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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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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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Updated: Sep 3, 2025

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

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Predicting cancer immunotherapy response from gut microbiomes using machine learning models.

Hai Liang¹, Jay-Hyun Jo¹, Zhiwei Zhang²

¹Dermatology Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD 20892, USA.

|July 25, 2022

Summary

This summary is machine-generated.

The gut microbiome can predict cancer immunotherapy response across various cancer types. Specific bacterial groups in the gut are associated with patient response to immunotherapy, offering potential for improved cancer treatment outcomes.

Keywords:

16S rRNA gut microbiome immunotherapy machine learning metagenomics

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Area of Science:

Oncology
Microbiome Research
Immunology

Background:

Cancer immunotherapy has improved survival, but response rates are suboptimal.
Gut microbiome composition is linked to immunotherapy response, particularly in melanoma.
Previous studies show inconsistent bacterial taxa associated with response across different cohorts.

Purpose of the Study:

To identify common gut microbiome features associated with immunotherapy response in a tumor-agnostic manner.
To determine if gut microbiome signatures of response are generalizable across different cancer types and patient cohorts.
To evaluate the predictive accuracy of gut microbiome features for immunotherapy outcomes.

Main Methods:

Meta-analysis of 16S rRNA gene sequencing data from a mixed tumor cohort and published melanoma datasets.
Multivariate statistical analysis (selbal) to identify bacterial genera associated with responders and non-responders.
Validation using shotgun metagenomic datasets and cross-platform analysis.

Main Results:

Identified specific gut bacterial taxa correlated with immunotherapy response irrespective of tumor type.
Two distinct groups of bacterial genera were associated with responders versus non-responders.
Statistical models demonstrated robust prediction accuracy of immunotherapy response using microbiome data.

Conclusions:

Baseline gut microbiome features can predict clinical outcomes in cancer patients receiving immunotherapy.
Certain gut microbiome features associated with immunotherapy response may be generalizable across diverse cancer types, patient cohorts, and sequencing platforms.
Machine learning models applied to microbiome data can reveal interactions relevant to improving cancer patient outcomes.