Medicine, Faculty of
DSpace
UBCV
Heroux, Devon Travis
Doctor of Philosophy - PhD
University of British Columbia
Immunotherapy, particularly immune checkpoint inhibition (ICI), has transformed cancer treatment and led to durable responses in subsets of patients. However, response rates remain low across many tumour types, and strategies to improve ICI efficacy are urgently needed. Metal-based chemotherapies, such as platinum (Pt)-containing agents like cisplatin and oxaliplatin, have long been used in oncology. Although oxaliplatin induces ICD, its systemic toxicity and inconsistent benefit in combination with ICI have limited its clinical applicability. Copper (Cu), unlike Pt, is a bioessential trace element required for mitochondrial respiration and redox regulation. These cellular processes are frequently upregulated in Cu-enriched tumour cells to support proliferation and metastasis. Historically, Cu chelation has been explored as a way to deprive tumour cells of this essential nutrient. More recently, an alternative strategy has emerged: delivering Cu therapeutically to induce a form of regulated cell death known as cuproptosis. This mechanism, and its potential to enhance anticancer immunity, remains underexplored in the context of immunotherapy. This thesis presents a systematic investigation of Cu in anticancer immunity, focusing on both therapeutic delivery and the influence of baseline Cu levels in the tumour microenvironment (TME). A liposomal formulation of the Cu complex of diethyldithiocarbamate (Cu(DDC)â) was developed and shown to activate adaptive immune responses in syngeneic mouse tumour models. A comparative screen of Cu-binding compounds revealed that the ability to transport Cu intracellularly (ionophore-like activity) was associated with ICD induction. Although both Cu(DDC)â and a Cu complexation of clioquinol (Cu(CQ)â) activated ICD markers, they elicited distinct transcriptional responses, suggesting compound-specific effects on cell death and immune activation. To assess the influence of baseline Cu content, CT26 and MC38 tumour models were characterized as high- and low-Cu environments with distinct isotopic signatures. Treatment with Cu(DDC)â or Cu(CQ)â in combination with ICI led to markedly different outcomes. CT26 showed enhanced therapeutic response, particularly during acute treatment, whereas MC38 displayed an antagonistic interaction. These findings support the therapeutic potential of Cu-based agents in cancer immunotherapy and highlight the importance of Cu carrier chemistry and tumour-specific Cu biology in guiding clinical development of Cu-based adjuncts to ICI.
Copper Ionophores as Inducers of Immunogenic Cell Death: A Mechanistic and Therapeutic Evaluation by Devon Travis Heroux MSc, McGill University, 2012 BSc (H), University of British Columbia, 2009 A THESIS 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) October 2025 © Devon Travis Heroux, 2025ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Copper Ionophores as Inducers of Immunogenic Cell Death: A Mechanistic and Therapeutic Evaluation. submitted by Devon Travis Heroux in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Interdisciplinary Oncology Examining Committee: Marcel Bally, Professor, Pathology and Laboratory Medicine, UBC Supervisor Julian Lum, Professor, Biochemistry and Microbiology, University of Victoria Supervisory Committee Member Karla Williams, Associate Professor, Pharmaceutical Sciences, UBC University Examiner David Schaeffer, Associate Professor, Pathology and Laboratory Medicine, UBC University Examiner John Bell, Professor, Medicine, University of Ottawa External Examiner Additional Supervisory Committee Member: Mads Daugaard, Associate Professor, Urologic Sciences, UBC Supervisory Committee Member iii Abstract Immunotherapy, particularly immune checkpoint inhibition (ICI), has transformed cancer treatment and led to durable responses in subsets of patients. However, response rates remain low across many tumour types, and strategies to improve ICI efficacy are urgently needed. Metal-based chemotherapies, such as platinum (Pt)-containing agents like cisplatin and oxaliplatin, have long been used in oncology. Although oxaliplatin induces ICD, its systemic toxicity and inconsistent benefit in combination with ICI have limited its clinical applicability. Copper (Cu), unlike Pt, is a bioessential trace element required for mitochondrial respiration and redox regulation. These cellular processes are frequently upregulated in Cu-enriched tumour cells to support proliferation and metastasis. Historically, Cu chelation has been explored as a way to deprive tumour cells of this essential nutrient. More recently, an alternative strategy has emerged: delivering Cu therapeutically to induce a form of regulated cell death known as cuproptosis. This mechanism, and its potential to enhance anticancer immunity, remains underexplored in the context of immunotherapy. This thesis presents a systematic investigation of Cu in anticancer immunity, focusing on both therapeutic delivery and the influence of baseline Cu levels in the tumour microenvironment (TME). A liposomal formulation of the Cu complex of diethyldithiocarbamate (Cu(DDC)â) was developed and shown to activate adaptive immune responses in syngeneic mouse tumour models. A comparative screen of Cu-binding compounds revealed that the ability to transport Cu intracellularly (ionophore-like activity) was associated with ICD induction. Although both Cu(DDC)â and a Cu complexation of clioquinol (Cu(CQ)â) activated ICD markers, they elicited distinct transcriptional responses, suggesting compound-specific effects on cell death and immune activation. To assess the influence of baseline Cu content, CT26 and MC38 tumour models were characterized as high- and low-Cu environments with distinct isotopic signatures. Treatment with Cu(DDC)â or Cu(CQ)â in combination with ICI led to markedly different outcomes. CT26 showed enhanced therapeutic response, particularly during acute treatment, whereas MC38 displayed an antagonistic interaction. These findings support the therapeutic potential of Cu-based agents in cancer immunotherapy and highlight the importance of Cu carrier chemistry and tumour-specific Cu biology in guiding clinical development of Cu-based adjuncts to ICI. iv Lay Summary Chemotherapy has been a cornerstone of cancer treatment since the 1940s, and immunotherapy has recently transformed the field by offering lasting remissions for some patients. Yet, most patients do not respond, underscoring the need for treatments that can both kill tumours and activate the immune system, generating durable, vaccine-like protection. Metals such as platinum have been used in cancer therapy since the 1970s but cause toxicity because they are foreign to the body. In contrast, copper is essential for life and is often elevated in tumours. This thesis explores the therapeutic potential of copper by using ionophores to deliver it into cancer cells. The approach not only kills tumour cells but also stimulates immune activation, enhancing the effectiveness of immunotherapy. The results highlight how the ionophore, tumour type, and treatment duration shape therapeutic success, advancing copper-based strategies for improved cancer outcomes. v Preface The studies presented in this dissertation were conceived and designed by me and Dr. Marcel Bally, with editing provided by Drs. Mads Daugaard, Julian Lum and Marcel Bally. Chapter 1 is based on a manuscript currently under revision: Heroux D, Sun XX, Nosrati Z, Bally MB. Am-bivalent role of copper in anticancer immunity and its therapeutic potential in nanomedicines. The article was organized, written, and edited by me. I prepared and illustrated all figures. Section 2 was contributed by Nosrati Z, and the original draft of Section 5 was written by Sun XX. I revised this section in 2025 to reflect recent findings. Chapter 3 is based on a manuscript published in the International Journal of Pharmaceutics: Heroux D, Leung AWY, Gilabert-Oriol R, Kulkarni J, Anantha M, Cullis PR, Bally MB. Liposomal delivery of a disulfiram metabolite drives copper-mediated tumor immunity. Int J Pharm. 2025 Aug 2;683:126010. I conducted all experiments, with the exception of the calreticulin assay (performed by Gilabert-Oriol R) and HPLC assistance (provided by Anantha M). Cryo-transmission electron microscopy images were generated by Kulkarni J at the University of British Columbia with resource support from Cullis PR. Leung AWY and Gilabert-Oriol R contributed to the design of the liposomal, immunogenic cell death (ICD), and animal studies, and provided editorial input. I wrote the manuscript, with editing by Bally MB and Leung AWY. Chapter 4 is based on a manuscript currently under revision: Heroux D, Leung AWY, Gilabert-Oriol R, Farzaneh S, Milne K, Wolf M, Alayoubi S, Singh H, MacFarlane T, Di Vito C, Nelson BH, Walsby CJ, Kalra J, Bally MB. Immunogenic cell death in colorectal cancer models is modulated by baseline and ionophore-induced copper accumulation. Preprint available at: https://doi.org/10.21203/rs.3.rs-7322091/v1 I conducted all experiments, except for ICP-MS Cu analysis (performed by Farzaneh S with support from Walsby J), immunohistochemistry (Milne K, MacFarlane T, and Di Vito C with support from Nelson BH), and selected ICD assays and replications (Wolf M, Alayoubi S, and Singh H). I wrote the manuscript, with editing by Bally MB and Leung AWY. vi Chapter 5 is based on a manuscript currently under revision: Heroux D, Sun XX, Zhang S, Sharifiaghdam M, Leung AWY, Farzaneh S, Milne K, MacFarlane T, Di Vito C, Nelson BH, Walsby C, Bally MB. Copper ionophores drive divergent responses to immune checkpoint inhibition across colorectal tumor models. Preprint available at: http://dx.doi.org/10.2139/ssrn.5442015 I conducted all experiments, with the exception of phagocytosis assays (Sun XX and Zhang C; Fig. 3EâH), ICP-MS Cu analysis (Farzaneh S, with support from Walsby J), cytokine analysis of plasma samples (Sharifiaghdam M, Fig. 6F, J), and immunohistochemistry (Milne K, MacFarlane T, and Di Vito C with support from Nelson BH; Fig. 4JâM, Fig. 6C, D, G, H, and Fig. 7F, N). Leung AWY contributed to study design and manuscript editing. I wrote the manuscript, with editing by Bally MB. All animal studies were conducted in accordance with the Canadian Council on Animal Care (CCAC) and approved by the UBC Institutional Animal Care Committee (IACC) under Animal Care Certificate A14-0290. All content in this thesis was written by me, except for co-authored sections as noted. I used OpenAI ChatGPT (GPT-4 and GPT-5) and Paperpal for grammar, style, and formatting improvements, and for technical assistance in R programming, including workflow optimization and code debugging.vii Table of Contents Abstract............................................................................................................................................. iii Lay Summary ..................................................................................................................................... iv Preface ............................................................................................................................................... v Table of Contents ............................................................................................................................. vii List of Tables ...................................................................................................................................... x List of Figures .................................................................................................................................... xi List of Illustrations ............................................................................................................................ xii List of Abbreviations ........................................................................................................................ xiii Acknowledgements ......................................................................................................................... xvi Dedication ...................................................................................................................................... xvii 1 Introduction ................................................................................................................................ 1 1.1 Thesis overview ............................................................................................................................... 1 1.2 Cuâs role in biology .......................................................................................................................... 3 1.3 Cuâs role in cancer and cancer treatments ........................................................................................ 3 1.3.1 Role of Cu in tumour angiogenesis ..................................................................................................................... 4 1.3.2 Role of Cu in tumour metastasis ......................................................................................................................... 5 1.3.3 Role of Cu in resistance to chemotherapy .......................................................................................................... 6 1.4 Cu-binding compounds .................................................................................................................... 6 1.4.1 Cu ionophores ..................................................................................................................................................... 7 1.4.2 Cu chelators ........................................................................................................................................................ 7 1.5 Cu and the immune system ............................................................................................................. 8 1.5.1 Cu excess and the immune system ..................................................................................................................... 9 1.5.2 Dietary Cu deficiency ........................................................................................................................................ 10 1.5.3 Cu chelation ...................................................................................................................................................... 10 1.6 Cu and immunogenic cell death of cancer cells .............................................................................. 11 1.6.1 Approved ICD-inducing treatments with an emphasis on disulfiram ............................................................... 12 1.7 Cu, Cu-complexes, and PD-L1 ......................................................................................................... 14 1.8 Cu-based nanomedicines and development of Metaplex technology ............................................. 18 1.9 Conclusions ................................................................................................................................... 20 2 Materials and Methods ............................................................................................................. 23 2.1 Materials ....................................................................................................................................... 23 2.2 Liposomal preparation and characterization .................................................................................. 23 2.2.1 Liposomal preparation ...................................................................................................................................... 23 2.2.2 Liposomal loading ............................................................................................................................................. 24 viii 2.3 In vitro cell-based assays ................................................................................................................ 25 2.3.1 Cell lines ............................................................................................................................................................ 25 2.3.2 Cytotoxicity assays ............................................................................................................................................ 25 2.3.3 Cu internalization assay .................................................................................................................................... 26 2.3.4 Phagocytosis Assay ........................................................................................................................................... 26 2.3.5 Cytokine Profiling .............................................................................................................................................. 26 2.3.6 Characterization of immunogenic cell death .................................................................................................... 27 2.4 In vivo characterization of liposomal formulations ......................................................................... 29 2.4.1 Dose range finding studies ................................................................................................................................ 29 2.4.2 Pharmacokinetic studies ................................................................................................................................... 30 2.4.3 Biodistribution and immune infiltration studies ............................................................................................... 30 2.4.4 In vivo efficacy studies ...................................................................................................................................... 30 2.4.5 Anti-tumour vaccination study ......................................................................................................................... 31 2.5 Analytical Assays ............................................................................................................................ 32 2.5.1 Atomic absorption spectroscopy ...................................................................................................................... 32 2.5.2 Inductively Coupled Plasma Mass Spectrometry (ICP-MS) ............................................................................... 32 2.5.3 HPLC analysis ..................................................................................................................................................... 32 2.6 Immunohistochemistry (IHC) Analysis ............................................................................................ 32 2.6.1 Tumour harvesting and tissue processing ......................................................................................................... 32 2.6.2 Multicolor immunofluorescent staining â primary panel ................................................................................. 33 2.6.3 Multicolor immunofluorescent staining â calreticulin/PD-L1 panel ................................................................. 33 2.6.4 Image acquisition and analysis .......................................................................................................................... 34 2.7 RNA Sequencing and Transcriptomic Analysis ................................................................................ 34 2.7.1 Bulk RNA sequencing ........................................................................................................................................ 34 2.7.2 Differential expression and pathway enrichment ............................................................................................. 35 2.7.3 Gene set enrichment and signature analysis .................................................................................................... 35 2.7.4 Integration with public datasets ....................................................................................................................... 35 2.7.5 Statistical analysis ............................................................................................................................................. 36 3 Liposomal delivery of a disulfiram metabolite drives Cu-mediated tumour immunity ................ 37 3.1 Introduction .................................................................................................................................. 37 3.2 Results ........................................................................................................................................... 39 3.2.1 Characterization of Cu(DDC)2 formulations prepared in BSA and liposomes ................................................... 39 3.2.2 External buffer with a pH of 7.5 is optimal for Cu(DDC)2 formation ................................................................. 41 3.2.3 Internal buffer with pH 7.5 increases the Cu(DDC)2 to liposomal lipid ratio .................................................... 43 3.2.4 Cu(DDC)2 association with liposomes while varying the initial Cu(DDC)2 to liposomal lipid ratio ................... 44 3.2.5 DDC has Cu ionophore-like properties and can move Cu across liposomal lipid bilayers as well as cellular membranes ..................................................................................................................................................................... 45 3.2.6 Cytotoxicity of liposomal and BSA Cu(DDC)2 formulations. .............................................................................. 48 3.2.7 Liposomal and BSA formulations of Cu(DDC)2 exhibit similar behaviour following intravenous administration in NRG mice. .................................................................................................................................................................... 49 3.2.8 Efficacy of liposomal and BSA Cu(DDC)2 formulations in an MDA-MB-231 xenograft model. .......................... 50 3.2.9 Liposomal Cu(DDC)2 exhibits enhanced activity in an immunocompetent 4T1 syngeneic model. ................... 51 3.2.10 Transcriptional and functional ICD responses to liposomal Cu(DDC)â ......................................................... 52 3.3 Discussion ...................................................................................................................................... 55 3.4 Conclusions ................................................................................................................................... 58 4 Immunogenic cell death in colorectal cancer models is modulated by baseline and ionophore-ix induced Cu accumulation .................................................................................................................. 59 4.1 Introduction .................................................................................................................................. 59 4.2 Results ........................................................................................................................................... 61 4.2.1 Cu-dependent ATP release and ionophore activity across compound classes. ................................................ 62 4.2.2 Cu-binding compounds enhance DAMP release in proportion to intracellular Cu levels. ................................ 64 4.2.3 Basal intracellular Cu levels predict sensitivity to Cu(CQ)â but not Cu(DDC)â. .................................................. 66 4.2.4 Gene ontology enrichment in CT26 cells following Cu treatment. ................................................................... 68 4.2.5 Transcript-level characterization of Cu-responsive and immunogenic cell death (ICD) genes in CT26 cells. ... 70 4.2.6 Distinct patterns of Cu accumulation and immune activation in CT26 and MC38 tumours during progression and after treatment. ........................................................................................................................................................ 72 4.2.7 Cu ionophore monotherapy modestly alters tumour growth without impacting body weight in CT26 and MC38 models. ................................................................................................................................................................. 75 4.3 Discussion ...................................................................................................................................... 76 5 Cu ionophores drive divergent tumour responses to immune checkpoint blockade .................... 80 5.1 Introduction .................................................................................................................................. 80 5.2 Results ........................................................................................................................................... 82 5.2.1 Cu-associated cell death and metabolic pathway signatures in murine syngeneic tumour models. ............... 83 5.2.2 Transcriptomic profiling of CT26 cells treated with Cu ionophores identifies distinct and overlapping pathways in cuproptosis and immune regulation. .......................................................................................................... 86 5.2.3 Cytotoxicity, cytokine release, and phagocytosis in BMDMs treated with Cu(DDC)â or Cu(CQ)â ..................... 89 5.2.4 In vivo Cu biodistribution and immune infiltration after Cu(DDC)â or Cu(CQ)â treatment ............................... 92 5.2.5 Divergent temporal effects of Cu ionophore and antiâPD-1 therapy in CT26 and MC38. ................................ 95 5.2.6 Immune and cytokine responses to Cu ionophore and antiâPD-1 therapy in CT26 and MC38 ........................ 97 5.2.7 Cu biodistribution and calreticulin exposure following Cu ionophore and antiâPD-1 treatment ................... 100 5.3 Discussion ..................................................................................................................................... 103 6 Summarizing Discussion .......................................................................................................... 105 6.1 Summary of results ....................................................................................................................... 105 6.1.1 Cu ionophore formulations ............................................................................................................................. 105 6.1.2 Cu or carrier? .................................................................................................................................................. 106 6.1.3 High vs low Cu tumour models ....................................................................................................................... 107 6.1.4 Isotopic fractions â a novel consideration in cuproptosis ............................................................................... 108 6.2 Future perspectives ...................................................................................................................... 109 6.2.1 Liposomal development and optimization ..................................................................................................... 109 6.2.2 Emerging Cu-based therapeutic strategies ..................................................................................................... 111 6.2.3 Epidemiologic approaches to drug repurposing ............................................................................................. 113 6.3 Conclusions .................................................................................................................................. 117 References ...................................................................................................................................... 118 x List of Tables Table 1.1. Serum and tissue Cu levels in normal and cancer patients. ..................................................................... 4 Table 1.2. Commonly used Cu chelators and ionophores ........................................................................................ 8 xi List of Figures Figure 1.1. Effect of Cu excess or Cu deprivation on immune cells. ......................................................................... 9 Figure 1.2. Effects of Cu excess or Cu deprivation in tumour cells. ........................................................................ 11 Figure 3.1. Characterization of Cu(DDC)2 formulations prepared in BSA and liposomes. ...................................... 41 Figure 3.2. External buffer with a pH of 7.5 is optimal for Cu(DDC)2 formation. .................................................... 42 Figure 3.3. Internal buffer with pH 7.5 increases the Cu(DDC)2 to liposomal lipid ratio. ....................................... 44 Figure 3.4. Cu(DDC)2 association with liposomes while varying the initial Cu(DDC)2 to liposomal lipid ratio. ....... 45 Figure 3.5. DDC has Cu ionophore-like properties and can move Cu across liposomal lipid bilayers as well as cellular membranes. ............................................................................................................................................... 47 Figure 3.6. Cytotoxicity of liposomal and BSA Cu(DDC)2 formulations. .................................................................. 49 Figure 3.7. Liposomal and BSA formulations of Cu(DDC)2 exhibit similar behaviour following intravenous administration in NRG mice. ................................................................................................................................... 50 Figure 3.8. Efficacy of liposomal and BSA Cu(DDC)2 formulations in an MDA-MB-231 xenograft model. ............. 51 Figure 3.9. Liposomal Cu(DDC)2 exhibits enhanced activity in an immunocompetent 4T1 syngeneic xenograft model. ..................................................................................................................................................................... 52 Figure 3.10. Cu(DDC)â activates ICD-associated transcriptional pathways and triggers DAMP release in vitro, leading to protection in a tumour vaccination model. ........................................................................................... 54 Figure 4.1. Cu-dependent ATP release and ionophore activity across compound classes. .................................... 64 Figure 4.2. Cu-binding compounds enhance DAMP release in proportion to intracellular Cu levels. .................... 65 Figure 4.3. Basal intracellular Cu levels predict sensitivity to Cu(CQ)â but not Cu(DDC)â. ...................................... 67 Figure 4.4. Gene ontology enrichment in CT26 cells following Cu treatment. ....................................................... 69 Figure 4.5. Transcript-level characterization of Cu-responsive and immunogenic cell death (ICD) genes in CT26 cells. ........................................................................................................................................................................ 72 Figure 4.6. Distinct patterns of Cu accumulation and immune activation in CT26 and MC38 tumours during progression and after treatment. ........................................................................................................................... 75 Figure 5.1. Cu-associated cell death and metabolic pathway signatures in murine syngeneic tumour models. ... 85 Figure 5.2. Transcriptomic profiling of CT26 cells treated with Cu ionophores identifies distinct and overlapping pathways in cuproptosis and immune regulation. ................................................................................................. 89 Figure 5.3. Cytotoxicity, cytokine release, and phagocytosis in BMDMs treated with Cu(DDC)â or Cu(CQ)â ......... 92 Figure 5.4. In vivo Cu biodistribution and immune cell infiltration following treatment with the liposomal Cu(DDC)â or Cu(CQ)â formulations. ......................................................................................................................... 95 Figure 5.5. Divergent effects of Cu ionophore and antiâPD-1 combination therapy in murine CT26 and MC38 tumour models. ...................................................................................................................................................... 97 Figure 5.6. Immune cell infiltration and cytokine response following Cu ionophore and antiâPD-1 combination therapy in mice with CT26 and MC38 tumours. ................................................................................................... 100 Figure 5.7. Cu biodistribution and calreticulin exposure following Cu ionophore and antiâPD-1 treatment ...... 102 Figure 6.1. Design and ROS activity of Cu(DDC)â and diCu(DDC)â liposomal formulations. .................................. 110 Figure 6.2. Ongoing case-control study of disulfiram uses and cancer outcomes in British Columbia. ............... 114 Figure 6.3. Integrated research program in drug repurposing for cancer. ........................................................... 115 xii List of Illustrations Illustration 3.1. Graphical abstract illustrating the development and immune activity of a copper diethyldithiocarbamate (Cu(DDC)â) liposomal formulation. ................................................................................... 39 Illustration 4.1. Graphical abstract illustrating ionophore-mediated copper delivery and activation of immunogenic cell death in colorectal cancer models. ............................................................................................ 61 Illustration 5.1. Graphical abstract summarizing copper ionophoreâinduced immune modulation in high- and low-copper colorectal cancer models. .................................................................................................................... 82 xiii List of Abbreviations 3H-CHE 3H-cholesteryl hexadecyl ether AAS Atomic absorption spectroscopy AD Alzheimerâs disease ALDEFLUOR Aldehyde dehydrogenase activity assay ALDH Aldehyde dehydrogenase ANOVA Analysis of variance ATCC American Type Culture Collection ATP Adenosine triphosphate ATSM Diacetyl-bis(N4-methylthiosemicarbazone) AUC Area under the curve BCL B-cell lymphoma BD Biodistribution BMDM Bone marrow-derived macrophage BSA Bovine serum albumin BTIC Brain tumour-initiating cell CBT Cu-based therapeutic CC Cu chaperone CDDP Cisplatin CED Convection-enhanced delivery CEM Cryo-electron microscopy CHE Cholesteryl hexadecyl ether COPD Chronic obstructive pulmonary disease CQ Clioquinol CRC Colorectal cancer CRG Cu response gene CRISPR Clustered Regularly Interspaced Short Palindromic Repeats CRT Calreticulin CSC Cancer stem cell Cu(DDC)2 Cu diethyldithiocarbamate Cu(CQ)2 Cu clioquinol DAMP Damage-associated molecular pattern DAPI 4Ê¹,6-diamidino-2-phenylindole DC Dendritic cell DDC Diethyldithiocarbamate DEG Differentially expressed gene DLAT Dihydrolipoamide S-acetyltransferase DMSO Dimethyl sulfoxide DMT Divalent metal transporter DNA Deoxyribonucleic acid DOTA 1,4,7,10-tetraazacyclododecane-N,Nâ,Nâ,Nââ-tetraacetic acid DOX Doxorubicin DSF Disulfiram DSPC 1,2-distearoyl-sn-glycero-3-phosphocholine DSPE 1,2-distearoyl-sn-glycero-3-phosphoethanolamine xiv DSPG 1,2-distearoyl-sn-glycero-3-phospho-(1Ê¹-rac-glycerol) EDTA Ethylenediaminetetraacetic acid EGFR Epidermal growth factor receptor ELISA Enzyme-linked immunosorbent assay EV Extracellular vesicle FASTQ Text-based format for storing biological sequence data FDA Food and Drug Administration GO Gene Ontology GQD Graphene quantum dot GSEA Gene set enrichment analysis GSH Glutathione GTSM Glyoxal-bis(thiosemicarbazone) HBSS Hankâs Balanced Salt Solution HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HIF Hypoxia-inducible factor HIV Human immunodeficiency virus HPLC High-performance liquid chromatography HRP Horseradish peroxidase HSA Human serum albumin IACC Immunoassay-acquired cancer cohort ICD Immunogenic cell death ICI Immune checkpoint inhibitor ICP-MS Inductively coupled plasma mass spectrometry IGF Insulin-like growth factor IHC Immunohistochemistry IN Intranasal IP Intraperitoneal IR Ionizing radiation LOX Lysyl oxidase MAPK Mitogen-activated protein kinase MOF Metal-organic framework MTD Maximum tolerated dose NADH Nicotinamide adenine dinucleotide NK Natural killer NSAID Non-steroidal anti-inflammatory drug PARP Poly (ADP-ribose) polymerase PBS Phosphate-buffered saline PDT Photodynamic therapy PK Pharmacokinetics QELS Quasi-elastic light scattering RCD Regulated cell death RES Reticuloendothelial system RNA Ribonucleic acid ROS Reactive oxygen species RT Radiation therapy SD Standard deviation SDT Sonodynamic therapy xv SEM Standard error of the mean SOD Superoxide dismutase TCA Trichloroacetic acid TEA Triethanolamine TETA Triethylenetetramine TISMO Tumour-initiated systemic modulation TMA Tissue microarray TME Tumour microenvironment TMZ Temozolomide TNFA Tumour necrosis factor alpha TPM Transcripts per million TV Tumour volume UV Ultraviolet VEGF Vascular endothelial growth factor xvi Acknowledgements Marcel, youâve been the kindest and most supportive supervisor Iâve ever worked with. Your leadership gave me the freedom to think widely and work deeply, and your encouragement let in light when it was needed most. From our earliest conversations, I began to understand the impact of my work through its ability to âtouch a patient,â and Iâm deeply grateful for the trust you gave me to take the scenic route. Iâm thankful to Mads and Julian for your steadfast support as committee members, especially when the timeline became alternative. Iâve appreciated your accessibility, guidance, and thoughtful feedback throughout the years. Ada, your friendship and endless patience were a guiding light when I entered the group. Your deep knowledge and calm presence is missed. Brent, thank you for the training and wealth of technical knowledge youâve provided over the years, keeping the lab running for us all. Norman, Iâm grateful for your help in explaining so many technical processes, often more than once. Iâm also thankful for the friendships of past and current lab members who enriched my time there. To my parents: thank you for giving me a loving home that valued the joy of learning. Mom, your perpetual kindness and reminders to make sure I was eating properly never went unnoticed. From providing childcare to dropping off sour doughs, youâve always been there for us. Dad, your strong work ethic set a standard of productivity, and your enthusiasm for ocean science was contagiousâIâll always be an ichthyologist at heart. Naty, your love and support have been the heart and soul of this mission from the beginning. Thank you for being there to share in the excitement when things were going well, and for believing in me when they werenât. Luna and Cielo, you are the best reason to wake up in the morning, and often throughout the night. You are my blessings. xvii Dedication To Naty, Luna, and Cielo 1 1 Introduction 1.1 Thesis overview Transition metals play a range of essential roles in biological processes. Zinc (Zn), iron (Fe), manganese (Mn), cobalt (Co), and copper (Cu) are all required for human health [1]. While Zn acts through dynamic changes in ion availability, the other metals function mainly as static cofactors involved in redox catalysis through Fenton chemistry. A more recent concept, metalloallostery, describes how metal ions can also modulate protein function through binding at allosteric sites [2,3]. Approximately 30 enzymes use Cu as a cofactor, including cytochrome c oxidase, superoxide dismutase, and lysyl oxidase, which support the electron transport chain, neutralize free radicals, and promote collagen crosslinking, respectively [3]. Cu has also been identified as a regulator of kinases such as MEK1/2 and ULK1/2, leading to activation of MAPK signaling and autophagy, both of which are often upregulated in cancer cells [4,5]. Elevated Cu levels are found in many tumour types and have been linked to increased proliferation, angiogenesis, and metastasis. Disrupting Cu homeostasis through either deprivation or excess can result in cell death by inhibiting cuproplasia (Cu-dependent growth) or activating death pathways, including apoptosis and caspase-independent mechanisms such as cuproptosis [6â9]. Clinically, both Cu chelation and Cu delivery strategies have been explored with varying success. A major consideration in these approaches is how Cu levels affect immune system function; Cu deficiency impairs multiple aspects of immune activity, whereas Cu supplementation in some settings has been shown to enhance immune cell responses. Cuâs role in tumour immunity involves a range of overlapping mechanisms. Cu signaling modulates immune-related pathways such as NF-κB and ALDH1, which influence the expression of cytokines and retinoic acidârelated signals [10â13]. These signaling changes can reshape the tumour immune microenvironment, shifting the balance between immune activation and suppression depending on context. In some cases, Cu contributes to immune evasion by upregulating and stabilizing PD-L1 expression on tumour cells [14â19]. Conversely, it has also been shown to support immune activation through the induction of immunogenic cell death (ICD) as part of Cu-based therapeutic (CBT) strategies, a form of cell death that releases damage-associated molecular patterns (DAMPs) that contribute towards an anti-tumour immune response [20]. 2 The activation of ICD to increase anticancer immunity has been a promising goal for therapeutic drug design [21,22], and the recent characterization of a newly described form of cell death caused by Cu overload, termed âcuproptosis,â has sparked renewed interest in CBT strategies with ICD induction as a primary goal [23â25]. However, this clinical momentum has not been matched by a clear understanding of Cuâs direct role in ICD induction, or of the diverse chemical carriers, such as ionophores, that transport it into cells. Ionophores bind and neutralize Cu ions, facilitating their movement across biological membranes. This class includes approved drugs such as disulfiram (DSF), used for alcohol use disorder, and clioquinol (CQ), an antifungal and antiparasitic. The hypothesis guiding this research was as follows: Cu has a direct role in induction of ICD, and parenteral administration of Cu ionophores will elevate tumour Cu levels, causing immunogenic cell death and development of anti-cancer immunity. Based on this hypothesis, the study aimed to: Aim 1: Characterize a novel liposomal formulation of a Cu-bound metabolite of disulfiram, Cu(DDC)2, and assess its activity in pre-clinical models of cancer developed in immunocompetent and immune compromised mice, and whether treated cells can induce a vaccination effect in immunocompetent animals. Aim 2: Determine whether Cu(DDC)2, other Cu ionophores and Cu can induce immunogenic cell death by measuring expression/release of damage-associated molecular patterns (DAMPs). Aim 3: Assess the use of an ICD inducer alone and in combination with known immunomodulators for use in the treatment of syngeneic murine models of cancer. The following section provides a foundational overview of Cu biology in both normal and cancer contexts, along with recent insights into its role in anti-cancer immunity. It examines how strategies to either deliver or deplete Cu influence immune responses within the tumour microenvironment. The effects of Cu modulation on both immune and tumour cells are described through mechanisms that promote immune activation or suppression. Together, this background sets the stage for exploring how targeted manipulation of Cu homeostasis might enhance therapeutic responses in cancer. 3 1.2 Cuâs role in biology Although present only in trace amounts, free copper (Cu) is highly reactive and can catalyze damaging redox reactions if not tightly regulated. The body maintains two main Cu reservoirs: a large, protein-bound pool and a smaller, labile fraction that remains metabolically accessible. Dietary Cu is absorbed in the small intestine following the reduction of Cu²âº to Cuâº by STEAP enzymes [26] enabling uptake into cells primarily via the CTR1 (SLC31A1) transporter, with CTR2 (SLC31A2) and DMT1 serving compensatory roles under conditions of stress or deficiency [26â28]. In circulation, Cu binds to albumin and transcuprein for transport, while in tissues, ceruloplasmin (CP) carries the majority of Cu. Once inside cells, Cu is directed to target compartments by a network of cytosolic chaperones that maintain intracellular homeostasis. Cu chaperone for superoxide dismutase (CCS) delivers Cuâº to superoxide dismutase 1 (SOD1) and to MAPK signaling proteins such as MEK1/2 [29,30]. In mitochondria, COX17 hands Cu off to SCO1/2 and COX11, enabling the proper assembly of cytochrome c oxidase [31]. In the trans-Golgi network, ATOX1 transfers Cu to ATP7A or ATP7B, facilitating enzyme metallation or Cu export [32]. Both CCS and ATOX1 also localize to the nucleus, where they participate in transcriptional regulation, including the activation of hypoxia-inducible factor-1α (HIF1α) [33]. To avoid toxicity, excess intracellular Cu is buffered by metallothioneins (MT1/2) and glutathione (GSH), which chelate free Cuâº and mitigate ROS generation [33]. These buffering systems are upregulated during oxidative stress, in part through NRF2-mediated pathways [34]. Cu export is primarily mediated by ATP7A in most tissues and by ATP7B in the liver; loss-of-function mutations in these transporters cause Menkes and Wilson diseases, respectively [26]. Given its dual roles in redox balance and signaling, Cu is frequently dysregulated in cancer, offering potential therapeutic opportunities. These are explored in the following sections. 1.3 Cuâs role in cancer and cancer treatments Cu is considered to be an important target in cancer due to its essential role in the cell and its dysregulation in many cancers [35]. In some cancers, alterations in Cu absorption, transport, metabolism, or excretion have led to higher Cu serum levels [37,38]. This may be due to higher demand for nutrients by cancer cells and this, in turn is linked to an elevation of serum and tissue levels of Cu in 4 various cancers including breast [39â42], ovarian [41,43â45], lung [46â49], colorectal [41,50â52], stomach [53,54], thyroid [55,56], acute leukemia [57,58], oral [59â61] and prostate [62,63] (Table 1). Moreover, various studies have confirmed serum Cu levels to correlate with grade of malignancy in some cancers and suggest serum Cu measurement as a screening tool for cancer diagnosis [64,65]. Ceruloplasmin, the major Cu-carrying protein in the blood, is another potential prognostic marker. Higher serum ceruloplasmin levels (4-8-fold over normal level) have been reported in numerous cancers during tumour progression, which returns to normal during tumour regression [66,67]. With this noted, it is important to ask what the role of Cu may be in various hallmarks of cancer. 1.3.1 Role of Cu in tumour angiogenesis Angiogenesis is a physiological process in which new blood vessels form from existing vasculature, typically occurring during tissue development or repair. In addition, it plays a critical role in the growth of cancer by developing blood vessels to provide oxygen, nutrients, and other essential factors [68,69]. The tumour-associated blood vessels are, however, abnormal and lack a basement membrane and are tortuous contributing to these blood vessels being âleakyâ to serum. Tumour angiogenesis is regulated by many diverse factors. This can include hypoxia that can initiate the formation and release of vascular endothelial growth factor (VEGF) a potent mediator of blood vessel formation [70]. Findings have linked tumour angiogenesis with higher Cu levels and ceruloplasmin expression in serum [71,72]. Cu also has an essential role in the regulation of HIF-1, and under hypoxic conditions in tumour cells, HIF-1 binds to the VEGF gene promoter and promotes angiogenesis [73]. On the other hand, depletion of Cu using Cu chelation therapy has been shown to inhibit angiogenesis in a wide variety of cancer cell and xenograft systems [68,74â77]. PSP-2 is a Cu (I) selective chelator that has induced significant anti-angiogenic activity due to a reduction in intracellular Cu level [78]. Table 1.1. Serum and tissue Cu levels in normal and cancer patients. Cancer Serum Cu Level (µg/dL) Tissue Cu Level (µg/g) Normal Cancer Normal Cancer Breast 50.6 ±12.8 105.6 ± 12.8 [40] 9.3 ± 2.3 21.0 ± 10.7 [79] 98.8 ± 24.3 167.3 ±37.9 [80] 1.58 ± 0.62 1.91 ± 0.56 [41] Ovarian 106.73 ± 26.37 146 ± 24.78 [44] 0.3 ± 0.1 0.7 ± 0.3 [45] 92.9 139.5 [81] 1.26 ± 0.45 2.16 ± 0.63 [41] Lung 109.5 ± 5.39 122.9 ± 3.77 [49] 1.01 ± 0.02 1.52 ± 0.08 [46] 128.5 ±5.23 162.4 ± 8.18 [48] 5.08 ± 1.09 8.23 ± 4.88 [47] 5 Colon 152.08 ± 112.56 154.60 ± 91.71 [50] 1.53 ± 0.35 1.90 ± 0.6 [41] 135.8 ± 30.5 138.6 ± 30.8 [52] 1.26 ± 0.37 1.47 ± 0.58 [51] Stomach 143.03 ± 3.25 171.94 ± 7.27 [53] 1.1 ± 0.4 1.7 ± 0.4 [54] Thyroid 105.87±10.68 131.61±33.9 [55] 4.23 ± 0.18 14.5 ± 2.6 [56] Leukemia 86.7 ± 25.3 132.8 ± 50.6 [58] 15 ± 4 * 52 ± 16 * [57] Oral 124.83 ± 20.68 151.20 ± 11.20 [60] 114.20 ± 38.69 209.85 ± 160.28 [59] 105.5 + 18.81 141.99 ± 21.44 [61] Prostate 97 ± 22 169 ± 31 [63] 94.45 ± 34.37 100.31 32.38 [62] * μg/106cells 1.3.2 Role of Cu in tumour metastasis Cu ions appear to be required for the formation of pre-metastatic niches as well as the establishment of metastasis through Cu-binding proteins. Lysyl oxidase (LOX) and LOX-like (LOXL1â4) proteins are Cu-dependant metalloenzymes, secreted under hypoxic conditions by various cancers such as breast, pancreatic, hepatic, and colorectal cancers [82â84]. LOX family proteins are characterized by their Cu-binding sites at the C-terminal domain of the protein. The catalytic function of LOX is activated once it binds to Cu as a co-factor [85]. As mentioned previously, to support intracellular Cu delivery, the CTR1 transmembrane protein transports Cu ions into cells. Thereafter, ATOX1 mediates the delivery of Cu to the ATP7A and ATP7B in the trans-Golgi network. ATP7A is responsible for pumping intracellular Cu to secreted LOX cuproenzymes. Subsequently, the Cu-loaded LOX mediates tumour progression through separate extracellular and intracellular mechanisms. Within extracellular mechanisms, LOX catalyzes the oxidation of lysine residues in elastin and collagen and forms a crosslinked elastin-collagen in the tumour microenvironment which facilitates tumour growth and invasion [86,87]. It has been shown that silencing ATOX1 and ATP7A genes can inhibit the LOX activity, tumour growth, and metastasis in various models of lung, breast and head and neck cancers [58-60]. In addition to this extracellular influence, LOX plays a key role in tumour cell migration and invasion. The by-product of LOX activation is hydrogen peroxide which stimulates the key signaling molecules focal adhesion kinase (FAK1) and proto-oncogene tyrosine-protein kinase (SRC), and promotes cell migration and metastasis [82,88]. Mediator of cell motility 1 (MEMO1) is another Cu-dependent enzyme involved in 6 tumour metastasis. MEMO1 is upregulated in tumours and its expression is correlated with cancer aggressiveness [89,90]. In mammary epithelial cells, MEMO1 mediates HER2-dependent cell migration through upregulation of IGF-IR/IRS1 signaling [91]. Recent studies have investigated MEMO1 inhibition as a potential therapeutic target for cancer treatment [92â94]. 1.3.3 Role of Cu in resistance to chemotherapy Platinum-based drugs are chemotherapeutic agents widely used for treatment of solid tumours including colon, breast, ovarian, bladder, brain, and non-small cell lung cancers [95â100]. The most common platinum agents are cisplatin, carboplatin, and oxaliplatin, which have unique role in the management of individual cancers [101][75]. However, resistance to platinum-based drugs develops due to increases in DNA repair, reductions in cellular accumulation, and increases in drug inactivation [93][102]. Studies have shown that cell entry and subcellular distribution of platinum-containing drugs are mediated through transporter proteins (CTR1 and CTR2) and chaperones (ATOX1, ATP7A and ATP7B), which are known to be involved in Cu homeostasis [103â105]. CTR1, the main Cu uptake transporter, regulates uptake and controls the accumulation and cytotoxic effect of platinum-containing drugs. Studies have shown a good correlation between CTR1/CTR2 expression and chemoresistance to platinum-based drugs [43,106,107]. Elevated concentrations of Cu and platinum-based compounds have opposite effects on the regulation of CTR1 and CTR2. When there is an excess amount of Cu and platinum-containing drugs, CTR1 is downregulated through endocytosis and degradation, while CTR2 expression is increased [107]. Moreover, it has been shown that silencing of CTR2 leads to an increase in the cellular accumulation of cisplatin and this has been proposed as a treatment to overcome platinum resistance [43]. Recent evidence demonstrates that higher expression of the Cu transporter ATP7A and ATP7B in different tumour types contributes to reduced sensitivity of platinum-based treatments due to an increase in drug efflux [78]. Several studies have investigated the inhibition of ATP7A and ATP7B as potential targets in cancer treatments. For example, silencing ATP7A and ATP7B expressions in ovarian cancer and breast cancers has improved platinum drug sensitivity [109â111]. 1.4 Cu-binding compounds As highlighted above Cu plays a role in cancer development in progression as well as in treatment response and resistance to certain therapeutics, notably the platinum-based agents. While this is understood, many investigators are pursuing the development of methods to modulate Cu levels. 7 However, the rationales are paradoxical as some strategies are attempting to deplete Cu while other strategies are attempting to increase Cu levels in the cells. This is further confused by those trying to develop Cu complexes as therapeutics and trying to deal with concerns that Cu is often viewed as a toxic metal. In order to better understand the issues, a summary of Cu-binding compounds and their use in the treatment of models of cancer has been summarized below. 1.4.1 Cu ionophores Cu ionophores efficiently transport Cu across biological membranes, providing a payload of Cu that can selectively induce cuproptosis in cancer cells [112,113]. The role of Cu ionophores in cancer has been recently reviewed by Oliveri et al [114], and will be briefly discussed here in the context of cancer immunity, an area of growing interest and attention. Table 2 describes commonly used Cu ionophores and their known cellular and immunological effects. The most commonly used of these compounds in cancer is disulfiram (DSF), which is an anti-alcohol abuse drug that has shown potential as an anticancer agent since the 1970s, although efforts to use it clinically have not been successful [115]. This may be due to its rapid systemic clearance and its requirement for Cu to be active against cancer. This is where this lab became interested in the DSF metabolite diethyldithiocarbamate (DDC), a well-known Cu-binding agent [116â120]. When DSF is ingested, it is converted in the blood to DDC, which may undergo oxidative biotransformation to diethylthiomethylcarbamate (Me-DTC) [121]. This compound acts as a suicide inhibitor for aldehyde dehydrogenases (ALDHs), although in the presence of Cu, DDC will form Cu(DDC)2, the complex responsible for DSFâs anticancer activity. This may be preferentially formed in cancer cells with higher levels of intracellular Cu, although most studies with DSF have been in combination with Cu, or as a soluble form of Cu(DDC)2 [115,122]. The mechanisms through which Cu ionophores can act against cancer or modulate immune responses are summarized in Table 2, but are further described in Section 5 of this review. 1.4.2 Cu chelators Contrary to the potential of Cu ionophores to augment anti-tumour responses, Cu chelation has been explored as a way of reducing intracellular Cu levels and inhibiting Cu-driven cancerous cell growth (i.e. cuproplasia) [123,124]. Commonly used chelators include D-penicillamine, trientine, and tetrathiomobdylate (TM) (Table 2), which differ from ionophores in that the binding coefficient to Cu is generally stronger and leads to the sequestration of Cu instead of transport of Cu as would be driven by 8 ionophores [125]. Cu chelators target cell metabolism, kinase pathways, and immunomodulating pathways including NF-kB, the inhibition of which potentially leads to a reduction of various cytokines including IL-1b, IL-6, and IL-8, known to modify immune components of the tumour microenvironment (TME). Cu chelation has shown efficacy in combination with monoclonal antibody therapy [126], immune activation [127], and oncolytic virotherapy [128,129], however, there are few studies on the direct role of Cu chelation in antitumour immune response and more research is needed to understand specific immune cell involvement in various cancers. Table 1.2. Commonly used Cu chelators and ionophores Cu Ionophores *(added Cu) Cellular effect Immune effect Disulfiram [15,18,130â132] *âNF-ÎºB, PSM, ALDH, *âROS *âICD, PD-L1; âRA Clioquinol [133] *âNF-ÎºB, PSM n.d. Pyrrolidine dithiocarbamate [133â135] *âNF-ÎºB, PSM, âCSP3 âTNF-Î±, âIL-12, âIL-10 Elesclomol [136] âROS, âATP7A n.d. Cu Chelators Penicillamine [137â139] âROS, âdsDNA breaks âTC, BC, NK, NP Tetrathiomobdylate [140,141] âNF-ÎºB, RAS/MAPK âTNF, IL-2, IL-4, IL-5, IFN-γ; âCD4+ infiltrate Trientine [142] âAngiogenesis âIL-8 Curcumin [143,144] âNF-ÎºB, âROS âIL-1, IL-6, IL-8, IL-12 PSM = proteasome, ICD = immunogenic cell death, RA = retinoic acid, CSP3 = caspase 3, TC = T cells, BC = B cells, NK = natural killer cells, NP = neutrophils, n.d. = no data. 1.5 Cu and the immune system Cu can modulate immune responses, but it remains unclear whether excess or deficiency is more beneficial. This reflects a limited understanding of how Cu shapes immune functionâboth generally and in the context of cancer immunotherapy. The following sections examine how Cu excess and deficiency influence these processes.9 Figure 1.1. Effect of Cu excess or Cu deprivation on immune cells. Effect of Cu excess or Cu deprivation on immune cells. Supraphysiological Cu generally leads to an activation of processes promoting immune response, while Cu deficiency leads to an inhibition of immune-activating pathways. Cu excess includes Cu ions or Cu in complexation, and Cu deficiency includes dietary Cu deficiency or Cu chelation. DC = dendritic cells, MP = macrophages, NP = neutrophils, NK = natural killer cells, BC = B cells, DTH = delayed-type hypersensitivity, WD = Wilsonâs disease, con A = concanavalin A, LPS = lipopolysaccharide. Created with Biorender.com 1.5.1 Cu excess and the immune system Cu levels above physiological requirements appear to have varied effects on the innate and adaptive immune system. White et al found that treating macrophages with Cu led to enhanced activity and intracellular killing of E. coli [145]. This effect was likely due to the Cu-catalyzed production of the hydroxyl radical from H2O2, allowing physiological levels of H2O2 to be lethal to bacteria [146]. An examination of the response of metallic allergens on dendritic cells (DC) found that CuSO4 caused the release of IL-8 [147], a marker of DC activation. A study in mice fed with high levels of Cu exhibited a significant decrease in delayed-type hypersensitivity responses (DTH) associated with T cell recruitment [148]. Further, a study assessing long-term high Cu intake in young men suggested that the Cu-containing diet decreased the levels of IL-2 receptor, which was associated with regulating T cell proliferation [149]. These results are in agreement with observational studies of patients with Wilsonâs disease (WD), a genetic disease cause by a mutation in the ATP7B gene. A study by Czlonkowska et al determined the effect of WD on immune function, and found that cell-mediated immunity was impaired, yet antibody response was greatly increased [150]. As discussed below Cu-complexes are being considered as therapeutics and these complexes have been shown to have a stimulatory effect on the immune system in mice. For example, a study found that injection of Cu2(3,5-diisopropylsalicylate)4 caused splenomegally with increased splenic macrophage 10 levels and enhanced T cell and B cell response [151]. It is not clear whether the effects were due to Cu or the Cu-complex. As noted above, some Cu complexes actually can act like Cu ionophores bringing Cu into cells. The effect of Cu is, however, uncertain as there are not many studies highlighting the impact of Cu deficiencies on immune function. Cu deficiency can have a profound effect on the immune system, resulting in a substantial decrease in cell-mediated and humoral immunity [152], and increased rates of infection and mortality in animals maintained on Cu-deficient diets [153]. Impaired cellular function may be a result of reduced Cu-dependent enzyme activity, and the effects of dietary and chelation-induced Cu deficiency are summarized here. 1.5.2 Dietary Cu deficiency The effects of insufficient dietary Cu on neutrophils have long been recognized, causing a decrease in circulating neutrophils in animals and humans. Low levels of Cu in the diet can be associated with the inhibition of respiratory burst and microbicidal functions [154]. Babu & Failla demonstrated that cellular Cu status, respiratory burst and yeast-killing ability of peritoneal macrophages decreases in severely Cu-deficient rats [139]. Furthermore, in a study comparing spleens from Cu-adequate and Cu-deficient rats, the NK cells from the Cu-adequate rats were five to sevenfold more cytotoxic [155]. Cu deficiency causes a general reduction in T lymphocytes, predominantly CD4+ cells, while the decrease in the CD8+ cells is less pronounced [156]. The B cell response required for antigen processing and T cell coordination as well as antibody production is seemingly impaired in Cu-deficient rats [157]. This does not appear to be due to a reduction in B cells. Some studies have actually suggested an increase in B cell populations in Cu-deficiency [152]. Finally, morphological studies in mice and rats have shown that Cu-deficiency is characterized by small thymuses and enlarged spleens accompanied by a notable a decrease in IL-2 secretion by rodent splenocytes. The IL-2 production is required for T cell proliferation. These studies looked at reducing Cu levels in the diet, but some investigators have pro-actively pursued chelation therapy that also leads to Cu reductions. 1.5.3 Cu chelation Research suggests that the effects of Cu-chelation on immune cells are similar to what has been observed when evaluating dietary Cu-restrictions, but there are notable exceptions. Interestingly, Cu-depletion through tetrathiomolybdate (TM) caused an increase in CD4+ T cell tumour infiltration in a murine breast cancer model, while decreasing myeloid-derived suppressor cell (MDSC) levels [140]. 11 The general decrease in the number and/or function of neutrophils, T cells, B cells, and NK cells were comparable to that of dietary deficiencies [138], [158]. Figure 1.2. Effects of Cu excess or Cu deprivation in tumour cells. Supraphysiological Cu leads to modulation of cytokine production, PD-L1 expression, and activation of immunogenic cell death, while Cu deficiency inhibits cytokine expression while decreasing PD-L1 exposure. Excess Cu includes Cu ions or Cu in complexation, and Cu deficiency includes dietary Cu deficiency or Cu chelation. DC = dendritic cells, TC = T cells, RA = retinoic acid, Cu (I) = Cu+, Cu (II) = Cu2+, ATP = adenosine triphosphate, CRT = calreticulin. Created with Biorender.com 1.6 Cu and immunogenic cell death of cancer cells Regulated cell death (RCD) is a type of cellular demise that relies on dedicated molecular signaling pathways, in contrast to the instantaneous demise of cells exposed to physical, chemical or mechanical forces [159]. Initially proposed to be immunologically silent [160], researchers have shown that various forms of RCD can be immunogenic, including chemotherapy and radiotherapy-induced apoptosis that was observed to activate an antitumour adaptive immune response [20]. In 1953, Mole described a 12 regression of tumours outside of the irradiated region, using the term abscopal, meaning âaway from the targetâ in Latin [161]. The abscopal effect was questioned for several decades due to its rarity of occurrence [162], while in 2005, Casares et al found that doxorubicin induces a caspase-dependent immune response [20]. This form of RCD has been referred to as immunogenic cell death (ICD), and was defined in 2018 by the Nomenclature Committee on Cell Death as âa form of RCD that is sufficient to activate an adaptive immune response in immunocompetent syngeneic hostsâ [159]. This requires two essential elements of ICD being activation of cytotoxic T lymphocyte (CTL)-driven adaptive immunity as well as the generation of immunological memory [163]. The immunogenicity of cell death depends on various factors, including the intrinsic antigenicity of the cells, as well the presence of adjuvant signals. ICD involves spatiotemporally exposure or release of danger signals which are required for the recruitment of antigen-presenting cells (APCs). These signals are collectively referred to as damage-associated molecular patterns (DAMPs) [164], and include the exposure of calreticulin (CRT) on the cellular surface, the secretion of high mobility group box 1 (HMGB1), and the release of adenosine triphosphate (ATP) [163,165,166]. These signals bind pattern recognition receptors (PRRs) in DCs and subsequently recruit CTLs into the tumour microenvironment [167,168]. Anticancer therapies including chemotherapy, targeted therapy, and radiation therapy have been shown to elicit clinically relevant ICD responses [169â171] and there is evidence that combination of certain ICD inducers with immune checkpoint inhibitors (ICI) leads to improved efficacy [172â176]. Despite clinical potential only a few bona fide ICD inducers have been employed for use in combination therapy with ICIs [169,177,178], although numerous FDA-approved ICD inducers are being investigated for this purpose [179â184]. 1.6.1 Approved ICD-inducing treatments with an emphasis on disulfiram Considerable efforts have been made in using combination therapy of ICD inducing agents and ICI in colon and rectal cancers, with the focus largely on oxaliplatin combined with PD-1/PD-L1 blocking antibodies [185â187]. Multiple trials have indicated that oxaliplatin is a more favourable ICI combination agent than cisplatin [188,189], and this is believed to be due to oxaliplatinâs potent role as an ICD inducer [190]. Supporting this is the finding that single nucleotide polymorphisms (SNPs) in ICD-related genes could affect clinical outcomes in patients treated with oxaliplatin [191]. Recently, a 13 combination of the antibody-drug conjugate enfortumab-vedotin (EV) and pembrolizumab was approved for patients with locally advanced or metastatic urothelial cancer [175]. EV is a notable inducer of ICD, causing ER stress and immune cell recruitment, which contribute to its clinical efficacy in combination with PD-1 therapy [175,192,193]. Another compound that has generated interest is DSF, which has recently been shown to activate ICD signals in human colorectal cancer (CRC) cell lines in combination with Cu [14]. Treatment of CRC with DSF/Cu led to cell surface exposure of CRT and HSP70, which promote the phagocytosis of tumour cells by APCs and are associated with the development of ICD [163,165]. A human CRC xenograft mouse model also showed the induction of ICD markers under treatment of DSF/Cu, with this study providing early evidence for the potential of ionophore-delivered Cu as an armament in ICD induction. Although breast cancer (BC) was not among the initial cancer types studied clinically with ICIs, there have been recent approvals for the indication of ICI in triple-negative breast cancer (TNBC), and numerous ongoing trials for TNBC and other subtypes, including HER2-positive cancer [194â198]. There is evidence that cancer stem cells (CSCs), believed to be responsible for tumour progression and metastasis [199] also interact with various immune cells, promoting immune silencing and avoidance of destruction [200]. Irradiation therapy (IR) is a standard treatment for BC and has been reported to induce ICD in breast and other cancers, enhancing the efficacy of ICI [201,202]. However, consistent with evidence that breast cancer stem cells (BCSCs) are treatment-resistant, Sun and colleagues demonstrated that IR triggered reduced levels of ICD signals in BCSCs [16]. A common molecular target for CSC inhibition is ALDH, which is believed to cause resistance by removing genotoxic aldehydes in BCSC [199]. There is evidence that DSF is a pan-ALDH inhibitor, and has been used in combination with Cu to potentially target CSCs in multiple cancers [203,204]. Sun et al found that resistance in ICD induction of irradiated cells was removed through pre-treatment with DSF/Cu by assessing ICD markers of CRT, HSP90, HMGB1 [16] representing the first study to use disulfiram with Cu to induce ICD in CSCs. While several recent approvals of ICIs for hepatocellular carcinoma (HCC) have significantly improved management of the disease [205], most patients yield limited benefits from immune-based therapies [206]. The immunosuppressive microenvironment in the liver is influenced by Kupffer cells, MDSCs, Tregs, and anti-inflammatory cytokines, and a plausible strategy in enhancing immunotherapy response is the conversion to an inflammatory TME [207]. DSF/Cu has been used to inhibit HCC tumour-initiating cells (TICs) [208], and a recent study by Gao and colleagues demonstrated the potential of 14 DSF/Cu to cause activation of several ICD markers including CRT, HMGB1, ATP, and IFN [15]. Significantly, a cancer vaccination model was used to provide the first in vivo ICD validation of DSF/Cu, and synergistic anti-tumour activity was demonstrated in combination with CD47 blockade. DSF/Cu treated cells underwent a significant gene enrichment in Cu response, suggesting the potential of ionophore-mediated Cu delivery for further applications of ICD induction. 1.7 Cu, Cu-complexes, and PD-L1 Other efforts to induce ICD with Cu have involved the synthesis of Cu (II) complexes to generate intracellular ROS. Kaur and colleagues prepared several compounds containing a Schiff base ligand (known to generate ROS in combination with Cu) with various lipophilic polypyridyl ligands (known to facilitate localization in the ER) [209]. The lead diphenylâ1,10âphenanthrolineâbearing complex was cytotoxic to both bulk breast cancer cells and BCSCs at sub-micromolar concentrations, induced various DAMPs including CRT and ATP, and promoted BCSC phagocytosis by macrophages. This represented the first cytotoxic Cu (II) complex to induce ICD in BCSC, and provided early rationale for use of Cu in ICD. However, when considering the use of Cu and ICD induction it is our contention that these should be considered in the context of ICIs such as the antibodies that have been developed to target Programmed Cell Death Ligand (PD-L1). PD-L1 is a trans-membrane protein normally expressed by DCs, MPs, some activated T cells and B cells, and tumour cells, in which it acts in a mechanism to escape anti-tumour immune responses [210]. The PD-1/PD-L1 pathway maintains immune tolerance in the tumour microenvironment, controlling T cell activation and cytokine secretion, leading to PD-1-mediated T cell exhaustion and reduced cytotoxicity against tumour cells [210]. This negative interaction can be inhibited by anti-PD-1/anti-PD-L1 antibodies, and since the approval of pembrolizumab for the treatment of advanced melanoma in 2014, PD-1/PD-L1 ICIs have been approved for use in many other tumours [211], although their efficacy is limited by various mechanisms of resistance in certain patients and side effects in others [212]. PD-L1 protein expression levels on tumours were the first potential predictive biomarker for sensitivity to ICI, and currently remain the best validated marker for cancer immunotherapy [213,214]. While high PD-L1 levels have been associated with increased tumour immune infiltration [215], its expression is correlated with worse prognosis in many tumours [216,217]. In the context of this review, it is important to consider the role of Cu and PD-L1 expression and targeted therapy. 15 Various mechanisms may lead to increased PD-L1 expression, including tumour microenvironment (TME) release of pro-inflammatory cytokines such as IFNy, TNFa and IL-6, which activate signaling pathways including EGFR, PI3K and AKT [218]. Cu (II) ions have been found to activate EGFR in the absence of its ligand and cause activation of the AKT and ERK pathways [219] and recently, Voli and colleagues reported that intra-tumour Cu influences PD-L1 levels [17]. The addition of Cu was shown to increase PD-L1 mRNA and protein levels, and upregulated the same target gene subset as IFNy, while Cu chelators decreased PD-L1 expression by inhibiting cancer cellsâ response to IFNy, TNFa, and IFNa/b. Cu chelation inhibited EGFR phosphorylation, causing an accumulation of ubiquitinated PD-L1 and subsequent proteasomal degradation, and decreased STAT3 phosphorylation, reducing the expression of several STAT target genes. Decreasing Cu levels in immunocompetent tumour-bearing mice led to an increase in tumour-infiltrating CD8+ T cells and NK cells, demonstrating the therapeutic potential of Cu chelation in the context of decreased PD-L1 expression levels. With this in mind, one may question the use of Cu-like ionophores that could mediate increased levels of Cu in tumour cells. However, this needs to be considered in the context of combinations of Cu-like ionophores with PD-L1 targeted therapeutics. Due to formation of the highly potent anti-cancer complex Cu(DDC)2 when DSF is administered with Cu, DSF is most frequently studied in combination with Cu in cancer research [220]. However, as many tumours contain elevated levels of Cu this may be sufficient for formation of active levels of Cu(DDC)2 [221], and due to other less studied mechanisms there has been some clinical interest in DSF given without Cu [222,223]. In a recent pre-clinical study, Zheng and colleagues uncovered a link between DSF-mediated overexpression of IRF7, a regulator of type I IFN, and upregulation of PD-L1 [19]. IRF7 binds to the PD-L1 promoter causing overexpression, a function that is attenuated by methyltransferases such as DNMT1. DNMT1 inhibitors such as decitabine have been studied clinically to improve the response of anti-PD-1/PD-L1 inhibition [224,225]. While DNMT1-mediated PD-L1 upregulation promotes immune escape, combination treatment with ICI promotes an antitumour immune response. Such data provide a rational for combining PD-L1 inhibitors with Cu-like ionophores such as DDC or DSF. Interestingly, DSF was found to inhibit DNMT1, and while this resulted in no antitumour effects as a single agent, the treatment decreased intratumoural T cell infiltration. Surprisingly, combination with anti-PD-1 mAb led to a synergistic antitumour immune responses, representing a novel therapeutic strategy for metastatic triple-negative breast cancer (TNBC) and other tumours. Another recent study 16 also showed synergy between DSF and anti-PD-1 treatment in melanoma, uncovering a stimulatory effect of DSF on CD8+ T cells [226]. The authors of this study also noted that DSF directly bound LCK, the first molecule to be recruited to the TCR complex. This enhanced its kinase activity and increased T cell effector response and antitumour immunity. The role of basal Cu in binding the DDC metabolite of DSF in this study is unclear, and further studies are needed to understand this and the necessity of exogenous Cu addition for DSF-mediated immune activation. As noted above, disulfiram and or DDC inhibit aldehyde dehydrogenase (ALDH) and this is why it is used to prevent alcohol consumption. ALDH is an enzyme responsible for oxidizing aldehydes resulting from metabolic processes, and is also important for the maintenance and differentiation of stem cells [227]. ALDHs regulate various pathways in cancer to promote tumourigenesis and cancer stem cell signaling, including minimizing ROS production and enhancing retinoic acid (RA) signaling [228,229]. ALDH is a negative prognostic marker for most tumour types [230,231] and promotes an immune suppressive landscape by inducing Treg and modulating macrophage polarization through the production of RA by multiple cell types [232â234]. ALDH is positively correlated with PD-L1 levels in CRC and lung cancer patients [235,236], and PD-L1 is known to promote various factors that maintain CSC stemness including OCT4 and Nanog through activation of the PI3K/AKT pathway [237]. Thus, dual ALDH and PD-1/PD-L1 axis inhibition is a reasonable anticancer strategy. Intratumoural Cu level has been found to have an inverse correlation with ALDH protein levels in osteosarcoma (OS) cells in that ALDHhigh highly metastatic cell lines having low Cu levels and a ALDHlow low metastatic line having high levels of Cu [238]. Many studies have linked DSF/Cu to ALDH inhibition [239â242], with evidence suggesting that DSF/Cu targets a stem-cell like ALDH population and reporting antitumour activity against an ALDHhigh population in vivo [241]. In contrast to this, Skrott et al recently suggested that anti-cancer activity of DSF/Cu is not due to ALDH inhibition, as neither DSF nor DSF/Cu directly inhibit ALDH in a short-term assay. They argued that the long DSF/Cu exposure times used in previous studies caused cell permeability, confounding results from the ALDEFLUOR assay used to measure ALDH function. They also argued that the bona fide inhibitor of ALDH was the non-toxic DSF metabolite S-methyl-N,N-diethylthiocarbamate-sulfoxide (Me-DTC-SO) [243], which is only generated in vivo and the formation of which is minimized when DSF is in the presence of Cu. Specifically, when Cu(DDC)2 is formed it can be an active anticancer agent targeting NPL4, a subunit of the p97/VCP segregase [244]. Altogether this provides compelling evidence that 17 challenges the long-held belief that DSF/Cu targets ALDH inhibition as a major anticancer mechanism. While the precise role of DSF/Cu in ALDH inhibition is under debate, many recently developed ALDH inhibitors have shown promising efficacy in tumours [245] and a recent in silico screen of natural products for inhibitors of ALDH isoforms led to the synthesis of a rutin-Cu complex that had sub-micromolar activity in a breast cancer cell line. Although there is promising evidence for the role of Cu and Cu compounds in immunomodulatory ALDH inhibition, contradictory reports and limited evidence indicate that further research is needed. It is also worth noting that Cu has been shown to initiate an inflammatory state in rats, causing the production of reactive oxygen species (ROS) and the activation of a downstream target of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) [246] as well as activate NF-kB and TNF-a in the spleen and thymus of chickens [247,248]. Cu also caused the overexpression of NF-kB-target cytokines in mice when given in feed [249], and Cu chelation through TM led to a decrease in NF-kB-mediated pro-inflammatory cytokines in microglial cells in mice [250] while inhibiting NF-kB in breast cancer cells [251]. Conversely, Kanemaru et al found that Cu, when given as Cu (II) ions or in a peptide complex, inhibited NF-kB in ovarian cancer cells and Jurkat T cells, and a number of other Cu complexes have demonstrated NF-kB pathway inhibition in cancer [244]â[246]. NF-κB is a transcription-factor family comprising five subunits that controls expression of target genes including IL6, TNFA, BCL2 and VEGF, influencing tumour cell proliferation, inflammation, and adaptive immunity [252]. NF-kB is viewed as a critical link between inflammation and tumourigenesis, and in certain tumour environments it can promote tumour proliferating effects through inflammation or immunosuppression [253]. Tumour cells produce a variety of NF-κB-induced cytokines and chemokines that influence the recruitment and activation of immune cells, and as well, NF-κB mediates transcription and protein stability of PD-L1 in tumour cells, contributing to CD8+ T cell exhaustion [254]. Although there is evidence that NF-κB activation and signaling in tumour cells may increase patient response to ICI [255,256]. NF-κB inhibition can also be beneficial for patient response. NF-κB inhibition downregulates genes involved in metastasis and angiogenesis in tumour cells [257] and has numerous effects on immune cells in the TME including stimulation of DCs, T cells and NK cells, and relieving suppression exerted by MDSCs and Tregs [254]. A well-studied compound is bortezomib, which inhibits NF-κB as a by-product of its proteasome inhibition function [258] and has been shown to 18 have synergistic effects with anti-PD-1 therapy [259], with ongoing clinical interest [260]. While pyrrolidine dithiocarbamate, 8-hydroxyquinoline metabolites and other Cu-complexing compounds have been shown to inhibit NF-κB in cancer [261,262], DSF is the most studied [130,263,264] and as an inhibitor of the proteasome system, DSF/Cu inhibits degradation of inhibitor-κB (IκB), leading to suppression of NF-κB nuclear translocation and activation. The activity of DSF/Cu has been used to inhibit 5-fluorouricil-induced NF-κB activation [130] and the reversal of chemoresistance in colon and breast cancers [130,265]. Although there is a dearth of studies that examine DSF/Cuâs NF-κB inhibition in cancer and the resulting modulation of immune phenotype, DSF has been examined for the inhibition of NF-kB-mediated cytokine output in a phase 2 clinical trial for coronavirus disease 2019 (COVID-19). The primary outcomes were a change in plasma inflammatory biomarker levels (e.g. IL-6 and IL-b) and viral load on days 5, 15 and 31 [266]. DSF has also been examined clinically with minor benefit in HIV latency reversion [267], and due to the context-dependent, possibly contradictory findings from studies of Cu and NF-κB interactions, further studies are needed to understand the role of DSF in anti-tumour and anti-viral immunity for the potential of this diversely active compound to be realized. While Cu modulation has clear potential to influence cancer progression and treatment response, particularly in the context of immunotherapy, its therapeutic development was historically limited by concerns around toxicity and formulation challenges. In oncology, however, toxicity is often acceptable within a defined therapeutic index, and Cuâs redox activity may offer advantages in promoting tumour-selective stress and immune activation. One long-standing hurdle has been the tendency of certain Cu complexes to form insoluble precipitates, limiting their viability as drug candidates. More recently, a growing number of groups have addressed this through nanoformulations, which can improve Cu solubility, stability, and tumour targeting. This shift has opened new directions for Cu-based therapeutic design. 1.8 Cu-based nanomedicines and development of Metaplex technology Cu-based nanomedicines have undergone a rapid evolution, transitioning from antimicrobial materials to highly engineered platforms designed to deliver immunomodulatory and cytotoxic effects. Early studies on Cu nanoparticles (Cu-NPs) and Cu oxide nanoparticles (CuO-NPs) demonstrated strong bactericidal and virucidal activity [268,269], which later led to their investigation in cancer contexts. However, their 19 translation was hindered by dose-limiting toxicities, pro-inflammatory off-target effects, and limited capacity for tumour-specific delivery [270â272]. The reactive nature of Cu ions in circulation, including their interaction with serum proteins and redox cycling in non-target tissues, presented considerable challenges to systemic application [273]. Initial work with CuO-NPs in tumour models demonstrated dose-dependent tumour suppression, but also substantial collateral tissue damage [274], reinforcing the need for more sophisticated delivery strategies that could direct Cu to tumours while minimizing systemic exposure. To overcome these issues, second-generation Cu nanomedicines incorporated Cu(II) ions into stabilized drug delivery systems. Two widely cited clinical examples are Vyxeos® (CPX-351) and Irinophore Câ¢, which utilize metal-coordinated drug encapsulation. Vyxeos® uses Cu to co-encapsulate daunorubicin and cytarabine at a synergistic 5:1 molar ratio, with Cu stabilizing the complex and modulating drug release kinetics [275â277]. Irinophore Câ¢ demonstrated that intraliposomal Cu could interact with irinotecanâs lactone and quinoline groups, significantly prolonging retention compared to liposomes loaded using pH gradients alone [278]. The selectivity of Cuâs interaction was highlighted by comparative studies: when other divalent cations such as Zn2+, Mg2+, or Mn2+ were used in place of Cu2+, the same retention could not be achieved [279â282]. Notably, Cuâs ability to coordinate with both the drug and the inner phospholipid leaflet was thought to reduce passive permeability, further enhancing retention. Although this irinotecan formulation did not advance into clinical trials, partly due to the approval of Onivyde, a different irinotecan formulation, the concept of using metals to coordinate compounds with metal-binding ligands was pursued further. These early studies firmly established Cu coordination as a viable strategy for modulating drug retention and pharmacokinetics, building a foundation for subsequent nanomedicine technologies. It was argued that the mechanism which helped to retain a drug like irinotecan in liposomes could depend on the metal ion itself and the ability of the functional group(s) of the drug or drug candidate to interact with the metal ion. This interaction not only can be used to change the release rate of encapsulated drugs but could be used to drive the loading of certain drugs/ligands. When considering this, the majority of drugs with defined metal-binding ligands were sparingly soluble in water. If the metal inside the liposome could complex the added agent with the metal binding ligand, then the solubility of the agent would now be determined by the liposomes, thus increasing the apparent solubility of the agent, making an insoluble but highly potent drug candidate suitable for parental 20 administration [280,283,284]. This breakthrough led to the development of what has been referred to as Metaplex technology [275],[283,285]. Metaplex technology relied on the use of liposomes prepared in solutions of concentrated Cu2+ ions. One of the first metal binding agents that was used to demonstrate the utility of Metaplex technology was diethyldithiocarbamate (DDC) which was known to form a Cu complex (Cu(DDC)2) that has potent anti-cancer activity, as described above. In 2017, Wehbe et al. reported the first i.v. injectable liposomal Cu(DDC)2 formulation that utilizes the Metaplex technology. The study provided valuable information on anti-tumour Cu-based drugs as the data presented in most previous studies had been largely collected with the use of DMSO, a solvent that is very commonly used to dissolve the Cu complexes in in vitro studies but must be eliminated in animal studies and in clinical trials [286], [283,287]. While the use of metals within liposomes to prepare potential anticancer agents is still being pursued, recent studies have demonstrated that for some of the drug candidates, such as DDC, the candidate could be added to solutions of liposomes with no encapsulated metal initially. The metal and the DDC could be added to the outside of the preformed liposomes, and the Cu(DDC)2 complex would subsequently become associated with the liposome. As suggested above, this was due to the Cu ionophore-like properties of DDC. In this context, it could be argued that some of the complexes being considered for therapeutic development could enhance Cu delivery to target cells of interest, such as cancer cells. Both the Cu ion and its coordinating ligand should be taken into account when evaluating the efficacy of such complexes, particularly in relation to immune-based therapies. 1.9 Conclusions The information presented in this review highlights growing interest in the role of copper (Cu) in disease development, progression, and treatment. Increasing evidence suggests that therapies altering Cu levels can modulate immune function and response, and that nanoparticle technologies relying on Cu may hold therapeutic value. Taken together, these findings point to Cuâs potential in treating diseases such as cancer, although its precise role remains unclear. If Cu-based formulations are to be considered viable therapeutics, the toxicity of the Cu²âº ion must be addressed. Some studies indicate that repeated oral dosing of Cu²âº can cause severe gastrointestinal and liver toxicity. However, in this teamâs experience, multiple in vivo studies using Cu-based Metaplex formulations and Cu-containing liposomal controls showed no overt signs of toxicity [283,287]. The CuSOâ doses used were significantly lower than the 21 toxic oral threshold in mice (100 mg/kg) [288]. If any of the Cu-based drug formulations move into clinical trials, the toxicity of the formulation will need to be carefully assessed. Since our teamâs focus is primarily on developing treatments for cancer indications, there is a general acceptance for using therapeutics that induce toxicity, but toxicities that can be managed through use of co-medications or through careful consideration of dose and dose-reductions when needed. Growing evidence has also suggested that when the compound of interest is complexed with metal ions, the metal-compound complex is often more therapeutically effective than the parent compound [286,289]. Some metal complexes activate or suppress new intracellular pathways or trigger distinctive mechanisms such as reactive oxygen species (ROS) generation or endoplasmic reticulum (ER) stress [290,291]. These effects may be driven directly by the Cu²âº ion or result from additive interactions. For oncology, Cu-based delivery systems offer a promising alternative to platinum-based therapies, which are associated with chemoresistance and broad systemic toxicity. Although Cu has been used therapeutically for its microbial properties for millenia, only in recent years has there been a growing understanding of Cuâs activity and therapeutic potential against cancer. The two opposing strategies of Cu chelation and delivery both offer promising mechanisms of tumour inhibition, each with inherent flaws that must be considered. While Cu chelation reduces cancer cell energy production by interfering with many Cu-dependent processes, removing Cu may have deleterious effect on nearly every component of the immune system. On the other hand, excess Cu has the potential to activate cellular ROS and cause Cu-mediated cell death while potentially enhancing certain elements of immune response. Recent findings suggest, for example, that combinations of Cu carriers with immune checkpoint inhibition may prove to be invaluable in unlocking the therapeutic potential of Cu in cancer [18,19,185,292â294]. As the role of Cu is context-dependent and largely unclear in immune-modulating pathways such as NF-kB and ALDH1, mechanistic studies will be important to clarify the direct role of excess Cu in tumour and immune cells. It is also unclear how Cu behaves mechanistically when delivered as Cu ions or through Cu-complexes that may act like ionophores. While more research is needed on determining the direct effect of Cu-based therapies on immune cells, the field of Cu nanomedicine is in an excellent position to combine recent findings on immune checkpoint blockade with tumour biology to translate these advances toward a synergistic therapy. 22 Beyond cancer, Cu-based therapeutics may have relevance in neurodegenerative diseases like Alzheimerâs, where Cu dysregulation is implicated in pathogenesis [295]. Liposomal or nanoparticle delivery of Cu complexes may help restore Cu balance and slow disease progression, especially given the challenge of reaching lesion sites without a delivery system. During the COVID-19 pandemic, the urgent need for antiviral and anti-inflammatory treatments drew attention to CuâNSAID complexes, which show enhanced anti-inflammatory activity and may help manage cytokine storms in critically ill patients [285]. In summary, there lie countless opportunities for Cu-based nanomedicines in the future, while at the same time providing investigators an opportunity to better understand the role of Cu in disease development, progression and treatment. 23 2 Materials and Methods 2.1 Materials 3[H]-cholesteryl hexadecyl ether (CHE) and picofluor-15 scintillation fluid were purchased from PerkinElmer, Inc. (Boston, MA). Roswell Park Memorial Institute (RPMI) 1640 medium, Dulbeccoâs Modified Eagleâs Medium (DMEM), Eagle's Minimum Essential Medium (EMEM), Ham's F-12 medium, 0.25% (w/v) trypsin-EDTA, Hankâs balanced salt solution (HBSS) without calcium and magnesium, primary antibody against CRT (product number PA3-900), secondary antibody conjugated to DyLight® 488 (product number 35552), Hoechst 33342 and CellMaskâ¢ Deep Red were obtained from Thermo Fisher Scientific (Waltham, MA, USA). Fetal bovine serum (FBS) and l-glutamine (Life Technologies) were obtained from Life Technologies (Carlsbad, CA). Ethidium homodimer-I was obtained from Biotium (Fremont, CA, USA.) Sucrose and chloroform were obtained from EMD Chemicals Inc. (Gibbstown, NJ). 1,2-Distearoyl-sn-glycero-3-phosphocholine (DSPC), cholesterol (Chol), sodium diethyldithiocarbamate trihydrate and all other chemicals were purchased from Sigma-Aldrich (Oakville, ON, Canada) as analytical grade reagents. 2.2 Liposomal preparation and characterization 2.2.1 Liposomal preparation For DSPC/Chol liposomes, the lipids were first weighed and dissolved in chloroform to achieve a 55:45 (DSPC:Chol) mol:mol ratio. 3[H]-CHE was added as a liposomal lipid marker prior to drying. Specific activity (approximately 20,000 â 25,000 DPM/µmol total lipid) was then determined by adding 10 μL of the sample to a vial, removing the chloroform and then adding 5 mL of picofluor scintillation fluid. All samples were measured in quadruplicate. The radioactivity was measured on a Packard 1900 TR Liquid Scintillation analyzer. For the bulk sample, chloroform was removed by drying with a stream of nitrogen gas until the samples became thick and syrup-like. To completely remove the chloroform, the samples were then placed under vacuum for at least 3 h, resulting in the formation of a bubbly lipid film. The dried lipid film was then hydrated in various solutions (300 mM Cu sulfate; 300 mM citric acid (pH 3.5); 150 mM NaCl, 20 mM citric acid, 20 mM MES, 20mM HEPES at pH 3.5, pH 5.5, or pH 7.5; sucrose HEPES (SH) pH 7.5 (300 mM sucrose, 20 mM HEPES), HEPES buffered saline (HBS) pH 7.5 (150 mM NaCl, 20 mM HEPES); 1 mL solution per 50 μmol of lipids. The sample was hydrated at 24 65 °C with constant stirring. The resulting multilamellar liposomes were then extruded fifteen times through two stacked 0.08 μm filters in a 10 mL or 100 mL thermobarrel extruder (LIPEX® Extruder, Evonik Industries, Vancouver, BC, Canada) at 65 °C. The resultant unilamellar liposomes had a mean diameter of approximately 100 ± 20 nm as determined by dynamic light scattering (ZetaPALS, Brookhaven Instruments Corp., Holtsville, NY). For Cu-containing liposomes, the extruded liposomes were passed through a Sephadex G50 column pre-equilibrated with sucrose/HEPES/EDTA (SHE) buffer pH 7.4 (300 mM sucrose, 20 mM HEPES, 15 mM EDTA) to remove unencapsulated Cu. The external SHE buffer was further exchanged with the desired external buffers through Sephadex chromatography where the matrix was pre-equilibrated with the desired buffer. The external buffers used in these studies include sucrose HEPES (SH) pH 7.5 (300 mM sucrose, 20 mM HEPES); HEPES buffered saline (HBS) pH 7.5 (150 mM NaCl, 20 mM HEPES); and 150 mM NaCl, 20 mM citric acid, 20 mM MES, 20mM HEPES at pH 3.5, pH 5.5, or pH 7.5. The final lipid concentration was determined by measuring 3[H]-CHE using liquid scintillation counting. 2.2.2 Liposomal loading Diethyldithiocarbamate (DDC) and CuSO4 were added step-wise to DSPC/Chol liposomes at drug-to-lipid ratios of 0.1 to 0.4 (mol/mol). The mixture was then continuously mixed with a mini stir-bar in water baths at temperatures of 4 °C and 25 °C. 80 μL aliquots from the Cu(DDC)2-liposome mixture were then taken at various time points (5, 15, 30) and centrifuged through 1 mL Sephadex G-50 mini-spin columns (680 Ãg for 3 min) to separate the unencapsulated DDC from liposome-associated DDC. For filtration studies, the samples were passed through 0.2 μm filters (Pall Corporation, New York City, NY). Cu(DDC)2 concentrations in the liposomes were determined by measuring absorbance at 435 nm on the Hewlett Packard/Agilent 8453 G1103A UVâVisible Spectrophotometer (Agilent Technologies, Santa Clara, CA, USA) and the liposomal lipid concentrations were measured by liquid scintillation counting. Samples visualized on the UVâvisible spectrophotometer were diluted in 100% HPLC-grade methanol. Liposomal Cu(CQ)â was prepared following a previously described method [296], with slight modifications. DSPC/cholesterol (55:45 mol%) lipid films were hydrated with 300âmM CuSOâ, followed by extrusion through two stacked 80ânm polycarbonate membranes as described above. External CuSOâ was removed and exchanged for HEPES-buffered saline (pH 7.4) using tangential flow 25 filtration (TFF). Clioquinol (CQ) was added to the Cu-loaded liposomes at a final drug-to-liposomal lipid molar ratio of 0.2 and mixed at room temperature to facilitate Cu(CQ)â complexation and bilayer association. The formulation was then filtered through 0.2âÎ¼m syringe filters prior to use. For both formulations, Cu complex formation was confirmed by UVâvisible spectrophotometry at 435ânm after dilution in 100% HPLC-grade methanol. Liposomal lipid concentrations were quantified by liquid scintillation counting as 3[H]-cholesteryl hexadecyl ether (CHE) was incorporated in all liposomal formulations. All sterile filtered formulations were used directly in cytotoxicity, RNA sequencing, and in vivo experiments. 2.3 In vitro cell-based assays 2.3.1 Cell lines All cell lines were maintained in their respective media supplemented with 10% fetal bovine serum (FBS) and 2âmM L-glutamine, unless otherwise specified. Cells were cultured in a humidified incubator at 37â°C with 5% COâ. Upon reaching approximately 90% confluence (70% for Capan-1), cells were washed with HBSS lacking calcium and magnesium, detached using 0.25% (w/v) trypsin-EDTA, and subcultured at a ratio of 1:4 to 1:10 depending on the desired seeding density. CT26 murine colon carcinoma cells (ATCC CRL-2638) were cultured in either DMEM or RPMI 1640. MDA-MB-231 human breast adenocarcinoma cells (ATCC HTB-26) were cultured in RPMI 1640. JIMT-1 Herceptin-resistant human breast carcinoma cells (DSMZ) were cultured in Hamâs F-12/DMEM. DU145 human prostate carcinoma cells (ATCC HTB-81) were cultured in EMEM, while LNCaP human prostate carcinoma cells (ATCC CRL-1740) were cultured in RPMI 1640. A2780cp, a cisplatin-resistant human ovarian carcinoma cell line (a gift from Mark W. Nachtigal, University of Manitoba), was cultured in DMEM:F12. 4T1 murine mammary gland tumour cells (ATCC CRL-2539) were cultured in DMEM. SKOV3 human ovarian adenocarcinoma cells (ATCC HTP-77) were cultured in McCoyâs 5A. MCF7 human breast adenocarcinoma cells (ATCC HTB-22) and A549 human lung adenocarcinoma cells (ATCC CCL-815) were cultured in RPMI 1640. BxPC3 human pancreatic adenocarcinoma cells (ATCC CRL-1687) were cultured in DMEM, and Capan-1 human pancreatic adenocarcinoma cells (ATCC HTB-79) were cultured in IMDM supplemented with 20% FBS and 4âmM L-glutamine. 2.3.2 Cytotoxicity assays Cells were seeded into 384-well black-walled, clear-bottom plates (Greiner Bio-One) in a volume of 26 50âÎ¼L per well using complete growth medium. Seeding densities were optimized for each cell line, typically between 1500 and 2000 cells per well. Following overnight attachment, cells were treated with the indicated compounds at the specified concentrations. After 72âhours, cell viability was assessed using dual staining with 4.87âÎ¼M Hoechst 33342 (Life Technologies) and 312.5ânM ethidium homodimer I (Biotium) to distinguish viable (Hoechst-positive, ethidium-negative) from non-viable (Hoechst-positive, ethidium-positive) cells. Plates were incubated with the dyes for 20 minutes at 37â°C in 5% COâ, then imaged using the IN Cell Analyzer 2200 (GE Healthcare). Cell counts were determined using Developer Toolbox 1.9 software (GE Healthcare), and viability was expressed as the fraction of live cells normalized to vehicle-treated controls. Graphs were generated using Prism software (version 9.0 or 10.0; GraphPad Software, La Jolla, CA). 2.3.3 Cu internalization assay MDA-MB-231 cells (1 x 106) were seeded in 6 well plates and treated the following day. At 24 hours post-treatment, cells were detached with Trypsin/EDTA solution (0.25% trypsin with EDTA 4Na), and were analyzed for protein concentration with Pierceâ¢ BCA Protein Assay Kit (Thermo Scientific), and Cu concentration as described above. The Cu concentration was normalized as nmol Cu per mg protein. 2.3.4 Phagocytosis Assay CT26 cells (5âÃâ10âµ cells/well) were seeded in 6-well plates and treated for 24 hours with Cu(DDC)â or Cu(CQ)â at the indicated concentrations. Following treatment, cells were detached using TrypLE Express (Gibco) and washed once with PBS to remove residual drug. Cells were then washed with pHrodo wash buffer and resuspended in labeling buffer containing 100âng/mL pHrodo Red dye (Sartorius), following the pHrodo Red Cell Labeling Kit protocol. Cells were incubated for 1 hour at 37â°C to allow for dye incorporation, then washed once with labeling buffer to remove excess dye. Labeled tumour cells were added directly to bone marrow-derived macrophages (BMDMs) (1âÃâ10âµ cells/well) in a 24-well plate at a tumour cell-to-BMDM ratio of 4:1 (i.e., 4âÃâ10âµ labeled tumour cells per well). Co-cultures were imaged using the Incucyte live-cell imaging system every 2 hours, with four images acquired per well per timepoint. Background fluorescence intensity measured at 0 hours was subtracted from all subsequent timepoints. The phagocytic index, defined as the total integrated pHrodo Red fluorescence per well, was used to quantify tumour cell uptake under acidic phagosomal conditions. 2.3.5 Cytokine Profiling 27 Cytokine levels were measured in both dissociated tumour lysates and plasma samples collected from treated mice using the V-PLEX Mouse Cytokine 10-Plex Panel (Mesoscale Diagnostics), which quantifies IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p70, IFN-γ, TNF-α, and KC/GRO. Samples were run in duplicate or triplicate on the MESO QuickPlex SQ 120 instrument. Data were obtained from a single experiment due to the practical limitations of repeating multiplex assays and were used to assess systemic and tumour-associated cytokine modulation following Cu ionophore and combination treatments. 2.3.6 Characterization of immunogenic cell death 2.3.6.1 ATP secretion For the determination of ATP secretion, CT26 cells (200,000 cells/well) were seeded in a 24-well plate (Corning catalog number 353047) in 300 µL/well of medium. Cells were allowed to attach and grow for 24 h before 100 µL/well of drugs or medium (untreated controls) was added. Cells were incubated with drugs for 24 h and caution was taken not to agitate plates during the experiment to avoid any possible mechanically-stimulated ATP release. At the end of the experiment, 50 µL/well of conditioned cell culture medium (cell supernatant) was transferred to a 96-well white-walled, clear bottom plate already containing 50 µL/well of CellTiter-Glo® 2.0 reagent (Promega, Madison, WI, USA). Pipetting was carefully done to avoid touching the bottom of the wells, so that cells remained adhered to the plate surface and only cell supernatant containing the secreted extracellular ATP (but no cells) was transferred to the new plate containing the reagent. To calculate ATP concentrations, a standard curve was prepared by diluting ATP (10â1000 nM) in medium without FBS. The 96-well plate with samples and CellTiter-Glo® 2.0 reagent was shaken for 2 min at room temperature and further incubated for a total of 15 min. The luminescence signal was recorded in arbitrary units with a FLUOstar OPTIMA microplate reader. 2.3.6.2 HMGB1 secretion To measure the release of HMGB1, CT26 cells were seeded on a 24-well plate in 300 µL/well of medium containing 200,000 cells/well and incubated for 24 h to allow cell attachment and proliferation. Then, 100 µL/well of drug or medium was added to cells and the cells were incubated for a further 24 h. At the end of the incubation, the extracellular concentration of HMGB1 was determined using the commercial HMGB1 enzyme-linked immunosorbent assay (ELISA) kit (IBL International, catalog number ST51011, Hamburg, Germany). Briefly, 5 µL/well of conditioned cell culture medium was 28 transferred to a pre-coated HMGB1 ELISA 96-well plate containing 105 µL/well of diluent buffer. Care was taken during pipetting to avoid touching the bottom of the well and cell supernatant (with no cells) was transferred to the HMGB1 ELISA 96-well plate. Alternatively, 10 µL/well of diluent buffer, standards or positive control (provided with the commercial kit) were added to wells containing 100 µL/well of diluent buffer. The 96-well plate was shaken for 30 seconds and incubated for 24 h at 37°C. The addition of the detection antibody and final detection reaction steps were done following the manufacturerâs instructions. The colorimetric signal was determined by measuring the absorbance at 450 nm (reference wavelength at 600 nm) with a Multiskan® Spectrum microplate reader (Thermo Fisher Scientific). To calculate the concentration of HMGB1, absorbance values were referred to the internal HMGB1 standard curve (range of 2.5â80 ng/mL). 2.3.6.3 Calreticulin surface exposure To quantify calreticulin (CRT) exposed on the cell membranes, a 96-well black-walled, clear bottom plate was coated with 50 µL/well of 25 µg/mL poly-D-lysine in water for 1 h at room temperature to facilitate cell adherence. The coating solution was aspirated and 20,000 CT26 cells/well were seeded in 100 µL/well. Cells were grown for 24 h and thereafter 100 µL of drugs or medium was added to each well. The cells were exposed to the drugs for 24 h, at which time the cells were washed 3x with 100 µL/well of HBSS with calcium and magnesium without phenol red and fixed with 50 µL/well of 4% methanol-free formaldehyde diluted in phosphate-buffered saline (PBS) for 20 min at room temperature. Methanol-free formaldehyde was used to minimize permeabilization of cell membranes during fixation. Cells were washed 3x with HBSS and then 50 µL/well of primary antibody against CRT (diluted 1:200 in staining buffer) was added for 1 h on ice. Cells were washed 3x with HBSS and stained with 50 µL/well of secondary antibody conjugated to DyLight® 488 (diluted 1:500 in staining buffer) for 30 min on ice and in the dark. The plate was washed 3x with HBSS and 50 µL/well of Hoechst 33342 (diluted 1:2000 in PBS) and CellMaskâ¢ Deep Red (diluted 1:1000) was added for 20 min at room temperature to stain the cell nuclei. Cells were finally washed 3x with HBSS and imaged in 100 µL/well of PBS with the IN Cell Analyzer 2200. Twenty images were acquired for each well. The flat field correction was applied during the image acquisition. Mean fluorescence intensity of CRT on the cell membranes was quantified for each individual cells by conducting a multi-target analysis with the IN Cell 2200 Workstation 3.7 software (GE Healthcare). The average of mean fluorescence intensities for all cells under the same condition was calculated and related to untreated cells in order to express the results as 29 fold-increase in CRT exposure. 2.4 In vivo characterization of liposomal formulations 2.4.1 Dose range finding studies The Cu(DDC)2 DSPC/Chol liposomes were prepared at a final DDC:total lipid ratio of 0.2 (mol:mol). Prior to administration Cu(DDC)2 DSPC/Chol liposomes, Control âemptyâ DSPC/Chol liposomes and the Cu(DDC)2 BSA formulation were filter-sterilized through sterile 50 mL vacuum-driven Steriflip filtration system (0.22 µm) Millipore, Billerica, MA, USA). After filtration, the liposomal formulations and BSA formulations were diluted to 0.1 mg Cu(DDC)2/mL for samples injected intraperitoneally (i.p.) at 1 mg/kg, or 0.8 mg Cu(DDC)2/mL for samples injected intravenously (i.v.) at 8 mg/kg. The control liposomes were diluted to match liposomal lipid concentration (175 mg/kg). In vivo experiments were conducted using female (NOD)-Rag1null-IL2rγnull (NRG) mice (for tolerability, efficacy, and pharmacokinetics studies), and BALB/c (for efficacy studies) from the Animal Resource Centre (ARC) at the BC Cancer Research Institute (Vancouver, BC, Canada) and were maintained in a pathogen-free environment, housed in groups of four. Mice were housed in microisolators with temperature and humidity monitoring, and were provided enrichment with free access to water and food. All studies adhered to the guidelines of the Canadian Council on Animal Care and were completed under A22-0274 (or previous approved versions), an animal care protocol approved by the Institutional Animal Care Committee (IACC) University of British Columbia. This research team is attempting to meet ARRIVE guidelines [23]. The IACC provided regular monitoring of work conducted under this protocol through a post-approval monitoring program. To define safe doses of the different Cu(DDC)2 formulations, NRG mice (n = 3) were injected i.p. Monday, Tuesday, Wednesday, Thursday, Friday for 2 weeks (M, T, W, Tr, F x2) or injected i.v. (lateral tail vein) for 2 weeks (M, W, F x2). Following injection, the health of the animals was evaluated through monitoring of body weight loss, appetite changes, and other behavioural changes such as lethargy and signs of stress. When signs of severe toxicity were observed, the animals were euthanized (isoflurane overdose followed by CO2 asphyxiation) for humane reasons. Necropsies were performed on all animals terminated for humane reasons. If the animals survived treatment, they were monitored for 14 days after the administration of the last dose before they were terminated and full necropsies were then completed. 30 2.4.2 Pharmacokinetic studies Pharmacokinetic studies with Cu(DDC)2 formulations were completed in 6â8âweeks old female NRG mice (nâ=â4 per time-point). All mice received a single iv dose (8 mg/kg Cu(DDC)2) prepared in liposomes with the 3[H]-CHE tracer or BSA, and blood was collected via cardiac puncture and placed into EDTA coated tubes. The blood was kept on ice and centrifuged (Beckman Coulter Allegra X-15R) at 1,500 Ã g for 15 min at 4°C. Plasma was collected and placed into a separate tube before assaying for Cu, Cu(DDC)2, and liposomal-associated [3H]-CHE which were measured with AAS, HPLC, and lipid scintillation counting, respectively. 2.4.3 Biodistribution and immune infiltration studies Biodistribution of Cu and immune profiling were conducted using female C57BL/6 and BALB/c mice bearing subcutaneous MC38 and CT26 tumours, respectively. Mice were housed in a pathogen-free facility and handled according to protocols approved by the University of British Columbia Animal Care Committee (protocol A22-0274). MC38 and CT26 cells (5 Ã 10âµ) were injected subcutaneously into the right flank. Starting on Day 3 post-inoculation, mice received intraperitoneal injections of Cu(DDC)â liposomes (1 mg/kg; 0.25 mg/kg Cu) or Cu(CQ)â liposomes (8.4 mg/kg; 0.80 mg/kg Cu) administered Monday through Friday for two consecutive weeks (MâF Ã2). Anti-PD-1 antibody (clone RMP1-14, 200 μg) was administered intraperitoneally on four occasions, once every 3â4 days, beginning on Day 3. Combination therapy groups received both regimens on their respective schedules. Tumour volumes were measured three times weekly using calipers and calculated as (length Ã width²)/2. 2.4.4 In vivo efficacy studies 2.4.4.1 Cu(DDC)2 and Cu(CQ)2 monotherapy In Chapter 3, based on the tolerated dose studies described above, efficacy studies were initiated using the tolerated dose administered to mice bearing established MDA-MB-231 and 4T1 TNBC models. Cells were grown and maintained in media as described above. For in vivo studies, cells were harvested from passage #3 to #10 when they reached a confluence of 80â90%. Cell counts and cell viability were determined using Cellometer Auto T4 (Nexcelom, Lawrence, MA, USA), and cells were then centrifuged at 200 Ã g for 7 min. After removal of supernatant, cells were resuspended in growth medium and inoculation was performed with 10 Ã 106 cells and 1 Ã 104 cells for MDA-MB-231 and 4T1 respectively, with subcutaneous injection into the right flank of mice in a volume of 50 μL using a 28-31 gauge needle. Treatment proceeded once the average tumour volume reached 100 mm3, and tumour size was determined three times per week by measuring tumour dimensions with callipers starting on first day of treatment. Tumour volumes were calculated based on a formula of (L x W2 )/2 , and tumours were allowed to grow to a maximum size of 800 mm3 before the animals were euthanized for humane reasons. Animals with nondetectable tumours by day 25 were excluded. In Chapter 4, female BALB/c and C57BL/6 mice bearing subcutaneous CT26 and MC38 tumours, respectively. Tumours were initiated by injecting 5âÃâ10âµ CT26 or MC38 cells subcutaneously into the right flank. To assess tumour growth inhibition, mice were treated with intraperitoneal injections of Cu(DDC)â liposomes (1âmg/kg; 0.25âmg/kg Cu) or Cu(CQ)â liposomes (8.4âmg/kg; 0.80âmg/kg Cu) beginning on day 3 post-inoculation. Treatments were administered once daily, Monday through Friday, for two consecutive weeks (10 total doses). Tumour volumes were measured three times per week using digital calipers and calculated as (length Ã width²)/2. Body weights were recorded throughout the treatment period. Tumour growth comparisons were performed on day 19. 2.4.4.2 Anti-PD-1 combination therapy Anti-PD-1 antibody (clone RMP1-14, 200 μg) was administered intraperitoneally at 5-10 mg/kg on four occasions, once every 3â4 days, beginning on Day 3. volumes were measured three times weekly using calipers and calculated as (length Ã width²)/2. 2.4.5 Anti-tumour vaccination study For the âvaccinationâ studies, CT26 cells (1 Ã 10â¶) were seeded in 6-well plates on Day â9, and on Day â8, cells were incubated for 24 hours with a concentration of Cu(DDC)â known to induce approximately 50% cell death (AnnexinVâº/PIâº) after 48 hours. At 24 hours post treatment (Day -7), cells were detached with fresh enzyme-free cell dissociation buffer (Thermo Fisher, Waltham, MA, USA), the cells were counted and assessed for viability using Cellometer Auto T4. Cells were suspended in phosphate-buffered saline (PBS) at 10x106 cells/mL, and 5x105 cells were inoculated sc (injection volume of 50 µL) into the left flank of mice using a 28-gauge needle. On Day 0, 5x105 untreated, parental CT26 cells were injected subcutaneously into the right flank of mice in a volume of 50 µL. Tumour growth in these animals were monitored over time until then animals reached the humane end-point (tumour mass of 800 mm3). 32 2.5 Analytical Assays 2.5.1 Atomic absorption spectroscopy Concentrations of Cu in plasma and tumour homogenates were measured by flameless AAS. Samples were diluted in 0.1% nitric acid and analyzed using the AAnalyst 600 Atomic Absorption Spectrometer (Perkin Elmer Life Sciences, Waltham, MA). Briefly, samples were dried at 110â°C for 30s, heated (45âs) to 130â°C, ashed at 1200â°C for 30 s, and atomized and cleaned at 2000â°C (5âs) and 2450â°C (4âs), respectively. The standard curve was generated with 0.1% nitric acid containing 0, 25, 50, 75, and 100 ng/ml Cu solution. 2.5.2 Inductively Coupled Plasma Mass Spectrometry (ICP-MS) Cu isotopic analysis was performed using a Thermo Scientific iCAP Qc instrument equipped with an autosampler for aqueous liquid samples. The system operated under kinetic energy discrimination (KED) mode with helium as the collision gas and argon at 0.3 mL/min. Injection volumes were 1 mL. Isotopes â¶Â³Cu and â¶âµCu were measured concurrently with internal standards indium and rhodium (10 ppb, Thermo Fisher). Quantification was based on a calibration curve prepared from a ruthenium standard (Sigma-Aldrich) at 0.1, 1, 10, 100, and 500 ppb concentrations dissolved in 2% ultrapure nitric acid. 2.5.3 HPLC analysis Cu(DDC)2 concentrations for plasma samples were determined with high-performance liquid chromatography (HPLC; Waters, Milford, MA) at an absorbance wavelength of 435 nm. Plasma samples were mixed with chilled 3% acidified (glacial acetic acid) methanol, and the samples were centrifuged at 14000 rpm for 15 min at 4 °C to remove precipitated plasma proteins. Then 20 μL of the supernatant was injected into a Luna Omega 3âÎ¼m C18 (2) 100 A (150âÃâ4.6âmm) column and C18 Symmetry shield guard column (Waters, Mississauga, ON, CA). The mobile phase consisted of 12% mobile phase âAâ (water) and 88% mobile phase âBâ (100% methanol), with a flow rate of 1 ml/min. Standard curves (ranged from 0.1 μg/mL to 4 μg/mL) were prepared where 3% acidified methanol was used as the dilution solvent. All standards and samples were kept at 4 °C prior to injections and the column temperature was set to 40 °C. 2.6 Immunohistochemistry (IHC) Analysis 2.6.1 Tumour harvesting and tissue processing 33 Tumours were harvested 72 hours after the final treatment for immune infiltration studies. Female BALB/c mice bearing subcutaneous CT26 tumours were used for all staining protocols described. Following harvest, tumours were fixed, processed through graded reagent alcohols and xylene (Fisher Scientific) using a TP1020 Carousel tissue processor (Leica), and then used to construct tissue microarrays (TMAs) with a Beecher Instruments manual tissue microarrayer. TMA blocks were sectioned at 4âµm using a Microm HM355 microtome. Slides were deparaffinized through xylene and graded reagent alcohols, followed by antigen retrieval using rodent decloaking solution in a decloaking chamber (Biocare Medical). All sections were then processed for multicolor immunofluorescent staining. 2.6.2 Multicolor immunofluorescent staining â primary panel Slides were loaded into an Intellipath FLX autostainer (Biocare Medical). The first round of staining began with the blocking of endogenous peroxidase activity using Peroxidased-1 (Biocare) for 5 minutes and non-specific binding using Rodent Block M (Biocare) for 30 minutes. CD3 (clone SP7, Abcam) was applied for 30 minutes, followed by MACH2 Rabbit-HRP polymer (Biocare) for 10 minutes and OPAL520 fluorophore (Akoya Biosciences) for 10 minutes. Slides were then removed and subjected to heat-mediated antigen retrieval in AR9 buffer (Akoya) via microwave treatment to denature antibodies while preserving fluorophore signals. The second round involved staining with FoxP3 (clone FJK-16s, Fisher Scientific), followed by Rat-on-Mouse HRP Polymer (Biocare) and OPAL620 (Akoya), with microwave retrieval in AR6 (Akoya). The third round employed PAX5 (clone EPR3770(2), Abcam), MACH2 Rabbit-HRP polymer, and OPAL650, followed again by AR6. In round four, CD8 (clone D4W2Z, Cell Signaling Technology) was applied, followed by MACH2 Rabbit-HRP and OPAL690, then AR6. In round five, background Sniper (Biocare) was used for 10 minutes in place of Rodent Block M, followed by Pan-Keratin (clone E6S1S, Cell Signaling Technology), MACH2 Rabbit-HRP, and OPAL570, then AR6. The final round included F4/80 (clone D2S9R, Cell Signaling Technology), MACH2 Rabbit-HRP, and OPAL540. Following staining, slides were mounted using Prolong Diamond antifade mountant (Fisher Scientific). 2.6.3 Multicolor immunofluorescent staining â calreticulin/PD-L1 panel A separate multiplex immunofluorescent panel was developed to evaluate PD-L1 and calreticulin expression. Although pan-keratin was included in this panel for epithelial segmentation, the CT26 model 34 did not express the keratins targeted by the E6S1S antibody strongly, limiting its utility for segmentation in this context. Antigen retrieval was performed using nuclear decloaking solution (Biocare Medical). Blocking was carried out on the Intellipath FLX using the same peroxidase and nonspecific binding inhibitors as the primary panel. In round one, PD-L1 (clone D5V3B, Cell Signaling Technology) was applied, followed by MACH2 Rabbit-HRP polymer and OPAL540, then AR9 microwave treatment. In round two, Calreticulin (clone EPR3924, Abcam) was applied, followed by MACH4 HRP polymer and OPAL620, then AR6. The final round applied Pan-Keratin (clone E6S1S, Cell Signaling Technology) with MACH4 HRP polymer and OPAL690. Slides were coverslipped using Prolong Diamond antifade mountant. 2.6.4 Image acquisition and analysis Slides were imaged using the Vectra 3 multispectral imaging system (Akoya Biosciences), with 1Ã1 image capture per core. Image analysis was conducted using inForm software (Akoya), beginning with a base algorithm that segmented tissues based on keratin, DAPI, and autofluorescence into epithelium, stroma, and âotherâ compartments (e.g., blank space, tissue folds, necrotic regions). Individual phenotyping algorithms were developed for mutually exclusive marker pairs (e.g., F4/80-CD3, CD8-PAX5, FoxP3) to define immune cell phenotypes. Results from inForm were compiled using Phenopreports (Akoya) to generate composite overlays and quantify cellular phenotypes. All analysis steps were conducted in triplicate to ensure reproducibility. For the Calreticulin/PD-L1 panel, tissue segmentation was performed as in the primary panel. The inForm phenotype function was used to derive PD-L1+ cell counts, and an H-score algorithm was applied to a representative image to calculate staining intensity and proportion metrics. 2.7 RNA Sequencing and Transcriptomic Analysis 2.7.1 Bulk RNA sequencing CT26 cells were seeded at 1âÃâ10â¶ cells per well in 6-well plates and treated with CuSOâ (800âµM), Cu(DDC)â (1âµM), or vehicle control for 24 hours. Total RNA was extracted using the RNeasy Plus Mini Kit (Qiagen), and RNA integrity was assessed using the Agilent 2100 Bioanalyzer, with only samples showing RNA Integrity Numbers (RIN) greater than 9 included for sequencing. Libraries were prepared using the Illumina Stranded mRNA Prep Kit according to the manufacturer's protocol. Sequencing was performed on an Illumina NextSeq 2000 platform, generating 59 base pair paired-end 35 reads (PE59Ã59). Raw sequencing reads were demultiplexed using Illuminaâs BCL Convert software. The resulting FASTQ files were aligned to the Mus musculus reference genome (mm10) using the DRAGEN RNA pipeline on the Illumina BaseSpace Sequence Hub. Gene-level count matrices were generated using the DRAGEN Differential Expression App, which performed transcript quantification, normalization, and filtering for downstream analysis. 2.7.2 Differential expression and pathway enrichment Differential expression analysis was performed using DESeq2. This was executed through the DRAGEN Differential Expression App on BaseSpace, which returned normalized gene-level counts and statistical results. Genes with adjusted p-values < 0.05 were considered differentially expressed. For comparative analyses including Venn diagram segmentations, an additional threshold of absolute logâ fold change â¥ 0.5 was applied to define treatment-specific and overlapping differentially expressed genes (DEGs). Count matrices were exported for further analysis in R. Functional enrichment was carried out using the clusterProfiler package, focusing on Gene Ontology (GO) biological process terms and curated pathway collections relevant to immune response, apoptosis, metal ion transport, oxidative stress, and cell death. Analyses were conducted on both full DEG lists and Venn-derived subsets. Results were visualized as dot plots and bubble plots, and enriched terms were ranked based on adjusted p-values. 2.7.3 Gene set enrichment and signature analysis Gene set enrichment analysis (GSEA) was performed using both clusterProfiler and fgseaMultilevel packages in R. Ranked gene lists based on logâ fold change values were analyzed against custom and publicly available gene sets. These included collections from the Molecular Signatures Database (MSigDB) a curated Cu-responsive gene set based on the Gene Ontology term âresponse to Cu ion,â and a 34-gene immunogenic cell death (ICD) signature described by Zhou et al. (2023). Each treatment conditionâCuSOâ, Cu(DDC)â, and Cu(CQ)ââwas evaluated to identify shared and unique transcriptional responses. Particular attention was given to cuproptosis-related genes including Fdx1, Dlat, Dld, Lipt1, Pdha1, Pdhb, and Lias. Enrichment results were visualized using running enrichment score (RES) plots, dot plots, ridge plots, and heatmaps. 2.7.4 Integration with public datasets To contextualize the observed transcriptomic responses, reference data from ten murine syngeneic 36 tumour models were obtained from Zhong et al. [297]. Raw RNA sequencing files were downloaded from the NCBI Sequence Read Archive (SRA) using the SRA Toolkit (fasterq-dump) and converted to FASTQ format. Quality assessment was conducted using FASTQC. As the goal was to evaluate baseline expression patterns, downstream analyses utilized the gene-level expression matrices published in the supplementary materials of the original study. This dataset enabled comparison of basal expression levels of Cu-handling genes, cuproptosis markers, and immune-related transcripts across CT26, MC38, and other syngeneic tumour models. Additional transcriptomic datasets were accessed through the TISMO portal (https://tismo.cistrome.org) and used for immune cell deconvolution and cross-referencing with treatment response profiles. Gene expression data were also retrieved from the DepMap portal (https://depmap.org), which provides molecular profiles of human cancer cell lines. 2.7.5 Statistical analysis All analyses were conducted in GraphPad Prism (v10). Data are reported as mean Â± standard error of the mean (SEM) or standard deviation (SD), as indicated in figure legends. Two-tailed unpaired t-tests were used for comparisons between two groups, including Cu quantification and isotopic analyses. For comparisons involving more than two groups, one-way ANOVA was applied. In Chapter 3, where multiple group comparisons were of interest (e.g., BSAâCu, liposomalâCu, and control), post hoc analysis was performed using Tukeyâs test. In Chapters 4 and 5, where comparisons aligned with pre-specified hypotheses (e.g., treatment groups versus vehicle, or PD-1 monotherapy versus combination therapy), targeted contrasts were applied. Tumour growth was evaluated at early and late time points using one-way ANOVA. Survival outcomes were assessed using the log-rank (MantelâCox) test. A two-sided p-value < 0.05 was considered statistically significant. 37 3 Liposomal delivery of a disulfiram metabolite drives Cu-mediated tumour immunity 3.1 Introduction Disulfiram (DSF) was approved by the FDA under the brand name Antabuse for treatment of alcohol abuse in 1951. However, for over 50 years, evidence has supported its potential as an anti-cancer agent [298,299]. Since the first report of a complete remission in a woman with metastatic breast cancer, several phase I/II trials have been conducted in patients with breast, lung, germ cell, and brain cancers [300â303]. These studies, however, suggest that disulfiram offers limited or no clinical benefit in these settings. This may be due to several factors, including its rapid elimination following oral administration and evidence that Cu (Cu) is required for activity [304]. To address the issue of rapid elimination, various formulations have been developed to alter disulfiramâs pharmacokinetics and biodistribution, including liposomes, micelles, and PLGA-based systems [305â307]. Notably, many of these formulation efforts did not directly consider the role of Cu in disulfiramâs anti-cancer activity. DSF is rapidly metabolized into diethyldithiocarbamate (DDC) and, when mixed with Cu, DDC forms Cu(DDC)2 complexes. It is argued that Cu(DDC)2 is the active agent that can kill cancer cells, exhibiting significant activity at doses less than 100 nM in vitro. Studies suggest that Cu(DDC)2 is a non-specific agent and its therapeutic potential for treatment of cancer may be due to a number of actions including inhibition of the proteasome [308,309], NF-kB [309], and multidrug resistance proteins such as p-glycoprotein [310]. In addition, DDC has been suggested to transport Cu into cells [311]. Intracellular Cu can exert various effects, including free radical scavenging, regulation of the electron transport chain [312], and modulation of immune responses through cytokine production and immune cell activity [312,313]. In our view, and that of others, Cu(DDC)2 is the species of primary interest for anticancer activity. However, the Cu(DDC)2 complex is water insoluble and therefore challenging to develop as a therapeutic agent. Efforts to prepare formulations of Cu(DDC)2 suitable for parenteral administration have been described by our group [117] and others [244,314,315]. The liposomal formulation previously described by our group formulated Cu(DDC)2 by the addition of DDC to Cu-containing liposomes. DDC was membrane-permeable, and when it diffused into the liposomes it would complex with Cu, precipitate, and become trapped. The apparent solubility of Cu(DDC)2 was increased substantially using 38 this method although precipitation during the preparation of the formulation sometimes interfered with sterile-filtration. More recently, an alternative formulation of Cu(DDC)2 was described where bovine serum albumin (BSA) was present during Cu(DDC)2 formation [244]. Specifically, these investigators used a step-wise addition of DDC and Cu chloride (CuCl2) in the presence of BSA. This formulation was shown to be effective in breast cancer models. In the current study, a new liposomal Cu(DDC)â formulation amenable to large-scale production is described, using a sequential addition method where DDC and Cu sulfate (CuSOâ) are added to Cu-free liposomes. Evidence is presented that DDC functions as a Cu ionophore, facilitating Cu transport into both liposomes and cultured cells. When tested in vivo, the formulation showed greater activity in immunocompetent breast cancer models compared to the same tumours in immunodeficient mice. Additional data suggest that Cu(DDC)â induces immunogenic cell death (ICD) [14,16], a form of cell death that promotes the release of DAMPs and triggers an adaptive immune response against tumours. These results provide proof-of-concept for using ionophore-like small molecules to facilitate controlled in vitro and in vivo evaluation of Cuâs biological effects. Together, these results support the use of this strategy as a flexible platform for investigating the immunological and ionophore-driven effects of Cu-based therapeutics. Adapted from Heroux D, Leung AWY, Gilabert-Oriol R, Kulkarni J, Anantha M, Cullis PR, Bally MB. Liposomal delivery of a disulfiram metabolite drives Cu-mediated tumour immunity. Int J Pharm. 2025 Aug 2;683:126010.39 3.2 Results Illustration 3.1. Graphical abstract illustrating the development and immune activity of a copper diethyldithiocarbamate (Cu(DDC)â) liposomal formulation. (A) Cu(DDC)â liposomes were prepared by combining diethyldithiocarbamate (DDC) and CuSOâ in pre-formed DSPC/cholesterol liposomes, leveraging DDCâs copper-ionophore-like activity for efficient intraliposomal loading. (B) Treatment with Cu(DDC)â slowed tumour growth in immunocompetent BALB/c mice but not in immunodeficient BALB/c nu/nu mice, indicating a role for adaptive immunity in therapeutic efficacy. (C) In a prophylactic vaccination model, mice inoculated with Cu(DDC)â-treated CT26 cells exhibited delayed tumour growth and improved tumour-free survival following challenge, confirming induction of an adaptive immune response consistent with immunogenic cell death (ICD). 3.2.1 Characterization of Cu(DDC)2 formulations prepared in BSA and liposomes Cu(DDC)2 may be useful as a therapeutic agent in the treatment of cancer, but is insoluble (Fig. 1H). Our team previously described a formulation for liposomal Cu(DDC)2 where the liposomes were prepared with encapsulated CuSO4 (Cu inside; Cu-i) (Fig. 1D-F) [116,117]; this increased the apparent solubility of Cu(DDC)2 more than 500-fold. Liposomes were selected not only for their ability to solubilize Cu(DDC)â, but also for their favorable tumour-targeting properties: their size range (typically 80â150 nm) enables passive accumulation in tumors via the enhanced permeability and retention (EPR) 40 effect, which arises from leaky tumor vasculature and impaired lymphatic drainage [316]. Subsequently, Skrott et al [244] described a Cu(DDC)2 formulation using BSA where DDC was added to a Cu-containing solution in the presence of BSA (Fig. 1G). No precipitation was noted. To assess whether Cu(DDC)2 remained in solution in the presence of âemptyâ liposomes, a similar approach was tried. Surprisingly, a liposomal formulation of Cu(DDC)2 could be prepared when DDC and CuSO4 were added sequentially to Cu-free liposomes (Cu outside; Cu-o) (Fig. 1A-C). This formulation led to the apparent solubilization of Cu(DDC)2 comparable to the Cu-i method. The association of Cu(DDC)2 with liposomes was measured over time at 4 oC and room temperature (RT). In both formulations, Cu Cu(DDC)2 rapidly associated with the liposomes at RT, and a final Cu(DDC)2 to liposomal lipid ratio of ~0.1 was measured after separating the liposomes from unassociated Cu(DDC)2 via a Sephadex G-50 column. The only difference in Cu(DDC)2 association with liposomes was observed when the incubation was done at 4 oC. At this temperature, the rate of Cu(DDC)2 association with the Cu-i method was slower, and complete association was only noted after 30 min. In contrast, when the Cu-o method was used, Cu(DDC)2 association with liposomes peaked at a Cu(DDC)2 to liposomal lipid ratio of ~0.05 (50% of expected) and there was visible Cu(DDC)2 precipitation. The two formulations prepared at 25oC were characterized by cryo-TEM (see Methods) and representative photomicrographs are provided in Fig. 1 C,F. No electron-dense liposome interior was noted in either formulation and the size and morphology of the two formulations were comparable. Previous studies have used Cu salts including CuCl2 for DDC complexation [244] and for initial formulation assessments, both CuClâ and CuSOâ were tested for Cu-o and BSA loading efficiency, with similar results observed (Fig.1H); all subsequent optimization steps were conducted using CuSOâ. Cu-o was stable at 6 months during storage at 4 oC, with minimal changes in size or polydispersity (Fig. 1I). 41 Figure 3.1. Characterization of Cu(DDC)2 formulations prepared in BSA and liposomes. (AâC) âCu-outsideâ (Cu-o) liposomes are prepared by adding 22 mM DDC to CuSOâ (2:1 molar ratio) in buffer with 300 mM sucrose HEPES (SH) (pH 7.5), forming Cu(DDC)â externally, with a Cu(DDC)â-to-lipid ratio of ~0.1 within 5 minutes at RT. The resulting formulation is simple, filterable, and leverages the enhanced permeability and retention (EPR) effect (green), with diameters of 110â150 nm. (DâF) âCu-insideâ (Cu-i) liposomes are pre-loaded with 300 mM CuSOâ and then incubated with 22 mM DDC externally, with similar loading kinetics as Cu-o at RT. This method also supports EPR-based delivery (green) but requires multiple steps and can produce precipitate, complicating filtration (yellow). (G) BSA-based Cu(DDC)â (~8 nm) is generated by combining 22 mM DDC with CuSOâ or CuClâ (2:1 molar ratio) in 1% BSA. This approach is simple and filterable (green) but lacks size-based tumor targeting. (H) Solubility of Cu(DDC)â formed in water, Cu-o liposomes, or BSA using CuSOâ or CuClâ. (I) Cu-o liposomes remain stable after 6 months at 4â°C, maintaining consistent Cu(DDC)â-to-lipid ratios (~0.075â0.077), size (~145â158ânm), and low PDI values, before and after column purification. Created with Biorender.com 3.2.2 External buffer with a pH of 7.5 is optimal for Cu(DDC)2 formation The formulation was evaluated as a function of internal and external buffers by varying buffer 42 compositions and pH. The results have been summarized in Fig. 2. Formation of Cu(DDC)2 is known to be dependent on pH, due in part to the hydrogenation of thiol groups at low pH [317]. Formation of Cu(DDC)2 was assessed at pH 3.5, pH 5.5 and pH 7.5 (Fig. 2A) which highlights that Cu(DDC)2 does not form at low pH. For the liposomal formulations (Fig. 2B), the liposomes were prepared with internal buffers containing: (i) citric acid (pH 3.5), (ii) SH (pH 7.5), or (iii) HBS (pH 7.5), and the external buffer consisted of a mixture of citric acid, HEPES, MES, and NaCl (CHMN) to enable good buffering capacity through a pH range of 3.5 to 7.5. Similar to the results in Fig. 2A, when the liposomes were placed in a pH 3.5 external buffer, little or no Cu(DDC)2 was associated with the liposomes. Under these conditions Cu(DDC)2 formation was not observed. When the external buffer was increased to pH 5.5 or 7.5, Cu(DDC)2 associated with the liposomes even when the trapped internal buffer was pH 3.5. The association of Cu(DDC)2 with liposomes was most efficient when the external pH was 7.5. Figure 3.2. External buffer with a pH of 7.5 is optimal for Cu(DDC)2 formation. (A) Cu(DDC)â, visible as a brown, water-insoluble precipitate, does not form in pH 3.5 buffer in the absence of liposomes, but becomes clearly visible at pH 5.5 and pH 7.5. (B) Cu(DDC)â was formulated with liposomes using the Cu-o method under varying internal and external pH conditions. Liposomes 43 were prepared with internal buffers of pH 3.5 (300 mM citric acid, CA), pH 7.5 (300 mM sucrose HEPES, SH), or pH 7.5 (300 mM HEPES-buffered saline, HBS), and external buffers of pH 3.5, 5.5, or 7.5 (20 mM HEPES, 20 mM MES, 20 mM citric acid, 150 mM NaCl, adjusted with 1 M NaOH). An aqueous DDC solution (22 mM) was added to achieve a final DDC:CuSOâ molar ratio of 2:1. After 30 minutes of incubation at room temperature, liposomes were purified through Sephadex G-50 spin columns to remove unassociated Cu(DDC)â. The excluded fractions were analyzed by UV-Vis spectroscopy and liquid scintillation counting to determine Cu(DDC)â and lipid concentrations. Representative images of liposome-stabilized Cu(DDC)â for each condition are shown above the corresponding bars. Data represent mean ± SEM. 3.2.3 Internal buffer with pH 7.5 increases the Cu(DDC)2 to liposomal lipid ratio Further characterization of the Cu(DDC)2 liposomes focused on liposomes prepared using an external pH of 7.5 and the efficiency of Cu(DDC)2 association was measured as a function of internal pH. When the internal pH was 3.5, Cu(DDC)2 association with the liposomes was reduced. A maximum Cu(DDC)2 to liposomal lipid ratio of 0.2 was achievable under these conditions. In contrast, Cu(DDC)2 to liposomal lipid molar ratios of approximately 1 were achievable when the internal pH was 7.5; albeit the capacity was greatest when using trapped HBS (pH 7.5) compared to SH (pH 7.5) (Fig. 3A). Although higher Cu(DDC)2 to liposomal lipid ratios could be achieved, the resulting formulations could not be filtered through a 0.22 µm filter, suggesting the presence of precipitated Cu(DDC)2. In fact, the only Cu(DDC)2 to liposomal lipid ratio that could be consistently passed through the sterile filtration filters was 0.1. To determine whether the Cu(DDC)2 was associated with the liposomes, the internal pH 7.5 formulations prepared at Cu(DDC)2 to liposomal lipid molar ratio of 0.1 were fractionated on a Sephadex G-50 column to assess co-elution of Cu(DDC)2 and liposomal lipid (Fig. 3B). If a Cu(DDC)2 precipitate formed, the precipitate would remain at the top of the column (data not shown). When Cu(DDC)2 formed in the presence of liposomes HBS (Fig. 3B) or SH (Fig. 3C) the Cu(DDC)2 co-eluted with the liposomes. 44 Figure 3.3. Internal buffer with pH 7.5 increases the Cu(DDC)2 to liposomal lipid ratio. Cu(DDC)2 was generated in liposomes with various internal buffers (citric acid, pH 3.5; SH or HBS, pH 7.5) and external buffers (SH or HBS, pH 7.5) with limiting concentrations of liposomal lipid to establish capacity of Cu(DDC)2 association with the liposomes (A). After 30 minutes, samples were passed through spin columns containing Sephadex G-50 beads to separate non-associated Cu(DDC)2. Data are plotted as meanâ±âSEM. The formulations containing internal/external buffers of SH (B) and HBS (C) were passed through 5 ml Sephadex G-50 columns, and fractions were analyzed for Cu(DDC)2 and liposomal lipid by UV-Vis spectroscopy and liquid scintillation counting. 3.2.4 Cu(DDC)2 association with liposomes while varying the initial Cu(DDC)2 to liposomal lipid ratio The conditions used to prepare Cu(DDC)2 liposomes were further characterized as shown in Fig. 4 A and B. The results are consistent with the results shown in Fig. 3, and suggest formulations prepared at a fixed liposomal lipid concentration (Fig. 4A) or a fixed Cu(DDC)2 concentration (Fig. 4B) to generate a range of theoretical Cu(DDC)2-to-liposomal lipid ratios (0.05, 0.1, 0.2) could be prepared. There were close associations between the theoretical and actual Cu(DDC)2-to-liposomal lipid ratios regardless of 45 approach. The results verify the 1:2 Cu to DDC ratio, but the 0.2 Cu(DDC)2-to-liposomal lipid ratio formulation could not be sterile filtered (0.22 µm filter). Figure 3.4. Cu(DDC)2 association with liposomes while varying the initial Cu(DDC)2 to liposomal lipid ratio. In order to determine the role of Cu(DDC)2 and lipid concentrations in loading efficiency, the Cu(DDC)2 formation was assessed when the liposomal lipid concentration was fixed at 20 mM (A) and while the Cu(DDC)2 was fixed at 2 mM (B). Liposome-associated Cu(DDC)2 and Cu were determined by UV-visible spectroscopy and AAS. Liposomal lipid concentration was determined by liquid scintillation counting. Data are plotted as meanâ±âSEM. 3.2.5 DDC has Cu ionophore-like properties and can move Cu across liposomal lipid bilayers as well as cellular membranes Other investigators have suggested that DDC can transport Cu through cellular membranes [311],[318]. It was postulated that the method of internalization in the present liposomal formulation was due to DDCâs ionophore-like activity, binding membrane-impermeable charged Cu ions and transporting the neutralized complex through the liposomal membrane. To test the Cu ionophore-like properties of DDC, DDC was added to liposomes and then excess Cu was added. Liposomes prepared with an internal buffer (20 mM HEPES, 20 mM MES, 20 mM citric acid, 150 mM NaCl) (pH of 3.5 â 7.5) and a pH 7.5 SH external (20 mM liposomal lipid) were mixed with DDC (4 mM) and then CuSO4 (2, 10 mM) and the Cu levels in the liposomes were then determined (Fig. 5A). The Cu(DDC)2 to liposomal lipid ratios were maintained at essentially 0.1. However, when high levels of external Cu were used (10 mM) all formulations had a higher than predicted Cu association. This was most evident when the internal pH was 5.5 and 7.5, where Cu to lipid ratios exceeded 0.3. 46 These studies also evaluated liposomal Cu to lipid levels when the concentration of DDC added to the liposomes increased from 2 mM to 8 mM and the concentration of Cu was increased from 2 mM to 10 mM. The level of Cu(DDC)2 that became associated with the liposomes could be predicted based on the stoichiometric ratio of the two components (Fig. 5B). However, the level of Cu that became associated with the liposomes suggested that there were enhanced Cu levels than what would be predicted based on DDC levels (Fig. 5C). An assessment of DDC-mediated transport of Cu into a cell line was completed. MDA-MB-231 cells were selected because subsequent efficacy studies were completed in mice with established sc tumours created following inoculation with this triple-negative breast cancer cell line. The association of Cu with these cells was determined at 0.5, 1, 2 and 4 hrs after 5 μM DDC and 2.5 μM Cu sulfate were added. The results shown in Fig. 5D, indicate that cell-associated Cu levels increased over time. In the absence of DDC or Cu there was less than 1 nmole Cu per mg cellular protein. In the presence of DDC and Cu, the level of Cu increased more than 40-fold to levels of 20 nmole Cu per mg cellular protein. The uptake of Cu into this cell line varied depending on the Cu salt used (Fig. 5E). In the absence of DDC the nmole Cu per mg cellular protein level was always less than 1.5, but in the presence of DDC this increased to 20, 30, 42, and 85 nmole Cu per mg cellular protein when Cu sulfate, Cu gluconate, Cu acetate and Cu chloride were added, respectively. 47 Figure 3.5. DDC has Cu ionophore-like properties and can move Cu across liposomal lipid bilayers as well as cellular membranes. To gain a better understanding of how Cu and DDC added to the outside of liposomes resulted in Cu(DDC)2 formation inside the liposomes an evaluation of Cu transport was assessed when there was Cu excess (relative to the established 2:1 ratio), liposomes containing internal buffers of pH 3.5, 5.5, and 7.5 (20 mM HEPES, 20 mM MES, 20 mM citric acid, 150 mM NaCl), with a SH external buffer (pH 7.5) were mixed with DDC and CuSO4 at the indicated ratios (A). The liposomes were separated on mini-Sephadex G-50 columns and Cu(DDC)2, liposomal lipid and Cu were measured with UV-Vis spectroscopy, liquid scintillation counting, and atomic absorption spectroscopy (AAS), respectively. When excess Cu was present the Cu to liposomal lipid ratio increased while the Cu(DDC)2 to liposomal lipid ratio remained approximately 0.1. When Cu was added to liposomes in the absence of DDC no Cu became associated with the liposomes. Liposomes were mixed with DDC (2, 4, 8 mM) and CuSO4 (1, 2, 4, 6, 8, 10 mM) and the Cu(DDC)2-to-liposomal lipid ratios (B) and Cu-to-lipid ratios (C) were determined. To determine whether addition of Cu and DDC to cells in cultures triple-negative breast 48 cancer cells (1x106 MDA-MB-231 cells) in T25 flasks were assessed for Cu levels after 5 μM DDC and 2.5 μM CuSO4 were added (D). Internal Cu was then measured at the times indicated with AAS. Several forms of Cu (acetate, chloride, gluconate, sulfate) were also combined with 5 μM DDC, or without DDC (E) and the Cu levels in the cells were determined as described in the Methods. * P < 0.01 3.2.6 Cytotoxicity of liposomal and BSA Cu(DDC)2 formulations. One goal of this research was to compare the therapeutic activity of the BSA formulation of Cu Cu(DDC)2 [244] to a liposomal formulation of Cu(DDC)2. Studies evaluated liposomal formulations where DDC and Cu were added to the outside of liposomes, a method that would be comparable to adding DDC and Cu to BSA in solution. Initial studies, summarized in Figure 6, used cells in culture to assess the cytotoxicity of the BSA and the liposomal Cu(DDC)2 formulations. The dose-response curves used to define the IC50 of the formulations (Cu-o, Cu-i, BSA) in MDA-MB-231 cells were identical (Fig. 6A). Using the Cu-o liposomal formulation, it was clear that the dose-response curves were essentially identical regardless of whether the cells were exposed to Cu(DDC)2 for 4 h or 72 h (Fig. 6B). Finally, a comparison of Cu(DDC)2 in DMSO was made using different tumour cell lines originating from breast (JIMT-1, MDA-MB-231), prostate (DU145, LNCap), and lung (A2780CP), and all demonstrated similar potency with IC50âs ranging from 0.05 to 0.13 μM (Fig. 6C). 49 Figure 3.6. Cytotoxicity of liposomal and BSA Cu(DDC)2 formulations. Cytotoxic effects of two liposomal Cu(DDC)2 formulations (Cu-i and Cu-o) were compared to Cu(DDC)2 formed in the presence of BSA in MDA-MB-231 cells (A). Cells were stained with Hoechst 33342 and ethidium homodimer I, and imaged and quantified using an IN Cell Analyzer 2200 with percentages of viable cells normalized to vehicle controls and expressed as fraction affected. Cytotoxicity of the liposomal Cu(DDC)2 formulation (Cu-o) was measured after a 4h and a 72 h exposure time (B). When cells were exposed to Cu(DDC)2 for 4h, the cells were washed with PBS after 4h and replenished with media, then imaged after a total of 72h. The IC50 (72 h exposure time) of the liposomal Cu(DDC)2 formulation (Cu-o) against the MDA-MB-231 cell line was approximately 100 nM. If Cu(DDC)2 was prepared in DMSO the IC50 (72h) was comparable for JIMT-1 (human breast cancer cell line), MDA-MB-231 (human breast cancer cells), DU145 (prostate cancer cell line), LNCaP (prostate cancer cell line), and A2780CP (a cisplatin-resistant lung cancer cell line) (C). Data points represent the mean ± SEM. 3.2.7 Liposomal and BSA formulations of Cu(DDC)2 exhibit similar behaviour following intravenous administration in NRG mice. The pharmacokinetics (PK) of Cu(DDC)2 formulated in liposomes (Cu-o formulation) were compared to Cu(DDC)2 formulated using BSA. The results are summarized in Fig. 7. The maximum tolerated dose (MTD) for both formulations was determined in immunodeficient NRG mice following iv injections on Monday, Wednesday, and Friday for 2 weeks (M, W, F x2) and 8 mg/kg was tolerated and thus used for the PK studies shown in Fig. 7. For these studies, a single iv injection of 8 mg/kg was given to NRG mice, and Cu(DDC)2 and liposomal lipid concentrations in the plasma were determined at the indicated time points. Cu(DDC)2 was rapidly eliminated from the plasma compartment, with approximately 99% of the Cu(DDC)2 eliminated within 5 min. After 20 min the Cu(DDC)2 level was below detectable limits of the assay used. The area under the curve (AUC) estimations for Cu(DDC)2 following administration of the Cu-o liposomal formulation (7.48 μg-hr/ml) and BSA formulation (7.66 μg-hr/ml) were comparable. The results were comparable to results obtained when measuring the plasma elimination of the Cu-i formulation [117]. It should be noted that > 75% of the injected liposomal lipid dose was present in the plasma compartment 30 min following iv injection and 40% was remaining 4 h after injection. It can be suggested that both methods provide an effective means to solubilize Cu(DDC)2 and that Cu(DDC)2 dissociates from the liposomes almost immediately following administration, while the liposomes are retained in the plasma compartment for extended time periods. 50 Figure 3.7. Liposomal and BSA formulations of Cu(DDC)2 exhibit similar behaviour following intravenous administration in NRG mice. Cu(DDC)2 formulated in liposomes and BSA were injected at a Cu(DDC)2 dose of 8 mg/kg. The level of Cu(DDC)2 in the plasma was measured by HPLC (A), and liposomal lipid was measured by assessing 3H-CHE as a non-exchangeable, non-metabolizable liposomal lipid marker by liquid scintillation counting (B). All data are plotted as meanâ±âSEM (n = 3-7). 3.2.8 Efficacy of liposomal and BSA Cu(DDC)2 formulations in an MDA-MB-231 xenograft model. The anti-tumour efficacy of the Cu-o and BSA Cu(DDC)2 formulations was assessed in a subcutaneous xenograft model of MDA-MB-231. This cell line was chosen, in part, because of the previously reported activity using the BSA Cu(DDC)2 formulations [244]. NRG mice with established tumours (â¥ 100 mm3) were injected iv using a Cu(DDC)2 dose of 8 mg/kg for the Cu-o and BSA formulation. The Cu(DDC)2 formulations were administered on M, W, F x 2 weeks (Fig. 8A). Treatment with these formulations delayed tumour progression. At 35 days there was a 37% reduction in tumour volume relative to controls for animals treated with the Cu-o liposomal formulation and a 32% reduction in tumour volume when the mice were treated with the BSA Cu(DDC)2 formulations. The difference in activity was not significant (p>0.05). The previous study assessing efficacy in MDA-MB-231 tumour-bearing mice was done using ip administration of the drug and the MTD was determined to be 1 mg/kg M-F x 2 as an adaptation of the >30 day dosing schedule used by Skrott et al. [244]. This dosing strategy was well tolerated for both formulations and the results have been summarized in Fig. 8C. Thirty-four days following treatment there was a 35% and 10% reduction in tumour volume when mice were injected (ip) with the Cu-o Cu(DDC)2 and BSA Cu(DDC)2 formulation, respectively. This was statistically significant (p<0.05) when compared to the vehicle control and between the formulations. The efficacy data obtained for animals treated with the BSA Cu(DDC)2 formulation was less than that reported by 51 Skrott et al. [244] and may be due to changes in the dosing length, injection volume, mice used, cell line used, and/or the animal facility used. Figure 3.8. Efficacy of liposomal and BSA Cu(DDC)2 formulations in an MDA-MB-231 xenograft model. Female NRG mice were inoculated subcutaneously with 1 Ã 107 MDA-MB-231 cells, and tumour-bearing mice (â¥ 100 mm3) were injected intravenously (IV) (A-B) at a Cu(DDC)2 dose of 8 mg/kg given Monday, Wednesday and Friday for two weeks. The data are plotted as mean ± SEM. For animals given an IP administration, 1 mg/kg Cu(DDC)2 was dosed daily Monday to Friday for two weeks (C). Data are shown as mean ± SEM. *P < 0.05; **P < 0.01 by one-way ANOVA with Tukeyâs post hoc test. 3.2.9 Liposomal Cu(DDC)2 exhibits enhanced activity in an immunocompetent 4T1 syngeneic model. The Cu-o Cu(DDC)2 formulation demonstrated modest efficacy in the immunodeficient NRG MDA-MB-231 sc model but was comparable to the BSA Cu(DDC)2 formulation in a side-by-side comparison in the same model, with a slight improvement observed following IP administration. There is evidence that disulfiram and/or Cu(DDC)2 has the potential to activate the immune system [14,16,319], thus it was important to determine the activity of the Cu(DDC)2 formulation in a syngeneic immunocompetent 52 model. Focusing on the Cu-o Cu(DDC)2 formulation, the activity of the formulation was determined in mice with established 4T1 (breast carcinoma cell line) tumours. The studies compared the activity when the tumours were established in immunocompetent mice (Balb/c) or in immunodeficient NRG mice. Mice with established tumours (100-150 mm3) were treated IV with the Cu-o Cu(DDC)2 formulation (8 mg/kg three times every 4 days (q4dx3)). The control group (dextrose treated) exhibited similar tumour growth rates in the immunocompetent and immunodeficient mice. The Cu-o Cu(DDC)2 formulation did not have a therapeutic effect in NRG mice with established 4T1 tumours. However, when the efficacy study was determined in Balb/c mice with established 4T1 tumours there was a 41% decrease in tumour volume (Fig. 9A), and at the time when all the NRG mice were euthanized due to tumour progression (tumour volumes >800 mm3) 73% of the 4T1 tumour bearing Balb/c mice were still alive (Fig. 9B). These results indicate a significant survival advantage in immunocompetent mice with the 4T1 tumours. Figure 3.9. Liposomal Cu(DDC)2 exhibits enhanced activity in an immunocompetent 4T1 syngeneic model. Female immunocompetent Balb/c and immune comprised Balb/c nu/nu were inoculated subcutaneously with 1x104 syngeneic 4T1 cells, and tumour-bearing mice (100-150 mm3) were injected i.v. with 8 mg/kg Cu(DDC)2 three times at intervals of 4 days (q4dx3). Tumour volumes on day 23 (A) and percentage increase in survival at day 32 (B) are plotted as mean ± SEM (n = 6-11). * P < 0.05 3.2.10 Transcriptional and functional ICD responses to liposomal Cu(DDC)â The benefits of Cu(DDC)â in immunocompetent mice prompted investigation into Cu(DDC)ââs potential to induce immunogenic cell death (ICD), a regulated form of cell death that promotes anti-tumour immunity via the release of DAMPs. ICD is marked by early surface exposure of calreticulin (CRT), ATP release during apoptosis, and HMGB1 release during late necrosis, along with inflammatory cytokine signaling. RNA-seq analysis of CT26 cells treated with Cu(DDC)â for 24 hours showed strong enrichment of pathways linked to apoptosis, cytokine-mediated signaling, NF-κB activation, ER stress, 53 and type I interferon response (Fig. 10AâB). Transcriptional changes included increased expression of Calr, Hmgb1, Il1a, Tnf, Ccl5, and Cu exporters such as Atp7a, while Slc31a1 (CTR1), the primary Cu importer, was upregulated with CuSOâ but remained unchanged with Cu(DDC)â (Fig. 10C). This may reflect differences in Cu uptake, as Cu(DDC)â likely bypasses CTR1 via passive diffusion or direct membrane interaction. Functional assays confirmed the induction of DAMPs by Cu(DDC)â, including a 7-fold increase in ATP release, a 4-fold increase in HMGB1 secretion, and a 2-fold increase in surface CRT exposure compared to untreated or singly treated controls (Fig. 10DâF). To assess whether this translated to in vivo immunogenicity, a prophylactic tumour vaccination model was used. CT26 cells treated in vitro with Cu(DDC)â at a dose inducing ~50% cell death were injected into the left flank of BALB/c mice, followed by a rechallenge with untreated CT26 cells one week later on the contralateral flank (Fig. 10G). On day 14 post-challenge, mice vaccinated with Cu(DDC)â-treated cells showed a 72% reduction in tumour volume relative to controls (Fig. 10H). Tumour-free survival reached 63% in the vaccinated group, compared to 10% in controls (Fig. 10I), indicating that Cu(DDC)â treatment elicits a protective, immune-mediated anti-tumour response consistent with ICD. 54 Figure 3.10. Cu(DDC)â activates ICD-associated transcriptional pathways and triggers DAMP release in vitro, leading to protection in a tumour vaccination model. (A) Schematic overview of the in vitro RNA-seq experimental design. CT26 cells were treated with CuSOâ (800 µM) or Cu(DDC)â (1 µM) for 24 h prior to transcriptomic analysis. (B) GSEA heatmap showing normalized enrichment scores (NES) for selected GO pathways related to Cu stress, ICD, immune signaling, and cell cycle in Cu- or Cu(DDC)â-treated cells. (C) Heatmap of differentially expressed genes (logâ fold change) involved in Cu stress, DAMP signaling, cytokine responses, and NF-55 κB response. (DâF) Independent in vitro assays confirming DAMP induction after 24 h treatment with Cu (12.5 µM), DDC (25 µM), or the combination. Extracellular ATP (D) was measured using CellTiter-Glo 2.0; HMGB1 release (E) by ELISA; and calreticulin exposure (F) by immunofluorescence. (G) Schematic of the prophylactic tumor vaccination model. CT26 cells were treated with Cu(DDC)â for 24 h, resulting in ~50% cell death, and injected subcutaneously into the right flank of BALB/c mice. After 7 days, mice were rechallenged with untreated CT26 cells on the contralateral flank, and tumor growth was monitored to an endpoint of 800 mm³. (HâI) Tumor growth (H) and tumor-free survival (I) following contralateral challenge in vaccinated vs. control mice. In vitro data are shown as mean ± SD; in vivo data as mean ± SEM. *P < 0.05; **P < 0.01. Created with Biorender.com 3.3 Discussion Disulfiram is rapidly converted to DDC following oral administration and DDC is further metabolized to diethylthiomethylcarbamate (Me-DTC) which is the active inhibitor of ALDH1 useful in the treatment of alcohol abuse [243]. The anticancer activity of disulfiram becomes apparent when DDC binds Cu forming Cu(DDC)2. However, attempts to administer DDC intravenously have shown rapid clearance, with a study reporting a terminal half-life of 3-6 minutes after a 4-hour infusion in humans [320]. Although a Phase 2 trial reported a positive effect of DDC in breast cancer, administering DDC alone may have limited efficacy in vivo, as Cu(DDC)2 is unlikely to form at active concentrations in tumours [300]. The liposomal Cu(DDC)2 formulation described in this study improves upon a previously developed preparation by our group that contained encapsulated Cu [116]. This new formulation is based on the sequential addition of DDC and CuSO4 and the subsequent formation of Cu(DDC)2 inside liposomes. The formation of Cu(DDC)2 inside liposomes was unexpected since it is known that Cu(DDC)2 is an insoluble precipitate. Internal and external buffer conditions were optimized, and a pH of 7.5 was determined to be optimal. Cu(DDC)2 could not be formed or incorporated into liposomes at an external pH of 3.5, likely to due to protonation of the Cu-binding thiol groups and/or decomposition of DDC [321,322]. When using pH 7.5 external buffers, there was a significant increase in Cu(DDC)2-to-lipid molar ratios compared to pH 3.5. However, at molar ratios exceeding 0.1, the formulation became difficult to filter, likely due to precipitation of Cu(DDC)â outside the liposomes. The use of liposomes allows for a controlled analysis of Cu movement across lipid bilayers. As shown (see Figs. 4 and 5), increasing Cu concentrations beyond the 2:1 DDC:Cu stoichiometry did not increase Cu(DDC)â formation, but did lead to a threefold increase in liposome-associated Cu. This indicates that DDC is capable of driving directional Cu transport from the exterior of the liposome into the liposomal 56 interior. Given the well-established increase in cytotoxicity observed when DDC is combined with Cu, these findings may reflect not only the activity of Cu(DDC)â itself but also the broader potential of Cu transport mediated by DDC to induce Cu-mediated cell death [323,324]. Cu accumulation and dysregulation have been linked to a range of tumour types, including breast and colon cancer, where elevated Cu levels correlate with tumour progression and stage [325â327]. This Cu-dependent cell growth and proliferation, or cuproplasia, can be targeted with Cu-selective chelators or with Cu ionophores that increase intracellular Cu levels and mediate cell death through accumulation of reactive oxygen species (ROS) in the process referred to as cuproptosis [328,329]. The role of Cu and/or Cu(DDC)2 in mediating anticancer effects may not be so simple and may involve the activation of immune responses. The immunomodulatory properties of DDC were first described by Renoux and colleagues, who found a stimulatory effect involving the activation of CD4+ T-cells, natural killer cells (NK) cells, and intraperitoneal cancer [221,330,331]. Recent reports have also indicated that disulfiram targets tumour-associated macrophages while increasing PD-L1 expression in cancer [18,332,333]. It has been shown that both disulfiram and Cu individually inhibit PARP and promote PD-L1 expression, whereas the combination of disulfiram with PD-1 inhibition could produce powerful antitumour effects [18,333,334]. In this context, the studies here assessed the hallmark features of immunogenic cell death (ICD), including increased surface calreticulin, ATP release, and HMGB1 secretion [14,16], specifically in CT26 cells treated with the Cu + DDC combination. To assess whether these molecular hallmarks translated to functional antitumour immunity, a prophylactic tumour vaccination assay was performed using Cu(DDC)â-treated CT26 cells. Vaccinated mice demonstrated delayed tumour growth and improved tumour-free survival upon rechallenge, supporting the ability of Cu(DDC)â to induce ICD in this setting. The induction of ICD is increasingly recognized as a key feature of certain chemotherapies, such as anthracyclines and oxaliplatin, and may also underlie the abscopal effect observed with radiation therapy [162,163]. These therapies convert immunologically âcoldâ tumours into âhotâ tumours, enhancing response to immune checkpoint inhibition (ICI). Clinical trials have demonstrated improved outcomes when ICD-inducing agents are combined with PD-1 or PD-L1 blockade [22,335,336], with a recent trial in triple-negative breast cancer reporting that doxorubicin was able to effectively prime tumours ICI [22]. Disulfiram has also been shown to enhance antitumour responses in mice when combined with PD-1 inhibition [51]. It is our contention that the novel formulation of Cu(DDC)2 57 developed here may be ideal for use in combination with ICI, likely through its ICD-inducing properties. In light of these findings, the Cu-o Cu(DDC)â formulation described here may serve as an effective immune priming agent in future combination approaches. While this version of the formulation was designed primarily for mechanistic evaluation, its structure is readily suitable for therapeutic development. It enables precise control over Cu delivery and elicits hallmark features of ICD, including DAMP release, transcriptional activation of stress responses, and functional immune memory. These properties, along with its compatibility with scalable production, offer a strong foundation for optimization. Future studies are focused on increasing Cu(DDC)â in liposomes following administration and assessing efficacy in tumour models grown in immunocompetent mice, particularly in combination with ICI. Given the formulationâs ability to engage both innate and adaptive immunity, this platform may provide a rational route for integrating Cu biology with immunotherapy in settings where immune-mediated toxicity and tumour responsiveness can be carefully balanced. In addition to tumor cell-directed effects, Cu(DDC)â may also act on immune cell populations. Disulfiram and its metabolites have been reported to influence lymphoid and myeloid subsets in vivo [337], and the liposomal formulation's accumulation in organs rich in immune cells such as the liver and spleen may amplify these effects. Although this study did not assess immune cells directly, ongoing work in syngeneic models is exploring these interactions in the context of immune-activating agents. The therapeutic activity observed in the MDA-MB-231 model suggests that Cu(DDC)â can exert effects independent of systemic immunity through direct tumor accumulation, and future studies will examine these biodistribution properties in models with differing immune contexts. Meanwhile, in the context of potential immunosuppression of other ICD-inducing agents (e.g., bortezomib [338]) transcriptional analyses in CT26 cells provide preliminary support for both direct and systemic immune involvement, and further analysis of infiltrates and cytokine profiles will clarify the impact of Cu(DDC)â on the tumor microenvironment across models. These findings support a model in which Cu(DDC)â mediates both tumor-intrinsic and immune-modulating actions. While this version of the formulation was designed primarily for mechanistic evaluation, its structure is readily suitable for therapeutic development. It enables precise control over Cu delivery and elicits hallmark features of ICD, including DAMP release, transcriptional activation of stress responses, and 58 functional immune memory. These properties, along with its compatibility with scalable production, offer a strong foundation for optimization. Future studies are focused on enhancing Cu(DDC)â loading into liposomes following administration, through further optimization of lipid composition and buffer conditions, including the incorporation of PEGylated and other circulation-prolonging systems, as well as co-delivery of disulfiram and Cu(DDC)2 to maximize ROS activation (see Fig. 6.1). Recent advances in stimuli-responsive liposomal deliveryâsuch as pH-, redox-, light-, and thermo-sensitive formulationsâoffer promising opportunities for site-specific drug release (see Section 6.2.2). These strategies are readily adaptable to the current formulation and are under evaluation in tumor models grown in immunocompetent mice, particularly in combination with immune checkpoint inhibitors. -Given the formulationâs ability to engage both innate and adaptive immune pathways, this platform may offer a rational approach to integrating Cu-based therapies with immunotherapy, especially in contexts where immune activation must be precisely modulated to maximize efficacy while limiting toxicity. 3.4 Conclusions The current study describes a novel formulation of Cu(DDC)â that is suitable for in vivo use and capable of activating the immune system to reduce tumour burden through an immunogenic cell death (ICD) mechanism. In addition to confirming that DDC can function as a Cu ionophore by transporting Cu across lipid bilayers, this work establishes a foundation for exploring Cu-based immunotherapies. Whether the observed effects are driven primarily by Cu(DDC)â itself or by DDC-mediated Cu accumulation remains to be clarified, but the induction of functional antitumour immunity supports its potential in combination with immune checkpoint inhibition. Continued development of this platform could enable targeted Cu delivery strategies that pair immune activation to enhance treatment outcomes. 59 4 Immunogenic cell death in colorectal cancer models is modulated by baseline and ionophore-induced Cu accumulation 4.1 Introduction Cu is an essential transition metal with significant roles in numerous biological processes, including oxidative phosphorylation, redox homeostasis, and immune function [339â341]. Given its involvement in diverse cellular activities, Cu homeostasis is tightly regulated, and its dysregulation has become a promising therapeutic target in cancer treatment. Manipulating Cu levels through depletion strategies, such as Cu chelation, aims to restrict cuproplasia, a phenomenon where Cu availability promotes tumour growth [75,124,328]. Conversely, Cu overload, driven by compounds known as Cu ionophores, has been explored as a strategy to disrupt cellular Cu homeostasis [26,112], triggering cell death through mechanisms distinct from traditional apoptosis or ferroptosis. Recently, a unique Cu-dependent cell death modality termed cuproptosis was identified, characterized primarily by mitochondrial protein aggregation induced by lipoylation stress, culminating in metabolic collapse and mitochondrial dysfunction [113]. Understanding the complexities underlying Cu-induced cell death pathways is therefore critical for exploiting Cu therapeutically. Beyond the induction of direct cytotoxic effects, Cu also exerts considerable influence on immune modulation within the tumour microenvironment. It has been shown that Cu can regulate cytokine production, promote macrophage polarization towards an M1-like pro-inflammatory phenotype, and facilitate T-cell activation and proliferation [342â347]. These immunomodulatory properties position Cu modulation as a potentially effective adjunctive strategy to augment conventional immunotherapies, including immune checkpoint inhibitors (ICIs). Yet, a significant gap in understanding remains regarding whether immunogenic cell death (ICD), marked by release of damage-associated molecular patterns (DAMPs) such as ATP, HMGB1, and calreticulin (CRT), represents a generalized response to elevated Cu or is uniquely influenced by specific Cu ionophore chemistries. Translating Cu modulation into effective cancer therapies remains challenging due to Cuâs narrow therapeutic window and the potential for toxicity from both deficiency and excess [348]. The distribution and availability of Cu are tightly regulated, making targeted accumulation in tumours difficult without affecting healthy tissues. Emerging technologies, including nanocarriers and metal organic frameworks, are being developed to improve specificity, safety, and pharmacokinetic profiles 60 for targeted Cu delivery [292,294,349â355]. Among these strategies, small-molecule Cu ionophores such as elesclomol, diethyldithiocarbamate (DDC) and clioquinol facilitate intracellular Cu accumulation and trigger oxidative stress mechanisms linked to immunogenic cell death (ICD) [293,356â358]. These compounds form lipophilic Cu complexes that enter cells directly, raising intracellular Cu levels through transporter-independent uptake. DDC, a major metabolite of disulfiram, and clioquinol, an antifungal agent, have both been shown to enhance Cu uptake and induce cytotoxic stress in cancer cells through processes including redox disruption and mitochondrial dysfunction [122,359]. Although these and other ionophores can raise intracellular Cu levels, their immunogenic effects vary and are not well defined. Differences in structure, Cu-binding affinity, and cellular targeting likely contribute to distinct patterns of ICD activation. Additionally, the role of baseline Cu levels in modulating these responses has not been fully elucidated and may vary by tumour type or treatment context. It remains unclear how differences in ionophore chemistry and tumour Cu levels translate into distinct therapeutic effects. This study examines how Cu delivery strategies and intracellular Cu homeostasis influence the induction of immunogenic cell death. A panel of Cu-binding compounds is assessed to define the relationship between ionophore activity, cytotoxicity, and ICD induction, and to determine whether Cu alone is sufficient to trigger DAMP exposure. To evaluate how baseline Cu levels affect susceptibility to Cu delivery, liposomal formulations of Cu ionophores are used to assess activity in tumour cells with distinct Cu phenotypes. Transcriptional responses across different Cu carriers are also analyzed to identify shared and distinct immunogenic programs. Finally, in vivo ICD is assessed by measuring calreticulin surface exposure following ionophore treatment in CT26 and MC38 tumour models, which were identified to contain high and low basal Cu content, respectively. Together, these experiments offer insights into how Cu levels and delivery mechanisms contribute to ICD induction in vivo. Adapted from: Heroux D, Leung AWY, Gilabert-Oriol R, Farzaneh S, Wolf M, Alayoubi S, Singh H, Walsby CJ, Kalra J, Bally MB. Immunogenic cell death in colorectal cancer models is modulated by baseline and ionophore-induced Cu accumulation. Apoptosis (in revision).61 4.2 Results Illustration 4.1. Graphical abstract illustrating ionophore-mediated copper delivery and activation of immunogenic cell death in colorectal cancer models. (A) Agents known to complex Cu and act like an ionophore to shuttle Cu across cell membranes were tested for their ability to enhance ICD marker stimulation in combination with Cu. (B) RNAseq analysis revealed Cu ionophore-induced activation of ICD gene signatures with activity in up to 1000-fold lower concentrations compared to CuSOâ. (C) Tumour Cu content was analyzed in high-Cu CT26 and low-Cu MC38 models, showing differences in Cu isotopic composition. (D) Calreticulin exposure was assessed in vivo, demonstrating model-specific responses to Cu ionophores. (E) Cu formulations were well-tolerated with modest single-agent activity, supporting their potential for safe Cu delivery in ICD induction. *Pâ<â0.05, **Pâ<â0.01. Created with Biorender.com. 62 4.2.1 Cu-dependent ATP release and ionophore activity across compound classes. Diethyldithiocarbamate (DDC) is a well-characterized Cu-binding compound historically used for heavy metal chelation in both industrial and clinical settings, with increasing interest in its anticancer potential when complexed with Cu [244,360]. Given recent evidence that Cu complexes of DDC can induce immunogenic cell death (ICD) [361], it was first evaluated whether DDC promotes ATP release, a hallmark of ICD, when paired with transition metals (Fig. 1AâC). DDC alone had minimal effect, but in combination with various Cu salts, it triggered a 3- to 9-fold increase in extracellular ATP. Other transition metals such as cobalt, iron, or zinc did not show a comparable effect. The terpyridine analog TTBT, tested in parallel, strongly induced ATP as a single agent (~8-fold), with Cu combination further enhancing this to 30- to 40-fold. In contrast to Cu, most other metals suppressed TTBT's ATP-releasing activity, with the exception of cobalt, which had no effect. Transition metals alone had limited impact on ATP release (Fig. 1C). Dose-response analysis confirmed that ATP release with Cu(DDC)2 was concentration-dependent and detectable at concentrations as low as 3.1 µM in B16-F10 cells (Fig. 1DâE). To better understand whether this ATP induction was primarily due to Cu transport or intrinsic biological effects of the ligands, a panel of Cu-binding compounds from several drug classes was profiled (Fig. 1F). Cytotoxicity was assessed with and without CuSOâ at 24 and 72 hours. Although ICâ â values varied widely, Cu potentiation, defined as a shift in ICâ â, was only observed with DDC, 8-hydroxyquinoline (8HQ), and pyrithione; known ionophore-like compounds [362]. To assess whether this potentiation correlated with Cu transport, cells were pulsed for 4 hours with each compound and CuSOâ and the intracellular Cu levels were measured (Fig. 1G). Ionophore-like compounds significantly increased intracellular Cu (2- to 8-fold). The terpyridine analogs TTBT and CPT also increased Cu content by 3- to 5-fold. Other compounds, including several polyphenols and chemotherapies known to bind divalent metals, had minimal effect or slightly decreased Cu accumulation. These findings suggest that Cu ionophore-like activity plays a key role in enhancing cytotoxicity in the presence of extracellular Cu. However, some compounds such as TTBT, CPT, and clioquinol (CQ) showed Cu transport without an increase in activity, which may reflect additional factors including treatment duration, subcellular localization, or Cu-independent stress responses. For example, DDC showed potentiation at 24 hours but not at 72 hours, possibly due to cumulative Cu uptake from the cell culture media that reduces differences between conditions over time. 63 64 Figure 4.1. Cu-dependent ATP release and ionophore activity across compound classes. (A) Extracellular ATP measured after 24 h exposure to diethyldithiocarbamate (DDC) with various Cu salts (CuAc, CuClâ, CuGlu, CuSOâ) or non-Cu transition metals (CoClâ, FeClâ, MnSOâ, NiSOâ, ZnSOâ); bar color corresponds to visible reaction color. (B) Extracellular ATP following treatment with a terpyridine analog (TTBT) and the same transition metal panel. (C) Transition metals alone at 25 μM. (DâE) Dose response curves for extracellular ATP following 24 h exposure to DDC or DDC:CuSOâ (2:1 molar ratio) in CT26 (D) and B16-F10 (E) cells. (F) Table of Cu-binding compounds from various drug classes with 24 h and 72 h ICâ â values measured with or without CuSOâ using an IN Cell Analyzer 2200; Cu potentiation (ICâ â shift) is highlighted in red. (G) Fold change in intracellular Cu levels after a 4 h treatment with Cu and each compound, relative to Cu alone, as a measure of Cu ionophore activity. Data are presented as mean ± SD. 4.2.2 Cu-binding compounds enhance DAMP release in proportion to intracellular Cu levels. To evaluate whether Cu-ligand combinations promote immunogenic signaling, three damage-associated molecular patterns (DAMPs)âATP release, HMGB1 release, and calreticulin (CRT) exposureâwere measure in CT26 cells following 24-hour treatment (Fig. 2AâC). Compounds were applied at doses optimized for ATP induction as single agents, either alone, with CuSOâ, or CuSOâ alone at the same concentration. The known ionophore group (brown bar) showed minimal activity across DAMPs when used alone, but addition of Cu markedly enhanced all three markers, as reflected in the fold-increase column. In contrast, compounds in the terpyridine class (pink bar) induced DAMP release as single agents, with little further enhancement upon Cu addition. TTBT was the only terpyridine compound that increased all three DAMPs with Cu. Other compound classes showed minimal effects regardless of the addition of Cu. To determine whether intracellular Cu levels alone accounted for DAMP induction, internalized Cu from Fig. 1G to DAMP responses following CuSOâ treatment alone (CuSOâ columns in Fig. 2AâC) were compared. There was a strong positive correlation between intracellular Cu content and release of ATP, HMGB1, and CRT (Fig. 2D), suggesting that Cu accumulation can directly stimulate ICD-associated pathways. The ionophore-like activity, defined as the fold increase in intracellular Cu over CuSOâ alone, was assessed to determine if Cu-induced enhancement of DAMP markers could be predicted. Ionophore-like activity was associated with HMGB1 and CRT release, and showed a strong trend with ATP (Fig. 2E). However, when analyzing all treatments, there was no consistent correlation between total intracellular Cu levels and DAMP release, likely due to Cu-independent effects in some compound classes. Together, these results support a model in which Cu ionophore-like activity drive 65 ICD-like responses by increasing intracellular Cu concentrations beyond Figure 4.2. Cu-binding compounds enhance DAMP release in proportion to intracellular Cu levels. (AâC) Heatmaps showing ATP (A), HMGB1 (B), and calreticulin (CRT) (C) levels in CT26 cells treated with Cu (Cu) alone, ligand alone, or Cu-ligand combinations. The âFold-increaseâ column shows the ratio of Cu+Ligand signal to the higher of either Cu or ligand alone. (D) Correlation between intracellular Cu content and DAMP marker release following treatment with increasing concentrations of CuSOâ, with intracellular Cu measured by atomic absorption spectroscopy (AAS). (E) Correlation 66 between ionophore activity, defined as the fold increase in intracellular Cu relative to CuSOâ alone, and DAMP marker release following Cu-ligand treatment. Created with Biorender.com. 4.2.3 Basal intracellular Cu levels predict sensitivity to Cu(CQ)â but not Cu(DDC)â. With intracellular Cu delivery emerging as a potential strategy to activate ICD and enhance immune responses, Cu uptake following addition of CuSOâ, Cu(DDC)â, and Cu(CQ)â was assessed across a panel of human and murine cell lines (Fig. 3AâC). Baseline intracellular Cu levels were measured, followed by a 4-hour treatment with each compound. Addition of Cu(DDC)â and Cu(CQ)â resulted in increases in intracellular Cu when compared to cells incubated with 100 µM CuSOâ. The Cu dose for the Cu(DDC)â and Cu(CQ)â studies were 20- and 4-fold less, respectively. To determine whether basal Cu levels were predictive of treatment sensitivity, eight cancer cell lines were assessed and the cytotoxicity following 72-hour exposure to Cu(DDC)â and Cu(CQ)â was determined (Fig. 3EâF). A heatmap of cuproptosis-related gene expression revealed notable heterogeneity in core regulators such as FDX1, DLAT, and PDHA1 across cell lines (Fig. 3D), with MDA-MB-231 displaying both high expression of multiple cuproptosis-associated genes and the greatest sensitivity to Cu(CQ)â, consistent with its elevated basal Cu levels. When ICâ â values were plotted against baseline Cu levels, no correlation was observed when Cu(DDC)â was added (Fig. 3H). In contrast, Cu(CQ)â cytotoxicity was significantly associated with basal Cu content (Fig. 3I), possibly due to the substantially higher dose of Cu used that contributed to greater Cu accumulation and downstream dysregulation. 67 Figure 4.3. Basal intracellular Cu levels predict sensitivity to Cu(CQ)â but not Cu(DDC)â. (AâC) Intracellular Cu levels following 4-hour treatment with 100âÎ¼M CuSOâ (A), 5âÎ¼M Cu(DDC)â liposome (B), or 25âÎ¼M Cu(CQ)â liposome (C), shown alongside baseline Cu levels in five human cancer cell lines. (D) Heatmap showing baseline mRNA expression of cuproptosis-related genes across six human cancer cell lines. Gene expression data were retrieved from the DepMap portal (depmap.org) and Z-score normalized by gene to visualize cell lineâspecific variation. The first seven genes (FDX1, DLAT, PDHA1, DLD, LIAS, LIPT1, PDHB) are core components of the cuproptosis pathway [113]. The final two genes (CD274, MELK) have been associated with sensitivity to Cu-induced cell death in prior studies [26,363]. (EâF) Cytotoxicity curves for Cu(DDC)â (E) and Cu(CQ)â (F) across all eight cell 68 lines following 72-hour treatment. (G) Baseline intracellular Cu in the same five cell lines as AâC, along with three additional lines analyzed in parallel; human cell lines are shown in red, and mouse cell lines in green. (H-I) ICâ â values for Cu(DDC)â and Cu(CQ)â plotted against baseline intracellular Cu concentrations to assess Cu sensitivity, with correlation coefficients (R and p-values) shown. Created with Biorender.com. 4.2.4 Gene ontology enrichment in CT26 cells following Cu treatment. To examine the transcriptomic effects of Cu treatments, gene ontology (GO) enrichment analysis was performed on CT26 cells treated with CuSOâ, Cu(DDC)â, or Cu(CQ)â at ICââ for 24 hours, using gene set enrichment based on logâ fold-changeâranked genes and mouse GO gene sets (Fig. 4). Enrichment maps revealed that CuSOâ primarily induced pathways related to ion transport, along with smaller clusters associated with cell motility and keratinization (Fig. 4A). Cu(DDC)â and Cu(CQ)â also showed enrichment in ion transport-related terms, likely reflecting their role in facilitating Cu uptake, but had broader transcriptional responses that suggested changes in immune and cellular regulatory processes. Cu(DDC)â treatment led to enrichment of pathways related to membrane organization, vesicle trafficking, and hormone transport (Fig. 4CâD), potentially reflecting intracellular responses to altered metal homeostasis. Cu(CQ)â, on the other hand, induced strong upregulation of immune-related pathways, including cytokine production, Toll-like receptor signaling, and regulation of adaptive immune responses (Fig. 4EâF). Some of the enriched categories also included terms associated with tolerance induction and suppression of lymphocyte activation. While the directionality of these responses remains uncertain, the pattern is consistent with activation of both immune stimulatory and regulatory programs. Together, these results indicate that while the core transcriptomic effects of CuSOâ and both ionophores involve metal ion processing, Cu(DDC)â and Cu(CQ)â engage distinct biological processes that may influence how Cu delivery is interpreted by the tumour cell, particularly in the context of immune activation. 69 Figure 4.4. Gene ontology enrichment in CT26 cells following Cu treatment. (A, C, E) Enrichment maps (EM) of Gene Ontology (GO) terms across the Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories for CuSOâ (A), Cu(DDC)â liposome (C), and Cu(CQ)â liposome (E) treatments at IC70 for 24 hours. Enrichment was performed using fgseaMultilevel on logâ fold-changeâranked genes with mouse GO gene sets. Nodes represent significantly enriched pathways (padj < 0.01), sized by normalized enrichment score (NES) and colored by manually assigned functional themes. Edges represent pathway similarity based on Jaccard index. (B, D, F) Dot plots of the top 10 positively enriched GO Biological Process (GOBP) pathways for CuSOâ (B), Cu(DDC)â (D), and Cu(CQ)â (F), ranked by NES. Dot size indicates gene count and color reflects adjusted p-value (padj). 70 4.2.5 Transcript-level characterization of Cu-responsive and immunogenic cell death (ICD) genes in CT26 cells. To determine whether transcriptional responses to Cu accumulation correspond with immunogenic stress signaling, the expression changes in Cu-responsive and ICD-related genes was profiled in CT26 cells. All three treatments activated genes associated with the Gene Ontology category "response to Cu ion," with the most pronounced expression in CuSOâ and Cu(DDC)â, despite Cu(DDC)â being administered at an 800-fold lower molar dose compared to CuSOâ (Fig. 5A). This suggests that intracellular delivery mechanisms may be more influential than the total amount of administered Cu in driving transcriptional response. Across the same conditions, the expression of a curated 31-gene immunogenic cell death (ICD) signature (Fig. 5B) was examined. Each treatment upregulated subsets of ICD genes, but with differing breadth and intensity. Volcano plots showed that CuSOâ induced widespread upregulation and downregulation of genes, consistent with a broad stress response (Fig. 5C). In contrast, Cu(DDC)â triggered more selective transcriptional activation, including strong upregulation of NLRP3 and TNF (Fig. 5D). Cu(CQ)â had fewer differentially expressed genes overall but included increased expression of P2RX7 and NLRP3 (Fig. 5E). These patterns suggest that while CuSOâ produces a diffuse transcriptional signature, ionophore-based treatments may more directly engage immune-relevant pathways, possibly due to differences in subcellular Cu localization or duration of signaling. Gene set enrichment analysis (GSEA) plots visualized the overall enrichment of the Cu response and ICD pathways (Fig. 5FâH). Although CuSOâ induced the most differentially expressed genes, its enrichment for the Cu ion response set was the weakest, likely due to widespread transcriptional changes diluting pathway-specific signals. Cu(CQ)â yielded the strongest and most selective enrichment of both Cu-responsive and ICD-associated genes despite lower overall transcriptional activation. Cu(DDC)â showed intermediate behavior, with strong activation of ICD components alongside robust Cu-responsive transcription. These findings support a model in which ionophore-based Cu delivery can modulate gene expression in a more targeted manner than extracellular Cu alone, potentially shaping immune engagement in distinct ways. 71 72 Figure 4.5. Transcript-level characterization of Cu-responsive and immunogenic cell death (ICD) genes in CT26 cells. (AâB) Heatmaps of logâ fold changes (LogâFC) for genes annotated in the Gene Ontology (GO) Biological Process term âresponse to Cu ionâ (A) and a curated ICD signature (B), following treatment with CuSOâ, Cu(DDC)â, and Cu(CQ)â. Hierarchical clustering was applied to genes (rows), with treatments (columns) ordered by condition. Red indicates upregulation; blue indicates downregulation. (CâE) Volcano plots for CuSOâ (C), Cu(DDC)â (D), and Cu(CQ)â (E) treatments, with genes from the ICD signature labeled and highlighted in yellow. Vertical dashed lines denote the absolute logâ fold change threshold, and the horizontal line indicates the adjusted p-value cutoff (padj < 0.05). Points are colored by direction of change: red (upregulated), blue (downregulated), and gray (non-significant). (FâH) Running enrichment score (RES) plots show the enrichment profiles for the GO âresponse to Cu ionâ (blue) and ICD (red) gene sets under CuSOâ (F), Cu(DDC)â (G), and Cu(CQ)â (H) treatments. The x-axis represents the ranked gene list (ordered by logâ fold change), and the y-axis displays the running enrichment score. Tick marks below each plot indicate the rank positions of genes from each gene set within the full ranked list. 4.2.6 Distinct patterns of Cu accumulation and immune activation in CT26 and MC38 tumours during progression and after treatment. As suggested in Figure 3, CT26 cells in vitro had significantly higher intracellular Cu levels than MC38 cells. Since this may affect therapeutic response to Cu delivery, Cu and immune-related features were characterized in syngeneic murine tumour models following SC injection of two cell lines (Fig. 6A). Cu levels in CT26 were approximately twice the level of MC38, but this difference was less pronounced in larger tumours (Fig. 6B). The common isotopes of Cu are â¶Â³Cu and â¶âµCu, and Cu isotopic fractionation is thought to reflect cellular metabolism and transporter activity [364â366]. While altered â¶âµCu/â¶Â³Cu ratios have been observed in cancer patients, recent data suggest that measurable isotopic shifts in serum may originate in the liver rather than directly in tumour tissue [367]. In this study, MC38 tumours showed higher â¶âµCu enrichment when compared to CT26 tumours, particularly in smaller tumours (Fig. 6C). Plasma levels of total and isotopic Cu were not significantly different when the plasma was obtained from these tumour-bearing mice (Fig. 6FâG). Large MC38 tumours had higher TNFα and IL-12 cytokine levels, which may reflect immune activation. In addition, elevated IL-10 levels were observed, and this may be associated with immune suppression. Interestingly, plasma cytokine levels showed a reverse trend, with higher TNFα and IL-12 in CT26 tumours and lower levels in MC38 tumours (Fig. 6D, H). CT26 tumours also had higher CD8âº infiltration when compared to MC38 tumours, suggesting greater immune priming in this model. There were similar levels of other lymphocyte populations and macrophages in these two tumour models (Fig. 6E). 73 To determine the in vivo susceptibility of Cu ionophore-like compounds and their ability to activate ICD in these models, CT26- and MC38-bearing mice were treated with one dose of Cu(DDC)â or Cu(CQ)â. CRT exposure was assessed 24 hours later by IHC (Fig. 6I). In the CT26 tumour model, Cu(CQ)â, delivering 0.80 mg/kg elemental Cu, caused an increase in CRT levels (albeit not significant), whereas there was little change in this model when Cu(DDC)â was given (delivering 0.25 mg/kg Cu; Fig. 6J). MC38 tumours had higher baseline CRT levels, but treatment with both ionophore-like compounds led to decreased CRT expression, including a significant (p<0.05) 7-fold reduction when the mice were injected with Cu(DDC)â and a 2-fold drop (p=0.051) when the mice were injected with Cu(CQ)â (Fig. 6K). These findings indicate important differences in Cu accumulation and immune-related responses between the two tumour models, with implications for how Cu-based therapeutics might differentially modulate ICD in distinct tumour contexts. 74 75 Figure 4.6. Distinct patterns of Cu accumulation and immune activation in CT26 and MC38 tumours during progression and after treatment. (A) Female BALB/c and C57BL/6 mice were subcutaneously inoculated with 5 Ã 10âµ CT26 or MC38 cells, respectively; tumours were collected at two timepoints. (B) Tumour Cu was measured by ICP-MS. (C) Tumour Î´â¶âµCu values were normalized to CT26 at the earlier timepoint. (D) Tumour cytokines were quantified using the Meso Scale Discovery Proinflammatory Panel. (E) Immune cell populations were assessed by IHC; CD4âº T cells were inferred from CD8â»FoxP3â»CD3âº cells. (FâG) Plasma Cu (F) and Î´â¶âµCu (G). (H) Plasma cytokine levels. (I) Mice bearing CT26 or MC38 tumours were treated intraperitoneally with Cu(DDC)â liposomes (1 mg/kg; 0.25 mg/kg Cu) or Cu(CQ)â liposomes (8.4 mg/kg; 0.80 mg/kg Cu) and tumours were collected 24 hours later. (JâK) Surface calreticulin (CRT) levels assessed by IHC in CT26 (J) and MC38 (K) tumours. Data are presented as mean ± SD. *pâ<â0.05, **pâ<â0.01, nsâ=ânot significant. 4.2.7 Cu ionophore monotherapy modestly alters tumour growth without impacting body weight in CT26 and MC38 models. The activity of monotherapy with Cu(DDC)â and Cu(CQ)â liposomes was assessed in CT26 and MC38 tumor models. Mice were treated three days after tumor cell inoculation and a total of 10 intraperitoneal doses were given over two weeks (Fig. 7A). Both agents produced modest growth inhibition compared to vehicle controls, with Cu(CQ)â showing a slightly greater effect in the CT26 model (Fig. 7BâC, EâF). Body weight was unaffected when using this dose schedule (Fig. 7D, G), and no significant adverse events were observed. These data suggest that Cu ionophore monotherapy is well tolerated but modestly suppresses tumor growth. The results suggest that these agents may be better suited for use in combination regimens aimed at enhancing immunotherapy through Cu-dependent or other immunomodulatory mechanisms. 76 Figure 4.7. Cu ionophore monotherapy modestly alters tumour growth without impacting body weight in CT26 and MC38 models. (A) Female BALB/c and C57BL/6 mice were subcutaneously inoculated with 5 Ã 10âµ CT26 or MC38 cells, respectively. Mice were treated intraperitoneally with Cu(DDC)â liposomes (1 mg/kg; 0.25 mg/kg Cu) or Cu(CQ)â liposomes (8.4 mg/kg; 0.80 mg/kg Cu) from day 3 post-inoculation, administered Monday through Friday for two weeks. (B, E) Tumour growth curves over time in CT26 (B) and MC38 (E) models. (C, F) Tumour volumes and (D, G) body weights at day 19 post-inoculation. Data are presented as mean ± SEM. 4.3 Discussion This study investigated the relationship between intracellular Cu and immunogenic cell death (ICD) and examined strategies to enhance cellular Cu delivery through compounds that act like ionophores. While 77 previous research has demonstrated robust ICD activation with Cu-containing treatments, [294,349,351,368], role of internal Cuâboth baseline and post-treatmentâhas remained largely unexplored. The data presented here suggest that intracellular Cu levels directly correlate with the activation of damage-associated molecular patterns (DAMPs), and that the ionophore-like activity of a compound significantly influences the magnitude of ICD induction when combined with Cu. Additionally, data suggests that baseline Cu and certain cuproptosis-related genes (CRGs) are associated with sensitivity to Cu delivery, and chemical differences in Cu carriers lead to differential pathway activation. This is even though shared ICD-related gene signatures are consistently activated. In vivo, CT26 and MC38 syngeneic murine colorectal cancer models, identified to have high and low total Cu and distinct isotopic profiles, exhibited variable activation of calreticulin (CRT) exposure. Together, these findings highlight the interplay between Cu availability, compound chemistry, and tumour-intrinsic factors, but also point to the need for caution when interpreting results across systems with distinct Cu handling and immune profiles. Since the initial characterization of ICD following doxorubicin treatment by Casares et al. in 2005 [20], numerous therapies, including other chemotherapies, kinase inhibitors, and photodynamic therapy, have been identified as ICD inducers [364,365]. The excitement generated from clinical trials combining immune checkpoint inhibition (ICI) with ICD-inducing agents has spurred new efforts to evaluate the DAMP stimulation capabilities of novel and existing compounds, including Cu-binding agents such as the metabolite of disulfiram (DSF), diethyldithiocarbamate (DDC). DSF has been studied for its anticancer properties for over 50 years [298], with more recent evidence showing that the complexation of DDC with Cu is responsible for its anticancer activity [356]. In the pivotal study by Tsvetkov et al., DDC and elesclomol were identified as cuproptosis activators, and CRISPR analysis of these two ionophores revealed a core set of essential genes required for cuproptosis [113]. In the study presented here, DDC was identified as the most active ionophore tested. DDC is capable of engaging Cu and ICD pathways at 1/800th the dose required when copper sulfate (CuSOâ) is used alone. Treatment simultaneously activated additional gene pathways involved in immune cell migration and responses. These results have implications for effective Cu delivery to tumour cells while minimizing systemic Cu toxicity. It is important to note, however, that in vivo studies revealed that activation of ICD transcriptional networks resulted in only modest CRT exposure in CT26 tumours. Conversely, MC38 tumours showed significant suppression of CRT exposure following both Cu(DDC)2 and Cu(CQ)2 78 treatments. Clioquinol (CQ) was historically used as an oral and topical antimicrobial agent but was withdrawn in the early 1970s after being implicated in an epidemic of subacute myelo-optic neuropathy (SMON) in Japan [366]. Subsequent studies suggest this toxicity may be linked to a genetic predisposition within the Japanese population, potentially due to prevalent mutations in ATP-binding cassette (ABC) transporters [367]. Despite this history, CQ has reemerged as a candidate therapeutic in neurodegenerative diseases and cancer [355,368]. In our studies, CQ exhibited distinct biological activity, including the ability to stimulate TNFα production in macrophages, consistent with findings by Du et al [369]. Although Cu(CQ)2 was less efficient than Cu(DDC)2 in mediating Cu ionophore-like activity, its cytotoxicity correlated significantly with basal Cu levels across cell lines, suggesting a potential role for endogenous Cu homeostasis in shaping treatment response. Cu(CQ)2 also induced robust cytokine-associated gene expression and resulted in a two-fold increase in calreticulin exposure in CT26 tumours (not statistically significant, p = 0.09), suggesting a possible trend toward enhanced immunogenicity. Importantly, both Cu(CQ)2 and Cu(DDC)2 demonstrated modest single-agent efficacy in vivo when administered at well-tolerated doses. The efficacy results, while unexciting, support the future integration of these complexes into next-generation Cu-based nanocarrier platforms currently under development. It is clear that the therapeutic potential of these complexes will be achieved when they are used in combination with other treatments. There is a particular interest in their role as part of an immunotherapy cocktail. The findings reveal marked differences in susceptibility to Cu-mediated cell death across tumour phenotypes. This appears to be the first study to evaluate both total Cu content and natural Cu isotope ratios as predictors of responsiveness to Cu ionophores. Previous reports have linked altered Cu isotopic signatures to cancer severity, with reduced serum 65Cu/63Cu ratios observed in patients with colon, breast, and liver cancers, and increased 65Cu observed within liver tumour tissue [359,361,370,371]. While tumour-specific isotopic data remain limited, declining serum 65Cu /63Cu ratios have shown potential as biomarkers of disease progression [361,370,371]. These shifts were often initially attributed to preferential 65Cu binding by lactate within tumours, although more recent models suggest hepatic fractionation as a more plausible driver [362]. In this study, CT26 tumours contained more total Cu than MC38, yet MC38 displayed a higher relative abundance of 65Cu, despite similar plasma Cu levels. Such isotopic fractionation may reflect differences in transporter activity or metabolic states [360,366], and 79 warrants further investigation to clarify the role of Cu isotopes in regulating cuproptosis sensitivity. The clinical translation of Cu-enhanced ICI therapy is currently constrained by incomplete understanding of the precise targets and mechanisms involved in Cu-mediated cell death. Our findings support adopting a precision approach to Cu delivery, factoring in ionophore chemistry, tumor microenvironment characteristics, and tumor-specific Cu metabolism to optimize immune responses to immunotherapy. 80 5 Cu ionophores drive divergent tumour responses to immune checkpoint blockade 5.1 Introduction Cuproptosis is a recently described form of regulated cell death, mechanistically distinct from apoptosis, ferroptosis, and other classical death pathways [113]. It is characterized by mitochondrial proteotoxic stress resulting from Cu binding to lipoylated components of the tricarboxylic acid (TCA) cycle, which induces aggregation of mitochondrial proteins, destabilization of iron-sulfur cluster proteins, and ultimately mitochondrial failure [26,113,353]. Since its identification, cuproptosis has emerged as a potent means of targeting metabolically active tumour cells and has gained attention for its ability to elicit immunogenic cell death (ICD) and promote antitumour immunity [353]. Cuproptosis-related gene (CRG) expression has been shown to reflect immune phenotype, therapeutic vulnerability, and patient outcomes [369â371]. In colorectal cancer, higher expression of validated CRG risk signatures is consistently associated with poorer survival [364,365], and the same studies have also linked elevated CRG expression to increased immune infiltration and altered immune cell composition, though the functional consequences of these immune changes remain uncertain. Pan-cancer studies have extended these findings by correlating CRG heterogeneity with drug sensitivity, immune subtype, and prognosis, revealing that the prognostic impact of cuproptosis can differ widely among cancers depending on metabolic dependencies and immune context [366]. These patterns suggest that cuproptosis activity captures fundamental aspects of tumour biology that may inform patient stratification and therapeutic design. Parallel to this molecular characterization, nanotechnology-enabled delivery systems have accelerated the therapeutic exploration of cuproptosis. These platforms, including metal-organic frameworks, biomimetic nanoparticles, and hybrid constructs with catalytic or targeting functions, are increasingly used to deliver Cu ionophores in combination with immune-modulating features [23,294,295,345â347,350,351,353,363,367â370]. Many incorporate additional functionalities such as glutathione depletion, Fenton-like reactivity, or oxygenation capacity to bypass resistance mechanisms and augment ICD [295,345,347,363]. Despite their varied designs, these systems often converge on shared outcomes: induction of immunogenic cell death, enhancement of antitumour immune activation, and improved response to immune checkpoint inhibitors. Several studies report improved synergy with immune 81 checkpoint blockade in models such as CRC, breast cancer, and melanoma, supporting the broader potential of Cu formulations to potentiate antitumour immunity [295,347,353,363,367]. Despite growing interest in cuproptosis, several aspects of immune modulation remain underexplored, including how tumor-intrinsic characteristics, treatment duration, and the identity of Cu ionophores collectively shape immune responses. While Cu accumulation is commonly viewed as central to the therapeutic effects observed, recent studies suggest that the chemical nature of Cu carriers may independently modulate immune infiltration, tumor burden, and synergistic interactions with immune checkpoint inhibitors [23,319,372]. Additionally, while acute induction of ICD by Cu exposure is well-documented [293,357,372], the consequences of sustained treatment are less clear and likely dependent on tumor context. Thus, understanding how tumor phenotype, kinetics of treatment, and carrier-specific mechanisms of Cu delivery integrate to influence immune outcomes remains an important open question. Here, immunological effects of Cu ionophore treatment were examined across two CRC models, CT26 and MC38, which differ markedly in basal Cu levels, metabolic profiles, and immune phenotypes. Using Cu(DDC)â and Cu(CQ)â as representative Cu complexes, transcriptional responses, Cu isotope incorporation, and immune cell infiltration were characterized over both short and extended treatment durations. Our results demonstrate that immune responses induced by cuproptosis vary substantially depending on tumor context, duration of treatment, and ionophore identity. Although both complexes elevate Cu levels within tumors and plasma, their immunological effects are distinct and carrier-specific. These findings highlight the importance of tumor metabolism, treatment timing, and ionophore chemistry, and provide a framework for investigating cuproptosis-mediated immune effects in cancer immunotherapy using syngeneic models. Adapted from Heroux D, Sun XX, Zhang C, Sharifiaghdam M, Leung AWY, Farzaneh S, Milne K, MacFarlane T, Di Vito C, Nelson BH, Walsby CJ, Bally MB. Cu ionophores drive divergent responses to immune checkpoint blockade across tumour models. Cancer Letters (in revision).82 5.2 Results Illustration 5.1. Graphical abstract summarizing copper ionophoreâinduced immune modulation in high- and low-copper colorectal cancer models. (A) In the high-Cu CT26 model, short-term treatment (days 1â10) with Cu ionophores, especially Cu(CQ)â, enhanced immune infiltration and reduced tumour growth when combined with αPD-1 therapy. (B) Prolonged treatment (days 10â17) led to an immunosuppressive state, marked by reduced lymphocyte infiltration, lower IFNγ levels, and slightly higher tumour volumes compared to αPD-1 therapy alone. Plasma Cu and â¶âµCu isotopic fractionation correlated with therapeutic response (plasma Cu, p < 0.05; â¶âµCu, p = 0.077), while tumour Cu (p = 0.073) and low â¶âµCu showed a trend (did not reach statistical significance) toward association with calreticulin exposure, suggesting a role of Cu in cell 83 death. (C) In the low-Cu MC38 model, Cu(CQ)â as a single agent reduced macrophage infiltration (p = 0.088) and showed a sustained antagonistic effect on tumour growth when combined with αPD-1. No statistically significant changes in tumour Cu levels, calreticulin exposure, or immune infiltration were observed. Plasma Cu and isotopic fractionation showed no significant correlations with tumour growth, with trends opposing those observed in CT26. These findings emphasize the need to consider basal tumour Cu levels and optimize dosing regimens to prevent immunosuppression and maximize therapeutic efficacy. Created with Biorender.com. 5.2.1 Cu-associated cell death and metabolic pathway signatures in murine syngeneic tumour models. Murine syngeneic tumour models are frequently used to characterize responses to therapy, often through targeting specific, well-characterized forms of cell death. The baseline susceptibilities of cell death pathways in varied syngeneic models is an important consideration for a targeted activation of these pathways, and there does not appear to be extensive comparisons of basal cell death pathway activation reported across diverse models. To assess the baseline expression of genes associated with various forms of cell death, RNA sequencing data of ten common cell lines representing malignancies of the colon (CT26, MC38), breast (4T1, EMT6), kidney (RENCA), blood/lymphoid (A20, EL4), skin (B16F10), lung (LLCsr) and germ cell (F9) were downloaded [297]. The sequencing data was mean-normalized and Gene Ontology (GO) pathways of cell death were compared using the Z-scores of each genes in each model, with the expression of seven cuproptosis-related genes used in the absence of an established pathway (Fig 1A). In recent years, the majority of cuproptosis-targeting therapies have focused on 4T1, and to a lesser extent, CT26, and the comparative expression of basal cuproptosis genes was highest in F9, 4T1, and CT26 cells. CT26 was notably highest in pyroptosis gene expression, which is associated with immunogenic cell death and the release of damage-associated molecular patterns (DAMPs). CT26 had high expression of the seven cuproptosis gene, while MC38 had variable levels and a relatively low expression of FDX1, the primary driver of cuproptosis (Fig 1B). Gene expression data of tumour-bearing mice that responded to anti-PD-1 therapy was accessed via the web-tool TISMO [373], and interestingly, while CT26 responders had decreased expression levels of all cuproptosis genes, three out of seven genes including FDX1 were elevated in MC38 responders (Fig 1C). Broader pathways related to Cu homeostasis, oxidative stress, metabolism, and immune response were assessed. Compared to MC38, CT26 cells had higher expression in cuproptosis-related pathways regulating Cu and reactive oxygen species (ROS), while MC38 cells had higher expression of pathways essential for cuproptosis including TCA cycle, oxidative phosphorylation, and protein lipoylation genes 84 (Fig 1D). CT26 cells were highly enriched in Cu storage genes including CP and HEPHL1 (Fig 1E), and an assessment of tumour Cu levels in BALB/c and C57BL/6 mice inoculated with CT26 and MC38 cells suggested higher Cu levels in CT26 tumours, although high variability meant that this difference did not reach significance. In contrast, MC38 had higher levels of genes related to both Cu influx, such as SLC31A1 (CTR1), and efflux, including ATP7A and the related chaperone ATOX1, suggesting that despite overall lower Cu levels, there is a high degree of Cu flux in the cell. It has been proposed that ATP7A contributes to intracellular isotopic fractionation of Cu, preferentially pumping the lighter 63Cu isotope, resulting in enrichment of intracellular 65Cu levels [366]. An assessment of isotopic Cu in CT26 and MC38 tumours did show an enrichment of 65Cu in MC38 tumours, with an increase in 65Cu levels, denoted as δ65Cu, of approximately 5% (Fig 1H). Gene set enrichment analysis (GSEA) of several pathways related to cuproptosis and immunity was performed with CT26 and MC38 expression data (Fig 1I-J), where the strong gene expression in Cu-related and cytokine-activation pathways in CT26 cells is contrasted with high TCA cycle expression in MC38 cells. 85 Figure 5.1. Cu-associated cell death and metabolic pathway signatures in murine syngeneic tumour models. RNA-seq data were obtained from Zhong et al., a study comprising in vitro transcriptomes of ten commonly used murine tumour cell lines [296]. Gene-level counts were normalized using transcripts per million (TPM), filtered for low expression, and Z-score standardized by mean-centering and scaling within each model to enable cross-model comparisons. (A) Heatmap of cell death pathway gene expression across models. (B) Expression of core cuproptosis genes identified by Tsvetkov et al. [113] (C) Differential expression in anti-PD-1 responders from baseline, extracted from TISMO [364]. (D) Broader pathway-level organization of genes related to Cu metabolism, oxidative stress, mitochondrial metabolism and immune signaling. (E-F) Expression of genes involved in Cu storage (E) and Cu transport (F). (G-H) Total tumour Cu levels (G) and Î´â¶âµCu isotopic signatures (H) in BALB/c and 86 C57BL/6 mice bearing CT26 or MC38 tumours harvested at ~200 mg. (I-J) Running enrichment scores (RES) from gene set enrichment analysis (GSEA) for CT26 (I) and MC38 (J), highlighting pathways relevant to cuproptosis. GSEA was performed using fgseaMultilevel on logâ fold-change-ranked genes. Tumour Cu levels were quantified via inductively coupled plasma mass spectrometry (ICP-MS) and analyzed using a two-tailed t-test. 5.2.2 Transcriptomic profiling of CT26 cells treated with Cu ionophores identifies distinct and overlapping pathways in cuproptosis and immune regulation. Many recent studies have demonstrated successful induction of cuproptosis in tumour models as a strategy to enhance immunotherapy responses. These approaches range from Cu ionophores to graphene quantum dots as well as metal-organic frameworks and function through modulatating core cuproptosis regulators such as Dlat and Fdx1 [349,355]. Their effects are often accompanied by activation of immunogenic cell death (ICD) markers like calreticulin and HMGB1 [294,350,354]. While all of these systems deliver Cu systemically, it remains unclear how the mode of delivery, particularly when considering ionophores, influences the downstream transcriptional programs that govern both cuproptosis and immune signaling. To address this, the studies here involved treating CT26 cells with Cu sulfate (CuSOâ, 800 µM), or liposome-encapsulated Cu(DDC)â (1 µM) or Cu(CQ)â (100 µM), each at their respective IC70, for 24 hours (Fig. 2A). Transcriptomic profiling revealed distinct treatment-specific responses. CuSOâ induced the largest number of differentially expressed genes (DEGs, n = 9545), compared to Cu(DDC)â (4014) and Cu(CQ)â (2411), potentially reflecting differences in membrane permeability to the specified agent which in the case of Cu(DDC)â and Cu(CQ)â may be influenced by liposome uptake (Fig. 2B). Shared DEGs were enriched for pathways related to hormone activity (Gdf15, Nppb, Ostn), myeloid-mediated immunity (Fcgr1, Nlrp6, Cd300a), and growth factor signaling (Fgf21, Il11, Gdf9), consistent with activation of both stress-adaptive and immune transcriptional programs (Fig. 2CâG). The coordinated induction of immune effectors and secreted factors suggests enhanced tumourâimmune crosstalk. CuSOâ uniquely activated pathways associated with channel activity, Cu(DDC)â upregulated genes related to protein secretion and cleavage, while Cu(CQ)â induced several cytokine signaling pathways. These findings highlight that although Cu delivery broadly activates immune-related pathways, ionophore-specific effects on non-Cu targets may be critical. To assess whether these responses reflected canonical cuproptosis, seven core genes were evaluated. Most were downregulated following treatment, including FDX1, which was strongly suppressed by 87 CuSOâ, modestly reduced by Cu(CQ)â, and unchanged with Cu(DDC)â (Fig. 2H). A colorectal cancer-derived cuproptosis signature showed similar downregulation in CuSOâ- and Cu(DDC)â-treated cells, with a stronger effect seen in the Cu(CQ)â group (Fig. 2I). Partitioning this signature by Venn diagram segments revealed that overlapping DEGs contributed most to this suppression, although ionophore-unique DEGs also contributed, suggesting additional cuproptosis-relevant effects beyond core Cu regulation (Fig. 2J). Shared upregulation of adaptive immune response pathways was observed across treatments, including gene programs involved in T cell differentiation and cytotoxic activity (Fig. 1J), with enrichment scores highest in the Cu(CQ)â group (Fig. 1K). All treatments also triggered strong activation of cytokine signaling pathways, encompassing cytokines linked to T cells, macrophages, and dendritic cells (Fig. 1L), with Cu(CQ)â and Cu(DDC)â showing greater overall enrichment (Fig. 1M). 88 89 Figure 5.2. Transcriptomic profiling of CT26 cells treated with Cu ionophores identifies distinct and overlapping pathways in cuproptosis and immune regulation. (A) Schematic representation of the liposomal formulations and Cu complexation with DDC and clioquinol (CQ) which can act as Cu ionophores. Within the liposomes, Cu(DDC)â and Cu(CQ)â are formed. These formulations are investigated for their use to enhance Cu delivery and mediate intracellular accumulation. (BâE) Venn diagram of differentially expressed genes (DEGs) in CT26 cells treated with CuSOâ (800 µM), Cu(DDC)â (1 µM), or Cu(CQ)â (100 µM) (their respective IC70) for 24 hours, results that highlight unique and overlapping gene expression profiles across treatments. DEGs were selected based on an absolute logâ fold change (log2fc) â¥ 0.5 and adjusted p-value (padj) < 0.05. Dot plots represent Gene Ontology (GO) enrichment analysis of DEGs using fgseaMultilevel, with pathway significance determined by padj < 0.05. Pathways with immune-related functions are highlighted in red. (F) Heatmap of cuproptosis core gene expression across treatments. (G) Running enrichment scores (RES) for a curated cuproptosis gene signature in human colorectal cancer (CRC) patient data, extracted from Jiang et al. [374]. The top panel represents RES for CT26 cells treated with CuSOâ, Cu(DDC)â, or Cu(CQ)â. The bottom panel shows RES analysis for genes identified as unique or shared in the Venn diagram segments (BâE), allowing comparison of their contribution to the cuproptosis response. (I, K) Heatmaps displaying differentially expressed genes from adaptive immune response and cytokine production involved in immune response pathways, identified from GO annotations. (J, L) Running enrichment scores (RES) of these pathways, demonstrating the distinct impact of each Cu treatment on immune signaling. 5.2.3 Cytotoxicity, cytokine release, and phagocytosis in BMDMs treated with Cu(DDC)â or Cu(CQ)â Macrophages comprise a substantial portion of tumour-infiltrating immune cells and play key roles in tumour progression, partly by modulating the recruitment and activation of other immune subsets [380]. To assess how Cu ionophores affect macrophage function, bone marrow-derived macrophages (BMDMs) were prepared (see Methods) from cells isolated from BALB/c mice and then treated with Cu(DDC)â or Cu(CQ)â (Fig 3A). These compounds showed ICâ â values of 0.083 µM and 8.77 µM, respectively. Cytokine profiling in the media was completed using ICs below the compound IC50s and revealed strong IL-6 induction, with increases of approximately 50-fold and 30-fold following Cu(DDC)â and Cu(CQ)â treatment, respectively. While Cu(DDC)â modestly increased IFNγ and TNFα levels in the media (~5-fold), Cu(CQ)â triggered a more pronounced inflammatory response, with a 70-fold increase in IL-1β and 20-fold increases in TNFα and KC/GRO in the media (Fig 3C-D). These data support the transcriptomic findings from CT26 cells (see Fig. 1M), where Cu(CQ)â elicited broader activation of immune signaling pathways. In addition, previous studies have shown that clioquinol can stimulate TNFα production in macrophages, contributing to its potential anti-tumour activity [376]. To examine whether treatment altered tumour cell immunogenicity in a manner that promotes uptake by 90 innate immune cells, CT26 cells were treated with the selected compounds for 24 hours and then these cells were added onto BMDMs (Fig. 3D). Internalization of the cells, as measured by cells labelled with pHrodoâ¢ Red dye (see Methods), showed a marked increase in cell uptake after the cells were treated with Cu(DDC)â and Cu(CQ)â resulting in a 9.6-fold and 10.5-fold increase in uptake when compared to untreated cells (i.e. vehicle treated cells), respectively (Fig. 3E, G). Cytokine analysis of BMDM supernatants revealed modest reductions in TNFα and KC/GRO, while IL-6 levels rose two-fold and four-fold when the cells were treated with Cu(DDC)â or Cu(CQ)â, respectively (Fig. 3F, H). These results suggest that direct BMDM exposure to Cu(DDC)â or Cu(CQ)â often drives robust cytokine response, while tumour cell treatment with Cu(DDC)â or Cu(CQ)â induced comparable effects on BMDM-mediated phagocytosis and cytokine release. 91 92 Figure 5.3. Cytotoxicity, cytokine release, and phagocytosis in BMDMs treated with Cu(DDC)â or Cu(CQ)â (A) Schematic of BMDM treatment. BMDMs were generated from BALB/c femur and tibia and differentiated for 7 days with macrophage colony-stimulating factor (M-CSF). Cells were treated with Cu(DDC)â or Cu(CQ)â for 72 hours, followed by cytotoxicity and cytokine analysis. (B) Doseâresponse curves for Cu(DDC)â and Cu(CQ)â in BMDMs. Viability was assessed at 72 h. Red arrows indicate the doses selected for cytokine analysis in (CâD). (CâD) Cytokine release in response to Cu(DDC)â (C) or Cu(CQ)â (D), measured from BMDM supernatants using the Meso Scale Discovery (MSD) Proinflammatory Cytokine Panel. Viability (red dashed line) and cytokine levels (solid lines) are shown, normalized to vehicle. (E) Schematic representation of the phagocytosis assay. CT26 cells were treated with Cu(DDC)â or Cu(CQ)â for 24 hours, collected, and then overlaid onto BMDMs. Phagocytosis was assessed over 24 hours using pHrodoâ¢ Red dye-stained CT26 cells as targets, with fluorescence measured in arbitrary units at each time point. (F, H) Phagocytosis of CT26 cells treated with Cu(DDC)â (F) or Cu(CQ)â (H). (G, I) Cytokine release following phagocytosis. Data are expressed as mean ± SD. 5.2.4 In vivo Cu biodistribution and immune infiltration after Cu(DDC)â or Cu(CQ)â treatment To determine how Cu ionophores influence Cu biodistribution in vivo plasma and tumours collected at 8 to 24 hours following treatment of CT26 and MC38 tumour-bearing mice with a single dose of Cu(DDC)â or Cu(CQ)â, was assessed (Fig. 4A). Tumour Cu content increased following treatment with Cu(DDC)â or Cu(CQ)â for both models. A significant elevation was observed in Cu(CQ)â-treated CT26 tumours and a similar, albeit not statistically significant, increase was observed following treatment with Cu(DDC)â (Fig. 4B, D). Plasma Cu levels rose by approximately 60% in Cu(CQ)â-treated mice with established CT26 tumours and showed a comparable trend in mice with established MC38 tumours. Tumour (CT26 and MC38 tumours) Cu levels did not change significantly when the tumour bearing mice were treated with Cu(DDC)â (Fig. 4C, E). To explore whether treatment with Cu(DDC)â or Cu(CQ)â altered Cu metabolism, the isotopic composition of Cu (Î´â¶âµCu) in isolated tumours and plasma were analyzed. Changes in Î´â¶âµCu reflect shifts in Cu uptake, retention, or intracellular processing and can serve as sensitive markers of altered Cu homeostasis [364,366,377]. A significant decrease in tumour Î´â¶âµCu was observed in CT26 tumours from animals treated with Cu(CQ)â, consistent with preferential retention of the lighter â¶Â³Cu isotope (Fig. 4F). The CT26 tumours from mice treated with Cu(DDC)â showed similar, albeit not-significant, pattern (Fig. 4F, H). These effects appeared localized, as plasma Î´â¶âµCu values remained stable across all groups (Fig. 4G, I), suggesting that the observed shifts reflect tumour-specific Cu processing rather than differences in compound isotopic composition. 93 To determine whether a single Cu ionophore dose modulated the tumour immune microenvironment the immune cells within the tumour were assessed. In animals bearing CT26 tumours, treatment with Cu(DDC)â or Cu(CQ)â significantly elevated F4/80âº macrophage levels. For animals with MC38 tumours, by contrast, treatment resulted in no change in CD8âº T cells (Fig. 4L) and a decrease in macrophages, If the treatment was the Cu(DDC)â formulation the reduction observed was statistically significant (Fig. 4M). Together, these findings suggest that Cu-based treatments rapidly alter Cu uptake and immune cell dynamics in a tumour- and formulation-dependent manner. 94 95 Figure 5.4. In vivo Cu biodistribution and immune cell infiltration following treatment with the liposomal Cu(DDC)â or Cu(CQ)â formulations. (A) Schematic of the in vivo pharmacokinetic and biodistribution (PK/BD) study design. BALB/c or C57BL/6 mice were subcutaneously inoculated with CT26 or MC38 cells, respectively. Once tumours reached ~200 mm³, mice were dosed with the liposomal Cu(DDC)â or Cu(CQ)â formulation. After 8 hours, plasma was collected; tumours were harvested at 24 hours for Cu and Cu-isotope analysis. (BâE) Cu content in tumour (B, D) and plasma (C, E) from CT26 (BâC) and MC38 (DâE) tumour-bearing mice, measured using inductively coupled plasma mass spectrometry (ICP-MS). (FâI) Cu isotopic composition (Î´â¶âµCu) in tumours (F, H) and plasma (G, I) for CT26 (FâG) and MC38 (HâI) models. (J) Schematic of immune profiling experiment. Mice bearing CT26 or MC38 tumours were treated with Cu(DDC)â or Cu(CQ)2. Tumours were collected 24 hours post-treatment for immunohistochemical (IHC) analysis. (KâN) Frequency of intratumoural CD8âº T cells and F4/80âº macrophages in CT26 (KâL) and MC38 (MâN) tumours. Data are shown as mean ± SD, *p < 0.05. 5.2.5 Divergent temporal effects of Cu ionophore and antiâPD-1 therapy in CT26 and MC38. To establish a baseline response to immunotherapy, two doses of antiâPD-1 monotherapy were evaluated in CT26 and MC38 tumour-bearing mice. A subcurative dose of 5 mg/kg was selected for subsequent combination studies (Fig. 5AâF). Mice with CT26 tumours were more responsive to antiâPD-1 monotherapy when compared to mice with MC38. There were complete responses observed in mice with CT26 tumours when the dose was 10 mg/kg in CT26 but not in mice with MC38 tumours. These results are consistent with the stronger expression of adaptive immunity and cytokine signaling pathways in CT26 (see Fig. 1). Using the 5 mg/kg dose, the efficacy of Cu(DDC)â and Cu(CQ)â, alone or in combination with PD-1 blockade was assessed (Fig. 5GâP). For mice with CT26 tumours, after five days of treatment and prior to any effects from monotherapies, both Cu ionophore combinations significantly reduced tumour volume relative to controls, with Cu(CQ)â showing continued suppression through day 10 (Fig. 5M). However, by day 17 this therapeutic advantage was not observed. Cu(DDC)â plus antiâPD-1 showed no survival benefit over monotherapy, while Cu(CQ)â was inferior to antiâPD-1 alone (Fig. 5N). In contrast, for mice with MC38 tumours an early increase in tumour growth was observed following combination treatment, with tumour volumes approximately twofold higher than controls by day 10 (Fig. 5O). This effect was not sustained and by day 17 tumour sizes in the combination groups were comparable to untreated controls, while antiâPD-1 monotherapy engendered tumour suppression (Fig. 5P). All treatments were well tolerated, with only minor and transient weight loss observed in Cu(DDC)â-treated animals (Fig. 5QâR). 96 97 Figure 5.5. Divergent effects of Cu ionophore and antiâPD-1 combination therapy in murine CT26 and MC38 tumour models. (AâB) Schematic of tumour efficacy study design for antiâPD-1 monotherapy in mice bearing CT26 (A) and MC38 (B) tumours. Female BALB/c and C57BL/6 mice were subcutaneously inoculated with 5 Ã 10âµ CT26 or MC38 cells, respectively (see Methods). Day 3 post-inoculation, mice were treated with antiâPD-1 antibody (BioXCell; 5 or 10 mg/kg) on the days indicated. (CâF). Tumour growth (C, E) and survival (D, F) curves for the CT26 and MC38 models following antiâPD-1 monotherapy. (GâH) Schematic of the combination studies with antiâPD-1 therapy and Cu(DDC)â or Cu(CQ)â in mice with CT26 (G) and MC38 (H) tumours. Mice received Cu(DDC)â or Cu(CQ)â Monday through Friday for two weeks (see Methods), with antiâPD-1 administered on the same schedule outlined in AâB. (IâL) Tumour growth (I, K) and survival (J, L) curves for CT26 and MC38 treated with vehicle, monotherapy, or combination therapy. (MâP) Tumour volume comparisons at early (M, O; days 7â10) and late (N, P; days 17-19) time points for each treatment group. (QâR) Body weight measurements throughout treatment for CT26 (Q) and MC38 (R) cohorts. Data are presented as mean ± SEM. Statistical significance was assessed using one-way ANOVA. *pâ<â0.05, **pâ<â0.01, ***pâ<â0.001. 5.2.6 Immune and cytokine responses to Cu ionophore and antiâPD-1 therapy in CT26 and MC38 To further investigate how Cu ionophores and antiâPD-1 therapy modulate the tumour immune microenvironment, immune cell infiltration and cytokine profiles were characterized in tumours from mice bearing CT26 and MC38 tumours. Samples were collected on day 17 (see Figure 6 for treatment scheme). This was three days after the final dose of treatment (Fig. 6A). In CT26 tumours, antiâPD-1 monotherapy increased tumour-infiltrating CD8âº, inferred CD4âº (CD8â»FoxP3â»CD3âº), FoxP3âº, and PAX5âº lymphocytes, but these populations were reduced to baseline or lower in tumour where the mice were given the combination of antiâPD-1 and Cu(DDC)â liposomes or Cu(CQ)â liposomes (Fig. 6C). The effect was most pronounced in animals treated with the combination of Cu(CQ)â plus antiâPD-1, suggesting that prolonged combination therapy may create an environment unfavorable to lymphocyte retention or recruitment. By contrast, macrophages (F4/80âº) remained elevated, indicating a selective reduction in lymphocyte infiltration rather than a broad suppression of immune cells. TISMO-based immune deconvolution showed that αPD-1 responders in CT26 were enriched for CD8âº T cells, B cells, and macrophages relative to baseline, with macrophage abundance specifically associated with response in CT26 but not MC38 (Fig. 6B); both M1- and M2-like signatures were positively correlated with response in CT26. PD-L1 expression within tumours remained unchanged in combination groups relative to antiâPD-1 monotherapy (Fig. 6D). Cytokine analysis in CT26 tumours suggested reduced IFNγ and TNFα levels in mice treated with the 98 combination of Cu(CQ)â plus antiâPD-1, consistent with impaired local immune activation (Fig. 6D). In contrast, plasma TNFα levels were significantly elevated in this treatment group, while plasma IFNγ remained relatively stable across treatments (Fig. 6E), illustrating differences between systemic and intratumoural inflammatory responses. Tumour IL-2 and IL-6 levels were modestly elevated following Cu(CQ)â monotherapy but returned to baseline with combination treatment; plasma levels of both cytokines remained unchanged. In MC38 tumours from treated mice, the combination of Cu(CQ)â plus antiâPD-1 resulted in significantly increased CD8âº and PAX5âº tumour lymphocyte infiltration and comparable levels of FoxP3âº and CD4âº lymphocytes when compared to agents used alone (Fig. 6G). However, IFNγ was significantly reduced in tumours from mice treated with the combination of Cu(CQ)â and antiâPD-1 (Fig. 6H), suggesting a possible shift toward T cell dysfunction or exhaustion. While TNFα and IL-2 levels remained stable, IL-6 was significantly reduced following Cu(DDC)â treatment. In contrast to observation with CT26 tumours, PD-L1 expression was elevated across all treatment groups in MC38, with the largest increase of approximately threefold observed in the Cu(CQ)â combination group (Fig. 6H). 99 100 Figure 5.6. Immune cell infiltration and cytokine response following Cu ionophore and antiâPD-1 combination therapy in mice with CT26 and MC38 tumours. (A) Schematic of experimental design for immune cell and cytokine profiling in CT26 and MC38 tumour bearing mice following treatment with vehicle, Cu(DDC)â liposomes, Cu(CQ)â liposomes, antiâPD-1 antibody, or combination therapy. Female BALB/c and C57BL/6 mice were inoculated with CT26 or MC38 cells (see Methods), respectively, and treated from day 3 to day 14 as outlined. Tumours and plasma were collected on day 17 for analysis. (B) Immune cell deconvolution of RNAseq data from Î±PD-1âtreated mice with CT26 and MC38 tumours using the TISMO reference framework. The heatmap shows relative inferred abundance of immune subsets, including CD8âº T cells, CD4âº T cells, regulatory T cells (Tregs), B cells, and macrophages, highlighting differences in immune composition between the two models. (C, G) Quantification of immune cell infiltration by immunohistochemistry (IHC) from paraffin-embedded tumour sections. Tumour microarrays (TMAs) were generated using 0.6 mm cores in triplicate. Cell types assessed included cytotoxic T cells (CD8âº), regulatory T cells (FoxP3âº), B cells (PAX5âº), and macrophages (F4/80âº). (D, H) PD-L1 expression in tumours assessed by IHC. (E, I) Tumour cytokine concentrations measured by the Meso Scale Discovery (MSD) V-PLEX Proinflammatory Panel 1 (Mouse), which includes IFNγ, TNFα, IL-2, and IL-6. (F, J) Cytokine concentrations in plasma from matched animals, using the same panel. Data are presented as mean ± SD. Statistical significance was assessed using one-way ANOVA. *pâ<â0.05, **pâ<â0.01, ***pâ<â0.001. 5.2.7 Cu biodistribution and calreticulin exposure following Cu ionophore and antiâPD-1 treatment To assess how treatment influences Cu distribution and early immune signaling, tumour and plasma Cu levels were measured as well as calreticulin (CRT) exposure, three days after the final dose was given (Fig. 7). In mice with CT26 tumours, tumour Cu trended higher in the Cu(CQ)â combination group, while plasma Cu was significantly elevated in both Cu(CQ)â-treated arms (Fig. 7AâB). Notably, plasma Cu levels were inversely correlated with tumour volume, suggesting a potential association between systemic Cu levels and treatment response in this model (Fig. 7C). Plasma Î´â¶âµCu was significantly reduced in mice receiving the combination of Cu(CQ)â and antiâPD-1. Following monotherapies there was no difference observed in plasma isotopic levels in comparison to untreated mice. This suggests treatment-induced shifts in Cu metabolism rather than an intrinsic isotopic bias in the compound (Fig. 7D; see also Fig. 4FâG). Low Î´â¶âµCu values were associated with a trend toward reduced tumour volume overall (Fig. 7E). These findings suggest that modulation of Cu homeostasis by Cu(CQ)â may contribute to growth control, although chronic exposure could compromise immune function as observed in Fig. 6. CRT exposure, a hallmark of immunogenic cell death that facilitates phagocytic uptake of expressing cells [165], appeared highest in Cu(CQ)â-treated tumours (Fig. 7F). CRT positivity showed an association with tumour Cu content (p = 0.078, Fig. 7G) but the effect was not significant. Tumours with higher CRT levels tended to have lower Î´â¶âµCu, suggesting a potential link between Cu flux and 101 immunogenic signaling (Fig. 7H). In the MC38 tumour model, tumour Cu levels increased following treatment with Cu(CQ)â but this was not observed in tumours given the combination of Cu(CQ)â and anti-PD1and no changes were observed in plasma Cu levels (Fig. 7IâJ). Unlike CT26, there was no significant relationship between plasma Cu levels and tumour volume, although a weak, non-significant trend in the opposite direction was noted (Fig. 7KâM). CRT exposure increased with Cu(CQ)â alone but declined in tumours from mice given the combination treatment (Fig. 7N). Tumour Cu or Î´â¶âµCu levels were not significantly associated with CRT expression levels in this tumour model. For CT26 tumours there was an association (not significant) between Cu metrics and CRT exposure. The results highlight potential model-specific differences in the link between Cu regulation and immunogenic stress. 102 Figure 5.7. Cu biodistribution and calreticulin exposure following Cu ionophore and antiâPD-1 treatment Tumour and plasma samples from the mice with CT26 and MC38 tumours were also analyzed for Cu accumulation, Cu isotope composition, and calreticulin (CRT) exposure. (A, I) Tumour Cu concentrations measured by inductively coupled plasma mass spectrometry (ICP-MS) (see Methods). (B, J) Plasma Cu concentrations. (C, K) Plasma Cu levels stratified by tumour volume (TV), separating mice into low and high tumour volume groups (bottom and top 50% of cohort, respectively). (D, L) Plasma Î´â¶âµCu values. (E, M) Plasma Î´â¶âµCu values by tumour volume group. (F, N) CRT exposure in tumours measured by immunohistochemistry, reported as the percentage of tumour cells with moderate (2+) or high (3+) CRT staining (see Methods). (G, O) Linear regression of CRT-positive tumour cell percentage versus tumour Cu concentration. (H, P) Tumour Î´â¶âµCu values stratified by CRT exposure, 103 dividing animals into low and high CRT based on cohort median. Data are presented as mean ± SEM. Statistical significance was assessed using one-way ANOVA for comparisons across treatment groups, and two-tailed unpaired t-tests for binary group comparisons. *pâ<â0.05 5.3 Discussion This study explored the role of Cu as an immune modulator in syngeneic models of colorectal cancer treated with a murine immune checkpoint inhibitor. By evaluating total and isotopic Cu at baseline and following treatment, plasma Cu levels and isotopic enrichment was identified as potential biomarkers of response. The results suggest that Cu carrier chemistry and treatment duration shape distinct transcriptional immune signatures, with acute and chronic Cu exposure producing divergent effects. These findings may inform the design of Cu-containing therapies and the selection of tumour models as part of a more precise strategy for integrating cuproptosis into immunotherapy. This is, according to available literature, the first study to assess total and isotopic Cu in the context of Cu delivery during immune checkpoint blockade. Previous work has linked Cu isotope fractionation with cancer severity [364,366,367,377], but has not evaluated these measures in response to Cu-based treatments. Although total tumour Cu levels were higher in CT26 tumours than MC38 tumours, the MC38 tumours were relatively enriched in 65Cu. These differences aligned with the expression of genes associated with Cu storage, transport, and may also reflect metabolic differences between the models. For example, 65Cu is more stable in oxygen-containing bonds and may be favored in oxidative phosphorylation dependent cells like MC38, while 63Cu binds more stably to sulfur-containing ligands, such as the Cu-storing protein ceruloplasmin enriched in CT26 [365]. Following treatment, higher total Cu and lower 65Cu /63Cu in CT26 tumours predicted CRT exposure and tumour regression, while in MC38 tumours these associations were not evident. Although Cu isotopic shifts remain an emerging area of study, these findings support further investigation of isotopic Cu as a metabolic and immunological biomarker. While Cu-based therapies have gained interest as activators of anti-tumour immunity, sustained Cu exposure may also impair immune function. Prior studies have shown that chronic Cu overload can deplete lymphocyte populations and promote pyroptosis in the spleen [378]. In this study, CT26 tumours from mice treated with Cu(CQ)2 and ICI exhibited early increases in immune infiltration and reduced tumour volume, but prolonged exposure led to declining TILs and cytokine expression. This suggests a time-dependent trade-off between immune stimulation and suppression. The effects also varied by tumour model and Cu-ionophore used. In MC38, the combination of Cu(CQ)â and anti-PD1 maintained 104 or increased immune cell infiltration but reduced IFNα expression, consistent with a shift toward immune exhaustion. These results highlight the need to consider both timing and tumour context when evaluating Cu-immune dynamics. Although several recent studies have applied Cu nanodelivery to enhance checkpoint blockade [293,294,351,357,368], most have focused on a limited range of tumour models and have not systematically evaluated how tumour-specific Cu metabolism or cuproptosis sensitivity may affect therapeutic outcomes. The comparative gene expression analysis studies reported here suggest that CT26 tumours express higher levels of core cuproptosis genes than MC38 tumours. Many recent studies targeting 4T1 tumours, which also express higher levels of core cuproptosis genes, and this may have contributed to anti-PD1 activity. These models exhibited elevated baseline immune infiltration and cytokine activation signatures, suggesting a more permissive immune microenvironment. While cuproptosis has been linked to oxidative phosphorylation, most notably by Tsvetkov et al. using galactose-adapted cells, data from the same study suggested that high-dose elesclomol treatment may activate alternative pathways [113]. In the tumour models used here and at the treatment doses evaluated, transcriptional markers of oxidative metabolism were not reliable predictors of ICD. Instead, Cu availability and expression of cuproptosis-related genes were more consistently associated with response. As the extended inter-model comparisons were based primarily on transcriptomic data, further in vivo validation will be important, along with validation of cell death mechanisms in vivo with varying dosing schedules. Bridging this gap across physiologically relevant systems will help define which tumour contexts are most likely to benefit from Cu-induced immunogenic cell death, with a major caveat of the limited number of syngeneic tumour models that can be reliably used. Together, the results illustrate how tumour-intrinsic Cu biology and ionophore chemistry shape the immune response to checkpoint blockade in a time and model-dependent manner. These insights support more selective and context-aware use of Cu-based strategies to boost immunotherapy efficacy. 105 6 Summarizing Discussion 6.1 Summary of results Cu has been used therapeutically since antiquity, with its earliest applications documented in Egyptian texts such as the Edwin Smith Papyrus (~1600 BCE), where it was applied in wound care and sterilization [379]. Greek and Roman physicians later used Cu compounds to treat skin lesions, ulcers, and eye diseases [379]. Though its biological functions were not yet understood, early use laid the foundation for subsequent investigation into Cu role in health and disease. Dysregulation of Cu homeostasis is recognized in a range of diseases including neurodegenerative disorders, and several cancers [380], and therapeutic strategies that either restrict or exploit Cu availability have emerged as promising interventions [381]. Among these, Cu ionophores have drawn interest for their ability to increase intracellular Cu levels and induce stress responses that lead to ICD. The recent identification of Cu-dependent cell death, or cuproptosis, by Tsvetkov et al. [113], published during the later stages of this thesis, has sparked increased attention toward Cu-based therapeutics for cancer. Building on this rationale, the studies presented here investigated the potential of Cu ionophores to enhance antitumour immunity, particularly in combination with immune checkpoint blockade. The results demonstrated several key findings that will aid in the pre-clinical development of Cu-mediated therapy. CQ and DDC have been studied for decades for their anticancer effects, and for the first time, nanoformulations of their corresponding Cu complexes have been compared directly in their capacity to modulate ICI in syngeneic tumour models. The CRC models CT26 and MC38, identified in our screen as having high and low intratumoural Cu respectively, further clarified the role of copper in response to Cu-based therapy. These findings support the hypothesis that tumours with high baseline Cu may be more susceptible to Cu overload and subsequent cuproptosis. The data also suggest that tumour sensitivity to Cu delivery may be linked to immune function, as while acute combination treatment with immune checkpoint inhibitors was beneficial, chronic Cu exposure abrogated these effects. 6.1.1 Cu ionophore formulations With the focus of this thesis on demonstrating the potential of Cu delivery to potentiate immunotherapy response, I chose to use liposomal formulations of the well-studied ionophores DDC and CQ. While the Cu(CQ)â formulation was previously developed by a former PhD student, Moe Wehbe, the Cu(DDC)â formulation in this study is an improved version of an earlier prototype. Here, rather than adding DDC 106 to Cu-containing liposomes, DDC and CuSOâ were added stepwise to sucrose-HEPES liposomes, leveraging DDCâs well-established ionophore capacity to transport Cu across membranes, now applied within a liposomal context. The resulting formulation offered better filtration and was simpler to prepare. Although it was not pharmacokinetically optimized, its partial tumour response made it suitable for combination studies with immune checkpoint inhibitors (ICI), which were the primary focus of this work. The contrasting maximum tolerated doses (MTDs) of Cu(DDC)â (1 mg/kg; 0.25 mg/kg Cu) and Cu(CQ)â liposomes (8.4 mg/kg; 0.80 mg/kg Cu) provided models of low and high Cu delivery at cytotoxic levels, enabling exploration of the differential roles of Cu and its chemical carrier. 6.1.2 Cu or carrier? This research began with two central questions. First, can Cu overload enhance anti-tumour immunity in cancer? And second, if Cu delivery does produce enhanced effects, are they attributable to the Cu ion itself, the bound complex, or a combination of both? In the context of Cu-based cancer therapies, the distinct roles of Cu versus its carrier have remained unclear. Clarifying this distinction has been a core objective of this thesis, contributing to the broader goal of improving tumour response in murine models. Ionophores such as DDC and elesclomol have been shown to shuttle substantial amounts of Cu into cells, bypassing natural transporters and redox barriers. However, individual carriers possess unique chemical properties that contribute to cellular effects beyond Cu alone. This nuance is often overlooked in the rapid development of Cu-based therapeutics and nanoformulations using diverse carriers, leading to a blurring of mechanistic roles between Cu and its delivery vehicle. Foundational studies like that of Tsvetkov et al. [113] have helped define shared pathways by which ionophores induce cuproptosis, identifying ten common genes required by both DDC and elesclomol in CRISPR screens. Yet, the broader and carrier-specific effects on other cellular pathways, particularly those involved in anti-tumour immunity, remain poorly understood. Chapters 4 and 5 of this thesis focus on comparative analyses of treatments with Cu(DDC)2 and Cu(CQ)2, and how these compare to CuSOâ alone. Transcriptomic profiling in Chapter 3 identified broad pathway activation patterns and focused on immunogenic cell death (ICD) genes. Chapter 4 further illustrated these effects through a Venn diagram 107 of unique and shared gene activations, highlighting the distinct signature of each Cu carrier. This approach revealed that Cu(CQ)2 induces numerous immune-related pathways not observed with Cu alone, suggesting its potential to synergize Cu-mediated cuproptosis and ICD via cytokine-driven activation of adaptive immunity. Future work in our lab will focus on validating specific expression differences, such as via qRT-PCR and Western blot, between carriers and across models. Although Cu(DDC)2 and Cu(CQ)2 were similarly modest as monotherapies in both CT26 (high Cu) and MC38 (low Cu) models (Fig. 4.7), it was hypothesized that combining these compounds, particularly Cu(CQ)2, with immune checkpoint inhibitors (ICI) would be necessary to fully harness the immunological benefits of ICD and cytokine signaling. 6.1.3 High vs low Cu tumour models The results in MC38 were unexpected: Cu(DDC)2 and Cu(CQ)2 completely reversed the effect of ICI, likely through activation of tumour cell cuproplasia in the context of immune exhaustion, compounded by diminished CRT exposure (Fig. 4.6, 5.5â5.7). In contrast, the initial response in CT26 was promising â Cu(CQ)2 delivered abundant Cu to tumours, resulting in improved CRT and immune infiltration in monotherapy (Fig. 4.6). This translated into significantly reduced tumour volume in early combination treatment, with the caveat of less precision in low tumour volume measures (Fig. 5.6). However, after one week of daily treatment, it became evident that chronic Cu exposure diminished efficacy, and the combination therapies performed slightly worse than ICI alone. This decline was most likely due to lymphocyte depletion in the tumour microenvironment rather than cuproplasia activation in CT26, as Cu levels remained elevated in plasma and tumours of responders across all treatments, suggesting a protective role of high Cu levels, in contrast to the opposite trend observed in MC38. These starkly divergent effects in CT26 and MC38 highlight the importance of considering both the tumour microenvironment and host immune context in the application of Cu therapies. Despite rapid progress in developing Cu-based therapeutics, mechanistic understanding of Cu-induced cell death remains limited, especially regarding biomarkers that can reliably predict cuproptosis activation. Intrinsic factors such as high Cu content, elevated CTR1 (SLC31A1) expression, protein lipoylation, and high expression of cuproptosis-related genes (CRGs) including FDX1, DLAT, LIPT1, and DLD have shown predictive value in preclinical models. Mitochondrial respiration has also been fundamentally 108 linked to cuproptosis, as shown by Tsvetkov et al., who reported 1000-fold higher activity in Cu-ionophoreâtreated lung cancer cells undergoing oxidative phosphorylation. Still, responses across tumour models are highly variable, and overlapping modes of cell death make prediction based on cuproptotic features alone challenging. Having identified CT26 and MC38 as differing in both total and isotopic Cu levels, it was then examined whether higher Cu levels in CT26 were more predictive of response than the elevated mitochondrial respiration and protein lipoylation observed in MC38. Although this comparison was complicated by distinct immune responses between strains, high transcriptomic expression of CRGs and high Cu content were confirmed as predictive markers in CT26. A cross-model transcriptomic comparison (Fig. 5.1) further identified 4T1 as possessing the highest cuproptotic signature, an important validation of this model's utility in the literature. It is my hope that this direct comparison of cell death pathways across commonly used syngeneic models may be informative beyond cuproptosis and help clarify the predictive relevance of these features. 6.1.4 Isotopic fractions â a novel consideration in cuproptosis While total Cu levels have been studied in the context of sensitization to Cu-based therapies and the induction of cuproptosis, to my knowledge, the role of varying isotopic ratios of Cu has not been reported. A PubMed search conducted in July 2025 for âcuproptosisâ returned 1,883 results since 2022, whereas a search for âcuproptosis isotopeâ yielded only 3 resultsânone referencing Cu isotopes or their biological significance in this context. Isotopic enrichment in plasma and tumours is an emerging field with potential as a highly sensitive prognostic biomarker [377,382,383]. As the mechanisms underlying isotopic fractionation remain under debate [367], additional evidence is needed from diverse models, particularly in the context of standard-of-care immunotherapies, for its clinical relevance to be realized. Current knowledge stems from a small number of studies since 2014 that found enrichment of 63Cu in serum from breast and liver cancer patients treated with chemotherapy, correlating with disease severity [377,383], alongside limited data suggesting 65Cu enrichment in tumour tissue [382]. This thesis examined 65Cu/63Cu in relation to baseline differences between high- and low-Cu tumour models (Fig. 4.6), as well as in response to ICI (Fig. 5.7). Lower 65Cu levels in CT26 tumours contrasted with trends toward increased 65Cu after Cu(CQ)2/ICI treatment in both plasma and tumour, which corresponded to improved response and CRT exposure. In contrast, MC38 tumours, characterized by higher baseline 65Cu, showed the opposite pattern or no correlation with outcomes. While interpretation is limited by 109 differences in host strain physiology and tumour origin, these findings offer preliminary evidence for the prognostic and predictive potential of 65Cu/63Cu in the setting of ICI. 6.2 Future perspectives 6.2.1 Liposomal development and optimization While the liposomal formulations of Cu(DDC)â and Cu(CQ)â were useful in assessing the direct effects of each ionophore on Cu delivery and immune checkpoint inhibition (ICI), further development is necessary to advance them as viable clinical candidates. A significant amount (~20 µM) of Cu(CQ)â remains in plasma 8 hours after IV injection [296], whereas Cu(DDC)â becomes undetectable within 15 minutes (Fig. 3.6; [117]), highlighting the need to optimize the release properties of the latter. Although PEGylation has been shown to prolong circulation time and was used by Dr. Wehbe in a Cu(DDC)â variant, it did not enhance efficacy in this context [117]. A pharmacokinetic screening of lipid composition, such as altering lipid chain lengths or incorporating stabilizing components, may help extend circulation and improve tumour deposition [316]. Additionally, although Cu(DDC)â activated markers of damage-associated molecular patterns (DAMPs) in vitro, it elicited minimal calreticulin (CRT) exposure in vivo. While Cu(DDC)â is the active cytotoxic form of disulfiram (DSF), its in situ formation may be critical for generating reactive oxygen species (ROS), and the pre-formed complex might be ROS-depleted. Supporting this, preliminary data showed that free DDC generated significantly more ROS at comparable doses (Fig. 6.1B). This discrepancy suggests that preserving or enhancing ROS-generating potential may be useful to improving the immunogenicity of Cu(DDC)â. To address this, and as an example of ongoing formulation development and optimization, a disulfiram-infused Cu(DDC)â liposome (diCu(DDC)â) was developed, incorporating DSF into the lipid bilayer prior to extrusion and film formation (Fig. 6.1C). This method, adapted from Rolle et al., who co-encapsulated DSF with doxorubicin [384], allows for the same Cu(DDC)â payload to be loaded while the embedded DSF remains unreactive with Cu until exposed to reducing conditions (Fig. 6.1D). The resulting diCu(DDC)â formulation significantly enhanced ROS production at low doses (Fig. 6.1E), providing a proof-of-concept for the modularity of the platform and its potential for further therapeutic refinement. The next section highlights emerging strategies in Cu delivery being advanced through innovations in carrier design and nanomedicine. 110 Figure 6.1. Design and ROS activity of Cu(DDC)â and diCu(DDC)â liposomal formulations. (AâB) Cu(DDC)â liposomes were generated by encapsulating pre-formed Cu(DDC)â complexes in DSPC/Chol liposomes. Compared to free DDC, these pre-formed Cu(DDC)â liposomes induced minimal ROS in cells, suggesting that DDC-mediated Cu scavenging contributes to ROS generation. (CâE) To combine cytotoxic Cu(DDC)â with ROS-inducing potential, disulfiram was embedded in the liposome bilayer during Cu(DDC)â loading. As shown in (D), disulfiram remains unreactive with Cu unless reduced to DDC. This stability enabled co-loading without disrupting Cu(DDC)â complexation. When assessed in cells (E), diCu(DDC)â liposomes restored ROS activity, suggesting that bilayer-embedded disulfiram can drive ROS generation independently of the Cu(DDC)â core. Created with Biorender.com. 111 6.2.2 Emerging Cu-based therapeutic strategies As this thesis neared completion, a wave of new studies emerged, driven in part by the recent surge of interest in cuproptosis and Cu-dependent cell death. While this work has focused on Cu ionophores delivered via liposomal formulations in CRC, other groups have pursued complementary strategies that examine different aspects of Cu biology and delivery. The following sections highlight notable advances, including novel small molecules, nanomedicines, and activation platforms, that build on the therapeutic potential of Cu in cancer treatment. 6.2.2.1 Novel Cu ionophores Novel small molecules have been developed recently to expand the range of Cu ionophores available for targeted cancer therapy. For example, YL21, a naphthoquinone derivative with two dithiocarbamate groups, forms stable Cu-thiolate complexes that efficiently deliver Cu to mitochondria [385]. Like DSF, YL21 significantly increases intracellular Cu levels when combined with Cu²âº, leading to mitochondrial dysfunction and protein aggregation. However, YL21 is more soluble in aqueous environments than DSF, which can improve Cu retention and reduce the need for additional formulation strategies to maintain bioavailability. In contrast, rhenium(I) complexes like Re5 do not bind Cu directly but instead act as indirect Cu carriers [386]. These complexes coordinate with nitrogen-donor ligands, such as bipyridine or phenanthroline, which do not strongly chelate Cu but can indirectly promote Cu accumulation. Once inside the mitochondria, Re5 can facilitate the reduction of Cu²âº to Cuâº in the presence of cellular reductants like NADH and GSH, producing highly reactive hydroxyl radicals through Fenton-like reactions. This approach bypasses the solubility and stability issues faced by direct Cu chelators like DSF, allowing for more precise control over intracellular Cu distribution and oxidative stress. Additionally, the positive charge of rhenium complexes drives sequestration into the negatively charged mitochondrial matrix, where the buildup of reduced Cu can increase oxidative stress and mitochondrial damage. 6.2.2.2 Novel Cu-based nanomedicines 6.2.2.2.1 Complementary agents Other Cu-based nanomedicine strategies have focused on developing nanoparticles that release Cu along with complementary agents to induce cancer cell death. One example is a core-shell nanoparticle 112 (CuP/Er) that releases Cu(II) and erastin in the acidic tumour microenvironment [387]. Erastin blocks a key antioxidant transporter, leading to glutathione depletion and increased lipid peroxidation, while Cu(II) binds to lipoylated TCA cycle proteins and disrupts mitochondrial function, promoting cuproptosis. In MC38 and 4T1 tumour models, this combination induced ICD through calreticulin exposure, HMGB1 release, and ATP secretion, and significantly inhibited tumour growth when used alongside anti-PD-L1 antibodies. Another approach integrates the IDO1 inhibitor NLG919 to overcome the immunosuppressive tumour microenvironment [372]. Here, a biomimetic nanoparticle (ECNM) combines Cu²âº, elesclomol (ES), and NLG919, with subsequent coating with 4T1 cell membranes for enhanced tumour targeting and stability. The NLG919 component blocks IDO1, reversing immune suppression and promoting DC maturation and T cell activation, while the Cu²âº and ES components induce cuproptosis, leading to DAMP stimulation and tumour response in 4T1 tumour models. 6.2.2.2.2 Immune cell carriers Building on the concept of cell membrane coatings, some systems take this approach further by using live immune cells as carriers. MetaCells, for example, are cellular Trojan horses that incorporate Fe-Cu metal-organic frameworks (MOFs) into live neutrophils, taking advantage of the natural tumour-homing capability of these cells [349]. Neutrophils are drawn to inflamed tumour sites, allowing targeted delivery of Fe-Cu MOFs directly into the tumour microenvironment. Once released, these MOFs generate reactive oxygen species (ROS), deplete glutathione, and activate both cuproptosis and ferroptosis, promoting antigen presentation and T cell activation. This strategy was also examined in 4T1 models, noting near-complete tumour regression. 6.2.2.3 Metal organic frameworks MOFs, like those used in the MetaCell platform, are highly porous, crystalline structures that allow precise control over the release of Cu²âº and other therapeutic agents within the tumour microenvironment. These frameworks can be engineered to respond to specific triggers, enhancing the selectivity and potency of Cu-based therapies. For example, a CaOâ@Cu-MOF nanoreactor was designed to release Cu²âº and the BRD4 inhibitor JQ-1 in response to GSH-rich and acidic conditions, generating Cuâº, blocking ATP7B, and producing oxygen to reduce hypoxia, promoting antigen presentation and T cell activation in CT26 colorectal cancer models [388]. Luo et al. developed ES-Cu-MOF nanoparticles by incorporating elesclomol and Cu²âº within a Cu-based MOF, allowing for pH-responsive release in the intracellular environment [389]. In fibrosarcoma models, the released cargo led 113 to mitochondrial stress, loss of FDX1, and features of ICD, including dendritic cell activation. 6.2.2.4 External activation External activation strategies, including photodynamic therapy (PDT) and sonodynamic therapy (SDT), have also been explored to improve the performance of Cu nanomaterials. PDT uses light to activate Cu-containing photosensitizers, generating reactive oxygen species (ROS) through energy transfer processes. However, the hypoxic tumour microenvironment (TME) can limit oxygen availability, reducing ROS production. To address this, systems like Au@SiOâ@CuâO nanocomposites have been developed, incorporating an oxygen-enriched core to boost Â¹Oâ production under light activation [390]. These designs also take advantage of Cuâs ability to deplete intracellular GSH, lowering the antioxidant capacity of tumour cells and amplifying oxidative damage [390â392]. SDT, in contrast, uses ultrasound to penetrate deeper tissues and activate Cu nanostructures through mechanical and thermal effects. For example, Yan et al. developed CuâO nanocubes coated with graphene quantum dots (GQDs) to create a sonosensitizer that releases Cuâº ions in acidic conditions while promoting ROS production under ultrasound [393]. The GQD shell stabilizes the CuâO core and improves electron transfer under ultrasound, enhancing ROS formation without requiring high oxygen levels. Similarly, Cu-cysteamine nanoparticles have been shown to generate ROS upon ultrasound activation, demonstrating efficacy in preclinical tumour models [394]. 6.2.3 Epidemiologic approaches to drug repurposing There are substantial advantages to investigating previously approved therapies for cancer, including the availability of large volumes of clinical data for pharmacoepidemiologic analysis. One of the main challenges in translating preclinical therapies to clinical trials is establishing the potential for efficacy in humans. Well-designed retrospective studies that leverage historical usage and health outcomes can provide compelling evidence of a compoundâs clinical relevance for new indications [395]. Disulfiram has been studied as a potential anticancer agent for decades, and researchers in Denmark have used national registries to assess its association with cancer diagnoses, identifying a potential protective effect in some tumour types [244,396]. The Danish national registries are exemplary in data integration and have enabled high-impact retrospective analyses that have shaped policy, such as widely cited studies demonstrating no association between vaccination and autism [397]. While Canadian 114 prescription registries are more fragmented, the British Columbia drug registry (PharmaNet) can be linked with other datasets, including Vital Statistics (deaths) and the BC Cancer Registry (cancer diagnoses, treatments). As an example of ongoing work in integrating population work in the nanomedicine development pipeline, in consultation with Dr. Parveen Bhatti at the UBC School of Population and Public Health, I designed a case-control study using these linked registries to examine associations between disulfiram prescriptions and cancer incidence or outcomes (Fig. 6.2). The proposal was deemed feasible by Population Data BC, which oversees access to the required datasets, and to strengthen my analytic skills and support this work, I also pursued formal training through the Graduate Certificate Program in Epidemiology at Stanford University. Figure 6.2. Ongoing case-control study of disulfiram uses and cancer outcomes in British Columbia. BC residents diagnosed with cancer will be identified through the BC Cancer Registry and matched to controls based on demographic and clinical criteria. Prescription records from PharmaNET will be used 115 to determine exposure, defined as filing â¥1 prescription for disulfiram. Mortality data will be obtained from Vital Statistics. Primary outcomes will include the protective effect of disulfiram and survival among individuals co-treated with immunotherapy or chemotherapy. Data sources span 1996â2025 and include BC Cancer Registry/Pharmacy, PharmaNET, and Vital Statistics. Created with Biorender.com. Recognizing the potential of BC patient records to broadly support drug repurposing efforts, not only for disulfiram but for other candidate agents, I developed a plan for a large-scale retrospective screen at BC Cancer to identify commonly used drugs that may enhance immunotherapy response (Fig. 6.3). Building on this population-based training through a Master of Public Health (MPH) at the Harvard T.H. Chan School of Public Health, selected drug candidates will be reformulated to optimize anticancer activity during upcoming postdoctoral studies. By using real-world patient data to inform candidate selection and development, the presence of clinical evidence for anticancer activity will serve as a central rationale for further clinical translation. Figure 6.3. Integrated research program in drug repurposing for cancer. Ongoing work bridges pharmacoepidemiologic analyses and translational nanomedicine development. At Harvard T.H. Chan School of Public Health, co-prescribed non-oncology medications (e.g., 116 antihypertensives, antidiabetics, lipid-lowering agents) are being evaluated for associations with improved cancer outcomes in patients treated at BC Cancer. These candidates are then reformulated using nanomedicine strategies to enhance tumour delivery. Preclinical studies assess safety and efficacy in combination with immune checkpoint inhibitors, with the long-term goal of advancing combination therapies for metastatic bladder cancer. Created with Biorender.com. 117 6.3 Conclusions This thesis provides a systematic assessment of the potential for Cu delivery through ionophores as an adjunct treatment for immunotherapy. A nanoformulation of the active form of DDC in Cu-complexation was developed, which showed robust activation of immune-related anticancer activity through the induction of ICD. The chemical form of Cu carriers was shown to have broad and therapeutically important implications, including the activation of different transcriptional signatures, specific activation and inhibition of immune cell populations, and overall efficacy in acute and chronic treatment conditions. To contextualize these findings and explore how tumoural factors modulate response, CT26 and MC38 were established as comparative models of high and low tumoural and isotopic Cu levels for evaluating Cu-based therapies. These results highlight the importance of a precision medicine approach to Cu delivery, where both the choice of ionophore and the timing of administration are tailored to the tumourâs Cu phenotype. This study suggests that in high-Cu tumours, short-term or pulsed dosing may enhance immune activation without inducing chronic stress, whereas treatment in low-Cu tumours may limit therapeutic benefit or even interfere with immunotherapy. Further studies across diverse models are needed to refine these insights and develop predictive and prognostic frameworks based on total and isotopic Cu phenotypes. The focus of this thesis involved using Cu-binding drugs, or drug metabolites, that have been approved for clinical use. With rigorous regulatory requirements to ensure safe and efficacious treatments, the unique benefits of existing toxicology and pharmacology, as well as real-world data on patient outcomes and side effects, make drug repurposing an attractive avenue of research, particularly in the context of optimization through nanomedicine. If DSF is identified to have a protective or synergistic effect in population studies, how much greater might this effect be when targeted to tumours in its active form? 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Thesis/Dissertation
eng
Interdisciplinary Oncology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Bally, Marcel B., 1956-
Copper ionophores as inducers of immunogenic cell death : a mechanistic and therapeutic evaluation
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