Using complex state-of-the-art mouse models of cancer to improve the understanding and treatment of human cancer

使用最先进的复杂小鼠癌症模型来提高对人类癌症的理解和治疗

• 批准号: MC_PC_21042
• 负责人: Karen Blyth
• 金额: $ 488.57万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MC_PC_21042
• 关键词: Using; complex; state; art; mouse

Cancer remains one of the most prevalent health conditions for our population and, whilst great strides have been made in diagnosis and treatment, 1 in 4 people in the UK currently die of the disease, affirming that the science community must address the need for better treatments. Cancer is a complex and dynamic process which involves the transformation of a normal cell into an aggressively malignant one through a series of genetic changes, but is also strongly influenced by the local environment in which a tumour co-evolves. The complex interactions and cross-talk between tumour cells and their neighbouring tissues is a vital component of how a cancer develops, spreads through the body and responds to anti-cancer therapies. These processes can only be fully explored in the context of the living animal. Mouse models have been crucial in deepening our understanding of cancer and in the discovery of novel therapies. For cancers of certain organs mouse models now exist that recapitulate the full cancer journey, but for other cancers these models have fallen short of replicating the complexity of human disease. There is an urgent need therefore to develop the next generation of mouse models that will allow the research community to better understand the biology underpinning the major cancer types, generate novel targets and rigorously apply these to a therapeutic pipeline with better prediction to patient response. This proposal brings together leading clinicians, cancer biologists, computational biologists partnered with technology clusters in a coordinated national effort. Our pertinent aims will be to exploit new and emerging technologies that will i) allow us to characterise how accurately mouse models reflect specific subsets of human cancers (so called disease-positioning); ii) build better mouse models that mirror the full spectrum of human tumour evolution; iii) create robust mouse models that significantly improve predictability of novel cancer treatments. In addressing these aims we will create an outward facing, easily accessible compendium of models and data for the UK community, opening out availability of resources, technologies and expertise. We will improve and extend our repertoire of models to cover those cancers where no quality models exist. With access to huge human data sets we can reiteratively refine the mouse models to faithfully reflect human disease, and utilize sophisticated imaging techniques to study the dynamic changes at the cell, tissue and organ level. This is vital to ensure that new knowledge gleaned from these models is applicable to human disease, and to ensure that therapeutic testing in mouse models accurately predicts how human cancers will respond to the same treatments. In collaboration with the Mary Lyon Centre (MLC), and leveraging the cluster expertise within the National Mouse Genetics Network, we will develop platforms for integrating many different types of data across our spectrum of models, increasing their value as predictive pre-clinical tools. Specialist expertise in surgery, radiotherapy, imaging and standard-of-care treatment will be centralised through MLC that will act as a repository of models for wider distribution and promote best practice and 3Rs (refinement, replacement and reduction) nationally. We aim to expand upon our established links with industry, particularly in the SME space, to promote job creation and commercialisation of new therapeutic discoveries and to leverage further significant investment in disease modelling through collaborative engagement. Ultimately we expect to translate new treatments and drug combinations validated in our cancer models into the clinic through our already extensive clinical network.

癌症仍然是我国人口最普遍的健康状况之一，虽然在诊断和治疗方面取得了巨大进步，但目前英国有四分之一的人死于这种疾病，这表明科学界必须解决更好治疗的需求。癌症是一个复杂而动态的过程，涉及通过一系列遗传变化将正常细胞转化为具有侵袭性的恶性细胞，但也受到肿瘤共同进化的局部环境的强烈影响。肿瘤细胞与其邻近组织之间复杂的相互作用和串扰是癌症如何发展，在体内扩散和对抗癌疗法作出反应的重要组成部分。这些过程只能在活体动物的背景下进行充分探索。小鼠模型在加深我们对癌症的理解和发现新疗法方面至关重要。对于某些器官的癌症，现在存在的小鼠模型可以概括整个癌症过程，但对于其他癌症，这些模型无法复制人类疾病的复杂性。因此，迫切需要开发下一代小鼠模型，使研究界能够更好地了解主要癌症类型的生物学基础，产生新的靶点，并将其严格应用于治疗管道，更好地预测患者的反应。该提案汇集了领先的临床医生，癌症生物学家，计算生物学家，与技术集群合作，共同努力。我们的相关目标将是利用新的和新兴的技术，这些技术将i）使我们能够确定小鼠模型如何准确地反映人类癌症的特定子集（所谓的疾病定位）; ii）建立更好的小鼠模型，反映人类肿瘤进化的全谱; iii）创建强大的小鼠模型，显着提高新型癌症治疗的可预测性。为了实现这些目标，我们将为英国社区创建一个面向外部、易于访问的模型和数据汇编，开放资源、技术和专业知识的可用性。我们将改进和扩展我们的模型库，以涵盖那些没有高质量模型的癌症。通过访问庞大的人类数据集，我们可以精确地改进小鼠模型，以忠实地反映人类疾病，并利用先进的成像技术来研究细胞，组织和器官水平的动态变化。这对于确保从这些模型中收集的新知识适用于人类疾病，并确保小鼠模型中的治疗测试准确预测人类癌症对相同治疗的反应至关重要。通过与玛丽里昂中心（MLC）合作，并利用国家小鼠遗传学网络内的集群专业知识，我们将开发平台，用于整合我们的模型范围内的许多不同类型的数据，提高其作为预测性临床前工具的价值。手术、放射治疗、成像和标准治疗方面的专家专长将通过MLC集中起来，MLC将作为模型库，供更广泛地分发，并在全国范围内推广最佳做法和3R（改进、替换和减少）。我们的目标是扩大我们与行业的联系，特别是在中小企业领域，以促进创造就业机会和新的治疗发现的商业化，并通过合作参与进一步利用疾病建模的重大投资。最终，我们希望通过我们已经广泛的临床网络，将在我们的癌症模型中验证的新疗法和药物组合转化为临床。

项目成果

?d T cells turn the tables on immune-evasive colon cancer.

?d T 细胞扭转了免疫逃避结肠癌的局面。

• DOI: 10.1016/j.medj.2023.02.007
• 发表时间: 2023
• 期刊: Med (New York, N.Y.)
• 作者: Coffelt SB

β-Catenin Drives Butyrophilin-like Molecule Loss and γδ T-cell Exclusion in Colon Cancer.

• DOI: 10.1158/2326-6066.cir-22-0644
• 发表时间: 2023-08-03
• 期刊: CANCER IMMUNOLOGY RESEARCH
• 影响因子: 10.1
• 作者: Suzuki, Toshiyasu;Kilbey, Anna;Casa-Rodriguez, Nuria;Lawlor, Amy;Georgakopoulou, Anastasia;Hayman, Hannah;Swe, Kyi Lai Yin;Nordin, Anna;Cantu, Claudio;Vantourout, Pierre;Ridgway, Rachel A.;Byrne, Ryan M.;Chen, Lei;Verzi, Michael P.;Gay, David M.;Vazquez, Ester Gil;Belnoue-Davis, Hayley L.;Gilroy, Kathryn;Kostner, Anne Helene;Kersten, Christian;Thuwajit, Chanitra;Andersen, Ditte K.;Wiesheu, Robert;Jandke, Anett;Blyth, Karen;Roseweir, Antonia K.;Leedham, Simon J.;Dunne, Philip D.;Edwards, Joanne;Hayday, Adrian;Sansom, Owen J.;Coffelt, Seth B.
• 通讯作者: Coffelt, Seth B.

Epithelial TGFβ engages growth-factor signalling to circumvent apoptosis and drive intestinal tumourigenesis with aggressive features.

• DOI: 10.1038/s41467-022-35134-3
• 发表时间: 2022-12-07
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Flanagan DJ;Amirkhah R;Vincent DF;Gunduz N;Gentaz P;Cammareri P;McCooey AJ;McCorry AMB;Fisher NC;Davis HL;Ridgway RA;Lohuis J;Leach JDG;Jackstadt R;Gilroy K;Mariella E;Nixon C;Clark W;Hedley A;Markert EK;Strathdee D;Bartholin L;Redmond KL;Kerr EM;Longley DB;Ginty F;Cho S;Coleman HG;Loughrey MB;Bardelli A;Maughan TS;Campbell AD;Lawler M;Leedham SJ;Barry ST;Inman GJ;van Rheenen J;Dunne PD;Sansom OJ
• 通讯作者: Sansom OJ

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data.

• DOI: 10.1158/1078-0432.ccr-22-1102
• 发表时间: 2022-09-15
• 期刊: CLINICAL CANCER RESEARCH
• 影响因子: 11.5
• 作者: Fisher, Natalie C.;Byrne, Ryan M.;Leslie, Holly;Wood, Colin;Legrini, Assya;Cameron, Andrew J.;Ahmaderaghi, Baharak;Corry, Shania M.;Malla, Sudhir B.;Amirkhah, Raheleh;McCooey, Aoife J.;Rogan, Emily;Redmond, Keara L.;Sakhnevych, Svetlana;Domingo, Enric;Jackson, James;Loughrey, Maurice B.;Leedham, Simon;Maughan, Tim;Lawler, Mark;Sansom, Owen J.;Lamrock, Felicity;Koelzer, Viktor H.;Jamieson, Nigel B.;Dunne, Philip D.
• 通讯作者: Dunne, Philip D.

MmCMS: mouse models' consensus molecular subtypes of colorectal cancer.

• DOI: 10.1038/s41416-023-02157-6
• 发表时间: 2023-03
• 期刊: British journal of cancer
• 影响因子: 8.8
• 作者: Amirkhah R;Gilroy K;Malla SB;Lannagan TRM;Byrne RM;Fisher NC;Corry SM;Mohamed NE;Naderi-Meshkin H;Mills ML;Campbell AD;Ridgway RA;Ahmaderaghi B;Murray R;Llergo AB;Sanz-Pamplona R;Villanueva A;Batlle E;Salazar R;Lawler M;Sansom OJ;Dunne PD
• 通讯作者: Dunne PD