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.

Public Health Relevance

Integrated analysis of spatial configurations of tissue pathologic features and underlying molecular biomarkers accounting for cancer progression is an important way to understand tumor expansion mechanisms. To advance in this research field, I propose to develop automated image analysis tools, database-based spatial integration methods, and mathematical models for tumor microenvironment representation to support quantitative investigations on Glioblastoma (GBM) brain tumor invasion mechanisms with definitive pathology imaging and multiplexed molecular biomarker data from whole-slide images of GBM specimens.

National Institute of Health (NIH)
Mentored Quantitative Research Career Development Award (K25)
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Special Emphasis Panel (ZCA1)
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Jakowlew, Sonia B
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Emory University
Schools of Medicine
United States
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