预测耐药性和伪雌性：是否能够预测肺癌的检查点抑制剂治疗结果的简约免疫编辑数学模型？
Predicting resistance and pseudoprogression: are minimalistic immunoediting mathematical models capable of forecasting checkpoint inhibitor treatment outcomes in lung cancer?
影响因子:1.80000
分区:数学4区 / 生物学4区 数学与计算生物学4区
发表日期:2024 Oct
作者:
Kevin Robert Scibilia, Pirmin Schlicke, Folker Schneller, Christina Kuttler
摘要
靶向PD-1/PD-L1在肺癌治疗中靶向PD-1/PD-L1的免疫检查点抑制剂（ICI）的应用增加产生了临床需求，以可靠地预测单个患者的治疗结果。为了弥合预测差距，我们以普通微分方程的形式检查了四个不同的数学模型，包括新型延迟响应模型。我们严格评估了他们的个人和组合预测能力，以通过模型衍生的结果概率相等地加权患者的进行性疾病（PD）状态。适合完整的治疗过程，新型延迟反应模型（R2 = 0.938）超过了最简单的模型（R2 = 0.865）。该模型组合能够以77％的总体精度可靠地预测患者PD结局（敏感性= 70％，特异性= 81％），仅通过用原发性肿瘤最长的直径测量值进行校准。它自主鉴定了51％的患者的子集，其中总体准确性为81％（敏感性= 81％，特异性= 81％）的患者。所有模型都显着超过了完全数据驱动的基于机器学习的方法。这些建模方法通过通过临床可用的测量数据来确定不同的治疗结果轨迹，为临床医生提供了动态的基线框架，以支持治疗决策。与其他预测工具和其他生物标志物以及更多的疾病信息（例如，可以进一步），可以进一步增强临床上可用的测量数据。我们认为，简单的模型配方可以广泛采用开发的模型对其他癌症类型。可以轻松地为其他治疗方式制定类似的模型。
Abstract
The increased application of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 in lung cancer treatment generates clinical need to reliably predict individual patients' treatment outcomes.To bridge the prediction gap, we examine four different mathematical models in the form of ordinary differential equations, including a novel delayed response model. We rigorously evaluate their individual and combined predictive capabilities with regard to the patients' progressive disease (PD) status through equal weighting of model-derived outcome probabilities.Fitting the complete treatment course, the novel delayed response model (R2=0.938) outperformed the simplest model (R2=0.865). The model combination was able to reliably predict patient PD outcome with an overall accuracy of 77% (sensitivity = 70%, specificity = 81%), solely through calibration with primary tumor longest diameter measurements. It autonomously identified a subset of 51% of patients where predictions with an overall accuracy of 81% (sensitivity = 81%, specificity = 81%) can be achieved. All models significantly outperformed a fully data-driven machine learning-based approach.These modeling approaches provide a dynamic baseline framework to support clinicians in treatment decisions by identifying different treatment outcome trajectories with already clinically available measurement data.Conjoint application of the presented approach with other predictive tools and biomarkers, as well as further disease information (e.g. metastatic stage), could further enhance treatment outcome prediction. We believe the simple model formulations allow widespread adoption of the developed models to other cancer types. Similar models can easily be formulated for other treatment modalities.