A Predictive Network-Based Immune Checkpoint Blockade Immunotherapeutic Signature Optimizing Patient Selection and Treatment Strategies.

Immune checkpoint blockade (ICB) therapy has brought significant advancements to the field of oncology. However, the diverse responses among patients highlight the need for more accurate predictive tools. In this study, insights are drawn from tumor-immunology pathways, and a novel network-based ICB immunotherapeutic signature, termed ICBnetIS, is constructed. The signature is derived from advanced biological network-based computational strategies involving co-expression networks and molecular interactions networks. The efficacy of ICBnetIS is established through its association with enhanced patient survival and a robust immune response characterized by diverse immune cell infiltration and active anti-tumor immune pathways. The validation process positions ICBnetIS as an effective tool in predicting responses to ICB therapy, analyzing ICB data from a broad collection of over 700 samples from multiple cancer types of more than 15 datasets. It achieves an aggregated prediction AUC of 0.784, which outperforms the other nine renowned immunotherapeutic signatures, indicating the superior predictive capability of ICBnetIS. To sum up, the findings suggest ICBnetIS as a potent tool in predicting ICB therapy responses, offering significant implications for patient selection and treatment optimization in oncology. The study highlights the role of ICBnetIS in advancing personalized treatment strategies, potentially transforming the clinical landscape of ICB therapy.