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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.

Mouse Models of Cancer Study

Mouse Models of Cancer Study

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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Related Experiment Video

Updated: May 12, 2025

Testing Cancer Immunotherapeutics in a Humanized Mouse Model Bearing Human Tumors

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Published on: December 16, 2022

Identify Modules Associated with Immunotherapy Response from Mouse Tumor Profiles for Stratifying Cancer Patients.

Dechen Xu¹, Jie Li^2,3, Li Zhou¹

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.

Interdisciplinary Sciences, Computational Life Sciences

|May 9, 2025

Summary

Predicting cancer immunotherapy response is crucial. This study uses mouse models to identify gene modules that accurately predict patient response to immune checkpoint inhibitors (ICIs), offering a novel approach for personalized cancer therapy.

Keywords:

Cross-species transfer learning Immunotherapy response Machine learning Prediction model

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Published on: October 9, 2018

Area of Science:

Oncology
Immunology
Bioinformatics

Background:

Immune checkpoint inhibitors (ICIs) offer significant cancer treatment benefits, but predicting patient response remains challenging.
Limited human immunotherapy response data necessitates leveraging preclinical models like syngeneic mouse tumor models.
Developing methods to translate findings from mouse models to human prediction is critical for advancing immunotherapy.

Purpose of the Study:

To develop a novel methodology for identifying immunotherapy response predictors from mouse tumor data.
To build and validate predictive models for immunotherapy response using mouse-derived gene modules.
To assess the translatability of mouse-based models for predicting human response to ICIs.

Main Methods:

Identification of gene modules associated with immunotherapy response in mouse tumor profiles using cancer gene panels.
Construction of prediction models for immunotherapy response based on identified mouse gene modules.
Transfer learning approach to apply mouse-based models for predicting human patient responses to ICIs.

Main Results:

Gene modules identified from mouse data serve as reliable predictors of immunotherapy response.
Mouse-based prediction models demonstrate successful transferability to human cancer patient data.
The proposed method shows superior performance compared to conventional biomarkers and existing mouse-based models in predicting drug response and survival outcomes.

Conclusions:

The developed methodology effectively utilizes mouse tumor profiles to identify robust predictors of immunotherapy response.
Mouse-derived models can be successfully transferred to predict human responses to immune checkpoint inhibitors.
This approach offers a promising strategy for enhancing personalized cancer therapy and improving patient outcomes.