权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Identifying neuroblastoma drivers and bringing them to the clinic

识别神经母细胞瘤驱动因素并将其带到诊所

基本信息

批准号：
10197505
负责人：
STEPHEN X SKAPEK
金额：
$ 43.74万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-02 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10197505
关键词：
11q 17q Algorithmic Analysis Bayesian Method Biological Biological Markers Biology Biometry Cell Line Cells Child Clinic Clinical Clinical Trials Clustered Regularly Interspaced Short Palindromic Repeats Combined Modality Therapy Communities Computational algorithm Copy Number Polymorphism Data Development Disease Gene Dosage Gene Expression Gene Expression Profiling Genes Genomic approach Genomics Goals Leadership Link MYCN gene Malignant Childhood Neoplasm Malignant Neoplasms Modeling Molecular Molecular Biology Molecular Genetics Mutate Mutation Neuroblastoma Nucleotides Oncogenic Outcome Parents Patients Pattern Pediatric Oncology Group Positioning Attribute Pre-Clinical Model Preclinical Testing Proteins Recurrence Research Research Personnel Rhabdomyosarcoma Risk Scientist Solid Testing Translating Tumor Suppressor Proteins Validation Work base candidate validation childhood cancer mortality cohort computational pipelines disorder risk effective therapy functional genomics genomic data high risk improved improved outcome innovation insight molecular targeted therapies mutant neuroblastoma cell new therapeutic target next generation sequencing novel novel strategies patient derived xenograft model precision medicine programs risk stratification stem success targeted biomarker tool tumor virtual young adult

项目摘要

Project Summary Our overall goal is to improve outcomes for children with forms of cancer that cannot be eradicated with current therapies. We are focused on neuroblastoma, an especially challenging form of childhood cancer that accounts for a large proportion of childhood cancer deaths each year, and we have assembled a team to explore a new approach in which a novel computational pipeline is applied to existing genomics data and a functional genomics screen to reveal new insights into neuroblastoma biology. We anticipate that new biomarkers for risk stratification and assignment of molecularly targeted therapy will stem from our work. In the US each year, over 700 children and young adults develop neuroblastoma, among the most common solid malignancies in children. Sadly, the chance of cure is low for those with high-risk disease, and this bleak outlook has only modestly improved with the application of multifaceted therapies in recent years. New molecular biology and molecular genetics tools at the close of the last century brought new insights into the underpinnings of neuroblastoma, including the fact that copy-number gain in the MYCN gene is among the most important determinants of biologic risk. However, that has not been translated into better treatment and other molecular derangements contribute to poor chances of survival for children with this disease. Many clinicians, scientists, and patients and their parents anticipated that the more recent genomics revolution would usher in “precision” medicine focused on the mutant forms of proteins anticipated to drive the disease. That promise has not been fully realized in cancers like neuroblastoma that lack highly-recurrent, targetable mutations. Our team came together to explore a new approach to help close this gap. Given the few recurrent mutations in this disease, we are considering neuroblastoma to be a cancer in which normal developmental programs are corrupted by altered gene expression and that the altered gene expression is often “hard-wired” into the cell by gains and losses in the copies of the genes encoding oncogenic drivers and tumor suppressors. We exploring the capacity for a new computational algorithm to identify those cancer drivers/suppressors using existing genomic datasets. Second, we propose to use a focused but high-throughput cell-based screen to quickly provide functional validation of the candidate neuroblastoma drivers. Finally, we are using this information to develop a new biologically-based tool for assigning risk and guiding treatment assignment for children with neuroblastoma. If successful, we can extend this developmental model to other forms of childhood cancer.

项目总结