Signaling Networks in Glioblastoma Drug Resistance

胶质母细胞瘤耐药性中的信号网络

• 批准号: 8397738
• 负责人: Jose Luis McFaline-Figueroa
• 金额: $ 4.22万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8397738
• 关键词: Acute; Affect; Alkylating Agents; Brain Neoplasms; Bromodeoxyuridine; Cell Aging; Cell Cycle; Cell Cycle Arrest; Cell Death; Cell Line; Cells; Computational Technique; Computer Simulation; DNA; DNA Damage; Development; Disease; Disease Resistance; Drug Exposure; Drug resistance; Excision; Galactosidase; Genome; Genomics; Glioblastoma; Immunoblotting; Invaded; Lead; Malignant - descriptor; Malignant neoplasm of brain; Measurement; Measures; Modeling; Molecular; Molecular Biology; Mono-S; Mutate; Nature; North America; Operative Surgical Procedures; Oral; Outcome; Pathway interactions; Patients; Phenotype; Play; Ploidies; Population; Propidium Diiodide; RNA; RNA Interference; Radiation therapy; Radio; Recurrence; Recurrent disease; Resistance; Role; Signal Transduction; Staining method; Stains; Systems Biology; Techniques; Therapeutic; Tissues; Toxic effect; Transcript; annexin A5; cancer cell; chemotherapy; combinatorial; comparative genomic hybridization; neoplastic cell; new therapeutic target; novel; prognostic indicator; response; senescence; standard of care; temozolomide; therapy resistant; transcription factor; tumor; tumorigenic

DESCRIPTION (provided by applicant): Glioblastoma is unfortunately both the most common and the most malignant form of brain cancer comprising approximately 50% of all brain tumors. Treatment for glioblastoma is multimodal in nature, consisting of surgical resection of the main tumor mass followed by concomitant chemo- and radio- therapy. Glioblastoma is a highly infiltrative tumor with cancer cells invading far into healthy adjacent tissue highlighting the nee for efficient chemotherapy to target these diffusive cells. Despite aggressive treatment, resistance and recurrence are hallmarks of the disease, underscoring the need to identify mechanisms by which glioblastoma cells acquire resistance to therapy. Frontline chemotherapy for glioblastoma consists of temozolomide, a DNA mono-alkylating agent. On average, TMZ extends survival by only one to two months, with recurrent glioblastoma showing a strong resistance to the alkylating agent. The p53 transcription factor is commonly described as the guardian of the genome due to its vast anti-tumorigenic activities. Although mutated in 35% of glioblastoma tumors its role as a prognostic indicator in the disease is not well defined. Recently, it has been suggested that p53 activity may protect glioblastoma cells from DNA damaging agents including temozolomide and may contribute to chemoresistance in p53 proficient tumors. However, as resistance to temozolomide also occurs in p53 deficient tumors, we hypothesize that the genomic and cellular signaling network alterations leading to temozolomide resistance are very different in p53 proficient and p53 deficient cells. In this study I will examine the response of glioblastoma cells to temozolomide in p53 proficient and p53 deficient contexts. Using computational modeling, cellular signaling network measurements, including quantitative immunoblotting and transcriptional profiling, will be compared to proliferation, cell cycle, senescence and cell death measurements to identify correlations between pathways responsive to damage and the phenotypic response of cells to temozolomide treatment. Furthermore, these techniques will be applied to temozolomide resistant cell lines produced from p53 proficient and p53 deficient glioblastoma cells to investigate how the signaling network changes as a result of acquired chemoresistance. Finally, perturbations at the molecular level of pathways predicted from our model to lead to sensitivity or resistance to temozolomide will identify targets most likely to alter therapeutic outcome. PUBLIC HEALTH RELEVANCE: Glioblastoma is unfortunately both the most common and most malignant form of brain cancer, which, despite aggressive treatment, displays a strong radio- and chemo-resistant phenotype. This proposal focuses on identifying mechanisms of cellular response and resistance to temozolomide, the frontline chemotherapeutic used in glioblastoma treatment, in the context of p53 proficient and p53 deficient glioblastoma cells. Using computational, systems biology and molecular biology approaches we aim to identify novel combinatorial therapies to potentiate temozolomide toxicity as well as ways to target resistant disease.

描述(申请人提供)：不幸的是，胶质母细胞瘤是最常见和最恶性的脑癌形式，约占所有脑肿瘤的50%。胶质母细胞瘤的治疗本质上是多模式的，包括手术切除主要肿瘤肿块，然后同时进行化疗和放射治疗。胶质母细胞瘤是一种高度浸润性的肿瘤，癌细胞侵袭到健康的邻近组织很远，因此需要有效的化疗来靶向这些扩散的细胞。尽管进行了积极的治疗，但耐药和复发是该病的特征，这突显了确定胶质母细胞瘤细胞获得治疗耐药性的机制的必要性。胶质母细胞瘤的一线化疗包括DNA单烷基化剂替莫唑胺。平均而言，TMZ仅将生存期延长一到两个月，复发的胶质母细胞瘤对烷化剂表现出很强的耐药性。P53转录因子通常被描述为基因组的守护者，因为它具有广泛的抗肿瘤活性。尽管在35%的胶质母细胞瘤中发生突变，但其作为疾病预后指标的作用尚未明确。最近，有研究表明，P53的活性可能保护胶质母细胞瘤细胞免受包括替莫唑胺在内的DNA损伤剂的伤害，并可能参与了P53熟练的肿瘤的化疗耐药。然而，由于替莫唑胺耐药也发生在p53缺失的肿瘤中，我们假设导致替莫唑胺耐药的基因组和细胞信号网络改变在p53熟练和p53缺失的细胞中是非常不同的。在这项研究中，我将研究在p53熟练和p53缺乏的情况下，胶质母细胞瘤细胞对替莫唑胺的反应。利用计算模型，细胞信号网络测量，包括定量免疫印迹和转录图谱，将与增殖、细胞周期、衰老和细胞死亡测量进行比较，以确定损伤反应通路和细胞对替莫唑胺治疗的表型反应之间的相关性。此外，这些技术将被应用于由p53熟练和p53缺陷的胶质母细胞瘤细胞产生的替莫唑胺耐药细胞系，以研究获得性化疗耐药导致的信号网络的变化。最后，从我们的模型预测的导致替莫唑胺敏感性或耐药性的通路的分子水平的扰动将确定最有可能改变治疗结果的靶点。 公共卫生相关性：不幸的是，胶质母细胞瘤是最常见和最恶性的脑癌形式，尽管进行了积极的治疗，但表现出强烈的放射和化疗耐药表型。这项建议侧重于在p53熟练和p53缺陷的胶质母细胞瘤细胞的背景下，确定细胞对替莫唑胺的反应和耐药性的机制。替莫唑胺是治疗胶质母细胞瘤的一线化疗药物。利用计算、系统生物学和分子生物学的方法，我们的目标是确定新的组合疗法来增强替莫唑胺的毒性，以及针对耐药疾病的方法。