Medicine, Faculty of
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UBCV
Johnson, Fraser
Doctor of Philosophy - PhD
University of British Columbia
High-throughput phenotype-based screening of large libraries of compounds without known targets can identify small molecules that elicit a desired cellular response, but additional approaches are required to find and characterize their targets and mechanisms of action. Through such a screen, the novel compound LCS3 was previously identified that selectively kills lung adenocarcinoma (LUAD) cells, but its mechanism of action remained unknown. This thesis used gene expression profiling to elucidate the cellular responses of LUAD cells to LCS3. I demonstrated that LCS3 induces NRF2 pathway activation and oxidative stress through the generation of reactive oxygen species in sensitive LUAD cell lines. I then developed and applied a thermal proteome profiling (TPP) approach and identified the disulfide reductases GSR and TXNRD1 as LCS3 targets. Through enzymatic assays using purified protein, I confirmed that LCS3 inhibits disulfide reductase activity through a reversible and uncompetitive mechanism. The results demonstrated that LCS3-sensitive LUAD cells are correspondingly sensitive to the synergistic inhibition of glutathione and thioredoxin pathways, suggesting a mechanistic overlap in cell death induced by LCS3 and lethality arising from disulfide reductase inhibition. I established that challenging resistant cells with oxidative stress increases reliance on the glutathione and thioredoxin pathways and sensitizes cells to LCS3 and dual disulfide reductase inhibition. Finally, a genome-wide CRISPR-Cas9 knockout screen identified the loss of NQO1 as a mechanism of LCS3 resistance. Together, this work shines light on the mechanism of action of LCS3 and demonstrates the potential utility of disulfide reductase inhibition in lung cancer. This work also highlights the ability of TPP to uncover novel targets of novel small molecules identified by high-throughput screens.
CHARACTERIZATION OF A SMALL MOLECULE INHIBITOR OF DISULFIDE REDUCTASES THAT INDUCES OXIDATIVE STRESS AND LETHALITY IN LUNG CANCER CELLS by Fraser Johnson B.Sc., The University of British Columbia, 2015 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Interdisciplinary Oncology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2022 © Fraser Johnson, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Characterization of a small molecule inhibitor of disulfide reductases that induces oxidative stress and lethality in lung cancer cells submitted by Fraser Johnson in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Interdisciplinary Oncology Examining Committee: Dr. William W. Lockwood, Associate Professor, Pathology and Laboratory Medicine, UBC Co-supervisor Dr. Gregg B. Morin, Associate Professor, Medical Genetics, UBC Co-supervisor Dr. Peter Stirling, Associate Professor, Medical Genetics, UBC Supervisory Committee Member Dr. Christopher Ong, Associate Professor, Urologic Sciences, UBC University Examiner Dr. Christopher A. Maxwell, Associate Professor, Pediatrics, UBC University Examiner Additional Supervisory Committee Member: Dr. Poul H. Sorensen, Professor, Pathology and Laboratory Medicine, UBC Supervisory Committee Member iii Abstract High-throughput phenotype-based screening of large libraries of compounds without known targets can identify small molecules that elicit a desired cellular response, but additional approaches are required to find and characterize their targets and mechanisms of action. Through such a screen, the novel compound LCS3 was previously identified that selectively kills lung adenocarcinoma (LUAD) cells, but its mechanism of action remained unknown. This thesis used gene expression profiling to elucidate the cellular responses of LUAD cells to LCS3. I demonstrated that LCS3 induces NRF2 pathway activation and oxidative stress through the generation of reactive oxygen species in sensitive LUAD cell lines. I then developed and applied a thermal proteome profiling (TPP) approach and identified the disulfide reductases GSR and TXNRD1 as LCS3 targets. Through enzymatic assays using purified protein, I confirmed that LCS3 inhibits disulfide reductase activity through a reversible and uncompetitive mechanism. The results demonstrated that LCS3-sensitive LUAD cells are correspondingly sensitive to the synergistic inhibition of glutathione and thioredoxin pathways, suggesting a mechanistic overlap in cell death induced by LCS3 and lethality arising from disulfide reductase inhibition. I established that challenging resistant cells with oxidative stress increases reliance on the glutathione and thioredoxin pathways and sensitizes cells to LCS3 and dual disulfide reductase inhibition. Finally, a genome-wide CRISPR-Cas9 knockout screen identified the loss of NQO1 as a mechanism of LCS3 resistance. Together, this work shines light on the mechanism of action of LCS3 and demonstrates the potential utility of disulfide reductase inhibition in lung cancer. iv This work also highlights the ability of TPP to uncover novel targets of novel small molecules identified by high-throughput screens. v Lay Summary Lung cancer is the leading cause of cancer-related deaths worldwide, largely due to the lack of effective therapies. Despite an advanced understanding of the genetic basis of lung cancer progression, most therapeutics are ineffective due to drug resistance. This research studies a new, highly effective drug that has not been previously characterized. The purpose of this work is to understand how this drug affects lung cancer cells to predict which patients could benefit from such treatment. I investigate cellular gene expression and apply a method called ‘thermal proteome profiling’ to identify two enzymes the drug inhibits. Through this characterization, I demonstrate how it kills malignant cells while remaining non-toxic to normal cells. This work identified a gene, which when inactivated, causes cancer cells to be resistant to this treatment. This knowledge can advance drug development and help oncologists tailor personalized treatments to ensure cancer patients are treated with appropriate therapeutics. vi Preface All chapters of this thesis include some text or data from work that has been published: Johnson, F. D., Ferrarone, J., Liu, A., Brandstädter, C., Munuganti, R., Farnsworth, D., Lu, D., Luu, J., Sihota, T., Jansen, S., Nagelberg, A., Shi, R., Forcina, G., Zhang, X., Cheng, G.S.W, Spencer Miko S.E., de Rappard-Yuswack, G., Sorensen, P.H., Guha, U., Becker, K., Djaballah, H., Somwar, R., Varmus, H., Morin, G.B., Lockwood, W.W. (2022). Characterization of a small molecule inhibitor of disulfide reductases that induces oxidative stress and lethality in lung cancer cells. Cell Reports 38, 110343. For the above publication, from which most of this thesis originates, I was responsible for writing the draft manuscript, editing the manuscript with other coauthors, and working with William Lockwood to address the editor and reviewer comments. I was responsible for assembling all data and carrying out most of the published experiments and analysis except for the following: The cell line panel sensitivity screens in Figure 8, Figure 10, and Figure 30 were carried out by the High Throughput Screening Core Facility at the Memorial Sloan Kettering Cancer Center or the Developmental Therapeutics Program at the National Cancer Institute. The gene expression profiling in Figure 14 was performed by William Lockwood and analyzed by me and Alvin Liu. Molecular docking was performed by Ravi Munuganti. All GSR and TXRND1 enzymatic assays were carried out by either me or Christina Brandstädter. Metabolite assays, the CRISPR screen, and validation of sgNQO1 and sgKEAP1 knockout cell lines were performed by John Ferrarone and interpreted by me and William Lockwood. All other experiments were carried out by me with some assistance from other lab mates: Jennifer Luu, Dylan Farnsworth, Daniel Lu, Rocky Shi, Tianna Sihota, Amy Nagelberg, vii Sophie Jansen, and Georgia de Rappard-Yuswack. I performed the TPP with guidance from Gregg Morin and Sandra Spencer Miko, and first confirmed GSR and TXNRD1 as targets of LCS3 by enzymatic assays. viii Table of Contents Abstract ......................................................................................................................... iii Lay Summary ................................................................................................................. v Preface .......................................................................................................................... vi Table of Contents ....................................................................................................... viii List of Tables ............................................................................................................... xii List of Figures............................................................................................................. xiii List of Abbreviations ................................................................................................... xv Acknowledgements ................................................................................................... xvii Dedication ................................................................................................................... xix Chapter 1: Introduction ................................................................................................. 1 1.1 Lung cancer ...................................................................................................... 1 1.2 Subtypes of lung cancer.................................................................................... 2 1.2.1 Small cell lung cancer ................................................................................ 2 1.2.2 Non-small cell lung cancer ......................................................................... 3 1.2.2.1 Oncogenic drivers of lung adenocarcinoma ........................................... 4 1.3 Targeted therapies for LUAD ............................................................................ 7 1.4 Non-oncogene dependencies ......................................................................... 10 1.5 Discovery of new therapeutics ........................................................................ 11 1.5.1 Biochemical screen-based drug discovery ............................................... 12 1.5.2 In silico drug discovery............................................................................. 14 1.5.3 Phenotype-based drug discovery ............................................................ 15 1.5.4 Properties of lead compounds for preclinical development. ..................... 17 1.5.4.1 cLogP ................................................................................................... 18 1.5.4.2 Lipinski’s rule of 5 ................................................................................. 18 1.6 High throughput phenotype-based chemical screen to identify selective inhibitors of lung cancer ............................................................................................. 19 1.6.1 Identification and characterization of LCS1 .............................................. 21 1.6.2 Identification of LCS3 ............................................................................... 23 ix 1.7 Drug targets beyond protein............................................................................ 24 1.8 Thesis theme and rationale for study .............................................................. 25 1.9 Objective and Hypothesis ............................................................................... 25 1.10 Specific aims and thesis outline ...................................................................... 26 Chapter 2: Characterization of LCS3 ......................................................................... 29 2.1 Introduction ..................................................................................................... 29 2.2 Investigating cell sensitivities to LCS3 ............................................................ 30 2.2.1 Lung cell line panel .................................................................................. 30 2.2.2 Clonogenics ............................................................................................. 31 2.2.3 NCI-60 panel ............................................................................................ 32 2.3 Determination of cytostasis or cytotoxicity ...................................................... 33 2.3.1 Scalable time-lapse analysis of cell death kinetics (STACK) ................... 33 2.3.2 LCS3 causes apoptosis in sensitive LUAD cells ...................................... 36 2.4 Gene expression profiling ............................................................................... 37 2.4.1 Enrichr analysis ....................................................................................... 42 2.5 NRF2 pathway ................................................................................................ 43 2.5.1 NRF2 pathway activation by LCS3 .......................................................... 43 2.6 Generation of ROS ......................................................................................... 46 2.7 Antioxidant rescue .......................................................................................... 47 2.8 Conclusions .................................................................................................... 48 2.9 Methods .......................................................................................................... 49 Chapter 3: Discovery of protein targets of LCS3...................................................... 55 3.1 Methods of small molecule target discovery ................................................... 55 3.1.1 Thermal proteome profiling ...................................................................... 56 3.2 LCS3 TPP ....................................................................................................... 56 3.2.1 Candidate selection ................................................................................. 60 3.2.1.1 Glutathione-S-transferase omega 1 (GSTO1) ...................................... 61 3.2.1.2 Disulfide reductases GSR and TXNRD1 .............................................. 64 3.3 LCS3 inhibits GSR and TXNRD1 .................................................................... 67 3.4 Enzyme kinetics of LCS3 inhibition ................................................................. 69 3.5 Molecular docking ........................................................................................... 70 x 3.6 Structure-activity relationship .......................................................................... 72 3.7 Inhibition of downstream molecular products .................................................. 76 3.8 Conclusions .................................................................................................... 78 3.9 Methods .......................................................................................................... 78 Chapter 4: Inducing lethality in lung cancer cells by exploiting susceptibility to oxidative stress ........................................................................................................... 86 4.1 Introduction ..................................................................................................... 86 4.2 Glutathione and thioredoxin inhibition synergy................................................ 86 4.3 Sensitization of resistant cells to LCS3 ........................................................... 90 4.4 Sensitization of resistant cells to disulfide reductase inhibition ....................... 91 4.5 Thioredoxin pathway inhibition activates NRF2 pathway in H23 cells ............. 93 4.6 Dual disulfide reductase inhibition in lung cancer cells ................................... 94 4.7 Genome-wide CRISPR/Cas9 knockout screen ............................................... 95 4.7.1 Results, enrichment of antioxidant genes ................................................ 95 4.7.1.1 Validation of CRISPR screen hit NQO1 ............................................... 97 4.7.1.2 CRISPR screen hit KEAP1 ................................................................... 99 4.7.1.3 CRISPR screen main findings ............................................................ 100 4.8 Conclusions .................................................................................................. 101 4.9 Methods ........................................................................................................ 102 Chapter 5: Conclusions and future directions........................................................ 105 5.1 Summary....................................................................................................... 105 5.2 Dual targeting the disulfide reductase pathways in cancer ........................... 108 5.3 The role of NQO1 in LCS3 activity ................................................................ 111 5.4 Other uses of TPP in target discovery .......................................................... 113 5.5 Limitations ..................................................................................................... 113 5.6 Future directions ........................................................................................... 115 5.6.1 Redox cycling ........................................................................................ 115 5.6.2 LCS3 structure and in vivo optimization ................................................. 116 5.6.3 Drawbacks to the pharmacological use of aryl nitro subgroups and potential solutions ................................................................................................ 117 5.6.4 MAPK pathway relevance and other cancer types ................................ 120 xi 5.6.5 Clinically targeting oxidative stress in oncology ..................................... 120 5.7 Significance................................................................................................... 121 References ................................................................................................................. 123 Appendices ................................................................................................................ 137 Appendix A - Cell lines ............................................................................................. 137 Appendix B - Oligonucleotides ................................................................................. 138 Appendix C - Reagents used ................................................................................... 139 xii List of Tables Table 1. FDA-approved targeted therapies for advanced NSCLC .................................. 9 Table 2. LCS3 derivative structures used in SAR study ................................................ 74 Table 3. Liquid chromatography gradient conditions ..................................................... 80 Table 4. Cell lines used ............................................................................................... 137 Table 5. Oligonucleotides used in Chapter 4 ............................................................... 138 Table 6. Reagents used .............................................................................................. 142 xiii List of Figures Figure 1. Classifications of lung cancer ........................................................................... 4 Figure 2. Genetic alterations of LUAD patient tumours ................................................... 6 Figure 3. Non-oncogene addiction in cancer ................................................................. 11 Figure 4. High-throughput screening pipeline ................................................................ 21 Figure 5. Molecular structure of LCS1 ........................................................................... 23 Figure 6. Molecular structure of LCS3 ........................................................................... 23 Figure 7. Proposed target discovery strategy and characterization of LCS3 ................. 28 Figure 8. Lung cell line sensitivity to LCS3 .................................................................... 31 Figure 9. Crystal violet staining of LCS3-treated lung cells ........................................... 32 Figure 10. NCI-60 cell line panel sensitivity to LCS3 ..................................................... 33 Figure 11. Cell death kinetics of LCS3-treated cell lines ............................................... 36 Figure 12. LCS3 induction of apoptosis in LUAD cell lines ............................................ 37 Figure 13. Schematic of expression profiling studies .................................................... 38 Figure 14. Gene expression of LCS3-treated LUAD cell lines ....................................... 40 Figure 15. GO analysis .................................................................................................. 41 Figure 16. LCS3 activation of the NRF2 pathway ......................................................... 45 Figure 17. LCS3 induction of ROS in sensitive cell lines ............................................... 46 Figure 18. Rescue of LCS3 toxicity by antioxidants ...................................................... 47 Figure 19. LCS3 TPP .................................................................................................... 58 Figure 20. TPP denaturation curves .............................................................................. 60 Figure 21. Redox pathway schematic of TPP hits ......................................................... 61 Figure 22. GSTO1 thermal denaturation curve ............................................................. 61 Figure 23. Investigation of LCS3-GSTO1 interaction .................................................... 63 Figure 24. TXNRD1 and GSR thermal denaturation curves .......................................... 64 Figure 25. Amino acid sequence alignment of human TXNRD1 GSR........................... 66 Figure 26. GSR and TXNRD1 three dimensional structures ......................................... 67 Figure 27. LCS3 inhibits GSR and TXNRD1 ................................................................. 69 Figure 28. Lineweaver-Burk plots of GSR and TXNRD1 ............................................... 70 xiv Figure 29. In silico docking of LCS3 into GSR and TXNRD1 ........................................ 72 Figure 30. LCS3 structure-activity relationship .............................................................. 75 Figure 31. LCS3 induces PRDX1 oxidation in sensitive cell lines ................................. 76 Figure 32. LCS3 induces GSH oxidation in LUAD cells ................................................ 77 Figure 33. LCS3 and disulfide reductase inhibition ....................................................... 89 Figure 34. Sensitization of resistant cells to LCS3 ........................................................ 91 Figure 35. TBHP sensitizes HPL1D to glutathione and thioredoxin inhibition ............... 92 Figure 36. TXNRD1 inhibition activates NRF2 pathway ................................................ 93 Figure 37. Working model of LCS3 mechanism ............................................................ 94 Figure 38. Schematic of genome-wide CRISPR-Cas9 knockout screen ....................... 95 Figure 39. CRISPR screen results ................................................................................ 96 Figure 40. Validation of CRISPR screen hit NQO1 ....................................................... 98 Figure 41. CRISPR screen hit KEAP1......................................................................... 100 Figure 42. Overview of approach to LCS3 characterization ........................................ 106 Figure 43. Nitro R group structure ............................................................................... 117 xv List of Abbreviations ARE antioxidant response element ARNT aryl hydrocarbon receptor nuclear translocator BRAF B-Raf proto-oncogene serine/threonine kinase ERK extracellular-signal-related kinase FAD flavin adenine dinucleotide GCLM glutamate-cysteine ligase modifier subunit GFP green fluorescent protein GO gene ontology GSH reduced glutathione GSR glutathione disulfide reductase GSS glutathione synthetase GSTO1 glutathione-S-transferase omega 1 GSTP1 glutathione-S-transferase pi 1 HMOX1 heme oxygenase 1 HPLC high performance liquid chromatography HSPA6 heat shock 70 kDa protein 6 IR ionizing radiation KEAP1 kelch-like ECH-associated protein 1 KRAS kirsten rat sarcoma 2 viral oncogene homolog LC/MS liquid chromatography/mass spectrometry LUAD lung adenocarcinoma MAPK mitogen-activated protein kinase MEK mitogen-activated protein kinase kinase MET hepatocyte growth factor receptor NADPH nicotinamide adenine dinucleotide phosphate NHBE normal human bronchial epithelial NQO1 NAD(P)H quinone oxidoreductase 1 NRF2 nuclear factor erythroid 2-related factor 2 xvi NSCLC non-small cell lung cancer PRDX1 peroxiredoxin-1 RB1 retinoblastoma 1 RO5 Lipinski’s rule of five ROS reactive oxygen species SAR structure-activity relationship SCLC small cell lung cancer SILAC stable isotope labeling by amino acids in cell culture STACK scalable time-lapse analysis of cell death kinetics TCGA The Cancer Genome Atlas TP53 tumor protein p53 TPP thermal proteome profiling TRX thioredoxin TTF-1 thyroid transcription factor-1 TXNRD1 thioredoxin reductase 1 3D three-dimensional xvii Acknowledgements This work was made possible because my lab colleagues and supervisors invested their time and energy into supporting me throughout my PhD studies. I would like to thank my supervisors, Drs. William Lockwood and Gregg Morin, who took a chance on me by accepting me into their labs. Dr. Morin’s experience, knowledge, and scientific rigor were resources throughout my graduate studies and thesis writing. Dr. Lockwood was endlessly accessible, supportive, and a guiding light during my PhD work. Dr. Lockwood provided scientific guidance and pragmatic solutions at every occasion, which I can credit for much of my success. I am also very thankful for the expertise and guidance of my PhD Supervisory Committee, Drs. Poul Sorensen and Peter Stirling. My graduate program, the University of British Columbia Interdisciplinary Oncology Program, co-directors Drs. Wan Lam and Kevin Bennewith supported me over the years and guided me into finding a co-supervised graduate student position. I’m grateful for the valuable time and attention the above faculty have invested into my success. I also would like to thank the many collaborators and undergraduate trainees who contributed to this work. My wife, Andrea, has supported me consistently throughout this PhD degree, despite that I was often consumed by the demands of my graduate studies. I’m blessed by her patience, compassion, and companionship, and I look forward to a bright future with her. My grandmother, Darlene, has played an enduring supportive role throughout my life and I will always owe her my thanks. My son, Mackenzie, motivated me to excel in my academic studies and is a major reason I’ve been able to complete this thesis. My xviii daughters, Olivia and Abigail, have also inspired me to succeed at this work. Ken and Susan Keir motivated and encouraged me to pursue secondary education, for which I am enormously thankful. I would like to acknowledge the funding agencies that supported my PhD through providing the following awards: The IODE War Memorial Scholarship, Frederick Banting and Charles Best Canada Graduate Scholarships Master’s and Doctoral Awards (CIHR CGS-M and CGS-D). The Canadian Cancer Society and CIHR provided travel funds which allowed me to present this research at international conferences. This project was funded by the BC Cancer Foundation, the Cancer Research Society, and Canadian Institutes of Health Research. xix Dedication This work is dedicated to everyone who has suffered from cancer.1 Chapter 1: Introduction Portions of this chapter have been published as: Johnson, F. D., Ferrarone, J., Liu, A., Brandstädter, C., Munuganti, R., Farnsworth, D., Lu, D., Luu, J., Sihota, T., Jansen, S., Nagelberg, A., Shi, R., Forcina, G., Zhang, X., Cheng, G.S.W, Spencer Miko S.E., de Rappard-Yuswack, G., Sorensen, P.H., Guha, U., Becker, K., Djaballah, H., Somwar, R., Varmus, H., Morin, G.B., Lockwood, W.W. (2022). Characterization of a small molecule inhibitor of disulfide reductases that induces oxidative stress and lethality in lung cancer cells. Cell Reports 38, 110343. 1.1 Lung cancer Lung cancer is the leading global cause of cancer mortality in both men and women, resulting in more than 1.7 million deaths per year (Bade and Dela Cruz, 2020; Pao and Girard, 2011). In Canada alone, 21,000 persons die of lung cancer each year and an estimated 1 in 15 Canadians will be diagnosed with lung cancer in their lifetimes (Canadian Cancer Society, 2021). The high number of lung cancer deaths is mainly attributed to its high incidence, late detection, and the lack of effective therapeutic strategies for patients with advanced disease (Ridge et al., 2013). Lung cancer is most prevalent in persons over the age of 70 and 75% of diagnosed lung cancer cases occur in tobacco smokers (Bade and Dela Cruz, 2020). Other known causes include air pollution, radon gas exposure, biomass burning and occupational exposures such as asbestos. It is also established that genetic factors (such as alpha-1 antitrypsin deficiency), chronic lung disease and lung infection all influence predisposition to lung cancer (Bade and Dela Cruz, 2020; Tubio-Perez et al., 2021). 2 1.2 Subtypes of lung cancer Lung cancer is divided into two main subtypes that have distinct histological and genetic features. Approximately 15% of lung cancer cases are classified as small cell lung cancer (SCLC), and 85% are non-small cell lung cancer (NSCLC) (Gridelli et al., 2015). These two categories of lung malignancy are outlined in the following sections. 1.2.1 Small cell lung cancer Microscopically, SCLC cells are rounded and have minimal cytoplasm with large nuclei. Positive immunohistochemistry staining for thyroid transcription factor-1 (TTF-1) and neuroendocrine markers chromogranin, CD56, and synaptophysin are used to confirm SCLC tumours (van Meerbeeck et al., 2011). To date, no targeted therapies have been shown to be clinically effective for the treatment of SCLC (Peifer et al., 2012). SCLC patients are generally responsive to combined chemotherapy; however, median survival is only 8-20 months, depending on tumour stage at diagnosis (van Meerbeeck et al., 2011). The genomic landscape of SCLC is heterogeneous and has few established driver mutations. Tumor protein p53 (TP53) and retinoblastoma 1 (RB1) losses are found in nearly all SCLC tumours, making these the most frequently lost genes in SCLC patient tumours (Dowlati et al., 2016; George et al., 2015; Peifer et al., 2012). The NOTCH receptor gene family acts as a tumour suppressor in SCLC and inactivating NOTCH gene family mutations are observed in approximately 25% of SCLC tumours (George et al., 2015). Pathogenetically relevant alterations are also commonly observed in genes encoding 3 chromatin modifiers (CREBBP, MLL, EP300) and kinases (BRAF, KIT, FGR1), offering a potential path forward for the development of targeted SCLC therapies (George et al., 2015; Peifer et al., 2012). 1.2.2 Non-small cell lung cancer NSCLC is comprised of squamous (30% of NCSLC cases) and non-squamous cancers, which are further divided into large-cell and adenocarcinoma subtypes (Figure 1). Lung adenocarcinoma (LUAD) represents 90% of non-squamous cases and is the most common histological lung cancer subtype (Gridelli et al., 2015). LUAD represents approximately half of all lung cancer cases and is a highly heterogeneous disease, both histologically and genetically (Travis et al., 2011). LUAD is glandular, has ample cytoplasm, and has multiple defined histological growth patterns including acinar, papillary, lepidic and solid. Due to such histological heterogeneity, pathologists often rely on immunohistochemistry to diagnose LUAD. LUAD is TTF-1 positive and negative for squamous lung cancer markers CK5/6 and p63, except for focal positivity (Travis et al., 2011). 4 Figure 1. Classifications of lung cancer Figure used with permission from Gridelli et al., 2015. 1.2.2.1 Oncogenic drivers of lung adenocarcinoma A consistent feature of oncogenic transformation is the accumulation of genetic alterations. In particular, smokers have a high frequency of somatic alterations—approximately 10 mutations per megabase (Gridelli et al., 2015). Next-generation sequencing of patient tumours has revealed that somatic mutations frequently occur in cell signaling pathways, leading to sustained proliferative signaling. Other genetic alterations facilitate metastasis, inhibition of apoptosis, replicative immortality, immune escape and other processes that promote tumour survival (Hanahan and Weinberg, 2011). 5 Alterations in the mitogen-activated protein kinase (MAPK) pathway are frequent in LUAD tumours. The serine/threonine kinase ERK is central to the MAPK pathway and transmits mitogen signals from upstream proteins such as EGFR, KRAS, MEK and MET (Guo et al., 2020). One cohort of treatment-naive LUAD patients underwent whole-exome sequencing by The Cancer Genome Atlas (TCGA) Research Network and revealed activating alterations in KRAS (32%), EGFR (11%) and BRAF (7%) and other MAPK components (Figure 2). In all, 62% of LUAD tumours were found to have activating alterations in the MAPK pathway (Cancer Genome Atlas Research, 2014). 6 Figure 2. Genetic alterations of LUAD patient tumours Figure used with permission from Cancer Genome Atlas Research, 2014. During cancer progression, malignant cells can become dependent on the continued activity of mutant oncoproteins for survival. As an example, over 90% of chronic myeloid leukemia patients harbour the BCR-ABL fusion oncogenes (O'Brien et al., 2003). The kinase inhibitor imatinib, which targets BCR-ABL, achieved strong clinical responses in chronic myeloid 7 leukemia patients (O'Brien et al., 2003). This is an example by which oncogene addiction in patient tumours has been successfully exploited therapeutically. In LUAD patients, activating MAPK pathway alterations often give rise to oncogene addiction—such oncogene dependency forms the mechanistic basis for targeted therapies. 1.3 Targeted therapies for LUAD Advances in the genetic characterization of patient lung tumours have allowed the development of inhibitors to precisely target driver mutations and their associated signaling pathways. Targeted therapies can be advantageous compared to chemotherapies, which were more commonly used as first-line treatments in the past and are often associated with limited efficacy and undesirable toxicities. By inhibiting specific oncogenic signaling pathways, it is possible to slow tumour growth without broad physiological adverse effects. Due to the high prevalence of growth signaling pathway alterations in NSCLC, many targeted therapies have been developed that inhibit and exploit such vulnerabilities. Table 1 lists FDA-approved targeted therapies for NSCLC and corresponding clinical trials in progress. Targeting actionable alterations in KRAS, EGFR, ALK, MET, BRAF, RET, ROS1, NTRK and HER2 provides a survival benefit relative to previous standards of care (Majeed et al., 2021). All the therapies listed on Table 1 target signaling kinases, with the exception of the KRASG12C inhibitor sotorasib. Until recently, KRAS inhibitors have been elusive due to the challenge of inhibiting the small GTPase active site (Salgia et al., 2021). KRAS mutations at the glycine 12 residue are associated with persistent activation of pro-8 proliferative GTPase activity; sotorasib specifically targets the KRASG12C mutation by the covalent, inactivating modification of the KRAS cysteine 12 residue and is being investigated in an ongoing phase I clinical trial (Lanman et al., 2020). In contrast, pan-KRAS inhibitors seek to target other regions of KRAS that are essential for its activity. The small molecule BBP-454 targets a recently appreciated KRAS binding pocket and is being investigated pre-clinically (Salgia et al., 2021). BI 1701963 is in Phase I clinical trials and disrupts KRAS by inhibiting its binding to the KRAS activator SOS1 (Salgia et al., 2021). Overall, pan-KRAS inhibitors are in early development but show promising clinical utility. The therapies listed on Table 1 are all small molecules except for the HER2-targeting monoclonal antibodies T-DM1 and T-DXd (trastuzumab). While each of these drugs show promise, the application of targeting kinase signaling pathways is relatively narrow within the broader landscape of cancer cell biology. Less than half of LUAD patients have tumours that are dependent on kinase oncogenes and nearly all these patients lack effective and durable targeted therapy options. Patients treated with MAPK pathway-disrupting kinase inhibitors such as geftinib often initially respond favorably, but later succumb to disease as treatment resistance inevitably develops (Mok et al., 2009b). This paradigm highlights the need for new and effective antineoplastic agents; hence, new cancer cell vulnerabilities must be explored to uncover the next generation of targeted therapies. 9 Table 1. FDA-approved targeted therapies for advanced NSCLC Each therapy with corresponding clinical trials, comparator treatment, and clinical efficacy. *Indicates data for treatment naïve patient. ǂDrugs not yet FDA-approved. Table adapted with permission from Majeed et al., 2021. 10 1.4 Non-oncogene dependencies Targeted therapies that inhibit mutated components of key cellular pathways such as EGFR have proven successful in LUAD treatment (Mok et al., 2009a; Paez et al., 2004; Pao et al., 2004). However, most patients are not candidates for these therapies and those that are, inevitably develop resistance to treatment (Mok et al., 2009b). While the current array of targeted cancer therapeutics is directed against the products of some mutated oncogenes and components of their signaling pathways, the growth or survival of cancer cells may also be dependent on the function of other genes that would provide additional targets for new classes of anticancer agents. Recent studies suggest that cancer cells can become reliant on genes that are not essential in normal cells—for example, to compensate for transformation-induced stress. It is increasingly appreciated that cancer cells rely on both oncogenes and non-oncogenes to sustain their survival and targeting such genes in the context of a malignant phenotype can produce synthetic lethality (Huang et al., 2019; Kaelin, 2005; Vyse et al., 2017). Figure 3 illustrates the concept of targeting a non-oncogenic dependency in cancer cells. These strategies, such as immunotherapy, which compromises cancer cells’ ability to evade immune challenges, can potentially allow a patient to overcome drug resistance. Currently, there are numerous targeted immunotherapies under clinical investigation; one such example is the PDL-1 targeting monoclonal antibody pembrolizumab, a checkpoint inhibitor that has shown significant benefit in NSCLC patients with PDL-1-positive tumour biopsy, relative to platinum therapy (Madden and Kasler, 2019). This is an example of how understanding a non-oncogene dependency of cancer cells can give rise to therapeutic opportunity. 11 Figure 3. Non-oncogene addiction in cancer Cancer cells harbor elevated levels of various stresses, caused by collateral events during the tumourigenic process. Tumour cells can be specifically killed by application of additional stress, or by inhibition of specific salvage pathways, whereas normal cells can tolerate these perturbations. Figure adapted with permission from Nagel et al., 2016. 1.5 Discovery of new therapeutics The first medicinal drugs in human history were discovered from natural sources such as herbs, roots and fungi (Jones, 2011). In the modern era, scientists rely on increasingly sophisticated strategies to identify novel therapeutics. Due to advances in genomics and molecular biology, a multitude of biomolecules have been identified that are hypothesized to have an important function in human health and disease. Target-based drug discovery strategies seek to identify chemical structures that interact with such pre-defined molecular targets and include biochemical screen-based drug discovery in silico drug discovery (Moffat et al., 2017). In the following sections, I will outline how drug discovery methodologies are employed by those seeking to develop the next generation of therapeutics. 12 1.5.1 Biochemical screen-based drug discovery Biochemical screens are assays that produce analytical measurements of a quantifiable biochemical signal and are the predominant method of lead compound identification (Lage et al., 2018). When 10,000 or more compounds are screened, a screen is considered to be high throughput. Recent advances in instrumentation and automation allow the testing of millions of compounds in either singlet or duplicate measurements (Mishra et al., 2008). Such large compound libraries are primarily used by the pharmaceutical industry, although they are becoming increasingly accessible to academic research institutions. The purpose of such biochemical screens is to identify bioactive compounds that interact with or modify a molecular target (Lage et al., 2018). For example, enzyme-based screening can involve the expression of a known enzyme target in E. coli, followed by enzyme purification and in vitro assays using a compound library (Manly, 1997). Such a biochemical system may facilitate a reaction that consumes or produces a molecule that emits a fluorescent signal, which can be detected by a plate reader and quantified to infer enzyme activity (Manly, 1997). Most biochemical screens are performed in small volume formats using fluorescence detection or scintillation proximity assay techniques because of their high sensitivity. The scintillation proximity assay is primarily used as a binding assay to identify protein interactors and measures the radioactive energy in unbound radioactive molecules in solution (Kahl and Felder, 2005). This assay design exploits the scintillation proximity effect, in which a radioligand such as 3H emits low-energy particles that are converted into light energy when in close proximity to an acceptor molecule that is immobilized on a scintillant-coated surface 13 (Wu and Liu, 2005). An advantage of biochemical screens is that they are highly focused and can reliably identify agonists or antagonists of specific proteins. Biochemical screen formats are relatively consistent and reproducible relative to screening in more complex cell-based or ex vivo biological systems. A main advantage of biochemical screens is that it is possible to identify a high number of chemical structures with demonstrated efficacy. Using such focused biochemical screens is the ideal way to perform a structure-activity relationship (SAR) study. Diverse chemical groups can be substituted to enhance the pharmacodynamic and pharmacokinetic properties of a lead compound; this generates potentially hundreds of chemical scaffolds that can be compared for efficacy, screened by phenotype, and potentially patented by companies or researchers. Hit compounds identified in in vitro biochemical screens do not require accessibility to the cell, which can lead to the discovery of pharmacophores that would otherwise be effluxed from the cell or unable to penetrate the cell membrane (Manly, 1997). This can be advantageous because pharmacophores identified in biochemical screens can be optimized through medicinal chemistry to enhance cell accessibility. Similarly, many compounds undergo metabolism or other biotransformation within cells and such compounds might be missed by whole-cell assays. Such hit compounds identified in biochemical screens can in most cases be structurally modified to resist metabolism (Manly, 1997). Thus, the biochemical screen hit discovery can serve as a precursor to compound structure optimization. It is important to note that high throughput library screens—while powerful—are costly and resource-intensive. Such large biochemical screens require trained personnel, large compound libraries, and necessitate counter screening to ensure identified hits are specific to their desired target. Furthermore, the highly focused and targeted nature of biochemical screens 14 can also be a disadvantage as identified structures may exhibit poor pharmacodynamics or pharmacokinetics in vivo. Ultimately, the goal of pharmacology is to develop drugs that are active in humans, not in in vitro reactions or plated cells (Manly, 1997). 1.5.2 In silico drug discovery Following a successful biochemical screen, the virtual screen of a compound database can allow the identification of structures with more favorable ligand-protein interactions. In select cases, in silico screening can provide a starting point for lead discovery but in silico screens are ideally suited for subsequent SAR after a biochemical screen hit has been identified (Hughes et al., 2011). The main strength of in silico screening is that it allows for the prioritization of structural features that are predicted to enhance binding inside a protein pocket, which narrows the search field for structure optimization and allows informed medicinal chemistry decisions. When the three-dimensional (3D) structure of a drug target is known (most commonly a protein), computational approaches can be used to predict and identify novel chemical interactors. This strategy, known as structure-based drug design, involves the in silico screening of large libraries of chemical structures to discover drug-target interactions (Macalino et al., 2015). Alternatively, ligand-based drug design allows discovery when the biomolecular target 3D structure is not known. When using ligand-based design, computational scientists may use knowledge of other ligands which bind to a desired site to identify a chemical structure that is predicted to interact with the biomolecular target. Both 15 structure-based and ligand-based drug design strategies rely on virtual screening to identify high-probability candidate interactors (Macalino et al., 2015). An advantage of in silico approaches is that they can identify a preliminary structure for further focused study, without the requirement for the cost-intensive screening of a large library of chemical structures (Hughes et al., 2011). While the requirement for personnel of high expertise, expensive software, and high computer processing capacity can limit the accessibility of in silico screens, such screens are more readily accessible by small companies and academic institutions than more resource-demanding biochemical screens. A major limitation of in silico screening is that its predictive capacity relies on imperfect virtual docking models and protein structures produced by X-ray crystallography. In many cases, the crystallized structure available for virtual docking is not available at all or was crystallized in an irrelevant biological conformation. As such, the results of in silico screening do not always translate into successful structure optimization. The molecular candidates produced by such screens require subsequent validation by biochemical experiments. Ideally, an in silico screen can produce hits that can be validated and found to have favorable potency and pharmacological properties. 1.5.3 Phenotype-based drug discovery An alternative to target-based drug discoveries is phenotypic drug discovery. Phenotype-based discovery involves the screening of chemical compounds for their ability to elicit a specific phenotype, such as inhibition of cancer cell growth or reporter gene activation 16 within living cells, without knowledge of any singular specific molecular target (Blay et al., 2020; Manly, 1997; Moffat et al., 2017). High throughput screening of large libraries of small molecules, using physiologically-relevant assays provides a starting point for identifying new drug candidates (Zheng et al., 2013). More commonly, a group of pre-selected chemical scaffolds that were identified by biochemical and in silico screens are screened for phenotype to identify compounds that elicit a desired effect in a living system. Phenotypic screens are not focused on a single biochemical readout and while more complex, phenotypic readouts can be highly relevant to human disease. Thus, the most consequential aspect of phenotypic drug discovery is the ability of a screening assay to predict a positive outcome in a living system (Moffat et al., 2017). Phenotypic drug discovery has the power to uncover new vulnerabilities that may not be predicted through other such means such as target-based discovery or genetic screening methods. In the cancer field, this phenotype-based approach is particularly well-suited to identifying non-oncogene dependencies, as such genes are not commonly altered in patient tumours. Phenotypic screens can result in the identification of such dependencies through finding targets that are necessarily ‘druggable’, whereas target-based discovery strategies can fail when a proposed drug target cannot be unsuccessfully targeted and is thereby found to be ‘undruggable’. Despite the power of phenotype-based drug discovery, ultimately identifying effector targets of the resulting screen hits can be a significant challenge and limitation to this strategy. As a result, phenotypic screens often require chemical proteomics methods and focused secondary screens to validate the molecular target(s) of a hit compound (Hughes et al., 17 2011). For these reasons, the pharmaceutical industry does not typically rely on phenotype-based screens at the discovery phase. Primarily, industry will use phenotype-based screens to select from hundreds of structures with confirmed biochemical activity for the purpose of in vivo prioritization. It is also less appropriate to investigate SAR using phenotype-based screens—this is ideally suited for biochemical assays because subtle improvements in molecular activity can be more accurately attributed to specific structural changes. Phenotypic screens take expertise to initiate and maintain, and rely on costly compound libraries and sophisticated, automated instrumentation. Thus, large phenotype-based screens are best suited for industry or the largest of publicly funded research institutions. Altogether, the path to drug development is likely to rely on the combined usage of biochemical, in silico, and phenotype-based experiments. Each of these designs has unique strengths and limitations and potentially offers unique solutions to various problems. 1.5.4 Properties of lead compounds for preclinical development. When a therapeutic is approved for public use, it has been selected for favorable characteristics such as safety, efficacy, ease of storage and manufacture. A lead compound is a compound that has been identified by biochemical, in silico, or phenotype-based screens that has demonstrated bioactivity but may require structure optimization and analog prioritization. At the initial drug discovery stage there are other chemical characteristics that are of consideration and will be described in the following sections. 18 1.5.4.1 cLogP The cLogP is an estimated measure of a chemical structure’s lipophilicity, the LogP. The LogP is the partition coefficient between octanol and water (coctanol/cH2O ) and an important molecular property that affects bioavailability. A high lipophilicity is associated with poor aqueous solubility, high metabolic clearance and toxicity (Gomtsyan, 2012; Sun, 2016). The calculated LogP (cLogP) uses a substructure-based method that computes the LogP value contributions from individual fragments, using the Pomona College MedChem Project—a database of 10,000 diverse chemical structures with experimentally determined LogP values (Sun, 2016). This method is highly accurate in calculating LogP and is used in place of experimental determination. 1.5.4.2 Lipinski’s rule of 5 The Lipinski’s rule of 5 (RO5) is a general rule to determine a chemical structure’s ‘drug-likeness’. Christopher Lipinski is a medicinal chemist who has spent his career working at Pfizer. Candidate compounds that adhere to the RO5 predictably have better oral absorption and have lower attrition rates in clinical trials (Lipinski et al., 2001). Lipinski developed the RO5 guideline for drug discovery in which poor absorption is predicted when there are more than 5 H-bond donors, 10 H-bond acceptors, the molecular weight is greater than 500 Da, and the cLogP is greater than 5 (Lipinski et al., 2001). A greater molecular weight often suggests a poor permeability across the intestinal and blood-brain barriers. A large number of hydrogen bond donors (OH and NH) or hydrogen bond acceptors (O and N) also impairs membrane bilayer permeability (Pardridge, 2012). In the era of high-19 throughput drug screening, the RO5 has become an important rule, allowing the exclusion of small molecules predicted to have poor bioavailability. With the advent of combinatorial chemistry, which allows the automated synthesis of large chemical libraries, it has become desirable to predict the bioavailability of a compound before being tested in vivo. Hence, the RO5 is a simple rule to determine drug-likeness that may help to eliminate drug candidates that are likely to fail in vivo experimentation. 1.6 High throughput phenotype-based chemical screen to identify selective inhibitors of lung cancer In 2009, Somwar et al. published a study in which they screened 189,290 small molecules for their ability to inhibit the growth of human LUAD cell lines. This screen identified four compounds, named Lung Cancer Screen (LCS) 1 to 4, which had favorable characteristics including low molecular weight, a low cLogP, and a confirmed ability to inhibit the growth of LUAD—but not normal lung cells—at a low half-maximal inhibitory concentration (IC50) (Somwar et al., 2009). In the preliminary screen, the small molecule library was screened for inhibition of cell proliferation at a concentration of 10μM in four LUAD cell lines: H1650 (EGFRdelE746-A750), H1975 (EGFRL858R,T790M), H3255 (EGFRL858), and H2030 (KRASG12C). The assay used an alamarBlue viability readout, which could not distinguish between cytotoxic or cytostatic effects. This initial screen considered all compounds that inhibited at least 50% of the viability of at least one cell line as positive hits, of which 6,552 hit compounds were 20 identified (Figure 4). The compounds were resynthesized and tested again for antiproliferative activity, which resulted in 669 preliminary hit validations. Next, the resulting hit compounds were tested for purity by liquid chromatography/mass spectrometry (LC/MS), assessed for solubility in culture media, and used for dose-response studies in LUAD cell lines and the two non-malignant control lung cell lines WI-38 and NHBE. The human embryonic lung fibroblast cell line WI-38 and normal, human primary bronchial epithelial cell line NHBE were used for counterscreening to favour the identification of selective inhibitors of lung cancer. This additional screen produced a list of 59 compounds which inhibited at least one LUAD cell line at a half maximal inhibitory concentration (IC50) ≤ 1μM. Of these 59 hits, 4 compounds showed a 10-fold selectivity of at least one LUAD line relative to the control cell lines, had accessible analog structures available, lacked structural similarity to ATP, and had a predicted ease of making chemical derivatives. The resulting four compounds were termed LCS1-4 (Somwar et al., 2009). 21 Figure 4. High-throughput screening pipeline Figure adapted with permission from Somwar et al., 2009. 1.6.1 Identification and characterization of LCS1 LCS1 (4,5-dichloro-2-m-tolylpyridazin-3(2H)-one) (Pubchem ID: CID779573) is a pyridazine ring-containing structure (Figure 5). Somwar et al. synthesized a library of 41 LCS1 analogues in a SAR study, to understand the relationship between a chemical structure and its biomolecular activity. The analogues were tested on a broader panel of 16 LUAD cell lines and two structural analogues were also found to have potent anti-proliferative activity in vitro. LCS1 analogue LCS-1.34 induced apoptosis in all three LUAD cell lines that were tested (Somwar et al., 2009). 22 Somwar et al. next sought to characterize LCS1 by determining the cellular response to treatment and identify its protein target(s). By performing transcriptome analysis of two cell lines treated with LCS1, Somwar et al. sought to identify which genes were significantly altered by LCS1 in both cell lines. Secondarily, LCS1 and its analogue LCS-1.28 were immobilized to sepharose beads, which when coupled with affinity chromatography and mass spectrometry, can identify protein interactors in cells sensitive to treatment. The proteins identified by affinity proteomics were assessed using a gene interaction network and resulting proteins were cross-searched against the transcriptome analysis. This work led to the identification of superoxide dismutase 1 (SOD1) as a target of LCS1. It was found that SOD1 expression in LUAD inversely correlates with LCS1 sensitivity and that LCS1-sensitive cells were correspondingly sensitive to siRNA-mediated knockdown of SOD1. Overexpression of SOD1 conferred LCS1 resistance and LCS1 inhibited enzymatic activity of purified SOD1 in vitro (Somwar et al., 2011). This suggests that a subset of LUAD cells are dependent on SOD1 for survival, likely due to its ability to counteract reactive oxygen species (ROS) that is associated with the cancer cell state. Through this work, Somwar and colleagues integrated affinity proteomics and gene expression analysis to identify SOD1 as a bonafide target of LCS1 and shed light on how LUAD is reliant on SOD1 for survival. 23 Figure 5. Molecular structure of LCS1 Note that the structure of LCS1 satisfies the RO5. It has a molecular weight of 255 Da (<500 Da), a cLogP of 2.89 (<5), less than 5 H-bond donors, and less than 10 H-bond acceptors. 1.6.2 Identification of LCS3 Somwar et al. also identified LCS3 (N-(4-chlorophenyl)-5-nitrofuran-2-carboxamide) (Pubchem ID: CID844044), a heterocyclic compound belonging to the 5-nitro-n-phenylfuran-2-carboxamide chemical class (Figure 6) (Somwar et al., 2009). LCS3 has a cLogP of 2.47, a molecular weight of 267 Da, less than 5 H-bond acceptors and less than 10 H-bond acceptors; thus, LCS3 satisfies the Lipinski’s RO5. LCS3 has not yet been characterized, which provides an opportunity to potentially define a new dependency in LUAD cells. Figure 6. Molecular structure of LCS3 24 1.7 Drug targets beyond protein Most therapeutics in clinical use and development seek to target proteins. Enzyme active sites, ion channel proteins and membrane receptors are among the most common drug targets. However, other biomolecules have also been proposed as targets. Carbohydrates are very important to cellular biological functions such as molecular recognition and adhesion; however they do not typically have ideal small molecule binding pockets (Giorgi et al., 2014; Muramatsu and Muramatsu, 2004). Cisplatin is an approved therapeutic which exerts its activity mainly through the targeting of nucleic acids in proliferative cells. Within cells, cisplatin is converted into a species that is more reactive with nucleophiles and covalently crosslinks DNA. Proliferating cells are unable to repair the resulting adducts, such as guanine-guanine crosslinks, and the unresolved DNA lesion triggers the apoptotic signaling cascade. Cisplatin also reacts with other nucleophiles—particularly mitochondrial and sulfydryl group-containing proteins—and therefore the bioactivities of cisplatin are numerous, complex, and associated with severe side effects (Fuertes et al., 2003; Lowe, 2018). Other DNA cross-linking agents such as doxorubicin exist and none are considered sequence specific; thus, these agents are not recognized as targeted therapy. While gene therapy applications are becoming advanced, these therapies are nucleic acid-based and do not rely on the selectivity of small molecules to target specific proteins. Radiotherapy is a widely used treatment for cancer patients that uses high doses of ionizing radiation (IR) to kill tumour cells. IR induces double strand breaks that cause the activation of cell cycle checkpoints and senescence in proliferative cells (Lei et al., 2020a). The radiolysis of water by IR results in oxidase-mediated ROS generation, which can cause 25 lethal levels of cellular damage (Lei et al., 2020a). Radiotherapy is an example of therapy that exploits non-oncogene dependencies in cancer in a mechanism which does not involve the direct targeting of proteins. 1.8 Thesis theme and rationale for study The high-throughput phenotypic small molecule screen conducted by Somwar et al. was successful in identifying four novel small molecules that selectively inhibit the proliferation of lung cancer cells. The structure of LCS3 is unpublished and its activity has not been described; thus, characterizing LCS3 can potentially reveal a novel cancer dependency or establish a small molecule candidate for further preclinical development. 1.9 Objective and Hypothesis The objective of this work is to characterize the novel small molecule LCS3 and determine its mechanism of action to understand if it can potentially be exploited for the benefit of lung cancer patients. I hypothesize that defining the molecular targets and mechanism of action of LCS3 in LUAD cells can reveal targetable molecular processes for the treatment of lung cancer. 26 1.10 Specific aims and thesis outline This work mainly comprises a manuscript published by the same title as this dissertation. I have divided this thesis body into three experimental chapters to address each of the three aims, and a final summary chapter. Aim 1: Characterize cellular sensitivities and responses to LCS3. In Chapter 2, I characterize the effect of LCS3 at selectively inducing cell death in LUAD cells. This characterization is facilitated by analysis of the transcriptomic and proteomic changes of LUAD cells upon LCS3 treatment. It identifies the NRF2 pathway and response to oxidative stress as predominant cellular changes in response to LCS3. It demonstrates the generation of reactive oxygen species (ROS) in sensitive LUAD cells. Aim 2: Identify the molecular target(s) of LCS3. The work presented in Chapter 3 utilizes thermal proteome profiling to identify protein interactors with LCS3. Through this method, I identify and validate the disulfide reductases GSR and TXNRD1 as bonafide LCS3 molecular targets. I demonstrate that LCS3 inhibits GSR and TXNRD1 through a reversible, uncompetitive mechanism of inhibition. I conducted a SAR study to understand which molecular subgroups confer LCS3 activity and provide a molecular docking analysis. Lastly, I demonstrate that downstream molecular events of GSR and TXNRD1 are inhibited in LUAD cells. 27 Aim 3: Characterize the mechanism of LCS3 activity. In Chapter 4, I demonstrate that LCS3-sensitive LUAD cells are sensitive to the synergistic inhibition of glutathione and thioredoxin pathways. I identify ROS as a prerequisite for LCS3 sensitivity and demonstrate that cancer cell death by oxidative stress and concomitant disulfide reductase inhibition is an example of synthetic lethality. I performed a genome-wide CRISPR-Cas9 knockout screen and identify NQO1 loss as a mechanism of LCS3 resistance. In Chapter 5, I conclude this dissertation by describing the significance of my work, assessing if the aims were achieved, and describing limitations and possible avenues for further investigation. The overall approach taken in this work is illustrated in Figure 7. 28 Figure 7. Proposed target discovery strategy and characterization of LCS3 Chapter 4 29 Chapter 2: Characterization of LCS3 Portions of this chapter have been published as: Johnson, F. D., Ferrarone, J., Liu, A., Brandstädter, C., Munuganti, R., Farnsworth, D., Lu, D., Luu, J., Sihota, T., Jansen, S., Nagelberg, A., Shi, R., Forcina, G., Zhang, X., Cheng, G.S.W, Spencer Miko S.E., de Rappard-Yuswack, G., Sorensen, P.H., Guha, U., Becker, K., Djaballah, H., Somwar, R., Varmus, H., Morin, G.B., Lockwood, W.W. (2022). Characterization of a small molecule inhibitor of disulfide reductases that induces oxidative stress and lethality in lung cancer cells. Cell Reports 38, 110343. 2.1 Introduction Somwar and colleagues identified LCS3 and found it to inhibit the proliferation of four LUAD cell lines, but not primary human bronchiolar epithelial cells (Somwar et al., 2009). However, since the identification of LCS3, it has not been characterized in further detail. The compound LCS1 was similarly identified in the same phenotypic screen and was revealed to induce lethality in LUAD cells by inhibition of SOD1 in a subsequent study, thereby demonstrating the utility of this characterization approach for defining new dependencies in LUAD cells (Somwar et al., 2011). To properly characterize LCS3, I investigated what cell types are most sensitive to LCS3 and how it inhibits proliferation. I aimed to understand the molecular events that transpire in cancer cells upon LCS3 treatment and investigated expression changes through microarray and SILAC analyses. My approach was to use information gained through these analyses to further explore relevant molecular pathways and more fully elucidate the phenotypic effects caused in cancer cells by LCS3. 30 2.2 Investigating cell sensitivities to LCS3 The purpose of screening compounds by phenotype using cancer cells is to find candidates for further preclinical development and/or to identify novel cancer dependencies. During the screening which led to the identification of LCS1 and LCS3, the rationale was to identify compounds that selectively kill cancer, but not normal, lung cells. It is therefore important to validate this screen and better characterize the effect of LCS3 on various cell types. 2.2.1 Lung cell line panel To assess the activity of LCS3 in a wider collection of NSCLC cell lines, the High Throughput Screening Core Facility at the Memorial Sloan Kettering Cancer Center screened a panel of twenty-seven cell lines for susceptibility to the effects of LCS3 on cell proliferation. Relevant oncogenic driver mutations of cell lines used can be found in Appendix A .- Cell lines Cells were exposed to concentrations of LCS3 ranging from 5nM to 10µM for 96 hours, and cell viability was determined using alamarBlue. Consistent with the original study (Somwar et al., 2009), LCS3 selectively inhibited the growth of 24/25 non-small cell lung cancer (NSCLC) cell lines (24 LUAD/bronchioalveolar carcinoma and 1 large cell carcinoma) at low micromolar concentrations (IC50 < 4µM); both of the non-transformed lung cell lines were relatively insensitive (IC50 > 10µM) (Figure 8). 31 Figure 8. Lung cell line sensitivity to LCS3 IC50s determined by cell growth assays using alamarBlue cell viability reagent. Cell lines resistant to LCS3 are in bold. 2.2.2 Clonogenics To corroborate the lung cell line sensitivity results with an orthogonal approach, I performed clonogenic assays using six lung cell lines treated with 1µM LCS3 for nine days. Consistent with the results shown in Figure 8, I found that all LUAD cell lines except HCC2279 were sensitive to LCS3 while the non-transformed lung epithelial line HPL1D was resistant to treatment (Figure 9). 32 Figure 9. Crystal violet staining of LCS3-treated lung cells LCS3 sensitivity determination by crystal violet staining; images are representative of 3 replicates. Cell lines resistant to LCS3 are in bold. 2.2.3 NCI-60 panel To understand the ability of LCS3 to induce cell death across a breadth of cancer cell types, LCS3 was submitted to the National Cancer Institute for NCI-60 human tumour cell lines screening (Figure 10). LCS3 caused toxicity across most types of cancer cell lines, with notable sensitivities in central nervous system and colorectal cancers. These data demonstrate that the ability of LCS3 to inhibit proliferation is not unique to LUAD and may justify investigation in cancers of other tissue origins. 33 Figure 10. NCI-60 cell line panel sensitivity to LCS3 IC50 values for inhibition of proliferation of the NCI-60 cell line panel by LCS3. 2.3 Determination of cytostasis or cytotoxicity I have determined that LCS3 inhibits the proliferation of LUAD cell lines but have not yet resolved if this is due to a cytostatic effect, in which cell division is arrested, or a cytotoxic effect, in which cell death pathways are activated. To resolve this, I will need to assess the viability of cancer cells upon LCS3 treatment. 2.3.1 Scalable time-lapse analysis of cell death kinetics (STACK) To determine the extent to which the loss of cell viability is attributable to inhibition of proliferation or to induction of cell death, I used the scalable time-lapse analysis of cell death kinetics (STACK) method, which pairs live-cell imaging with fluorescence microscopy to detect the onset and rate of cell death (Forcina et al., 2017). I transduced the fusion gene 34 H2B-mCherry by lentivirus into lung cell lines to provide nuclear fluorescence; the resulting cell lines are denoted as H23N, H1650N, H1975N, H358N, HCC2279N, and HPL1DN. SYTOX Green—a nuclear dye which is unable to permeate the cell membrane of viable cells—was added to map the onset of toxicity in response to LCS3 or to a positive control lethal small molecule, the GPX4 inhibitor RSL3 (Figure 11A and Figure 11B). The overlay of mCherry (red) and SYTOX green (green) allows the counting of dead cells against the counted viable cells (red only). I observed a more rapid increase in dead cells (the lethal fraction) in H23N, H1650N, H1975N and H358N cells than in HCC2279N cells or the non-transformed HPL1DN cells at most LCS3 concentrations, indicating that LCS3 inhibits cell growth through a mechanism that is cytotoxic, rather than cytostatic (Figure 11A and Figure 11B). I calculated the onset of cell death (Do) and the rate of cell death (DR) for each cell line (Figure 11C); together, these data suggest that LCS3-sensitive cell lines undergo cell death that is both rapid in onset and synchronous throughout the cell population in vitro. The synchrony of this cell death suggests that the mechanism of cell death may not be cell cycle-dependent, but rather a concerted initiation of cell death in all cells. To resolve this, LCS3 could be compared to cell cycle-dependent chemotherapeutic agents such as cisplatin and etoposide using this STACK system. 35 A B C 36 Figure 11. Cell death kinetics of LCS3-treated cell lines Cell lines resistant to LCS3 are in bold. (A) Lethal fraction of lung cell lines treated with LCS3 determined by overlap of nuclei marker H2B-mCherry and dead cell marker SYTOX Green. Error bars are +/- SEM. (B) Incucyte images taken at 72 hours after treatment with LCS3 or GPX4 inhibitor RSL3 (positive death control). Cell lines expressing nuclei marker H2B-mCherry (red) and treated with dead cell marker SYTOX Green (green). Representative images shown of 4 technical replicates. (C) STACK calculation of Do and DR for 4μM LCS3 in cell lines. 2.3.2 LCS3 causes apoptosis in sensitive LUAD cells I next sought to determine if the cytotoxicity induced by LCS3 occurs through an apoptotic mechanism. To this end, I treated cells with 3µM LCS3 for 96 hours and performed western blots for protein markers of apoptosis. I found that LCS3 increased cleavage of caspase 3, caspase 7 and/or PARP1 in all LCS3-sensitive LUAD cell lines (Figure 12A). I also stained cells for annexin V and confirmed by flow cytometry that LCS3-sensitive, but not LCS3–resistant cell lines undergo apoptosis in response to 3µM LCS3 (Figure 12B). Together, these data suggest that LCS3 selectively kills LUAD cell lines, at least in part through the induction of apoptosis. 37 Figure 12. LCS3 induction of apoptosis in LUAD cell lines Cell lines resistant to LCS3 are in bold. (A) Western blot of apoptotic proteins in lung cell lines treated with LCS3 for 96 hours. (B) Apoptotic cell fraction of LCS3-treated lung cell lines determined by flow cytometry detection of Annexin V stained cells. Error bars are +/- SD; n = 3. 2.4 Gene expression profiling To understand the molecular processes governing the cellular response to LCS3, Dr. Lockwood and I profiled the transcriptome and proteome by RNA microarray and stable isotope labeling of amino acids in cell culture (SILAC) (Zhang et al., 2021). A workflow that is depicted in Figure 13 was used and the results will be described in the following sections. An Affymetrix microarray contains an array of oligonucleotides on a semiconductor chip that hybridize antisense copy RNA derived from mRNA samples (Stears et al., 2003). The copy A B 38 RNA, produced in the presence of biotinylated ribonucleotides, is hybridized to oligonucleotides, washed, labeled with streptavidin-phycoerythrin, and imaged by a confocal scanner to quantify relative transcript abundance in clinical or experimental samples (Stears et al., 2003). SILAC is a quantitative proteomics method that allows the determination of relative protein expression at the proteome level. Growing one experimental cell line condition in media containing normal amino acids (light), and another in media containing isotopically-labeled amino acids (heavy), allows the complete incorporation of heavy amino acids into all peptides of a single experimental condition (Mann, 2006). The purpose of heavy amino acid incorporation is that both samples can be pooled and coupled with mass spectrometry for relative proteome comparison. Peptides in the heavy-labeled sample will experience a known mass shift relative to the light sample, which allows the distinction of peptides derived from each condition. In this project I sought to identify differentially expressed mRNA and proteins upon LCS3 treatment in LUAD cells. The microarray and SILAC experiments were performed by Drs. William Lockwood and Xu Zhang and the analyses for both experiments were performed by me and Alvin Liu. Figure 13. Schematic of expression profiling studies 39 To assess temporal changes in RNA abundance induced by LCS3, two sensitive LUAD cell lines—H23 (KRAS mutant) and H1650 (EGFR mutant)—were treated with 3µM LCS3 for 3, 6 or 12 hours and profiled at each time point with Affymetrix RNA microarrays (Figure 14A). I combined genes that were significantly upregulated by LCS3 relative to the vehicle control of all time points in H23 and H1650 to determine a core set of 71 LCS3-modulated genes. Dr. Xu Zhang performed SILAC in H23 cells treated with 3µM LCS3 for 24 hours (Figure 14B) and I determined a list of genes encoding the proteins that were significantly upregulated (Figure 14C). Three genes were upregulated in both microarray and SILAC datasets: HSPA6, HMOX1, and GCLM. These genes are all characteristic of a response to oxidative stress, which will be elaborated on in the following sections. The genes SLC7A11, SRXN1 were upregulated in the microarray dataset and are also associated with the response to oxidative stress. I then performed a gene ontology (GO) analysis using both the transcriptome and proteome upregulated gene lists to identify biological processes that are affected by LCS3 (Figure 15) (Ashburner et al., 2000). 40 A B C Figure 14. Gene expression of LCS3-treated LUAD cell lines (A) Volcano plots of microarray data showing significant differentially expressed genes in H23 and H1650 cells treated with 3µM LCS3 for 3, 6, or 12 hours relative to control; n = 2. (B) Z-score plot of SILAC data showing significant differentially expressed genes in H23 treated with 3μM LCS3 for 24 hours; n = 2. (C) Venn diagram showing significantly upregulated genes in microarray and SILAC datasets. 41 SILAC Microarray Figure 15. GO analysis Gene ontology analysis of biological processes upregulated by LCS3 determined by transcriptome and proteome profiling. Red bars signify a direct relation to redox homeostasis. 42 The GO analyses of the transcriptome and proteome expression datasets suggest that LCS3 elicits a cellular response to oxidative stress, including pathways responsive to increased levels of reactive oxygen species (ROS). ROS include radicals, ions, or molecules that have an unpaired electron in the valence electron shell, including superoxide, hydroxyl radicals, hydrogen peroxide, peroxyl radicals, and other highly reactive species (Liou and Storz, 2010). It is widely recognized that cancer cells produce increased levels of ROS relative to non-malignant cells, due to irregular mitochondrial, metabolic, and oxidase activities, as well as hyperactive growth signaling (Liou and Storz, 2010; Postovit et al., 2018; Storz, 2005). Cancer cells often lack the means to eliminate ROS; thus they sustain elevated levels of ROS and require the expression of cytoprotective genes to survive (Liou and Storz, 2010). 2.4.1 Enrichr analysis I then queried the computational systems biology research tool Enrichr to predict what proteins control the transcriptional response to LCS3 (Ashburner et al., 2000; Chen et al., 2013). In agreement with the GO analysis, Enrichr predicted that the top four most significant upstream effectors of LCS3-induced transcriptome changes are all proteins that are crucial to the cellular response to oxidative stress (Figure 16A). These regulators, including MAFK, CEBPB, and BACH1, each function as cofactors in the transcriptional activation of nuclear factor erythroid 2-related factor 2 (NRF2), which serves as the master regulator of the cellular response to ROS (Nguyen et al., 2005; Padmanabhan et al., 2006). 43 2.5 NRF2 pathway In a normal cellular state, NRF2 is sequestered by Kelch-like ECH-associated protein 1 (KEAP1). KEAP1 also serves as an adaptor protein for the CUL3-based E3 ubiquitin ligase complex, which through ubiquitinylation targets NRF2 for proteosomal degradation and accelerates NRF2 turnover (Nguyen et al., 2005). Upon oxidative or electrophilic stresses, crucial redox-sensitive KEAP1 cysteine residues become oxidized, impairing the ability of KEAP1 to repress NRF2 (Padmanabhan et al., 2006). The resulting stabilization of NRF2 facilitates its translocation to the nucleus, localization to the antioxidant response element (ARE), and transcription of cytoprotectant genes (Hur et al., 2010; Rushmore and Pickett, 1990) (Figure 16B). 2.5.1 NRF2 pathway activation by LCS3 Using the microarray data, I examined the expression of genes using an established NRF2 gene expression signature (Malhotra et al., 2010) at each time point during LCS3 treatment in H23 and H1650 cell lines. I observed a notable increase in the induction of NRF2-regulated genes (Figure 16C). To confirm the activation of NRF2, I treated H23 and H1650 cell lines with 3µM LCS3 and assessed the expression of NRF2 targets by western blot to measure the abundance of the protein products. I found noticeable increases in the protein levels of NRF2 and of the products of selected downstream targets of NRF2 in both cell lines (Figure 16D). The 44 response was similar to that seen with AI-1, a compound that enhances NRF2 levels by covalently inactivating KEAP1 (Hur et al., 2010). To determine if LCS3 induces enhanced binding of NRF2 to its consensus DNA sequence—the ARE (Rushmore and Pickett, 1990)—upon LCS3 treatment, I introduced an ARE-green fluorescent protein (GFP) reporter into H23 and H1650 cell lines (Figure 16E) and found that LCS3 induced the production of GFP in both cell lines in a dose-dependent manner (Figure 16F). 45 Figure 16. LCS3 activation of the NRF2 pathway (A) Enrichr prediction of upstream transcription regulators of LCS3-induced RNA expression changes. (B) Schematic of NRF2 pathway. (C) Heatmap of NRF2 transcriptome signature in response to 3μM LCS3. (D) Western blot showing induction of NRF2-regulated proteins by 24 hours treatment with LCS3 or KEAP1 inactivator AI-1 (positive control). (E) Schematic of antioxidant response element-green fluorescent protein (ARE-GFP) reporter which was transduced by lentivirus into H23 and H1650 cell lines. (F) GFP expression of ARE-GFP relative to pathway control in response to AI-1 or LCS3. Data shown are the mean + SEM; n = 3. A B C D E F 46 2.6 Generation of ROS Since NRF2 is a principal regulator of redox homeostasis and the response to oxidative stress, I asked if LCS3 increased ROS levels in lung cells. I found that ROS accumulated in LCS3-sensitive cancer cell lines H23 and H1650, but not in the resistant HCC2279 cancer cell line or the non-transformed HPL1D lung cell line (Figure 17). Collectively, these findings indicate that LCS3-sensitive LUAD cell lines respond to LCS3 by accumulating ROS and activating the NRF2 transcription program. Figure 17. LCS3 induction of ROS in sensitive cell lines Flow cytometry with oxidative stress sensor H2DCFDA detecting induction of ROS after 90 minutes of treatment. Representative histograms are shown; n = 3. 47 2.7 Antioxidant rescue I next sought to determine if antioxidant compounds would rescue LCS3-induced cell death. I tested the ability of three antioxidant agents to rescue LCS3 toxicity in H23 cells: N-acetylcysteine (NAC), glutathione monoethyl ester (GSH-MEE), and ferrostatin-1 (FS-1). Reduced glutathione (GSH) is a tripeptide thiol that scavenges ROS upon electrophilic and oxidative stresses (Ruffmann and Wendel, 1991). GSH-MEE is efficiently transported into the cell and converted to GSH by cellular esterases (Tsan et al., 1989), becoming available to scavenge ROS. NAC increases intracellular cysteine concentration, which often limits the biosynthesis of cellular GSH (Ruffmann and Wendel, 1991). FS-1 is a lipophilic antioxidant that has an N-cyclohexyl subgroup that serves as a lipophilic anchor within cellular membranes (Dixon et al., 2012). All three antioxidants reduced the toxicity of LCS3 in H23 cells, suggesting that ROS and/or glutathione depletion play a role in LCS3-mediated LUAD toxicity (Figure 18). Figure 18. Rescue of LCS3 toxicity by antioxidants Proliferation rescue experiment of LCS3-induced cell death in sensitive cell line H23 by antioxidants. Incucyte data shown are the mean +/- SEM and representative of two independent experiments. 48 2.8 Conclusions In this chapter, I have outlined my approach to characterize the phenotype elicited in lung cancer cells by LCS3. Through combining cell line screen and STACK experiments, assays for apoptosis, expression profiling at the transcriptome and proteome levels, NRF2 reporter experiments, and ROS detection assays, I characterized the LUAD cellular response to LCS3. I determined that LCS3 selectively induces lethality, apoptosis and oxidative stress in lung cancer cells and activates the NRF2 pathway. Only three genes were overexpressed in both the microarray and SILAC datasets—HSPA6, HMOX1, and GCLM. Both HMOX1 and GCLM are present in the NRF2 gene signature and HSPA6 is a heat shock protein known to be upregulated in response to oxidative stress to act as a molecular chaperone for oxidase enzymes (Liao et al., 2021; Malhotra et al., 2010). Thus, it is noteworthy that all three genes significantly upregulated in both datasets are associated with the response to oxidative stress. The discrepancy between mRNA and protein expression can be explained by numerous dynamic post-transcriptional and post-translational regulatory mechanisms. Such processes include RNA processing, miRNA-mediated mRNA degradation, RNA-binding protein-mediated regulation of mRNA translation, and protein degradation through the ubiquitin-proteasome pathway, that cooperatively affect the multi-step flow of genetic information and contribute to the decoupling of mRNA and protein expression (Huntzinger and Izaurralde, 2011; Moore and von Lindern, 2018; Tai and Schuman, 2008; Wu et al., 2020). Collectively, the work presented in this chapter demonstrates that global expression profiling can be successfully applied to uncover molecular changes upon treatment of novel compounds identified by high-throughput drug screens. 49 2.9 Methods Cell line-LCS3 screens Determination of IC50 values of the lung cell line panel was performed using the fluorescent dye alamarBlue as a readout of cell proliferation. Cell lines were fingerprinted by STR profiling to confirm identity and mycoplasma tested prior to use. LCS3 was submitted to the Developmental Therapeutics Program at the National Cancer Institute for assessment of growth inhibition in the NCI-60 cell line panel using the established NCI workflow (https://dtp.cancer.gov/discovery_development/nci-60/default.htm). Crystal violet stain Ten thousand cells were seeded in triplicate into 6-well plates and then treated the following day with 0.1% DMSO or 1µM LCS3. After nine days, cells were washed twice in ice-cold PBS before fixed in ice-cold methanol at room temperature for 10 minutes. Cells were then stained with 0.5% (vol/vol) crystal violet solution at room temperature for 10 minutes, on a rotating shaker. Once stain was completed, cells were rinsed with PBS thrice and left overnight to air dry. Incucyte cell growth experiments Cells were seeded in clear 96-well plates at 4,000 cells per well. All experiments were seeded in at least triplicate wells except for the antioxidant rescue experiment in Figure 18, which was seeded in duplicate. One day after seeding, media containing drug treatments 50 was added to each well and plates were placed in an Incucyte S3 live-cell imaging system within an incubator maintained at 37ºC with 5% CO2. Images were taken at specified time intervals. For STACK experiments—H23N, H1650N, H1975N, H358N, HPL1DN, were seeded into 96 well plates. Twenty-four hours after seeding, cells were treated with 20nM SYTOX Green and 10µM RSL3 or 500nM-16µM LCS3. STACK analysis was performed by determining the lethal fraction of each cell line under each condition, determined by counting the dead cells (green and red overlap) and dividing by the total cells (red objects). Onset of cell death (Do) and the rate of cell death (DR) for each cell line were determined by fitting a plateau followed by one phase association model with GraphPad Prism. Constraints used were Xo >1, Yo >1, 0 <plateau >1 and K >1. Xo was represented as Do and K*100 was represented by DR. Western blotting Cells were harvested and washed with ice-cold PBS before being scraped in PBS, transferred into 15mL conical tubes and centrifuged at 1500rpm for 5min. Cells were resuspended in RIPA lysis buffer supplemented with HALTTM protease and phosphatase inhibitors. After transfer to 1.5mL microfuge tubes and lysis sonication, cells were centrifuged at 15,000 rpm at 4ºC for 5 minutes and lysates were then transferred to a new tube. Protein concentrations were determined by BCA assay as directed by the manufacturer and samples were prepared with Laemmli sample buffer, 100mM DTT and heating at 95ºC on a dry block heater for 5 minutes. Samples were separated by SDS-PAGE on a 4-12% Bis Tris gel and transferred onto a polyvinylidene fluoride membrane. 51 Membranes were blocked with 5% bovine serum albumin and incubated overnight at 4ºC with primary antibodies in 5% BSA in TBS-T. Following incubation with primary antibodies, membranes were washed three times for five minutes with TBS-T and incubated for one hour with 1:15,000 HRP-linked anti-rabbit or anti-mouse antibodies or 1:15,000 DyLight anti-rabbit IgG 800 conjugate. Membranes were then washed three times for five minutes with TBS-T and imaged with a Bio-Rad ChemiDoc MP imager or in a dark room on x-ray film. If HRP was used, either Pico or Fempto Chemiluminescent substrate was added prior to imaging. Annexin V assay 100,000 cells of each cell lines were seeded into 6-well plates. The day after seeding, cells were treated with 0.1% DMSO or 3µM LCS3 in triplicate. After 48 hours, cells were collected by trypsinization and prepared for flow cytometry according to manufacturer’s instructions. Transcript expression profiling (microarray) H23 and H1650 cells were treated with DMSO for 3 or 6 hours in duplicate, or 3µM LCS3 for 3, 6, or 12 hours in duplicate, and profiled using the Affymetrix Human GeneChip 1.0 ST array platform as previously described (Lockwood et al., 2012). Raw data were normalized by robust multiarray analysis and a generalized linear model was applied via the rma and limma packages respectively (Irizarry et al., 2003; Ritchie et al., 2015). Genes with a Benjamini-Hochberg adjusted p-value <0.05 in the LCS3 treated groups versus vehicle 52 control were considered differentially expressed. Significantly upregulated genes in all treatment conditions were pooled and submitted to DAVID Bioinformatics Database v6.8 for functional analysis (Huang da et al., 2009). I also used this upregulated transcript gene set to search the computational systems biology research tool Enrichr ENCODE TF ChIP-seq 2015 query function to predict the upstream regulators of LCS3-induced transcriptome changes (Ashburner et al., 2000; Chen et al., 2013). The transcriptome dataset has been uploaded to NCBI Gene Expression Omnibus (GSE178311). Protein expression profiling (SILAC) H23 cells were cultured either in normal media (light) or media with Lys and Arg substituted with 13C615N2 Lys and 13C6 Arg (heavy). One replicate of each treatment condition was cultured in light media and one in heavy. Cells were maintained in label-containing media and passaged until 99% labeling efficiency was achieved. Cells were treated in duplicate (one heavy and one light) with 3µM LCS3 for 24 hours and then harvested by cell scraping. Samples were mixed 1:1, digested with trypsin, and prepared for mass spectrometry as previously described (Zhang et al., 2017; Zhang et al., 2021). The SILAC proteome dataset has been uploaded to PRIDE as project accession PXD027228. Normalized Z-scores were calculated for each identified protein replicate based on its ratio of treatment-to-vehicle (Yang et al., 2015). Z-score values were statistically combined by Stouffer’s Z method. Proteins with Z > 1.5 were considered differentially expressed. The 53 upregulated proteins were entered as input into DAVID Bioinformatics Database v6.8 to determine upregulated biological processes by gene ontology (Huang da et al., 2009). Antioxidant Response Element reporter assay The ARE-GFP lentivirus and pathway control-GFP lentivirus were transduced into H23 and H1650 cells with 8µg/mL polybrene and selected with blasticidin. Two hundred thousand cells of H23 and H1650 pathway control-GFP and ARE-GFP cells were seeded into 6-well plates. One day after seeding, cells were treated with DMSO (0.1%), AI-1 (10µM), or LCS3 (1-3µM) in triplicate. After 72 hours, cells were washed with PBS, trypsinized and analyzed by flow cytometry. Relative GFP to pathway control is the mean MFI (FL-1) of each sample condition relative to the mean DMSO-treated pathway control. Detection of ROS In 6-well plates, cells grown to 90% confluence were treated in triplicate with inhibitors. Thirty minutes into treatment, 10µM H2DCFDA was added as co-treatment to each well. After 1 additional hour, cells were washed twice with PBS, trypsinized, and stained with 1µg/mL propidium iodide. H2DCFDA fluorescence (FL-1) was detected in propidium iodide-negative cells (FL-3) by a FACSCalibur Flow Cytometer. 54 Cell lines Cell line information including sourcing, sex, mutation status, and media requirements are detailed in Table 4. All media were supplemented with 1% penicillin-streptomycin and FBS concentrations specified on Table 4. Cells were passaged by washing with Dulbecco’s phosphate buffered saline (PBS), incubation with trypsin-EDTA (0.25%) until cells became detached from cell culture plates, and then transfer by pipette into cell culture plates with media. Cells were maintained in a humidified incubator at 37ºC and 5% CO2. All cell lines were routinely tested for mycoplasma contamination to ensure negativity. H23N, H1650N, H1975N, H358N, HPL1DN, HCC2279N cells were generated from parental lines by the transduction of pLenti6-H2B-mCherry by lentivirus (gift from Torsten Wittman) (Pemble et al., 2017). Lentivirus was produced by transfection of 293FT cells with lentiviral construct in combination with psPAX2 and pMD2.G packaging and envelope plasmids (gift from Didier Trono) using Lipofectamine 3000 transfection reagent. Media was changed the next day and lentivirus-containing supernatant was collected the following day. This virus-containing media was then syringed through a 0.45µm filter and combined with 8µg/mL polybrene. This media was then added to parental cells the day after seeding at 50% confluence. After 24 hours exposure to lentivirus media, cells were passaged into 2µg/mL puromycin selection for 3 days. 55 Chapter 3: Discovery of protein targets of LCS3 Portions of this chapter have been published as: Johnson, F. D., Ferrarone, J., Liu, A., Brandstädter, C., Munuganti, R., Farnsworth, D., Lu, D., Luu, J., Sihota, T., Jansen, S., Nagelberg, A., Shi, R., Forcina, G., Zhang, X., Cheng, G.S.W, Spencer Miko S.E., de Rappard-Yuswack, G., Sorensen, P.H., Guha, U., Becker, K., Djaballah, H., Somwar, R., Varmus, H., Morin, G.B., Lockwood, W.W. (2022). Characterization of a small molecule inhibitor of disulfide reductases that induces oxidative stress and lethality in lung cancer cells. Cell Reports 38, 110343. 3.1 Methods of small molecule target discovery Failure to identify or characterize the molecular targets of novel anti-cancer compounds has been a major limitation of phenotype-based chemical library screens. One of the most widely used approaches in target discovery is affinity proteomics, which involves the covalent linking of a derivatized small molecule to an immobilized matrix—such as magnetic or carbohydrate beads—coupled with affinity chromatography and mass spectrometry (Somwar et al., 2011). The principal disadvantage with this approach is that it involves modifying the native structure of the compound of interest and the resulting shift in chemical properties may impact drug-protein interactions (Kubota et al., 2019). It is difficult to distinguish protein binders from effectors using this method, as no metric of affinity exists and because affinity chromatography often isolates a large number of protein interactors. This method also requires numerous stringent washes, which may lead to the loss of potential targets. 56 3.1.1 Thermal proteome profiling TPP entails subjecting intact cells or protein lysates, in the presence and absence of a small molecule, to a temperature gradient to precipitate insoluble heat-denatured proteins. The soluble fraction of protein is then recovered and the abundance of protein is quantified by performing tandem mass spectrometry (Franken et al., 2015; Miettinen et al., 2018; Savitski et al., 2014). It is expected that proteins interacting with the compound of interest will be stabilized or destabilized, leading to a change in the temperature at which the target protein denatures and precipitates out of solution. TPP quantifies the heat denaturation curve displacement for thousands of individual proteins—a displaced denaturation curve indicates the protein is a potential binding partner of the small molecule. In this way, TPP exploits the thermodynamic changes to protein stability that occur upon binding to a ligand or small molecule and can be used to identify even rare proteins that are thermally affected by such interactions. 3.2 LCS3 TPP After establishing that the cellular response to LCS3 includes apoptosis and oxidative stress, I sought to identify molecular targets of LCS3 through a proteomics-based approach. I used TPP to identify proteins in H23 cell lysates that formed thermally-stable complexes with LCS3 (Franken et al., 2015)—the workflow is illustrated in Figure 19A. A total of 5,593 proteins were detected by mass spectrometry in the lysates, and I employed the non-parametric analysis of response curves (NPARC) (Childs et al., 2019) to rank them according to their ability to form heat-stable complexes with LCS3 Figure 19B. This strategy 57 prioritized 77 proteins involved in diverse cellular functions, including cell structure and motility, RNA processing, and metabolism (Figure 19C and Figure 19D). Denaturation curves were plotted for all identified proteins and the top 20 proteins by NPARC (adjusted p-value significance) are shown in Figure 20. The ‘oxidation-reduction process’ GO category contained the most proteins on this list and was ranked the most significant; in view of the oxidative stress observed after addition of LCS3, the effector target protein(s) seemed likely to belong to this functional class. Three of the most highly ranked candidates within this category were glutathione-S-transferase omega-1 (GSTO1; ranked 1st overall), thioredoxin reductase 1 (TXNRD1—ranked 2nd), and glutathione disulfide reductase (GSR—ranked 6th); all of which showed greater thermal stabilization relative to the median proteome than most other proteins (Figure 19). GSTO1 is known to play a protective role against oxidative stress by adding glutathione to protein thiols that are otherwise vulnerable to irreversible oxidation (Menon and Board, 2013). The disulfide reductases TXNRD1 and GSR are central to the thioredoxin and glutathione antioxidant systems, which perform diverse cellular roles; most notably, they facilitate the reduction of ROS and oxidized cellular proteins (Winyard et al., 2005). These and other proteins ranked with high significance by TPP and their relation to redox pathways is illustrated in Figure 21. Each of these TPP hit proteins functions uniquely in the maintenance of redox homeostasis, may plausibly interact with LCS3, and deserves further investigation as potential drug targets. 58 Figure 19. LCS3 TPP (A) Schematic workflow of TPP methodology; n = 2. (B) TPP NPARC analysis. Proteins with high F-statistic and p-value < 0.01 identify LCS3-interactor candidates. (C) Functional classification groups of protein hits. False discovery rate-adjusted p-values (FDR) of significant biological processes are shown. (D) Overview of function classification and hit proteins in redox homeostasis category. A C B C 59 -log adj p: 10 -log adj p: 9.4 -log adj p: 6.6 -log adj p: 7.7 -log adj p: 4.5 -log adj p: 4.5 -log adj p: 4.5 -log adj p: 8.3 -log adj p: 4.6 -log adj p: 4.6 -log adj p: 4.9 -log adj p: 4.5 -log adj p: 4.4 -log adj p: 3.9 -log adj p: 3.6 -log adj p: 3.3 -log adj p: 3.4 -log adj p: 3.5 -log adj p: 3.8 -log adj p: 3.6 60 3.2.1 Candidate selection Following a TPP experiment, NPARC determines a list of proteins which exhibit differential thermal denaturation between treatment conditions. I defined proteins with an NPARC p-value < 0.01 as significant, as this criteria for selection yielded 77 hits. Primary attention was given to proteins which were most significantly ranked (top ten) and had denaturation curves that were robust (good non-parametric curve fit and agreement between replicates) and that function in maintaining cellular redox homeostasis. Many of the TPP hits fall into common redox pathways and a redox pathway schematic of the TPP hits is illustrated in Figure 21. Briefly, several of the TPP hits intersect with either the core glutathione pathway (GSR, GPX4)—which is dependent on the intracellular uptake of cysteine and its biosynthesis into glutathione—or peripheral glutathione pathways (GSTO1, PTGES2). TXNRD1 and PRDX4 function in the thioredoxin redox cycle. PYCR1, MTRR and HK2 are mitochondrial localized and serve diverse biochemical functions. My investigation of TPP hits is described in the following sections. Figure 20. TPP denaturation curves Normalized denaturation curves of median proteome and top 20 TPP hits by NPARC significance are shown. The negative log of adjusted p-values are shown in red. 61 Figure 21. Redox pathway schematic of TPP hits 3.2.1.1 Glutathione-S-transferase omega 1 (GSTO1) I examined the ability of LCS3 to interact with the top-ranked TPP hit, GSTO1 and the enlarged denaturation curve is shown in Figure 22. Figure 22. GSTO1 thermal denaturation curve 62 I was unable to obtain purified enzymatically active GSTO1 to assay its activity in vitro in the presence of LCS3; however, previous work has shown that GSTO1 inhibitors can be screened by their ability to bind to the catalytically active site. The cell-permeable fluorescent probe 5-chloromethylfluorescein diacetate (CMFDA) irreversibly binds the GSTO1 active site, and competition for the site by small molecules diminishes in-gel fluorescence (Ramkumar et al., 2016). I performed an in-gel fluorescence assay by pre-incubating purified GSTO1 with LCS3 or the known GSTO1 inhibitor C1-27 as a positive control (Ramkumar et al., 2016). C1-27, but not LCS3, prevented binding of CMFDA, implying that LCS3 does not occupy the GSTO1 active site and is thus unlikely to be an inhibitor of the enzyme (Figure 23A). I also tested the responses of LCS3-sensitive and LCS3-resistant cell lines to C1-27 and did not observe any differential sensitivities to C1-27 among cell lines (Figure 23B), suggesting that LCS3 and C1-27 exert toxicity by different means. Thus, I hypothesized that although GSTO1 may bind LCS3 due to its xenobiotic metabolism function or protein similarity to true LCS3 effectors, it is unlikely to mediate the cell inhibitory effects of LCS3. 63 Figure 23. Investigation of LCS3-GSTO1 interaction (A) In-gel fluorescence assay (top section) to determine GSTO1 active site occupancy by LCS3 and positive control GSTO1 inhibitor C1-27. Lower section is a western blot showing total GSTO1 in each lane. (B) Incucyte proliferation experiment of both LCS3-sensitive and LCS3-resistant cell lines treated with either LCS3 or GSTO1 inhibitor C1-27. A B 64 3.2.1.2 Disulfide reductases GSR and TXNRD1 The results with GSTO1 prompted the examination of the other TPP screen hits as potential targets of LCS3. The second-ranked candidate from the TPP study, TXNRD1, is structurally and functionally similar to another candidate, GSR—ranked 6th. The relation of these enzymes will be discussed in the following section and enlarged denaturation curves of both proteins are shown in Figure 24. Figure 24. TXNRD1 and GSR thermal denaturation curves Together, GSR and TXNRD1 are essential components of the two systems that regulate the cellular redox state and other diverse biological processes through thioredoxin and glutathione. TXNRD1 and GSR possess FAD- and NADPH-dependent disulfide reductase activity and reduce thioredoxin (TRX) and glutathione disulfide (GSSG) substrates, respectively. After reduction, TRX and GSH participate in many reactions that scavenge ROS and reduce cellular substrates (Hanschmann et al., 2013). The antioxidant activity of TRX is primarily through reducing peroxiredoxin proteins, which are among the most abundant proteins in mammalian cells (Lu and Holmgren, 2014). In addition, GSH is a versatile adaptor molecule and an essential cofactor for enzymes that require thiol-reducing 65 equivalents, particularly in those with redox-active cysteines in their catalytic sites (Deponte, 2013). These disulfide reductases are evolutionarily linked, as metazoan TXNRD1 is homologous to GSR and not the ancestral thioredoxin reductases found in archaea, eubacteria, protists, fungi and plants (Miller et al., 2018). GSR has conserved amino acids that interact with cofactors FAD and NADPH and all GSR residues that interact with glutathione are conserved in TXNRD1, despite the inability of TXNRD1 to reduce glutathione as substrate (Sandalova et al., 2001). Both enzymes function as homodimers and their active sites are situated at the homodimer interfaces (Sandalova et al., 2001). Sandalova and colleagues aligned the amino acid sequence of rat thioredoxin reductase with human GSR and found that one third of the homodimer interfaces were conserved between the two proteins, which highlights their structural similarity (Sandalova et al., 2001). In a similar fashion, I aligned human TXNRD1 and GSR and found 35.6% overall sequence similarity and conservation in many of the amino acid residues predicted to have functional significance by Sandalova and colleagues (Figure 25). The quaternary protein structures of TXNRD1 and GSR are also highly similar, suggesting that a small molecule such as LCS3 could plausibly interact with both structures (Figure 26). I previously showed that the monoethyl ester form of glutathione, GSH-MEE, with its efficient uptake by cells, was able to partially rescue cells from LCS3-induced toxicity, suggesting that GSH levels may be important in regulating the response to LCS3 (Figure 18). In addition, TXNRD1 and other glutathione synthesis genes (GCLM, GCLC) were transcriptionally upregulated in response to LCS3 (Figure 16), which may indicate compensational gene regulation in response to pathway disruption. 66 Figure 25. Amino acid sequence alignment of human TXNRD1 GSR This figure is inspired from Sandalova et al, 2001. Possible functions of individual residues of rat TXNRD1 and human GSR were predicted by Sandalova et al, 2001 and those predictions are integrated into this figure. Blue dot, flavin binding; red dot, NADPH binding; green dot, participation in dimer interface; pink dot, active site. Figure and legend adapted with permission from Sandalova et al., 2001. Copyright 2001 National Academy of Sciences. 67 Figure 26. GSR and TXNRD1 three dimensional structures The human GSR (PDB 1XAN) and human TXNRD1 (PDB 2CFY) three dimensional structures are presented. Both dimer structures are displayed with two FAD ligands. GSR structure also shows the xanthene inhibitor (middle ligand). 3.3 LCS3 inhibits GSR and TXNRD1 I next determined whether GSR and TXNRD1 are direct binding targets of LCS3 by performing in vitro assays with purified protein. Both enzyme reactions catalyze the transfer of electrons from NADPH to either GSSG or Ellman’s reagent (DTNB), which has a disulfide bond that can be reduced by TXNRD1, replacing the requirement for a purified TRX substrate (Carlberg and Mannervik, 1985; Gromer et al., 1998). All enzymatic assays were performed by either me or Dr. Christina Brandstädter. Both GSR and TXNRD1 were 68 inhibited by LCS3 (IC50 = 3.3µM and 3.8µM, respectively) (Figure 27A). I also sought to establish the mechanism of enzymatic inhibition by LCS3. Pre-incubation of an enzyme with a covalent inhibitor increases enzymatic inhibition (indicated by a lower IC50) and I used this strategy to assess the reversibility of the LCS3-protein interaction. As a positive control, I pre-incubated GSR with the covalent disulfide reductase inhibitor 2-AAPA (50µM); as anticipated, the longer the pre-incubation period, the greater the enzymatic inhibition. In contrast, pre-incubation with 3µM LCS3 did not affect the amount of inhibition, suggesting that LCS3 inhibits GSR by a reversible mechanism (Figure 27B). 69 Figure 27. LCS3 inhibits GSR and TXNRD1 (A) In vitro enzymatic assays of purified GSR and TXNRD1 in the presence of LCS3. ‘BLANK’ signifies no inhibitor was present. Shown data are representative of two independent experiments. (B) Pre-incubation assay with purified GSR and specified pre-incubation durations with covalent GSR inhibitor 2-AAPA (positive control) and LCS3. DMSO sample is included in both graphs. Error bars are +/- SEM of one representative experiment. 3.4 Enzyme kinetics of LCS3 inhibition To understand how LCS3 affects the enzyme kinetics of GSR and TXNRD1, Dr. Christina Brandstädter performed enzyme kinetics assays and I generated Lineweaver-Burk plots (Figure 28). Plotting the reciprocal of enzymatic rates and the reciprocal of substrate concentrations for each concentration of LCS3 produced lines of best fit that are parallel—a strong indication that LCS3 acts on both GSR and TXNRD1 through an uncompetitive inhibitory mechanism (Yoshino and Murakami, 2009). This suggests that LCS3 does not A B 70 compete directly with the cellular substrates of these enzymes in their free state, but rather inhibits the enzymes following the formation of an enzyme-substrate complex (Waldrop, 2009). These data suggest that LCS3—as an uncompetitive inhibitor—preferentially binds the disulfide reductases at predominantly high concentrations of oxidized substrates, GSSG or DTNB, and does not bind the enzymes in the absence of substrate. Figure 28. Lineweaver-Burk plots of GSR and TXNRD1 Shown data are representative of two independent experiments. 3.5 Molecular docking Based on these observations, my collaborator Dr. Ravi Munuganti performed in silico molecular docking studies to predict how LCS3 physically interacts with GSR and TXNRD1. The molecular docking simulation predicts that the nitro group of LCS3 forms a crucial hydrogen bond with GSR at His126 (Figure 29). Moreover, the furan moiety of LCS3 is predicted to form an arene-arene electrostatic interaction with nearby Phe122. Additional hydrophobic contacts were predicted between LCS3 and Val118, His119 and Tyr451. Enzymatic activity of GSR is dependent on its homodimer formation, in which a side-chain 71 extending from each subunit binds and processes the GSSG substrate (Sandalova et al., 2001). This homodimerization is indispensable for GSR activity and a strong hydrogen bond interaction has been described between the imidazole rings of His119—His126’ (Karplus and Schulz, 1987). The molecular docking data suggest that the LCS3 nitro subgroup displaces the His119—His126’ hydrogen bond between subunits. I theorize that this displacement, in addition to the other LCS3-GSR interactions noted earlier, confer the ability of LCS3 to inhibit enzymatic activity. In a similar fashion, TXNRD1 homodimerizes to perform its catalytic function and LCS3 is predicted to strongly bind the side chain residues responsible for homodimer formation. The nitro group of LCS3 is predicted to form a hydrogen bond with Trp561 of TXNRD1 and LCS3 is also predicted to interact via hydrogen bond formation with Asp 232 and an arene-cation interaction with Arg566 (Figure 29). Critical hydrophobic contacts are predicted between LCS3 and Leu225, Ala229 and Tyr236. Arg566 is situated at the homodimer interface on a segment which functions as a guiding bar to stabilize the flexible C-terminal arm during electron transfer to the TRX substrate (Fritz-Wolf et al., 2011). Arg566 has been reported to form hydrogen bonds with Asp232 (Fritz-Wolf et al., 2007), and the arene-cation and hydrogen bond formation of LCS3 with both these residues may disrupt this structural interaction. Together, the molecular docking data predict that LCS3 forms molecular interactions with residues in the homodimer interfaces of both GSR and TXNRD1 and suggest that the reversible occupancy of these intermolecular cavities by LCS3 disrupts disulfide reductase activity. 72 Figure 29. In silico docking of LCS3 into GSR and TXNRD1 Schematic of LCS3 arene-arene and arene-cation interactions with amino acid residues of GSR and TXNRD1, respectively. Figure was generated by Ravi Munuganti. 3.6 Structure-activity relationship In light of the docking analysis that indicated how LCS3 likely interacts with GSR and TXNRD1, Dr. William Lockwood and I probed the structure-activity relationship (SAR) between the chemical structure of LCS3 and its anti-proliferative activity. The High Throughput Screening Core Facility at the Memorial Sloan Kettering Cancer Center conducted a cell-based sensitivity screen to investigate how LCS3 and 26 structural analogues (Table 2) inhibit the growth of a panel of lung cell lines. These compounds are identified by internal Sloan Kettering Identifiers (SKI) and were not purchased through a vendor. It was revealed that only compounds that possess the nitro functional group exhibit toxicity in LUAD cells (Figure 30A), demonstrating the necessity of this moiety for the inhibition of cell growth caused by LCS3. I next evaluated if the nitro subgroup is essential for enzymatic inhibition of GSR by an in vitro enzymatic assay with purified protein. I found that a high concentration of LCS3 (20µM) abolished nearly all GSR activity, but the same concentration of an LCS3 analogue (N-(4-chlorophenyl)furan-2-carboxamide; PubChem ID 73 CID790315) lacking the nitro subgroup did not inhibit GSR (Figure 30B). Thus, these data suggest that the nitro functional group of LCS3 is essential for its ability to inhibit enzymatic activity and subsequently elicit death of cancer cells. These data also support the molecular docking analysis, which predicts that the nitro subgroup of LCS3 forms crucial hydrogen bonds with GSR His126 and TXNRD1 Trp561. 74 Table 2. LCS3 derivative structures used in SAR study Compound identifiers are Sloan Kettering Institute (SKI) identification numbers used by the High Throughput Screening Core Facility at Memorial Sloan Kettering Cancer Center. PubChem ID: CID790315 75 Figure 30. LCS3 structure-activity relationship (A) Proliferation screen of 23 NSCLC lines treated with LCS3 and 26 structural analogues. Nitro subgroup-containing compounds are indicated by a green box and compounds lacking a nitro subgroup are indicated by a red box. (B) In vitro enzymatic GSR activity assay of LCS3 and the inactive analogue that lacks the nitro functional group. Data shown are of a single replicate experiment, which is representative of 3 independent experiments. Substrate concentrations: 800µM NADPH, 8mM GSSG. PubChem ID: CID790315 A B 76 3.7 Inhibition of downstream molecular products I then assessed whether LCS3 inhibits the downstream products of GSR and TXNRD1 activity within cells. I performed a redox western blot, in which protein thiols are alkylated during harvest and electrophoresis is completed under reducing conditions. Together, these protocol modifications preserve the oxidation state of proteins, allowing the determination of the relative oxidation state of peroxiredoxin 1 (PRDX1), a direct substrate of TRX and a surrogate for the detection of TRX activity (Neumann et al., 2009; Torrente et al., 2020). Auranofin, a covalent inhibitor of TXNRD1, served as a positive control (Snyder et al., 1987). Addition of LCS3 markedly increased PRDX1 oxidation in LCS3-sensitive LUAD cell lines after 2 hours, to a degree comparable to that produced by the known TXNRD inhibitor, auranofin; but, LCS3 did not increase oxidation of PRDX1 in the insensitive cell lines HPL1D or HCC2279 (Figure 31). Figure 31. LCS3 induces PRDX1 oxidation in sensitive cell lines Cells treated with DMSO, LCS3 or TXNRD1 inhibitor auranofin (positive control) for 2 hours, harvested and electrophoresed by redox western blot. 77 To assess the effects of LCS3 on GSR, Dr. John Ferrarone determined the intracellular ratio of GSH to GSSG after exposing LUAD cell lines to 2-8µM LCS3. Addition of LCS3 decreased the concentration of GSH and increased the concentration of GSSG in LCS3 sensitive cell lines, resulting in a dose-dependent decrease in the [GSH]/[GSSG] ratio (Figure 32). These data suggest that LCS3 interacts with GSR and TXNRD1, inhibits their enzymatic activities, and reduces the levels of their products in LCS3-sensitive LUAD cells. Figure 32. LCS3 induces GSH oxidation in LUAD cells Error bars are +/- SD. 78 3.8 Conclusions In this chapter, I have outlined the approach taken to identify and validate the molecular targets of LCS3. I performed TPP and generated a list of 77 hits for further investigation. Through enzymatic assays using purified protein, Dr. Christina Brandstädter and I confirmed that LCS3 is a reversible, uncompetitive inhibitor of disulfide reductases GSR and TXNRD1. Dr. John Ferrarone and I demonstrated that LCS3 inhibits the downstream molecular products of these enzymes in vitro. Through virtual docking, Dr. Ravi Munuganti modeled the binding of LCS3 to the homodimer interface of GSR and TXNRD1 and an SAR study revealed that the nitro subgroup is indispensable for LCS3 activity. This raises a potential problem, as nitro subgroups are often flagged by medicinal chemists for undesirable toxicities. In Chapter 5, I discuss pharmacological applications of nitro subgroups and their limitations. Together, these experiments have allowed me to find molecular targets of the novel small molecule LCS3. More generally, this strategy can serve as a proof-of-concept for target discovery and be applied for other novel small molecules identified by high-throughput chemical screens. 3.9 Methods Redox western blots Redox western blots were performed by seeding 1.5 million cells in a 10cm plate and treating for two hours with each treatment condition the following day using established methods (Torrente et al., 2020). Briefly, cells were treated with 6µM auranofin or LCS3 for two hours. Thirty minutes before harvest, 300µL of 1mg/mL catalase dissolved in alkylation buffer (40mM HEPES, 50mM NaCl, 1mM EGTA, HALTTM protease and phosphatase 79 inhibitors, pH7.4) was added to each plate. Immediately prior to harvest, cell media was aspirated and replaced with ice cold PBS. The PBS was then aspirated and replaced with 400µL 200mM NEM dissolved in alkylation buffer. Plates were incubated for 10 minutes at room temperature on an orbital shaker and 40µL 10% CHAPS dissolved in alkylation buffer was added to each plate. Cell plates were incubated on the orbital shaker for an additional 10 minutes before being scraped, transferred into 1.5mL microfuge tubes and incubated on ice for 30 minutes. Lysates were further processed by the standard western blot sample preparation protocol described above except sonication was not used for lysis and DTT was not added to the Laemelli sample buffer. TPP Five million H23 cells were seeded in two 15cm plates and harvested the next day by 2x cold PBS wash and gentle scraping using a cell lifter. Cells were pelleted at 1000rpm, resuspended in cold PBS, and lysed by three freeze-thaw cycles using an ethanol-dry ice bath. Protein concentration was determined by BCA assay and diluted in PBS to a concentration of 1mg/mL. Lysates (1.2mL volume) were then treated with either 0.1% DMSO or 2µM LCS3 in duplicate, incubated for 20 minutes at room temperature and then placed on ice, as specified by the TPP protocol (Franken et al., 2015). Treated lysates were aliquoted into 100µL volumes in eleven PCR tubes, which were then heated to Celsius temperatures of 37º, 41º, 44º, 47º, 50º, 53º, 56º, 59º, 63º, 67º for 3 minutes by thermocycler, placed in a room temperature water bath for 3 minutes, and then snap frozen in an ethanol-dry ice bath. The lysates were thawed together on ice, transferred into 80 thickwall polycarbonate centrifuge tube and ultracentrifuged at 100,000 xg for 20 minutes at 4ºC in a Beckman TL-100 ultracentrifuge. Following ultracentrifugation, 90μL of lysate was carefully pipetted from the surface as not to capture denatured proteins pelleted by centrifugation. The proteins were then digested, reduced, alkylated and cleaned up by single-pot solid-phase-enhanced sample preparation (SP3) by established methods (Hughes et al., 2019). Pooled mixtures of peptides from each temperature condition were desalted and checked for quality on a LTQ Orbitrap VelosTM mass spectrometer. Peptide samples were then TMT labelled according to manufacturer specifications. In the multiplexed tandem mass tag (TMT) preparation, in the 11th channel (TMT11-131C) I included an untreated H23 lysate mix, which was not subjected to temperature challenge or ultracentrifugation, to enhance discovery of underrepresented peptides. Peptides were desalted by stage tipping, fractionated by HPLC and speed vacuumed to dryness before reconstitution in 1% formic acid, 1% DMSO. Reconstituted peptides were injected into an Orbitrap FusionTM mass spectrometer via a 100um internal diameter capillary column, prepared in house, that was packed with Dr. Maisch C18 (3um) beads. The peptides were separated using a 2 hour solvent gradient specified in Table 3. Time (mins: seconds) Duration (mins: seconds) Flow rate (nl/min) % Buffer A: % Buffer B 0:00 0:00 425 97:3 2:00 2:00 425 93:7 96:00 94:00 425 28:72 113:00 17:00 425 40:60 114:00 1:00 425 20:80 120:00 6:00 425 20:80 Table 3. Liquid chromatography gradient conditions Buffer A: HPLC grade water, 0.1% trifluoroacetic acid Buffer B: Acetonitrile, 0.1% trifluoroacetic acid 81 TPP data were processed by Proteome Discoverer software and resulting detected proteins were ranked by a non-linear regression data analysis (NPARC) using an open source R package (Childs et al., 2019). Proteins with an adjusted p-value < 0.01 were considered hits. This analysis produced a list of 77 protein candidates, which were then clustered into gene ontology biological processes by DAVID Bioinformatics Database v6.8 (Huang da et al., 2009) for further analysis. The raw TPP data has been uploaded to PRIDE (PXD030294). Enzymatic assays GSR activity was initially demonstrated by me according to the Cayman Chemical Assay Kit protocol (703202) and then validated by Dr. Christina Brandstädter. Briefly, 20nM purified human GSR, was incubated with treatments in the presence of 800µM NADPH in 125µL assay buffer. To initiate the reaction, 75µL of 8mM oxidized GSSG was added to each well and the absorbance was measured at 340nm for the duration of the experiment by a Cytation 3 multi-mode imaging plate reader. Dr. Christina Brandstädter of the Dr. Katja Becker laboratory independently performed GSR and TXNRD1 activity assays using established methods (Carlberg and Mannervik, 1985; Kanzok et al., 2002). Human GSR was recombinantly produced in E. coli and purified using IPTG, Ni-NTA agarose and imidazole (Nordhoff et al., 1993) and native human TXNRD1 was isolated from human placenta (Gromer et al., 1998). The GSR assay was performed by 82 incubating purified recombinant GSR with NADPH, GSSG, and inhibitor while measuring NADPH consumption through reading absorbance at 340nm according to an established protocol (Carlberg and Mannervik, 1985). DTNB reduction assays were performed with isolated native human TXNRD1 by incubation with NADPH, DTNB, and inhibitor while measuring DTNB reduction through reading absorbance at 412nm using an established method (Kanzok et al., 2002). All measurements were conducted in two biological independent replicates and Lineweaver-Burk plots show the data of one representative experiment. IC50 values were calculated using the Quest Graph™ IC50 Calculator. AAT Bioquest, Inc, https://www.aatbio.com/tools/ic50-calculator. Docking studies Performed by Dr. Ravi Munuganti. Protein preparation: 2GH5, a crystal structure of GSR complexed with a Fluoro-Analogue of the Menadione Derivative (1.70 Å resolution) and 2ZZ0, a crystal structure of TXNRD1 (2.80 Å resolution) were used for molecular docking studies. For protein structure preparation, all solvent molecules were deleted and the bond order for the protein was adjusted. Missing hydrogen atoms were added, and side chains were energy-minimized using the OPLS-2005 force field, as implemented by Maestro 2017v. The ligand binding region was defined by a 12 Å box centered on the crystallographic ligands of the crystal structures. Van der Waals scaling factors were not applied; the default settings were used for all other adjustable parameters. Ligand preparation: LCS3 was built using MOE version 2017. Hydrogen atoms were added after these structures were “washed” (a procedure including salt disconnection, removal of minor 83 components, deprotonation of strong acids, and protonation of strong bases). The following energy minimization was performed with the MMFF94x force field, as implemented by the MOE, and the optimized structures were exported into the Maestro suite in SD file format. Molecular Docking: docking experiments were performed using Glide included in the Schrodinger Package, Maestro interface. For docking, standard-precision (SP) docking method was adopted to generate the minimized pose, and the Glide scoring function (Glide Score) was used to select the final poses for ligands. Amino acid sequence alignment of TXNRD1 and GSR Human TXNRD1 and human GSR FASTA files were downloaded from UniProt (www.uniprot.org) (UniProt, 2019). The downloaded sequences were pasted into Clustal Omega Multiple Sequence Alignment tool (Sievers et al., 2011). Residue function predictions were used from a publication by Sandalova et al. (2001), in which the functions of rat TXNRD1 and human GSR amino acid residues were predicted. Virtual three-dimensional structure viewing of TXNRD1 and GSR The human GSR (PDB 1XAN) (Savvides and Karplus, 1996) and human TXNRD1 (PDB 2CFY) (Debreczeni et al., 2006) structures were visualized in the RCBS Protein Data Bank viewer (https://www.rcsb.org/) (Burley et al., 2021). Water molecules were removed, ligand molecules were highlighted, and monomer units were colourized in the virtual model. 84 Measurement of GSH, GSSG, NAPDH, and NADP+ levels For each cell line, a total of 5,000 cells/well were seeded into 96-well plates. Twenty-four hours later, media was replaced with media containing DMSO or specified concentrations of LCS3. After 6 hours of treatment, cells were washed with 150µL of PBS. GSH, GSSG, NADPH, and NADP+ levels were then quantified using either the GSH/GSSG-Glo assay or the NADP/NADPH-Glo assay according to manufacturer instructions. In-gel fluorescence assay In 200µL PCR tubes, 0.1% DMSO, LCS3, or C1-27 was added to 0.5µg of recombinant GSTO1 in PBS in 12uL total volume and incubated in a thermocycler at 37ºC for 20 mins in a competitive CMFDA binding assay described previously (Ramkumar et al., 2016). Six µL of 3µM CMFDA was then added to each sample and incubated for an additional 20 mins. Samples were heated in a thermocycler at 95ºC for five minutes and mixed with Laemmli sample buffer and DTT and loaded into a 4-12% Bis-Tris gel. Samples were separated on the gel and the entire gel was imaged on a Bio-Rad ChemiDoc MP with the fluorescein image setting. The gel was then transferred to PVDF and blotted for GSTO1 as described in the western blot methods section. 85 SAR study High-throughput screening of LCS3 and related derivatives for SAR assessment and determination of IC50 values was performed as described previously (Somwar et al., 2009) by a microplate-based assay using the fluorescent dye alamarBlue as a readout of cell growth. 86 Chapter 4: Inducing lethality in lung cancer cells by exploiting susceptibility to oxidative stress Portions of this chapter have been published as: Johnson, F. D., Ferrarone, J., Liu, A., Brandstädter, C., Munuganti, R., Farnsworth, D., Lu, D., Luu, J., Sihota, T., Jansen, S., Nagelberg, A., Shi, R., Forcina, G., Zhang, X., Cheng, G.S.W, Spencer Miko S.E., de Rappard-Yuswack, G., Sorensen, P.H., Guha, U., Becker, K., Djaballah, H., Somwar, R., Varmus, H., Morin, G.B., Lockwood, W.W. (2022). Characterization of a small molecule inhibitor of disulfide reductases that induces oxidative stress and lethality in lung cancer cells. Cell Reports 38, 110343. 4.1 Introduction After identifying disulfide reductases GSR and TXNRD1 as targets of LCS3, I next wanted to understand the biological significance of this mechanism. It is well-established that combined inhibition of the glutathione and thioredoxin pathways synergizes to induce cell death in some types of cancer cells (Bebber et al., 2021; Habermann et al., 2017; Harris et al., 2019; Harris et al., 2015; Mandal et al., 2010; Yan et al., 2019; You and Park, 2016). It is of biological significance to understand why lung cancer cells are susceptible to dual disulfide reductase pathway inhibition and to determine if this dependency can potentially be exploited for the treatment of LUAD. 4.2 Glutathione and thioredoxin inhibition synergy After demonstrating that LCS3 inhibits the disulfide reductases GSR and TXNRD1, I aimed to establish whether loss of these enzymatic activities is toxic to LUAD but not non-cancerous lung epithelial cells. I hypothesized that if LCS3 exerts cytotoxicity by synergistic 87 inhibition of these two compensatory pathways which are illustrated within Figure 21, then combining LCS3 with an agent that inhibits either pathway should further sensitize LUAD cells to LCS3. This assumes that co-treatment of LCS3 with a compound that inhibits one of its effectors will reduce the concentration of LCS3 required to inhibit both pathways and elicit a lethal phenotype. I tested the combinatorial effects of LCS3, the TXNRD1 inhibitor auranofin, and the GSH synthesis inhibitor buthionine-sulfoximine (BSO)—using all three dual pairings (LCS3 + auranofin, LCS3 + BSO, and auranofin + BSO)—on the growth of LUAD cell lines (Figure 33A and Figure 33B). Consistent with other reports, I found the combination of auranofin and BSO to be highly synergistic in LUAD cell lines, demonstrating the compensatory nature of the glutathione and thioredoxin pathways. The effects of LCS3 were also synergistic with either BSO or auranofin in both cell lines (Figure 33A), supporting the case that LCS3 may exert toxicity by disrupting both redox pathways. Additionally, I found that siRNA-mediated knockdown of GSR (siGSR) sensitized cells to LCS3, which was also observed when combining auranofin and siGSR—this shared property of sensitizing LUAD cells to GSR depletion further suggests at least a partial overlap exists between the mechanisms of LCS3 and auranofin (Figure 33C). However, I did not observe additive effects when combining glutathione pathway disruptor BSO and siRNA targeting of TXNRD1 (Figure 33D), suggesting that other thioredoxin reductases may compensate for loss of TXNRD1, but not for the inhibitory effects of LCS3 or auranofin. TXNRD2 or TXNRD3 may compensate for TXNRD1 loss, as TXNRD1 depletion does not phenocopy auranofin-mediated TXNRD1 inhibition. Together, these data are supportive of a 88 mechanism by which LCS3 disables both the glutathione and thioredoxin pathways; in this way, increased reliance on either of the two pathways, after addition of pathway specific inhibitors or siRNAs, impairs the ability of LUAD cells to maintain redox balance and consequently sensitizes LUAD cells to LCS3. 89 Figure 33. LCS3 and disulfide reductase inhibition Incucyte proliferation experiment data are shown as mean +/- SEM. (A) LCS3 and inhibitors of the glutathione and thioredoxin pathways (BSO and auranofin, respectively) alone or in combination. (B) Heatmaps showing combination interactions between inhibitors of the glutathione and thioredoxin pathways. (C) Incucyte growth experiment demonstrating interaction between GSR depletion by siRNA and either LCS3 or TXNRD1 inhibitor auranofin. KIF11 siRNA is a cell death positive control. (D) Incucyte growth experiment demonstrating the lack of combination synergy between BSO and TXNRD1 depletion by siRNA. A B C D 90 4.3 Sensitization of resistant cells to LCS3 I have shown that LCS3-sensitive LUAD cell lines are sensitive to combined inhibition of glutathione and thioredoxin pathways and noted that insensitive cell lines have low basal levels of ROS (Figure 17). Consequently, I asked whether supplying ROS to insensitive cell lines would increase reliance on glutathione and thioredoxin pathways and thereby sensitize resistant cells to LCS3. I treated LCS3-resistant cell lines HPL1D and HCC2279 with LCS3 alone and in combination with tert-butyl hydroperoxide (TBHP), a direct-acting exogenous source of ROS. These cell lines were insensitive to LCS3 or TBHP alone, but growth was strongly inhibited when the agents were combined (Figure 34A and Figure 34B), implying that LCS3-resistant cells can be sensitized to LCS3 by the addition of ROS. It appears that, when challenged with oxidative stress, these LCS3-resistant cell lines become dependent on the glutathione and thioredoxin pathways to maintain control of redox balance; dual inhibition of these pathways by LCS3 allows ROS to accumulate and compromises cell survival. 91 Figure 34. Sensitization of resistant cells to LCS3 (A) LCS3 or TBHP alone or in combination in LCS3-resistant cell lines. Data are mean +/- SEM and are representative of two independent experiments. (B) Incucyte micrographs taken at 72 hours after HPL1D and HCC2279 cells were treated with LCS3 and TBHP alone or in combination. Scale bar, 400μm. 4.4 Sensitization of resistant cells to disulfide reductase inhibition I next evaluated if sensitization of LCS3-resistant cells to ROS could be phenocopied with known inhibitors of the glutathione and thioredoxin pathways. When HPL1D cells were grown with TBHP and a combination of auranofin and either BSO or siGSR, their growth was inhibited, although they were tolerant of auranofin, TBHP, or GSR depletion alone. A B 92 Combination of TBHP and BSO induced a degree of toxicity; however, this effect was increased with the addition of auranofin (Figure 35). These data are consistent with my prior observation that TBHP sensitizes LCS3-resistant cells to LCS3 and support the hypothesis that inhibition of LCS3-induced toxicity is mediated through disulfide reductase inhibition. Figure 35. TBHP sensitizes HPL1D to glutathione and thioredoxin inhibition TBHP affects viability of HPL1D upon dual disulfide reductase pathway inhibition achieved by auranofin and either GSR knockdown or BSO. Data are mean +/- SEM and representative of two independent experiments. 93 4.5 Thioredoxin pathway inhibition activates NRF2 pathway in H23 cells I hypothesized that inhibition of the glutathione and thioredoxin pathway would synergistically activate the NRF2 pathway, as observed in LCS3-treated LUAD cells. Unexpectedly, I found that auranofin alone induced NRF2 target gene expression in H23 cells. Additionally, auranofin induced GFP expression in the H23 ARE-GFP reporter cell line (Figure 36A and Figure 36B). Together, these data suggest that auranofin and LCS3 have a shared ability to induce NRF2 activation in LUAD. Figure 36. TXNRD1 inhibition activates NRF2 pathway (A) Western blot of NRF2 and its target genes after 24 hours treatment with BSO, auranofin, or KEAP1 inactivator AI-1. (B) GFP expression of ARE-GFP relative to pathway control in response to 24 hours treatment with AI-1, BSO, or auranofin. Data shown are mean + SEM; n = 3. A B 94 4.6 Dual disulfide reductase inhibition in lung cancer cells Thus far, I have determined that LCS3 inhibits the glutathione and thioredoxin pathways and that this inhibition is synergistically lethal to LUAD cells. I found that cells exhibiting higher basal levels of ROS—including the majority of LUADs—are more likely to be sensitive to LCS3-mediated inhibition of the glutathione and thioredoxin pathways. This is supported by experiments showing that TBHP is highly synergistic with LCS3 and dual disulfide reductase pathway inhibition in resistant cells. My working model for this mechanism is illustrated in Figure 37. Figure 37. Working model of LCS3 mechanism A working model of how LCS3-mediated dual inhibition of glutathione and thioredoxin pathways elicits accumulation of ROS and cell death in LUAD cells. 95 4.7 Genome-wide CRISPR/Cas9 knockout screen To complement the effort to identify targets of LCS3 with TPP and to interrogate potential mediators of the LCS3 response systematically, Dr. John Ferrarone performed a genome-wide, loss-of-function CRISPR-Cas9 screen in LCS3-sensitive LUAD cells. H358 cells, which harbour the KRASG12C mutation, were infected with a lentiviral pool generated using the Toronto KnockOut Library v3 (TKOv3) library, which contains Cas9 and 70,948 gRNAs targeting 18,053 protein-coding genes (Hart et al., 2017). Transduced cells were passaged in the presence of DMSO, 4µM LCS3, or 8µM LCS3 for 14 population doublings. DNA from the three cultures was assessed for the degree of enrichment or depletion of the sgRNAs in the original library in response to either concentration of LCS3 (Figure 38). Figure 38. Schematic of genome-wide CRISPR-Cas9 knockout screen 4.7.1 Results, enrichment of antioxidant genes The sgRNAs targeting the genes encoding NQO1 and glutathione-S-transferase P (GSTP1) were significantly enriched, while those targeting glutamate-cysteine ligase (GCLM) and KEAP1 were significantly depleted, in cultures grown at both concentrations of LCS3. It is notable that many proteins encoded by the affected genes play prominent roles in 96 regulating redox balance (Figure 39) (Lei et al., 2020b; Nguyen et al., 2005). The sgRNAs targeting the genes encoding NQO1 and KEAP1 were, respectively, the most significantly enriched and the most significantly depleted under both LCS3 conditions. Figure 39. CRISPR screen results Beta score scatterplots and CRISPR screen gene rankings. Figure was generated by John Ferrarone. 97 4.7.1.1 Validation of CRISPR screen hit NQO1 NQO1, an NRF2 target gene, is a NADPH dehydrogenase that reduces quinones during detoxification and is involved in the cellular adaptation to stress (Siegel et al., 2012); however, it has been reported that NQO1 activity can elicit a bioreductive activation of substrates that is associated with the production of ROS and exacerbation of redox imbalance (Wu et al., 2018). While loss of NQO1 had no effect on cellular fitness in H358 cells under normal growth conditions, it greatly increased the relative ability of cells to proliferate in the presence of LCS3 (Figure 39). This observation was confirmed by establishing stable cell lines in which the NQO1 gene was knocked out by CRISPR-Cas9 (performed by Dr. John Ferrarone) and by inhibiting NQO1 with the small molecule ES936 (performed by me); both approaches demonstrated nearly complete protection against LCS3-mediated toxicity (Figure 40A and Figure 40B). In addition, I observed a dramatic decrease in the induction of ROS by LCS3 in H358 cells in which NQO1 was suppressed by either sgNQO1 or ES936 (Figure 40C and Figure 40D), mirroring the reduction in toxicity (Dehn et al., 2003). In addition, cells in which the NQO1 gene was inactivated by sgNQO1 did not activate the NRF2 pathway relative to the control cell line (Figure 40E). Thus, the phenotype of NQO1-null cells—the loss of LCS3-induced, ROS-mediated cell toxicity—is not likely mediated by changes in regulation of NRF2. In H358 sgChr19 control cells, but not H358 sgNQO1 cells, LCS3 induces an increase in NADPH, further suggesting that the cellular effects of LCS3 are inhibited upon knockout of NQO1 (Figure 40F). 98 Figure 40. Validation of CRISPR screen hit NQO1 (A) Incucyte cell proliferation experiment of H358 sgNQO1 and control cells treated with LCS3. (B) Cell proliferation rescue experiment of LCS3-induced cell death in H358 by NQO1 inhibitor ES936. (C) Flow cytometry with H2DCFDA in cells treated with LCS3 or positive control 2-AAPA in H358 sgNQO1 cell lines; n = 3. (D) Flow cytometry with H2DCFDA in cells treated with LCS3 and ES936 alone or in combination; n = 3. (E) Western blot of H358 sgNQO1 and control cell lines treated with LCS3. (F) Metabolite assay to determine relative concentrations of NADPH and NADP+ in LCS3-treated H358 cells. A C F B D E sgChr19-SAFE 99 4.7.1.2 CRISPR screen hit KEAP1 The depletion of KEAP1 sgRNAs in the genetic screen, implying selection against KEAP1-null cells during growth in LCS3, was unexpected (Figure 39). KEAP1 is a negative regulator of NRF2, and elevated NRF2 activity would be predicted to allow cells to better tolerate the induction of ROS by LCS3. To test this, Dr. John Ferrarone used the CRISPR-Cas9 method to inactivate KEAP1 in H358 cells with multiple sgRNAs and found no significant effects on sensitivity of cell growth to LCS3, despite an increase in NRF2 levels (Figure 41A and Figure 41B). This finding appears to conflict with the results of the knockout screen. However, NRF2 controls the transcription of NQO1 and GSTP1 (Lei et al., 2020c), genes that were depleted in cells that grew preferentially in the presence of LCS3. Therefore, it is possible that the selection against KEAP1 sgRNAs in the context of the screen is related to the induction of these and other NRF2 targets. Indeed, cells bearing sgRNAs for the transcription factor ARNT—which, together with NRF2, controls NQO1 expression by binding to the Xenobiotic Response Element motifs in response to electrophilic stress (Oh and Park, 2015)—were also positively selected in 8µM of LCS3, which further supports this theory (Figure 39). 100 Figure 41. CRISPR screen hit KEAP1 (A) Incucyte growth experiment demonstrating KEAP1 deletion does not impact LCS3 sensitivity in H358 cells. Note that sgChr19-SAFE control data are the same as shown in Figure 40A alongside sgNQO1 cell lines. (B) Western blot showing NRF2 pathway activation of H358 cells with KEAP1 deletion 4.7.1.3 CRISPR screen main findings The results of the CRISPR screen support a framework in which LCS3 disrupts redox homeostasis in sensitive LUAD cell lines and highlights NQO1 activity as a key determinant of LCS3 sensitivity and a potential biomarker for response. This view is further supported by additional results from the CRISPR screen. GCLM catalyzes the ligation of cysteine to glutamate in the first and rate-limiting step of glutathione biosynthesis (Bansal and Simon, 2018; Harris et al., 2015); inactivation of GCLM is predicted to impair the downstream glutathione pathway and yield an effect similar to the knockdown of GSR or to the addition of BSO—factors that cooperate with LCS3. Therefore, the selection against the suppression of GCLM in the CRISPR screen is consistent with the observed synergies between LCS3 and other glutathione pathway disruptors. A B 101 The sgRNAs for glutathione synthetase (GSS), which regulates the second step of glutathione biosynthesis (Balendiran et al., 2004; Bansal and Simon, 2018), were also significantly depleted in the CRISPR screen, but only in the cultures grown in 8µM LCS3 (Figure 39). This finding suggests that LCS3 increases reliance on glutathione and further supports a mechanism by which LCS3 elicits cancer cell death via oxidative stress mediated by disruption of the glutathione and thioredoxin pathways. Another potentially informative gene depleted in the CRISPR screen, GSTP1, encodes a xenobiotic detoxification enzyme which conjugates GSH to electrophilic compounds to facilitate the excretion of hydrophilic metabolites (Chen et al., 2010). Thus, sgRNA-mediated inactivation of GSTP1 would plausibly promote cell survival in the context of LCS3 treatment by increasing the availability of free GSH. 4.8 Conclusions To summarize, I demonstrated synergistic interaction between LCS3 and both thioredoxin pathway inhibition and glutathione pathway inhibition in LUAD cells. Importantly, I found that exogenous ROS sensitizes resistant lung cells to both LCS3 and dual disulfide reductase pathway inhibition. Together this is supportive of a mechanism in which LCS3-induced lethality in LUAD cells is through disulfide reductase pathway inhibition. Additionally, Dr. John Ferrarone performed a genome-wide CRISPR-Cas9 knockout screen and identified numerous antioxidant genes as being essential to LCS3 resistance. Dr. John Ferrarone and I identified NQO1 as an essential mediator of LCS3 activity and have not yet elucidated how NQO1 interacts with LCS3. Together, these data justify further investigation 102 into how ROS and disulfide reductase pathway inhibition can be exploited therapeutically for the treatment of LUAD. 4.9 Methods Genome-wide loss-of-function CRISPR-Cas9 knockout screen The genetic screen was performed with the Toronto Knockout version 3 (TKOv3) library (Hart et al., 2017). Lentivirus was generated from the TKOv3 library in low passage (<10) 293FT cells using Lipofectamine 3000. Approximately 400 million target H358 cells were infected with the TKOv3 library virus at a multiplicity of infection (MOI) of 0.5, in order to achieve an average 200-fold representation of the sgRNAs in each condition and replicate of the screen. Cells were selected on puromycin for 5 days and then 14 million cells were seeded into media containing DMSO, LCS3 4µM, or LCS3 8µM in triplicate. Cells were passaged every 3 days and after 14 population doublings in the DMSO condition, 14 million cells from each condition were harvested for genomic DNA extraction. sgRNA inserts were amplified with NEBNext High-Fidelity 2X PCR Master Mix. Samples were then pooled in equimolar concentrations, gel purified, and sequenced on a NextSeq 500 kit. Sequencing reads were trimmed and aligned to the reference library using Cutadapt and Bowtie, respectively (Kechin et al., 2017; Langmead and Salzberg, 2012). This alignment returned a raw read count table, and the sgRNAs with less than 30 raw read counts were excluded from further analysis. The raw read counts were then analyzed with MAGeCK-MLE in order to obtain a beta-score for each gene (Wang et al., 2019). To identify gene knockouts that 103 conferred resistance to LCS3, the beta-scores for the genes in the LCS3-treated conditions (4 or 8µM) were subtracted from the beta-scores in the DMSO-treated condition. siRNA knockdown 400,000 cells were first seeded into a 6-well plate. The next day, cells were transfected with siRNA by combining 10µl of 20µM siRNA and 5µL Dharmafect in 800µL OptiMEM and adding to a 6-well plate in RPMI media to a total volume of 4mL, for a final siRNA concentration of 50nM. The siRNAs used were non-targeting pool siCtrl, siGSR, siTXNRD1, and siKIF11. Generation of KEAP1 and NQO1 knockout cell lines The sgNQO1, sgKEAP1, and sgChr19-SAFE control H358 cell lines were produced as follows and sgRNA sequences are provided in Table 5. The sgChr19-SAFE RNA targets a safe harbour site on Chromosome 19 that allows safe genomic integration with minimal untended expression changes. The sgKEAP1 and sgChr19-SAFE RNAs were cloned into the lentiCRISPR v2 (LCv2) backbone (gift from Feng Zhang) (Sanjana et al., 2014) using the BsmBI restriction site. The sgNQO1 and sgChr19-SAFE RNAs were cloned into the pLenti-U6-tdTomato-P2A-BlasR (LRT2B) backbone (gift from Lukas Dow) (Zafra et al., 2018), also using the BsmBI restriction site. First, H358 cells were transduced with lentivirus containing LCv2 sgKEAP1 or sgChr19-SAFE. Next, these lines were transduced with lentivirus containing LRT2B sgNQO1 or sgChr19-SAFE. Cells were cross-selected in puromycin and blasticidin. 104 Synergy experiments For combination experiments in Figure 33B, the fraction of growth inhibition was calculated by subtracting the fraction of treatment condition confluence over maximal confluence from 1 at the confluence end point, which was defined as the first time point of each cell line in which the mean control condition surpassed 97% confluence. 105 Chapter 5: Conclusions and future directions Portions of this chapter have been published as: Johnson, F. D., Ferrarone, J., Liu, A., Brandstädter, C., Munuganti, R., Farnsworth, D., Lu, D., Luu, J., Sihota, T., Jansen, S., Nagelberg, A., Shi, R., Forcina, G., Zhang, X., Cheng, G.S.W, Spencer Miko S.E., de Rappard-Yuswack, G., Sorensen, P.H., Guha, U., Becker, K., Djaballah, H., Somwar, R., Varmus, H., Morin, G.B., Lockwood, W.W. (2022). Characterization of a small molecule inhibitor of disulfide reductases that induces oxidative stress and lethality in lung cancer cells. Cell Reports 38, 110343. 5.1 Summary Lung cancer is the greatest cause of cancer death both in Canada and worldwide, and new therapeutic strategies are needed to improve outcomes (Bade and Dela Cruz, 2020; Society, 2021). To address this need, I employed a chemical biology approach to seek new druggable targets and cell survival dependencies that could potentially be exploited for the treatment of lung cancer. The novel small molecule LCS3, which was uncovered previously in a high-throughput, phenotype-based drug screen designed to identify compounds that selectively inhibit LUAD growth (Somwar et al., 2009), was thoroughly characterized by this thesis work. My main approach was to investigate the mechanism of action and effector targets of LCS3 to define and evaluate a potentially new LUAD treatment strategy. The multifaceted approach to characterize LCS3 is illustrated in Figure 42. 106 Figure 42. Overview of approach to LCS3 characterization Using an expanded lung cell line panel, I found in Chapter 2 that LCS3 selectively induces apoptotic cell death in LUAD cell lines. I then performed an integrative gene expression and proteomic analysis of treated cells and found evidence that cellular response to LCS3 involves activation of the NRF2 pathway. I observed LCS3-induced ROS in sensitive LUAD cells, but not insensitive LUAD cell line HCC2279 or non-transformed cell line HPL1D. The antioxidant agents NAC, GSH-MEE, and FS-1 were able to rescue some LCS3 toxicity, implicating oxidative stress and GSH deprivation in the mechanism of cell death. I next sought to define the molecular targets of LCS3 to have a more complete understanding of its mechanism of action. In Chapter 3, I applied TPP and identified candidate protein interactors of LCS3. Enzymatic assays revealed that the disulfide reductases GSR and TXNRD1 are inhibited by LCS3 through a reversible, uncompetitive 107 mechanism. In silico molecular docking analysis shed light on the LCS3-protein interactions and suggests that the nitrofuran subgroup of LCS3 interacts with amino acid side chains situated near the homodimer interfaces of GSR and TXNRD1. An SAR investigation demonstrated that the nitro moiety of LCS3 is indispensable for its activity at inducing cell death and inhibiting disulfide reductase activity. Moreover, the downstream molecular products of GSR and TXNRD1 were inhibited by LCS3 in LUAD cells, suggesting that LCS3-mediated inhibition of disulfide reductase activity causes profound cellular effects which may compromise LUAD cell viability. Chapter 4 is centered on exploring the utility of disulfide reductase inhibition in LUAD and the broader mechanism of LCS3. I investigated combination treatments of LCS3 and inhibitors of the glutathione and thioredoxin pathways. I found that both BSO and auranofin synergize with LCS3 at killing LUAD cells, suggesting that LCS3 could affect cell viability through disruption of both pathways. I demonstrate that resistant lung cells can be sensitized to LCS3 by addition of exogenous ROS, which suggests that oxidative stress-prone LUAD cells are intrinsically sensitive to disulfide reductase inhibition—an example of synthetic lethality. LCS3-resistant HPL1D cells were sensitized to dual disulfide reductase pathway inhibition by TBHP, further supporting the notion that oxidative stress increases reliance on disulfide reductase activity. The genome-wide CRISPR-Cas9 knockout screen revealed that LCS3 activity is dependent on NQO1 activity and that glutathione biosynthesis pathway enzymes GSS and GCLM are vital for LUAD survival to LCS3. Overall, I succeeded across the three aims to demonstrate LCS3 is a reversible, uncompetitive inhibitor of GSR and TXNRD1 that selectively kills LUAD cells in an oxidative 108 stress-dependent manner. Therefore, the potential utility of LCS3 lies in targeting the redox homeostasis dependency as a therapeutic strategy. Further investigations are warranted to fully explore how LCS3, and the redox pathways can be exploited for the treatment of LUAD and other cancers, especially for those with poor outcomes and limited therapeutic options. 5.2 Dual targeting the disulfide reductase pathways in cancer Increased production of ROS is associated with oncogene signaling and transformation in lung and other solid cancers (Harris et al., 2015); hence targeting redox homeostasis has become an attractive approach for the development of new cancer therapies. LUAD cells commonly have genetic alterations that lead to NRF2 pathway activation and increased cellular cysteine through upregulation of system Xc- in order to synthesize GSH and other ROS-scavenging metabolites (Saigusa et al., 2020). It has also been demonstrated that the ETS-1 transcription factor, which is downstream of RAS-ERK signaling, directly activates the Xc- promoter in cells expressing oncogenic KRAS (Lim and Leprivier, 2019). It is noteworthy that gene encoding for Xc-, SLC7A11, was upregulated in both LUAD cell lines of our microarray dataset. This suggests that cells respond to LCS3 by increasing glutathione biosynthesis and that targeting this cysteine transporter could weaken the cellular response to oxidative stress or LCS3. Demonstrating the importance of these programs for cell fitness and survival, strategies that inhibit key NRF2 effectors or cause cysteine depletion can induce apoptosis and other forms of cancer cell death both in vitro and in vivo (Dixon et al., 2012; Stepkowski and Kruszewski, 2011). Indeed, another compound, known as LCS1, from the original screen in which LCS3 was identified, was 109 subsequently found to inhibit SOD1, a key NRF2 gene product that mitigates ROS by transforming superoxide anions into hydrogen peroxide (Somwar et al., 2011). My findings in this work build on the concept that at least some cancer cells are exquisitely sensitive to oxidative stress and demonstrate a reliance on the glutathione and thioredoxin systems to maintain redox homeostasis and survival. While normal cells can tolerate LCS3 because of low basal levels of ROS, adding exogenous ROS to mimic the oncogenic state leads to LCS3 susceptibility and lethality (Figure 34), suggesting that cells in a state of oxidative stress have increased dependence on the glutathione and thioredoxin systems. Furthermore, in LUAD cells, disruption of either of these pathways alone does not have a major effect on cell viability; however, their combined inhibition in the presence of ROS leads to synthetic lethality. This context-dependent sensitivity to LCS3 indicates that strategies that target the glutathione and thioredoxin pathways may yield a favorable therapeutic ratio, with selective cytotoxicity in cancer cells. The concept of dual targeting of the glutathione and thioredoxin pathways in cancer has gained attention in the last decade (Bebber et al., 2021; Habermann et al., 2017; Harris et al., 2019; Harris et al., 2015; Mandal et al., 2010; Yan et al., 2019; You and Park, 2016). While the complete mechanism of action resulting from the combined inhibition of these pathways remains unclear, a discernible synergy arises from their interconnection and compensatory nature. As an example, glutaredoxin-2 (GLRX2) is reduced non-enzymatically by GSH but can also be reduced by thioredoxin reductases when GSH is depleted (Johansson et al., 2004). In a reciprocal fashion, glutaredoxin-1 (GLRX1) can rescue TXNRD1 loss by catalyzing the transfer of electrons from GSH to TRX (Du et al., 2012; Prigge et al., 2012). Thus, the compensation provided by these connected pathways 110 in alleviating oxidative stress in cancer cells is supportive of a therapeutic strategy based on dual pathway inhibition. LCS3 serves as an important proof of principle in this regard; further work based on this compound should be carried out in animal models to investigate if targeting this dependency in LUAD or in other malignancies is a viable therapeutic avenue for clinical development. While it is known that ROS are generated as a byproduct of cancer cell growth (Trachootham et al., 2006), a more comprehensive understanding of the redox landscape could potentially help guide rational patient selection for drugs that exploit this sensitivity (Liou and Storz, 2010; Storz, 2005). In the lung cell line panel, HCC2279 was the sole LCS3-resistant LUAD cell line (Figure 8). I observed that this cell line has low basal ROS, which may explain why it is non-responsive (Figure 17); however, the underlying dynamics that regulate this state are unclear. The CRISPR-Cas9 knockout screen in H358 cells, which are sensitive to LCS3 treatment, demonstrated key factors that may be useful as biomarkers for predicting sensitivity to dual glutathione and thioredoxin suppression. Numerous sgRNAs that were enriched or depleted in cells grown in this screen disrupt genes that are involved in regulating key processes in glutathione metabolism and oxidative stress response, further supporting the proposed mechanism of action of LCS3. However, the finding that cells containing KEAP1 sgRNAs were depleted in the screen was unanticipated (Figure 39), since I predicted that activation of the NRF2 pathway would protect cells against LCS3-mediated oxidative stress. While the pleiotropic effects of NRF2—which include activating many genes that are associated with increased sensitivity to LCS3—may explain this finding, the effects of KEAP1 disruption on LCS3 sensitivity could not be validated by creating stable KEAP1-defective clones of H358 cells. This 111 suggests that the temporal relationships of KEAP1 disruption and LCS3 treatment may also be a factor, with simultaneous suppression of KEAP1 and treatment with LCS3 having detrimental effects on cell viability. In contrast, inactivation of KEAP1 before addition of LCS3 may permit adaptation of the relevant pathways to protect against the toxic consequences of ROS. In other words, the role of the KEAP1/NRF2 pathway in mediating sensitivity and resistance to LCS3 and other oxidative stress-inducing compounds may depend on the timing and balance of these regulatory factors. 5.3 The role of NQO1 in LCS3 activity The most striking finding from the CRISPR knockout screen was the apparent dependence of LCS3 sensitivity on NQO1 activity (Figure 39), supporting a key role for this enzyme in LCS3-induced cell death. This finding was validated by creating stable NQO1 knockout cells and using an inhibitor of NQO1; both strategies protected H358 cells from LCS3 (Figure 40A and Figure 40B). It is noteworthy that NQO1 has been implicated in the bioreduction of nitroheterocyclic compounds, which is essential for inducing their anticancer activities. This process catalyzes the reduction of a nitro group of a compound into a nitro anion radical, which can subsequently react with oxygen to form a superoxide anion and hydrogen peroxide, inducing oxidative stress and toxicity in cancer cells (Colucci et al., 2008; Rao and Mason, 1987). As an example of this, the structurally related antibiotic nitrofurantoin has been reported to generate a nitro anion radical by redox cycling, a process which results in both the production of ROS and the regeneration of the parent molecule (Wang et al., 2008). The SAR analysis demonstrated that the nitro moiety is essential for the anti-proliferative and enzymatic inhibition activities of LCS3, suggesting 112 that the electron-accepting properties of the nitro group confer intermolecular affinity for the reductases GSR and TXNRD1, which are electron donors to GSSG and thioredoxin substrates. Interestingly, the in vitro assays with purified proteins demonstrate that LCS3 causes direct enzyme inhibition, suggesting that LCS3 activity does not require redox cycling or another form of NQO1-dependent biotransformation to inhibit GSR and TXNRD1. However, it is possible that structural analogues of LCS3 that lack a nitro group are not metabolized by NQO1 and consequently fail to generate ROS via redox cycling and thus do not exhibit activity in cell-based experiments. Thus, redox cycling mediated by NQO1, which is characteristically high in cancer cells, could explain why LCS3 induces ROS only in cancer and not in normal cells. While my demonstration that dual inhibition of the glutathione and thioredoxin pathways by BSO and auranofin recapitulates the cytotoxicity of LCS3, it is possible that NQO1 potentiates these effects through redox cycling and concomitant generation of ROS. I speculate that LCS3 may be unique in its ability both to cause oxidative stress in NQO1-expressing cancer cells and to abrogate the ability of cancer cells to cope with accumulated ROS, through parallel disruption of the glutathione and thioredoxin systems. Therefore, it is both interesting and unexpected that the highly reactive nitro group—which could be viewed as a suboptimal structural feature from a medicinal chemistry perspective—may be responsible for the selective and potent activities of LCS3. If so, further medicinal chemistry may optimize its inhibitory and pharmacological properties. Together, these data suggest that NQO1 may serve as a potential biomarker to predict LCS3 response in future preclinical studies. It will surely also be useful to determine which derivatives of LCS3, if any, are produced by biotransformation and to assess their bioactivities. 113 5.4 Other uses of TPP in target discovery As opposed to traditional affinity-based methods, TPP offers the potential to identify proteins that bind compounds that are therapeutic candidates, without the need for chemical derivatization; in this way, TPP limits false-positive findings and streamlines the workflow (Lyu et al., 2020). Despite the advantages of TPP, it has not been widely utilized in target discovery. One group recently determined a target of the autophagy inhibitor indophagolin using a TPP-based strategy (Carnero Corrales et al., 2021). Another group identified CCNK/CDK12 as targets of the novel compound NCT0T, which was identified in a high-throughput screen to identify selective inhibitors of colorectal cancer spheroids (Dieter et al., 2021). To my knowledge, few other studies have used TPP to identify the effector targets of any small molecule inhibitor—rather than off-target binders of well-characterized compounds—in human cells. 5.5 Limitations Although I observe evidence of apoptosis, I found that antioxidants can partially rescue LCS3-induced toxicity (Figure 18), suggesting evidence of a ROS-related cell death pathway, such as ferroptosis. Further interrogation of mechanisms of cell death activated by LCS3 will be a crucial topic of further study. My experiments are limited to in vitro assessment on cancer cell viability. Notably, the low micromolar IC50 is likely to be prohibitively high for LCS3 to be considered for in vivo use; thus, chemical optimization will be necessary for the development of a derivative with increased potency and favorable pharmacokinetics. 114 Identifying main effectors of novel small molecules is challenging to achieve. I have already outlined earlier in this chapter how the derivatization and immobilization of small molecules in affinity proteomics methods can have unforeseen consequences in affecting small molecule-protein interactions. For example, if the small molecule is derivatized at the site which interacts with a protein, then the protein binding may be disrupted. Alternatively, TPP does not require that the small molecule of interest is derivatized. However, other limitations for this method exist. Firstly, not every protein is identified by mass spectrometry, so this may prevent real small molecule effectors from being identified. Preparation for mass spectrometry creates bias in various cellular compartments such as underrepresentation of surface-membrane proteins. To compensate for this compartment bias, the developers of the TPP method created a protocol using mild detergent to facilitate cell extraction that effectively increases enrichment of membrane-bound proteins detected by TPP (Reinhard et al., 2015). It should also be noted that the significance determined by NPARC does not always translate into increased strength of chemical-protein interaction. One of the great challenges of TPP is being unable to systematically interrogate all TPP hits. For these reasons, effectors of small molecules may not always be identified by TPP. I demonstrate sensitization of LCS3-resistant lines to disulfide reductase inhibition but could more comprehensively explore this dynamic. For example, I could investigate glutathione dynamics in sensitive and resistant cell lines by temporally mapping GSH depletion upon BSO or LCS3 treatment. This may reveal if GSH depletion coincides with sensitization to ROS and give clues as to how and when cells become sensitized to oxidative stress. To firmly establish the relative contributions of thioredoxin and glutathione pathway disruption 115 in LUAD and normal cells, more comprehensive experiments would need to be carried out in a large number of diverse cell lines. For example, I only tested the ability of auranofin to induce NRF2 activation in the H23 cell line—this should be established in other LUAD models. Additionally, the relative dependence on each disulfide reductase pathway should be investigated in additional cell lines to better understand the responsiveness of lung cancer to this potential therapeutic mechanism of action. To further confirm that LCS3 toxicity is dependent on dual inhibition of GSR and TXNRD1, rescue with mutant forms of these proteins that remain active in the presence of LCS3 would be optimal. Randomized mutagenesis of GSR and TXNRD1 cDNAs and expression in LUAD cells could be useful in this context as previously described (Yenerall et al., 2020). The creation of resistant cells by LCS3 dose-escalation to identify genetic factors involved in resistance could also help in this context. 5.6 Future directions 5.6.1 Redox cycling The mechanistic role of NQO1 in facilitating LCS3 toxicity was not investigated at depth in this study and the hypothesis of NQO1-mediated redox cycling should be studied. I propose investigating the possibility of LCS3 redox cycling by a similar approach that used to demonstrate the cytochrome P450 reductase-mediated redox cycling of nitrofurantoin (Wang et al., 2008). Briefly, H2O2 quantification can be fluorometrically determined with a plate reader by the horse-radish peroxidase (HRP)-catalyzed oxidation of Amplex Red (Zhou et al., 1997). Adding HRP, NADPH, and Amplex Red to H23 cell lysates in the 116 presence and absence of LCS3 can reveal if LCS3 induces H2O2 accumulation in lysates, which may be indicative of redox cycling. If confirmed, this reaction can subsequently be performed in the presence of purified recombinant NQO1, instead of H23 cell lysates, to confirm that H2O2 generation is NQO1-dependent. This proposed pair of experiments can help to determine if redox cycling of LCS3 likely occurs; however, it is not yet known if redox cycling can be pharmacologically advantageous. The conclusions resulting from these experiments would surely be a major consideration in the further preclinical development of LCS3 or its analogues. 5.6.2 LCS3 structure and in vivo optimization LCS3 has demonstrated anti-cancer activity and should be investigated further. Until LCS3 or its analogues are tested in vivo, one cannot know how favorable the pharmacokinetics or safety profiles are. Before in vivo testing, the chemical properties of LCS3 analogues should be considered. Figure 30 shows LCS3 analogues that exhibit anti-proliferative activity in vitro. The structures of the active compounds from this cell line screen (Table 2) should be considered and analogues with favorable solubility properties should be selected for in vivo experiments. It is also not yet known if the nitro functional group is truly indispensible for LCS3 activity, or if the moiety can be substituted with a similar chemical group. I will outline the features of nitro-containing compounds in the subsection ahead. 117 5.6.3 Drawbacks to the pharmacological use of aryl nitro subgroups and potential solutions The dilemma of using aryl nitro groups in pharmaceutical development is well-established: the nitro group is an integral part of many pharmacophores, while it often also behaves as a toxicophore (Nepali et al., 2019). The pharmacophore ability of a nitro group arises from its unique chemical properties. The nitro group is small, prone to form hydrogen bonds, moderately-to-highly polar, and strongly electron-withdrawing (Patterson and Wyllie, 2014). The oxygen atoms of the nitro group resonance structure (Figure 43) possess delocalized electrons and collectively carry an almost full negative charge that confers polarity (Lowe, 2007). When being used for therapeutic purposes in a living system, nitro groups are most commonly situated on aryl or heterocyclic rings, whereby the immediately adjacent, positively charged nitrogen atom withdraws electron density from the ring; this inductive electron withdrawal effect polarizes the pi electron system and facilitates many protein-ligand interactions (Meanwell, 2011), (Lowe, 2007). Figure 43. Nitro R group structure The main problem for nitro subgroup use in pharmacology is that the nitro is nearly completely oxidized and there are many cellular reductases that can transform it to a more reduced and reactive state (Lowe, 2007). It is through such biotransformation that the nitro group can become activated into a more reactive species that exerts biological effects 118 (Patterson and Wyllie, 2014). Nitros are first reduced to hydroxylamines, nitrosos, or nitro anion radicals—all of which tend to be highly reactive. The terminal amine (NH2) species is less reactive—it is the intermediates which are often mutagenic, carcinogenic, hepatotoxic, and generally undesirable for use in vivo (Lowe, 2007) (Patterson and Wyllie, 2014). Consequently, these known toxicity issues have resulted in the nitro group becoming ‘flagged’ or categorized as a structural alert or toxicophore by many medicinal chemists (Nepali et al., 2019). Despite this awareness, there are many successful pharmacological applications of nitro groups. In summary, the pharmacological activity of the nitro subgroup is defined by both its chemical structure—small, electron-withdrawing, and polar—and its biotransformation-associated activity. Both properties will need to be considered by any efforts to achieve bioisosteric replacement of the nitro subgroup. It is not yet established if the activity of LCS3 is dependent on its bioactivation and investigating this research question should be prioritized. There are several instances in which nitroheterocyclic compounds gain therapeutic activity through flavoenzyme-mediated redox cycling and LCS3 may undergo such bioactivation as well (Nepali et al., 2019). If the activity of LCS3 is determined not to be dependent on biotransformation, an appropriate drug development strategy would be to replace the nitro subgroup with a suitable bioisostere. Bioisosteres are chemical structures of similar size and electronic properties that can sometimes be substituted for one another in medicinal chemistry applications to improve pharmacokinetics or mitigate the toxicities of a lead structure. The most widely used aryl nitro isotere is pyridine, which is sufficiently unreactive (Meanwell, 2011). In the example of 119 LCS3, the entire nitrofuran heterocyclic ring could plausibly be substituted with a pyridine structure. Carboxylate (R—CO2-) subgroups exhibit a similar resonance to the nitro and have provided successful examples of its bioisosteric replacement; however, carboxylate subgroups are less electron-withdrawing nitros (Meanwell, 2011)). Trifluoromethyl (CF3) and pentafluorosulfanyl (SF5) are highly electron-withdrawing and may serve as suitable nitro bioisosteres (Adeniji et al., 2012; Sumii et al., 2019). Nitriles and dinitriles are both rigid, electron-withdrawing and polar—both cyano subgroups are less reactive than the nitro and are established bioisosteres (Fleming et al., 2010; Sasho et al., 1995; Wang et al., 2021b). If it is determined that bioactivation of the LCS3 nitro subgroup is not required for its activity, such bioisosteric replacements described in this section should be considered for structure optimization. A structure-guided investigation into the virtual docking of bioisosteres into the crystal structures of GSR and TXNRD1 may prove to be useful. The aryl nitro will need to be investigated by SAR using bioisosteres—through collaboration with a medicinal chemist and through in vitro enzymatic assays and cell-based assays. Together, these biochemical and phenotype-based experiments would allow the determination of which structures maintain the activities of inhibiting disulfide reductases and inducing cancer cell death. Ultimately, clinical translation will require successful in vivo studies and collectively, these avenues provide a path to improving efficacy and tolerability. 120 5.6.4 MAPK pathway relevance and other cancer types A striking feature of the NSCLC genomic landscape is the high incidence of activating MAPK pathway alterations (Cancer Genome Atlas Research, 2014). Due to the central theme of MAPK pathway genomic alteration and the demonstration in this thesis that LUAD cells are sensitive to LCS3, it would be logical to investigate if LCS3 has any interaction with the MAPK pathway and specifically, the ERK phosphorylation state. This may also direct future studies into the activity of LCS3 in other cancer types. The NCI-60 cell line screen revealed that central nervous system and colorectal cancer cell lines are highly sensitive to LCS3 (Figure 10) and both cancer types frequently have activating alterations in RAS or other MAPK pathway components (Fang and Richardson, 2005; Nicos et al., 2016). Thus, it should be investigated if there are intrinsic properties of MAPK pathway activation that sensitize cells to LCS3 or if LCS3 sensitization results from extrinsic characteristics such as MAPK pathway-induced changes to redox gene regulation or oxidative stress. 5.6.5 Clinically targeting oxidative stress in oncology Numerous clinical trials have investigated antioxidants such as NAC and the NRF2 inducer sulforaphane as an adjuvant therapy to either decrease tumour burden or mitigate therapy-induced oxidative damage (Forman and Zhang, 2021; Vassalle et al., 2020). While these trials aim to alleviate oxidative stress in cancer cells to improve patient outcomes, the strategy of increasing ROS to kill cancer cells has only been explored in a limited clinical capacity. Various chemotherapeutics (e.g. doxorubin), targeted agents (e.g. erlotinib), and repurposed therapies (e.g. auranofin) have been noted to increase ROS in cancer cells pre-121 clinically, but exacerbation of oxidative stress is not understood to be a main driver of therapeutic efficacy in clinical care (Buranrat and Connor, 2015; Li et al., 2016; Shan et al., 2016; Wang et al., 2021a). However, there are several examples of use in clinical oncology in which therapeutics aim to exacerbate oxidative stress. For example, the glutathione pathway disruptor BSO was clinically investigated in the early 1990s but its use was discontinued due to liver toxicity. The erastin analogue PRLX 93936 has been investigated in clinical phase I/II trials for multiple myeloma and advanced cancers, and arsenic trioxide is a known, strong inducer of ROS and is FDA-approved for promyelocytic leukemia (Hayes et al., 2020; O'Dwyer et al., 1992). Few other examples of targeting patient tumours through the induction of oxidative stress have been investigated and one must be cognizant of past safety challenges to succeed in advancing this treatment strategy. It has been reported that oxidative stress inhibits multiple stages of metastasis in vivo and targeting oxidative stress in patients may reduce metastatic burden (Gill et al., 2016; Piskounova et al., 2015). Thus, the overarching goal of improving patient outcomes using this approach will require a broad and collaborative effort. 5.7 Significance TPP has not been widely applied in the context of identifying the molecular target(s) of a novel compound and its successful application in this project should advance its broader acceptance and utilization. There is a dire need for better targeted therapies to treat lung cancer and no drugs have been clinically approved that target redox pathways for this purpose. The oxidative stress and cell death phenotype caused by LCS3 in lung cancer cells but not normal cells suggests that inhibition of redox homeostatic processes may 122 serve as an attractive treatment strategy. By defining the mechanism and molecular targets of LCS3, as well as identifying genetic determinants of response, this work may help to guide the preclinical development of LCS3 or mechanistically similar drugs. 123 References Adeniji, A. O., Twenter, B. M., Byrns, M. 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Analytical biochemistry 253, 162-168. 137 Appendices Appendix A - Cell lines Table 4. Cell lines used Cell line information including sourcing, sex, mutation status, and media requirements. 138 Appendix B - Oligonucleotides Table 5. Oligonucleotides used in Chapter 4 139 Appendix C - Reagents used REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Rabbit monoclonal anti-β-actin Cell Signaling Technology Cat# 4970P Mouse monoclonal anti-GAPDH Santa Cruz Cat# sc-47724 Rabbit monoclonal anti-cleaved PARP Cell Signaling Technology Cat# 5625S Rabbit polyclonal anti-cleaved caspase 3 Cell Signaling Technology Cat# 9661S Rabbit monoclonal anti-cleaved capase 7 Cell Signaling Technology Cat# 9491S Rabbit monoclonal anti-HO-1 (HMOX1) Cell Signaling Technology Cat# 43966S Rabbit monoclonal anti-GCLM Abcam Cat# Ab126704 Mouse monoclonal anti-NQO1 Cell Signaling Technology Cat# 3187S Rabbit monoclonal anti-NRF2 Cell Signaling Technology Cat# 12721 Rabbit monoclonal anti-KEAP1 Cell Signaling Technology Cat# 8047S Rabbit monoclonal anti-PRDX1 Cell Signaling Technology Cat# 8499S Rabbit polyclonal anti-GSTO1 Thermo Fisher Cat# PA5-83383 HRP-linked anti-rabbit IgG Cell Signaling Technology Cat# 7074S HRP-linked anti-mouse IgG Cell Signaling Technology Cat# 7076S DyLight anti-rabbit IgG 800 Cell Signaling Technology Cat# 5366S Bacterial and virus strains ARE-GFP lentivirus GenTarget LVP981-B-PBS Pathway control lentivirus GenTarget Path-Ctr5-PBS Chemicals, peptides, and recombinant proteins Polybrene Sigma Cat# H9268-10G Lipofectamine 3000 Thermo Fisher Cat# L3000-001 Dharmafect Dharmacon Cat# T-2001-03 OptiMEM Gibco Cat# 31985-062 Blasticidin Life Cat# R210-01 Puromycin dihydrochloride Sigma Cat# 540222-100MG H2DCFDA Enzo Cat# 89152-832 Propidium iodide Thermo Fisher Cat# P1304MP Purified human recombinant GSR Cedarlane Cat# CLENZ202-2 Purified human recombinant GSTO1 Sigma Cat# GS75-100ug CMFDA Cedarlane Cat# G783003 NADPH Sigma Cat# N1630-100MG GSSG (oxidized) Sigma Cat# G4376-1G DTNB Sigma Cat# D8130-5G 140 REAGENT or RESOURCE SOURCE IDENTIFIER Chemicals, peptides, and recombinant proteins Imidazole Carl Roth Cat# X998.4 IPTG Carl Roth Cat# CN08.2 Ni-NTA agarose Invitrogen Cat# LSR90101 RIPA lysis buffer Thermo Fisher Cat# P123227 HALTTM protease and phosphatase inhibitors Thermo Fisher Cat# PI-78444 Laemmli sample buffer Bio-Rad Cat# 161-0747 DTT Sigma Cat# D9779-5g Bovine Serum Albumin Sigma Cat# A3912-100g Pico chemiluminescent substrate Thermo Fisher Cat# PI34577 Fempto chemiluminescent substrate Thermo Fisher Cat# PI34095 RPMI Gibco Cat# 11875119 DMEM Gibco Cat# 12430063 Fetal bovine serum Gibco Cat# 1283020 Penicillin-streptomycin Gibco Cat# 15140-122 Dulbecco’s phosphate buffered saline Gibco Cat# 14190250 0.25% Trypsin-EDTA Gibco Cat# 25200114 Methanol Thermo Fisher Cat# A433P-4 DMSO Thermo Fisher Cat# BP231-100 Crystal violet staining solution Sigma Cat# HT90132-1L SYTOX green Thermo Fisher Cat# S7020 LCS3 Chembridge Cat# 5181525 LCS3 inactive analogue without nitro group Chembridge Cat# 5246153 NAC Sigma Cat# A7250-25G GSH-MEE Sigma Cat# 353905 AI-1 Sigma Cat# SML0009-5MG TBHP Sigma Cat# 458139-100ML BSO Sigma Cat# B2515-500MG Auranofin Sigma Cat# A6733-10MG Ferrostatin-1 Sigma Cat# SML0583-5MG RSL3 Selleck Chemicals Cat# S8155 ES936 Santa Cruz Cat# 192820-78-3 2-AAPA Sigma Cat# A4111-5MG C1-27 MedChemExpress Cat# HY-111530 NaCl Fisher Scientific Cat#S671-10 HEPES Sigma Cat# H3375-100G EGTA Sigma Cat# 4100-50GM CHAPS Detergent Thermo Fisher Cat# 28299 NEM Sigma Cat# E3876-5G Bovine catalase Sigma Cat# C9322-10G alamarBlue Thermo Fisher Cat# DAL1025 141 REAGENT or RESOURCE SOURCE IDENTIFIER Critical commercial assays NADP/NADPH-Glo assay Promega Cat# G9071 GSH/GSSG-Glo assay Promega Cat# V6611 NextSeq 500/550 High Output Kit v2.5 Illumina Cat# 20024907 NEBNext High-Fidelity 2x PCR Master Mix New England Biolabs Cat# M0541 BCA assay kit Thermo Fisher Cat# P123227 Annexin V assay kit BD Bioscience Cat# 556547 TMT 10plex isobaric label reagent set plus TMT11-131C label reagent Thermo Fisher Cat# A34808 Deposited data Transcriptome dataset This thesis GEO: GSE178311 SILAC dataset This thesis PRIDE: PXD027228 TPP dataset This thesis PRIDE: PXD030294 Experimental models: Cell lines See Table 4 This thesis N/A Oligonucleotides sgRNAs - see Table 5 This paper N/A siCtrl Dharmacon Cat# D-001810-10-05 siGSR Dharmacon Cat# E-009647-00-0005 siTXNRD1 Dharmacon Cat# L-008236-00-0005 siKIF11 Dharmacon Cat# L-003317-00-0005 Recombinant DNA Toronto KnockOut version 3 library (TKOv3) Hart et al., 2017 Addgene# 90294 pLenti6-H2B-mCherry Pemble et al., 2017 Addgene# 89766 lentiCRISPR v2 (LCv2) Sanjana et al., 2014 Addgene# 52961 pLenti-U6-tdTomato-P2A-BlasR (LRT2B) Zafra et al., 2018 Addgene# 110854 psPAX2 Didier Trono Addgene# 12260 pMD2.G Didier Trono Addgene# 12259 Software and algorithms R Environment The R Project for Statistical Computing https://www.r-project.org/ R Studio Rstudio Team https://www.rstudio.com/ Bowtie2 Langmead and Salzberg , 2012 http://bowtie-bio.sourceforge.net/bowtie2/index.shtml Cutadapt Kechin et al., 2017 https://cutadapt.readthedocs.io/en/stable/ TPP analysis script for this study This paper https://github.com/Lockwood-Lab/LCS3_project.git 142 REAGENT or RESOURCE SOURCE IDENTIFIER Software and algorithms MAGeCKFlute Wang et al., 2019 https://www.bioconductor.org/packages/release/bioc/html/MAGeCKFlute.html DAVID Bioinformatics Database v6.8 Huang et al., 2009 https://david.ncifcrf.gov/ Enrichr Chen et al., 2013 https://maayanlab.cloud/Enrichr/ Prism v8.3.1 Graphpad https://www.graphpad.com/scientific-software/prism/ ImageLab v6.0.1 Bio-Rad https://www.bio-rad.com/fr-ca/product/image-lab-software?ID=KRE6P5E8Z Inkscape v1.1.1 Inkscape Team https://inkscape.org/ Molecular Operating Environment Chemical Computing Group https://www.chemcomp.com/Products.htm Protein Data Bank RCSB https://www.rcsb.org Maestro molecular modeling platform Schrödinger https://www.schrodinger.com/platform Proteome Discoverer Thermo Fisher https://thermo.flexnetoperations.com BioRender 2021 BioRender https://biorender.com/ Other SDS-PAGE 4-12% Bis Tris gel NuPage Cat# NP0335BOX PVDF membrane Millipore Cat# IPVH00010 X-ray film Thermo Fisher Cat# PI34091 IncuCyte S3 Sartorius Cat# 4647 Cytation 3 multi-mode imaging plate reader BioTek Discontinued ChemiDoc MP Bio-Rad Cat# 12003154 FACSCalibur Flow Cytometer BD Biosciences Discontinued 0.45µm syringe filter Starstedt Cat# 83.1826 5ml syringe BD Bioscience Cat# BD-309646 Thickwall polycarbonate centrifuge tubes Beckman Coulter Cat# 343775 TL-100 ultracentrifuge Beckman Coulter Discontinued TLA-100 fixed-angle rotor Beckman Coulter Cat# 343840 Orbitrap FusionTM mass spectrometer Thermo Fisher Cat# IQLAAEGAA PFADBMBCX LTQ Orbitrap VelosTM mass spectrometer Thermo Fisher Discontinued Table 6. Reagents used 143 Data and code availability • Microarray, SILAC, and TPP data are uploaded as GEO and PRIDE accessions GSE178311, PXD027228, and PXD030294, respectively. • Code used for TPP analysis is available at (https://github.com/Lockwood-Lab/LCS3_project.git).
Thesis/Dissertation
eng
Interdisciplinary Oncology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lockwood, William W.
Morin, Gregg B.
Characterization of a small molecule inhibitor of disulfide reductases that induces oxidative stress and lethality in lung cancer cells
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