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\begin{document}

\title{Searching for the needle in the haystack: the application of a
literature searching tool to retrieve information to allow optimum
classification of MMR variants}



\author[1]{Varun Kaushik}%
\author[1]{Finlay Macrae}%
\author[1]{John Paul Plazzer}%
\affil[1]{The Royal Melbourne Hospital}%


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  \date{May 08, 2024}


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\begin{abstract}
Background: Pathogenic 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. Given the
onerous nature of extracting information related to genetic variants,
literature searching tools which harness artificial intelligence may aid
in retrieving information to allow optimum variant classification.
Methods: 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
on ClinVar. Variants were searched for using a traditional method
(Google Scholar) and literature searching tool (Mastermind Genomenon)
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. Results: 916 articles were
returned by both methods. Mastermind averaged four relevant articles per
search, Google Scholar, three. Of relevant Mastermind articles, 193/308
(62.7\%) were unique to it, compared to 87/202, (43.0\%) for Google
Scholar. All 6/80 (20\%) variants with pathogenic or likely pathogenic
InSiGHT classifications have newer VUS assertions on ClinVar.
Conclusion: Mastermind on average returned a more relevant literature
search. Google Scholar still found unique information, suggesting that
Mastermind could play a complementary role.%
\end{abstract}%



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