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.

项目摘要 神经胶质瘤患者的治疗在很大程度上是不成功的，即使最初的治疗显示出一些 尽管效果不佳，但长期预后仍然很差，因为疾病的复发几乎是肯定的。显著 建立复发性胶质瘤治疗方法的挑战是弥漫性浸润性生长和假定的 广泛的肿瘤异质性允许肿瘤细胞“逃逸”，甚至对治疗产生抗性。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影响肿瘤的生长的。 会随着治疗的变化而变化虽然这种需要很容易说明，但即使不是不可能， 因为它需要跟踪和表征肿瘤内的变化， 同一个病人此外，由于患者与患者之间的差异性以及对统计功效的需求，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，以及确定新的有效治疗靶点的长期目标，我们 国际胶质瘤纵向分析联盟（GLASS）自2014年以来， 建立了最大的（仍在扩大的）不同时间点测序的肿瘤样本数据库 任何癌症类型。这些样本的数据（在12个国家的34个机构收集）现已 汇总并整合国际子研究中的患者和表型数据。玻璃 数据集由跨研究/研究中心验证关键参数的基础设施支持， 可以进行复杂的综合分析。对这一独特数据集的初步分析表明， 使我们能够创建一个重现过程的肖像，并发现新的分子脆弱性， 以成功的治疗干预为目标。 我们现在准备进一步利用GLASS数据来确定驱动胶质瘤演变的关键过程。到 为此，我们提出：目的1-为了检验克隆多样性（肿瘤异质性）显著增加的假设， 目的2 -检验免疫编辑导致选择胶质瘤的假设 能够逃避免疫反应的细胞。 在完成这些目标后，我们将对治疗和免疫系统如何 驱动克隆（肿瘤细胞）选择，导致神经胶质瘤肿瘤异质性。在此过程中，我们还将 建立和分享有效分析这类多维度、多时间 点数据总之，这些努力的结果应该确定新的治疗途径， 更可靠的结果。