Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers - Jin, Wenyi; Yang, Qian; Chi, Hao; Wei, Kongyuan; Zhang, Pengpeng; Zhao, Guodong; Chen, Shi; Xia, Zhijia; Li, Xiaosong (Frontiers in Immunology, 2022)
Despite immunotherapy has benefited many patients with different cancers, its clinical application and improvement have still been hindered by drug resistance. It is of great clinical implications to foster a reliable approach to identify suffers who sensitive to given immunotherapeutic agent. Herein, we proposed ELISE pipeline (Ensemble Learning for Immunotherapeutic Response Evaluation) to generate robust and high accurate approaches to predict individual response to immunotherapies. ELISE employed iterative univariable logistic regression to select genetic features of patients, Monte-Carlo Tree Search for hyperparameters tuning. In each trial, ELISE selected multiple models for integration based on add or concatenate stacking strategies, including deep neural network, automatic feature interaction learning via self-attentive neural networks, deep factorization machine, compressed interaction networks and linear neural network, and adopted the best trial to generate final approach. Shapley additive explanations (SHAP) algorithm was applied to interpret ELISE, which was then validated in an independent test set. Regarding prediction of responses to atezolizumab within esophageal adenocarcinoma (EAC) patients, ELISE demonstrated a superior accuracy (Area Under Curve [AUC] = 100.00%). AC005786.3 (Mean [|SHAP value|] = 0.0097) was distinguished as the most valuable contributor to ELISE output, followed by SNORD3D (0.0092), RN7SKP72 (0.0081), EREG (0.0069), IGHV4-80 (0.0063), MIR4526 (0.0063). Mechanistically, immunoglobulin complex, immunoglobulin production, adaptive immune response, antigen binding, etc., were downregulated in ELISE-neg EAC subtypes and resulted in unfavorable responses. More encouragingly, ELISE could be extended to accurately estimate the responsiveness of various immunotherapeutic agents against other cancers, including PD1/PD-L1 suppressor against metastatic urothelial cancer (AUC = 88.86%), and MAGE‑A3 immunotherapy against metastatic melanoma (AUC = 100.00%). In conclusion, this study presented deep insights into integrating ensemble deep learning with self-attention mechanism for predicting immunotherapies responses to human cancers, and proposed ELISE as a general tool to generate reliable approaches.