Digital Twins for Targeted Therapy in Head and Neck Cancer: From Molecular Stratification to Resistance-Aware Combination Strategies.

Head and neck squamous cell carcinoma (HNSCC) remains clinically challenging because of its marked inter- and intra-tumour heterogeneity, the dynamic emergence of therapeutic resistance, and the limited ability of current biomarkers to guide treatment adaptation over time. Recent advances in digital twin (DT) technology have motivated the development of patient-specific, continuously updated computational models capable of integrating multi-scale data to support precision oncology. However, no current DT framework for HNSCC combines molecular stratification, longitudinal monitoring of resistance, pharmacodynamic modelling, and clinical decision support within a single adaptive system. In this review, we critically examine the current state of DT-enabled approaches for targeted therapy in HNSCC and propose a conceptual framework for their future clinical implementation. We discuss how genomic and multi-omic stratification, mechanistic imaging models, pharmacokinetic/pharmacodynamic modelling, longitudinal liquid biopsy (ctDNA and exosomes), ex vivo functional testing, toxicity prediction, and artificial intelligence could be integrated into a continuously updated patient-specific model. We further examine the biological mechanisms driving resistance to targeted therapies and immunotherapy, highlighting how these dynamic processes should inform adaptive therapeutic decision-making. We discuss existing DT-like approaches and evaluated them according to their capacity to fulfil the DT criteria, while also presenting DT-enabling technologies. Among the frameworks currently available, the deep reinforcement learning-based DITTO platform represents the closest approximation to a clinically relevant HNSCC digital twin. However, it does not yet incorporate molecular signalling networks, longitudinal resistance biomarkers, or multimodal biological data. We therefore identify the integration of these complementary data layers as the principal challenge and opportunity for the next generation of DTs. Collectively, this review provides a conceptual DT framework in which four main data domains could fulfil different roles within the DT. By sharing different parameters across these layers, the framework could forecast emerging resistance and update model predictions longitudinally. Such a DT could support biomarker-guided patient stratification, adaptive treatment selection, rational combination therapies, toxicity prediction, and future clinical trial design in HNSCC. We also highlight challenges and limitations that need to be addressed for future clinical translation, including data integration, interpretability, clinical validation, workflow integration, and ethical and regulatory considerations.