Precision Molecular Pathology of Glioblastoma

Description

This volume provides a balanced and realistic review of the current state of glioblastoma, ranging from traditional histological review, molecular pathology of glioma, modern radiomics, neurosurgical focus, and integration of treatment plans by neuro-oncologists. The book reviews basic principles such as epidemiology and etiology, and modern 2016 WHO classification of CNS tumors. Chapters cover a general overview of common molecular techniques used in molecular pathology, molecular pathology in a developing country, key drivers of patient outcomes and predictors of response to radiation and/or chemotherapy treatment, and immunohistochemical surrogates for key molecular pathology. It concludes with reviews on radiomics, animal and stem cell models of glioblastoma, and a chapter on the emerging field of Glioblastoma Neuroscience.    Precision Molecular Pathology of Glioblastoma is intended for pathology residents and fellows interested in glioblastoma, general surgical pathologists who need reviews on how to implement modern glioblastoma classification, as well as neuro-radiologists, oncologists, and radiation oncologists needing a holistic perspective to glioblastoma diagnosis and management. Foreword Preface Contents Contributors Part I: General Principles Chapter 1: Epidemiology and Etiology of Glioblastoma Introduction Epidemiological Features of GB Etiology of Glioblastoma Ionizing Radiation Allergies and Atopic Disease Aspirin and Other Medications Exposures with Mixed or Limited Evidence Cellular Phone Use Low Frequency Electromagnetic Fields Hormonal Exposures Dietary Factors Non-radiation Occupational Exposures Smoking Height, Weight and Metabolic Traits Mendelian Cancer Syndromes and Rare Variants Common Genetic Variants in Adult GB Telomere Length Genetic Ancestry Conclusion References Chapter 2: The Role of Molecular Genetics of Glioblastoma in the Clinical Setting Introduction Isocitrate Dehydrogenase (IDH) O6-Methylguanine-DNA Methyl- Transferase (MGMT) Epidermal Growth Factor Receptor (EGFR) Phosphatase and Tensin Homolog (PTEN) Deletion BRAF V600E Mutation FGFR-TACC Gene Fusions Platelet Derived Growth Factor Receptor Alpha (PDGFRA) CDKN2A/B Homozygous Deletion Telomerase Reverse Transcriptase Promoter Mutation (TERTp) Conclusion References Chapter 3: Neurosurgical Approach to the Patient with Glioblastoma Introduction Epidemiology and Survival Clinical Presentation Operative Candidate Selection Diagnostic Workup Preoperative Performance Status Surgical Strategies Extent of Resection Gross Total Resection Subtotal Resection/Debulking Stereotactic and Neuronavigation-Assisted Biopsy Surgical Adjuncts and Special Techniques Neurophysiological Monitoring and Awake Craniotomy Neuronavigation Intraoperative Magnetic Resonance Imaging Intraoperative Ultrasound Imaging (iUS) Fluorescence-Guided Surgery Therapeutic Adjuncts Brachytherapy In Situ Chemotherapy Reoperation Postoperative Care Complications Conclusions References Chapter 4: Machine Learning Approaches Pertinent to Glioma Classification Background Classical Machine Learning Approaches Gene Expression Based Subtypes Methylation Based Subtypes Deep Learning Based Approaches Prevailing Challenges and Current Trends Key Takeaways References Chapter 5: Histopathology of Adult and Pediatric Glioblastoma Introduction Small Cell GBM Giant Cell GBM Gliosarcoma Epithelioid GBM GBM with Primitive Neuronal (PN) Component Glioblastoma with Gemistocytes Granular Cell Glioblastoma Other Patterns of GBM Fascicular Lipomatous Oligodendroglioma Component Myxoid Epithelial Differentiation Histopathology of GBM: Pediatric Versus Adult IDH Wild-Type GBMs in Children IDH Wild-Type Primary GBMs (Mid-older Adults) Versus IDH Mutant Secondary GBMs (Adolescent-Younger Adults) Genetic Susceptibility and Related Syndromes Associated with GBM Neurofibromatosis Type I (NF1) Turcot Syndrome Li-Fraumeni Syndrome (LFS) L-2-Hydroxyglutaric Aciduria (L2-HGA) Ollier’s Disease (OD) / Maffucci Syndrome (MS) Conclusion References Chapter 6: Principles of Radiation Therapy for Glioblastoma Patients Introduction Basics of Radiation Therapy Radiation Planning Techniques Radiation Toxicity The Evolution of Radiation Therapy for GBMs Dose Escalation Studies Decreasing Radiation Volumes to Minimize Toxicity Molecular Biomarkers and Radiation Therapy in GBM Hypofractionated Radiation Therapy in Elderly Patients Re-irradiation of GBM Summary and Conclusions References Chapter 7: Implementing Molecular Pathology in a Developing Country Developed Countries and Developing Countries South America and Brazil The Revolutions of Pathology The Dilemmas of Pathology in Brazil, as an Example of Developing Country Heterogeneous Distribution of Health Care Professionals and Structure The Need of Different Techniques and Assays for the Integrated Diagnosis of CNS Tumors Cost of Equipment and Reagents Specialized Work Force Needed for Molecular Pathology Experience from a Large Cancer Center in Brazil References Part II: Molecular Pathology of Glioblastoma Chapter 8: Molecular Stratification of Adult and Pediatric High Grade Gliomas Introduction Glioblastoma, IDH Wildtype, WHO Grade 4 Background Defining Mutations RTK/PI3K/AKT Pathway TP53/ MDM2/MDM4/CDKN2A Pathway RB/CDK4/CDKN2A-p16INK4a Pathway Telomere Maintenance Other Genetic and Epigenetic Alterations Glioblastoma Variants Gene Expression Profiling and Genomic Subtypes Astrocytoma, IDH-Mutant, WHO Grade 4 Background IDH1/2 Mutation Genetic Landscape of IDH-Mutant Astrocytomas, WHO Grade 4 Gene Expression Profiling Diffuse Midline Glioma, H3 K27M-Mutant, WHO Grade 4 Background H3 K27M Mutation Genetic Landscape of Diffuse Midline Gliomas H3 K27M Mutations in Other Tumor Types High Grade Gliomas with H3 G34R/V Mutation, WHO Grade 4 Background H3 G34R/V Mutation Genetic Landscape of High Grade Gliomas with H3 G34R/V Mutations Pediatric High Grade Gliomas, Other Background PDGFRA-Driven MYCN/MYC-Driven EGFR-Driven Infantile High Grade Gliomas Germline-Mutations Associated with High Grade Glioma Susceptibility Neurofibromatosis Type 1 Li-Fraumeni Syndrome Constitutional Mismatch Repair Deficiency/Turcot Syndrome Type 1 References Additional Resources Chapter 9: Genomic Heterogeneity of Aggressive Pediatric and Adult Diffuse Astrocytomas Introduction Gene Expression Profiling Genomic Structural Changes and Copy Number Alterations DNA Methylation Profiling Single Nucleotide Variants and Deregulated Cancer Cell Signaling Pathways Future Directions and Conclusion References Chapter 10: Immunohistochemical Surrogates for Molecular Pathology IDH1 The Interpretation of the Immunohistochemistry IDH2 ATRX The Interpretation of the Immunohistochemistry P53 The Interpretation of the Immunohistochemistry H3K27M The Interpretation of the Immunohistochemistry H3K27me3 The Interpretation of the Immunohistochemistry H3G34 The Interpretation of the Immunohistochemistry BRAFV600E The Interpretation of the Immunohistochemistry EGFR The Interpretation of the Immunohistochemistry EGFRvIII The Interpretation of the Immunohistochemistry TERT MGMT CDKN2A Conclusion References Part III: Key Molecular Pathways in Glioblastoma Development and Progression Chapter 11: Machine Learning-Based Automated Methods for Brain Tumor Segmentation, Subtype Classification, Tracking and Patient Survival Prediction Introduction Background Review Brain Tumor Segmentation Tumor Growth Modeling Longitudinal Brain Tumor Tracking Artificial Neural Networks Deep Learning Method: Convolutional Neural Network Patient Survival Prediction Methodology Machine Learning-Based Methods: Brain Tumor Growth Modeling, Segmentation and Tracking Biophysical Tumor Growth Model Feature Fusion-Based Tumor Segmentation Method Joint Label Fusion-Based Tumor Segmentation Deep Learning-Based Method: Brain Tumor Segmentation, Subtype Classification and Patient Survival Prediction CANet-Based Tumor Segmentation CANet-Based Tumor Subtype Classification CANet-Based Survival Prediction Experimental Setup Dataset Brain Tumor Subregion Dice Score Coefficient (DSC) Results and Discussions Machine Learning-Based Method Feature Fusion Label Fusion Deep Learning Method CANet-Based Tumor Segmentation CANet-Based Tumor Subtype Classification CANet-Based Survival Prediction Discussion Conclusion References Chapter 12: Rodent Brain Tumor Models for Neuro-Oncology Research Introduction Carcinogen Induces Rodent Brain Tumor Models Chemically Induced Murine GBM Models Genetically Induced Murine Glioma Models Cre/LoxP Site Specific Recombination Driven Murine Models Flp/FRT RCAS Avian Sarcoma Leukosis Virus Mediated GEM Model Transposon Derived GEM Models CRISPR-Cas9 Spontaneous Tumor Derived Implantable Murine Models Human Glioma Xenograft Models Humanized Mouse Models Conclusion and Future Prospects References Chapter 13: Stem Cell Based Modelling of Glioblastoma Introduction Studying Glioblastoma Cancer Stem Cells Isolating GSCs Alternative Approaches to Isolating and Studying GSCs Expanding Isolated GSCs Validating GSCs Functionally Evaluating Changes in the Stem Cell State Conclusions and Future Directions References Chapter 14: The Neuroscience of Glioblastoma Introduction Synapses Drive Glioma How Gliomas and Seizures Interact Concluding Thoughts References Index