TCRID - Accelerating T-Cell receptor research and target identification through AI

**TCRID -通过人工智能加速T细胞受体研究和靶点识别 ** 传统的癌症治疗（如化疗、放疗、手术）通常无法治愈，无法防止癌症复发，需要持续干预。基于T细胞受体（TCR）的细胞疗法是一种新的、有前景的治愈性治疗方法，可用于治疗其他方法无法治疗的晚期实体癌，如骨癌、肺癌和胃肠道癌。它们由来自患者的免疫细胞（T细胞）组成，这些细胞通过癌症靶向TCR进行基因重编程，繁殖并重新引入患者体内以摧毁癌细胞。癌症靶向TCR很少见，但可以在癌症患者中找到，并且是开发基于TCR的细胞疗法的重要组成部分。然而，识别这种癌症靶向TCR是一个非常具有挑战性，耗时且资本密集型的实验室过程，并且缺乏自动化和简化该过程的计算方法。Exogene利用人工智能（AI）显著加快发现新的癌症靶向TCR，同时大幅降低发现过程所需的时间和成本。与目前可用的展示分析不同，Exogene正在构建AI模型，通过结合内部湿实验室TCR筛选，10亿个数据点的训练数据集、结构建模和尖端的深度学习。这项极具前景的技术将加速英国18万多名晚期实体癌患者（全球超过900万人）的新型救命癌症治疗方法的开发，否则这些患者的治疗选择和预期寿命有限。该项目的结果将发表在同行评审期刊上，并将加强英国在推进基于TCR的细胞疗法以应对全球癌症方面的世界领先地位。

**TCRID - Speeding up T-Cell receptor research and target identification through AI**Traditional cancer therapies (e.g. chemotherapy, radiotherapy, surgery) are often not curative, do not prevent cancer from re-emerging and require continuous intervention.T-cell-receptor(TCR)-based cell therapies are new, promising curative treatments for otherwise untreatable advanced solid cancers, such as bone, lung and gastrointestinal cancers. They consist of immune cells (T-cells), derived from patients, that are genetically reprogrammed with a cancer-targeting TCR, multiplied, and re-introduced into patients to destroy cancer cells.Cancer-targeting TCRs are rare but can be found in cancer patients, and are an essential component to developing TCR-based cell therapies. However, identifying such cancer-targeting TCRs is an incredibly challenging, time-consuming and capital-intensive laboratory process, and there is a lack of computational methods to automate and streamline the process. Exogene leverages artificial intelligence (AI) to significantly accelerate the discovery of new cancer-targeting TCRs, while drastically reducing the time and cost required for the discovery process.Unlike currently available display assays, which focus on the analysis of 1 TCR at the time, Exogene is building AI models to predict TCR-target interactions at massive scale by combining in-house wet lab TCR screening, a 1 billion data-point training dataset, structural modelling and cutting-edge deep learning.This highly-promising technology will accelerate the development of novel life-saving cancer treatments for more than 180,000 patients with advanced solid cancers in the UK (more than 9 million worldwide) that would otherwise have limited treatment options and life expectancy. The results of the project will be published in a peer-reviewed journal and will strengthen the UK's world-leading position in advancing TCR-based cell therapies to tackle cancer worldwide.