  Cancer remains a global challenge due to its growing clinical and economic
burden. Its uniquely personal manifestation, which makes treatment difficult,
has fuelled the quest for personalized treatment strategies. Thus, genomic
profiling is increasingly becoming part of clinical diagnostic panels.
Effective use of such panels requires accurate drug response prediction (DRP)
models, which are challenging to build due to limited labelled patient data.
Previous methods to address this problem have used various forms of transfer
learning. However, they do not explicitly model the variable length sequential
structure of the list of mutations in such diagnostic panels. Further, they do
not utilize auxiliary information (like patient survival) for model training.
We address these limitations through a novel transformer based method, which
surpasses the performance of state-of-the-art DRP models on benchmark data. We
also present the design of a treatment recommendation system (TRS), which is
currently deployed at the National University Hospital, Singapore and is being
evaluated in a clinical trial.
