The development and characterization of a human mesothelioma in vitro 3D model to investigate immunotoxin therapy.

BackgroundTumor microenvironments present significant barriers to penetration by antibodies and immunoconjugates. Tumor microenvironments, however, are difficult to study in vitro. Cells cultured as monolayers exhibit less resistance to therapy than those grown in vivo and an alternative research model more representative of the in vivo tumor is more desirable. SS1P is an immunotoxin composed of the Fv portion of a mesothelin-specific antibody fused to a bacterial toxin that is presently undergoing clinical trials in mesothelioma.Methodology/principal findingsHere, we examined how the tumor microenvironment affects the penetration and killing activity of SS1P in a new three-dimensional (3D) spheroid model cultured in vitro using the human mesothelioma cell line (NCI-H226) and two primary cell lines isolated from the ascites of malignant mesothelioma patients. Mesothelioma cells grown as monolayers or as spheroids expressed comparable levels of mesothelin; however, spheroids were at least 100 times less affected by SS1P. To understand this disparity in cytotoxicity, we made fluorescence-labeled SS1P molecules and used confocal microscopy to examine the time course of SS1P penetration within spheroids. The penetration was limited after 4 hours. Interestingly, we found a significant increase in the number of tight junctions in the core area of spheroids by electron microscopy. Expression of E-Cadherin, a protein involved in the assembly and sealing of tight junctions and highly expressed in malignant mesothelioma, was found significantly increased in spheroids as compared to monolayers. Moreover, we found that siRNA silencing and antibody inhibition targeting E-Cadherin could enhance SS1P immunotoxin therapy in vitro.Conclusion/significanceThis work is one of the first to investigate immunotoxins in 3D tumor spheroids in vitro. This initial description of an in vitro tumor model may offer a simple and more representative model of in vivo tumors and will allow for further investigations of the microenvironmental effects on drug penetration and tumor cell killing. We believe that the methods developed here may apply to the studies of other tumor-targeting antibodies and immunoconjugates in vitro

Grow and Merge: A Unified Framework for Continuous Categories Discovery

Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories. In this work, we focus on the application scenarios where unlabeled data are continuously fed into the category discovery system. We refer to it as the {\bf Continuous Category Discovery} ({\bf CCD}) problem, which is significantly more challenging than the static setting. A common challenge faced by novel category discovery is that different sets of features are needed for classification and category discovery: class discriminative features are preferred for classification, while rich and diverse features are more suitable for new category mining. This challenge becomes more severe for dynamic setting as the system is asked to deliver good performance for known classes over time, and at the same time continuously discover new classes from unlabeled data. To address this challenge, we develop a framework of {\bf Grow and Merge} ({\bf GM}) that works by alternating between a growing phase and a merging phase: in the growing phase, it increases the diversity of features through a continuous self-supervised learning for effective category mining, and in the merging phase, it merges the grown model with a static one to ensure satisfying performance for known classes. Our extensive studies verify that the proposed GM framework is significantly more effective than the state-of-the-art approaches for continuous category discovery.Comment: This paper has already been accepted by 36th Conference on Neural Information Processing Systems (NeurIPS 2022