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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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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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Updated: Jan 12, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

NeoTImmuML: a machine learning-based prediction model for human tumor neoantigen immunogenicity.

Yan Shao¹, Shuguang Ge¹, Ruizhe Dong¹

¹School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.

Frontiers in Immunology

|November 7, 2025

Summary

We developed NeoTImmuML, a machine learning model, and TumorAgDB2.0, an upgraded database, to efficiently predict tumor neoantigen immunogenicity for personalized cancer vaccines.

Keywords:

SHAP database ensemble model immunogenicity machine learning tumor neoantigens

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Area of Science:

Oncology
Immunology
Bioinformatics

Background:

Tumor neoantigens are key targets for personalized cancer immunotherapies.
Experimental neoantigen identification is time-consuming, hindering vaccine development.

Purpose of the Study:

To create an efficient tool for predicting neoantigen immunogenicity.
To expand the TumorAgDB database with recent neoantigen data.

Main Methods:

Developed NeoTImmuML, a weighted ensemble machine learning model.
Integrated LightGBM, XGBoost, and Random Forest algorithms.
Upgraded TumorAgDB with two years of public neoantigen data to create TumorAgDB2.0.

Main Results:

TumorAgDB2.0 contains 187,223 entries.
NeoTImmuML showed strong predictive performance on test datasets.
Peptide hydrophilicity and length were identified as key immunogenicity determinants.

Conclusions:

TumorAgDB2.0 is a valuable resource for neoantigen research.
NeoTImmuML provides an efficient and interpretable method for predicting neoantigen immunogenicity.
These tools support personalized neoantigen vaccine design and cancer immunotherapy development.