  Identifying the mutations that drive cancer growth is key in clinical
decision making and precision oncology. As driver mutations confer selective
advantage and thus have an increased likelihood of occurrence, frequency-based
statistical models are currently favoured. These methods are not suited to
rare, low frequency, driver mutations. The alternative approach to address this
is through functional-impact scores, however methods using this approach are
highly prone to false positives. In this paper, we propose a novel combination
method for driver mutation identification, which uses the power of both
statistical modelling and functional-impact based methods. Initial results show
this approach outperforms the state-of-the-art methods in terms of precision,
and provides comparable performance in terms of area under receiver operating
characteristic curves (AU-ROC). We believe that data-driven systems based on
machine learning, such as these, will become an integral part of precision
oncology in the near future.
