Kerioui Marion, Bertrand Julie, Desmée Solène, Le Tourneau Christophe, Mercier François, Bruno René, Guedj Jérémie
Université Paris Cité, INSERM, IAME, Paris, France.
Université de Tours, Université de Nantes, INSERM SPHERE, UMR 1246, Tours, France.
JCO Precis Oncol. 2023 Feb;7:e2200368. doi: 10.1200/PO.22.00368.
Several studies have raised the hypothesis that immunotherapy may exacerbate the variability in individual lesions, increasing the risk of observing divergent kinetic profiles within the same patient. This questions the use of the sum of the longest diameter to follow the response to immunotherapy. Here, we aimed to study this hypothesis by developing a model that estimates the different sources of variability in lesion kinetics, and we used this model to evaluate the impact of this variability on survival.
We relied on a semimechanistic model to follow the nonlinear kinetics of lesions and their impact on the risk of death, adjusted on organ location. The model incorporated two levels of random effects to characterize both between- and within-patient variability in response to treatment. The model was estimated on 900 patients from a phase III randomized trial evaluating programmed death-ligand 1 checkpoint inhibitor atezolizumab versus chemotherapy in patients with second-line metastatic urothelial carcinoma (IMvigor211).
The within-patient variability in the four parameters that characterize individual lesion kinetics represented between 12% and 78% of the total variability during chemotherapy. Similar results were obtained during atezolizumab, except for the durability of the treatment effects, for which the within-patient variability was markedly larger than during chemotherapy (40% 12%, respectively). Accordingly, the occurrence of divergent profile consistently increased over time in patients treated with atezolizumab and was equal to about 20% after 1 year of treatment. Finally, we show that accounting for the within-patient variability provided a better prediction of most at-risk patients than a model relying solely on the sum of the longest diameter.
Within-patient variability provides valuable information for the assessment of treatment efficacy and the detection of at-risk patients.
多项研究提出了这样的假设，即免疫疗法可能会加剧个体病灶的变异性，增加在同一患者体内观察到不同动力学特征的风险。这对使用最长直径之和来跟踪免疫疗法的反应提出了质疑。在此，我们旨在通过开发一个模型来研究这一假设，该模型可估计病灶动力学变异性的不同来源，并使用此模型评估这种变异性对生存的影响。
我们依靠一个半机制模型来跟踪病灶的非线性动力学及其对死亡风险的影响，并根据器官位置进行调整。该模型纳入了两个层次的随机效应，以表征患者间和患者内对治疗反应的变异性。该模型是根据一项III期随机试验中的900名患者进行估计的，该试验评估了程序性死亡配体1检查点抑制剂阿替利珠单抗与化疗在二线转移性尿路上皮癌患者中的疗效（IMvigor211）。
表征个体病灶动力学的四个参数的患者内变异性在化疗期间占总变异性的12%至78%。在使用阿替利珠单抗治疗期间也获得了类似的结果，但治疗效果的持续性除外，其患者内变异性明显大于化疗期间（分别为40%和12%）。因此，在接受阿替利珠单抗治疗的患者中，不同特征的出现随时间持续增加，在治疗1年后约为20%。最后，我们表明，考虑患者内变异性比仅依赖最长直径之和的模型能更好地预测大多数高危患者。
患者内变异性为评估治疗疗效和检测高危患者提供了有价值的信息。
