Computational Methods for Identifying Non-coding Cancer Drivers

从肿瘤全基因组序列（WGS）获得的大多数变体发生在非肿瘤细胞中。 基因组的编码区。虽然蛋白质编码区的变异已经收到了 大多数人的注意力，许多研究现在已经注意到非- 癌症中的编码变异。鉴定驱动肿瘤的功能性非编码变体 增长仍然是一个挑战，也是使用全基因组测序的瓶颈， 诊所癌症驱动因素通常通过其高频率来识别， 突变发生在患者身上。然而，突变率是高度异质性的非- 编码区和许多非驱动元件的突变频率高于 其他的，如黑色素瘤中转录因子结合的区域或 在结肠癌细胞分裂后期复制。在这份提案中，我们将使用高- 通量合并的CRISPR筛选和预测非生物学特性的新计算方法 编码癌症驱动程序。我们将定量衡量数千个非- 使用我们创新的高通量CRISPR筛选编码突变， 非编码基因组天然环境中的修饰（即，非报告基因测定） 与癌症相关的表型（细胞生长）。屏幕的结果将用作 用于开发NC_Driver的训练数据，NC_Driver是一种计算癌症驱动预测 工具. NC_Driver将整合高功能影响的信号， 多个肿瘤样本中的变异，以识别非编码突变， 癌症中的正选择我们将确定启动子，增强子和CTCF中的驱动程序 绝缘子CTCF绝缘子是突变最多但研究最少的调节元件 在癌症基因组中。使用这种综合的实验和计算方法， 我们将识别高置信度的候选驱动程序。最后，我们将执行功能 评估结直肠癌和前列腺癌中的优先非编码驱动因素。我们将 在患者来源的细胞培养物中使用CRISPR/Cas9基因组编辑来测试20个高排名的 候选驱动启动子/增强子/绝缘子突变。总的来说，这项提案 解决了在非编码基因组中识别驱动程序的关键需求， 术语使每个患者的基因组测序受益最大。

Most variants obtained from tumor whole-genome sequences (WGS) occur in non- coding regions of the genome. Although variants in protein-coding regions have received the majority of attention, numerous studies have now noted the importance of non- coding variants in cancer. Identification of functional non-coding variants that drive tumor growth remains a challenge and a bottleneck for the use of whole-genome sequencing in the clinic. Cancer drivers are generally identified by the high frequency at which their mutations occur across patients. However, mutation rate is highly heterogeneous in non- coding regions and many non-driver elements show higher mutation frequency than others, such as regions bound by transcription factors in melanoma or regions replicating late during cell division in colon cancer. In this proposal, we will use high- throughput pooled CRISPR screen and novel computational methods to predict non- coding cancer drivers. We will quantitatively measure the impact of thousands of non- coding mutations using our innovative high-throughput CRISPR screen that directly ties modifications in the native context of the non-coding genome (i.e. not a reporter assay) to a cancer relevant phenotype (cell growth). The results of the screen will be used as training data for the development of NC_Driver, a computational cancer driver prediction tool. NC_Driver will integrate the signals of high functional impact with the recurrence of variants across multiple tumor samples to identify the non-coding mutations under positive selection in cancer. We will identify drivers in promoters, enhancers and CTCF insulators. CTCF insulators are the most mutated yet least studied regulatory elements in the cancer genome. Using this integrative experimental and computational approach, we will identify high-confidence candidate drivers. Finally, we will perform functional evaluation of prioritized non-coding drivers in colorectal and prostate cancers. We will use CRISPR/Cas9 genome editing in patient-derived cell cultures to test 20 high-ranking candidate driver promoter/enhancer/insulator mutations. Overall, this proposal addresses the critical need to identify drivers in the non-coding genome and over long- term enable the maximal benefit of genome sequencing for each patient.