Cancer Variant Interpretation Group UK (CanVIG-UK): an exemplar national subspecialty multidisciplinary network

Garrett, Alice; Callaway, Alison; Durkie, Miranda; Çubuk, Cankut; Alikian, Mary; Burghel, George J.; Robinson, Rachel; Izatt, Louise; Talukdar, Sabrina; Side, Lucy; Cranston, Treena; Palmer-Smith, S.; Baralle, Diana; Berry, Ian; Drummond, James; Wallace, Andrew; Norbury, Gail; Eccles, Diana; Ellard, Sian; Lalloo, Fiona; Evans, D. Gareth; Woodward, Emma R.; Tischkowitz, Marc; Hanson, Helen; Turnbull, Clare

doi:10.1136/jmedgenet-2019-106759

Cited by 39 publications

(39 citation statements)

References 21 publications

(39 reference statements)

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“…With these technological advances comes a separate and new set of challenges including accurate classification of the variants identified [96]. Equally important is ensuring that, for mutations detected in the mainstream setting, at-risk family members are offered testing for the familial PV where appropriate [97].…”

Section: Detection Of Mutations In Known Breast Cancer Genesmentioning

confidence: 99%

From BRCA1 to Polygenic Risk Scores: Mutation-Associated Risks in Breast Cancer-Related Genes

2021

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Background: There has been huge progress over the last 30 years in identifying the familial component of breast cancer. Summary: Currently around 20% is explained by the high-risk genes BRCA1 and BRCA2, a further 2% by other high-penetrance genes, and around 5% by the moderate risk genes ATM and CHEK2. In contrast, the more than 300 low-penetrance single-nucleotide polymorphisms (SNP) now account for around 28% and they are predicted to account for most of the remaining 45% yet to be found. Even for high-risk genes which confer a 40–90% risk of breast cancer, these SNP can substantially affect the level of breast cancer risk. Indeed, the strength of family history and hormonal and reproductive factors is very important in assessing risk even for a BRCA carrier. The risks of contralateral breast cancer are also affected by SNP as well as by the presence of high or moderate risk genes. Genetic testing using gene panels is now commonplace. Key-Messages: There is a need for a more parsimonious approach to panels only testing those genes with a definite 2-fold increased risk and only testing those genes with challenging management implications, such as CDH1 and TP53, when there is strong clinical indication to do so. Testing of SNP alongside genes is likely to provide a more accurate risk assessment.

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Section: Detection Of Mutations In Known Breast Cancer Genesmentioning

confidence: 99%

From BRCA1 to Polygenic Risk Scores: Mutation-Associated Risks in Breast Cancer-Related Genes

2021

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“…Aside from the technical aspects of how to control for variable batch effects in sequencing and how to deal with tissue-specific splicing and splicing artefacts apparent in read mapping, a critical part of this will be the development of clinical guidelines relating to how splicing abnormalities should be interpreted in terms of their pathogenicity in variant classification. Initial attempts at such guidelines have been made in relation to cancer susceptibility genes but it is likely that a much more nuanced and perhaps experimentally evidenced approach will be needed in order to try to take account of the complexity of RNA metabolism and splice isoform regulation [123]. Beyond diagnostics, funding of translational research into therapeutic splicing manipulation will be key.…”

Section: Figurementioning

confidence: 99%

Translating RNA Splicing Analysis into Diagnosis and Therapy

Douglas¹,

Baralle²

2021

OBM Genetics

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A large proportion of rare disease patients remain undiagnosed and the vast majority of such conditions remain untreatable whether diagnosed or not. RNA splicing analysis is able to increase the diagnostic rate in rare disease by identifying cryptic splicing mutations and can help in interpreting the pathogenicity of genomic variants. Whilst targeted RT-PCR analysis remains a highly sensitive tool for assessing the splicing effects of known variants, RNA-seq can provide a more comprehensive transcriptome-wide analysis of splicing. Appropriate care should be taken in RNA-seq experimental design since sample quality, processing, choice of library preparation and sequencing parameters all introduce variability. Many bioinformatic tools exist to aid both in the prediction of splicing effects from DNA sequence and in the handling of RNA-seq data for splicing analysis. Once identified, splicing abnormalities may be amenable to correction using antisense oligonucleotide compounds by masking cryptic splice sites or blocking key splice regulatory elements, or by use of alternative corrective technologies such as trans-splicing. A growing number of such drugs have started to enter clinical use, most notably nusinersen for the treatment of spinal muscular atrophy. By bringing together the fields of RNA diagnostics and antisense therapeutics, it is becoming feasible to envisage the development of a truly personalised medicine pipeline. This has already been shown to be possible in the case of milasen, an n=1 bespoke antisense drug, and the growth and convergence of these technologies means that similar therapeutic opportunities should arise in the near future.

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“…CanVIG-UK was established in 2017 as part of the UK-ACGS activity supporting adoption and dissemination of the ACMG framework for variant interpretation. 5 CanVIG-UK currently comprises >100 clinical scientists, clinical geneticists and genetic counsellors, with representation from each of the 25 Molecular Diagnostic Laboratories and Clinical Genetics Services of the UK (NHS) and Republic of Ireland. The group meets monthly to undertake multidisciplinary review and interpretation of problematic clinically detected variants.…”

Section: American College Of Medical Genetics (Acmg) Variant Interprementioning

confidence: 99%

“…3 Additional evidence elements not present in the original 2015 ACMG framework have been introduced in recent specifications of the framework by ClinGen expert groups, as well as the UK-ACGS and CanVIG-UK. [4][5][6][7][8][9] Frequently encountered within CanVIG-UK are variants for which there is uncertainty and inconsistency regarding the combination of multiple evidence elements, in particular those for which (1) the evidence elements available do not conform to any of the combinations specified in the original ACMG framework; (2) a proposed combination of evidence elements is of contentious legitimacy; and (3) there are conflicting evidence elements, that is, towards both pathogenicity and benignity.…”

Section: Combining Evidence Items Under the Acmg Frameworkmentioning

confidence: 99%

“…Permissible and non-permissible combinations of concordant evidence elements (CanVIG-UK consensus) Co-usage permitted, provided that there is a schema predefining distinct data types used for each, thus preventing 'double counting' of phenotypic features (eg PP4 is applied for molecular/tumour assay data, indicating specificity at gene level); PS4 is applied at patient level, counting the strength of features in cases and the number of cases/families5 …”

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Combining evidence for and against pathogenicity for variants in cancer susceptibility genes: CanVIG-UK consensus recommendations

et al. 2020

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Accurate classification of variants in cancer susceptibility genes (CSGs) is key for correct estimation of cancer risk and management of patients. Consistency in the weighting assigned to individual elements of evidence has been much improved by the American College of Medical Genetics (ACMG) 2015 framework for variant classification, UK Association for Clinical Genomic Science (UK-ACGS) Best Practice Guidelines and subsequent Cancer Variant Interpretation Group UK (CanVIG-UK) consensus specification for CSGs. However, considerable inconsistency persists regarding practice in the combination of evidence elements. CanVIG-UK is a national subspecialist multidisciplinary network for cancer susceptibility genomic variant interpretation, comprising clinical scientist and clinical geneticist representation from each of the 25 diagnostic laboratories/clinical genetic units across the UK and Republic of Ireland. Here, we summarise the aggregated evidence elements and combinations possible within different variant classification schemata currently employed for CSGs (ACMG, UK-ACGS, CanVIG-UK and ClinGen gene-specific guidance for PTEN, TP53 and CDH1). We present consensus recommendations from CanVIG-UK regarding (1) consistent scoring for combinations of evidence elements using a validated numerical ‘exponent score’ (2) new combinations of evidence elements constituting likely pathogenic’ and ‘pathogenic’ classification categories, (3) which evidence elements can and cannot be used in combination for specific variant types and (4) classification of variants for which there are evidence elements for both pathogenicity and benignity.

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Cancer Variant Interpretation Group UK (CanVIG-UK): an exemplar national subspecialty multidisciplinary network

Cited by 39 publications

References 21 publications

From <b><i>BRCA1</i></b> to Polygenic Risk Scores: Mutation-Associated Risks in Breast Cancer-Related Genes

From <b><i>BRCA1</i></b> to Polygenic Risk Scores: Mutation-Associated Risks in Breast Cancer-Related Genes

Translating RNA Splicing Analysis into Diagnosis and Therapy

Combining evidence for and against pathogenicity for variants in cancer susceptibility genes: CanVIG-UK consensus recommendations

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