Dr. Matthew Schu, Sr. Bioinformatician at Q2 Solutions – EA Genomics, Dr. Victor Weigman, Director of Translational Genomics at Q2 Solutions – EA Genomics and Dr. Mark Kiel, CSO at Genomenon presented a poster at AACR this week on an automated approach to gene panel design. The full title and a link to the poster presentation is:
In the poster, Drs. Schu, Weigman and Kiel discuss a novel text-mining infrastructure that automatically identifies disease-gene-treatment associations from the titles and abstracts of more than 26M scientific publications in PubMed and disease-gene-variant associations from the full-text of 3.9M prioritized articles.
They demonstrated the rational gene panel design approach in creating an Acute Myeloid Leukemia (AML) 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.
The final in silico panel design was compared to commercially-available panels and included several genes with a clear association to AML with potential clinical significance that are otherwise not present on any such panel.