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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：
10192676
负责人：
EKTA KHURANA
金额：
$ 47.39万
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/10192676
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）中获得的大多数变异发生在非