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 classifier that...
Mar 15 2024German Cancer Research Center Making a personalized T cell therapy for cancer patients currently takes at least six months; scientists at the German Cancer Research Center 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 classifier that halves this time.
The development of such therapies is a complicated process. First, doctors isolate tumor-infiltrating T cells 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.
A team led by Platten and co-study head Ed Green has now 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.
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Machine learning classifier accelerates the development of cellular immunotherapiesMaking 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 classifier that...
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