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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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Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Computational genomics tools for dissecting tumour-immune cell interactions.

Hubert Hackl¹, Pornpimol Charoentong¹, Francesca Finotello¹

¹Division of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria.

Nature Reviews. Genetics

|July 5, 2016

Summary

Computational genomics tools are essential for analyzing complex tumor-immune interactions in cancer immunology research. This review aids in selecting the right tools for genomic data analysis and pipeline assembly.

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Area of Science:

Cancer immunology
Genomic data analysis
Computational biology

Background:

Cancer immunotherapy breakthroughs and reduced high-throughput sequencing costs drive research into tumor-immune cell interactions.
Vast amounts of genomic data necessitate advanced computational tools for processing, analysis, and visualization.

Purpose of the Study:

To review computational genomics tools for cancer immunology.
To provide guidance on tool selection and analytical pipeline assembly.

Main Methods:

Literature review of existing computational genomics tools.
Analysis of tool requirements and functionalities.

Main Results:

Identification of various computational tools for mining tumor immunologic and genomic data.
Information provided to assist researchers in tool selection and pipeline construction.

Conclusions:

Computational genomics tools are critical for advancing cancer immunology research.
Effective tool selection and pipeline assembly are key to gaining mechanistic insights from complex genomic data.