Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy
Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy
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DOI：
10.1126/sciadv.aay6298
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发表时间：
2020-04-01
期刊：
影响因子：
13.6
通讯作者：
Cristini, Vittorio
中科院分区：
文献类型：
--
作者：
Butner, Joseph D.;Elganainy, Dalia;Cristini, Vittorio
我们提出了一种免疫检查点抑制剂治疗的机械数学模型，以解决肿瘤学对患者对免疫治疗反应的早期、广泛适用的读数（生物标志物）的需求。该模型建立在免疫系统和癌症之间复杂的生物和物理相互作用的基础上，并且仅使用标准护理CT进行通知。我们回顾性地将该模型应用于来自多项临床试验的245例接受抗CTLA-4或抗PD-1/PD-L1抗体治疗的患者。我们发现，模型参数明显识别了常见（n = 18）和罕见（n = 10）恶性肿瘤类型的患者，他们从这些单药治疗中获益或未获益，首次再分期（中位数53天）的准确率高达88%。此外，这些参数成功区分了假进展和真进展，为假进展的独特生物物理特征提供了以前未识别的见解。我们的数学模型为个性化肿瘤学和工程免疫治疗方案提供了临床相关工具。
We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti-CTLA-4 or anti-PD-1/PD-L1 antibodies. We found that model parameters distinctly identified patients with common (n = 18) and rare (n = 10) malignancy types who benefited and did not benefit from these monotherapies with accuracy as high as 88% at first restaging (median 53 days). Further, the parameters successfully differentiated pseudo-progression from true progression, providing previously unidentified insights into the unique biophysical characteristics of pseudo-progression. Our mathematical model offers a clinically relevant tool for personalized oncology and for engineering immunotherapy regimens.