Abstract 7438: Mapping T cell reactivity against the human genome using machine learning uncovers target specific TCRs against novel tumour antigens

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Abstract Introduction: T cell engagers (TCEs) are a class of targeted therapies with demonstrated clinical potential in solid tumors. A main challenge currently facing TCEs is the availability of clean cancer targets with a wide therapeutic window. Intracellular targets presented to T cells by HLA complexes are strongly linked to disease mechanisms and are therefore very attractive, but drugging HLA-presented targets has been challenged by our ability to map the specificity of T cell repertoires. We here present the T-Cα platform, which represents a fundamental change in TCR discovery methodology, enabling us to discover >10, 000 TCRs per week against 100s of targets. This data has enabled us to train machine learning (ML) models and to demonstrate the ability of these models to extrapolate beyond the training data. In effect, this results in the discovery of novel therapeutically relevant TCRs and enhances our understanding of the interface between T cell reactivity and the human genome. Experimental Procedures: We used the T-Cα platform to discover more than 50 novel targets across all areas of the genome, including regions previously considered non-coding. We screened billions of T cells from 100s of donors against these targets and other targets of interest. We generated single-cell transcriptomic data as part of the screens and predicted the potency of each TCR at the discovery stage. For the purposes of model development, we generated matched negative data with non-binding TCRs during the course of the screens. These datasets, representing 10, 000s of TCR-epitope pairs, were collated alongside available public data and a machine learning model was trained to predict TCR specificity. The TCRs were functionally validated downstream for potency and cross-reactivity. Results: We have generated, to our knowledge, the largest high-quality database of TCR-epitope pairs against 100s of targets. We demonstrate that with this data, alongside matched negatives, we can train models that are able to predict TCR specificity with high concordance with known epitope pairings. We used the model to predict the specificity of unseen TCRs against novel targets of therapeutic interest in oncology. We further demonstrate that the model can extrapolate beyond the training data and identify novel target-specific TCRs suitable for the development of TCEs. Conclusions: We have developed a uniquely high-throughput, high-content TCR discovery platform with which we are able to discover >10, 000 specific TCRs per week against 100s of targets. We have trained a machine learning model which can accurately extrapolate to unseen TCRs, enhancing our understanding of T cell reactivity against the human genome. With the T-Cα platform, we have built a pipeline of novel T cell engagers for the treatment of solid tumors, the first of which we expect to enter IND enabling studies mid 2025. Citation Format: Nathaniel J. Davies, Amalia Martinez, Maria Busz, David Cook, Xiaoyan Pan, Juan Bolivar, Simon Wright, Eleanor Bagg, Graham Ogg, Sophie Johnson, Sophie Wells, Sophie Richard, Samantha Drennan, Samuel Ward, Sarah Leonard, Thomas L. Andresen. Mapping T cell reactivity against the human genome using machine learning uncovers target specific TCRs against novel tumour antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7438.

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