In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes

The human immune system consists of a highly intelligent network of billions of independent, self-organized cells that interact with each other. Machine learning (ML) is an artificial intelligence (AI) tool that automatically processes huge amounts of image data. Immunotherapies have revolutionized the treatment of blood cancer. Specifically, one such therapy involves engineering immune cells to express chimeric antigen receptors (CAR), which combine tumor antigen specificity with immune cell activation in a single receptor. To improve their efficacy and expand their applicability to solid tumors, scientists optimize different CARs with different modifications. However, predicting and ranking the efficacy of different off-the-shelf immune products (e.g., CAR or Bispecific T-cell Engager [BiTE]) and selection of clinical responders are challenging in clinical practice. Meanwhile, identifying the optimal CAR construct for a researcher to further develop a potential clinical application is limited by the current, time-consuming, costly, and labor-intensive conventional tools used to evaluate efficacy. Particularly, more than 30 years of immunological synapse (IS) research data demonstrate that T cell efficacy is not only controlled by the specificity and avidity of the tumor antigen and T cell interaction, but also it depends on a collective process, involving multiple adhesion and regulatory molecules, as well as tumor microenvironment, spatially and temporally organized at the IS formed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. The optimal function of cytotoxic lymphocytes (including CTL and NK) depends on IS quality. Recognizing the inadequacy of conventional tools and the importance of IS in immune cell functions, we investigate a new strategy for assessing CAR-T efficacy by quantifying CAR IS quality using the glass-support planar lipid bilayer system combined with ML-based data analysis. Previous studies in our group show that CAR-T IS quality correlates with antitumor activities in vitro and in vivo. However, current manually quantified IS quality data analysis is time-consuming and labor-intensive with low accuracy, reproducibility, and repeatability. In this study, we develop a novel ML-based method to quantify thousands of CAR cell IS images with enhanced accuracy and speed. Specifically, we used artificial neural networks (ANN) to incorporate object detection into segmentation. The proposed ANN model extracts the most useful information to differentiate different IS datasets. The network output is flexible and produces bounding boxes, instance segmentation, contour outlines (borders), intensities of the borders, and segmentations without borders. Based on requirements, one or a combination of this information is used in statistical analysis. The ML-based automated algorithm quantified CAR-T IS data correlates with the clinical responder and non-responder treated with Kappa-CAR-T cells directly from patients. The results suggest that CAR cell IS quality can be used as a potential composite biomarker and correlates with antitumor activities in patients, which is sufficiently discriminative to further test the CAR IS quality as a clinical biomarker to predict response to CAR immunotherapy in cancer. For translational research, the method developed here can also provide guidelines for designing and optimizing numerous CAR constructs for potential clinical development. Author summary Adoptive transfer of chimeric antigen receptor (CAR)-modified immune cells (including CAR-T and CAR-NK cells) have revolutionized the treatment of cancer with success in clinical trials treating multiple myeloma, leukemia, sarcoma, and neuroblastoma. However, CAR-modified immune cells (particularly CAR-T cells) must form a functional immunological synapse (IS) with their susceptible tumor cells to be effective in clinics. Currently, there are no effective biomarkers to predict CAR efficacy in vivo. In this study, we develop a state-of-the-art machine learning (ML) detection, and segmentation method to measure the quality of the CAR-T cell IS using CAR-T samples from patients. We automate the IS quality analysis to develop effective prognostic applications of CAR-T therapies for cancer patients. The fast, easy-to-implement Synapse Predicts Efficacy (SPE) assay we propose will streamline CAR development and selection, ultimately optimizing clinical outcome(s) for patients undergoing these rapidly evolving immunotherapies. This technology can lead to development of fast and easy tools to predict CAR-T cell effectiveness in cancer patients.

Automated summary

Machine learning is used to process large amounts of image data in the human immune system, where immunotherapies play a crucial role in blood cancer treatment. Scientists aim to improve the efficacy of engineered immune cells expressing chimeric antigen receptors (CAR) to treat solid tumors, but face challenges in predicting and ranking the efficacy of different immune products and selecting clinical responders. Optimal cytotoxic lymphocyte function depends on the immunological synapse (IS) quality, prompting the development of a novel ML-based method to quantify CAR IS quality for potential clinical applications.