Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Validation structures for sequence variants of uncertain significance in hereditary cancer

Abstract

Hereditary cancer syndromes are among the most common inherited disorders and contribute to nearly 10% of solid tumours. While genetic testing is now central to diagnosis, surveillance, and cascade prevention, its impact is constrained by the persistent challenge of variants of uncertain significance (VUS), which comprise almost 40% of reported hereditary cancer syndrome-associated variants in ClinVar. These unresolved classifications undermine the interpretive power of testing, limiting its translational and preventive potential. In this review, we examine the foundations of variant interpretation, the role of expert-guided specifications, and emerging methods for VUS reclassification, including population-level data, RNA- and protein-based functional assays, computational predictors, and long-read sequencing. We further highlight how systematic re-evaluation structures and curation infrastructures translate new evidence into clinical practice. We conclude with an outlook on future directions to reduce the burden of VUS and increase the clinical utility of hereditary cancer syndrome testing.

This is a preview of subscription content, access via your institution

Access options

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Imyanitov EN, Kuligina ES, Sokolenko AP, Suspitsin EN, Yanus GA, Iyevleva AG, et al. Hereditary cancer syndromes. World J Clin Oncol. 2023;14:40–68.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Vos JR, Giepmans L, Röhl C, Geverink N, Hoogerbrugge N. ERN GENTURIS. Boosting care and knowledge about hereditary cancer: European Reference Network on Genetic Tumour Risk Syndromes. Fam Cancer. 2019;18:281–4.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Idos GE, Kurian AW, Ricker C, Sturgeon D, Culver JO, Kingham KE, et al. Multicenter prospective cohort study of the diagnostic yield and patient experience of multiplex gene panel testing for hereditary cancer risk. JCO Precis Oncol. 2019. https://doi.org/10.1200/PO.18.00217.

  4. Ceyhan-Birsoy O, Jayakumaran G, Kemel Y, Misyura M, Aypar U, Jairam S, et al. Diagnostic yield and clinical relevance of expanded genetic testing for cancer patients. Genome Med. 2022;14:92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, 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.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Amendola LM, Jarvik GP, Leo MC, McLaughlin HM, Akkari Y, Amaral MD, et al. Performance of ACMG-AMP variant-interpretation guidelines among nine laboratories in the Clinical Sequencing Exploratory Research consortium. Am J Hum Genet. 2016;98:1067–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Amendola LM, Muenzen K, Biesecker LG, Bowling KM, Cooper GM, Dorschner MO, et al. Variant classification concordance using the ACMG-AMP variant interpretation guidelines across nine genomic implementation research studies. Am J Hum Genet. 2020;107:932–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Harrison SM, Dolinsky JS, Knight Johnson AE, Pesaran T, Azzariti DR, Bale S, et al. Clinical laboratories collaborate to resolve differences in variant interpretations submitted to ClinVar. Genet Med. 2017;19:1096–104.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Vears DF, Sénécal K, Borry P. Reporting practices for variants of uncertain significance from next generation sequencing technologies. Eur J Med Genet. 2017;60:553–8.

    Article  PubMed  Google Scholar 

  10. O’Daniel JM, McLaughlin HM, Amendola LM, Bale SJ, Berg JS, Bick D, et al. A survey of current practices for genomic sequencing test interpretation and reporting processes in US laboratories. Genet Med. 2017;19:575–82.

    Article  PubMed  Google Scholar 

  11. Caswell-Jin JL, Gupta T, Hall E, Petrovchich IM, Mills MA, Kingham KE, et al. Racial/ethnic differences in multiple-gene sequencing results for hereditary cancer risk. Genet Med. 2018;20:234–9.

    Article  PubMed  Google Scholar 

  12. Ndugga-Kabuye MK, Issaka RB. Inequities in multi-gene hereditary cancer testing: lower diagnostic yield and higher VUS rate in individuals who identify as Hispanic, African or Asian and Pacific Islander as compared to European. Fam Cancer. 2019;18:465–9.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2016;44:D862–8.

    Article  CAS  PubMed  Google Scholar 

  14. Cline MS, Liao RG, Parsons MT, Paten B, Alquaddoomi F, Antoniou A, et al. BRCA Challenge: BRCA Exchange as a global resource for variants in BRCA1 and BRCA2. PLoS Genet. 2018;14:e1007752.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Fokkema IFAC, Kroon M, López Hernández JA, Asscheman D, Lugtenburg I, Hoogenboom J, et al. The LOVD3 platform: efficient genome-wide sharing of genetic variants. Eur J Hum Genet. 2021;29:1796–803.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Perez G, Barber GP, Benet-Pages A, Casper J, Clawson H, Diekhans M, et al. The UCSC Genome Browser database: 2025 update. Nucleic Acids Res. 2025;53:D1243–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Spurdle AB, Healey S, Devereau A, Hogervorst FBL, Monteiro ANA, Nathanson KL, 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.

    Article  CAS  PubMed  Google Scholar 

  18. Benet-Pagès A, Laner A, Nassar LR, Wohlfrom T, Steinke-Lange V, Haeussler M, et al. Reclassification of VUS in BRCA1 and BRCA2 using the new BRCA1/BRCA2 ENIGMA track set demonstrates the superiority of ClinGen ENIGMA Expert Panel specifications over the standard ACMG/AMP classification system. Genet Med Open. 2025;3:101961.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Brnich SE, Abou Tayoun AN, Couch FJ, Cutting GR, Greenblatt MS, Heinen CD, et al. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med. 2019;12:3.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lee K, Krempely K, Roberts ME, Anderson MJ, Carneiro F, Chao E, et al. Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline CDH1 sequence variants. Hum Mutat. 2018;39:1553–68.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Luo X, Maciaszek JL, Thompson BA, Leong HS, Dixon K, Sousa S, et al. Optimising clinical care through CDH1-specific germline variant curation: improvement of clinical assertions and updated curation guidelines. J Med Genet. 2023;60:568–75.

    Article  PubMed  Google Scholar 

  22. Thompson BA, Spurdle AB, Plazzer JP, Greenblatt MS, Akagi K, Al-Mulla F, et al. Application of a 5-tiered scheme for standardized classification of 2,360 unique mismatch repair gene variants in the InSiGHT locus-specific database. Nat Genet. 2014;46:107–15.

    Article  CAS  PubMed  Google Scholar 

  23. Yin X, Richardson M, Laner A, Shi X, Ognedal E, Vasta V, et al. Large-scale application of ClinGen-InSiGHT APC-specific ACMG/AMP variant classification criteria leads to substantial reduction in VUS. Am J Hum Genet. 2024;111:2427–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sauer M, Lucas MC, Prokosch V, Keßler T, Risch T, Laner A, et al. Improving genetic diagnosis of hereditary tumor syndromes: From expanded gene panels to functional genomics. Int J Cancer. 2025. https://doi.org/10.1002/ijc.70274.

  25. Lucas MC, Keßler T, Scharf F, Steinke-Lange V, Klink B, Laner A, et al. A series of reviews in familial cancer: genetic cancer risk in context variants of uncertain significance in MMR genes: which procedures should be followed?. Fam Cancer. 2025;24:42.

    Article  CAS  PubMed  Google Scholar 

  26. Chen S, Francioli LC, Goodrich JK, Collins RL, Kanai M, Wang Q, et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature. 2024;625:92–100.

    Article  CAS  PubMed  Google Scholar 

  27. Marco-Puche G, Lois S, Benítez J, Trivino JC. RNA-Seq perspectives to improve clinical diagnosis. Front Genet. 2019;10:1152.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Horton C, Hoang L, Zimmermann H, Young C, Grzybowski J, Durda K, et al. Diagnostic outcomes of concurrent DNA and RNA sequencing in individuals undergoing hereditary cancer testing. JAMA Oncol. 2023;4. https://doi.org/10.1001/jamaoncol.2023.5586.

  29. Smirnov D, Schlieben LD, Peymani F, Berutti R, Prokisch H. Guidelines for clinical interpretation of variant pathogenicity using RNA phenotypes. Hum Mutat. 2022;43:1056–70.

    Article  CAS  PubMed  Google Scholar 

  30. Montalban G, Bonache S, Moles-Fernández A, Gisbert-Beamud A, Tenés A, Bach V, et al. Screening of BRCA1/2 deep intronic regions by targeted gene sequencing identifies the first germline BRCA1 variant causing pseudoexon activation in a patient with breast/ovarian cancer. J Med Genet. 2019;56:63–74.

    Article  CAS  PubMed  Google Scholar 

  31. Fulk K, Turner M, Eppolito A, Krukenberg R. RNA sequencing uncovers clinically actionable germline intronic MSH2 variants in previously unresolved Lynch syndrome families. BMJ Case Rep. 2022;15:e249580.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Landrith T, Li B, Cass AA, Conner BR, LaDuca H, McKenna DB, et al. Splicing profile by capture RNA-seq identifies pathogenic germline variants in tumor suppressor genes. NPJ Precis Oncol. 2020;4:4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Drost M, Tiersma Y, Thompson BA, Frederiksen JH, Keijzers G, Glubb D, et al. A functional assay-based procedure to classify mismatch repair gene variants in Lynch syndrome. Genet Med. 2019;21:1486–96.

    Article  CAS  PubMed  Google Scholar 

  34. Drost M, Tiersma Y, Glubb D, Kathe S, van Hees S, Calléja F, et al. Two integrated and highly predictive functional analysis-based procedures for the classification of MSH6 variants in Lynch syndrome. Genet Med. 2020;22:847–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Rayner E, Tiersma Y, Fortuno C, van Hees-Stuivenberg S, Drost M, Thompson B, et al. Predictive functional assay-based classification of PMS2 variants in Lynch syndrome. Hum Mutat. 2022;43:1249–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Jia X, Burugula BB, Chen V, Lemons RM, Jayakody S, Maksutova M, et al. Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk. Am J Hum Genet. 2021;108:163–75.

    Article  CAS  PubMed  Google Scholar 

  37. Scott A, Hernandez F, Chamberlin A, Smith C, Karam R, Kitzman JO. Saturation-scale functional evidence supports clinical variant interpretation in Lynch syndrome. Genome Biol. 2022;23:266.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Findlay GM, Daza RM, Martin B, Zhang MD, Leith AP, Gasperini M, et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature. 2018;562:217–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dace P, Forrester NM, Zanti M, Cubitt L, Terwagne C, Buckley M, et al. Saturation genome editing of BRCA1 across cell types accurately resolves cancer risk. medRxiv. 2025. https://doi.org/10.1101/2025.08.11.25333423.

  40. Matreyek KA, Starita LM, Stephany JJ, Martin B, Chiasson MA, Gray VE, et al. Multiplex assessment of protein variant abundance by massively parallel sequencing. Nat Genet. 2018;50:874–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kotler E, Segal E, Oren M. Functional characterization of the p53 “mutome. Mol Cell Oncol. 2018;5:e1511207.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Giacomelli AO, Yang X, Lintner RE, McFarland JM, Duby M, Kim J, et al. Mutational processes shape the landscape of TP53 mutations in human cancer. Nat Genet. 2018;50:1381–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Huang H, Hu C, Na J, Hart SN, Gnanaolivu RD, Abozaid M, et al. Functional evaluation and clinical classification of BRCA2 variants. Nature. 2025;638:528–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Bouvet D, Bodo S, Munier A, Guillerm E, Bertrand R, Colas C, et al. Methylation tolerance-based functional assay to assess variants of unknown significance in the MLH1 and MSH2 genes and identify patients with Lynch syndrome. Gastroenterology. 2019;157:421–31.

    Article  CAS  PubMed  Google Scholar 

  45. Rath A, Radecki AA, Rahman K, Gilmore RB, Hudson JR, Cenci M, et al. A calibrated cell-based functional assay to aid classification of MLH1 DNA mismatch repair gene variants. Hum Mutat. 2022;43:2295–307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen W, Frankel WL. A practical guide to biomarkers for the evaluation of colorectal cancer. Mod Pathol. 2019;32:1–15.

    Article  PubMed  Google Scholar 

  47. Papathomas TG, Oudijk L, Persu A, Gill AJ, van Nederveen F, Tischler AS, et al. SDHB/SDHA immunohistochemistry in pheochromocytomas and paragangliomas: a multicenter interobserver variation analysis using virtual microscopy: a Multinational Study of the European Network for the Study of Adrenal Tumors (ENS@T). Mod Pathol. 2015;28:807–21.

    Article  CAS  PubMed  Google Scholar 

  48. Teixeira LA, Candido Dos Reis FJ. Immunohistochemistry for the detection of BRCA1 and BRCA2 proteins in patients with ovarian cancer: a systematic review. J Clin Pathol. 2020;73:191–6.

    Article  CAS  PubMed  Google Scholar 

  49. Thompson BA, Goldgar DE, Paterson C, Clendenning M, Walters R, Arnold S, et al. A multifactorial likelihood model for MMR gene variant classification incorporating probabilities based on sequence bioinformatics and tumor characteristics: a report from the Colon Cancer Family Registry. Hum Mutat. 2013;34:200–9.

    Article  CAS  PubMed  Google Scholar 

  50. Pejaver V, Byrne AB, Feng BJ, Pagel KA, Mooney SD, Karchin R, et al. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet. 2022;109:2163–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Tavtigian SV, Greenblatt MS, Harrison SM, Nussbaum RL, Prabhu SA, Boucher KM, et al. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med. 2018;20:1054–60.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Frazer J, Notin P, Dias M, Gomez A, Min JK, Brock K, et al. Disease variant prediction with deep generative models of evolutionary data. Nature. 2021;599:91–5.

    Article  CAS  PubMed  Google Scholar 

  53. Quinodoz M, Peter VG, Cisarova K, Royer-Bertrand B, Stenson PD, Cooper DN, et al. Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity. Am J Hum Genet. 2022;109:457–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science. 2023;381:eadg7492.

    Article  CAS  PubMed  Google Scholar 

  55. Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N, et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 2019;47:D941–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Chang MT, Asthana S, Gao SP, Lee BH, Chapman JS, Kandoth C, et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat Biotechnol. 2016;34:155–63.

    Article  CAS  PubMed  Google Scholar 

  57. UniProt Consortium. UniProt: The universal protein knowledgebase in 2025. Nucleic Acids Res. 2025;53:D609–17.

  58. Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, et al. Predicting splicing from primary sequence with deep learning. Cell. 2019;176:535–48.e24.

    Article  CAS  PubMed  Google Scholar 

  59. Canson DM, Davidson AL, de la Hoya M, Parsons MT, Glubb DM, Kondrashova O, et al. SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion. Bioinformatics. 2023;39:btad179.

  60. Wagner N, Çelik MH, Hölzlwimmer FR, Mertes C, Prokisch H, Yépez VA, et al. Aberrant splicing prediction across human tissues. Nat Genet. 2023;55:861–70.

    Article  CAS  PubMed  Google Scholar 

  61. Bournazos AM, Riley LG, Bommireddipalli S, Ades L, Akesson LS, Al-Shinnag M, et al. Standardized practices for RNA diagnostics using clinically accessible specimens reclassifies 75% of putative splicing variants. Genet Med. 2022;24:130–45.

    Article  CAS  PubMed  Google Scholar 

  62. Nattestad M, Goodwin S, Ng K, Baslan T, Sedlazeck FJ, Rescheneder P, et al. Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line. Genome Res. 2018;28:1126–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Thibodeau ML, O’Neill K, Dixon K, Reisle C, Mungall KL, Krzywinski M, et al. Improved structural variant interpretation for hereditary cancer susceptibility using long-read sequencing. Genet Med. 2020;22:1892–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Filser M, Schwartz M, Merchadou K, Hamza A, Villy MC, Decees A, et al. Adaptive nanopore sequencing to determine pathogenicity of BRCA1 exonic duplication. J Med Genet. 2023;60:1206–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Bjørnstad PM, Aaløkken R, Åsheim J, Sundaram AYM, Felde CN, Østby GH, et al. A 39 kb structural variant causing Lynch Syndrome detected by optical genome mapping and nanopore sequencing. Eur J Hum Genet. 2024;32:513–20.

    Article  PubMed  Google Scholar 

  66. Aganezov S, Goodwin S, Sherman RM, Sedlazeck FJ, Arun G, Bhatia S, et al. Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing. Genome Res. 2020;30:1258–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Gulsuner S, AbuRayyan A, Mandell JB, Lee MK, Bernier GV, Norquist BM, et al. Long-read DNA and cDNA sequencing identify cancer-predisposing deep intronic variation in tumor-suppressor genes. Genome Res. 2024;34:1825–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lucas MC, Novoa EM. Long-read sequencing in the era of epigenomics and epitranscriptomics. Nat Methods. 2023;20:25–29.

  69. O’Neill K, Pleasance E, Fan J, Akbari V, Chang G, Dixon K, et al. Long-read sequencing of an advanced cancer cohort resolves rearrangements, unravels haplotypes, and reveals methylation landscapes. Cell Genom. 2024;4:100674.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Fehlberg Z, Stark Z, Best S. Reanalysis of genomic data, how do we do it now and what if we automate it? A qualitative study. Eur J Hum Genet. 2024;32:521–8.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the team at the Medizinisch Genetisches Zentrum (MGZ), Munich for their valuable discussions and support. Figures were created with BioRender.com. The authors also acknowledge the constructive feedback from colleagues within the hereditary cancer research community, which helped refine the focus and clarity of this review.

Funding

This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Contributions

MCL and TK contributed equally to the conception and design of the review. ABP, EHF, AL, and BK provided critical input, references, and revisions. All authors discussed the content, revised the manuscript for important intellectual content, and approved the final version for submission.

Corresponding authors

Correspondence to Morghan C. Lucas or Barbara Klink.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lucas, M.C., Keßler, T., Benet-Pagès, A. et al. Validation structures for sequence variants of uncertain significance in hereditary cancer. Eur J Hum Genet (2026). https://doi.org/10.1038/s41431-026-02073-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41431-026-02073-2

Search

Quick links