健康人和一名接受抗 PD-1 免疫疗法的非小细胞肺癌患者体内 [18F]F-AraG 的全身动态成像和动力学模型。

Negar Omidvari, Jelena Levi, Yasser G Abdelhafez, Yiran Wang, Lorenzo Nardo, Megan E Daly, Guobao Wang, Simon R Cherry
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引用次数: 0

摘要

免疫疗法,尤其是检查点抑制剂,如抗程序性细胞死亡蛋白1(anti-PD-1)抗体,通过增强免疫系统靶向和杀死癌细胞的能力,改变了癌症治疗方法。然而,预测免疫疗法的反应仍然具有挑战性。18F-阿拉伯核糖基鸟嘌呤([18F]F-AraG)是一种靶向活化T细胞的分子成像示踪剂,可通过无创量化肿瘤微环境和身体其他部位的免疫细胞活性来促进治疗反应评估。本研究旨在获得[18F]F-AraG全身药代动力学的初步数据,并将其作为一种潜在的定量生物标记物用于免疫反应评估。研究方法研究包括对 4 名健康受试者和 1 名非小细胞肺癌患者进行 90 分钟的全身动态扫描。采用阿凯克信息准则模型选择的区室模型分析各器官的示踪剂动力学。此外,还分析了原发性肺肿瘤的 7 个亚区域和 4 个纵隔淋巴结。进行了实际可识别性分析,以评估动力学参数估计的可靠性。计算了SUV均值、组织与血液SUV比值(SUVR)和洛根图斜率(K Logan)与总分布容积(V T)的相关性,以确定动力学建模的潜在替代物。结果:K Logan和SUVR与V T之间存在很强的相关性,这表明它们可以作为V T的替代物,尤其是在血容量比例较低的器官中。此外,实际可识别性分析表明,动态[18F]F-AraG PET 扫描有可能缩短至 60 分钟,同时保持所有相关器官的量化准确性。该研究表明,虽然[18F]F-AraG SUV 图像可以提供免疫细胞分布的信息,但要准确量化治疗后的免疫反应,可能需要动力学建模或图形分析方法。虽然 SUVmean 在治疗后的不同肿瘤亚区显示出不同的变化,但 SUVR、K Logan 和 V T 在所有分析的肿瘤亚区都显示出一致的上升趋势,具有很高的实际可识别性。结论我们的研究结果凸显了[18F]F-AraG动态成像作为一种非侵入性生物标记物量化癌症患者对免疫疗法的免疫反应的前景。令人鼓舞的全身动力学建模结果还表明,这种示踪剂有可能被更广泛地应用于研究 T 细胞在疾病免疫发病机制中的作用。
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Total-Body Dynamic Imaging and Kinetic Modeling of [18F]F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy.

Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune system's capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-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 after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. 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.

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