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Updated: Jun 16, 2026

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

Published on: August 18, 2023

A novel computational framework for tumor-specific T cell antigen identification using a deep neural network.

Salman Khan¹, Islam Uddin², Fawaz Khaled Alarfaj³

¹Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11451, Riyadh, Saudi Arabia.

Journal of Computer-Aided Molecular Design

|June 15, 2026

Summary

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Tumor Immunotherapy

Tumor Immunotherapy

Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.

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This study introduces a novel Deep Neural Network framework for identifying tumor-specific T-cell antigens, improving cancer immunotherapy precision. The AI-driven approach enhances antigen prediction accuracy and efficiency, outperforming existing methods.

Area of Science:

Oncology
Immunology
Bioinformatics

Background:

Accurate identification of tumor-specific T-cell antigens is critical for advancing cancer immunotherapy.
Current artificial intelligence (AI) and machine learning (ML) methods face challenges with the complexity and sequence dependency of antigen data, leading to suboptimal predictions.

Purpose of the Study:

To develop a Deep Neural Network (DNN)-based framework for enhanced computational tumor T-cell antigen identification.
To address limitations in existing AI/ML approaches for antigen prediction.

Main Methods:

Utilized hybrid sequence encoding: Position-Specific Scoring Matrix with Discrete Wavelet Transform (PsePSSM-DWT) and Protein Bidirectional Encoder Representations from Transformers (ProtBERT-BFD).
Implemented a Shapley Additive exPlanations (SHAP)-based global feature selection strategy for efficient feature selection.

Keywords:

Antigen identification Cancer immunotherapy Computational immunology Deep learning Deep neural networks (DNNs)Tumor T-cell antigens

Related Experiment Videos

Last Updated: Jun 16, 2026

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

Published on: August 18, 2023

Trained a DNN model on the optimized feature set for antigen identification.

Main Results:

The proposed DNN framework achieved an average accuracy of 96.16% and a Matthew's correlation coefficient of 0.923.
Demonstrated significant performance improvement over conventional machine learning and state-of-the-art methods.
Established a robust computational baseline for T-cell antigen identification.

Conclusions:

The developed framework offers a powerful tool for precision-driven, AI-assisted discovery of tumor antigens.
Provides a foundation for integrating multi-omics data and real-time immunotherapy workflows.
Represents a significant advancement in computational approaches for cancer immunotherapy.