Analysis of dynamics of body composition using computerised tomography scans and their association to molecular taxonomy in pancreatic cancer (Glasgow

以上头衔以（格拉斯哥领先）结束。关键词：胰腺癌，分子亚型，精准医学，肿瘤-宿主相互作用，医学影像学该项目将主要涉及计算和人工智能方法，包括生物信息学（算法开发），首先进行与CT扫描图像分析（检查身体成分）和全身炎症标记物相关的胰腺肿瘤特征分析，以确定遗传特征和基因组畸变，基于此多组学患者队列数据集分析。重要的基因通路也将使用多重IF染色进行评估，以便使用数字病理学方法分析空间蛋白质组学。这是为了描述可能导致临床表型的潜在分子模式，并随后定义支持所确定的每种表型的关键生物学途径。然后将根据预后对这些关系进行排序，并在患者队列数据中进一步验证结果以确认临床相关性，以便尝试和预测患者预后。CT扫描图像分析将在iCASE工业合作伙伴Collective Mind Radiology开发的可信研究环境中进行。

The above title ends in (Glasgow lead).Studentship strategic priority area: Basic & Clinical ResearchKeywords: Pancreatic cancer, molecular subtypes, precision medicine, tumour-host-interaction, medical imagingAbstract: This project will primarily involve computational and AI methods including bioinformatics (algorithm development) to first carry out analysis of pancreatic tumour profiles in correlation with CT scan image analysis (to examine body composition) and systemic inflammation markers to define genetic characteristics and genomic aberrations, based on this multi-omic patient cohort dataset analysis. Gene pathways of importance will also be assess using multiplex IF staining so that spatial proteomics can be analysed using a digital pathology approach. This is to characterise potential molecular patterns that may contribute to clinical phenotypes, and subsequently define key biological pathways underpinning each phenotype identified. These relationships will then be ranked based on prognosis, and findings will be further validated in patient cohort data to confirm clinical relevance, in order to try and predict patient outcome. CT scan image analysis will be carried out on trusted research environment developed by the iCASE industrial partner Collective Mind Radiology.