Spatial-BrTME: Multicellular spatial dynamics of immunotherapy response in breast cancer

Spatial-BrTME：乳腺癌免疫治疗反应的多细胞空间动力学

• 批准号: EP/Y014995/1
• 负责人: Hamid Ali
• 金额: $ 161.87万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY014995%2F1
• 关键词: Spatial; BrTME; Multicellular; spatial; dynamics; Spatial; BrTME; Multicellular; spatial; dynamics

Immunotherapy has revolutionised cancer treatment, but its role in breast cancer is unclear. For breast cancer patients to benefit, we must understand why some respond whereas others don't, and identify a pragmatic biomarker to distinguish between them. Immunotherapy depends on spatial organisation of the tumour microenvironment (TME) because it targets T cell interactions, but the principles of TME organisation are poorly understood. How immunotherapy remodels this structure during treatment is also unknown but may explain why responses differ. To understand immunotherapy response in breast cancer, and to uncover a reliable discriminatory biomarker, I propose to dissect multicellular TME structure in situ by highly multiplexed imaging of tissues. Imaging mass cytometry (IMC) uses time-of-flight mass spectrometry to localise the expression of 44 proteins at subcellular resolution in tissues. Using IMC, we will analyse thousands of samples from hundreds of breast cancer patients recruited to randomised trials of immunotherapy where longitudinal samples (at baseline, on-treatment, and post-treatment) have been collected. Using automated image analysis, graph theory and spatial statistics we will identify multicellular configurations that recur across tumours and chart how these evolve under therapy in responders versus non-responders. Findings arising from these analyses are disconnected from routine clinical pathology however, because equivalent assays are not possible in that setting, frustrating translation. We will bridge this gap by using the large digital pathology resource accrued for these trials to develop novel machine-learning tools to transfer features learned in high-dimensional space to routine digital pathology stains. Together, this programme will elucidate the pathologic basis of immunotherapy response and take first steps toward a new clinical discipline of augmented pathology.

免疫疗法彻底改变了癌症治疗，但其在乳腺癌中的作用尚不清楚。为了使乳腺癌患者受益，我们必须理解为什么有些反应，而另一些人则没有反应，并确定务实的生物标志物以区分它们。免疫疗法取决于肿瘤微环境（TME）的空间组织，因为它靶向T细胞相互作用，但是TME组织的原理知之甚少。免疫疗法如何在治疗过程中重塑这种结构，但可以解释为什么反应有所不同。为了了解乳腺癌中的免疫疗法反应，并发现可靠的歧视性生物标志物，我建议通过高度多重的组织成像来剖析多细胞TME结构。成像质量细胞术（IMC）使用飞行时间质谱法将44种蛋白在组织中的细胞分辨率下定位。使用IMC，我们将分析来自数百名乳腺癌患者的数千种样本，这些患者被招募到免疫疗法的随机试验中，其中已收集了纵向样本（基线，治疗和治疗后）。使用自动图像分析，图理论和空间统计数据，我们将确定跨肿瘤复发的多细胞构型，并列出这些构型如何在响应者与非反应者中的治疗中进化。这些分析引起的发现与常规临床病理学断开，因为在这种情况下不可能进行同等的测定，这令人沮丧的翻译。我们将通过使用这些试验的大型数字病理资源来开发新颖的机器学习工具来传递在高维空间中学习的特征来常规数字病理学污渍，从而弥合这一差距。该计划将共同阐明免疫疗法反应的病理基础，并迈向新的增强病理临床学科的第一步。