Identifying Metabolic Vulnerabilities in Lung Cancer

识别肺癌的代谢脆弱性

• 批准号: 10438711
• 负责人: Elsa R Flores
• 金额: $ 202.9万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-25 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10438711
• 关键词: Address; Biology; Brassicaceae; Cancer Center; Cancer Etiology; Cancer Patient; Cell Line; Cells; Cessation of life; Collaborations; Country; Cysteine; Cysteine Metabolism Pathway; Data; Data Science Core; Development; Disease; Doctor of Philosophy; Drug resistance; Ensure; Family; Florida; Genes; Genotype; Goals; Immune; Immunosuppression; Immunotherapy; Knowledge; Maintenance; Malignant neoplasm of lung; Manuscripts; Metabolic; Metabolic Pathway; Metabolism; Modeling; Molecular; Molecular Profiling; Mutate; Myeloid-derived suppressor cells; Newly Diagnosed; Non-Small-Cell Lung Carcinoma; Operative Surgical Procedures; Organoids; PERK kinase; Pathogenesis; Pathology; Patients; Pre-Clinical Model; Prognosis; Program Research Project Grants; Radiation; Regulation; Research; Resource Sharing; Sampling; Signal Transduction; Survival Rate; TP53 gene; Therapeutic; Tissue Microarray; Tyrosine Kinase Inhibitor; chemotherapy; data repository; endoplasmic reticulum stress; experimental study; genetic signature; in vivo; innovation; lung small cell carcinoma; machine learning algorithm; metabolomics; mouse model; neoplastic cell; novel; patient subsets; pre-clinical; programs; targeted treatment; therapeutic target; therapeutically effective; transcription factor; tumor; tumor microenvironment

OVERALL PROJECT SUMMARY IDENTIFYING METABOLIC VULNERABILITIES IN LUNG CANCER Lung Cancer is the most common cause of cancer deaths world-wide. Tyrosine kinase inhibitors and immunotherapy have been shown to be effective in a subset of patients; however, the overall survival rate for this disease remains low especially for metastatic disease. Moreover, small cell lung cancer (SCLC) patients have a poor prognosis, and there especially exists a gap in knowledge in understanding SCLC and identifying effective therapeutic strategies. Our goal in this proposal is to understand the underlying biology of key drivers in lung cancer by identifying metabolic vulnerabilities that can ultimately be used as single agents or combined with immunotherapy to target lung cancer therapeutically. We will achieve this goal by engaging experts that have developed preclinical models with common molecular signatures in non-small cell (NSCLC) and small cell lung cancer (SCLC) and cutting-edge metabolomics. We have an active and collaborative group that meets twice monthly with projects and manuscripts that are co-authored by the leaders of each project and core. Additionally, our Program Project Grant (PPG) team is located at Moffitt Cancer Center, which is an ideal place to study the pathogenesis of lung cancer. Florida is number 2 in the country in terms of newly diagnosed lung cancer patients. Moffitt treats 10% of these cases. The PPG consists of four projects and four cores. These projects and cores collaborate and synergize to meet four objectives: i. To identify metabolic vulnerabilities in lung cancers through integrative analysis of in vivo and ex vivo models with common molecular signatures, including p53, NRF2/KEAP1, and MYC (Project #1, led by Dr. Flores, Project #2, led by Dr. DeNicola, Project #3, led by Drs. Cleveland and Haura, and Project #4, led by Dr. Rodriguez with support from the Administrative Core #1, led by Drs. Flores and Haura, Preclinical Models and Pathology Core #2, led by Drs. Cress and Karreth, Metabolism Core #3, led by Dr. Koomen, and Data Science Core #4, led by Dr. Fridley), ii. To identify metabolic vulnerabilities that synergize with immunotherapy through examining the tumor microenvironment and gaining a deep molecular understanding of myeloid derived suppressor cells (MDSCs). (Project #4 in collaboration with Projects #1 and #2 and Core #2), iii. To build mouse models as a platform to understand the metabolic pathways utilized by lung cancers with different genetic signatures and to assess therapeutic strategies for lung cancer. (Core #2 supporting Projects #1-4), and iv. To share resources and data locally and globally to obtain an integrated molecular understanding of metabolic vulnerabilities in lung cancer. (Core #4 leading efforts from All Projects and Cores).

总体项目摘要 确定肺癌的代谢脆弱性 肺癌是全球癌症死亡的最常见原因。酪氨酸激酶抑制剂和 免疫疗法已显示在一部分患者中有效。但是，总体生存率 这种疾病仍然很低，特别是对于转移性疾病。此外，小细胞肺癌（SCLC）患者 预后不佳，并且在理解SCLC和识别的知识方面尤其存在差距 有效的治疗策略。我们在此提案中的目标是了解关键驱动因素的基本生物学 在肺癌中，通过确定最终可以用作单一药物或组合的代谢脆弱性来确定代谢脆弱性 免疫疗法以治疗靶向肺癌。我们将通过吸引专家来实现这一目标 已经开发了非小细胞（NSCLC）和小细胞中常见分子特征的临床前模型 肺癌（SCLC）和尖端代谢组学。我们有一个积极且协作的小组，聚会两次 每个项目和核心领导者共同撰写的项目和手稿每月一次。此外， 我们的计划项目赠款（PPG）团队位于莫菲特癌症中心，这是研究该中心的理想场所 肺癌的发病机理。就新诊断的肺癌患者而言，佛罗里达州在该国排名第二。 莫菲特（Moffitt）治疗了其中10％的案件。 PPG由四个项目和四个核心组成。这些项目和核心 协作并协同实现四个目标：i。通过识别肺癌中的代谢脆弱性 具有常见分子特征的体内和离体模型的综合分析，包括p53， NRF2/KEAP1和MYC（由弗洛雷斯（Flores）博士领导的项目＃1的项目＃1，由Denicola博士领导，项目＃3，由Drs领导。 克利夫兰（Cleveland）和豪拉（Haura）以及项目＃4，由罗德里格斯（Rodriguez 由Drs。弗洛雷斯（Flores）和豪拉（Haura），临床前模型和病理核心＃2，由Drs领导。 Cress和Karreth，新陈代谢 由Koomen博士领导的Core＃3和Data Science Core＃4，由Fridley博士领导），II。识别代谢 通过检查肿瘤微环境并获得了与免疫疗法协同疗法的脆弱性 对髓样衍生的抑制细胞（MDSC）的深层理解。 （与项目＃4合作 项目＃1和＃2以及核心＃2），iii。构建鼠标模型作为平台以了解代谢途径 由具有不同遗传特征的肺癌使用，并评估肺癌的治疗策略。 （Core＃2支持项目＃1-4）和IV。在本地和全球共享资源和数据以获得 对肺癌代谢脆弱性的综合分子理解。 （核心＃4的主要努力 项目和核心）。