Background: Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with a poor prognosis, necessitating the discovery of novel and reliable molecular biomarkers for improved clinical management. Traditional bulk transcriptomic analyses often mask the cellular heterogeneity and spatial complexity of the tumor microenvironment, limiting the identification of robust biomarkers. This study aimed to identify and validate key driver genes in HNSCC through a comprehensive multi-omics and machine learning-based approach. Materials and methods: Transcriptomic data from multiple GEO datasets (GSE29330, GSE6631, GSE138206) and the TCGA-HNSC cohort were integrated and analyzed to identify consensus differentially expressed genes (DEGs). A suite of four machine learning algorithms (LASSO, SVM-RFE, XGBoost, Boruta) was employed to screen for core candidate genes. The cellular origins and spatial distribution of these core genes were subsequently dissected using public single-cell (GSE215403) and spatial transcriptomics (GSE252265) data. Finally, the expression of the key gene, SAM and SH3 domain-containing 1 (SASH1), was validated at the protein level via Western blot in HNSCC cell lines, and its clinical and therapeutic value was assessed through survival, clinical correlation, and drug sensitivity analyses. Results: An integrated analysis of bulk transcriptomic data identified 159 consensus DEGs, from which four core genes (COL1A1, EMP1, MYH11, SASH1) were robustly selected by all four machine learning algorithms. Multi-omics validation revealed that SASH1 was specifically downregulated within the malignant cell population and its expression was spatially exclusive from the COL1A1-high fibrotic stromal regions. Western blot confirmed the significant downregulation of SASH1 protein in HNSCC cells compared to controls. Importantly, low SASH1 expression was significantly associated with poorer overall survival in the TCGA cohort ( P < 0.05), a prognostic value not observed for the other core genes. Functional analyses linked SASH1 to critical pathways including cell cycle and adhesion. Furthermore, SASH1 expression levels correlated with sensitivity to multiple targeted drugs, including ATR and Aurora kinase inhibitors. Conclusion: By systematically integrating multi-platform transcriptomics, machine learning, and multi-dimensional validation, this study identifies SASH1 as a robust prognostic biomarker and a potential predictor of therapeutic response in HNSCC. The established multi-omics pipeline provides a meaningful framework for biomarker discovery and highlights SASH1 as a promising target for advancing precision medicine in HNSCC.

Background: Tumors, as intricate ecosystems, comprise oncocytes and the highly dynamic tumor stroma. Tumor stroma, representing the non-cancerous and non-cellular composition of the tumor microenvironment (TME), plays a crucial role in oncogenesis and progression, through its interactions with biological, chemical, and mechanical signals. This review aims to analyze the challenges of stroma mimicry models, and highlight advanced personalized co-culture approaches for recapitulating tumor stroma using patient-derived tumor organoids (PDTOs). Methods: This review synthesizes findings from recent studies on tumor stroma composition, stromal remodeling, and the spatiotemporal heterogeneities of the TME. It explores popular stroma-related models, co-culture systems integrating PDTOs with stromal elements, and advanced techniques to improve stroma mimicry. Results: Stroma remodeling, driven by stromal cells, highlights the dynamism and heterogeneity of the TME. PDTOs, derived from tumor tissues or cancer-specific stem cells, accurately mimic the tissue-specific and genetic features of primary tumors, making them valuable for drug screening. Co-culture models combining PDTOs with stromal elements effectively recreate the dynamic TME, showing promise in personalized anti-cancer therapy. Advanced co-culture techniques and flexible combinations enhance the precision of tumor-stroma recapitulation. Conclusions: PDTO-based co-culture systems offer a promising platform for stroma mimicry and personalized anti-cancer therapy development. This review underscores the importance of refining these models to advance precision medicine and improve therapeutic outcomes.

The inherent complexity and heterogeneity of tumors pose substantial challenges for the development of effective oncology therapeutics. Organoids, three-dimensional (3D) in vitro models, have become essential tools for predicting therapeutic responses and advancing precision oncology, with established correlations to clinical outcomes in patient-derived models. These systems have transformed preclinical drug screening by bridging the gap between conventional two-dimensional (2D) cultures and in vivo models, preserving tumor histopathology, cellular heterogeneity, and patient-specific molecular profiles. Despite their potential, limitations in tumor organoid biology, including inter-batch variability and microenvironmental simplification, can undermine their reliability and scalability in large-scale drug screening. To overcome these challenges, the integration of advanced technologies such as artificial intelligence (AI), automated biomanufacturing, multi-omics analytics, and vascularization strategies has been explored. This review highlights the “Organoid plus and minus” framework, which combines technological augmentation with culture system refinement to improve screening accuracy, throughput, and physiological relevance. We are convinced that the future of drug development hinges on the convergence of these multidisciplinary technologies with standardized biobanking and co-clinical validation frameworks. This integration will position organoids as a cornerstone for personalized drug discovery and therapeutic optimization, ultimately advancing the development of efficacy in oncology.

Cancer immunotherapy has become a powerful clinical strategy for cancer management, while its efficacy is frequently limited by primary and acquired resistance. The tumor microenvironment (TME) plays a pivotal role in mediating such resistance through multifaceted mechanisms involving cellular, metabolic, mechanical, and microbial components. This review systematically examines how the TME contributes to immunotherapy failure. We compare resistance mechanisms common to both immune checkpoint inhibitors (ICIs) and chimeric antigen receptor T-cell (CAR-T) therapies, two cornerstone modalities in clinical practice. Furthermore, we discuss emerging strategies designed to overcome these barriers, including immune microenvironment, stromal normalization, metabolic modulation, and microbiota engineering. By integrating recent preclinical and clinical insights, this review aims to provide a comprehensive framework for understanding and targeting microenvironmental resistance, ultimately facilitating the translation of novel combination therapies into improved patient outcomes.