Quantitative Analysis of GBM Invasion Mechanisms with New Imaging Protocol

描述(申请人提供)：胶质母细胞瘤(GBM，WHO IV级)是最常见和最高级别的星形细胞瘤，预后均较差。与较低级别的胶质瘤(II级和III级)相比，基底节细胞的径向生长速度几乎是后者的10倍。三种病理生理特征，包括坏死、坏死周围的细胞性假性病变和微血管增生，被认为是GBM预后的标志，并被认为与疾病急剧加速的进展有关。此外，已发现一组胶质瘤干细胞(GCS)与新血管的形成有关，导致GBM肿瘤向外生长。然而，由于生物标记物观察和数据分析的能力有限，这些病理特征和相关的信号网络在GBM进展中的确切作用及其相互作用机制仍不清楚。拟议的项目将通过利用计算能力和机器智能来分析大规模病理图像和空间参考的多路生物标记物数据，为GBM研究创造一条新的途径。一个全面的系统和分析基础设施将被开发出来，以定量研究1)与GBM侵袭相关的病理结构和GSC的空间分布，2)负责GBM进展的原位信号基因和调控网络的功能，以及3)跨GBM微环境的形态、分子和信号网络变化的意义。将开发一套可伸缩的图像处理算法，用于分析表型特征和生物标记物的空间分布和共定位，并将其应用于GBM的全切片病理Brightfield和量子点免疫组织化学图像。表型结构的衍生特征和边界以及生物标记物的空间分布将被存档并整合到病理学和原位分子成像数据数据库中，在那里可以调用科学查询来支持对GBM肿瘤进展机制的分析。特别是，多重量子点免疫组织化学(mQD-IHC)，一种新的生物标记物染色技术，将被用来显示多个感兴趣的生物标记物在同一组织空间参考中的位置和表达。在大规模病理图像处理、高性能数据计算、用于多路生物标志物研究的mQD-IHC染色技术以及由定制数据库的高通量数据查询能力支持的表型-基因型数据集成方面的专业知识，使一个独特的研究工具能够研究GBM肿瘤扩张机制。值得注意的是，建议的研究工作可以推广到其他涉及大规模数据和表型-基因信息集成的计算型癌症研究。因此，它将对未来的翻译研究产生广泛的影响。

DESCRIPTION (provided by applicant): Glioblastoma (GBM, WHO grade IV) is the most common and highest-grade astrocytoma with uniformly dismal prognosis. In contrast to the lower grade gliomas (grades II and III), GBMs present radial growth rates almost 10 times as fast. Three pathophysiologic features, including necrosis, cellular pseudopalisades surrounding necrosis, and microvascular hyperplasia, are considered as hallmarks for GBM prognosis and are believed to be relevant to the drastically accelerated disease progression. Additionally, a population of Glioma Stem Cells (GCSs) has been found pertinent to the new neovascular formation, leading to GBM tumor outward growth. However, the definitive roles of these pathologic features and the associated signaling networks in GBM progression and their interactive mechanisms remain ill defined, because of limited capacity of biomarker observation and data analysis. The proposed project will create a new avenue for GBM research by leveraging computational power and machine intelligence for analyses of large-scale pathology images and spatially referenced, multiplexed biomarker data. A comprehensive system and analytical infrastructure will be developed to quantitatively investigate 1) spatial distributions f GBM invasion-related pathologic structures and GSCs, 2) functions of the in-situ signaling genes and regulatory networks responsible for GBM progression, and 3) the significance of morphologic, molecular, and signaling network variation across GBM microenvironments. A set of scalable image processing algorithms for analyses of spatial distributions and co-localizations of phenotypic features and biomarkers will be developed and applied to whole-slide pathology brightfield and quantum dot immunohistochemistry images of GBMs. Derived features and boundaries of phenotypic structures, and spatial distributions of biomarkers will be archived and integrated in a database for pathology and in-situ molecular imaging data where scientific queries can be invoked to support the analysis of GBM tumor progression mechanisms. In particular, multiplexed quantum dot immunohistochemistry (mQD-IHC), a new biomarker staining technique, will be leveraged to show the locations and expressions of multiple biomarkers of interest within the same tissue spatial reference. The combined expertise in large-scale pathology image process, high performance data computation, mQD-IHC staining technique for multiplexed biomarker investigation, and phenotype- genotype data integration supported by high-throughput data query power from customized database enables a unique research vehicle to investigate GBM tumor expansion mechanisms. Notably, the proposed research work can be generalized to catalyze other computational cancer research involving large-scale data and phenotype-genotype information integration. Therefore, it will present a broad impact on future translational research.