  The complicated, evolving landscape of cancer mutations poses a formidable
challenge to identify cancer genes among the large lists of mutations typically
generated in NGS experiments. The ability to prioritize these variants is
therefore of paramount importance. To address this issue we developed
OncoScore, a text-mining tool that ranks genes according to their association
with cancer, based on available biomedical literature. Receiver operating
characteristic curve and the area under the curve (AUC) metrics on manually
curated datasets confirmed the excellent discriminating capability of OncoScore
(OncoScore cut-off threshold = 21.09; AUC = 90.3%, 95% CI: 88.1-92.5%),
indicating that OncoScore provides useful results in cases where an efficient
prioritization of cancer-associated genes is needed.
