Abstract
Background: Chemotherapy, targeted therapy and radiotherapy are the cornerstones of cancer treatment. However, therapeutic resistance-not only to these classic modalities but also to novel therapeutics like immune checkpoint inhibitors (ICIs) and antibody-drug conjugates-remains a major hurdle. Resistance significantly limits efficacy and increases recurrence rates. A deep understanding of the molecular mechanisms driving this resistance is critical for developing personalized therapeutic strategies and improving patient outcomes. Recent Advances: Patient-derived cancer organoids have emerged as a powerful preclinical platform that faithfully recapitulates the genetic, phenotypic, and histological characteristics of original tumors. Consequently, PDOs are being widely utilized to evaluate drug responses, investigate resistance mechanisms, and discover novel therapeutic targets for a range of therapies. Limitations: While organoid models have been instrumental in studying resistance, significant limitations persist. First, standard organoid-only models lack key tumor microenvironment components, such as immune cells, limiting immunotherapy research. Second, there is a significant lack of research on acquired resistance, particularly in lung cancer. This gap is largely driven by the clinical infeasibility of rebiopsy in patients with progressive diseases. Third, the absence of standardized protocols for generating and validating resistance models hinders reproducibility and complicates clinical translation. Conclusions: This review summarizes recent advances in using organoid models to study resistance to chemotherapy, radiotherapy, and novel therapeutics (ICIs and ADCs). We emphasize the critical need for standardization in resistance organoid research. We also propose future directions to overcome existing challenges, including the integration of co-culture systems (to include the TME) and advanced technologies (e.g., scRNA-seq, Spatial Transcriptomics). Our specific focus is on advancing lung cancer resistance modeling to enable functional precision medicine.