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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.

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Updated: Jul 14, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Machine-learning Modeling for Personalized Immunotherapy- An Evaluation Module.

Xiaonan Ying¹, Biaoru Li²

¹University of Nebraska Medical Center, Omaha, NE 68131, USA.

Biomedical Journal of Scientific & Technical Research

|October 11, 2023

Summary

This summary is machine-generated.

This study introduces a new machine-learning model to personalize cancer immunotherapy. By analyzing single-cell genomics, it predicts optimal immune-cell therapies and targeted drugs for improved patient outcomes.

Keywords:

Gene Expression Machine-Learning Pathway Analysis Personalized Immunotherapy Single-Cell Genomic Analysis Targeting Therapy Tumor-Infiltrating Lymphocytes

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Area of Science:

Oncology
Immunology
Computational Biology

Background:

Current immune-cell and targeted therapies for cancer face challenges in safety (cytokine releasing syndrome), specificity (off-targeting), and cost.
Personalized immunotherapy strategies are emerging to address these limitations in cancer treatment.

Purpose of the Study:

To develop a novel immunotherapy module utilizing machine learning and single-cell genomics.
To predict optimal immune-cell therapies and targeted drugs for individual cancer patients.

Main Methods:

Discovery of quiescent genes in tumor-infiltrating immune cells.
Single-cell genomics analysis to study heterogeneous immune responses against neoantigens.
Development of a machine-learning model to assess optimal immunotherapy strategies.

Main Results:

The machine-learning model, integrated with single-cell genomic data, can predict optimal treatments.
Identification of potential personalized immune-cell (e.g., T-cells) and targeted drug (e.g., PD1, CTLA4 inhibitors) combinations.

Conclusions:

This new generation of immunotherapy module offers a predictive approach for personalized cancer treatment.
The integration of machine learning and single-cell genomics holds promise for overcoming current immunotherapy challenges.