New ATTACH System Overcomes Key Bottleneck in Personalized Immunotherapy Development

A new platform developed by researchers at The University of Texas MD Anderson Cancer Center quickly finds and isolates rare, tumor-reactive immune cells that are especially good at recognizing and attacking cancer cells, even without knowing which tumor targets are recognized by the immune cells. This approach addresses a major bottleneck in immunotherapy development and could potentially accelerate the creation of personalized treatments.

The platform, called ATTACH (Assessment of T cells Tethered to Antigen Class I Histocompatibility), identifies the strongest interactions between T cells and cancer-specific proteins, isolating only the most effective, tumor-reactive T cells for further study and therapeutic use.

The study, published today in the Journal for ImmunoTherapy of Cancer, was led by Alexandre Reuben, Ph.D., assistant professor of Thoracic/Head and Neck Medical Oncology, and Amanda Montoya, senior research assistant in the Reuben lab. 

This approach turns an extremely rare population of tumor-reactive T cells into something we can reliably detect, study and use, opening new possibilities for truly personalized medicine. It overcomes key challenges associated with antigen-specific T cell isolation, allowing the tumor itself to reveal which immune cells are most effective against it instead of trying to guess those targets in advance."

Alexandre Reuben, Assistant Professor of Thoracic/Head and Neck Medical Oncology, Reuben lab

What are Tumor-Reactive T cells and Why is it Hard to Find Them?

Tumors contain many T cells, but only a small fraction recognize cancer-specific antigens – the abnormal and unique surface proteins found in tumors. These T cells are considered tumor-reactive because they are able to effectively recognize and destroy cancer cells or recruit other immune cells to the tumor. 

However, identifying tumor-reactive T cells is a challenge. Because tumors are highly complex and constantly mutating, different areas of the same tumor can display different antigens, making them difficult to recognize by a single type of T cell. Existing methods to identify and isolate tumor-reactive T cells require scientists to know specific antigens beforehand, but this new platform aims to bypass this requirement.

What is the New Platform and How Does It Improve Upon Existing Methods?

The ATTACH platform is a microfluidic system that separates tumor-reactive T cells based on how strongly they recognize and attach to cancer cells – a property known as "avidity." ATTACH does not need predefined tumor antigens, but uses the tumor itself to present natural cancer-specific antigens.

The researchers then apply immune cells taken from the tumor, allowing the reactive T cells to bind to cancer cells, similar to throwing spaghetti at the wall to see what sticks. By applying very gentle fluid flow to wash away any T cells that either have a weak bond or are unsuitable for fighting cancer cells, the researchers effectively isolate only the optimal tumor-reactive T cells.

This method increased the relative number of cancer-specific T cells up to tenfold in lab models, even with extremely rare cells that make up only 0.1% of the tumor. Importantly, the platform also preserved tumor-reactive T cell function after isolation, without needing special instruments.

How Does This Platform Help Patients With Cancer?

This new platform offers a rapid and accessible approach for isolating tumor-reactive T cells for research, and it has the potential to speed up and improve the development of immunotherapy treatments because it overcomes major hurdles with existing methods. Simplifying the isolation and enrichment of rare tumor-reactive T cells also provides a useful framework for future technologies based on bond strength.

Source:
Journal reference:

Montoya A, et al. (2026) Unbiased avidity-based isolation of antigen-specific T cells. Journal for ImmunoTherapy of Cancer. DOI: 10.1136/jitc-2026-014960. https://jitc.bmj.com/content/14/7/e014960 

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