Explainable deep learning approaches and clinical insights for cancer biomarker identification

Biomarkers play a pivotal role in contemporary cancer immunotherapy by guiding diagnosis, patient stratification, therapeutic decision-making, and longitudinal assessment of treatment responses. Despite the transformative impact of immune checkpoint inhibitors, adoptive cell therapies, and neoantigen-based vaccines, durable clinical benefit is achieved in only a subset of patients, highlighting the critical need for accurate predictive and prognostic biomarkers. Technological advances are rapidly expanding the biomarker repertoire through high-resolution approaches such as single-cell and spatial omics, circulating tumor DNA analysis, immune-related gene expression signatures, and microbiome profiling. These platforms enable deeper characterization of immune dynamics, resistance mechanisms, and therapeutic responsiveness. Recent advances in artificial intelligence, machine learning, and deep learning have fundamentally reshaped immunotherapy biomarker discovery by enabling the integration of complex, high-dimensional multiomics, radiomic, and clinical datasets into unified predictive frameworks. Deep learning models have demonstrated superior performance in predicting immune checkpoint inhibitor responses, immune-related adverse events, and mechanisms of therapeutic resistance across multiple cancer types. The incorporation of explainable AI approaches further enhances clinical interpretability by linking algorithmic predictions to biologically validated immune processes. Future progress will depend on multimodal biomarker integration, analytical standardization, and rigorous prospective validation, alongside addressing regulatory, economic, and implementation challenges to advance precision cancer immunotherapy.