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（由申请人提供）：不幸的是，胶质母细胞瘤是最常见也是最恶性的脑癌，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块，然后同时进行化疗和放疗。胶质母细胞瘤是一种高度浸润性肿瘤，癌细胞深入健康邻近组织，突出需要有效的化疗来靶向这些扩散细胞。尽管进行了积极的治疗，耐药性和复发是该疾病的标志，这强调需要确定胶质母细胞瘤细胞获得治疗耐药性的机制。胶质母细胞瘤的一线化疗包括替莫唑胺（一种 DNA 单烷基化剂）。平均而言，TMZ 只能延长生存期一到两个月，复发性胶质母细胞瘤对烷化剂表现出很强的抵抗力。 p53 转录因子因其巨大的抗肿瘤活性而通常被描述为基因组的守护者。尽管 35% 的胶质母细胞瘤存在突变，但其作为该疾病预​​后指标的作用尚不明确。最近，有人提出p53活性可以保护胶质母细胞瘤细胞免受包括替莫唑胺在内的DNA损伤剂的侵害，并且可能有助于p53丰富的肿瘤的化疗耐药性。然而，由于对替莫唑胺的耐药性也发生在 p53 缺陷的肿瘤中，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 精通和 p53 缺陷的细胞中非常不同。在这项研究中，我将检查 p53 充足和 p53 缺乏的情况下胶质母细胞瘤细胞对替莫唑胺的反应。使用计算模型，将细胞信号网络测量（包括定量免疫印迹和转录分析）与增殖、细胞周期、衰老和细胞死亡测量进行比较，以确定损伤响应途径和细胞对替莫唑胺治疗的表型反应之间的相关性。此外，这些技术将应用于由 p53 充足和 p53 缺陷的胶质母细胞瘤细胞产生的替莫唑胺耐药细胞系，以研究信号网络如何因获得性化学耐药性而变化。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分子水平的扰动将确定最有可能改变治疗结果的靶标。 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，尽管经过积极治疗，但仍表现出强烈的放射和化学抗性表型。该提案的重点是在 p53 充足和 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（胶质母细胞瘤治疗中使用的一线化疗药物）的反应和耐药机制。使用计算、系统生物学和分子生物学方法，我们的目标是确定新的组合疗法以增强替莫唑胺的毒性以及针对耐药性疾病的方法。