C.G., C.Y.L., H.L.R., M.S.W., L.D., A.C.W.

Corresponding author

Correspondence to Jennifer M. Yeh.

Ethics declarations

Competing interests

This project was funded by the National Institutes of Health (NIH) (5R01HD090019–04, principal investigator [PI] A.C.W.), which had no role in the design of the study, collection and analysis of data and decision to publish. K.D.C. was supported by NIH grant K01-HG009173. R.C.G. is co-founder of Genome Medical, Inc. and has received compensation for advising AIA, Grail, Humanity, Kneed Media, Plumcare, UnitedHealth, Verily, VibrentHealth, and Wamberg. H.L.R. serves on the Scientific Advisory Board for Genome Medical, Inc. The other authors declare no competing interests.

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Yeh, J.M., Stout, N.K., Chaudhry, A. et al. Universal newborn genetic screening for pediatric cancer predisposition syndromes: model-based insights. Genet Med (2021). https://doi.org/10.1038/s41436-021-01124-x

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