A virtuous cycle - using evidence to improve genetic testing
3 June 2015
From an analytical perspective the successful application of genetic and genomic medicine depends on the ability to distinguish disease causing ‘pathogenic’ genetic differences (variants) from those that are harmless, and to ascertain the degree of disease risk conferred by these variants. Identifying pathogenic variants and estimating their contribution to disease risk is by no means trivial. For the majority of the 80 million variants uncovered in the human genome, there is as yet no clear understanding of their role in health and disease.
Special reports last week in the NEJM highlight the importance of improving standards for data collection and sharing from genetic tests in order to increase knowledge on genetic variants, and thereby improve clinical services for patients. The papers also underscore the need for systematic interpretation of the clinical significance of genomic variants, and considerations as to how this information is used when developing new genetic tests.
Collecting and sharing evidence from genetic tests
Rehm and colleagues described the operation of the Clinical Genome Resource (ClinGen) programme. Supported by the US National Institute of Health, ClinGen is intended to be an authoritative central resource that defines the clinical relevance of genomic variants for use in precision medicine and research. Currently the assessment of whether a variant is clinically significant, i.e. potentially disease causing, is often subjective, and further confounded by obstacles to accessing all the relevant available information on the variant. For example the authors found that the interpretation of the importance of same variant by multiple clinical laboratories can differ. Since at least one interpretation must be wrong, this disparity in interpretation could therefore lead to inappropriate medical intervention.
ClinGen comprises a range of complementary resources to enable improvements in data sharing and variant interpretation. This includes the ClinVar database, the primary site for the deposition and retrieval of variant data and annotations. As of 4 May 2015, data on 172,055 variants existed within ClinVar. More than 118,000 unique variants had a clinical interpretation, although 21% of these were variants of uncertain significance. The consolidation of data into one resource from different submitters is helping to refine the interpretation, as variants that have differing assertions of clinical significance among submitters can be identified and revaluated. Since a key goal of ClinGen is to resolve differences in interpretation, the American College of Medical Genetics and Genomics (a ClinGen grantee) have been working with expert groups to develop new standards for interpreting genetic variants.
Role of evidence in gene panel tests
Accurate information on the clinical significance of variants is vital in the development of new genetic tests. Gene panel tests enable parallel testing of multiple genes implicated in particular disease phenotypes - a set of clinical symptoms - and are becoming a practical option with advances in sequencing technology. Tests have been developed that incorporate known pathogenic variants for particular inherited disorders (e.g. inherited eye disorders). Several companies are now offering such gene-panel tests for personalised risk prediction, leading to debate as to their appropriateness. These tests have been developed for disease such as breast cancer, with panels covering many genes, many of which are cited as increasing an individual’s risk, however, evidence of a robust association between the variants and risk is often not available for all of the variants on the panel. Consequently, they may not provide accurate enough information to aid clinical counselling.
The special report by Easton and colleagues discusses the key issues to be addressed when assessing clinical validity and utility of such tests, and the factors to be considered when including particular risk variants into a panel test. The paper also reviewed the evidence for specific breast cancer genes and showed that there are difficulties in assigning risk and demonstrating robust evidence of an association with breast cancer for many variants. The authors urge for an awareness of the limitation of panel tests in personalised risk prediction and the importance of systematic data collection in order to improve risk estimates for counselling purposes.
Facilitating advances in genomic medicine in the UK though improved data sharing
In two complementary projects, the PHG Foundation has been examining the analytical and technical challenges of Clinical Genome Analysis, as well as the legal, social and practical challenges of implementing gene panel, whole exome and whole genome sequencing in clinical practice - Realising Genomics. The work has highlighted the need for improvements in the underlying evidence base required for data interpretation, by addressing the multifaceted barriers to sharing of genetic and clinical data by NHS genetic laboratories. The PHG Foundation and the Association for Clinical Genetics Science (ACGS) will be holding a joint meeting later this month to discuss the pressing considerations around the sharing of data, and to explore ways to improve data sharing to support the effective development and delivery of genetic and genomic services.