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反应，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，因为它以 T 细胞相互作用为目标，但人们对 TME 组织的原理知之甚少。免疫疗法在治疗过程中如何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，并发现可靠的区分性生物标志物，我建议通过组织的高度多重成像原位解剖多细胞 TME 结构。成像质谱流式细胞仪 (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个样本，这些患者被招募参加随机免疫治疗试验，其中收集了纵向样本（基线、治疗中和治疗后）。利用自动图像分析、图论和空间统计，我们将识别肿瘤中重复出现的多细胞结构，并绘制出治疗后有反应者与无反应者中这些结构如何演变的图表。然而，这些分析得出的结果与常规临床病理学无关，因为在这种情况下不可能进行等效的测定，这令人沮丧的翻译。我们将利用这些试验积累的大量数字病理学资源来开发新颖的机器学习工具，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，从而弥补这一差距。该计划将共同阐明免疫治疗反应的病理学基础，并为增强病理学的新临床学科迈出第一步。