Profiling Tumor Infiltrating Immune Cells with CIBERSORT.

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Tumor infiltrating leukocytes (TILs) are key components of the tumor microenvironment.
• TILs correlate with patient prognosis and therapeutic response.
• Current methods like immunohistochemistry and flow cytometry have limitations in standardization and phenotypic marker scope.

Purpose of the Study:

• To introduce CIBERSORT, a computational method for quantifying immune cell fractions.
• To demonstrate the application of CIBERSORT for characterizing TILs in tumor samples.
• To provide a primer on using CIBERSORT with gene expression profiles.

Main Methods:

• CIBERSORT utilizes support vector regression.
• It leverages prior knowledge of purified leukocyte subset expression profiles.
• The method infers cellular components from bulk tissue gene expression profiles (GEPs).

Main Results:

• CIBERSORT accurately estimates the immune cell composition of tumor biopsies.
• The method is applicable to both microarray and RNA-Seq data.
• CIBERSORT offers a versatile computational approach to immune cell quantification.

Conclusions:

• CIBERSORT provides a robust and standardized method for TIL analysis.
• This computational approach overcomes limitations of traditional immune cell enumeration techniques.
• CIBERSORT facilitates deeper understanding of the tumor immune microenvironment.