Clinical Scientists, Cancer Genetics, and the Cancer Variant Interpretation Group (CanVIG)

Healthcare Science Awareness Week 2020 (6 to 15 March) shines a spotlight on the role of healthcare scientists working within the NHS and aims to inspire the future workforce. Here we look at the involvement of clinical scientists in the work of CanGene-CanVar around cancer variant interpretation.


A particular thank you to Miranda Durkie, whose talk at our Pan Program Meeting has helped with the development of this blog.


What are Clinical Scientists?

There are clinical scientists who have a specific training in an NHS diagnostics, with focus in a particular area, such as molecular genetics, cytogenetics, histopathology, haematology or clinical biochemistry.


What is the role of a Clinical Scientist in molecular genomics?

The role of a clinical scientist is to identify changes in the genome and interpret what impact this may have in disease.


Seems simple? In fact, there are approximately 5 million differences (of variants) in the genome of one person compared to the next. The average gene length is >25,000 base pairs. A single substitution of just one base for another could cause dramatically increased risk of disease. Clinical scientist must utilise available evidence to determine if a genetic change identified in an individual or family is pathogenic (disease-causing) or benign (not-harmful).


What is required for germline interpretation of cancer variants?

Two types of genomic variants may require analysis by a molecular genomics clinical scientist:

  • variants present only in the tumour (somatic or acquired variants) that inform on how to manage the tumour

  • variants present throughout the body since conception (germline or constitutional variants) that inform on cancer risk (and sometimes treatment too).


Germline cancer genetic variant interpretation involves combining different sources of evidence to determine the effect that a genetic variant has on cancer risk. The types of evidence are:

  1. Population data - Using large population datasets to identify the variant frequency across populations and comparing this to the frequency in affected individuals.

  2. Segregation data - Analysis of biological family members to determine if the variant is linked to the phenotype (i.e. increased risk of cancer).

  3. Functional data - Identifying the affect that a variant has on the gene and/or gene products.

  4. Literature - Analysis of literature to identify and collate evidence about the variant. This could include, for example, functional information about the variant or review of other families/individuals that the variant has been identified in.

  5. Clinical features - Analysis of phenotypic/clinical features of the patient to assess consistency with pathogenicity.

  6. De novo/in trans (allelic) - This involves testing of the parental samples to determine (i) whether the variant occurs in cis (the same copy of the gene) or in trans (different copies of the gene) and (ii) whether the variant is de novo, or new, in the individual i.e. absent from both parents.

  7. In silico tools (computational predictions) - Bioinformatic/computational tools that predict the effect of a gene variant.


The Association of Clinical Genomic Sciences (ACGS) have developed Best Practice Guidelines for Variant Classification[1], based upon the American College of Medical Genetics and Genomics guidelines[2]. These general guidelines enable the different evidence types to be given a score of how strongly they are supportive of pathogenicity or benignness. The overall classification of a variant is then based on the amount of supporting evidence and the probability that the variant is pathogenic or not.


What are the challenges in cancer variant interpretation?

There is often an incomplete picture of the effect of variants due to lack of, or conflicting, evidence across all the above categories. Consequently, a high proportion of cancer variants are classified as a ‘variant of insignificant significance’. For example, in ClinVar 36% of BRCA1 and 45% of BRCA2 variants are classified as variants of uncertain significance. This classification should ensure caution in management of the patient, but could mean people at higher risk of developing cancer are not receiving appropriate intervention, or people could have unnecessary concern by lack of certainty regarding their risk.


Unfortunately, studies have also demonstrated inconsistencies with the interpretation of evidence and classification of variants, which could lead to individuals and families receiving incorrect information about the effect of their variant or differential clinical management following genetic testing.


What improvements are needed to help improve variant interpretation?

Some potential areas for improvements in laboratories are:

  1. Increase in appointment of clinical Bioinformaticians.

  2. Wider access to high quality automated software, including automation of clinical variant interpretation guidelines.

  3. Resources that collate evidence and variant interpretations.

  4. Development and increase in the workforce and available resources.


Additionally, as the clinicians see the patients and refer to clinical genetics. The clinical scientist could better interpret cancer variants with:

  • better family history on referral to genetics

  • information relating to cancer histology

  • follow up data from the individuals and their outcomes

  • collaborative working through MDTs.

Cancer genetic variant interpretation has improved hugely over the last two decades thanks to improvements in knowledge and the tools/equipment for gene sequencing. However, there is much more to discover through research and development that will help to improve cancer genetic variant interpretation, including better in silico tools, but also additional challenges looming, such as variant interpretation in lower penetrance genes.


What is the Cancer Variant Interpretation Group UK (CanVIG-UK)?

CanVIG-UK is a multi-disciplinary group established in 2017. The purpose of CanVIG-UK) is to advance outcomes for patients by improving the accuracy and consistency of interpretation of variants in Cancer Susceptibility genes across the UK clinical-laboratory community.


We have six specific objectives:

  1. Creation of a national multi-disciplinary professional network and regular forum

  2. Training and Education

  3. Detailed specification for Germline Cancer Genetics of the UK-ACGS Best Practice Guidelines for Variant Interpretation

  4. Ratification of additional guidance in Germline Cancer Genetics relevant to the UK Clinical-Laboratory community

  5. Development of an online platform (CanVar-UK) to facilitate information-sharing and variant interpretation within the UK Clinical-Laboratory community

  6. UK contribution to international variant interpretation endeavours


What is CanVar-UK?

CanVar-UK is a freely-accessible online platform for variant interpretation being developed through CanGene-CanVar through which we:

  • have collated from a wide array of sources evidence on >1 million variants in 95 cancer susceptibility genes;

  • provide links to relevant external sites;

  • include CanVIG-UK consensus classification for specific variants;

  • display population variant frequency data collated from UK laboratories by PHE.


How are Clinical Scientists involved in CanGene-CanVar?

  • Clinical scientists from each of the 25 molecular genomics laboratories contribute to the monthly CanVIG meetings.

  • The senior clinical scientists on the CanVIG steering advisory group meet monthly to guide the activities of CGCV work-package 2, ensuring we get the best outputs from our CGCV work package 2 team (clinical fellow, Dr Alice Garrett; research clinical scientist, Laura King; and bioinformatician/software engineer, Cankut Cubuk).

  • Dr Rachel Butler, Operational Director of the South West Genomics Laboratory Hub, is a member of our Science Advisory Committee, offering critical review of the entire program at our annual SAC meetings.

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[1] https://www.acgs.uk.com/quality/best-practice-guidelines/ [2] https://www.nature.com/articles/gim201530

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This work was supported by Cancer Research UK [C61296/A27223]