Deconvoluting tumor-infiltrating immune cells from RNA-seq data using quanTIseq.

Area of Science:

• Cancer Immunology
• Computational Biology
• Immunotherapy

Background:

• Tumor-infiltrating immune cells significantly impact cancer progression and treatment outcomes.
• Accurate quantification of these immune cells is vital for advancing cancer immunology and immunotherapy.
• Existing methods may require complex sample processing or lack comprehensive analysis.

Purpose of the Study:

• To demonstrate the capabilities of quanTIseq, a computational pipeline for quantifying immune-cell fractions from bulk RNA sequencing (RNA-seq) data.
• To showcase the application of quanTIseq in analyzing both circulating and tumor-infiltrating immune cells.
• To illustrate how quanTIseq can reveal the immunological effects of various cancer treatments.

Main Methods:

• Utilized quanTIseq, a computational pipeline, to analyze RNA-seq data from blood and tumor samples.
• Applied quanTIseq to publicly available datasets, including preprocessed RNA-seq data for circulating immune cells and raw RNA-seq data from melanoma patient tumors.
• Validated quanTIseq results for circulating immune cells using matched flow cytometry data.

Main Results:

• Successfully quantified immune-cell fractions from bulk RNA-seq data using quanTIseq.
• Demonstrated the ability of quanTIseq to analyze circulating immune cells and validated findings with flow cytometry.
• Showcased quanTIseq's utility in analyzing tumor immune infiltrates from melanoma patients before and during kinase inhibitor treatment, revealing drug-induced immunological changes.

Conclusions:

• quanTIseq provides a robust computational method for quantifying immune-cell fractions from RNA-seq data.
• The tool is versatile, applicable to both circulating and tumor-infiltrating immune cells across different sample types.
• quanTIseq facilitates the study of immune responses in cancer and the evaluation of treatment effects in immunotherapy and targeted therapy research.