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.

项目概要/摘要 癌症中已发现超过 300 万个体细胞突变和融合基因。然而，我们的能力 预测这些躯体畸变的功能后果和治疗相关性仍然是一个主要问题 挑战。更关键的是，我们还没有解决如何有效地针对新形态像差的挑战 对肿瘤细胞内在或肿瘤微环境过程的功能影响是 通过监管流程、结合伙伴或细胞位置的重大变化而改变，导致 新颖且不可预测的功能。为了应对这一挑战，根据 PAR-21-274，我们提出了 CTD2中心将聘用 确认， 生物标志物。 最先进的高通量计算和实验方法 描述、验证和瞄准新颖的新形态驱动因素，并提名相关的预测因素 我们选择了神经胶质瘤、肉瘤和子宫内膜癌作为概念证明，因为它们所代表的 由点突变和融合基因驱动且涵盖不同类型的癌症类型未得到满足的需求较高 起源组织。在我们确定的驱动基因中，约 15% 的点突变和约 30% 的融合是 估计具有新形态效应。 理解 转移 可能 决定 策略 我们的申请将解决 PAR 中列出的三个关键领域：(i) 改进 通路和细胞线路中基因功能的研究对于癌症的发生、进展和发展非常重要 在一些人类肿瘤的背景下； (ii) 识别并确认候选生物靶标，以及 相关的预测标记物，涉及癌症病因学，易于调节； (三) 如何将这些特定于环境的新形态途径与已建立的结合起来利用 以免疫系统为目标，并确定耐药机制。基于我们的成功 我 在当前的 CTD2 项目中，我们组建了一个协作、高效、跨学科的团队，其中包括 Drs.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），并将追求三个具体目标；目标一：发展 用于预测新形态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背后的机制。目标 3：阐明治疗方法 新形态驾驶员畸变造成的责任。我们选择评估新形态驱动程序 因为它们作为一个类别，具有新的和不可预测的功能，这些功能会混淆分子明智的决策， 可能导致许多患者靶向治疗失败。通过了解新形态 畸变影响肿瘤细胞和肿瘤中蛋白质和细胞途径的下游功能 微环境中，我们将确定可以直接在人体临床试验中进行测试的治疗机会，例如 在当前的 CTD2 项目中得到了证明（已启动超过 10 项试验）。重要的是，我们专注于建模已知和 本地模型中预测的新形态突变和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) 事件进行建模。