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Identifying and Predicting Molecular Signatures in Glioblastoma Using Imaging-Derived

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Identifying and Predicting Molecular Signatures in Glioblastoma Using Imaging-Derived ABSTRACT Identifying and Predicting Molecular Signatures in Glioblastoma Using Imaging-Derived Phenotypic Traits by Dalu Yang This thesis addresses the relationship between molecular status of glioblastoma patients and imaging-derived phenotypic traits. Glioblastoma (GBM) is the most common and aggressive malignant brain tumor. GBM’s complex heterogeneity in gene expression leads to considerably various responses to current treatment among different patients. Thus, a deeper understanding of tumor biology and alternative personalized therapeutic intervention is urgently demanded. Magnetic Resonance Imaging (MRI) and histologic images are routinely used for GBM diagnosis. The imaging-derived phenotypic traits can be linked to tumor molecular signatures. In this thesis, we explore the imaging-genomic relationship in GBM via three approaches. First, we aim to find MRI-derived texture features that best discriminate GBM molecular subtypes. Second, we aim to find gene networks that determine the radiologically-defined tumor sub- compartment volumes. The third approach aims to quantify GBM histologic hallmarks and correlate them with biological pathway activities. Our study shows that linking imaging traits with tumor molecular status has potential clinical relevance and provides biological insight. Acknowledgments I would like to first thank my advisors, Dr. Arvind Rao and Dr. Ashok Veeraraghavan for their generous guidance and support, without which it would not have been possible for me to work on such an interesting thesis topic. I am also very grateful to Shivali Narang, Dr. Joonsang Lee, Peng Chu, Dr. Pankaj Singh, and Dr. Ganiraju Manyam, who have provided me with data, links, and insights that are key to the success of the experiments in this thesis. I am thankful to my girlfriend who really helped me overcome the procrastination on writing. Finally, I would like to express my gratitude to all my family and friends for their support and understanding on my choice of pursuing a graduate degree. Contents Acknowledgments ..................................................................................................... iv Contents .................................................................................................................... v List of Figures ........................................................................................................... viii List of Tables ............................................................................................................. ix Chapter 1 ................................................................................................................. 10 Introduction ............................................................................................................. 10 1.1. Background ............................................................................................................. 10 1.2. Motivation .............................................................................................................. 12 1.3. Thesis Layout .......................................................................................................... 13 Chapter 2 ................................................................................................................. 15 Predicting Molecular Subtypes with MRI Texture Features ....................................... 15 2.1. Introduction ............................................................................................................ 15 2.2. Pre-processing of MRI scans .................................................................................. 17 2.3. Texture Feature Extraction ..................................................................................... 20 2.3.1. Segmentation-based Fractal Texture Analysis (SFTA) ..................................... 20 2.3.2. Run-length matrix (RLM) ................................................................................. 22 2.3.3. Local binary patterns (LBP) .............................................................................. 23 2.3.4. Histogram of oriented gradients (HOG) .......................................................... 25 2.3.5. Haralick texture features ................................................................................. 26 2.4. Classification Algorithm .......................................................................................... 27 2.5. Experiments and Results ........................................................................................ 28 2.5.1. Dataset ............................................................................................................. 28 vi 2.5.2. Prediction Performance Evaluation ................................................................. 29 2.5.3. Results .............................................................................................................. 31 Chapter 3 ................................................................................................................. 34 Discovering Molecular Networks Underlying MRI Volumetric Features ..................... 34 3.1. Introduction ............................................................................................................ 34 3.2. Methods and Materials .......................................................................................... 36 3.2.1. Dataset ............................................................................................................. 36 3.2.2. Automated Tumor Segmentation in MRI ........................................................ 37 3.2.3. Determination of Cutpoints on Tumor Subcompartment Volumetrics .......... 39 3.2.4. Identification of Gene Networks using Context-Specific Subnetwork Discovery ................................................................................................................................... 40 3.2.5. Pathway Identification using IPA ..................................................................... 41 3.2.6. Identification of Perturbagens using Connectivity Map .................................. 41 3.3. Results .................................................................................................................... 42 3.3.1. Tumor Subcompartment Volumes Stratify OS in a Statistically Significant Manner ...................................................................................................................... 42 3.3.2. MIS Extracted by COSSY Algorithm Contains Closely Interacting Molecular Nodes ......................................................................................................................... 43 3.3.3. Ingenuity Pathway Analysis Identifies Phenotype Related Pathways Containing MIS genes ................................................................................................ 47 3.3.4. Connectivity Map Identifies Perturbagens Reversing Phenotype Associated Molecular signatures ................................................................................................. 49 Chapter 4 ................................................................................................................. 51 Identifying Molecular Programs Underlying Features of Whole Slide Tissue Images in Glioblastoma ............................................................................................................ 51 4.1. Introduction ............................................................................................................ 51 4.2. Materials and Methods .......................................................................................... 52 4.2.1. Whole Slide Tissue Image Data........................................................................ 52 4.2.2. Pathway Activity Data ...................................................................................... 53 4.2.3. Extracting Image Tiles and Removing Artifact ................................................. 53 4.2.4. Segmenting Microvascular Proliferation ......................................................... 54 4.2.5. Segmenting Necrosis ....................................................................................... 56 vii 4.2.6. Correlating MVP and Necrosis Proportions with Pathway Activities .............. 57 4.3. Results .................................................................................................................... 58 4.3.1. Distribution of the MVP and necrosis proportions ......................................... 58 4.3.2. MVP and Necrosis Proportions are correlated with Abstract Processes of Different Pathways .................................................................................................... 59 Chapter 5 ................................................................................................................. 62 Discussion and Future Directions .............................................................................. 62 5.1. Performance of Predicting GBM Subtypes............................................................. 62 5.2. Biological Relevance of the MRI traits ................................................................... 65 5.3. Pathways Correlating with Features derived from WSIs ....................................... 69 5.4. Potential Future Directions .................................................................................... 71 Chapter 6 ................................................................................................................
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