  Precision oncology, the genetic sequencing of tumors to identify druggable
targets, has emerged as the standard of care in the treatment of many cancers.
Nonetheless, due to the pace of therapy development and variability in patient
information, designing effective protocols for individual treatment assignment
in a sample-efficient way remains a major challenge. One promising approach to
this problem is to frame precision oncology treatment as a contextual bandit
problem and to apply sequential decision-making algorithms designed to minimize
regret in this setting. However, a clear prerequisite for considering this
methodology in high-stakes clinical decisions is careful benchmarking to
understand realistic costs and benefits. Here, we propose a benchmark dataset
to evaluate contextual bandit algorithms based on real in vitro drug response
of approximately 900 cancer cell lines. Specifically, we curated a dataset of
complete treatment responses for a subset of 7 treatments from prior in vitro
studies. This allows us to compute the regret of proposed decision policies
using biologically plausible counterfactuals. We ran a suite of Bayesian bandit
algorithms on our benchmark, and found that the methods accumulate less regret
over a sequence of treatment assignment tasks than a rule-based baseline
derived from current clinical practice. This effect was more pronounced when
genomic information was included as context. We expect this work to be a
starting point for evaluation of both the unique structural requirements and
ethical implications for real-world testing of bandit based clinical decision
support.
