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Randomized prospective evaluation of genome sequencing versus standard-of-care as a first molecular diagnostic test

Abstract

Purpose

To evaluate the diagnostic yield and clinical relevance of clinical genome sequencing (cGS) as a first genetic test for patients with suspected monogenic disorders.

Methods

We conducted a prospective randomized study with pediatric and adult patients recruited from genetics clinics at Massachusetts General Hospital who were undergoing planned genetic testing. Participants were randomized into two groups: standard-of-care genetic testing (SOC) only or SOC and cGS.

Results

Two hundred four participants were enrolled, 202 were randomized to one of the intervention arms, and 99 received cGS. In total, cGS returned 16 molecular diagnoses that fully or partially explained the indication for testing in 16 individuals (16.2% of the cohort, 95% confidence interval [CI] 8.9–23.4%), which was not significantly different from SOC (18.2%, 95% CI 10.6–25.8%, P = 0.71). An additional eight molecular diagnoses reported by cGS had uncertain relevance to the participant’s phenotype. Nevertheless, referring providers considered 20/24 total cGS molecular diagnoses (83%) to be explanatory for clinical features or worthy of additional workup.

Conclusion

cGS is technically suitable as a first genetic test. In our cohort, diagnostic yield was not significantly different from SOC. Further studies addressing other variant types and implementation challenges are needed to support feasibility and utility of broad-scale cGS adoption.

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Fig. 1: Proband participant enrollment flowchart.
Fig. 2: Molecular diagnoses (probands only) made by clinical genome sequencing (cGS) and standard-of-care (SOC).
Fig. 3: Clinical relevance of clinical genome sequencing (cGS) molecular diagnoses and suspicious variant of uncertain significance (VUS) results. Each variant identified by cGS was reviewed for clinical relevance by the research team and referring clinical provider.

Data availability

Variants reported by cGS have been submitted to ClinVar. De-identified genomic and phenotype data will be made available on the National Human Genome Research Institute (NHGRI) AnVIL platform (pending approval of our application by AnVIL). Data access requests can be made per instructions here https://anvilproject.org/learn/accessing-data/requesting-data-access#accessing-controlled-access-data. For additional information, contact C.A.A-T. (ctse@mgh.harvard.edu).

References

  1. 1.

    Ross, L. F., Saal, H. M., David, K. L. & Anderson, R. R. Technical report: ethical and policy issues in genetic testing and screening of children. Genet. Med. 15, 234–245 (2013).

    Article  Google Scholar 

  2. 2.

    Manning, M. & Hudgins, L. Array-based technology and recommendations for utilization in medical genetics practice for detection of chromosomal abnormalities. Genet. Med. 12, 742–745 (2010).

    CAS  Article  Google Scholar 

  3. 3.

    National Comprehensive Cancer Network. Genetic/familial high-risk assessment: breast, ovarian, and pancreatic (Version 1.2020). https://www.nccn.org/professionals/physician_gls/pdf/genetics_bop.pdf (2020).

  4. 4.

    Xue, Y., Ankala, A., Wilcox, W. R. & Hegde, M. R. Solving the molecular diagnostic testing conundrum for Mendelian disorders in the era of next-generation sequencing: single-gene, gene panel, or exome/genome sequencing. Genet. Med. 17, 444–451 (2015).

    CAS  Article  Google Scholar 

  5. 5.

    Yang, Y. et al. Molecular findings among patients referred for clinical whole-exome sequencing. JAMA. 312, 1870–1879 (2014).

    CAS  Article  Google Scholar 

  6. 6.

    Lee, H. et al. Clinical exome sequencing for genetic identification of rare Mendelian disorders. JAMA. 312, 1880–1887 (2014).

    Article  Google Scholar 

  7. 7.

    Vissers, L. E. L. M. et al. A clinical utility study of exome sequencing versus conventional genetic testing in pediatric neurology. Genet. Med. 19, 1055–1063 (2017).

    Article  Google Scholar 

  8. 8.

    de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. https://doi.org/10.1056/NEJMoa1206524 (2012).

  9. 9.

    Stark, Z. et al. A prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders. Genet. Med. 18, 1090–1096 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Lionel, A. C. et al. Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test. Genet. Med. 20, 435–443 (2018).

    CAS  Article  Google Scholar 

  11. 11.

    Meienberg, J., Bruggmann, R., Oexle, K. & Matyas, G. Clinical sequencing: is WGS the better WES? Hum. Genet. 135, 359–362 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Thiffault, I. et al. Clinical genome sequencing in an unbiased pediatric cohort. Genet. Med. 21, 303–310 (2019).

    Article  Google Scholar 

  13. 13.

    Kalia, S. S. et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet. Med. 19, 249–255 (2017).

    Article  Google Scholar 

  14. 14.

    Girdea, M. et al. PhenoTips: patient phenotyping software for clinical and research use. Hum. Mutat. 34, 1057–1065 (2013).

    Article  Google Scholar 

  15. 15.

    Smith, H. S. et al. Clinical application of genome and exome sequencing as a diagnostic tool for pediatric patients: a scoping review of the literature. Genet. Med. 21, 3–16 (2019).

    CAS  Article  Google Scholar 

  16. 16.

    Cirino Allison, L. et al. A comparison of whole genome sequencing to multigene panel testing in hypertrophic cardiomyopathy patients. Circ. Cardiovasc. Genet. 10, e001768 (2017).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Kingsmore, S. F. et al. A randomized, controlled trial of the analytic and diagnostic performance of singleton and trio, rapid genome and exome sequencing in ill infants. Am. J. Hum. Genet. 105, 719–733 (2019).

    CAS  Article  Google Scholar 

  18. 18.

    Clark, M. M. et al. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. npj Genomic Med. https://doi.org/10.1038/s41525-018-0053-8 (2018).

  19. 19.

    Scocchia, A. et al. Clinical whole genome sequencing as a first-tier test at a resource-limited dysmorphology clinic in Mexico. npj Genomic Med 4, 1–12 (2019).

    Article  Google Scholar 

  20. 20.

    Huang, A. Y. et al. MosaicHunter: accurate detection of postzygotic single-nucleotide mosaicism through next-generation sequencing of unpaired, trio, and paired samples. Nucleic Acids Res. 45, e76 (2017).

    CAS  Article  Google Scholar 

  21. 21.

    Dolzhenko, E. et al. ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions. Bioinformatics (Oxf.) 35, 4754–4756 (2019).

    CAS  Article  Google Scholar 

  22. 22.

    Chen, X. et al. Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data. Genet. Med. 22, 945–953 (2020).

    CAS  Article  Google Scholar 

  23. 23.

    Fogel, B. L. et al. Exome sequencing in the clinical diagnosis of sporadic or familial cerebellar ataxia. JAMA Neurol. 71, 1237–1246 (2014).

    Article  Google Scholar 

  24. 24.

    Pyle, A. et al. Exome sequencing in undiagnosed inherited and sporadic ataxias. Brain. 138, 276–283 (2015).

    Article  Google Scholar 

  25. 25.

    Kang, C. et al. High degree of genetic heterogeneity for hereditary cerebellar ataxias in Australia. Cerebellum. 18, 137–146 (2019).

    CAS  Article  Google Scholar 

  26. 26.

    Galatolo, D., Tessa, A., Filla, A. & Santorelli, F. M. Clinical application of next generation sequencing in hereditary spinocerebellar ataxia: increasing the diagnostic yield and broadening the ataxia-spasticity spectrum. A retrospective analysis. Neurogenetics. 19, 1–8 (2018).

    CAS  Article  Google Scholar 

  27. 27.

    Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

    Article  Google Scholar 

  28. 28.

    Rehm, H. L. et al. ACMG clinical laboratory standards for next-generation sequencing. Genet. Med. 15, 733–747 (2013).

    Article  Google Scholar 

  29. 29.

    Hegde, M. et al. Development and validation of clinical whole-exome and whole-genome sequencing for detection of germline variants in inherited disease. Arch. Pathol. Lab. Med. 141, 798–805 (2017).

    CAS  Article  Google Scholar 

  30. 30.

    Mak, C. C. et al. Exome sequencing for paediatric-onset diseases: impact of the extensive involvement of medical geneticists in the diagnostic odyssey. npj Genomic Med. 3, 19 (2018).

    Article  Google Scholar 

  31. 31.

    Baldridge, D. et al. The Exome Clinic and the role of medical genetics expertise in the interpretation of exome sequencing results. Genet. Med. 19, 1040–1048 (2017).

    CAS  Article  Google Scholar 

  32. 32.

    Vears, D. F., Elferink, M., Kriek, M., Borry, P. & van Gassen, K. L. Analysis of laboratory reporting practices using a quality assessment of a virtual patient. Genet. Med. 30, 1–9 (2020).

    Google Scholar 

  33. 33.

    Clark, M. M. et al. Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aat6177 (2019).

  34. 34.

    Delikurt, T., Williamson, G. R., Anastasiadou, V. & Skirton, H. A systematic review of factors that act as barriers to patient referral to genetic services. Eur. J. Hum. Genet. 23, 739–745 (2015).

    Article  Google Scholar 

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Acknowledgements

This study was funded by the Department of Medicine at Massachusetts General Hospital. Illumina supplied a portion of the sequencing reagents to enable this study. M.U. was supported in part by NIDDK K23DK114551. We thank the patients and families for participating in this study. We are grateful to Stephanie Harris, Lauren O’Grady, Marcie Steeves, Jin Yun Helen Chen, Megan Hawley, Erica Blouch, Linda Rodgers, Kristen Shannon, David Sweetser, Paula Goldenberg, Frances High, Amel Karaa, Angela Lin, Stephanie Santoro, Steven Lubitz, Christopher Newton-Cheh, and Jeremy Schmahmann for referring patients to the study. We also thank Edyta Malolepsza, Harrison Brand, and members of Michael Talkowski’s laboratory for their assistance with SV calling and analysis.

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Authors

Contributions

Conceptualization: D.G.B., C.A.A-T., R.C.P., C.H., C.P., C.Y.L., P.N., K.G.A., A.V.K., S.K., H.L.R., M.U. Data curation:C.A.A-T., D.G.B., R.C.P., C.H., C.P., K.O., L.M.M., M.S.L., C.P., C.E.L. Formal analysis: C.A.A-T., R.C.P., D.G.B., C.H., C.E.L., M.U., C.Y.L. Funding acquisition: S.K., H.L.R., A.V.K. Investigation: C.A.A-T., D.G.B., R.C.P., H.H., C.H., C.E.L. Methodology: D.G.B., R.C.P., C.Y.L., M.S.L., C.A.A-T., M.U., H.L.R., P.N., A.V.K., K.G.A., S.K., C.P., C.H. Project administration: C.P., C.H., D.G.B., K.O., C.E.L., R.C.P. Resources: C.H., M.S.L., L.M.M., K.O., P.N., A.V.K., K.G.A., S.K., H.L.R., M.U. Software: n/a. Supervision: P.N., C.Y.L., A.V.K., K.G.A., S.K., H.L.R., M.U. Validation: C.A.A-T., H.L.R., D.G.B., R.C.P., C.H., C.E.L., M.U. Visualization: D.G.B., R.C.P., C.A.A-T., M.U., A.V.K. Writing—original draft: C.A.A-T., D.G.B., R.C.P., M.U. Writing—review & editing: C.A.A-T., D.G.B., R.C.P., P.N., C.H., C.E.L, C.Y.L., P.N., A.V.K., K.G.A., H.L.R., M.U.

Corresponding author

Correspondence to Deanna G. Brockman.

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Ethics Declaration

All study participants provided written consent or assent. This study was completed as a demonstration project in the MGH Center for Genomic Medicine and was approved by the Massachusetts General Brigham Institutional Review Board. Additional information about the study can be found on Clinicaltrials.gov (NCT03829176).

Competing interests

The authors declare no competing interests.

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Brockman, D.G., Austin-Tse, C.A., Pelletier, R.C. et al. Randomized prospective evaluation of genome sequencing versus standard-of-care as a first molecular diagnostic test. Genet Med 23, 1689–1696 (2021). https://doi.org/10.1038/s41436-021-01193-y

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