Identifying Treatment Options using RNA-Based Synthetic Lethality and Rescue

尽管迄今为止已经通过使用基因组生物标志物来预测对治疗的反应和对结果的描述来定义精确肿瘤学，但迄今为止，大部分癌症患者仍然没有从这些治疗中受益。批量RNA提取方法的进步允许这种“组学”数据越来越多地用于临床。ENLIGHT算法源自Ruppin等人的SELECT，并由PANGEA Biomed推进，使用全外显子组RNA-seq和合成致死率来优先考虑患者的治疗选择。这是一项试验性单臂开放标签II期试验，使用ENLIGHT算法（PANGEA Biomed）对从FFPE组织中提取的全外显子组RNA-seq进行研究，以确定FDA批准的参与者治疗方法。该项目是与PI法蒂玛卡尔扎伊和几个参与调查的合作内部研究。

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.