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 已在癌症中鉴定出300多万个体细胞突变和融合基因。然而，我们有能力 预测功能后果和这些躯体异常的治疗相关性仍然是一个主要的 挑战。更关键的是，我们还没有解决如何有效地瞄准新形性像差的挑战 其中对肿瘤细胞内在或肿瘤微环境过程的功能后果是 通过监管流程、结合伙伴或蜂窝位置的关键变化而改变，导致 到新奇和不可预测的功能。为了应对这一挑战，针对PAR-21-274，我们提出了 CTD2中心将雇用 识别， 生物标志物。 最先进的、高吞吐量的计算和实验方法 表征、验证和瞄准新的新形态驱动因素，并提名相关的预测 我们选择了胶质瘤、肉瘤和子宫内膜癌作为概念验证，因为它们代表 高度未满足的需要由点突变和融合基因驱动并包含分歧的癌症类型 起源的组织。在我们鉴定的驱动基因中，~15%的点突变和~30%的融合是 估计会有新形体的效果。 理解 转移 可能 确定 策略 我们的应用程序将解决PAR中列出的三个关键领域：(I)改进 在癌症的发生、发展和发展中起重要作用的基因通路和细胞连接 在少数人类肿瘤的背景下；(2)确定和确认候选生物靶点；以及 相关预测标记物，涉及癌症病因学，可受调节；和(Iii) 如何利用这些特定于上下文的新形态路径与已建立的 以免疫系统为目标，并识别抵抗机制。基于我们的成功 L 目前的CTD2项目，我们已经组建了一个协作的、富有成效的跨学科团队，其中包括Mills博士 (肿瘤生物学/临床试验)，Deneen(分子遗传学/电穿孔肿瘤模型)，梁(计算 生物学)和Chen(创新算法)，并将追求三个具体目标；目标1：开发 预测新形性驾驶员像差的计算算法。目标2：确定和澄清 潜在的新形性驾驶员像差的潜在机制。目标3：阐明治疗方法 由新形态化的驾驶员偏差所产生的责任。我们选择评估新形态的驱动因素 因为作为一个类别，它们具有新的和不可预测的功能，这些功能混淆了分子知情的决定， 潜在地导致了许多患者靶向治疗的失败。通过理解新形态化 异常会影响肿瘤细胞和肿瘤中蛋白质和细胞通路的下游功能。 微环境，我们将确定可在人体临床试验中直接测试的治疗机会，如 在当前的CTD2项目中展示了这一点(推出了10个试验)。重要的是，我们专注于建模已知的和 在原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。