This presentation focusses on two upcoming strategies facilitating CAR T cell generation. Short-term CAR T cells accelerate production times but may bear an increased risk for severe cytokine release syndrome. In vivo CAR T generation relies on T cell-specific vectors as off-the-shelf product while proof for clinical benefit remains to be provided.

We introduce a novel class of innate immune receptor-based CARs designed to program myeloid cells in vivo using mRNA/LNP, overcoming limitations of current CAR therapies in solid tumors. Preclinical data demonstrate robust anti-tumor efficacy, leading to the initiation of first-in-human clinical trials for TROP2 and GPC3-targeting CARs, highlighting significant potential for advancing cancer immunotherapy.

Tonic signaling from CAR expression is proposed to be associated with CAR T exhaustion. Our study describes an in vitro model for investigating tonic signaling in mRNA CAR T cells, which has not been fully characterized. Among approximately 80 tested permutations of structural elements, a few optimal CAR designs showed improved antigen-dependent T cell immune responses in vitro. Additionally, several formats of mRNA were also evaluated for CAR expression persistence.

To improve the efficiency of design and development of CAR constructs, we built the APEX model by fine tuning protein-language model ESM2 with a dataset comprised of binder (scFv, Fab, and VHH) and antigen pairs curated from the Structural Antibody Database. The model achieved robust performance on affinity prediction and can be expanded for other property prediction. Model adoption into CAR campaign facilitates binder prioritization to reduce discovery cycle time.

• Dive into the challenges with engineering immune cell engagers where machine learning might have an impact (i.e. development speed, therapeutic efficacy, clinical safety)
• Discuss the data and models needed to develop superior therapies. 
• Highlight different machine learning methods being applied to the design and optimisation of immune cell engagers. 
• Explore future directions for machine learning in immune cell engagers. 

The accumulation of biological data, including DNA sequencing data in the Sequence Read Archive (SRA) and protein structures in the Protein Data Base (PDB) are the primary substrates underlying recent advances in the use of artificial intelligence in biological systems. Here, we present our lab's recent work on both mining and extrapolating from these repositories of petabyte-scale data, including our identification of potential new modalities for immunotherapy.

T-cell engagers (TCEs) are a promising approach for solid tumors, with the first TCE for a major solid tumor approved in 2024. However, progress is limited by a lack of tumor-selective targets that maximize efficacy while minimizing on-target, off-tumor toxicity. We developed the ATLAS platform, a curated single-cell RNA sequencing resource that leverages both healthy and tumor-derived datasets to identify highly precise TCE targets. Using ATLAS, we discovered LY6G6D as a tumor-specific colorectal cancer (CRC) target, guiding the engineering of a best-in-class LY6G6D TCE for CRC.

T cells recognize intracellular targets presented by HLA to enable potent anti-tumor immune responses, and these targets can be leveraged to generate off-the-shelf therapeutics using T cell-bispecific engagers to treat a broad patient population. We've developed 3T-TRACE to rapidly identify the antigens of orphan T cells from patient tumors and 3T-PRIME, a TCR mimetic platform to rapidly generate potent and specific binders for therapeutic development.

Immuno-STATs represent a Fc fusion molecules capable of engaging, activating, and amplifying disease-specific T-cells. Our leading clinical candidate, CUE-101, specifically targets HPV E7-specific T cells and has shown remarkable efficacy in patients with advanced head and neck cancer. With this clinical success and our platform's proven safety, we introduce the Immuno-STAT platform. It enables the targeting of various tumor types in addition to AI applications.

The tumor microenvironment can inhibit the efficacy of cancer therapies through mechanisms such as poor trafficking and exhaustion of immune cells. Here, to address this challenge, we exploited the safety, tumor tropism and ease of genetic manipulation of non-pathogenic Escherichia coli (E. coli) to deliver key immune-activating cytokines to tumors via surface display on the outer membrane of E. coli K-12 DH5a.

Oncolytic viral therapies for cancers are frequently limited to intralesional injection due to non-specific localization of systemically injected viral particles. We developed a modified vesicular stomatitis virus (VSV) that harbors tumor-activating moiety. This novel virus preferentially distributes to tumor tissues and showed enhanced safety compared to wild-type VSV. When incorporated with a single chain, biologically active interleukin-12 (IL-12)-the novel virus mounts effective control against tumor growth.