The Single-Cell Landscape of Cancer-Fighting T Cells

The Single-Cell Landscape of Cancer-Fighting T Cells

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The Single-Cell Landscape of Cancer-Fighting T Cells

December 21, 2021

T Cells and Cancer Immunotherapies

How do cancer immunotherapies direct the immune system to recognize cancer cells as pathogenic? The key player in this process is the T cell, a type of immune cell that recruits other immune cells to destroy pathogens when it identifies its target antigen.

Many cancer immunotherapies rely on T cells- checkpoint inhibitors, for example, block the molecules that negatively regulate T cells to induce a stronger T cell response in tumor tissue. CAR T cell therapy involves infusing patients with modified T cells that can more effectively and robustly target cancer cells.

But all of these immunotherapies revolve around the T cells being able to effectively infiltrate the tumor, and T cell tumor infiltration differs greatly between cancer types due to different tumor microenvironments.

Building a Tumor Infiltrating T Cell Atlas

In a paper published in Science last week, Zheng et al applied single-cell RNA sequencing (scRNA-seq) and bioinformatic analyses to build a pan-cancer atlas of tumor-infiltrating T cells, with some interesting insights

First, they identified what they called “potentially tumor-reactive populations” or pTRPs of T cells, populations of cells that seemed to be involved in reacting to the cancer cells. Then they characterized the states of the T cells, whether they were tissue-resident memory CD8+ T cells, regulatory T cells, or “exhausted” T cells, a state that T cells enter in severe viral infection or fighting cancer.

Insights from scRNA-seq Analysis

By characterizing the distinct populations of tumor-reactive cells across cancer types and the cellular states of those T cell populations, the researchers were then able to group patients based on their immune type. The immune types they identified correlated to patient outcomes like survival and response to immune checkpoint therapy. An example of an immune type would be a group with high levels of exhausted CD8+ T cells versus a group with high levels of tissues-resident memory CD8+ T cells.

“Immune-typing” and its Implications

This bioinformatic analysis of scRNA-seq data has great implications for cancer immunotherapies. Being able to “immune-type” a patient adds another tool to be used by clinicians and researchers. The aim is to build a multi-omic biomarker profile of the patient and their specific cancer to provide the most effective treatment possible. Now that this foundation of a pan-cancer T cell atlas exists, more cancer researchers, computational biologists, and bioinformaticians can contribute data and analysis to build this great new resource

Jane Cook, Journalist & Content Writer, Bridge Informatics

Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in the management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email [email protected] or [email protected].


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