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Systematic Characterization and Targeting of Neomorphic Drivers in Cancer

癌症新形态驱动因素的系统表征和靶向

基本信息

批准号：
10717973
负责人：
Benjamin Deneen
金额：
$ 88.37万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-19 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10717973
关键词：
Address Affect Algorithm Design Algorithms Alleles Area Bar Codes Binding Biological Biological Markers Brain CRISPR/Cas technology Cancer Etiology Cell Line Cells Clinical Trials Code Communities Complement Computational Biology Computational algorithm Connective Tissue DNA DNA Binding Data Data Set Development Electroporation Endometrial Carcinoma Ensure Event Failure Funding Gene Fusion Genes Genetic Glioma Goals Grant Heterogeneity Human Immune Immune Targeting Immune system Isogenic transplantation Location Malignant Neoplasms Mesoderm Modeling Molecular Molecular Genetics Mutation Neoplasm Metastasis Outcome Pathway interactions Patient-Focused Outcomes Patients Point Mutation Population Population Dynamics Post-Translational Protein Processing Process Productivity Proteins Proteomics RNA Interference Sampling Series Somatic Mutation Spatial Distribution System Systems Biology Testing Therapeutic Tissues Translating Tumor Biology Validation cancer initiation cancer type driver mutation drug sensitivity fusion gene gene cloning gene function genomic aberrations improved innovation loss of function neoplastic cell novel patient derived xenograft model patient screening personalized cancer therapy prediction algorithm predictive marker protein protein interaction resistance mechanism response sarcoma screening screening program success targeted agent targeted treatment therapy resistant three dimensional structure transcriptome transcriptome sequencing transplant model tumor tumor microenvironment tumor-immune system interactions vector

项目摘要

PROJECT SUMMARY/ABSTRACT More than 3 million somatic mutations and fusion genes have been identified in cancer. However, our ability to predict functional consequences and the therapeutic relevance of these somatic aberrations remains a major challenge. More critically, we have not solved the challenge of how to effectively target neomorphic aberrations where functional consequences on tumor cell-intrinsic or tumor microenvironment processes are altered through critical changes in regulatory processes, binding partners, or cellular locations, leading to novel and unpredictable functions. To address this challenge, in response to PAR-21-274, we propose a CTD2 Center that will employ identify, biomarkers. state-of-the-art, high-throughput computational and experimental approaches to characterize, validate, and target novel neomorphic drivers as well as nominate related predictive We have selected glioma, sarcoma, and endometrial cancers for proof of concept as they represent high unmet need cancer types that are driven by point mutations and fusion genes and encompass divergent tissues of origin. Of the driver genes we identified, ~15% of the point mutations and ~30% of the fusions are estimated to have neomorphic effects. understanding metastasis possibly determine strategies Our application wil address three key areas listed in the PAR: (i) improve of gene functions in pathways and cellular wiring important in cancer initiation, progression, and within the context of a few human tumors; (ii) identify and confirm candidate biological targets, and associated predictive markers, involved in cancer etiology which are amenable to modulation; and (iii) how these context-specific neomorphic pathways can be harnessed in combination with established that target the immune system, and identify mechanisms of resistance. Based on the success of our l current CTD2 project, we have assembled a collaborative, productive, interdisciplinary team comprising Drs. Mills (tumor biology/clinical trials), Deneen (molecular genetics/electroporation tumor models), Liang (computational biology), and Chen (innovative algorithms) and will pursue three Specific Aims; Aim 1: To develop computational algorithms for predicting neomorphic driver aberrations. Aim 2: To identify and elucidate mechanisms underlying potential neomorphic driver aberrations. Aim 3: To elucidate therapeutic liabilities engendered by neomorphic driver aberrations. We have chosen to evaluate neomorphic drivers because they, as a class, have new and unpredictable functions that confound molecularly informed decisions, potentially contributing to the failure of targeted therapy in many patients. By understanding how neomorphic aberrations affect downstream function within the protein and cellular pathways in tumor cells and the tumor microenvironment, we will identify therapeutic opportunities that can be directly tested in human clinical trials, as demonstrated in the current CTD2 project (>10 trials launched). Importantly, our focus on modeling known and predicted neomorphic mutations and fusion genes in autochthonous models will complement other CTD2 Centers' modeling of loss-of-function (LOF) events using RNAi and CRISPR/Cas9 strategies.

项目总结/文摘