Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization
Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization
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DOI：
10.1371/journal.pcbi.1007495
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发表时间：
2019-11-01
影响因子：
4.3
通讯作者：
Craig, Morgan
中科院分区：
文献类型：
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作者：
Cassidy, Tyler;Craig, Morgan
溶瘤病毒疗法，包括改良的单纯疱疹病毒talimogene laherparepvec（T-VEC），作为抗肿瘤免疫效应的有效激发剂，已显示出巨大的前景。特别地，OPTIM试验证明了与使用外源性施用粒细胞-巨噬细胞集落刺激因子（GM-CSF）的全身免疫疗法治疗相比，T-VEC的上级抗癌作用。理论上，利用外源性细胞因子免疫疗法和溶瘤病毒疗法的组合方法将引起甚至更大的免疫应答并改善患者结果。然而，联合免疫刺激和T-VEC治疗的方案安排尚未建立。在这里，我们校准了敏感和耐药肿瘤细胞和免疫相互作用的计算生物学模型，用于在计算机临床试验中实施，以测试和个性化组合免疫和病毒治疗。通过个性化和优化组合溶瘤病毒疗法和免疫刺激疗法，我们显示了晚期黑色素瘤个体的模拟患者结局的改善。更重要的是，通过对个体化治疗方案的评估，我们确定了GM-CSF和T-VEC联合治疗的决定因素，这些决定因素可以转化为临床可行的给药策略，而无需进一步个性化。我们的研究结果作为一个跨学科的方法来确定联合治疗的概念验证，并建议有前途的途径，调查量身定制的组合免疫疗法/溶瘤virotherapy.Author摘要生物疗法的抗癌治疗的出现对患者的结果产生了显着的影响。靶向外源性药物，包括溶瘤病毒，与现有的，更普遍的，免疫疗法如外源性细胞因子相结合，显示出继续改善癌症护理的巨大前景。然而，确定最佳的联合方案可能是困难的，因为测试拟议的时间表将需要大量的患者参加临床试验。幸运的是，计算生物学可以帮助解决治疗计划，同时帮助解开驱动治疗反应的机制。在这项工作中，我们将GM-CSF和拉他莫基（T-VEC）溶瘤病毒疗法的数学模型整合到虚拟临床试验中，以优化其联合给药。使用该平台，我们推断出晚期黑色素瘤患者的临床可行的组合方案，与GM-CSF和T-VEC单药治疗和标准组合策略相比，该方案显著改善了虚拟患者结局。我们的研究结果概述了一种合理的治疗优化方法，对我们如何有效地设计和实施临床试验以最大限度地提高其成功率，以及我们如何用联合免疫和病毒疗法治疗黑色素瘤具有有意义的后果。
Oncolytic virotherapies, including the modified herpes simplex virus talimogene laherparepvec (T-VEC), have shown great promise as potent instigators of anti-tumour immune effects. The OPTiM trial, in particular, demonstrated the superior anti-cancer effects of T-VEC as compared to systemic immunotherapy treatment using exogenous administration of granulocyte-macrophage colony-stimulating factor (GM-CSF). Theoretically, a combined approach leveraging exogenous cytokine immunotherapy and oncolytic virotherapy would elicit an even greater immune response and improve patient outcomes. However, regimen scheduling of combination immunostimulation and T-VEC therapy has yet to be established. Here, we calibrate a computational biology model of sensitive and resistant tumour cells and immune interactions for implementation into an in silico clinical trial to test and individualize combination immuno- and virotherapy. By personalizing and optimizing combination oncolytic virotherapy and immunostimulatory therapy, we show improved simulated patient outcomes for individuals with late-stage melanoma. More crucially, through evaluation of individualized regimens, we identified determinants of combination GM-CSF and T-VEC therapy that can be translated into clinically-actionable dosing strategies without further personalization. Our results serve as a proof-of-concept for interdisciplinary approaches to determining combination therapy, and suggest promising avenues of investigation towards tailored combination immunotherapy/oncolytic virotherapy.Author summary The advent of biological therapies for anti-cancer treatment has had a significant impact on patient outcomes. Targeted xenobiotics, including oncolytic viruses, in combination with existing, more general, immunotherapies like exogenous cytokines show great promise for continuing to improve cancer care. However, determining optimal combination regimens can be difficult, given that testing proposed schedules would require large cohorts of patients enrolled in clinical trials. Fortunately, computational biology can help to address treatment scheduling while simultaneously helping to unravel the mechanisms driving therapeutic responses. In this work, we integrate a mathematical model of GM-CSF and talimogene laherparepvec (T-VEC) oncolytic virotherapy into a virtual clinical trial to optimize their administration in combination. Using this platform, we inferred a clinically-actionable combination schedule for patients with late-stage melanoma that significantly improved virtual patient outcome when compared to GM-CSF and T-VEC monotherapies, and a standard combination strategy. Our results outline a rational approach to therapy optimization with meaningful consequences for how we effectively design and implement clinical trials to maximize their success, and how we treat melanoma with combined immuno- and virotherapy.