Regulation of anti-tumour immunity by GSK-3, a potential target for immunotherapy

本项目主要目的是研究糖原合成酶激酶3 （GSK-3）在免疫应答中的作用。先前的研究表明，细胞毒性淋巴细胞（即CD8+ T细胞和自然杀伤[NK]细胞）的抗肿瘤活性可以通过抑制GSK-3而增强。然而，由于GSK-3在许多组织中普遍存在，这给药物设计带来了困难。因此，通过基因敲除模型，本项目旨在揭示GSK-3调节抗肿瘤免疫的机制，并确定与该途径相关的其他靶点，这些靶点可能更适合用于免疫治疗。确定的靶点将在体外使用免疫学和生化方法进行验证，并评估它们对肿瘤识别和杀伤的调节途径的影响。然后将确定GSK-3下游的这些途径本身是否可药物化，并将评估它们作为未来开发的治疗靶点的潜力。确定的GSK-3靶标将在CD8+ T细胞和NK细胞中使用免疫学（如肿瘤杀伤测定、细胞因子释放、免疫检查点表达）和生化/遗传方法（酶测定、基因和蛋白质表达）进行分析；这些方法包括实验室技术，如从血液/组织中纯化免疫细胞、elisa和流式细胞术、western blotting和定量RT-PCR。生物信息学也将成为该项目的一部分，包括基因集富集分析（GSEA）、网络分析和统计方法，以比较和分析来自实验室模型的转录组数据与来自癌症患者队列的数据集。

The main purpose of this project is to study the role of Glycogen synthase kinase-3 (GSK-3) in the immune response. Previous work has shown that the anti-tumour activity of cytotoxic lymphocytes (namely CD8+ T cells and natural killer [NK] cells) can be enhanced by the inhibition of GSK-3. However, due to GSK-3 being largely ubiquitous in many tissues, this has created difficulties in drug design. Therefore, with the use of genetic knockout models, this project aims to uncover the mechanism behind GSK-3 regulation of anti-tumour immunity and identify other targets linked to this pathway which may be more suitable candidates for immunotherapy.Identified targets will be validated in vitro using immunological and biochemical approaches and their impact on modulating these pathways on tumour recognition and killing will be assessed. It will then be determined whether these pathways downstream of GSK-3 are themselves druggable and they will be evaluated for their potential as therapeutic targets for future development.Identified targets of GSK-3 will be analysed in CD8+ T cells and NK cells using immunological (e.g. tumour killing assays, cytokine release, immune checkpoint expression) and biochemical/genetic methods (enzyme assays, gene and protein expression); these approaches include laboratory techniques such as immune cell purification from blood/tissue, ELISAs and flow cytometry, western blotting, and quantitative RT-PCR. Bioinformatics will also form part of the project, including gene-set enrichment analysis (GSEA), network analysis and statistical methods to compare and analyse transcriptome data from laboratory models with datasets derived from cancer patient cohorts.