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Computational Methods for Identifying Non-coding Cancer Drivers

识别非编码癌症驱动因素的计算方法

基本信息

批准号：
10411390
负责人：
EKTA KHURANA
金额：
$ 12.41万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10411390
关键词：
Accounting Address Attention Benchmarking Biological Assay CRISPR screen CRISPR/Cas technology Cancer Patient Catalogs Cell Culture Techniques Cell Line Cell Survival Cell division Cells Clinic Clustered Regularly Interspaced Short Palindromic Repeats Code Colon Colon Carcinoma Colorectal Cancer Computer Models Computing Methodologies DNA Sequence Data Development Disease Dissection Elements Enhancers Evaluation Frequencies Gene Expression Genes Genetic Genome Heterogeneity Institutes Knock-in Learning Malignant Neoplasms Malignant neoplasm of prostate Measures Methods Modification Mutagenesis Mutate Mutation Nature Open Reading Frames Patients Phenotype Play Prostate Proteins Recurrence Regulatory Element Reporter Research Role Running Sampling Screening Result Signal Transduction Somatic Mutation Statistical Algorithm Structure Targeted Resequencing Testing Training Transcript Untranslated RNA Validation Variant biobank cancer genome cancer type cell growth cohort colon cancer cell line driver mutation genome editing genome sequencing genome-wide high throughput screening innovation knock-down melanoma novel patient derived xenograft model precision medicine promoter tool transcription factor tumor tumor growth tumor heterogeneity tumor microenvironment tumorigenesis tumorigenic whole genome

项目摘要

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.

从肿瘤全基因组序列(WGS)获得的大多数变异发生在非基因组的编码区。尽管蛋白质编码区的变体已经收到了大多数人注意到，许多研究现在都注意到了非癌症中的编码变体。致癌功能非编码变异体的鉴定增长仍然是全基因组测序应用的一个挑战和瓶颈诊所。癌症驱动因素通常是由癌症的高频率来识别的突变会在患者中发生。然而，在非传染性疾病中，突变率具有高度的异质性。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区域结肠癌细胞分裂后期复制。在本提案中，我们将使用高- 吞吐量池CRISPR筛选和预测非对癌症驱动因素进行编码。我们将定量衡量数以千计的非使用我们创新的高通量CRISPR屏幕编码突变，直接连接在非编码基因组的天然背景下的修改(即不是报告分析) 与癌症相关的表型(细胞生长)。筛选的结果将用作用于开发计算癌症驱动程序预测的NC_DRIVER的训练数据工具。NC_DRIVER将高功能影响的信号与重复性跨多个肿瘤样本的变异以识别以下非编码突变癌症中的阳性选择。我们将确定推动者、增强者和CTCF中的驱动因素绝缘体。CTCF绝缘子是变异最多但研究最少的调节元件在癌症基因组中。使用这种实验和计算相结合的方法，我们将确定高信心的候选司机。最后，我们将执行函数结直肠癌和前列腺癌中优先非编码驱动因素的评估。我们会在患者来源的细胞培养中使用CRISPR/Cas9基因组编辑来测试20个高排名候选驱动程序启动子/增强子/绝缘子突变。总体而言，这项建议解决了识别非编码基因组中的驱动因素以及长期- TERM使每个患者的基因组测序受益最大。