Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Qian Yang
  • Kongyuan Wei
  • Pengpeng Zhang
  • Guodong Zhao
  • Shi Chen
  • Zhijia Xia
  • Xiaosong Li

Detail(s)

Original languageEnglish
Article number1025330
Journal / PublicationFrontiers in Immunology
Volume13
Online published1 Dec 2022
Publication statusPublished - 2022
Externally publishedYes

Link(s)

Abstract

Introduction: Despite the many benefits immunotherapy has brought to patients with different cancers, its clinical applications and improvements are still hindered by drug resistance. Fostering a reliable approach to identifying sufferers who are sensitive to certain immunotherapeutic agents is of great clinical relevance.

Methods: We propose an ELISE (Ensemble Learning for Immunotherapeutic Response Evaluation) pipeline to generate a robust and highly accurate approach to predicting individual responses to immunotherapies. ELISE employed iterative univariable logistic regression to select genetic features of patients, using Monte Carlo Tree Search (MCTS) to tune hyperparameters. 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 network, and linear neural network, then adopted the best trial to generate a final approach. SHapley Additive exPlanations (SHAP) algorithm was applied to interpret ELISE, which was then validated in an independent test set.

Result: 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), and MIR4526 (0.0063). Mechanistically, immunoglobulin complex, immunoglobulin production, adaptive immune response, antigen binding and others, 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%).

Discussion: This study presented deep insights into integrating ensemble deep learning with self-attention as a mechanism for predicting immunotherapy responses to human cancers, highlighting ELISE as a potential tool to generate reliable approaches to individualized treatment.

Research Area(s)

  • Humans, Deep Learning, Neural Networks, Computer, Melanoma, Immunotherapy/methods, Attention

Bibliographic Note

Copyright © 2022 Jin, Yang, Chi, Wei, Zhang, Zhao, Chen, Xia and Li.

Citation Format(s)

Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers. / Jin, Wenyi; Yang, Qian; Chi, Hao et al.
In: Frontiers in Immunology, Vol. 13, 1025330, 2022.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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