Making a personalised 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 tumour-reactive T cell receptors for patients can be replaced with a machine learning classifier that halves this time.

Personalised 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: immune T cells from a patient are equipped in the laboratory to recognise the patient’s own unique tumour, and then reinfused in large numbers to effectively kill the tumour cells.

The development of such therapies is a complicated process.

First, doctors isolate tumour-infiltrating T cells (TILs) from a sample of the patient's tumour tissue.

This cell population is then searched for T-cell receptors that recognise tumour-specific mutations and can thus kill tumour cells.

This search is laborious and has so far required knowledge of the tumour-specific mutations that lead to protein changes that are recognised by the patients‘ immune system.

During this time the tumour is constantly mutating and spreading, 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, 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 tumour-reactive T-cell receptors independently of knowledge of the respective tumour epitopes, the process could be considerably simplified and accelerated."

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 characterise each cell.

The T cell receptors expressed by these TILs were then individually tested in the lab to identify those that were recognised and killed patient tumour cells.

The researchers then combined these data to train a machine learning model to predict tumour reactive T cell receptors.

The resulting classifier could identify tumour reactive T cells from TILs with 90% accuracy, works in many different types of tumour, and accommodates data from different cell sequencing technologies.

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

"We are now focusing on bringing this technology into clinical practice here in Germany. To finance further development, we have founded the biotech start-up Tcelltech," adds Michael Platten.

"predicTCR is one of the key technologies of this new DKFZ spin-off."

Source: German Cancer Research Center