Systems immunology meets clinical translation: Multi-omic approaches to predict therapy response in cancer and autoimmune disease

Abstract

The immune system is governed by complex, interconnected molecular and cellular networks that shape disease risk and treatment outcome. Conventional biomarkers in cancer and autoimmune disease often rely on single parameters and fail to capture immune heterogeneity, resulting in variable therapeutic responses. Systems immunology integrates genomics, transcriptomics, epigenomics, proteomics, metabolomics, and microbiome profiling to generate holistic models of immune function. This review examines how multi-omic integration improves the prediction of therapy response across solid and hematologic malignancies and autoimmune disorders, including rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, and inflammatory bowel disease. Advances in machine learning, network modeling, and spatial biology are discussed as critical enablers of immune stratification and biomarker discovery. Although integrated approaches outperform single-omics predictors, challenges remain in clinical validation, interpretability, and cost-effectiveness. We propose a translational framework in which targeted multi-omic panels and computational modeling guide personalized immunotherapy, shifting immune-based treatment from empirical intervention toward predictive, systems-driven precision medicine.