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Genome-Scale Modeling of Redox Metabolism and Therapeutic Response in Radiation-Resistant Tumors

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Genome-Scale Modeling of Redox Metabolism and Therapeutic Response in Radiation-Resistant Tumors GENOME-SCALE MODELING OF REDOX METABOLISM AND THERAPEUTIC RESPONSE IN RADIATION-RESISTANT TUMORS A Dissertation Presented to The Academic Faculty by Joshua Elliott Lewis In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in Bioinformatics in the Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology & Emory University May 2020 COPYRIGHT © 2020 BY JOSHUA E. LEWIS GENOME-SCALE MODELING OF REDOX METABOLISM AND THERAPEUTIC RESPONSE IN RADIATION-RESISTANT TUMORS Approved by: Dr. Melissa L. Kemp, PhD, Advisor Dr. David S. Yu, MD, PhD Department of Biomedical Engineering Department of Radiation Oncology Georgia Institute of Technology Emory University Dr. Greg Gibson, PhD Dr. Lee A. Cooper, PhD School of Biological Sciences Department of Pathology Georgia Institute of Technology Northwestern University Dr. Eberhard O. Voit, PhD Department of Biomedical Engineering Georgia Institute of Technology Date Approved: March 20, 2020 To my family ACKNOWLEDGEMENTS My development as a scientist can be attributed to the synergistic involvement of a number of people who I would like to gratefully acknowledge. First, I would like to thank my PhD advisor, Dr. Melissa Kemp, for providing me with tremendous guidance, challenging me to continually improve, and instilling in me a passion for scientific research. Next, I would like to thank my committee members for their valuable input and advice throughout my PhD training: Dr. Lee Cooper, Dr. Greg Gibson, Dr. Eberhard Voit, and Dr. David Yu. I would also like to thank research collaborators Dr. Cristina Furdui and Dr. David Boothman, as well as my previous and current financial support through the MD/PhD program (NIH T32 GM008169-29), Computational Biology and Predictive Health Training Grant (NIH T32 GM105490), and Ruth L. Kirschstein National Research Service Award (NIH F30 CA224968). I would like to thank the members of the Kemp lab, both past and present, for providing an enjoyable and intellectually stimulating environment to work and learn, as well as bagels during morning lab meetings. I would also like to thank my friends, especially those in the Emory MD/PhD program, who have made this long and arduous journey so much more bearable. Finally, I would like to thank my family for their unwavering support and acceptance that I will still be in school for a very long time. I especially want to thank my parents, who made countless sacrifices to allow me to pursue my dreams and try to make a positive difference in the world. iv TABLE OF CONTENTS ACKNOWLEDGEMENTS iv LIST OF TABLES viii LIST OF FIGURES ix LIST OF SYMBOLS AND ABBREVIATIONS xxvi SUMMARY xxxvi CHAPTER 1. Introduction 1 1.1 Research Objectives and Specific Aims 2 CHAPTER 2. Background 6 2.1 Radiation Therapy 6 2.1.1 Epidemiology 6 2.1.2 Mechanism of action 7 2.1.3 Predictors of radiation therapy response 8 2.2 Redox Metabolism 10 2.2.1 Redox cofactors 10 2.2.2 ROS-scavenging enzymes 15 2.2.3 Role of redox metabolism in tumor response to radiation therapy 16 2.3 Radiation-Sensitizing Chemotherapies 18 2.3.1 General principles 18 2.3.2 Redox metabolism of radiation-sensitizing chemotherapies 19 2.4 Flux Balance Analysis 23 2.4.1 Mathematical representation 24 2.4.2 Genome-scale metabolic reconstructions 26 2.4.3 FBA objective functions 27 2.4.4 Implementation of model constraints using biological data 28 CHAPTER 3. FBA models of NAD(P)H-driven ß-lapachone sensitization 31 3.1 Introduction 31 3.2 Methods 33 3.2.1 Computational methods 33 3.2.2 Experimental methods 49 3.3 Results 50 3.3.1 Construction of genome-scale metabolic models for HNSCC cell lines 50 3.3.2 Predicted NADPH production across the HNSCC metabolome 55 3.3.3 Simulated silencing of IDH1 highlights NADPH flux re-routing 57 3.3.4 Upregulation of NADH-producing fluxes in rSCC-61 61 3.3.5 ß-lapachone sensitivity and effects on NADPH production 64 3.3.6 Cell line-specific changes in ß-lapachone sensitivity 66 v 3.4 Discussion 74 CHAPTER 4. FBA models of redox metabolism in TCGA tumors 80 4.1 Introduction 80 4.2 Methods 83 4.2.1 Computational methods 83 4.2.2 Experimental methods 103 4.3 Results 110 4.3.1 Automated bioinformatics pipeline for integrating multi-omic data 110 4.3.2 Compartmental differences in redox metabolic fluxes 113 4.3.3 Heterogeneity in personalized metabolic flux profiles 120 4.3.4 Impact of IDH1 R132 mutations on glioma NADPH production 123 4.3.5 Simulated genome-wide knockout screen for identifying redox targets 127 4.3.6 Disparities in redox metabolism and H2O2-scavenging systems 135 4.4 Discussion 141 CHAPTER 5. Machine learning classifiers for prediction of radiation response 148 5.1 Introduction 148 5.2 Methods 151 5.2.1 Computational methods 151 5.2.2 Experimental methods 161 5.3 Results 164 5.3.1 Dataset-independent ensemble for radiation response classification 164 5.3.2 Gene expression classifier implicates cellular metabolism 173 5.3.3 Genome-scale metabolic models accurately predict metabolite production 181 5.3.4 Multi-omic classifier identifies clinical subpopulations of cancer patients 191 5.3.5 Metabolic features highlight network-level involvement 199 5.3.6 Personalized predictions of non-invasive clinical and metabolic biomarkers 208 5.4 Discussion 213 CHAPTER 6. FBA models of radiation-sensitizing chemotherapeutics 218 6.1 Introduction 218 6.2 Methods 221 6.2.1 Integration of chemotherapeutic metabolism modules into FBA models 221 6.2.2 Multi-feature objective function screen 229 6.2.3 Machine learning regressors for radiation sensitizing effect 231 6.2.4 Code availability 231 6.3 Results 232 6.3.1 Optimal FBA objective functions utilize redox cofactors 232 6.3.2 FBA models predict chemotherapy response and radiation sensitization 235 6.3.3 Machine learning regressors identify sensitization biomarkers 237 6.4 Discussion 244 CHAPTER 7. Conclusions and future directions 248 7.1 Conclusions 248 7.1.1 FBA models of redox metabolism in TCGA tumors 248 7.1.2 Machine learning classifiers for prediction of radiation response 249 vi 7.1.3 FBA models of radiation-sensitizing chemotherapeutics 250 7.2 Future Directions 250 7.2.1 FBA models of redox metabolism in TCGA tumors 250 7.2.2 Machine learning classifiers for prediction of radiation response 252 7.2.3 FBA models of radiation-sensitizing chemotherapeutics 254 REFERENCES 256 vii LIST OF TABLES Table 2-1 Alternative isoforms of cytosolic and mitochondrial GPx, Grx, Prx, 16 and TR. Table 3-1 Compartmental concentrations of water, oxygen, and ions used in the 43 model. Table 3-2 Metabolite concentration upper and lower bounds from Park et al. 44 Table 3-3 Metabolite formulation of DMEM/F-12 media used to model the 47 extracellular environment. Table 3-4 Experimentally-measured metabolite concentration values and ranges 48 in SCC-61 and rSCC-61 cells. Table 3-5 Comparison of model and experimental results for canonical NADPH- 72 producing genes (left) and discovered HNSCC-specific genes of interest (right). Table 4-1 Objective functions used in FBA and FVA. 84 Table 4-2 Turnover numbers of normal and neomorphic IDH1 reactions for 102 given IDH1 mutations. Table 4-3 Matched radiation-sensitive and radiation-resistant cell lines. 104 Table 4-4 siRNA’s used for each gene target 106 Table 5-1 Hyperparameter ranges for Bayesian optimization with gradient 155 boosting classifiers. Table 5-2 Hyperparameter ranges for Bayesian optimization with the random 158 forest classifier. Table 5-3 Hyperparameter ranges for Bayesian optimization with the logistic 159 regression classifier with L1 regularization. Table 5-4 Hyperparameter ranges for Bayesian optimization with the neural 159 network classifier with L1 regularization. Table 5-5 Matched radiation-sensitive and radiation-resistant cell lines. 162 viii LIST OF FIGURES Figure 2-1 Comparison of number of radiation-sensitive and radiation-resistant 7 TCGA patients between different cancer types. Figure 2-2 Many processes in the NAD+ metabolic network are disrupted in 11 cancer, including the production of NAD+ intermediates and consumption of NAD+ by signaling processes necessary for tumor cell survival. Figure 2-3 Major inputs and outputs to/from the cellular pools of 12 NAD+, NADH, NADP+, and NADPH, including pathways and therapies pertinent to the cellular response to radiation therapy. Figure 2-4 Major NADPH biogenesis pathways and reactions. Interconnection 14 of well-studied biochemical reactions that convert oxidized NADP+ to reduced NADPH, labeled with their associated enzymes and grouped into biochemical pathways (colored boxes). Figure 2-5 Overview of cisplatin metabolism, including import/export 20 reactions, mechanism of action, and drug clearance. Figure 2-6 Overview of cyclophosphamide metabolism, including 21 import/export reactions, mechanism of action, and drug clearance. Figure 2-7 Overview of doxorubicin metabolism, including import/export 23 reactions, mechanism of action, and drug clearance. Figure 3-1 Pipeline for determining turnover number (kcat) values for every 42 Recon 2 reaction with an available enzyme commission (EC) number. The number and percentage of needed kcat values filled at every step is given. All data is extracted from the BRENDA database (25). Subsystem refers to the metabolic subsystem assigned to every reaction within the Recon 2 model. Figure 3-2 Incorporation of disparate sources of biological information into the 52 mathematical modeling framework. Figure 3-3 Correlation between experimental proteomic and experimental gene 53 expression values (as ratios of rSCC-61 to SCC-61) corresponding to the same NCBI gene ID. Figure 3-4 Correlation between experimental proteomic and model proteomic 53 values.
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