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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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Related Experiment Video

Updated: Dec 26, 2025

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

Computational methods in tumor immunology.

Bhavneet Bhinder¹, Olivier Elemento¹

¹Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States.

Methods in Enzymology

|March 18, 2020

Summary

This summary is machine-generated.

Computational approaches analyze high-throughput data to understand tumor-immune interactions and improve cancer immunotherapy response. These methods explore tumor ecosystems to identify factors influencing treatment sensitivity or resistance.

Keywords:

Checkpoint blocking Deconvolution Deep learning Immune clusters Immune escape Immune scores Immunotherapy Neoantigen prioritization Neoantigens Resistance to therapy Survival Tumor heterogeneity Tumor immunity Tumor microenvironment Tumor mutation burden

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Area of Science:

Computational biology
Cancer immunology
Bioinformatics

Background:

Cancer immunotherapies, particularly checkpoint blocking antibodies, show success in some patients.
Tumor-immune crosstalk is crucial for understanding response heterogeneity.
High-throughput technologies generate vast datasets on the tumor ecosystem.

Purpose of the Study:

To describe computational approaches for analyzing high-throughput data in tumor immunology.
To systematically interrogate tumor immunity within its microenvironment.
To identify mechanisms of resistance and sensitivity to cancer therapies, especially immunotherapy.

Main Methods:

Leveraging genomic, transcriptomic, and epigenomic data.
Analyzing digitized histopathology images.
Integrating diverse high-throughput datasets using computational tools.

Main Results:

Computational methods enable efficient analysis and quantification of tumor immunity parameters.
These approaches facilitate the integration of multi-omics and imaging data.
Identification of key factors influencing immunotherapy outcomes.

Conclusions:

Computational approaches are essential for dissecting complex tumor-immune interactions.
Systematic interrogation of tumor ecosystems aids in understanding treatment response.
These methods are vital for advancing cancer immunotherapy development.