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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.

Cytotoxic T Cells-mediated Immune Response

Cytotoxic T Cells-mediated Immune Response

Cytotoxic T cells are a vital component of the immune system. They have the remarkable ability to identify and target antigens on infected or abnormal cells. These antigens often originate from intracellular pathogens such as viruses or abnormal proteins cancer cells produce.
Immunological surveillance is the ability of immune cells to monitor and eliminate infected cells with intracellular pathogens, neoplastically transformed cells, and cells with non-self antigens. Cytotoxic T cells and NK...

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Related Experiment Video

Updated: May 25, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Explainable machine learning identifies features and thresholds predictive of immunotherapy response.

Khoa A Tran^1,2, Venkateswar Addala³, Lambros T Koufariotis³

¹Cancer Program, QIMR Berghofer Medical Research Institute, 300 Herston Road, Brisbane, Australia. khoa.tran@qimrb.edu.au.

Scientific Reports

|May 23, 2026

Summary

This summary is machine-generated.

Predicting immunotherapy response in melanoma is crucial. Machine learning models integrating clinical and sequencing data identified key features like mutation status, immune cell abundance, and LAG3 expression, aiding precision oncology.

Keywords:

Biomarkers Explainability Immunotherapy Machine learning Melanoma Multi-omics Treatment response

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Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Related Experiment Videos

Last Updated: May 25, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Area of Science:

Oncology
Bioinformatics
Immunology

Background:

Predicting patient response to immunotherapy is a significant challenge in precision oncology.
Reliable biomarkers are needed to guide treatment decisions for melanoma patients.

Purpose of the Study:

To develop and validate machine learning models for predicting immunotherapy response in melanoma.
To identify key clinical, genomic, and transcriptomic features associated with treatment outcomes.

Main Methods:

Integrated clinical data with DNA and RNA sequencing from 229 melanoma samples.
Developed predictive models using machine learning on 138 samples, optimizing with SHAP (SHapley Additive exPlanations).
Validated the optimal random forest model on an independent cohort of 53 patients.

Main Results:

Random forest identified as the optimal classifier.
SHAP analysis revealed mutation features, immune cell abundance, and LAG3 expression as significant predictors of response.
Identified potential numerical thresholds for features differentiating good versus poor responders.

Conclusions:

Machine learning models integrating multi-omic data can predict immunotherapy response in melanoma.
Explainability methods like SHAP highlight intrinsic and extrinsic factors influencing treatment success.
This approach has potential for clinical translation in biomarker discovery and personalized treatment strategies.