Decoding immunotherapy response through computational modeling

Immunotherapy has seen success in treating patients with cancer, but variable responses underscore the need for effective patient stratification and therapy planning. Computational tools integrating multi-omics, imaging and machine learning have advanced, yet reliable personalized predictions remain challenging. This review analyzes the field through four converging paradigms: classical machine learning, deep learning, graph and network modeling, and mechanistic systems biology. We examine the evolution from correlational features to representation learning, relational inference, and causal simulation of tumor-immune dynamics, highlighting the shift towards multi-modal fusion and interpretable, clinically deployable models. By providing an integrated review of these computational tools, we hope to bring the community closer to achieving precision immuno-oncology for personalized cancer treatments. Multiomics data has revolutionized immuno-oncology studies by facilitating both populational and personalized facets of treatment planning. Here the authors review current computational tools, stratified into four ‘paradigms’, for analyzing multiomics data and facilitating optimization of cancer therapy.