Using artificial intelligence (AI), scientists at the Ludwig International Cancer Center have developed a powerful predictive model to identify the most powerful tumor-killing immune cells that can be used in cancer immunotherapies.
Combined with additional algorithms, the prediction model, described in the current issue of Nature Biotechnology, can be applied to personalized cancer treatments that tailor therapy based on the unique cellular makeup of each patient’s tumors.
“The implementation of artificial intelligence in cell therapy is new and can change the rules of the game, offering new clinical options to patients,” says Alexandre Harari, who led the study, in a press release from the Ludwig Lausanne Center.
Cellular immunotherapy involves extracting immune cells from a patient’s tumor, possibly modifying them through genetic engineering to enhance their natural cancer-fighting abilities, and reintroducing them into the body after they have multiplied.
T cells are one of two main types of white blood cells, or lymphocytes, that circulate in the blood and patrol to find virally infected or cancerous cells.
T cells that infiltrate solid tumors are known as tumor-infiltrating lymphocytes, or TILs. But not all of these TILs are effective in recognizing and attacking tumor cells.
“Only a fraction is, in fact, reactive to tumors,” explained Harari. “The challenge we set ourselves was to identify those few TILs that are equipped with T-cell receptors capable of recognizing antigens on the tumor.”
To do this, the team developed a new predictive model based on artificial intelligence, called TRTpred, which can classify T-cell receptors (TCRs) according to their tumor reactivity. The researchers used 235 TCRs collected from patients with metastatic melanoma, already classified as either tumor-reactive or non-reactive.
The team loaded the gene expression – or transcriptomic – profiles of T cells bearing each TCR into a machine learning model to identify patterns that differentiate tumor-reactive T cells from inactive ones.
“TRTpred can learn from a population of T cells and create a rule that can then be applied to a new population,” explained the researcher. “Thus, when faced with a new TCR, the model can read its transcriptomic profile and estimate whether or not the cell is reactive to tumors.”
The TRTpred model analyzed TILs from 42 melanoma and gastrointestinal, lung, and breast cancer patients and identified tumor-reactive TCRs with approximately 90% accuracy.
The researchers further refined their selection process for these TILs by applying a secondary algorithmic filter to screen for only those tumor-reactive T cells with “high avidity”—that is, those that bind strongly to tumor antigens.
The team then introduced a third filter to maximize recognition of various tumor antigens.
To validate their approach, the team grew human tumors in mice and showed that certain TIL cells can eliminate tumors when transferred into mice.
“This method promises to overcome some of the shortcomings of current TIL-based therapy, especially for patients facing tumors that are currently unresponsive to such therapies,” said director of the Ludwig Center in Lausanne, George Coukos, co-author of the study.
Now the team plans to launch a phase I clinical trial that will test the technology on patients, which could lead to an entirely new type of T-cell therapy.
