Toward automation of germline variant curation in clinical cancer genetics

  • A Correction to this article was published on 21 March 2019

Abstract

Purpose

Cancer care professionals are confronted with interpreting results from multiplexed gene sequencing of patients at hereditary risk for cancer. Assessments for variant classification now require orthogonal data searches and aggregation of multiple lines of evidence from diverse resources. The clinical genetics community needs a fast algorithm that automates American College of Medical Genetics and Genomics (ACMG) based variant classification and provides uniform results.

Methods

Pathogenicity of Mutation Analyzer (PathoMAN) automates germline genomic variant curation from clinical sequencing based on ACMG guidelines. PathoMAN aggregates multiple tracks of genomic, protein, and disease specific information from public sources. We compared expertly curated variant data from clinical laboratories to assess performance.

Results

PathoMAN achieved a high overall concordance of 94.4% for pathogenic and 81.1% for benign variants. We observed negligible discordance (0.3% pathogenic, 0% benign) when contrasted against expert curated variants. Some loss of resolution (5.3% pathogenic, 18.9% benign) and gain of resolution (1.6% pathogenic, 3.8% benign) were also observed.

Conclusion

Automation of variant curation enables unbiased, fast, efficient delivery of results in both clinical and laboratory research. We highlight the advantages and weaknesses related to the programmable automation of variant classification. PathoMAN will aid in rapid variant classification by generating robust models using a knowledgebase of diverse genetic data (https://pathoman.mskcc.org).

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Fig. 1: Comparison of PathoMAN results against expertly curated variants.
Fig. 2: Comparison of PathoMAN results against expertly curated variants.

Change history

  • 21 March 2019

    An update to the original published conflict of interest for author Liying Zhang, PhD. L.Z. received compensation from Future Technology Research LLC (seminar on precision medicine), Roche Diagnostics Asia Pacific, BGI, Illumina (speaking activities at conferences/workshop). L.Z.'s family member has a leadership position and ownership interest of Shanghai Genome Center. This correction has been made.

References

  1. 1.

    Offit K. Multigene testing for hereditary cancer: when, why, and how. J Natl Compr Canc Netw. 2017;15:741–743.

  2. 2.

    Desmond A, Kurian AW, Gabree M, et al. Clinical actionability of multigene panel testing for hereditary breast and ovarian cancer risk assessment. JAMA Oncol. 2015;1:943–951.

  3. 3.

    Richards S, Aziz N, Bale 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. 2015;17:405–424.

  4. 4.

    Pandey KR, Maden N, Poudel B, et al. The curation of genetic variants: difficulties and possible solutions. Genomics Proteomics Bioinformatics. 2012;10:317–325.

  5. 5.

    Maxwell KN, Hart SN, Vijai J, et al. Evaluation of ACMG-guideline-based variant classification of cancer susceptibility and non-cancer-associated genes in families affected by breast cancer. Am J Hum Genet. 2016;98:801–817.

  6. 6.

    Pritchard CC, Mateo J, Walsh MF, et al. Inherited DNA-repair gene mutations in men with metastatic prostate cancer. N Engl J Med. 2016;375:443–453.

  7. 7.

    Mandelker D, Zhang L, Kemel Y, et al. Mutation detection in patients with advanced cancer by universal sequencing of cancer-related genes in tumor and normal DNA vs guideline-based germline testing. JAMA. 2017;318:825–835.

  8. 8.

    Zhang J, Walsh MF, Wu G, et al. Germline mutations in predisposition genes in pediatric cancer. N Engl J Med. 2015;373:2336–2346.

  9. 9.

    Lek M, Karczewski KJ, Minikel EV, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–291.

  10. 10.

    Hu C, Hart SN, Polley EC, et al. Association between inherited germline mutations in cancer predisposition genes and risk of pancreatic cancer. JAMA. 2018;319:2401–2409.

  11. 11.

    Robinson DR, Wu YM, Lonigro RJ, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548:297–303.

  12. 12.

    Findlay GM, Daza RM, Martin B, et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature. 2018;562:217–222.

  13. 13.

    Amendola LM, Jarvik GP, Leo MC, et al. Performance of ACMG-AMP variant-interpretation guidelines among nine laboratories in the Clinical Sequencing Exploratory Research Consortium. Am J Hum Genet. 2016;99:247

  14. 14.

    Harrison SM, Dolinsky JS, Knight Johnson AE, et al. Clinical laboratories collaborate to resolve differences in variant interpretations submitted to ClinVar. Genet Med. 2017;19:1096–1104.

  15. 15.

    Breast Cancer Association Consortium. Commonly studied single-nucleotide polymorphisms and breast cancer: results from the Breast Cancer Association Consortium. J Natl Cancer Inst. 2006;98:1382–1396.

  16. 16.

    Hart SN, Maxwell KN, Thomas T, et al. Collaborative science in the next-generation sequencing era: a viewpoint on how to combine exome sequencing data across sites to identify novel disease susceptibility genes. Brief Bioinform. 2016;17:672–677.

  17. 17.

    Complexo,Southey MC, Park DJ, et al. COMPLEXO: identifying the missing heritability of breast cancer via next generation collaboration. Breast Cancer Res. 2013;15:402.

  18. 18.

    Chenevix-Trench G, Milne RL, Antoniou AC, et al. An international initiative to identify genetic modifiers of cancer risk in BRCA1 and BRCA2 mutation carriers: the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). Breast Cancer Res. 2007;9:104.

  19. 19.

    Patel RY, Shah N, Jackson AR, et al. ClinGen Pathogenicity Calculator: a configurable system for assessing pathogenicity of genetic variants. Genome Med. 2017;9:3.

  20. 20.

    Rehm HL, Berg JS, Brooks LD, et al. ClinGen—the Clinical Genome Resource. N Engl J Med. 2015;372:2235–2242.

  21. 21.

    Balmana J, Digiovanni L, Gaddam P, et al. Conflicting interpretation of genetic variants and cancer risk by commercial laboratories as assessed by the Prospective Registry of Multiplex Testing. J Clin Oncol. 2016;34:4071–4078.

  22. 22.

    Walsh MF, Gaddam P, Digiovanni L, et al. Prospective registry of multiplex testing (PROMPT): a web-based platform to assess cancer risk of genetic variants. J Clin Oncol. 2016;34 15 suppl:1518.

  23. 23.

    Whiffin N, Walsh R, Govind R, et al. CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation. Genet Med. 2018;20:1246–1254.

  24. 24.

    Li Q, Wang K. InterVar: clinical interpretation of genetic variants by the 2015 ACMG-AMP Guidelines. Am J Hum Genet. 2017;100:267–280.

  25. 25.

    Kelly MA, Caleshu C, Morales A, et al. Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen’s Inherited Cardiomyopathy Expert Panel. Genet Med. 2018;20:351–359.

  26. 26.

    Jarvik GP, Browning BL. Consideration of cosegregation in the pathogenicity classification of genomic variants. Am J Hum Genet. 2016;98:1077–1081.

  27. 27.

    Nykamp K, Anderson M, Powers M, et al. Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria. Genet Med. 2017;19:1105–1117.

  28. 28.

    Knudson AG Jr. Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci USA. 1971;68:820–823.

  29. 29.

    Olsen JH, Hahnemann JM, Borresen-Dale AL, et al. Cancer in patients with ataxia-telangiectasia and in their relatives in the Nordic countries. J Natl Cancer Inst. 2001;93:121–127.

  30. 30.

    Vijai J, Topka S, Villano D, et al. A recurrent ERCC3 truncating mutation confers moderate risk for breast cancer. Cancer Discov. 2016;6:1267–1275.

  31. 31.

    Bouaoun L, Sonkin D, Ardin M, et al. TP53 variations in human cancers: new lessons from the IARC TP53 database and genomics data. Hum Mutat. 2016;37:865–876.

  32. 32.

    Janatova M, Kleibl Z, Stribrna J, et al. The PALB2 gene is a strong candidate for clinical testing in BRCA1- and BRCA2-negative hereditary breast cancer. Cancer Epidemiol Biomarkers Prev. 2013;22:2323–2332.

  33. 33.

    Southey MC, Goldgar DE, Winqvist R, et al. PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS. J Med Genet. 2016;53:800–811.

  34. 34.

    Guidugli L, Carreira A, Caputo SM, et al. Functional assays for analysis of variants of uncertain significance in BRCA2. Hum Mutat. 2014;35:151–164.

  35. 35.

    Spurdle AB, Healey S, Devereau A, et al. ENIGMA—evidence-based network for the interpretation of germline mutant alleles: an international initiative to evaluate risk and clinical significance associated with sequence variation in BRCA1 and BRCA2 genes. Hum Mutat. 2012;33:2–7.

  36. 36.

    Beroud C, Letovsky SI, Braastad CD, et al. BRCA Share: a collection of clinical BRCA gene variants. Hum Mutat. 2016;37:1318–1328.

  37. 37.

    Findlay GM, Boyle EA, Hause RJ, et al. Saturation editing of genomic regions by multiplex homology-directed repair. Nature. 2014;513:120–123.

  38. 38.

    Starita LM, Ahituv N, Dunham MJ, et al. Variant interpretation: functional assays to the rescue. Am J Hum Genet. 2017;101:315–325.

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Acknowledgements

We thank Sabine Topka, Semanti Mukherjee, Maria Carlo, and Zoe Steinsnyder for helpful suggestions to improve the manuscript. Research reported in this paper was supported by National Cancer Institute of the National Institutes of Health under award numbers R21CA029533, P50CA221745, as well as Cycle for Survival, the Breast Cancer Research Foundation, and the V Foundation for Cancer Research. It is also supported by the Cancer Center core grant P30CA008748 and the Robert and Kate Niehaus Center for Inherited Cancer Genomics. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funding agencies.

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Correspondence to Joseph Vijai PhD.

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Disclosure

L.Z. received compensation from Future Technology Research LLC (seminar on precision medicine), Roche Diagnostics Asia Pacific, BGI, Illumina (speaking activities at conferences/workshop). L.Z.'s family member has a leadership position and ownership interest of Shanghai Genome Center. Y.K. was a past employee of Bioreference Laboratories, a subsidiary of OPKO Health, with employment ending in January 2016. K.C. declares institutional support for therapeutic clinical trial from AstraZeneca and Syndax Pharmaceuticals outside the submitted work. Z.S. declares her immediate family member works at the department of Ophthalmology at MSKCC. She also holds consulting/advisory roles with Allergan, Adverum Biotechnologies, Alimera Sciences, Biomarin, Fortress Biotech, Genentech, Novartis, Optos, Regeneron, Regenxbio, Spark Therapeutics. M.R. declares grants, personal fees and nonfinancial support from AstraZeneca, personal fees from McKesson, grants and personal fees from Pfizer, nonfinancial support from Myriad, nonfinancial support from Invitae, grants from AbbVie, grants from Tesar, grants from Medivation outside the submitted work. V.J. and K.O. declare that they hold patent on the “Diagnosis and Treatment of ERCC3 mutant cancer” PCT/US18/22588. The other authors declare no conflicts of interest.

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Keywords

  • pathogenicity
  • ACMG
  • germline
  • cancer
  • curation