Machine learning-aided quantification of antibody-based cancer immunotherapy by natural killer cells in microfluidic droplets

Abstract

Natural killer (NK) cells have emerged as an effective alternative option to T cell-based immunotherapies, particularly against liquid (hematologic) tumors. However, the effectiveness of NK cell therapy has been less than optimal for solid tumors, partly due to the heterogeneity in target interaction leading to variable anti-tumor cytotoxicity. This paper describes a microfluidic droplet-based cytotoxicity assay for quantitative comparison of immunotherapeutic NK-92 cell interaction with various types of target cells. Machine learning algorithms were developed to assess the dynamics of individual effector-target cell pair conjugation and target death in droplets in a semi-automated manner. Our results showed that while short contacts were sufficient to induce potent killing of hematological cancer cells, long-lasting stable conjugation with NK-92 cells was unable to kill HER2⁺ solid tumor cells (SKOV3, SKBR3) significantly. NK-92 cells that were engineered to express FcγRIII (CD16) mediated antibody-dependent cellular cytotoxicity (ADCC) selectively against HER2⁺ cells upon addition of Herceptin (trastuzumab). The requirement of CD16, Herceptin and specific pre-incubation temperature served as three inputs to generate a molecular logic function with HER2⁺ cell death as the output. Mass proteomic analysis of the two effector cell lines suggested differential changes in adhesion, exocytosis, metabolism, transport and activation of upstream regulators and cytotoxicity mediators, which can be utilized to regulate specific functionalities of NK-92 cells in future. These results suggest that this semi-automated single cell assay can reveal the variability and functional potency of NK cells and may be used to optimize immunotherapeutic efficacy for preclinical analyses.