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综合分析确定了定义NF1肿瘤异质性的候选肿瘤微环境和细胞内信号通路。

Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1.

作者信息

Banerjee Jineta, Allaway Robert J, Taroni Jaclyn N, Baker Aaron, Zhang Xiaochun, Moon Chang In, Pratilas Christine A, Blakeley Jaishri O, Guinney Justin, Hirbe Angela, Greene Casey S, Gosline Sara Jc

机构信息

Computational Oncology, Sage Bionetworks, Seattle, WA 98121, USA.

Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, PA 19102, USA.

出版信息

Genes (Basel). 2020 Feb 21;11(2):226. doi: 10.3390/genes11020226.

Abstract

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions.

摘要

1型神经纤维瘤病(NF1)是一种单基因综合征,会引发多种症状,包括认知障碍、骨骼异常以及良性神经鞘瘤的生长。几乎所有NF1患者都会出现皮肤神经纤维瘤(cNFs),其出现在皮肤表面,而40%-60%的患者会出现丛状神经纤维瘤(pNFs),这些肿瘤深深嵌入周围神经。患有pNFs的患者一生中这些肿瘤恶变为恶性周围神经鞘瘤(MPNSTs)的几率约为10%。这些肿瘤预后严重,除手术外几乎没有其他治疗选择。鉴于这些肿瘤患者缺乏可用的治疗选择,确定可药物作用的途径或其他关键分子特征有助于正在进行的治疗发现研究。在这项工作中,我们使用统计和机器学习方法分析了77个具有基因组数据的NF1肿瘤,以表征区分这些肿瘤的关键信号通路,并确定药物开发的候选对象。我们确定了潜在基因表达变量的子集,这些子集可能在cNFs、pNFs、其他神经纤维瘤和MPNSTs的识别和病因学中具有重要意义。此外,我们还表征了这些潜在变量与基因变异、免疫反卷积预测和蛋白质活性预测之间的关联。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e9fa/7073563/8a7c8e40765e/genes-11-00226-g001.jpg

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