Precision oncology places an increased demand on cancer diagnostics to characterize tumors at the time of diagnosis. Tissue diagnostics for precision oncology increasingly rely on multi-valent assays to characterize cancer biology and identify therapeutic targets. The power of molecular imaging for cancer diagnosis has been well demonstrated by [18F]fluorodexoyglucose positron emission tomography (FDG PET/CT), widely used in clinical oncology. PET tracers targeted to facets of tumor biology now enable multi-valent molecular imaging to identify therapeutic targets. Early studies show that combined tracer studies, commonly FDG plus a target- specific tracer, are highly predictive of tumor response to targeted therapy. However, emissions from different PET tracers are indistinguishable to a PET scanner, requiring separate imaging sessions on separate days for multiple 18F-based tracers (~ 2 hour half-life) in the same patient. This limits the clinical practicality of multi-tracer PET, and fails to exploit the full power of multi-tracer PET to quantify tumor in vivo biology and biologic heterogeneity for highly variable process such as cancer metabolism. We will overcome this limitation by taking advantage of recent developments in volumetric PET scanners, fast reconstruction, and 4D image analysis methods from our laboratories to develop Multi-Tracer Volumetric PET (MTV-PET) to generate multi-valent, quantitative biologic parametric images for two or more tracers in a single session to guide precision oncology and translational cancer biology research. As such, our proposed technology development project addresses a need, described under Priority Area B for ?new capabilities for advancing precise clinical diagnosis of cancer patients?. Using methods developed in our laboratories, we now propose to integrate and enhance these methodologies to develop and validate Multi-Tracer Volumetric PET (MTV-PET) to generate quantitative biologic parametric images for two or more tracers in a single session. We will develop and test this approach for simultaneous glucose and glutamine metabolism imaging, with the goal of guiding metabolism-targeted therapy such as inhibitors of glutaminase (GLS) and lactate dehydrogenase (LDH). The project will focus on the technology developments (largely computational) that enable multi-tracer imaging. Ongoing and separately funded work on clinical studies will acquire data from two separate imaging PET sessions using current technology and will provide data for method development. We will first optimize tracer dose timing, image reconstruction, and image time binning for dual tracer MTV-PET (Aim 1), followed by implementation and technical validation of image analysis using a mixture analysis approach (Aim 2). This will set the stage for technical validation of MTV-PET (Aim 3) and an exploratory aim (Aim 4) testing image analytics based on machine learning. Successful completion of our technology development will yield a new method for multi-tracer PET that would change the landscape for cancer imaging diagnostic biomarker and precision oncology research, consistent with goals of RFA-CA-17-023 Priority Area B.
Our proposed technology development in response to RFA-CA-17-023 (Integration and Validation of Emerging Technologies to Accelerate Cancer Research) addresses a need for more precise diagnosis for precision oncology, under Priority Area B: ?New capabilities for advancing precise clinical diagnosis of cancer patients?. We will advantage of recent developments in volumetric positron emission tomography (PET) scanners, fast reconstruction, and 4D image analysis to develop methods for multi-tracer PET with the goal of generating quantitative, multi-parametric whole-body images of specific aspects of cancer biology, including cancer metabolism as the focus of our proposed technology development projects. Successful completion of our proposed technology development will yield a clinically practical method for multi-tracer PET that would provide multi-valent, whole body molecular parametric images that would change the landscape for cancer imaging diagnostic biomarkers and precision oncology research, targeted to RFA-CA-17-023 Priority Area B.
| Pantel, Austin R; Ackerman, Daniel; Lee, Seung-Cheol et al. (2018) Imaging Cancer Metabolism: Underlying Biology and Emerging Strategies. J Nucl Med 59:1340-1349 |
