Identifying Metabolic Vulnerabilities in Lung Cancer

识别肺癌的代谢脆弱性

• 批准号: 10676730
• 负责人: Elsa R Flores
• 金额: $ 201.92万
• 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/10676730
• 关键词: 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; Operative Surgical Procedures; Organoids; PERK kinase; Pathogenesis; Pathology; Patients; Pre-Clinical Model; Prognosis; Program Research Project Grants; Proliferating; 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; machine learning algorithm; metabolomics; mouse model; neoplastic cell; novel; patient subsets; pre-clinical; programs; small cell lung carcinoma; synergism; 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).

整体项目总结 识别肺癌的代谢易损性 肺癌是世界范围内最常见的癌症死亡原因。酪氨酸激酶抑制剂和 免疫疗法已被证明在部分患者中有效；然而， 这种疾病的发病率仍然很低，特别是对于转移性疾病。此外，小细胞肺癌(SCLC)患者 预后不良，尤其是在对小细胞肺癌的认识和识别方面存在差距。 有效的治疗策略。我们在这项提案中的目标是了解关键驱动因素的潜在生物学 通过识别最终可用作单一药物或联合使用的代谢易损性来治疗肺癌 以免疫疗法为靶点治疗肺癌。我们将通过聘请专家来实现这一目标 开发了在非小细胞(NSCLC)和小细胞中具有共同分子特征的临床前模型 肺癌(SCLC)和前沿代谢组学。我们有一个积极合作的小组，他们两次会面 每月有项目和手稿，这些项目和手稿由每个项目和核心的领导人共同撰写。另外， 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 肺癌的发病机制。就新诊断的肺癌患者而言，佛罗里达州在全国排名第二。 莫菲特治疗了其中10%的病例。PPG由四个项目和四个核心组成。这些项目和核心 合作和协同以实现四个目标：i.通过以下方式确定肺癌的代谢易感性 具有共同分子特征的体内和体外模型的综合分析，包括p53， NRF2/Keap1和MYC(项目#1，由Flores博士领导，项目#2，由DeNicola博士领导，项目#3，由Dr. 克利夫兰和豪拉，以及由罗德里格斯博士领导的项目4，在行政核心#1的支持下，领导 作者：Flores和Haura，《临床前模型和病理学核心2》，由Cress和Karreth博士领导，《新陈代谢》 核心3，由Koomen博士领导，数据科学核心4，由Fridley博士领导)，II。识别新陈代谢 通过检查肿瘤微环境与免疫治疗协同作用的脆弱性 对髓系抑制细胞(MDSCs)有深入的分子理解。(项目#4，与 项目#1和#2和核心#2)，3.建立小鼠模型作为了解代谢途径的平台 具有不同基因特征的肺癌患者可利用这一技术，并评估肺癌的治疗策略。 (核心#2支持项目1-4)和四.在本地和全球共享资源和数据以获得 肺癌代谢易损性的综合分子理解。(核心#4领导所有人的努力 项目和核心)。

项目成果

Deep Learning of Histopathology Images at the Single Cell Level.

• DOI: 10.3389/frai.2021.754641
• 发表时间: 2021
• 期刊: Frontiers in artificial intelligence
• 影响因子: 4
• 作者: Lee K;Lockhart JH;Xie M;Chaudhary R;Slebos RJC;Flores ER;Chung CH;Tan AC
• 通讯作者: Tan AC