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

• 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
• 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

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.

项目摘要/摘要 在癌症中已经发现了超过300万个体细胞突变和融合基因。但是，我们的能力 预测功能后果和这些体细胞像差的治疗相关性仍然是主要的 挑战。更重要的是，我们没有解决如何有效靶向新态畸变的挑战 在肿瘤细胞内部或肿瘤微环境过程中的功能后果是 通过调节过程，结合伴侣或细胞位置的关键变化而改变，领先 到新颖和不可预测的功能。为了应对这一挑战，回应PAR-21-274，我们提出了一个 将雇用的CTD2中心 确认， 生物标志物。 最先进的高通量计算和实验方法 表征，验证和靶向新颖的新形态驱动因素以及提名相关的预测 我们选择了神经胶质瘤，肉瘤和子宫内膜癌作为概念证明 高未满足的癌症类型是由点突变和融合基因驱动的，并且包含分歧 原产组织。我们确定的驱动基因中，约有15％的点突变和约30％的融合物为 估计具有新形态效应。 理解 转移 可能 决定 策略 我们的申请将介绍标准杆中列出的三个关键领域：（i）改进 基因在途径和细胞接线中的功能在癌症的起步，进展和 在一些人类肿瘤的背景下； （ii）识别并确认候选生物学靶标，以及 相关的预测标志物，参与癌症病因，可以调节； （iii） 如何将这些特定环境特异性的新态途径与已建立的 该靶向免疫系统，并确定抗性机制。基于我们的成功 l 当前的CTD2项目，我们组成了一个由DRS组成的协作，富有成效的跨学科团队。米尔斯 （肿瘤生物学/临床试验），Deneen（分子遗传学/电穿孔肿瘤模型），Liang（计算 生物学）和陈（创新算法），将追求三个特定目标；目标1：发展 用于预测新型驱动器像差的计算算法。目标2：识别和阐明 潜在的新形态驱动器像差的机制。目标3：阐明治疗 新手形驾驶员畸变产生的负债。我们选择评估新态驱动器 因为他们作为班级具有新的和不可预测的功能，使分子知情的决定混淆了， 有可能导致许多患者的靶向治疗失败。通过了解新态 畸变会影响肿瘤细胞和肿瘤中蛋白质和细胞途径内的下游功能 微环境，我们将确定可以在人类临床试验中直接测试的治疗机会，因为 在当前的CTD2项目（> 10次试验）中进行了证明。重要的是，我们专注于建模已知和 预测的新形态突变和自围模型中的融合基因将补充其他CTD2 中心使用RNAi和CRISPR/CAS9策略对功能丧失（LOF）事件的建模。