Abstract
Pathogenic constitutional genomic variants in the mismatch repair (MMR) genes are the drivers of Lynch syndrome; optimal variant interpretation is required for the management of suspected and confirmed cases. The International Society for Hereditary Gastrointestinal Tumours (InSiGHT) provides expert classifications for MMR variants for the US National Human Genome Research Institute's (NHGRI) ClinGen initiative and interprets variants with discordant classifications and those of uncertain significance (VUSs). Given the onerous nature of extracting information related to variants, literature searching tools which harness artificial intelligence may aid in retrieving information to allow optimum variant classification. In this study, we described the nature of discordance in a sample of 80 variants from a list of variants requiring updating by InSiGHT for ClinGen by comparing their existing InSiGHT classifications with the various submissions for each variant on the US National Centre for Biotechnology Information's (NCBI) ClinVar database. To identify the potential value of a literature searching tool in extracting information related to classification, all variants were searched for using a traditional method (Google Scholar) and literature searching tool (Mastermind) independently. Descriptive statistics were used to compare: the number of articles before and after screening for relevance and the number of relevant articles unique to either method. Relevance was defined as containing the variant in question as well as data informing variant interpretation. A total of 916 articles were returned by both methods and Mastermind averaged four relevant articles per search compared to Google Scholar's three. Of relevant Mastermind articles, 193/308 (62.7%) were unique to it, compared to 87/202, (43.0%) for Google Scholar. For 24 variants, either or both methods found no information. All 6/80 (20%) variants with pathogenic or likely pathogenic InSiGHT classifications have newer VUS assertions on ClinVar. Our study demonstrated that for a sample of variants with varying discordant interpretations, Mastermind was able to return on average, a more relevant and unique literature search. Google Scholar was able to retrieve information that Mastermind did not, which supports a conclusion that Mastermind could play a complementary role in literature searching for classification. This work will aid InSiGHT in its role of classifying MMR variants.
Highlights
Lynch syndrome (LS) is the most common aetiology of hereditary colorectal neoplasia with a prevalence of 3% to 5% amongst colorectal cancer patients.[1]
We hypothesized that amongst a sample of variants submitted to ClinVar with different pathogenicity assignments, Mastermind would add incremental value to the initial literature search for a variant being classified in the mismatch repair (MMR) classification process by providing a more relevant initial literature search and retrieve more unique information, compared to a standard Google Scholar search, for a particular variant
Our study has showed that for a sample of MMR variants with discordant classifications, Mastermind added incremental value to the initial literature search for a variant in question by providing a greater proportion of relevant articles overall and on average per search
Summary
Lynch syndrome (LS) is the most common aetiology of hereditary colorectal neoplasia with a prevalence of 3% to 5% amongst colorectal cancer patients.[1]. There are variant-oriented search systems that could improve the quality of search results and by extension improve the efficiency of the curation process.[7] These literature searching tools are able to find articles that mention specific variants using artificial intelligence and natural language processing They have been purported to increase the yield of a literature search compared to traditional search methods.[7] Whilst the literature does describe open source tools such as tmVar2.0 and LitVar which have been demonstrated to yield more articles than a standard PubMed search, the question as to whether these tools can be applied to a practical setting such as variant curation and interpretation remains unanswered.[8,9] For such tools to be useful, they would need to return articles that are relevant to the biocurator's task of classification. We set out to ask the question as to whether literature searching tools could add incremental value to the initial literature search to retrieve information for the classification of MMR variants submitted with different pathogenicity assignments
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