Gene Expression Signatures Identify Novel Therapeutics for Metastatic Pancreatic Neuroendocrine Tumors

基因表达特征可识别转移性胰腺神经内分泌肿瘤的新型治疗方法

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作者:Aaron T Scott, Michelle Weitz, Patrick J Breheny, Po Hien Ear, Benjamin Darbro, Bart J Brown, Terry A Braun, Guiying Li, Shaikamjad Umesalma, Courtney A Kaemmer, Chandra K Maharjan, Dawn E Quelle, Andrew M Bellizzi, Chandrikha Chandrasekharan, Joseph S Dillon, Thomas M O'Dorisio, James R Howe

Conclusions

We employed a complementary bioinformatics approach to identify novel therapeutics for pNETs by analyzing gene expression in metastatic tumors. The potential utility of these drugs was confirmed by in vitro cytotoxicity assays, suggesting drugs targeting MEK and TOP2A may be highly efficacious against metastatic pNETs. This is a promising strategy for discovering more effective treatments for patients with pNETs.

Purpose

Pancreatic neuroendocrine tumors (pNETs) are uncommon malignancies noted for their propensity to metastasize and comparatively favorable prognosis. Although both the treatment options and clinical outcomes have improved in the past decades, most patients will die of metastatic disease. New systemic therapies are needed. Experimental design: Tissues were obtained from 43 patients with well-differentiated pNETs undergoing surgery. Gene expression was compared between primary tumors versus liver and lymph node metastases using RNA-Seq. Genes that were selectively elevated at only one metastatic site were filtered out to reduce tissue-specific effects. Ingenuity pathway analysis (IPA) and the Connectivity Map (CMap) identified drugs likely to antagonize metastasis-specific targets. The biological activity of top identified agents was tested in vitro using two pNET cell lines (BON-1 and QGP-1).

Results

A total of 902 genes were differentially expressed in pNET metastases compared with primary tumors, 626 of which remained in the common metastatic profile after filtering. Analysis with IPA and CMap revealed altered activity of factors involved in survival and proliferation, and identified drugs targeting those pathways, including inhibitors of mTOR, PI3K, MEK, TOP2A, protein kinase C, NF-kB, cyclin-dependent kinase, and histone deacetylase. Inhibitors of MEK and TOP2A were consistently the most active compounds. Conclusions: We employed a complementary bioinformatics approach to identify novel therapeutics for pNETs by analyzing gene expression in metastatic tumors. The potential utility of these drugs was confirmed by in vitro cytotoxicity assays, suggesting drugs targeting MEK and TOP2A may be highly efficacious against metastatic pNETs. This is a promising strategy for discovering more effective treatments for patients with pNETs.

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