Modeling Tumor Evolution in Glioma

项目总结 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 效果不佳，长期预后仍然较差，因为该病几乎肯定会复发。一个重要的 建立复发胶质瘤治疗方法的挑战是弥漫性浸润性生长和假定 肿瘤的广泛异质性使肿瘤细胞得以“逃逸”，甚至对治疗产生抵抗力。按顺序 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 随着治疗的进行而进化。尽管这样的需要很容易说出来，但即使不是不可能，也是困难的， 要利用现有资源来实现，因为它需要跟踪和表征 同一个病人。此外，由于患者之间的差异，以及对统计能力的需要，这些 各种类型的调查还需要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(GRAS)联盟。自2014年以来，Glass一直在 建立了最大的(仍在扩大中)不同时间点测序的肿瘤样本数据库 在所有癌症类型中。来自这些样本的数据(从12个国家的34个机构收集)现在是 与国际亚研究中的患者和表型数据进行汇总和整合。玻璃杯 数据集由基础设施提供支持，该基础设施可跨研究/站点标准化关键参数，从而 复杂的、综合的分析是可能的。对这一独特数据集的初步分析表明，它将 使我们能够创建重复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玻璃数据来识别推动胶质瘤演变的关键过程。至 为此，我们建议：Aim1-检验克隆多样性(肿瘤异质性)显著 受治疗的影响，目标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，我们将对如何治疗和免疫系统有新的见解 驱动克隆(肿瘤细胞)选择，从而导致胶质瘤肿瘤的异质性。在这一过程中，我们还将 建立和共享有效分析这种类型的多维、多时间的工具/方法 点数据。综上所述，这些努力的结果应该会确定新的治疗途径， 更可靠的结果。

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.