A Quantitative PET/CT Research Resource for Co-Clinical Imaging of Lung Cancer Therapies

用于肺癌治疗联合临床成像的定量 PET/CT 研究资源

• 批准号: 10301566
• 负责人: A McGarry Houghton
• 金额: $ 66.03万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10301566
• 关键词: Address; Animal Model; Atypical lymphocyte; Cancer Etiology; Cancer Patient; Cessation of life; Clinical; Clinical Research; Clinical Trials; Development; Diagnosis; Digital Imaging and Communications in Medicine; Future; Genetically Engineered Mouse; Glean; Goals; IL8RB gene; Image; Immune checkpoint inhibitor; Immunotherapy; Industry Standard; Infiltration; Informatics; Link; Lung Adenocarcinoma; Malignant - descriptor; Malignant neoplasm of lung; Medical Imaging; Metadata; Methods; Modeling; Mus; Non-Small-Cell Lung Carcinoma; Oncology; Outcome; PD-1/PD-L1; PET/CT scan; Phase; Positron-Emission Tomography; Protocols documentation; Resources; Site; Squamous Cell Lung Carcinoma; Structure; Testing; Therapeutic; Work; animal imaging; anti-PD-1; anti-PD-L1 therapy; cancer imaging; cancer therapy; checkpoint therapy; clinical imaging; co-clinical trial; data resource; data sharing; digital; early detection biomarkers; human disease; imaging informatics; imaging modality; improved; improved outcome; innovation; member; monocyte; mouse model; neutrophil; novel; oncology trial; open data; open source; pre-clinical; preclinical imaging; preclinical study; programs; quantitative imaging; recruit; response; success; tumor; tumor microenvironment; web-accessible

Project Abstract The specific goal of this proposal is to develop and optimize methods for quantitative pre-clinical PET imaging for immune checkpoint inhibitor (ICI) therapies in non-small cell lung cancer (NSCLC). We will leverage an existing co-clinical trial using our genetically-engineered mouse model (GEMM) of lung adenocarcinoma to develop, test and implement the methods and populate a web-accessible research resource. This web- accessible research resource will in turn leverage recent developments in quantitative cancer imaging informatics using the industry-standard DICOM format. In response to PAR-18-841 there are four components to our proposal: (1) appropriate models, (2) a co-clinical trial including a therapeutic goal, (3) quantitative preclinical and clinical PET imaging, and (4) an innovative quantitative cancer imaging informatics platform. Our longer term goal is to improve outcomes for NSCLC patients, as lung cancer is still the leading cause of cancer deaths worldwide. Although ICI therapy has been a tremendous clinical benefit for some NSCLC patients, only ~20% of NSCLC patients respond to anti-PD1/PDL1 therapy. Using PET co-clinical imaging to improve ICI therapies for NSCLC is challenged by a lack of suitable informatics methods to capture and track necessary meta-information, appropriate response criteria for preclinical imaging, which in turn is a consequence in part of the lack of quantitative preclinical PET imaging methods that are linked to quantitative clinical PET imaging methods. We will address these challenges with three Specific Aims: 1, Develop and optimize quantitative preclinical quantitative imaging methods and protocols. These methods will involve novel long-lived phantoms that can cross-calibrate multiple preclinical and clinical PET scanners. This will be tested with partner members of the Co-Clinical Imaging Research Resources Program. 2. Implement the optimized methods in our co-clinical trial of ICI treatment of NSCLC with a GEMM lung adenocarcinoma model and a GEMM model lung squamous cell carcinoma. From this we will evaluate how the information gleaned from the pre-clinical and clinical studies can be used to inform future pre-clinical studies in terms of optimized mouse imaging protocols and response criteria. 3. Share data and resources on co-clinical trials using quantitative PET imaging using a web-accessible open science approach (open source + open data) by extending the DICOM standard for pre-clinical small animal imaging with DICOM-compliant structures that provide necessary quantitative meta-data. These methods and resources developed during this project will be distributed to accelerate the development of needed effective cancer therapies by improving the utility of early-phase oncology trials using co-clinical studies with PET imaging. In addition, we will determine, and potentially improve, the utility of PET imaging as a biomarker for early assessment of response in co-clinical immunotherapy studies by using an appropriate mouse model.

项目摘要 该提案的具体目标是开发和优化定量临床前PET成像的方法 用于非小细胞肺癌（NSCLC）的免疫检查点抑制剂（ICI）治疗。我们将利用 使用我们的肺腺癌基因工程小鼠模型（GEMM）进行的现有联合临床试验， 开发、测试和实施这些方法，并充实可在网上查阅的研究资源。这张网-- 可访问的研究资源将反过来利用定量癌症成像的最新发展 使用行业标准DICOM格式的信息。针对PAR-18-841，有四个组成部分 我们的建议：（1）适当的模型，（2）包括治疗目标的共同临床试验，（3）定量 临床前和临床PET成像，以及（4）创新的定量癌症成像信息学平台。 我们的长期目标是改善NSCLC患者的预后，因为肺癌仍然是导致NSCLC的主要原因。 全球癌症死亡人数尽管ICI治疗对一些NSCLC患者有巨大的临床获益， 在20例NSCLC患者中，只有约20%的NSCLC患者对抗PD 1/PDL 1治疗有反应。使用PET协同临床成像， 由于缺乏合适信息学方法来捕获和跟踪， 必要的元信息，临床前成像的适当反应标准，这反过来又是一个 部分原因是缺乏与定量相关的定量临床前PET成像方法 临床PET成像方法。我们将以三个具体目标应对这些挑战： 1、开发和优化定量临床前定量成像方法和方案。这些方法 将涉及新型长寿命体模，可以交叉校准多个临床前和临床PET扫描仪。 这将与合作临床成像研究资源计划的合作伙伴成员一起进行测试。 2.在我们ICI治疗GEMM肺的非小细胞肺癌联合临床试验中实施优化方法 腺癌模型和GEMM模型肺鳞状细胞癌。从这一点上，我们将评估如何 从临床前和临床研究中收集的信息可用于为未来的临床前研究提供信息， 优化的小鼠成像方案和响应标准。 3.使用可通过网络访问的开放式平台共享关于使用定量PET成像的联合临床试验的数据和资源 科学方法（开放源代码+开放数据），扩展DICOM标准用于临床前小动物 使用DICOM兼容结构进行成像，提供必要的定量元数据。 本项目期间开发的这些方法和资源将被分发以加速开发 通过使用联合临床试验提高早期肿瘤学试验的效用， PET成像研究。此外，我们还将确定并可能提高PET成像的实用性， 在联合临床免疫治疗研究中，通过使用适当的 小鼠模型