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.

癌症个性化治疗是全球面临的一个挑战，在临床和经济上都具有不断增长的负担。为了有效利用临床诊断面板中的基因组分析，需要准确的药物反应预测模型，本研究提出了一种基于转换器的创新方法，超越了基准数据上最先进的药物反应预测模型的性能，同时还设计了一种治疗建议系统，并正在新加坡国立大学医院进行临床试验评估。