Cancer Patients Digital Twins to Investigating disease fragmentation and its impact on drug response in AML trials

癌症患者数字孪生研究 AML 试验中的疾病碎片化及其对药物反应的影响

• 批准号: 2881649
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2881649
• 关键词: Cancer; Patients; Digital; Twins; Investigating

As not all patients respond equally to the same treatments, there may be clues in a patient's genome that can predict how they will respond to different treatments and whether there are common genomic signatures that influence response to chemotherapy. In particular, Cancer Patients Digital Twins (CPDTs) can assist in the digital or virtual representation of a patient, creating a replica of an individual in combination with AI algorithms that take into account multiple factors to aid prediction or diagnosis.This project will use digital twins of cancer patients to investigate disease fragmentation and its impact on drug response in AML trials. Using genomic data available from a number of AML trials for around 2500 patients, including their treatment and survival data, the project will focus on:i. Use machine learning approaches to identify disease fragmentation and patient response to specific drugs. ii. Look for genetic explanations for differences in drug response that are common to different treatment pathways; and iii. Develop models that use genomic data to predict drug response within standard AML treatment pathways, providing ways to tailor interventions for maximum impact. iv. Develop and design Cancer Patient Digital Twins (CPDTs) to help predict drug response and AML treatment. By taking a human-centred AI approach this project aims to explore and unpack the most appropriate data of AML trials to design and deploy a Cancer Patients Digital Twins (CPDTs) to predict drug responses and AML treatment pathways. Through this project we will identify: i. What are the key features, models and elements to better represent and model a digital cancer patient twin for disease fragmentation and drug response in AML. ii. What patient's genome data might be more relevant to predict how AML patients will respond to different treatments. iii. What are the most common genomic signatures that will influence response to chemotherapy. iv. Follow a human-centred AI approach to unpack the data streams, data interactions alongside with technological interactions to design Cancer Patients Digital Twins (CPDTs) for AML With the NHS CYSGODI (CYmru Service for Genomic Oncology Diagnoses) service generating genomic data for various haematological cancers, this work has a direct route to impact on routinely generated datasets.

由于并非所有患者对相同的治疗都有相同的反应，因此患者的基因组中可能有线索可以预测他们对不同治疗的反应，以及是否存在影响化疗反应的共同基因组特征。特别是，癌症患者数字双胞胎（CPDTs）可以帮助数字或虚拟表示患者，结合考虑多种因素的人工智能算法创建个体的复制品，以帮助预测或诊断。该项目将使用癌症患者的数字双胞胎来研究疾病碎片及其对AML试验中药物反应的影响。利用从大约2500名患者的一些AML试验中获得的基因组数据，包括他们的治疗和生存数据，该项目将重点关注：使用机器学习方法来识别疾病碎片和患者对特定药物的反应。二.寻找不同治疗途径常见的药物反应差异的遗传学解释;和iii.开发使用基因组数据预测标准AML治疗途径中药物反应的模型，提供定制干预措施以实现最大影响的方法。四.开发和设计癌症患者数字双胞胎（CPDT），以帮助预测药物反应和AML治疗。 通过采用以人为本的人工智能方法，该项目旨在探索和解压缩AML试验的最合适数据，以设计和部署癌症患者数字双胞胎（CPDT）来预测药物反应和AML治疗途径。 通过这个项目，我们将确定：i。哪些关键特征、模型和元素可以更好地代表和模拟AML中疾病碎片化和药物反应的数字癌症患者双胞胎。二.哪些患者的基因组数据可能更有助于预测AML患者对不同治疗的反应。三.影响化疗反应的最常见的基因组特征是什么？四.遵循以人为本的人工智能方法来解包数据流，数据交互以及技术交互，以设计AML的癌症患者数字双胞胎（CPDT）随着NHS CYSGODI（CYmru基因组肿瘤诊断服务）服务为各种血液癌症生成基因组数据，这项工作有一个直接的途径来影响常规生成的数据集。