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

Abstract

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. Trial Registration: ClinicalTrials.gov NCT00881920.

CAR-cell therapy in the era of solid tumor treatment: current challenges and emerging therapeutic advances

“…Researchers are currently looking to improve the efficacy of CAR-cell therapy by using various strategies including the Artificial Intelligence (AI) which could serve to counter many hurdles associated with CAR-cell therapy [ 278 , 279 ]. In fact, radiomics, a quantitative approach to medical imaging may be useful for predicting novel cancer-associated antigens, new molecules in immune cells as well as analyzing safety and efficacy of CAR-cells [ 278 , 279 ]. At the larger scale, AI can be used in automated CAR-T cell manufacturing which allows shorter production and delivery times to positively increase the number of patient treatments [ 280 ].…”

scifAI: Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies

“…In other studies, the potential of synapse formation was also investigated for CAR T cell therapy, where investigators used the mean intensity of stainings such as F-actin and P-CD3ζ per cell, clustering of tumor antigen and polarization of perforin-containing granules as a measure of synapse formation quality. These features varied between different CAR T cells and correlated with their effectiveness in vitro and in vivo as well as with clinical outcomes 39,40 . In our work, we improved this by incorporating 296 biologically motivated features such as texture, intensity statistics and synaptic related features.…”

Newer generations of multi-target CAR and STAb-T immunotherapeutics: NEXT CART Consortium as a cooperative effort to overcome current limitations

“…Several ex-vivo approaches based on artificial intelligence analyses of F-actin reorganization at the IS have assessed the IS quality made by CAR-T cells ( 32 , 33 ). Indeed, in vitro learning-based IS quality based on F-actin measurements correlates with patient clinical outcomes upon CAR-T therapy ( 34 ). This strategy could provide guidelines for designing and optimizing CAR constructs and also optimize T cell-redirecting strategies such as STAb-T cells (see below) for potential clinical developments.…”