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In this study, we describe the discovery of antibodies against a MAGE-A4 peptide-major histocompatibility complex (pMHC). These antibodies will form the basis for the tumor-binding arm of T-cell engagers (TCEs) against this target. Bispecific CD3 TCEs have the potential to transform cancer treatment by redirecting T cells to tumor targets, but technological barriers have limited their development for solid tumors. Targets for TCEs have generally been limited to surface-expressed proteins, however, access to intracellular proteins that are mutated and/or differentially expressed in cancer cells would expand the target pool. Peptides of these intracellular proteins presented on MHC class I (MHC-I) provide opportunities for TCE development. Technologies powering discovery of rare antibodies that are ultra-specific, high-affinity pMHC binders are needed to expand this promising class of tumor targets. We have developed a technology platform for the discovery of optimal TCEs, including a diverse panel of CD3-binding antibodies and an antibody discovery and development engine that includes multispecific engineering capabilities, powered by OrthoMabTM. We are applying this platform to develop TCEs against MAGE-A4, an intracellular tumor target expressed by many solid tumors, but not by healthy tissues. Using proprietary immunization technologies, we triggered robust, diverse antibody responses against a complex of a human MAGE-A4 peptide presented on MHC-I. We used high-throughput microfluidic technology to screen single B cells using a multiplexed bead-binding assay to identify antibodies specific to the target, but not closely-related pMHCs. We then expressed and purified antibodies for downstream validation and characterization. Antibody specificity was initially validated using a panel of related pMHC complexes, and developability properties were assessed, including hydrophobicity, self-association, polyspecificity, stability, and aggregation. With complex data integration and analysis, we identified a panel of diverse and developable antibodies that bind with high affinity to a human MAGE-A4 peptide sequence of 10 amino acids presented on MHC-I (HLA:02*01). Strategic selection and pairing of these target-binding antibodies with our large and diverse panel of fully human CD3-binders will power the discovery of ultra-specific MAGE-A4 TCEs with optimal potency and cytokine release. Citation Format: Davide Tortora, Peter Bergqvist, Grace P. Leung, Elena Vigano, Antonios Samiotakis, Harveer Dhupar, Wei Wei, Shirley R. Zhi, Yukiko Sato, Allison Goodman, Cindy-Lee Crichlow, Melissa Cid, Jessica Fernandes Scortecci, Ping Xiang, Ahn Lee, Vivian Li, Stephanie Masterman, Sherie Duncan, Aaron Yamniuk, Kush Dalal, Tim Jacobs, Raffi Tonikian, Bryan C. Barnhart. Breaking barriers to access intracellular targets with T-cell engagers: Discovery of diverse, developable, and ultra-specific antibodies against a MAGE-A4 pMHC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1891.

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity., Author summary Brain cell types have different electrical features, determined by the genes that each cell expresses. By combining data from hundreds of articles studying individual cell types in isolation, we developed a dataset that combines neuron gene expression patterns with their electrical characteristics. We asked if patterns of gene expression could predict a neuron’s electrical features; for example, if a neuron that expresses more of a sodium channel also tends to fire action potentials more frequently. We found hundreds of such statistical correlations that also replicated across brain cell types and regions. These relationships provide a starting point for understanding how alterations in the gene expression result in alterations in electrical functioning of neurons and brain circuits.