Innovative organ-on-a-chip platforms for exploring tumorigenesis and therapy in head and neck cancer

Head and neck cancer (HNC) presents significant research challenges due to the complexity of its tumor microenvironment (TME) and the heterogeneity across different cancer subtypes. Recent advancements in three-dimensional (3D) culture models and organ-on-a-chip (OOC) technology offer new avenues for mimicking the TME and enhancing the study of tumor biology, drug responses, and personalized treatment strategies. This study aims to summarize the current state of these models in HNC research and their potential in bridging the gap between preclinical models and clinical applications.

This review synthesizes findings from recent literature on the use of 3D models such as tumor spheroids, organoids, and co-culture systems in HNC research. A focus is placed on their applications in different cancer types, including laryngeal, oral, and nasopharyngeal cancers. Additionally, the integration of OOC technology in studying cancer metastasis, immunotherapy, and radiotherapy is discussed. Relevant studies on the role of AI and robotics in improving model efficiency and scalability are also examined.

The review identifies key developments in 3D model systems and OOC technologies, highlighting their ability to replicate patient-specific tumor behaviors and predict therapeutic responses. While these models have advanced the understanding of HNC pathophysiology, challenges remain in terms of technical limitations, validation, and physiological relevance. The integration of AI and robotics has shown promise in enhancing the scalability and data analysis capabilities of these models.

Advancements in 3D and OOC technologies are essential for overcoming the current limitations in HNC research. These models offer valuable insights into tumor biology and therapeutic efficacy, and their integration with artificial intelligence (AI) can further accelerate the development of personalized treatment strategies. However, further validation and refinement are needed before these models can be widely translated into clinical practice, offering a more effective and individualized approach to treating HNC.