Matthew Cannon
Matthew earned an undergraduate degree in Biochemistry and a doctorate degree in Biomedical Sciences from The Ohio State University. He contributed to bioinformatics and experimental therapeutics-based research to screen FDA-approved drugs for new indications for sickle cell disease (SCD). Matthew joined Dr. Alex Wagner’s lab as a postdoctoral scientist to continue his training and currently leads research and development for the next version of the Drug-Gene Interaction Database (DGIdb).
Abstract
Matthew Cannona, James Stevensona, Kori Kuzmaa, Colin O’Sullivana, Katherine Millera, Olivia Grischowa, Adam Coffmanb, Susanna Kiwalab, Joshua McMichaelb, Dorian Morrisseyb, Kelsy Cottob, Obi L. Griffithb, Malachi Griffithb, Alex Wagnera
aNationwide Children’s Hospital, Columbus, OH, USA; bWashington University, St. Louis, MO, USA
The Drug-Gene Interaciton Database (DGIdb, www.dgidb.org) is a publicly accessible resource that aggregates over 100,000 drug-gene interaction claims across 30 interaction types aid both researchers and clinicians in identifying associations between genes of interest and available therapeutics. By incorporating peer-reviewed data sources and publications, DGIdb integrates 102,426 gene records and 57,498 drug records from 40 drug-gene interaction data sources to drive hypothesis generation in precision medicine. The background process that normalizes drugs to a harmonized ontological concept has been upgraded. These improvements have increased concept normalization for drugs by 20%. Drug normalization is now available as a stand-alone service (https://normalize.cancervariants.org/therapy/). Leveraging our platform’s ability to find relationships between disease-critical genes and available therapeutics, DGIdb has been used in clinical interpretation pipelines to find drugs for specific diseases with an emphasis on regulatory approval status. DGIdb now uses annotations from Drugs@FDA to provide more accurate approval descriptors as well as references to active ANDA and NDA applications, when available. Lastly, to enhance the annotation potential for DGIdb in precision medicine pipelines, we have updated our druggable gene category sources with an additional curated list of 2,217 genes. Used alone or in combination with existing categories such as the heavily-utilized ‘clinically actionable’ category, this additional source will give precision medicine and interpretation pipelines the power to find concise, actionable annotations for specific diseases including pediatric cancers and epilepsy. We will illustrate the use of these features and DGIdb as a precision medicine resource in the evaluation of variants from four central nervous system tumors