Abstract LB-009: An automated approach to identifying disease-gene associations from the medical literature to inform gene panel design
Authors: Kiel Mark , Schu Matthew , Schwartz Steve , Weigman Victor 
Published: July 2017

Rational approaches to the design of gene panels for next-generation molecular diagnostic assays are made challenging by the amount and complexity of information that must be manually collected and assessed from the medical literature. We have developed a novel text-mining infrastructure that automatically identifies disease-gene-treatment associations from the titles and abstracts of more than 30M scientific publications in PubMed and disease-gene-variant associations from the full-text of 3.1M prioritized articles. For this work, curated lists of diseases and genes comprising 11.7K and 50.9K total entries, respectively, were used as initial search parameters to prioritize full-text content. Next, custom-designed algorithms were used to scan full-text articles using a comprehensive variant lists comprising 602M total entries for every possible cDNA or protein variant in all gene transcripts sorted by biological outcome as second-tier search parameters. In total, we identified 909K putative disease-gene associations (24 articles per association on average) and 543K total variants distributed across all genes. This information was organized according to the strength of the association based on the total number and quality of individual citations and the position of disease-gene and disease-gene-variant key terms within the text. We decided to demonstrate this approach in AML to create an NGS panel, where outcome is very poor and lists of well-known mutations and biomarkers for treatment are not well characterized outside of cytogenetics. In total, 11K unique variants in 151 genes were found to be associated with AML and ranked according to the number of citations for each. Each variant had been cited at least once from 3,865 individual scientific publications. These variants were further classified according to the journals in which they appeared and the resulting data was manually inspected for accuracy. Additionally, custom search algorithms identified recurrent gene amplifications, gene deletions and gene-fusions resulting from translocations and were also catalogued and incorporated in the final panel design. The final in silico panel design was compared to commercially-available panels and included several genes with a clear association to acute myeloid leukemia with potential clinical significance that are otherwise not present on any such panel. In conclusion, we have developed and tested a tool that rapidly and comprehensively interrogates, organizes and displays genomic targets for diseases and has promising applications for clinical panel construction with significantly reduced curation and research time.

Citation Format: Mark Kiel, Matthew Schu, Steve Schwartz, Victor Weigman. An automated approach to identifying disease-gene associations from the medical literature to inform gene panel design [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-009. doi:10.1158/1538-7445.AM2017-LB-009