  Modern cancer -omics and pharmacological data hold great promise in precision
cancer medicine for developing individualized patient treatments. However, high
heterogeneity and noise in such data pose challenges for predicting the
response of cancer cell lines to therapeutic drugs accurately. As a result,
arbitrary human judgment calls are rampant throughout the predictive modeling
pipeline. In this work, we develop a transparent stability-driven pipeline for
drug response interpretable predictions, or staDRIP, which builds upon the PCS
framework for veridical data science (Yu and Kumbier, 2020) and mitigates the
impact of human judgment calls. Here we use the PCS framework for the first
time in cancer research to extract proteins and genes that are important in
predicting the drug responses and stable across appropriate data and model
perturbations. Out of the 24 most stable proteins we identified using data from
the Cancer Cell Line Encyclopedia (CCLE), 18 have been associated with the drug
response or identified as a known or possible drug target in previous
literature, demonstrating the utility of our stability-driven pipeline for
knowledge discovery in cancer drug response prediction modeling.
