  Cancer, a leading cause of death globally, occurs due to genomic changes and
manifests heterogeneously across patients. To advance research on personalized
treatment strategies, the effectiveness of various drugs on cells derived from
cancers (`cell lines') is experimentally determined in laboratory settings.
Nevertheless, variations in the distribution of genomic data and drug responses
between cell lines and humans arise due to biological and environmental
differences. Moreover, while genomic profiles of many cancer patients are
readily available, the scarcity of corresponding drug response data limits the
ability to train machine learning models that can predict drug response in
patients effectively. Recent cancer drug response prediction methods have
largely followed the paradigm of unsupervised domain-invariant representation
learning followed by a downstream drug response classification step.
Introducing supervision in both stages is challenging due to heterogeneous
patient response to drugs and limited drug response data. This paper addresses
these challenges through a novel representation learning method in the first
phase and weak supervision in the second. Experimental results on real patient
data demonstrate the efficacy of our method (WISER) over state-of-the-art
alternatives on predicting personalized drug response.
