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Personalized cellular immunotherapies are considered promising new treatment options for various types of cancer.

New ML Model Identifies Tumor-Reactive T Cells Quickly

icTCR helps identify suitable T cells from patient samples in under a few days, not months

German Cancer Research Center (Deutsches Krebsforschungszentrum)
Published:Mar 18, 2024
|2 min read
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Making a personalized T-cell therapy for cancer patients currently takes at least six months; scientists at the German Cancer Research Center (DKFZ) and the University Medical Center Mannheim have shown that the laborious first step of identifying tumor-reactive T-cell receptors for patients can be replaced with a machine learning (ML) classifier that halves this time.

Personalized cellular immunotherapies are considered promising new treatment options for various types of cancer. One of the therapeutic approaches currently being tested are so-called T-cell receptor transgenic T cells. The idea behind this is that immune T cells from a patient are equipped in the laboratory to recognize the patient’s own unique tumor, and then reinfused in large numbers to effectively kill the tumor cells.

The development of such therapies is a complicated process. First, clinicians isolate tumor-infiltrating T cells (TILs) from a sample of the patient's tumor tissue. This cell population is then searched for T-cell receptors that recognize tumor-specific mutations and can, thus, kill tumor cells. This search is laborious and has so far required knowledge of the tumor-specific mutations that lead to protein changes that are recognized by the patients‘ immune system. The tumor continues mutating and metastasizing, making this step a race against time.

"Finding the right T-cell receptors is like looking for a needle in a haystack, costly and time-consuming," says Michael Platten, MD, head of department at the DKFZ and director of the Department of Neurology at the University Medical Center Mannheim. "With a method that allows us to identify tumor-reactive T-cell receptors independent of knowledge of the respective tumor epitopes, the process could be considerably simplified and accelerated."

A team led by Platten and co-study head Edward Wilhelm Green, PhD, has presented a new technology that can achieve precisely this goal in a recent publication. As a starting point, the researchers isolated TILs from a melanoma patient's brain metastasis and performed single-cell sequencing to characterize each cell. 

The T-cell receptors expressed by these TILs were then individually tested in the lab to identify those that were recognized and killed patient tumor cells. The researchers then combined these data to train an ML model, predicTCR, to predict tumor-reactive T-cell receptors. The resulting classifier could identify tumor-reactive T cells from TILs with 90 percent accuracy, work in many different types of tumor, and accommodate data from different cell sequencing technologies.

“predicTCR enables us to cut the time it takes to identify personalized tumor-reactive T-cell receptors from over three months to a matter of days, regardless of tumor type,” said Green.- 

- This press release was originally published on the German Cancer Research Center (DKFZ) website