Using PDX animal models to identify and stratify adenoid cystic carcinoma patients presenting an enhanced response to HDAC inhibitors.

American journal of cancer research(2023)

引用 0|浏览1
暂无评分
摘要
Adenoid cystic carcinoma (ACC) patients face a highly infiltrative and metastatic disease characterized by poor survival rates and suboptimal response to available therapies. We have previously shown that sensitization of ACC tumors to chemotherapy using histone deacetylase inhibitors (HDACi) constitutes a promising therapeutic strategy to manage tumor growth. Here, we used patient-derived xenografts (PDX) from ACC tumors to evaluate the effects of in vivo administration of the HDAC inhibitor Entinostat combined with Cisplatin over tumor growth. RNA from PDX tumor samples receiving the proposed therapy were analyzed using NanoString technology to identify molecular signatures capable of predicting ACC response to the therapy. We also used an RNAseq dataset from 68 ACC patients to validate the molecular signature identified by the NanoString platform. We found that the administration of Entinostat combined with Cisplatin resulted in a potent tumor growth inhibition (TGI) ranging from 38% to 106% of the original tumor mass. Enhanced response to therapy is consistent with the reactivation of tumor suppressor genes, including SFRP1, and the downregulation of oncogenes like FGF8 and CCR7. Nanostring data from PDX tumors identified a genetic signature capable of predicting tumor response to therapy. We further stratified 68 ACC patients containing RNAseq data accordingly to the activity levels of the identified genetic signature. We found that 23% of all patients exhibit a genetic signature consistent with a high ACC tumor response rate to Entinostat and Cisplatin. Our study provides compelling preclinical data supporting the deployment of a powerful systemic anticancer therapy crafted and explicitly tested for ACC tumors.
更多
查看译文
关键词
Precision medicine,acetylation,histone,senescence,tumor genome landscape
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要