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

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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.