JNCI：新发现可将溶瘤病毒抗癌疗效增加1000倍！

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梅奥诊所的研究人员发现了一种分子交流通路在决定麻疹病毒溶瘤疗法治疗卵巢癌和恶性脑瘤中发挥着关键作用，而此前研究人员本认为肿瘤的这种通路有缺陷。这项发现促使研究人员开发出了一种可以预测病人治疗疗效的算法，相关研究成果于近日发表在《Journal of the National Cancer Institute》上。

“这项发现及这个算法将帮助我们给最适合用溶瘤病毒疗法进行治疗的病人进行个性化治疗。”该研究通讯作者Evanthia Galanis博士说道。“我们还将知道哪些病人将从肿瘤病毒疗法和其他免疫疗法联合治疗中获益。”

此前研究人员一直认为肿瘤中这个激活的通路（叫做干扰素响应通路）存在缺陷。但是据研究人员的最新研究结果，事实并非如此。他们对可能反映病毒疗法的疗效的基因突变和信号进行了检测。

研究人员在人类卵巢癌和脑癌小鼠模型及病人身上进行了相关实验。他们发现一个变强的基因信号可以预测治疗敏感性和耐受性，随后的研究也发现使用鲁索替尼（一种FDA批准的治疗恶性血液疾病的药物）进行处理可以克服耐药性。这个药物靶向干扰素响应通路，可以将麻疹溶瘤病毒疗法的疗效提高1000倍。

研究人员认为这些发现将帮助在未来涉及溶瘤病毒的临床试验中选择合适的病人，同时也将改变这种新疗法的设计和在临床中的使用，包括开发有效的联合疗法。

（生物谷Bioon.com）

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Constitutive Interferon Pathway Activation in Tumors as an Efficacy Determinant Following Oncolytic Virotherapy


Background
Attenuated measles virus (MV) strains are promising agents currently being tested against solid tumors or hematologic malignancies in ongoing phase I and II clinical trials; factors determining oncolytic virotherapy success remain poorly understood, however.

Methods
We performed RNA sequencing and gene set enrichment analysis to identify pathways differentially activated in MV-resistant (n = 3) and -permissive (n = 2) tumors derived from resected human glioblastoma (GBM) specimens and propagated as xenografts (PDX). Using a unique gene signature we identified, we generated a diagonal linear discriminant analysis (DLDA) classification algorithm to predict MV responders and nonresponders, which was validated in additional randomly selected GBM and ovarian cancer PDX and 10 GBM patients treated with MV in a phase I trial. GBM PDX lines were also treated with the US Food and Drug Administration–approved JAK inhibitor, ruxolitinib, for 48 hours prior to MV infection and virus production, STAT1/3 signaling and interferon stimulated gene expression was assessed. All statistical tests were two-sided.

Results
Constitutive interferon pathway activation, as reflected in the DLDA algorithm, was identified as the key determinant for MV replication, independent of virus receptor expression, in MV-permissive and -resistant GBM PDXs. Using these lines as the training data for the DLDA algorithm, we confirmed the accuracy of our algorithm in predicting MV response in randomly selected GBM PDX ovarian cancer PDXs. Using the DLDA prediction algorithm, we demonstrate that virus replication in patient tumors is inversely correlated with expression of this resistance gene signature (ρ = –0.717, P = .03). In vitro inhibition of the interferon response pathway with the JAK inhibitor ruxolitinib was able to overcome resistance and increase virus production (1000-fold, P = .03) in GBM PDX lines.

Conclusions
These findings document a key mechanism of tumor resistance to oncolytic MV therapy and describe for the first time the development of a prediction algorithm to preselect for oncolytic treatment or combinatorial strategies.
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