Total-body Dynamic Imaging and Kinetic Modeling of ¹⁸F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy

Immunotherapies, especially the checkpoint inhibitors such as anti-PD-1 antibodies, have transformed cancer treatment by enhancing immune system's capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. ¹⁸F-AraG is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by non-invasive quantification of immune cell activity within tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of ¹⁸F-AraG, as a potential quantitative biomarker for immune response evaluation.

Methods: The study consisted of 90-min total-body dynamic scans of four healthy subjects and one non-small cell lung cancer (NSCLC) patient, scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection were employed to analyze tracer kinetics in various organs. Additionally, seven sub-regions of the primary lung tumor and four mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess reliability of kinetic parameter estimation. Correlations of SUVmean, SUVR (tissue-to-blood ratio), and Logan plot slope $\left(K_{Logan}\right)$ with total volume-of-distribution $\left(V_T\right)$ were calculated to identify potential surrogates for kinetic modeling.

Results: Strong correlations were observed between $K_{Logan}$ and SUVR values with $V_T$, suggesting that they can be used as promising surrogates for $V_T$, especially in organs with low blood-volume fraction. Moreover, the practical identifiability analysis suggests that the dynamic ¹⁸F-AraG PET scans could potentially be shortened to 60 minutes, while maintaining quantification accuracy for all organs-of-interest. The study suggests that although ¹⁸F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response post-therapy. While SUVmean showed variable changes in different sub-regions of the tumor post-therapy, the SUVR, $K_{Logan}$, and $V_T$ showed consistent increasing trends in all analyzed sub-regions of the tumor with high practical identifiability.

Conclusion: Our findings highlight the promise of ¹⁸F-AraG dynamic imaging as a non-invasive biomarker for quantifying the immune response to immunotherapy in cancer patients. The promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.