Digital Twins through Multimodal Machine Learning Unlock Precision Stratification in Head and Neck Cancer

Background: Head and neck cancer (HNC) represents an aggressive malignancy with favorable outcomes when detected early, yet inter-patient heterogeneity profoundly influences progression and therapeutic response. While genomic profiling has advanced disease classification and progression insights, comorbidities, particularly chronic obstructive pulmonary disease (COPD), hypertension, and cardiovascular disease, alongside demographic factors, exert substantial effects on tumor behavior, treatment tolerance, and survival. This study integrates clinical, demographic, and high-dimensional genomic data through a digital twin framework to identify and characterize patient subgroups exhibiting shared profiles, enabling precision stratification beyond conventional analyses.Methods: We applied unsupervised dimensionality reduction—principal component analysis (PCA) followed by uniform manifold approximation and projection (UMAP)—to generate an integrated embedding of multimodal patient data. Reference patients were projected into the UMAP space, and digital twin cohorts were identified using Euclidean nearest-neighbor similarity. Subgroup differences were then characterized via association tests, differential gene expression (DGE) analysis, pathway enrichment, and Kaplan-Meier (KM) survival analysis. Additionally, machine learning models were trained to predict survival and rigorously benchmarked.Results: The UMAP-based digital twin framework effectively captured patient heterogeneity, resolving finer subgroup structures than traditional clustering. KM analyses revealed marked survival disparities between twin-defined subgroups and the broader cohort. Clinical/demographic factors—including COPD, hypertension, and sex—exhibited significant inter-subgroup differences, as did specific medication patterns suggestive of personalized therapeutic opportunities. DGE profiling identified biologically relevant gene signatures that distinguish twin groups. We also deployed an interactive R Shiny application to facilitate twin identification, visualization, and downstream analyses.Conclusion: This digital twin approach advances precision oncology in HNC by enabling data-driven patient matching and subgroup discovery across multimodal features. By refining prognostic insights and highlighting tailored interventions, it paves the way for enhanced clinical decision-making.