Identifying Treatment Options using RNA-Based Synthetic Lethality and Rescue

尽管精确肿瘤学迄今已通过使用基因组生物标志物来定义 对于治疗反应的预测和结果的说明， 迄今为止，癌症患者仍然不能从这些疗法中获益。的推进 批量RNA提取方法允许这种“组学”数据越来越多地用于临床研究。 基础ENLIGHT算法，源自Ruppin等人的SELECT，并由PANGEA改进 Biomed使用全外显子组RNA-seq和合成致死率来优先考虑治疗选择， 患者这是一项使用ENLIGHT算法的试验性单臂开放标签II期试验 （PANGEA Biomed）对从FFPE组织中提取的全外显子组RNA-seq进行分析，以鉴定FDA批准的 治疗参与者。本项目是与PI法蒂玛合作的校内研究 卡尔扎伊和几名参与调查的人员。

Although precision oncology has been defined to date by the use of genomic biomarkers for prediction of response to treatment and prognostication of outcomes, a large fraction of patients with cancer still do not benefit from these therapies to date. The advancement of bulk RNA extraction methods allows this "-omics" data to be used increasingly on a clinical basis. The ENLIGHT algorithm, derived from SELECT by Ruppin et al. and advanced by PANGEA Biomed, uses whole-exome RNA-seq and synthetic lethality to prioritize therapeutic options for patients. This is a pilot single-arm open-label phase II trial using the ENLIGHT algorithm (PANGEA Biomed) on whole-exome RNA-seq extracted from FFPE tissue to identify FDA-approved therapies for participants. This project is a collaborative intramural study with PI Fatima Karzai and with several participating investigators.