Modelling the monstrosities: experimental and computational systems for studying polyploid giant cancer cells.

backgroundPolyploid Giant Cancer Cells (PGCCs) are a malformed subpopulation of tumor. They play a crucial role in metastasis, recurrence, and therapy resistance. However, the inconsistent model systems and a lack of standardization have hindered mechanistic understanding and clinical translation. This review highlights the pluralistic research for clinical application by methodically analyzing various model systems used in PGCC research to fill the gap in the literature.

methodsAs of November 2025, scholarly literature gathered from Google Scholar, PubMed, and ScienceDirect focused on examining the development, characteristics, and functional involvement of PGCCs in cancer.

resultsIn vitro approaches, although limited in their physiological relevance, enable detailed mechanistic studies and facilitate the screening of drugs. Ex vivo tumor explants and organoids preserve patient-specific traits with translational potential, while in vivo models, such as Drosophila and mouse xenografts, provide insight into PGCC function in complex tissue environments. By mapping model capabilities against PGCC research priorities, we demonstrate that no single system comprehensively recapitulates PGCC biology, necessitating integrated, multi-model experimental strategies that we outline in this study. More specifically, integrating patient-derived organoids with lineage-traced xenografts and single-cell omics enables continuous tracking of PGCC development and functional diversity, facilitating mechanistic studies of metastasis, drug resistance, and identification of clinical biomarkers for patient stratification.

conclusionConsidering the current lack of PGCC-targeted therapies, the convergence of model modification and the development of single-cell and imaging capabilities indicates significant progress toward therapeutically relevant findings. The ongoing development of these models is thus crucial for translating PGCC biology into predictive diagnoses and effective treatment methods.