Uncovering the Secrets of Cancer-Fighting T Cells: New Hope for Immunotherapy With Tres

In cancer immunotherapy, despite major advances achieved by enabling the immune system to actively recognize and attack cancer cells, many tumor-reactive T cells struggle to survive in the immunosuppressive tumor microenvironment .

A central focus of current cancer research is understanding the molecular features of T cells that mount an effective immune response against tumors. As a new approach, scientists have developed a computational application called the tumor-resilient T cell model (Tres = tumor resilient T cell).

What Is the Tres Model?

Tres takes data obtained from solid tumors (arising in tissues or organs), specifically single-cell transcriptomic data , and uses them to identify resistance to immunosuppressive signals such as TGF-beta, TRAIL, and PGE2 through signatures of resilient T cells . By analyzing single-cell datasets, the Tres model can predict the clinical efficacy of T cells in immune checkpoint blockade and adoptive cell transfer.

The Tres model can be used in 4 areas:

1. Search: This feature allows researchers to ask whether a particular gene is a positive (supportive) or negative (inhibitory) marker in tumor-resilient T cells. It is used to understand the gene's effect on T-cell resilience in the tumor microenvironment. For example, it can determine whether T cells are more resilient when a gene is active.

2. Predict: This function analyzes gene expression profiles from T cells or T-cell-enriched samples to predict their resilience in solid tumors. By entering T-cell genetic information, researchers can obtain predictions about how these cells will perform in the tumor microenvironment.

3. Prioritize: This tool helps researchers enter specific gene sets and prioritize gene markers that play important roles in tumor-resilient T cells. It is used to identify genes requiring further investigation and determine which should be pursued as potential therapeutic targets.

4. Evaluate: This function tests whether an entered gene signature can predict the clinical efficacy of T cells, particularly in immune checkpoint blockade and adoptive cell therapy. This is important for assessing the likelihood that particular genetic profiles will respond to treatment and understanding which patients will respond better to these therapies.

In cancer treatment, T cells must first overcome the tumor's hostile environment to reach their targets. Many, however, cannot survive in the hostile surroundings of cancer cells. The new Tres model will help identify biomarkers of T cells that can withstand this challenging environment and continue destroying cancer cells.

Tres: A New Tool for Predicting T-Cell Resilience

Researchers at the Center for Cancer Research (CCR), led by cancer data science expert Peng Jiang, published their research on Tres in May 2022 in Nature Medicine . This computer application analyzes gene activity to determine T-cell resilience in the tumor environment.

Developing the Tres Model and Its Clinical Applications

To build the Tres model, Trang Vu, a postdoctoral researcher in Jiang's laboratory, and Yu Zhang, a visiting doctoral student, used single-cell RNA transcriptomes from existing patients to identify T cells with gene signatures that allowed them to multiply even in a highly immunosuppressive tumor environment.

Applying the model to T cells from patients with melanoma, breast, lung, or blood cancers, Vu and Zhang calculated resilience scores to predict how well the cells survived in the tumor environment. Patients with the most resilient T cells were more likely to respond well to immunotherapy, and the resilience scores accurately predicted outcomes for patients trying various immunotherapies.

The FIBP Gene and Enhancing T-Cell Function

Jiang also discovered that Tres could help uncover new T-cell regulators and identify targets for improving T-cell function. As a proof of principle, Jiang's team linked the activity of a gene called FIBP to T-cell resilience. By switching this gene off, they enhanced the ability of T cells to kill cancer cells in cell culture and in mice. This finding suggests that the FIBP gene could be a potentially important drug target .

Making Tres Publicly Available

Tres is available to scientists online free of charge. Jiang says the cancer research community can use it to discover new biomarkers. The tool aims to provide useful insights for improving existing immunotherapies and developing new treatment options.