Targeted cancer therapies, including monoclonal antibodies and lipid nanomedicines, continue to enhance cancer treatment by increasing specificity and prolonging survival. Their therapeutic potential remains, however, limited by tumor heterogeneity, adaptive resistance, and complex microenvironmental factors. Conventional preclinical models, including cancer cell lines, patient-derived xenografts, and organoids, have helped elucidate mechanisms but fail to accurately predict long-term, patient-specific outcomes. This is a methodological gap that still hinders precision oncology, where the main goal is to tailor therapies to individual patients. In this review, we describe conventional in vitro models while focusing on recent developments in the generation and use of human-induced pluripotent stem cell (hiPSC)-derived cancer models. These systems offer distinct opportunities to bridge translational gaps by leveraging oncogenic mutations, providing renewable patient-specific platforms, and combining with lineage tracing, multi-omics, and organ-on-chip technologies. We also evaluate the role of hiPSC-derived models in complementing the existing platforms and discuss current limitations and future development, such as epigenetic mapping, nanoscale testing, and AI-driven analytics, thereby making hiPSC-based cancer models a new, highly promising tool for precision oncology.
Pant et al. (Mon,) studied this question.