RMISTCR - Rapidly mining the immune system for rare therapeutic T-Cell Receptors to treat solid tumour cancers

RMISTCR - 快速挖掘免疫系统中罕见的治疗性 T 细胞受体来治疗实体瘤癌症

• 批准号: 10070808
• 金额: $ 59.79万
• 依托单位: EXOGENE LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10070808
• 关键词: RMISTCR; Rapidly; mining; immune; system

**Need**According to Cancer Research UK, ~367,000 new cancer cases are diagnosed every year in the UK. Cancer kills 165,000 people each year in the UK. There is the urgent need for "new curative treatments for solid tumour cancer... cheaply and effectively" (NHS-Long Term Plan).**Challenge**T-cell-receptor-based cell therapies are promising curative treatments for otherwise untreatable advanced solid tumours. Consisting of immune cells known as T-cells, they are derived from patients, then genetically reprogrammed with a cancer-targeting T-cell receptor (TCR), before being multiplied, and reintroduced into patients to destroy cancer cells.Unfortunately finding these curative TCRs is a needle in a haystack problem and a major challenge using existing technologies in the lab such as mammalian display, which has an average discovery time of ~6 years and costs \\\>£10million/biopsy. There is an urgent need for computational methods to automate and streamline the process.**Innovation**We intend to remove this roadblock to identifying rare, cancer-targeting TCRs through the use of advanced AI (neural networks and deep learning algorithm architecture) and a novel high-throughput library-on-library wet lab screening technique (lentiviral display).This will enable our platform to rapidly screen billions of TCRs from patients in order to computationally identify rare TCRs that can target and destroy cancer cells. The AI learns from the billions of interactions between TCRs and cancer cells that we analyse at our labs using lentiviral display.**Impact**Unlike currently available display assays, which focus on the analysis of one TCR at the time, our innovative wet-lab+AI approach enables rapid TCR screening to run concurrently, facilitating the comprehensive screening of hundreds of thousands TCR in ~4 weeks, (~£3.5k/biopsy), dramatically increasing the chances of identifying rare cancer specific TCRs.This is a fundamental stepping stone to developing life-saving cell therapies for a wide range of untreatable solid tumours and helping millions of patients worldwide in their fight against cancer.The technology will speed-up UK drug discovery, accelerate the development of new life-saving cancer treatments for previously untreatable solid tumours, and ultimately reduce treatment costs to the NHS.The Biomedical Catalyst project will validate our approach, strengthen our AI- training data and enable us to demonstrate clinical validity using real blood samples. Whilst from a commercial perspective, the results of the project outputs will unlock a partnership with Immunocore, UK world-leading TCR therapy developer.

根据英国癌症研究中心的数据，英国每年约有36.7万例新确诊癌症病例。在英国，每年有16.5万人死于癌症。迫切需要“治疗实体肿瘤的新疗法……廉价而有效”(NHS-长期计划)。基于T细胞受体的细胞疗法有望治愈无法治愈的晚期实体肿瘤。由被称为T细胞的免疫细胞组成，它们来自患者，然后用癌症靶向T细胞受体(TCR)进行基因重新编程，然后增殖，然后重新注入患者体内以摧毁癌细胞。不幸的是，找到这些治愈的TCR是大海捞针问题，使用现有的实验室技术是一个重大挑战，例如哺乳动物显示，它的平均发现时间为6年，每次活组织检查的成本为1000万GB。迫切需要计算方法来自动化和简化这一过程。**创新**我们打算通过使用先进的AI(神经网络和深度学习算法体系结构)和一种新型的高通量库上湿实验室筛选技术(慢病毒显示)来消除识别罕见的、针对癌症的TCR的障碍。这将使我们的平台能够从患者中快速筛选数十亿TCR，以便通过计算识别能够靶向并摧毁癌细胞的罕见TCR。人工智能从我们实验室使用慢病毒显示分析的TCR和癌细胞之间的数十亿个相互作用中学习。**影响**与目前可用的显示分析不同，我们创新的湿实验室+人工智能方法使快速TCR筛查能够同时运行，促进在~4周内对数十万TCR进行全面筛查，(~GB 3.5k/活检)，这极大地增加了识别罕见癌症特异性TCR的机会。这是为各种无法治疗的实体肿瘤开发挽救生命的细胞疗法并帮助全球数百万患者与癌症作斗争的根本踏脚石。这项技术将加快英国的药物发现，加快针对以前无法治疗的实体肿瘤的新的挽救生命的癌症治疗方法的开发，并最终降低NHS的治疗成本。Biomedical Catalyst项目将验证我们的方法，加强我们的人工智能培训数据，并使我们能够使用真实的血液样本来证明临床有效性。虽然从商业角度来看，项目产出的结果将开启与英国世界领先的TCR疗法开发商免疫核心的合作伙伴关系。