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

基本信息

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
项目类别：
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

项目摘要

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 成像方法用于非小细胞肺癌（NSCLC）的免疫检查点抑制剂（ICI）疗法。我们将利用现有的联合临床试验使用我们的肺腺癌基因工程小鼠模型（GEMM）开发、测试和实施这些方法并填充可通过网络访问的研究资源。这个网- 可用的研究资源反过来将利用定量癌症成像的最新发展使用行业标准 DICOM 格式的信息学。响应 PAR-18-841 有四个组成部分我们的建议：（1）适当的模型，（2）包括治疗目标的联合临床试验，（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 患者的治疗结果，因为肺癌仍然是导致非小细胞肺癌 (NSCLC) 的主要原因全球癌症死亡人数。尽管 ICI 疗法对某些 NSCLC 具有巨大的临床益处患者中，只有约 20% 的 NSCLC 患者对抗 PD1/PDL1 治疗有反应。使用 PET 临床联合成像由于缺乏合适的信息学方法来捕获和跟踪，改善 NSCLC 的 ICI 疗法面临挑战必要的元信息、临床前成像的适当反应标准，这反过来又是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。我们将通过三个具体目标来应对这些挑战： 1、开发并优化临床前定量成像方法和方案。这些方法将涉及新型长寿命模型，可以交叉校准多个临床前和临床 PET 扫描仪。这将与联合临床影像研究资源计划的合作伙伴成员一起进行测试。 2. 在我们用 GEMM 肺 ICI 治疗 NSCLC 的联合临床试验中实施优化方法腺癌模型和肺鳞状细胞癌GEMM模型。由此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小鼠成像协议和响应标准的条款。 3. 使用可通过网络访问的开放平台共享使用定量 PET 成像的联合临床试验的数据和资源通过扩展临床前小动物的 DICOM 标准的科学方法（开源 + 开放数据）使用符合 DICOM 标准的结构进行成像，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提高早期肿瘤学试验的效用，提供所需的有效癌症治疗方法 PET 成像研究。此外，我们将确定并可能改进 PET 成像的实用性一种生物标志物，用于通过使用适当的方法对临床联合免疫治疗研究中的反应进行早期评估鼠标模型。