T-cell immunotherapies have improved outcomes in blood cancer patients but have fallen short in solid tumors. To expand use of the modality to solid tumors, researchers at UMC Utrecht developed a system to study the dynamic interactions of immune cells and patient cancer organoids.

The system, details of which were published July 25 in Nature Biotechnology, integrated multiple single-cell readouts to reveal differences between the behavior of engineered T-cell products and a gene signature associated with serial killing.

Behav3D, the system developed by the researchers, builds on work to use patient-derived organoids to understand patient-specific responses to treatment and the use of imaging to characterize the dynamics and organization of the 3D structures. Specifically, the study went beyond earlier efforts by combining organoid and imaging technology to analyze functional single-cell behavior with transcriptomic profiling.

"[What is] unique about this approach is that we are looking at cell therapy efficacy by studying the behavior of the T cells. This revealed a huge variety in behavior, like very potent behaviors, such as killing of multiple tumor cells in sequence, but also ineffective behaviors, with cells just sitting around and doing nothing. This suggested to us that there is room to improve clinical efficacy by promoting the most potent tumor-targeting behaviors," Florijn Dekkers, PhD, one of the lead authors of the paper and a senior researcher at the Princess Máxima Center in the Netherlands, said in a statement.

Dekkers and her collaborators generated the insights by using the system to study the behavior of more than 150,000 engineered T cells. The work uncovered ideas for how to decipher and manipulate how engineered immune cells target solid tumors. As well as revealing differences between T cells and the serial killing gene signature, the researchers designed an optimal sequence of T-cell combination therapy and provided proof of concept for a strategy to increase antitumor potency through cell selection.

The researchers looked at cancer metabolome-sensing engineered T cells (TEGs), a therapy that is activated upon the detection of metabolic changes in tumor cells. The lab of two of the authors of the paper, Zsolt Sebestyén, PhD, and Dr.Jürgen Kuball, developed TEGs. In an analysis of super engager TEGs, the team found 27 genes with no previously described T-cell function. Behav3D is also applicable to other types of T-cell therapy, such as CAR T cells, and to various subtypes of cancer.

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