Modeling Tumor Evolution in Glioma

神经胶质瘤的肿瘤进化建模

• 批准号: 10019611
• 负责人: Roel GW Verhaak
• 金额: $ 18.9万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10019611
• 关键词: 19q; Affect; Aftercare; Aneuploidy; Antibody-drug conjugates; Appearance; Automobile Driving; Biological Models; CDK4 gene; CDKN2A gene; Cell Cycle; Cells; Chemotherapy and/or radiation; Chromosome Arm; Chromosome Segregation; Clinical Trials; Collection; Communities; Complex; Computer Analysis; Country; DNA sequencing; Data; Data Analyses; Data Pooling; Data Set; Databases; Defect; Diagnosis; Diffuse; Disease; Epidermal Growth Factor Receptor; Event; Evolution; Funding; Future; Gene Abnormality; Genetic Transcription; Genomics; Glioma; Goals; Growth and Development function; Hospitals; Immune; Immune Evasion; Immune response; Immune system; Immunologic Surveillance; Immunotherapy; Infiltrative Growth; Infrastructure; Institution; International; Investigation; Laboratories; Magnetic Resonance Imaging; Malignant Neoplasms; Mind; Mitotic spindle; Modeling; Molecular; Molecular Profiling; Mutation; Operative Surgical Procedures; Outcome; PDGFRA gene; Pathology; Pathway interactions; Patients; Pattern; Portraits; Positioning Attribute; Primary Neoplasm; Process; Property; Protocols documentation; Quality of life; RB1 gene; Receptor Protein-Tyrosine Kinases; Recurrence; Recurrent tumor; Regimen; Research Institute; Resistance; Resistance development; Resources; Sampling; Scientist; Site; Source; Specimen; Standardization; Testing; The Cancer Genome Atlas; Therapeutic Intervention; Time; Tissues; Treatment outcome; Tumor Debulking; Tumor-Associated Process; Universities; Work; base; cancer type; cdc Genes; cohort; computerized data processing; data standards; dosage; effective therapy; experimental study; immunogenic; in vivo Model; insight; longitudinal analysis; longitudinal dataset; molecular subtypes; multidimensional data; mutant; neoantigens; neoplastic cell; novel; outcome forecast; patient variability; phenotypic data; response; therapy development; therapy resistant; tool; treatment effect; treatment response; treatment strategy; tumor; tumor heterogeneity

PROJECT SUMMARY Treatment of patients with glioma has been largely unsuccessful, and even if initial treatment shows some effect, the long-term prognosis remains poor because recurrence of the disease is near-certain. A significant challenge in establishing treatments for recurrent gliomas is that the diffuse infiltrative growth and presumed extensive tumor heterogeneity allows tumor cells to “escape” and even develop resistance to therapy. In order for better treatments to be developed we need to understand both the extent of tumor heterogeneity and how it evolves in response to treatment. Although such a need is easily stated it has been difficult, if not impossible, to achieve with current resources because it requires tracking and characterizing tumor changes within the same patient. Moreover, because of patient to patient variability, and the need for statistical power, these types of investigations also require standardized data from hundreds of patients. With these requirements in mind, and the long-term goal of identifying new, effective treatment targets, we initiated the international Glioma Longitudinal Analysis (GLASS) Consortium. Since 2014, GLASS has established the largest (and still expanding) database of tumor samples sequenced at different time points among any cancer type. Data from these samples (collected at 34 institutions in 12 countries) are now aggregated and integrated with patient and phenotypic data across international sub studies. The GLASS dataset is supported by an infrastructure that standardizes key parameters across studies/sites so that complex, integrated analyses are possible. Preliminary analysis of this unique dataset demonstrated that it will allow us to create a portrait of the recurrence process and discover novel molecular vulnerabilities that can be targeted for successful therapeutic intervention. We are now poised to further exploit the GLASS data to identify critical processes driving glioma evolution. To do this we propose: Aim1 - To test the hypothesis that clonal diversity (tumor heterogeneity) is significantly impacted by treatment, and Aim 2 - To test the hypothesis that immunoediting results in the selection of glioma cells that are capable of evading the immune response. Upon completion of these aims, we will have gained new insights into how treatment and the immune system drive the clonal (tumor cell) selection that leads to glioma tumor heterogeneity. In the process, we will also establish and share the tools/approaches needed for valid analyses of this type of multi-dimensional, multi-time point data. Taken together, the results of these efforts should identify novel avenues for treatment with better, more reliable outcomes.

项目概要 神经胶质瘤患者的治疗基本上不成功，即使初步治疗显示出一些效果 效果，长期预后仍然很差，因为疾病复发几乎是肯定的。一个重要的 建立复发性神经胶质瘤治疗方法的挑战在于，弥漫性浸润性生长和推测的 广泛的肿瘤异质性使得肿瘤细胞能够“逃脱”，甚至对治疗产生耐药性。为了 为了开发更好的治疗方法，我们需要了解肿瘤异质性的程度以及它是如何发生的。 随着治疗的反应而发展。尽管这种需求很容易表达，但即使不是不可能，也是很困难的， 利用现有资源来实现，因为它需要跟踪和描述肿瘤变化的特征 同一个病人。此外，由于患者之间的差异以及对统计功效的需要，这些 各种类型的调查还需要来自数百名患者的标准化数据。 考虑到这些要求以及确定新的、有效的治疗目标的长期目标，我们 发起了国际胶质瘤纵向分析（GLASS）联盟。自 2014 年以来，GLASS 已 建立了最大（且仍在扩展）的不同时间点测序肿瘤样本数据库 任何癌症类型中。这些样本的数据（从 12 个国家的 34 个机构收集）现已公布 汇总并整合国际子研究中的患者和表型数据。玻璃 数据集由基础设施支持，该基础设施标准化了研究/站点的关键参数，以便 复杂的综合分析是可能的。对这个独特数据集的初步分析表明，它将 让我们能够描绘复发过程并发现新的分子漏洞 以成功的治疗干预为目标。 我们现在准备进一步利用 GLASS 数据来识别驱动神经胶质瘤进化的关键过程。到 为此，我们建议： 目标 1 - 检验克隆多样性（肿瘤异质性）显着的假设 受治疗的影响，目标 2 - 检验免疫编辑导致神经胶质瘤选择的假设 能够逃避免疫反应的细胞。 完成这些目标后，我们将对治疗和免疫系统如何产生新的见解 驱动导致神经胶质瘤肿瘤异质性的克隆（肿瘤细胞）选择。在此过程中，我们还将 建立并分享有效分析此类多维、多时间所需的工具/方法 点数据。总而言之，这些努力的结果应该能够找到新的治疗途径，提供更好、更有效的治疗方法。 更可靠的结果。