  The genomic profile underlying an individual tumor can be highly informative
in the creation of a personalized cancer treatment strategy for a given
patient; a practice known as precision oncology. This involves next generation
sequencing of a tumor sample and the subsequent identification of genomic
aberrations, such as somatic mutations, to provide potential candidates of
targeted therapy. The identification of these aberrations from sequencing noise
and germline variant background poses a classic classification-style problem.
This has been previously broached with many different supervised machine
learning methods, including deep-learning neural networks. However, these
neural networks have thus far not been tailored to give any indication of
confidence in the mutation call, meaning an oncologist could be targeting a
mutation with a low probability of being true. To address this, we present here
a deep bayesian recurrent neural network for cancer variant calling, which
shows no degradation in performance compared to standard neural networks. This
approach enables greater flexibility through different priors to avoid
overfitting to a single dataset. We will be incorporating this approach into
software for oncologists to obtain safe, robust, and statistically confident
somatic mutation calls for precision oncology treatment choices.
