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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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Related Experiment Video

Updated: Apr 28, 2026

Using 22C3 Anti-PD-L1 Antibody Concentrate on Biopsy and Cytology Samples from Non-small Cell Lung Cancer Patients

Using 22C3 Anti-PD-L1 Antibody Concentrate on Biopsy and Cytology Samples from Non-small Cell Lung Cancer Patients

Published on: September 25, 2018

Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non-Small Cell Lung Cancer

Rayan Mubarak¹, Fahim Islam Anik², Jean T Rodriguez³

¹Cypress Bay High School, Weston, FL, United States.

JMIR Bioinformatics and Biotechnology

|January 13, 2026

Summary

This study uses deep learning to identify genomic biomarkers for non-small cell lung cancer (NSCLC) patients who respond to PD-1 immunotherapy. The DeepImmunoGene model identified 36 key genes, improving prediction accuracy for personalized treatment strategies.

Keywords:

DeepImmunoGene RNA-seq analysis biomarkers deep neural network differential gene expression immunotherapy lung cancer machine learning programmed cell death receptor-1

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Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

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Published on: February 7, 2021

Area of Science:

Genomics
Immunotherapy
Machine Learning
Cancer Research

Background:

Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality.
Programmed cell death receptor-1 (PD-1) immunotherapy shows promise but lacks universal response.
Predictive biomarkers are crucial for optimizing NSCLC treatment strategies.

Purpose of the Study:

To leverage deep neural networks (DNNs) for identifying genomic biomarkers predicting PD-1 immunotherapy response in NSCLC.
To develop and validate the DeepImmunoGene model for accurate biomarker discovery.
To enable personalized treatment approaches for NSCLC patients.

Main Methods:

RNA-sequencing data from 355 NSCLC patients were analyzed.
Differentially expressed genes were identified and preprocessed.
Machine learning models (SVM, XGBoost, DNN) were trained to predict immunotherapy response.
Feature selection and permutation importance analysis were used to identify key predictive genes.

Main Results:

A DNN model achieved 82% accuracy, outperforming SVM and XGBoost.
The DeepImmunoGene model, trained on 98 selected genes, reached 87% accuracy and 95% AUC.
36 upregulated genes in responders and 62 in non-responders were identified as potential biomarkers.

Conclusions:

The DeepImmunoGene model effectively predicts immunotherapy outcomes in NSCLC.
Identified genomic biomarkers can aid in patient stratification for PD-1 therapy.
This approach supports the development of personalized medicine for NSCLC.