A novel sensor platform for early detection of pancreatic cancer

用于早期检测胰腺癌的新型传感器平台

• 批准号: BB/X004775/1
• 负责人: David Jenkins
• 金额: $ 23.17万
• 依托单位: Plymouth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX004775%2F1
• 关键词: novel; sensor; platform; early; detection

This project aims to develop a point-of-care diagnostic tool for the early-onset detection of pancreatic cancer. Pancreatic cancer is predicted to become the second leading cause of cancer-death in the next few years. It is mostly asymptomatic, and currently 80% of cases are diagnosed at an advanced stage. Late detection leads to an extremely poor survival prognosis, with average survival of around 5 months after diagnosis. An analysis methodology that leads to early diagnosis, such as that proposed here, will have a profoundly positive effect on pancreatic cancer survival. When tumours are present in the body, specific proteins, indicative of disease progression, are produced and appear in the blood. These proteins, referred to as biomarkers, are indicators of disease. In pancreatic cancer there are two significant biomarkers: CA19-9 and CEA. Unfortunately, these biomarkers alone cannot be used to make a diagnosis in the general population. However, a large number of other proteins have been identified and linked to disease progression. For our project, 30 biomarkers indicative of pancreatic cancer have been carefully selected. Current methods to identify key biomarkers lack the necessary sensitivity and specificity for the detection of early-stage pancreatic cancer, are time consuming to run, and require skilled operators to ensure results are reliable. Therefore, a new approach is needed to achieve early onset pancreatic cancer detection. Effective point-of-care diagnosis will significantly reduce preventable cases. Here we propose to develop an integrated sensor platform that makes measurements indicating the presence of biomarkers using novel sensors. It will then make use of machine leaning approaches to combine these measurements with secondary data, to enhance diagnosis. The secondary data will include 'risk' factors from patient medical history, such as having diabetes. To detect biomarkers at the very low levels they manifest themselves at pancreatic cancer onset, we propose to design a novel sensitive and selective transistor-based sensor system. Normally transistors are operated by directly applying an electrical signal to their channels. Here the sensor system will be based upon an array of transistors which are tuned for the detection of specific biomarkers by using "aptamers" placed onto their channels. The detection process relies upon on specific biomarkers binding to aptamers, which acts like an input signal to change the overall transistor electrical characteristics, which can be subsequently measured. To ensure effective and reliable biomarker detection, it is essential the transistor sensors are built in a consistent fashion, especially regarding aptamer-loaded channel construction, since this greatly affects operation. To achieve this, a 3D-bioprinter will be used to deliver controlled volumes of aptamers to the transistor in an automated fashion. The experimental phase of the research will systematically progress from simple to complex detection tasks. Phase 1 of will focus on creating the sensors for initial characterisation studies. In the second phase, sensor capacity to detect known biomarker concentrations will determine the sensor sensitivity and detection limits. The final stage 3 will examine biomarker detection in serum samples from patients with pancreatic cancer at varying stages of disease progression, as well as healthy controls. Sensor data will be combined with risk-life factors to train a machine learning system to detect the presence of pancreatic cancer. Finally, we will evaluate sensor performance as a diagnostic tool to predict early-onset pancreatic cancer.

该项目旨在开发一种护理点诊断工具，用于早发胰腺癌。预计胰腺癌将成为未来几年癌症死亡的第二大原因。它主要是无症状的，目前有80％的病例在高级阶段被诊断出。晚期检测导致生存预后极低，诊断后约5个月的平均存活率约为5个月。一种导致早期诊断的分析方法，例如此处提出的诊断，将对胰腺癌的生存产生深远的积极影响。当体内存在肿瘤时，会产生特定的蛋白质，表明疾病进展并出现在血液中。这些蛋白质（称为生物标志物）是疾病的指标。在胰腺癌中，有两个重要的生物标志物：CA19-9和CEA。不幸的是，仅这些生物标志物不能用于在普通人群中进行诊断。但是，已经确定了许多其他蛋白质并与疾病进展有关。对于我们的项目，已经仔细选择了30种指示胰腺癌的生物标志物。当前识别关键生物标志物的方法缺乏检测早期胰腺癌的必要敏感性和特异性，很耗时运行，需要熟练的操作员来确保结果可靠。因此，需要一种新的方法来实现早期发作胰腺癌检测。有效的护理点诊断将大大减少可预防的病例。在这里，我们建议开发一个集成的传感器平台，该平台进行测量，表明使用新型传感器存在生物标志物。然后，它将利用机器倾斜方法将这些测量结果与辅助数据相结合，以增强诊断。次要数据将包括患者病史的“风险”因素，例如患有糖尿病。为了在非常低的水平上检测生物标志物，它们在胰腺癌发作中表现出来，我们建议设计一种新型敏感和选择性的基于晶体管的传感器系统。通常，晶体管是通过直接将电信号应用于其通道的。在这里，传感器系统将基于一系列晶体管，这些晶体管通过使用放置在其通道上的“适体”来调整以检测特定的生物标志物。检测过程依赖于与适体结合的特定生物标志物，这就像输入信号一样，可以改变整体晶体管电气特性，随后可以测量。为了确保有效且可靠的生物标志物检测，晶体管传感器以一致的方式构建至关重要，尤其是在适体的通道构造方面，因为这极大地影响了操作。为了实现这一目标，将使用3D-Bioprinter以自动化的方式向晶体管传递适体的控制量。研究的实验阶段将从简单到复杂的检测任务系统地发展。将重点侧重于创建最初表征研究的传感器。在第二阶段，传感器检测已知生物标志物浓度的能力将确定传感器的灵敏度和检测极限。最后的第3阶段将检查胰腺癌患者在疾病进展的不同阶段以及健康对照阶段的血清样品中的生物标志物检测。传感器数据将与风险生活因素结合使用，以训练机器学习系统以检测胰腺癌的存在。最后，我们将评估传感器性能作为预测早发胰腺癌的诊断工具。