Using human genetics to identify novel biomarkers for enhanced prediction and early detection of cancer

癌症是全球第二大死因。尽管近几十年来在治疗方面取得了重大进展，但许多癌症的预后仍然很差，因为个体通常表现为疾病晚期，此时治愈性治疗不再是一种选择。NHS长期计划的目标是到2028年，将早期发现的癌症比例从50%提高到75%，从而有可能治愈。改善癌症的早期检测和预测是实现这一目标的重要手段。蛋白质组学反向孟德尔随机化（RMR）是一种新的人类遗传学方法，它使用种系遗传变异来识别与遗传倾向相关的循环蛋白。该方法可用于识别与疾病病因学有关的循环蛋白，以及非因果的“旁观者”蛋白，这些蛋白可能是疾病预测和检测的信息性生物标志物。在这个项目中，学生将使用蛋白质组学RMR框架来识别与癌症遗传易感性相关的蛋白质。这些发现将通过英国生物银行（UKB）的直接蛋白质测量和癌症数据进行验证。将利用UKB的相关医疗记录，将循环蛋白数据整合到当前的临床癌症风险预测模型中，评估鉴定蛋白的潜在临床应用。

Cancer is the 2nd leading cause of death globally. Despite important advances in treatment in recent decades, the prognosis for many cancers remains poor because individuals typically present with advanced disease at which point curative treatment is no longer an option. The NHS Long Term Plan aims to increase the percentage of cancers caught at an early stage, thus potentially curable, from 50-75% by 2028. Improvements in early detection and prediction of cancer is an important vehicle to achieve that goal. Proteomic Reverse Mendelian randomization (RMR) is a novel human genetics approach that uses germline genetic variants to identify circulating proteins associated with a genetic tendency to develop disease. The approach can be used to identify circulating proteins implicated in disease aetiology as well as non-causal "bystander" proteins that may nonetheless be informative biomarkers for disease prediction and detection. In this project, the student will use a Proteomic RMR framework to identify proteins associated with a genetic predisposition to develop cancer. These findings will be validated using direct protein measures and cancer data in UK Biobank (UKB). The potential clinical application of the identified proteins will be assessed by integrating circulating protein data into current clinical cancer risk prediction models, using linked medical records in UKB.