Unlocking the Potentials of Transcriptomics in Predicting Pan Cancer Immune Therapy Response: A Deep Learning Approach Using PHZNet

The effectiveness of immune checkpoint blockade (ICB) therapy, a critical strategy in cancer immunotherapy, is often limited by a high rate of primary resistance. Identifying patients likely to respond to this therapy is a significant challenge. To tackle this issue, we introduce a novel deep learning model known as the Permutable Hybrid Zipped Network (PHZNet), which is specifically tailored for classifying patient responses to Immune Checkpoint Blockade (ICB) therapy. We achieved 89.67% accuracy on the transcriptomics public dataset from the Faculty of Pharmacy, Universit'e de Montr'eal, outperforming other excellent models. In addition, to explore interpretability, based on the concept of model distillation, we propose to use XGBoost gradient boosting decision trees to distill PHZNet and thus uncovering key gene features influencing therapy response. Our study provides a reliable basis for clinical decision-making by predicting whether patient benefits from ICB therapy across pan-cancer. Furthermore, it paves the way for understanding the ICB treatment response mechanism and provides critical gene targets for future immune treatment strategies.