iPRISM: Intelligent Predicting Response to Cancer Immunotherapy through Systematic Modeling - Yinchun Su; Siyuan Li; Qian Wang; Bingyue Pan; Jiyin Lai; Guangyou Wang; Junwei Han; Qingfei Kong (Advanced Intelligent Systems)
Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Herein, we present iPRISM (Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling), which is a novel network‐based model that integrates multiomics data to predict immunotherapy outcomes. In this approach, iPRISM incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune‐related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. Using stepwise logistic regression, we identified key predictive features and validated iPRISM across multiple cohorts including melanoma, bladder cancer, non‐small cell lung cancer, and stomach adenocarcinoma. We also find that iPRISM outperforms the existing methods, achieving high predictive accuracy and demonstrating significant prognostic value for overall and progression‐free survival. By identifying key genetic and immunological factors, this model provides a new insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms. iPRISM can be accessed at CRAN: https://CRAN.R‐project.org/package=iPRISM.