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

通路分析和模式识别的收敛通过 IAP 拮抗预测对最新一代 TRAIL 治疗药物的致敏。

  • 影响因子:7.70
  • DOI:10.1038/s41418-020-0512-5
  • 作者列表:"Vetma V","Guttà C","Peters N","Praetorius C","Hutt M","Seifert O","Meier F","Kontermann R","Kulms D","Rehm M
  • 发表时间:2020-02-21
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

: 第二代基于 TRAIL 的治疗药物,结合致敏联合治疗,最近已进入临床试验。然而,最佳患者选择的可靠反应预测因子尚不可用。在这里,我们证明了一种新的和翻译相关的六价 TRAIL 受体激动剂,IZI1551,与临床测试的 IAP 拮抗剂 Birinapant 联合,在各种黑色素瘤模型中有效诱导细胞死亡, 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。在一组 16 个黑色素瘤细胞系中,对 IZI1551/Birinapant 的反应性是异质性的,观察到完全耐药和明显的协同作用。TRAIL 通路调控因子的表达模式使我们能够开发一种组合标记物,以较高的准确性预测强效细胞杀伤。IZI1551/Birinapant 反应性不仅可以预测细胞系,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 (80-100% 预测精度)。数学参数减少确定了 11 个对确保预测准确性至关重要的蛋白,其中 x 连锁凋亡抑制蛋白 (XIAP) 和 procaspase-3 评分最高,家族成员 Bcl-2 强烈代表。应用于计算机模拟试验中 365 例转移性黑色素瘤患者队列的表达数据, 预测因子提示,高达 30% 的患者肿瘤可预期出现 IZI1551/Birinapant 反应性。总的来说,黑色素瘤模型的反应频率非常令人鼓舞, 预测黑色素瘤对最新一代基于 TRAIL 的治疗药物和 IAP 拮抗剂组合的敏感性的能力可以解决基于这些新药的临床试验中对患者选择策略的需求。

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