The emergence of large-scale omics data and foundational models has renewed efforts in the field of cancer drug response prediction (DRP). Despite recent progress, challenges such as limited tumor heterogeneity in standard cell lines and inconsistencies in experimental protocols across studies persist. However, these challenges also open significant opportunities for innovation. The complex nature of drug responses, influenced by variations in new patients and new drugs, presents a critical area for advancing validation approaches that traditional machine learning approaches often overlook. This review provides a comprehensive overview of the current state of DRP using advanced machine-learning models, discussing data sources, model designs, and evaluation methods. We introduce a unified framework for testing these models with a focus on clinically relevant metrics. By evaluating a range of foundational and deep-learning models within this framework, we identify performance gaps and propose concrete strategies to advance these computational models for reliable use in personalized cancer treatment, thereby unlocking their full clinical potential.
Real et al. (Thu,) studied this question.