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

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.

免疫疗法已经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，我们必须了解为什么有些人有反应而另一些人没有，并确定一种实用的生物标志物来区分它们。免疫疗法依赖于肿瘤微环境（TME）的空间组织，因为它靶向T细胞相互作用，但对TME组织的原理知之甚少。免疫疗法如何在治疗期间重塑这种结构也是未知的，但可以解释为什么反应不同。为了了解乳腺癌的免疫治疗反应，并揭示一个可靠的歧视性生物标志物，我建议解剖多细胞TME结构原位组织的高度多重成像。成像质谱细胞术（IMC）使用飞行时间质谱法以亚细胞分辨率定位组织中44种蛋白质的表达。使用IMC，我们将分析来自数百名乳腺癌患者的数千份样本，这些患者被招募到免疫治疗的随机试验中，其中收集了纵向样本（基线，治疗期间和治疗后）。使用自动图像分析，图论和空间统计学，我们将确定在肿瘤中复发的多细胞配置，并绘制这些配置在治疗应答者与非应答者中如何演变。然而，这些分析产生的结果与常规临床病理学无关，因为在该环境中不可能进行等同的测定，从而使翻译变得令人沮丧。我们将通过使用这些试验积累的大型数字病理学资源来弥合这一差距，以开发新的机器学习工具，将在高维空间中学习到的特征转移到常规数字病理学染色中。总之，该计划将阐明免疫治疗反应的病理基础，并朝着增强病理学的新临床学科迈出第一步。