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通路分析与模式识别的融合预测了 IAP 拮抗作用对最新一代 TRAIL 治疗药物的敏感性。

Convergence of pathway analysis and pattern recognition predicts sensitization to latest generation TRAIL therapeutics by IAP antagonism.

机构信息

Institute of Cell Biology and Immunology, University of Stuttgart, Stuttgart, Germany.

Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland.

出版信息

Cell Death Differ. 2020 Aug;27(8):2417-2432. doi: 10.1038/s41418-020-0512-5. Epub 2020 Feb 21.

DOI:10.1038/s41418-020-0512-5
PMID:32081986
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7370234/
Abstract

Second generation TRAIL-based therapeutics, combined with sensitising co-treatments, have recently entered clinical trials. However, reliable response predictors for optimal patient selection are not yet available. Here, we demonstrate that a novel and translationally relevant hexavalent TRAIL receptor agonist, IZI1551, in combination with Birinapant, a clinically tested IAP antagonist, efficiently induces cell death in various melanoma models, and that responsiveness can be predicted by combining pathway analysis, data-driven modelling and pattern recognition. Across a panel of 16 melanoma cell lines, responsiveness to IZI1551/Birinapant was heterogeneous, with complete resistance and pronounced synergies observed. Expression patterns of TRAIL pathway regulators allowed us to develop a combinatorial marker that predicts potent cell killing with high accuracy. IZI1551/Birinapant responsiveness could be predicted not only for cell lines, but also for 3D tumour cell spheroids and for cells directly isolated from patient melanoma metastases (80-100% prediction accuracies). Mathematical parameter reduction identified 11 proteins crucial to ensure prediction accuracy, with x-linked inhibitor of apoptosis protein (XIAP) and procaspase-3 scoring highest, and Bcl-2 family members strongly represented. Applied to expression data of a cohort of n = 365 metastatic melanoma patients in a proof of concept in silico trial, the predictor suggested that IZI1551/Birinapant responsiveness could be expected for up to 30% of patient tumours. Overall, response frequencies in melanoma models were very encouraging, and the capability to predict melanoma sensitivity to combinations of latest generation TRAIL-based therapeutics and IAP antagonists can address the need for patient selection strategies in clinical trials based on these novel drugs.

摘要

第二代以 TRAIL 为基础的治疗药物与增敏联合治疗最近已进入临床试验。然而,对于最佳患者选择,还没有可靠的反应预测因子。在这里,我们证明了一种新型的、具有转化相关性的六价 TRAIL 受体激动剂 IZI1551,与已在临床测试的 IAP 拮抗剂 Birinapant 联合使用,可有效诱导各种黑色素瘤模型中的细胞死亡,并且可以通过结合通路分析、数据驱动建模和模式识别来预测反应性。在 16 个黑色素瘤细胞系的面板中,对 IZI1551/Birinapant 的反应性存在异质性,观察到完全耐药和明显的协同作用。TRAIL 通路调节剂的表达模式使我们能够开发出一种组合标志物,能够以高精度预测有效的细胞杀伤。不仅可以预测 IZI1551/Birinapant 对细胞系的反应性,还可以预测 3D 肿瘤细胞球体和直接从患者黑色素瘤转移灶中分离的细胞的反应性(预测准确率为 80-100%)。数学参数减少确定了 11 种对确保预测准确性至关重要的蛋白质,其中 X 连锁凋亡抑制剂蛋白 (XIAP) 和前半胱天冬酶-3 评分最高,Bcl-2 家族成员也强烈代表。在概念验证的计算临床试验中,对 n = 365 名转移性黑色素瘤患者的队列表达数据进行应用,该预测器表明,多达 30%的患者肿瘤可能对 IZI1551/Birinapant 有反应。总体而言,黑色素瘤模型中的反应频率非常令人鼓舞,并且能够预测黑色素瘤对最新一代以 TRAIL 为基础的治疗药物和 IAP 拮抗剂的组合的敏感性,可以满足基于这些新型药物的临床试验中患者选择策略的需求。

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