Quantitative Analysis of GBM Invasion Mechanisms with New Imaging Protocol

描述（由申请人提供）：胶质母细胞瘤（Glioblastoma， GBM， WHO分级IV级）是最常见、级别最高的星形细胞瘤，预后普遍较差。与较低级别胶质瘤（II级和III级）相比，GBMs的径向生长速度几乎是其10倍。坏死、坏死周围的细胞假性突起和微血管增生这三个病理生理特征被认为是GBM预后的标志，并被认为与疾病进展的急剧加速有关。此外，神经胶质瘤干细胞（GCSs）群体已被发现与新的新血管形成有关，导致GBM肿瘤向外生长。然而，由于生物标志物观察和数据分析的能力有限，这些病理特征和相关信号网络在GBM进展中的确切作用及其相互作用机制仍然不明确。该项目将利用计算能力和机器智能分析大规模病理图像和空间参考、多路生物标志物数据，为GBM研究开辟一条新的途径。将建立一个全面的系统和分析基础设施，定量研究1)GBM侵袭相关病理结构和GSCs的空间分布，2)GBM进展的原位信号基因和调控网络的功能，以及3)GBM微环境中形态、分子和信号网络变化的意义。将开发一套可扩展的图像处理算法，用于分析表型特征和生物标志物的空间分布和共定位，并应用于GBMs的全切片病理明场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志物的空间分布将被存档并整合到病理和原位分子成像数据的数据库中，可以调用科学查询来支持GBM肿瘤进展机制的分析。特别是，多路量子点免疫组织化学（mQD-IHC），一种新的生物标志物染色技术，将被用来显示相同组织空间参考中多个感兴趣的生物标志物的位置和表达。结合大规模病理图像处理、高性能数据计算、用于多重生物标志物研究的mQD-IHC染色技术以及高通量数据查询功能支持的表型-基因型数据集成等专业知识，使研究GBM肿瘤扩展机制成为一种独特的研究工具。值得注意的是，所提出的研究工作可以推广到催化其他涉及大规模数据和表型-基因型信息整合的计算癌症研究。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.