Deconvolution of intra-tumoural heterogeneity using whole section images in pancreatic cancer

关键词：机器学习，肿瘤内异质性，胰腺癌，亚型分型，人工智能分子亚型分型是一种在显微镜下看起来非常相似的癌症进行亚分类的方法，对于更好地定义预后和治疗反应的分子分类至关重要。使用mRNA表达（转录组）进行亚型分型是一种流行的方法，因为它不仅提供了基因表达的全局概况，允许（无）监督聚类，它还为发现支持每种亚型的致癌途径提供了途径。一般来说，在转录组测序之前，RNA是从一小块几毫米大小的组织中提取的。因此，它没有考虑整个肿瘤或肿瘤内异质性（ITH）。虽然ITH已被证明在各种癌症类型中普遍存在，但在整个肿瘤规模上描述它们的特征仍然具有技术和经济上的挑战性。因此，迫切需要一种新的解决方案来更好地了解ITH和转录组亚型，以个性化和改善胰腺癌患者的治疗。该项目将开发一种新的策略，利用先进的人工智能（AI）模型在计算机上构建大型胰腺癌患者队列的空间分辨转录组谱。它将在计算机视觉和医学图像分析、生物信息学和转录组分析中使用尖端的深度学习技术，并将利用具有良好特征的胰腺癌队列的可用性（ICGC、TCGA和来自国家Precision-Panc试验的数据）。

Keywords: Machine learning, intratumoural heterogeneity, pancreatic cancer, subtyping, artificial intelligenceMolecular subtyping is a methodology to subclassify cancer that looks otherwise very similar under the microscope and is essential to better define molecular taxonomy of prognosis and treatment response. Subtyping using mRNA expression (transcriptome) is a popular method, as it not only gives a global overview of gene expression to allow (un)supervised clustering, it also provides runways to discovery of carcinogenesis pathways that underpins each subtype. In general, RNA is extracted from a small piece of tissue of a few millimetres size, prior to transcriptome sequencing. Therefore, it does not take the whole tumour or intra-tumoural heterogeneity (ITH) into consideration.While ITH has been shown to be pervasive across cancer types, characterising them at the whole tumour scale remains technically and financially challenging. Hence, a novel solution to better understand ITH and transcriptomic subtyping is urgently needed to personalise and improve treatments for pancreatic cancer patients. This project will develop a novel strategy that leverages advanced artificial intelligence (AI) models to construct spatially resolved transcriptomic profiles of large pancreatic cancer patient cohorts in silico. It will use cutting edge deep learning techniques in computer vision and medical image analysis, bioinformatics and transcriptome analysis, and will leverage the availability of well-characterised pancreatic cancer cohorts (ICGC, TCGA and data from the national Precision-Panc trial).