Immunogenic cell death in colorectal cancer models is modulated by baseline and ionophore-induced copper accumulation

Immunogenic cell death in colorectal cancer models is modulated by baseline and ionophore-induced copper accumulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Immunogenic cell death in colorectal cancer models is modulated by baseline and ionophore-induced copper accumulation Devon Heroux, Ada W Y Leung, Roger Gilabert-Oriol, Saeid Farzaneh, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7322091/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Copper ionophores represent a promising therapeutic strategy for disrupting copper homeostasis in tumors and triggering immunogenic forms of cell death. However, the extent to which these agents induce immunogenic stress varies with ionophore and tumor-intrinsic factors that remain poorly defined. Here, a panel of copper-binding compounds was evaluated to determine how copper delivery, intracellular copper accumulation, and tumor copper biology influence immunogenic cell death (ICD). This study focused on colorectal cancer models with divergent copper phenotypes. The results showed that ICD marker induction, including ATP release, HMGB1 release, and calreticulin exposure, correlated strongly with intracellular copper accumulation and was significantly enhanced by the ionophore used. Using transcriptomic analysis in CT26 cells, this study provides evidence that copper ionophores activate shared ICD-associated gene signatures while also engaging distinct transcriptional programs based on the ionophore structure. In vivo, CT26 and MC38 tumors exhibited contrasting copper levels, isotopic signatures, and ICD marker responses following treatment. These findings suggest that baseline copper metabolism may influence both the magnitude and nature of immune stress responses in treated tumors. Together, the results highlight the importance of tumor copper biology and ionophore identity in shaping immunogenic outcomes and provide a framework for the rational design of copper-based therapeutics. Copper ionophores immunogenic cell death cuproptosis clioquinol diethyldithiocarbamate damage-associated molecular patterns Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Copper (Cu) is an essential transition metal that plays a significant role in numerous biological processes, including oxidative phosphorylation, redox homeostasis, and immune function[ 1 – 3 ]. 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 copper levels through depletion strategies, such as copper chelation, aims to restrict cuproplasia, a phenomenon in which copper availability promotes tumor growth[ 4 – 6 ]. Conversely, copper overload, driven by compounds known as copper ionophores, has been explored as a strategy to disrupt cellular copper homeostasis[ 7 , 8 ], triggering cell death through mechanisms distinct from those of traditional apoptosis or ferroptosis. Recently, a unique copper-dependent cell death modality termed cuproptosis, characterized primarily by mitochondrial protein aggregation induced by lipoylation stress, culminating in metabolic collapse and mitochondrial dysfunction, was identified[ 9 ]. Therefore, understanding the complexities underlying copper-induced cell death pathways is critical for the therapeutic exploitation of copper. Beyond the induction of direct cytotoxic effects, Cu also exerts a considerable influence on immune modulation within the tumor microenvironment. It has been shown that copper can regulate cytokine production, promote macrophage polarization towards an M1-like pro-inflammatory phenotype, and facilitate T-cell activation and proliferation[ 10 – 15 ]. These immunomodulatory properties position Cu modulation as a potentially effective adjunctive strategy to augment conventional immunotherapies, including immune checkpoint inhibitors (ICIs). However, a significant gap in our understanding remains regarding whether immunogenic cell death (ICD), marked by the release of damage-associated molecular patterns (DAMPs) such as ATP, HMGB1, and calreticulin (CRT), represents a generalized response to elevated copper or is uniquely influenced by specific copper ionophore chemistries. Translating copper modulation into effective cancer therapies remains challenging due to its narrow therapeutic window and the potential for toxicity from both deficiency and excess[ 16 , 17 ]. The distribution and availability of Cu are tightly regulated, making targeted accumulation in tumors difficult without affecting healthy tissues. Emerging technologies, including nanocarriers and metal–organic frameworks, are being developed to improve the specificity, safety, and pharmacokinetic profiles of targeted copper delivery[ 18 – 28 ]. Among these, small-molecule copper ionophores, such as elesclomol, diethyldithiocarbamate (DDC), and clioquinol (CQ) facilitate intracellular copper accumulation and trigger oxidative stress mechanisms linked to immunogenic cell death (ICD)[ 20 , 29 – 31 ]. These compounds form lipophilic copper complexes that enter cells directly, increasing intracellular copper levels through transporter-independent uptake. DDC, a major metabolite of disulfiram (DSF), and CQ, an antifungal agent, have both been shown to enhance copper uptake and induce cytotoxic stress in cancer cells through redox disruption and mitochondrial dysfunction[ 32 , 33 ]. Although these and other ionophores can increase intracellular Cu levels, their immunogenic effects vary and are not well defined. Differences in structure, copper-binding affinity, and cellular targeting likely contribute to the distinct patterns of ICD activation. Additionally, the role of baseline copper levels in modulating these responses has not been fully elucidated and may vary according to the tumor type or treatment context. It remains unclear how differences in ionophore chemistry and tumor Cu levels translate into distinct therapeutic effects. This study examined how copper delivery strategies and intracellular copper homeostasis influence the induction of immunogenic cell death. We assessed a panel of copper-binding compounds to define the relationship between ionophore activity, cytotoxicity, and ICD induction and to determine whether copper alone is sufficient to trigger DAMP exposure. To evaluate how baseline copper levels affect susceptibility to copper delivery, we used liposomal formulations of copper ionophores in tumor cells with distinct copper phenotypes. We also analyzed transcriptional responses across different Cu carriers to identify shared and distinct immunogenic programs. Finally, we assessed in vivo ICD by measuring calreticulin surface exposure following ionophore treatment in CT26 and MC38 tumor models, which were identified to contain high and low basal copper content. Together, these experiments offer insights into how copper levels and delivery mechanisms contribute to ICD induction in vivo. 2. Materials and Methods 2.1. Materials 1,2-Distearoyl-sn-glycero-3-phosphocholine (DSPC) and cholesterol (Chol) were obtained from Avanti Polar Lipids Inc. (Alabaster, AL). 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, Dulbecco’s Modified Eagle’s medium (DMEM), Eagle's Minimum Essential Medium (EMEM), Ham's F-12 medium, 0.25% (w/v) trypsin-EDTA, Hank’s 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 CellMask™ Deep Red were obtained from Thermo Fisher Scientific (Waltham, MA). Fetal bovine serum (FBS) and l-glutamine (Life Technologies) were obtained from Life Technologies (Carlsbad, CA, USA). Ethidium homodimer I was obtained from Biotium (Fremont, CA). Sucrose and chloroform were obtained from EMD Chemicals Inc. (Gibbstown, NJ, USA). Copper-binding ligands, including sodium diethyldithiocarbamate trihydrate (DDC), clioquinol (CQ), 2,2′:6′,2″-terpyridine (TER), 4′-(4-chlorophenyl)-2,2′:6′,2″-terpyridine (CPT), and 4,4′,4″-tri-tert-butyl-2,2′:6′,2″-terpyridine (TTBT) were obtained from Sigma-Aldrich (Oakville, ON, Canada) as analytical grade reagents. 2.2 Analytical assays 2.2.1 Atomic absorption spectroscopy The copper content in the in vitro samples was assessed using flameless atomic absorption spectroscopy (AAS). Samples (media or cell lysates) were first digested overnight in 20% nitric acid at room temperature to ensure complete dissolution of the copper species. Following digestion, the samples were centrifuged to remove debris and the supernatants were diluted in 0.1% nitric acid. Measurements were performed using a PerkinElmer AAnalyst 600 instrument (Waltham, MA, USA). The thermal protocol included drying at 110°C for 30 s, heating to 130°C for 45 s, ashing at 1200°C for 30 s, atomization at 2000°C for 5 s, and a final cleaning phase at 2450°C for 4 s. 2.2.2 Inductively coupled plasma mass spectrometry (ICP-MS) The total copper levels and copper isotopic compositions in the in vivo samples were analyzed using a Thermo Scientific iCAP Qc ICP-MS system fitted with an autosampler for aqueous sample handling. The instrument was operated in kinetic energy discrimination (KED) mode, using helium as the collision gas and an argon flow of 0.3 mL/min. A sample volume of 1 mL was injected during each run. Copper isotopes ⁶³Cu and ⁶⁵Cu were measured simultaneously, with indium and rhodium (10 ppb, Thermo Fisher) included as internal standards to ensure analytical accuracy. The quantification was performed using a calibration series prepared from copper standards (Sigma-Aldrich) at concentrations of 0.1, 1, 10, 100, and 500 ppb in 2% ultrapure nitric acid. 2.3 Liposomal Cu(DDC)₂ and Cu(CQ)₂ formulations To prepare DSPC/Chol liposomes, lipids were dissolved in chloroform at a 55:45 molar ratio of DSPC to cholesterol. The radiolabeled lipid tracer 3 [H]-CHE was added prior to solvent evaporation. Specific activity (approximately 20,000–25,000 DPM/µmol total lipid) was assessed by transferring 10 µL of the lipid solution to a scintillation vial, evaporating chloroform, and adding 5 mL of Picofluor scintillation fluid. Radioactivity was quantified in quadruplicate using a Packard 1900 TR Liquid Scintillation Analyzer. Liposomal Cu(DDC)₂ was prepared according to a previously described method[ 34 ] with several modifications. The formulation involved in situ complexation of CuSO₄ and diethyldithiocarbamate (DDC) within preformed DSPC/cholesterol (55:45 mol%) liposomes. Liposomes were hydrated in 300 mM sucrose and 10 mM HEPES buffer (pH 7.4), and initially formed as multilamellar vesicles via thin-film hydration. Vesicles were then sequentially extruded through two stacked 80 nm polycarbonate membranes (Avanti Polar Lipids) using a 10 mL mini-extruder (15 passes) to obtain unilamellar liposomes. DDC and CuSO 4 were added stepwise to the suspension at a final drug-to-lipid molar ratio of 0.2, followed by gentle stirring at room temperature to promote Cu(DDC)₂ formation and incorporation into the lipid bilayer. The final formulation was sterilized by filtration through 0.2 µm syringe filters (Pall Corporation) to eliminate aggregates and unencapsulated material. Liposomal Cu(CQ)₂ was prepared according to a previously described method[ 35 ] with slight modifications. DSPC/cholesterol (55:45 mol%) lipid films were hydrated with 300 mM CuSO₄, followed by extrusion through two stacked 80 nm polycarbonate membranes, as described above. External CuSO 4 was removed and exchanged for HEPES-buffered saline (pH 7.4) using tangential flow 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)₂ complexation and bilayer association. The formulation was then filtered through 0.2 µm syringe filters prior to use. For both formulations, copper complex formation was confirmed by UV–visible spectrophotometry at 435 nm 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 the sterile filtered formulations were used directly for cytotoxicity, RNA sequencing, and in vivo experiments. 2.4 In vitro characterization assays 2.4.1 Tissue culture All cell lines were maintained in their respective media supplemented with 10% FBS and 2 mM l-glutamine, unless otherwise noted. The CT26 murine colon carcinoma cell line was obtained from the American Type Culture Collection (ATCC) (Manassas, VA, USA) (CRL-2638) and cultured in DMEM. The MDA-MB-231 human breast adenocarcinoma cell line was obtained from ATCC (Cat # HTB-26) and cultured in RPMI 1640. The A549 human lung adenocarcinoma cell line was obtained from ATCC (Cat # CCL-815) and cultured in RPMI 1640 medium. The SKOV3 human ovarian adenocarcinoma cell line was obtained from ATCC (Cat # HTB-77) and was cultured in McCoy’s 5A medium. The MCF7 human breast adenocarcinoma cell line was obtained from ATCC (Cat # HTB-22) and cultured in RPMI 1640. The BxPC3 human pancreatic adenocarcinoma cell line was obtained from ATCC (Cat # CRL-1687) and cultured in DMEM. The Capan-1 human pancreatic adenocarcinoma cell line was obtained from ATCC (Cat # HTB-79) and cultured in IMDM medium with 20% FBS and 4 mM L-glutamine. All the cells were grown and maintained in a humidified incubator at 37°C with 5% CO 2 . When cells reached 90% confluence (70% for Capan-1), they were washed with HBSS without calcium and magnesium, detached with 0.25% (w/v) trypsin-EDTA, and passaged into a new cell culture flask. Cells were subcultured at a ratio of 1:4 to 1:10 depending on the desired cell density. 2.5.2 Cytotoxicity assays Cells were seeded in 384-well black-walled, clear-bottom plates (Greiner Bio-One) in 50 µL of growth medium per well at densities optimized for each cell line (typically 1500–2000 cells/well). Following overnight attachment, cells were treated with the compounds at the indicated concentrations. After 72 hours, cell viability was assessed by staining with Hoechst 33342 (4.87 µM) and ethidium homodimer I (312.5 nM) to distinguish viable (Hoechst-positive, ethidium-negative) from non-viable (Hoechst-positive, ethidium-positive) cells. Plates were incubated with dyes for 20 min at 37°C, imaged using the IN Cell Analyzer 2200 (GE Healthcare), and analyzed using Developer Toolbox 1.9 software to quantify viable and dead cells. Viability was expressed as the fraction of live cells normalized to that of vehicle-treated controls. Graphs were generated using GraphPad Prism 10.0 (GraphPad Software, La Jolla, CA, USA). 2.6. In vitro assessment of immunogenic cell death (ICD) 2.6.1 ATP release assay: CT26 cells were seeded at a density of 200,000 cells/well in 24-well plates (Corning, Cat. no. 353047) with 300 µL of culture medium and allowed to adhere for 24 h. Next, 100 µL of treatment solution or control medium was added per well, and the cells were incubated for another 24 h without disturbing the plate to prevent non-specific ATP release. At the end of the treatment, 50 µL of supernatant was carefully transferred from each well—avoiding cell contact—to a 96-well white-walled, clear-bottom plate that contained 50 µL of CellTiter-Glo® 2.0 reagent (Promega). An ATP standard curve (10–1000 nM prepared in serum-free medium) was used. The plates were shaken at room temperature for 2 min and incubated for 15 min. Luminescence was measured using a FLUOstar OPTIMA plate reader and used to quantify extracellular ATP levels. 2.6.2 HMGB1 quantification by ELISA: To evaluate HMGB1 release, CT26 cells (200,000 cells/well) were plated in 300 µL of medium in 24-well plates and cultured for 24 h. The cells were then treated with 100 µL of drugs or control medium and incubated for another 24 h. After treatment, 5 µL of the supernatant was transferred to a pre-coated HMGB1 ELISA plate ( Cat. No. ST51011) containing 105 µL of diluent. The standards, controls, and samples were prepared according to the manufacturer’s protocol. After shaking for 30 s, the plates were incubated at 37°C for 24 h. Subsequent steps, including antibody addition and substrate development, were performed in accordance with the manufacturer’s instructions. The absorbance was recorded at 450 nm (reference at 600 nm) using a Multiskan® Spectrum reader (Thermo Fisher). HMGB1 concentrations were determined using a standard curve ranging from 2.5–80 ng/mL. 2.6.3 Surface calreticulin (CRT) detection: A 96-well black-walled, clear-bottom plate was first coated with 50 µL of 25 µg/mL poly-d-lysine to enhance cell attachment and incubated for 1 h at room temperature. After removing the coating solution, 20,000 CT26 cells were seeded per well in 100 µL of the medium and allowed to adhere for 24 h. Cells were then treated with 100 µL of drug or control medium and incubated for 24 h. Following treatment, the cells were washed three times with HBSS (with Ca²⁺ and Mg²⁺, without phenol red), fixed with 4% methanol-free formaldehyde in PBS for 20 min, and washed again. A primary anti-CRT antibody (1:200 in staining buffer) was applied for 1 h on ice, followed by three washes and incubation with DyLight® 488-conjugated secondary antibody (1:500) for 30 min on ice in the dark. After additional washes, the cells were counterstained with Hoechst 33342 (1:2000 in PBS) and CellMask™ Deep Red (1:1000) for 20 min, washed again, and imaged in PBS using the IN Cell Analyzer 2200. Twenty fields per well were captured using flat-field correction. Image analysis was conducted using IN Cell Workstation 3.7 software (GE Healthcare) to quantify CRT fluorescence intensity on the cell membrane. Mean intensities were averaged across all cells per condition and normalized to untreated controls to express CRT exposure as fold change. 2.7. RNA Sequencing and Transcriptomic Analysis 2.7.1 Bulk mRNA sequencing Sample quality control was performed using an Agilent 2100 Bioanalyzer or an Agilent 4200 TapeStation. Qualifying samples were then prepared following the standard protocol for Illumina Stranded mRNA prep (Illumina). Sequencing was performed on the Illumina NextSeq2000 with Paired End 59bp × 59bp reads. Sequencing data were demultiplexed using Illumina's BCL Convert. De-multiplexed read sequences were then aligned to the Homo sapiens (hg38 no Alts, with decoys) /Mus musculus (mm10) reference sequence using the DRAGEN RNA app on Basespace Sequence Hub. ( https://supportdocs.illumina.com/SW/DRAGEN_v41/Content/SW/DRAGEN/TPipelineIntro_fDG.htm ). Differential expression analysis was performed using DESeq2. ( https://bioconductor.org/packages/release/bioc/html/DESeq2.html ) using DRAGEN software. Differential Expression app on Basespace ( https://basespace.illumina.com/apps/14229215/DRAGEN-Differential-Expression ). 2.7.2 Gene Set Enrichment Analysis (GSEA) Ranked gene lists based on log2 fold change values were subjected to Gene Set Enrichment Analysis (GSEA) using the clusterProfiler package in R. Background gene sets included [gene set sources, for example, Molecular Signatures Database (MSigDB)] and a custom-curated copper/ICD gene set. Enrichment was visualized using dot plots, ridge plots, and enrichment maps, highlighting the pathways associated with immune responses, oxidative stress, and metal ion homeostasis. 2.7.3 Gene Ontology and Pathway Analysis Gene Ontology (GO) enrichment analysis was performed using clusterProfiler to identify the biological processes and pathways enriched among the differentially expressed genes. Pathways of interest, including the immune response, apoptosis, and stress response pathways, were specifically investigated. Enriched pathways were visualized as bubble plots, and GO terms were prioritized based on adjusted p-values. 2.7.4 Copper-responsive and ICD gene signature analysis Gene sets specific to copper response and immunogenic cell death (ICD) were used to assess treatment-induced transcriptional changes. The copper-responsive set was derived from the Gene Ontology Biological Process term “response to copper ion,” while the ICD gene set consisted of 34 curated genes previously identified by Zhou et al. (2023). 34 Ranked gene lists (log₂ fold change) for CuSO₄, Cu(DDC)₂, and Cu(CQ)₂ treatments were analyzed using GSEA implemented in clusterProfiler. Running enrichment score (RES) plots and heatmaps were generated to visualize the magnitude and direction of pathway engagement across conditions. 2.8 Murine tumor studies All animal experiments were approved by the University of British Columbia Animal Care Committee (protocol A22-0274) and performed in female BALB/c and C57BL/6 mice bearing subcutaneous CT26 and MC38 tumors, respectively. Tumors were initiated by injecting 5 × 10⁵ CT26 or MC38 cells subcutaneously into the right flank. 2.8.1 Efficacy studies To assess tumor growth inhibition, mice were treated with intraperitoneal injections of Cu(DDC)₂ liposomes (1 mg/kg; 0.25 mg/kg copper) or Cu(CQ)₂ liposomes (8.4 mg/kg; 0.80 mg/kg copper) beginning on day 3 post-inoculation. Treatments were administered once daily, Monday through Friday, for two consecutive weeks (10 total doses). Tumor volumes were measured three times per week using digital calipers and were calculated as (length × width²)/2. Body weight was recorded throughout the treatment period. Tumor growth was compared on day 19. 2.8.2 CRT exposure studies To evaluate in vivo ICD responses, mice with CT26 or MC38 tumors were treated with a single intraperitoneal dose of Cu(DDC)₂ or Cu(CQ)₂ liposomes when tumors reached ~ 200 mm³. Tumors were collected 24 h later and processed for immunohistochemical (IHC) analysis of calreticulin (CRT) surface exposure, described below. 2.8.3 IHC analysis Tumor Harvesting and Tissue Processing Tumors were harvested 72 h after final treatment for immune infiltration. Tumors were processed through graded reagent alcohols and xylene (Fisher) using a TP1020 Carousel tissue processor (Leica), and then used to construct tissue microarrays with a Beecher Instruments manual tissue microarrayer before being sectioned at 4 µm on a Microm HM355 microtome. The slides were deparaffinized using xylene and graded reagent alcohols, followed by antigen retrieval using a rodent decloaking solution in a decloaking chamber (Biocare). Multicolor Immunofluorescent Staining The slides were loaded into an Intellipath FLX autostainer (Biocare) for multicolor immunofluorescence staining. The first round of staining started with blocking endogenous peroxidases and non-specific binding using Peroxidased-1 for 5 min and Rodent Block M for 30 min (Biocare). CD3 (clone SP7, Abcam) was applied for 30 min, followed by MACH2 Rabbit-HRP polymer (Biocare) for 10 min, and OPAL520 (Akoya) for 10 min. The slides were then removed from the stainer and microwaved in AR9 (Akoya) to denature the reagents from the previous round, leaving the floor in place. Round 2 proceeded similarly using FoxP3 (clone FJK-16s, Fisher), rat–mouse HRP polymer (Biocare), and OPAL620, followed by AR6 (Akoya). Round 3 used PAX5 (clone EPR3770(2), Abcam), MACH2 Rabbit-HRP, and OPAL650, followed by AR6. Round 4 included CD8 (clone D4W2Z, Cell Signaling Technology), MACH2 rabbit-HRP, and OPAL690, followed by AR6. Round 5 used a background sniper (Biocare) for 10 min in place of rodent block M, followed by pan-keratin (clone E6S1S; Cell Signaling Technology), OPAL570, and AR6. The final round included F4/80 (clone D2S9R, Cell Signaling Technology), MACH2 rabbit-HRP, and OPAL540. Slides were then removed from the Intellipath FLX and coverslipped with a ProLong Diamond antifade mountant (Fisher). Image Acquisition and Analysis Slides were imaged using the Vectra 3 multispectral imaging system (Akoya), capturing 1 × 1 images per core for analysis using the inForm image analysis software (Akoya). A base algorithm was written to segment the tissue based on keratin, DAPI, and autofluorescence into the epithelium, stroma, and other regions (e.g., blank space, folds, and advanced necrosis). Individual phenotyping algorithms have been developed to identify mutually exclusive markers (e.g., F4/80-CD3, CD8-PAX5, and FoxP3). The results from the inForm were combined in Phenopreports (Akoya) to overlay markers and define phenotypes. This process was performed in triplicates. 2.9. Statistical analysis GraphPad Prism 10.0 software was used for all statistical analyses. In vitro data are presented as mean ± standard deviation (SD), and in vivo data as mean ± standard error of the mean (SEM), as noted in figure legends. Group comparisons were performed using unpaired two-tailed t-tests. This included comparisons of tumor volume at day 19, plasma and tumor copper levels, cytokine levels, and calreticulin staining. A P-value < 0.05 was considered statistically significant. 3. Results Diethyldithiocarbamate (DDC) is a well-characterized copper-binding compound that has been historically used for heavy metal chelation in both industrial and clinical settings, with increasing interest in its anticancer potential when complexed with copper[ 36 , 37 ]. Given recent evidence that copper complexes of DDC can induce immunogenic cell death (ICD)[ 38 ], we first evaluated whether DDC promotes ATP release, a hallmark of ICD, when paired with transition metals (Fig. 1 A–C). DDC alone had a minimal effect, but in combination with various copper 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 the copper combination further enhancing this to 30- to 40-fold. In contrast to copper, most other metals suppressed TTBT's ATP-releasing activity of TTBT, with the exception of cobalt, which had no effect. Transition metals alone had a limited effect on ATP release (Fig. 1 C). Dose-response analysis confirmed that ATP release by CuDDC was concentration-dependent and detectable at concentrations as low as 3.1 µM in B16-F10 cells (Fig. 1 D–E). To better understand whether this ATP induction was primarily due to copper transport or the intrinsic biological effects of the ligands, a panel of copper-binding compounds from several drug classes was profiled (Fig. 1 F). Cytotoxicity was assessed with and without CuSO₄ at 24 and 72 hours. Although the IC₅₀ values varied widely, copper potentiation, defined as a shift in IC₅₀, was only observed for DDC, 8-hydroxyquinoline (8HQ), and pyrithione, which are known ionophore-like compounds[ 39 ]. To assess whether this potentiation correlated with copper transport, cells were pulsed for 4 hours with each compound and CuSO₄ and the intracellular copper levels were measured (Fig. 1 G). Ionophore-like compounds significantly increased intracellular copper levels (2- to 8-fold). The terpyridine analogs TTBT and CPT also increased the copper content by 3- to 5-fold, respectively. Other compounds, including several polyphenols and chemotherapies known to bind divalent metals, had minimal effects or slightly decreased copper accumulation. These findings indicate that copper ionophore-like compounds significantly enhance intracellular copper accumulation and potentiate cytotoxicity in the presence of extracellular copper. While some compounds such as TTBT, CPT, and clioquinol increased copper levels without corresponding increases in cytotoxicity, others such as DDC showed time-dependent potentiation, with effects observed at 24 h but not at 72 h. To evaluate whether copper-ligand combinations promote immunogenic signaling, three damage-associated molecular patterns (DAMPs)–ATP release, HMGB1 release, and calreticulin (CRT) exposure–were measured in CT26 cells following 24-hour treatment (Fig. 2 A–C). The compounds were applied at doses optimized for ATP induction as single agents (Fig. S1 ), either alone, with CuSO₄, or CuSO₄ alone at the same concentration. The known ionophore group (brown bar) showed minimal activity across DAMPs when used alone, but the addition of copper 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 a single agent with little further enhancement upon copper addition. TTBT was the only terpyridine compound that increased all three DAMPs in the presence of copper. The other compound classes showed minimal effects regardless of the addition of copper. To determine whether intracellular copper levels alone accounted for DAMP induction, internalized copper from Fig. 1 G was compared to the DAMP responses following CuSO₄ treatment alone (CuSO₄ columns in Fig. 2 A–C). There was a strong positive correlation between the intracellular copper content and the release of ATP, HMGB1, and CRT (Fig. 2 D), suggesting that copper accumulation can directly stimulate ICD-associated pathways. The ionophore-like activity, defined as the fold increase in intracellular copper over CuSO₄ alone, was assessed to determine whether copper-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. 2 E). However, when analyzing all treatments, there was no consistent correlation between total intracellular copper levels and DAMP release, likely because of copper-independent effects in some compound classes. Together, these results support a model in which copper ionophore-like activity drives ICD-like responses by increasing intracellular copper concentrations beyond the threshold achieved with soluble copper alone. With intracellular copper delivery emerging as a potential strategy to activate ICD and enhance immune responses, copper uptake following addition of CuSO₄, Cu(DDC)₂, and Cu(CQ)₂ was assessed across a panel of human and murine cell lines (Fig. 3 A–C). Baseline intracellular copper levels were measured, followed by a 4-hour treatment with each compound. Addition of Cu(DDC)₂ and Cu(CQ)₂ resulted in increases in intracellular copper when compared to cells incubated with 100 µM CuSO₄. The copper doses for the Cu(DDC)₂ and Cu(CQ)₂ studies were 20- and 4 fold less, respectively. To determine whether basal copper levels were predictive of treatment sensitivity, eight cancer cell lines were assessed, and cytotoxicity after 72-hour exposure to Cu(DDC)₂ and Cu(CQ)₂ was determined (Fig. 3 E–F). A heatmap of cuproptosis-related gene expression revealed notable heterogeneity in core regulators such as FDX1, DLAT, and PDHA1 across cell lines (Fig. 3 D), with MDA-MB-231 cells displaying both high expression of multiple cuproptosis-associated genes and the greatest sensitivity to Cu(CQ)₂, consistent with its elevated basal copper levels. When IC₅₀ values were plotted against baseline copper levels, no correlation was observed for Cu(DDC)₂ (Fig. 3 H), whereas Cu(CQ)₂ cytotoxicity showed a significant association with basal copper content (Fig. 3 I). To examine the transcriptomic effects of copper treatments, gene ontology enrichment analysis was performed with CT26 cells treated with CuSO₄, Cu(DDC)₂, or Cu(CQ)₂ at IC₇₀ for 24 hours, a dose selected to capture death-related signaling while limiting confounding from growth inhibition. Gene set enrichment was performed using log₂ fold-change–ranked genes and mouse GO gene sets (Fig. 4 ). Enrichment maps revealed that CuSO₄ primarily induced pathways related to ion transport along with smaller clusters associated with cell motility and keratinization (Fig. 4 A). Cu(DDC)₂ and Cu(CQ)₂ also showed enrichment in ion transport-related terms, likely reflecting their role in facilitating copper uptake; however, these agents had broader transcriptional responses that suggested changes in immune and cellular regulatory processes. Cu(DDC)₂ treatment led to enrichment of pathways related to membrane organization, vesicle trafficking, and hormone transport (Fig. 4 C–D), potentially reflecting intracellular responses to altered metal homeostasis. Cu(CQ)₂, 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. 4 E–F). Some 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 the activation of both immunostimulatory and regulatory programs. These results indicate that, while the core transcriptomic effects of CuSO₄ and both ionophores involve metal ion processing, Cu(DDC)₂ and Cu(CQ)₂ engage distinct gene expression programs that reflect different cellular responses to copper delivery. To determine whether transcriptional responses to copper accumulation corresponded with immunogenic stress signaling, the expression changes in Cu-responsive and ICD-related genes were profiled in CT26 cells. All three treatments activated genes associated with the Gene Ontology category "response to copper ion," with the most pronounced expression in CuSO₄ and Cu(DDC)₂, despite Cu(DDC)₂ being administered at an 800-fold lower molar dose compared to CuSO₄ (Fig. 5 A). This suggests that intracellular delivery mechanisms may be more influential than the total amount of copper administered to drive the transcriptional response. Under the same conditions, the expression of a curated 31-gene immunogenic cell death (ICD) signature (Fig. 5 B) was examined. Each treatment upregulated subsets of ICD genes but with differing breadths and intensities. Volcano plots showed that CuSO₄ induced widespread upregulation and downregulation of genes, which was consistent with a broad stress response (Fig. 5 C). In contrast, Cu(DDC)₂ triggered more selective transcriptional activation, including strong upregulation of NLRP3 and TNF (Fig. 5 D). Cu(CQ)₂ had fewer differentially expressed genes overall but included increased expression of P2RX7 and NLRP3 (Fig. 5 E). These patterns suggest that CuSO₄ induces a broader transcriptional response, whereas ionophore-based treatments result in a more selective activation of immune-related gene pathways (Fig. 5 E). Gene set enrichment analysis (GSEA) plots visualized the overall enrichment of the copper response and ICD pathways (Fig. 5 F–H). Although CuSO₄ induced the most differentially expressed genes, its enrichment for the copper ion response set was the weakest, likely because of widespread transcriptional changes that dilute pathway-specific signals. Cu(CQ)₂ yielded the strongest and most selective enrichment of both Cu-responsive and ICD-associated genes, despite lower overall transcriptional activation. Cu(DDC)₂ showed intermediate behavior, with strong activation of ICD components and robust copper-responsive transcription. These findings support a model in which ionophore-based copper delivery can modulate gene expression in a more targeted manner than extracellular copper alone, potentially shaping. As shown in Fig. 3 , CT26 cells in vitro had significantly higher intracellular copper levels than MC38 cells. Since this may affect the therapeutic response to copper delivery, copper and immune-related features were characterized in syngeneic murine tumor models following s.c. injection of the two cell lines (Fig. 6 A). Copper levels in CT26 cells were approximately twice the level in MC38 cells, but this difference was less pronounced in larger tumors (Fig. 6 B). The common isotopes of copper are ⁶³Cu and ⁶⁵Cu, and copper isotopic fractionation is thought to reflect cellular metabolism and transporter activity[ 41 – 43 ]. While altered ⁶⁵Cu/⁶³Cu 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 tumor tissue[ 44 ]. In this study, MC38 tumors showed higher ⁶⁵Cu enrichment than CT26 tumors, particularly smaller tumors (Fig. 6 C). Plasma levels of total and isotopic copper were not significantly different when plasma was obtained from tumor-bearing mice (Fig. 6 F–G). Large MC38 tumors have higher TNFα and IL-12 cytokine levels, which may reflect immune activation. In addition, elevated IL-10 levels were observed, which may be associated with immunosuppression. Interestingly, plasma cytokine levels showed an opposite trend, with higher TNFα and IL-12 levels in CT26 tumors and lower levels in MC38 tumors (Fig. 6 D, H). CT26 tumors also showed higher CD8 + infiltration than MC38 tumors, suggesting greater immune priming in this model. Similar levels of other lymphocyte populations and macrophages were observed in these two tumor models (Fig. 6 E). To determine the in vivo susceptibility of copper 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)₂ or Cu(CQ)₂. CRT exposure was assessed 24 h later using IHC (Fig. 6 I). In the CT26 tumor model, Cu(CQ)₂, 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)₂ was given (delivering 0.25 mg/kg Cu; Fig. 6 J). MC38 tumors 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)₂ and a 2-fold decrease (p = 0.051) when the mice were injected with Cu(CQ)₂ (Fig. 6 K). These findings indicate important differences in copper accumulation and immune-related responses between the two tumor models, with implications for how Cu-based therapeutics might differentially modulate ICD in distinct tumor contexts. The activity of monotherapies with Cu(DDC)₂ and Cu(CQ)₂ 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. 7 A). Both agents produced modest growth inhibition compared to the vehicle controls, with Cu(CQ)₂ showing a slightly greater effect in the CT26 model (Fig. 7 B–C, E–F). Body weight was unaffected by this dose schedule (Fig. 7 D, G), and no significant adverse events were observed. These data suggest that copper ionophore monotherapy is well tolerated but modestly suppresses tumor growth. These results suggest that these agents may be better suited for use in combination regimens aimed at enhancing immunotherapy through copper-dependent or other immunomodulatory mechanisms. 4. Discussion This study investigated the relationship between intracellular copper and immunogenic cell death (ICD) with a focus on enhancing cellular copper delivery using ionophore-like compounds. Although prior studies have demonstrated that copper-containing treatments can stimulate ICD-related responses such as calreticulin (CRT) exposure and DAMP release[19,25,26,45], the specific contributions of intracellular copper levels, both at baseline and following treatment, remain largely uncharacterized. Here, we show that intracellular copper accumulation correlates with the activation of ICD signatures, and that the ionophore-like activity of a compound significantly influences the magnitude of this response. Compounds that induced greater intracellular copper uptake more robustly activated ICD-associated pathways, including those related to TNFα signaling and antigen presentation. Interestingly, several copper-binding compounds, including TTBT, CPT, and clioquinol, increased intracellular copper levels but failed to enhance cytotoxicity or CRT expression. These findings suggest that copper uptake alone is insufficient to induce cell death or ICD, and that compound-specific factors, such as subcellular localization, redox potential, or engagement of parallel stress pathways, likely modulate biological outcomes[46]. Among the tested ionophores, diethyldithiocarbamate (DDC) has emerged as the most potent. DDC activated ICD-related transcriptional programs at doses nearly 800-fold lower than CuSO₄ and induced additional gene pathways related to vesicle trafficking and immune-cell recruitment. This broad activity profile may contribute to enhanced therapeutic efficacy but also introduces complexity in distinguishing specific immunogenic signals from generalized stress responses. Notably, the cytotoxic enhancement observed with DDC was time-dependent, evident at 24 h, but diminished by 72 h, possibly due to cumulative copper equilibration from the culture media. Clioquinol (CQ), which was historically used as an antimicrobial agent and was withdrawn following an outbreak of subacute myelo-optic neuropathy (SMON) in Japan[47], displayed distinct biological behavior. Subsequent evidence suggests that SMON may be linked to genetic susceptibility to ABC transporter variants[48]. CQ has since re-emerged as a candidate therapeutic agent in oncology and neurodegeneration[33,49]. In this study, Cu(CQ)₂ exhibited moderate ionophore-like activity, with cytotoxicity significantly correlated with basal intracellular copper levels. This correlation supports a model in which endogenous copper homeostasis influences the sensitivity of cancer cells to Cu(CQ)₂. Furthermore, Cu(CQ)₂ treatment upregulated pro-inflammatory cytokine transcripts, including TNFα and NLRP3, and led to a two-fold increase in CRT exposure in CT26 tumors, although this difference was not statistically significant (p = 0.09). These findings indicate that, although CQ is a weaker ionophore than DDC, it may retain immunomodulatory potential in settings where tumors exhibit elevated basal copper levels. Transcriptomic profiling revealed that while CuSO 4 , Cu(DDC)₂, and Cu(CQ)₂ all activated metal ion-related pathways, they diverged substantially in their immune signaling signatures. CuSO₄ induced a broad, stress-associated transcriptional response consistent with high extracellular copper exposure, while Cu(DDC)₂ and Cu(CQ)₂ each activated more selective subsets of immune-related genes. Gene ontology analysis confirmed that Cu(CQ)₂ preferentially engaged in cytokine production and adaptive immune pathways, whereas Cu(DDC)₂ was more strongly associated with vesicle organization and trafficking. These findings support the notion that although all three compounds target core copper-handling mechanisms, the downstream biological consequences vary considerably based on the chemical properties and intracellular behavior of the compound. In vivo, the relationship between intracellular copper and ICD marker expression was variable. In CT26 tumors, both Cu(DDC)₂ and Cu(CQ)₂ treatments modestly increased surface calreticulin exposure. In contrast, MC38 tumors, which had lower baseline copper levels and distinct isotopic signatures, showed reduced CRT expression following treatment. These results suggest that differences in tumor-intrinsic copper metabolism may influence the effectiveness of copper-based ICD induction. While the in vitro data revealed compound-specific activation of immune-related genes, the in vivo outcomes highlight the need to consider tumor copper handling and the metabolic state when evaluating therapeutic responses. An additional insight from this study is the potential relevance of stable copper isotope profiling as a biomarker for tumor copper metabolism. Previous studies have linked shifts in 65 Cu/ 63 Cu ratios to cancer severity, with lower serum 65 Cu/ 63 Cu observed in patients with colon, breast, and liver cancer, and higher 65 Cu abundance detected in liver tumor tissue[41,43,50,51]. These isotopic shifts were initially attributed to the selective binding of 65 Cu by lactate, but more recent work suggests that hepatic fractionation is a more likely explanation[44]. In our study, CT26 tumors exhibited higher total copper levels than MC38 tumors, while MC38 tumors showed an increased relative abundance of 65 Cu despite similar plasma copper content. This tumor-specific fractionation may reflect differences in copper transporter expression or metabolic states, and supports further investigation into the role of isotopic profiling as a functional readout of tumor copper biology. Although both Cu(DDC)₂ and Cu(CQ)₂ demonstrated only modest single-agent efficacy in vivo, their distinct immune and transcriptional signatures suggest their therapeutic potential in combination with other treatments. Their ability to modulate immune-relevant pathways and influence ICD markers, even modestly, indicates their suitability as partners in combination regimens. These include checkpoint inhibitors, T cell–engaging therapies, or other immune-enhancing agents that rely on increased antigen presentation. The partial immunogenic responses observed in vivo are consistent with previous reports of copper-induced tumor regression and further support the strategy of rational combination therapy. Taken together, these findings highlight the complexity of copper-mediated ICD and the need for precision in therapeutic design. The biological activity of copper-based treatments depends not only on compound chemistry, but also on tumor-specific factors, including basal copper levels, isotopic distribution, and copper-handling capacity. Future efforts should focus on developing targeted delivery systems, such as nanocarriers, that can selectively increase copper levels in tumors while minimizing systemic toxicity. Stratifying tumors based on Cu content and isotopic profiles may help identify patients most likely to benefit from copper-based therapies. To advance the clinical utility of copper-enhanced immunotherapy, future strategies will need to align compound properties with tumor-specific copper biology and the immune context, using targeted delivery systems and biomarker-driven patient selection to guide treatment decisions. Abbreviations Cu Copper DDC diethyldithiocarbamate Cu(DDC) 2 copper diethydithiocarbamate CQ clioquinol Cu(CQ) 2 copper clioquinol DSF disulfiram CHE 3 [H]-Cholesteryl Hexadecyl Ether DSPC 1 , 2-Distearoyl-sn-Glycero-3-Phosphocoline IC 50 inhibitory concentration of 50 i.p intraperitoneal SD standard deviation SEM standard error of the mean TFF tangential flow filtration ATP adenosine triphosphate HMGB1 high mobility group box 1 CRT calreticulin DAMP damage-associated molecular patterns , RES , running enrichment score GO gene ontology TCA tricarboxylic acid IHC immunohistochemistry Declarations Ethics Statement All animal procedures were conducted in accordance with the Canadian Council on Animal Care (CCAC) guidelines and approved by the University of British Columbia Institutional Animal Care Committee under Animal Care Certificate A14-0290. Human-derived cell lines were obtained from established commercial sources and used in accordance with the institutional biosafety guidelines. No primary human tissue or patient data were used. Data Availability Statement The datasets supporting the findings of this study are available from the corresponding author upon request. Funding This research was funded by grants from the Canadian Institutes of Health Research (153132), Canadian Cancer Society (705290), and the NanoMedicines Innovation Network (DRG 03190). Devon Heroux acknowledges additional support from the NanoMedicines Innovation Network Doctoral Award. We gratefully acknowledge the additional support from the BC Cancer Foundation. Credit authorship contribution statement Devon Heroux: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – original draft, writing – review and editing, Visualization, Project administration. Ada W.Y. Leung: Conceptualization, Methodology, Formal Analysis, Investigation. Roger Gilabert-Oriel: Conceptualization, Methodology, Formal Analysis, Investigation. Saeid Farzaneh: Methodology, Formal Analysis, Investigation. Katy Milne: Methodology, Formal Analysis, Investigation. Marina Wolf: Methodology, Investigation. Sana Alayoubi: Investigation. Himmeshwar Singh: Investigation. Trevor MacFarlane: Investigation . Chantal Di Vito: Investigation . Brad H. Nelson. Resources and supervision . Charles J. Walsby: Resources, Supervision. Jessica Kalra: Conceptualization, Resources, Supervision. Marcel B. Bally: Conceptualization, Resources, Writing – review and editing, Supervision, Funding acquisition. Declaration of Competing Interests The authors have no conflicts of interest to declare. Acknowledgements The authors would like to thank the Investigational Drug Program/PharmaCore at the BC Cancer Research Institute for their contribution to the in vivo studies and Norman Chow for his technical assistance. Figures were created using BioRender.com. 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Proceedings of the National Academy of Sciences. ;112:982–5. https://doi.org/10.1073/pnas.1415151112 Télouk P, Puisieux A, Fujii T, Balter V, Bondanese VP, Morel A-P et al (2015) Copper isotope effect in serum of cancer patients. A pilot study. Metallomics 7:299–308. https://doi.org/10.1039/C4MT00269E Additional Declarations No competing interests reported. Supplementary Files ApoptosisSupFigures.docx image1.png Graphical abstract. Ionophore-mediated copper delivery enhances immunogenic cell death in copper-dysregulated colorectal cancer models. (A) Agents known to complex copper and act like ionophores to shuttle copper across cell membranes were tested for their ability to enhance ICD marker stimulation in combination with copper. (B) RNAseq analysis revealed copper ionophore-induced activation of ICD gene signatures with up to 1000-fold lower concentrations compared to CuSO₄. (C) Tumor copper content was analyzed in high-copper CT26 and low-copper MC38 models, showing differences in the copper isotopic composition. (D) Calreticulin exposure was assessed in vivo , demonstrating model-specific responses to copper ionophores. (E) Copper formulations were well tolerated with modest single-agent activity, supporting their potential for safe copper delivery during ICD induction. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7322091","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497747883,"identity":"1b90c6c1-23ea-4a27-85e5-5441e91956e9","order_by":0,"name":"Devon Heroux","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYDCCGxBKzoBkLcaka0ncQLQOvtvNxz58bDucvp398LMPPxjs5AlqkbxzLHnmzLa03J09acYzexiSDRsIaTG4kWPMzNtmk7vhBg8zMwPDAUYitOR/BmqRSDeAarEnxhZmkC0JMC2JBLVI3kgzZpxxLs0Q5BfGHoPkZIJa+G4kP2b4UHZY3pz98GOGHxV2tgS1oLuTRPWjYBSMglEwCrADAOmBORNuvz+wAAAAAElFTkSuQmCC","orcid":"","institution":"BC Cancer Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Devon","middleName":"","lastName":"Heroux","suffix":""},{"id":497747885,"identity":"594a8408-adce-436d-82dc-e32dd564b783","order_by":1,"name":"Ada W Y Leung","email":"","orcid":"","institution":"BC Cancer Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ada","middleName":"W Y","lastName":"Leung","suffix":""},{"id":497747886,"identity":"b0e188bb-8ead-45c5-aa13-6aa786488312","order_by":2,"name":"Roger Gilabert-Oriol","email":"","orcid":"","institution":"BC Cancer Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Roger","middleName":"","lastName":"Gilabert-Oriol","suffix":""},{"id":497747887,"identity":"074ee430-f142-42b9-8607-e7bceaa45e85","order_by":3,"name":"Saeid Farzaneh","email":"","orcid":"","institution":"Simon Fraser University","correspondingAuthor":false,"prefix":"","firstName":"Saeid","middleName":"","lastName":"Farzaneh","suffix":""},{"id":497747888,"identity":"8db84df3-f85c-4a8d-aee2-d2ad3ef9ebeb","order_by":4,"name":"Katy Milne","email":"","orcid":"","institution":"BC Cancer","correspondingAuthor":false,"prefix":"","firstName":"Katy","middleName":"","lastName":"Milne","suffix":""},{"id":497747889,"identity":"8621e213-32bd-4482-b11c-c1af5f1e002e","order_by":5,"name":"Marina Wolf","email":"","orcid":"","institution":"BC Cancer Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Wolf","suffix":""},{"id":497747890,"identity":"53d7c6b0-f056-4374-a9fb-89eccdd96c86","order_by":6,"name":"Sana Alayoubi","email":"","orcid":"","institution":"BC Cancer Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Sana","middleName":"","lastName":"Alayoubi","suffix":""},{"id":497747891,"identity":"1202b2e6-e3f5-4caf-b863-e2adcafc2b27","order_by":7,"name":"Himmeshwar Singh","email":"","orcid":"","institution":"BC Cancer Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Himmeshwar","middleName":"","lastName":"Singh","suffix":""},{"id":497747892,"identity":"8e3b7c30-a41e-47f3-b860-bc4dcdaddf49","order_by":8,"name":"Trevor MacFarlane","email":"","orcid":"","institution":"BC Cancer","correspondingAuthor":false,"prefix":"","firstName":"Trevor","middleName":"","lastName":"MacFarlane","suffix":""},{"id":497747893,"identity":"9c1b7bac-3731-433d-9d4a-0c49542fb5eb","order_by":9,"name":"Chantal Vito","email":"","orcid":"","institution":"BC Cancer","correspondingAuthor":false,"prefix":"","firstName":"Chantal","middleName":"","lastName":"Vito","suffix":""},{"id":497747894,"identity":"d21057bd-a014-4fda-9a85-ca065a33500d","order_by":10,"name":"Brad H Nelson","email":"","orcid":"","institution":"BC Cancer","correspondingAuthor":false,"prefix":"","firstName":"Brad","middleName":"H","lastName":"Nelson","suffix":""},{"id":497747895,"identity":"6b024763-5550-48a9-b461-ad314f02aa7f","order_by":11,"name":"Charles J Walsby","email":"","orcid":"","institution":"Simon Fraser University","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"J","lastName":"Walsby","suffix":""},{"id":497747896,"identity":"9d12a54c-5aac-4c38-b2e1-6839b02a1aed","order_by":12,"name":"Jessica Kalra","email":"","orcid":"","institution":"BC Cancer Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Kalra","suffix":""},{"id":497747897,"identity":"7d974c0e-3c37-4a42-9759-46ccef8fac99","order_by":13,"name":"Marcel B Bally","email":"","orcid":"","institution":"BC Cancer Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Marcel","middleName":"B","lastName":"Bally","suffix":""}],"badges":[],"createdAt":"2025-08-07 22:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7322091/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7322091/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89302662,"identity":"937ee026-1e18-4e2d-b889-51d6e4b10cfd","added_by":"auto","created_at":"2025-08-18 14:41:07","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":428626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCopper-dependent ATP release and ionophore-like activity across compound classes\u003cbr\u003e\n \u003c/strong\u003e(A) Extracellular ATP measured after 24 h exposure to diethyldithiocarbamate (DDC) with various copper salts (CuAc, CuCl₂, CuGlu, CuSO₄) or non-copper transition metals (CoCl₂, FeCl₃, MnSO₄, NiSO₄, ZnSO₄); 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. (D–E) Dose response curves for extracellular ATP following 24 h exposure to DDC or DDC:CuSO₄ (2:1 molar ratio) in CT26 (D) and B16-F10 (E) cells. (F) Table of copper-binding compounds from various drug classes with 24 h and 72 h IC₅₀ values measured with or without CuSO₄ using an IN Cell Analyzer 2200; copper potentiation (IC₅₀ shift) is highlighted in red. (G) Fold change in intracellular copper levels after a 4 h treatment with copper and each compound, relative to copper alone, as a measure of copper ionophore activity. Data are presented as mean ± SD.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/613fe257c77ebc200926023a.jpeg"},{"id":89304817,"identity":"475295e5-fab3-408d-b8b7-fbc1dba5bfc7","added_by":"auto","created_at":"2025-08-18 14:57:07","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":415762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCopper-binding compounds enhance DAMP release in proportion to intracellular copper levels. \u003c/strong\u003e(A–C) Heatmaps showing ATP (A), HMGB1 (B), and calreticulin (CRT) (C) levels in CT26 cells treated with copper (Cu) alone, ligand alone, or Cu-ligand combinations. The “Fold-increase” column shows the ratio of Cu+Ligand signal to the higher of either Cu or ligand alone. (D) Correlation between intracellular copper content and DAMP marker release following treatment with increasing concentrations of CuSO₄, with intracellular copper measured by atomic absorption spectroscopy (AAS). (E) Correlation between ionophore activity, defined as the fold increase in intracellular copper relative to CuSO₄ alone, and DAMP marker release following Cu-ligand treatment.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/39f285a2439f48f4199b0491.jpeg"},{"id":89302665,"identity":"2460e3a2-7e95-428b-8a63-49296b8048d7","added_by":"auto","created_at":"2025-08-18 14:41:07","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":446585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBasal intracellular copper levels predict sensitivity to Cu(CQ)₂ but not Cu(DDC)₂. \u003c/strong\u003e(A–C) Intracellular copper levels following 4-hour treatment with 100 μM CuSO₄ (A), 5 μM Cu(DDC)₂ liposomes (B), or 25 μM Cu(CQ)₂ liposomes (C), shown with baseline copper levels measured (AAS) in five 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 line–specific variation. The first seven genes (FDX1, DLAT, PDHA1, DLD, LIAS, LIPT1, PDHB) are core components of the cuproptosis pathway [9]. The final two genes (CD274, MELK) have been associated with sensitivity to copper-induced cell death in prior studies [8,40]. (E–F) Cytotoxicity curves for Cu(DDC)₂ (E) and Cu(CQ)₂ (F) across eight cancer cell lines following 72-hour treatment. (G) Baseline intracellular copper in the indicated cancer cell lines; human cell lines are shown in red, and mouse cell lines in green. (H-I) IC₅₀ values for Cu(DDC)₂ and Cu(CQ)₂ plotted against baseline intracellular copper concentrations to assess copper sensitivity, with correlation coefficients (R and p-values) shown.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/c8871ac3f7e52b7bcb3a3c8d.jpeg"},{"id":89302670,"identity":"4fc1efe8-7eab-48f8-bdc1-4a3ca73f1441","added_by":"auto","created_at":"2025-08-18 14:41:07","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":571212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene ontology enrichment in CT26 cells following copper treatment.\u003cbr\u003e\n\u003c/strong\u003e(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 CuSO₄ (A), Cu(DDC)₂ liposome (C), and Cu(CQ)₂ liposome (E) treatments at IC\u003csub\u003e70\u003c/sub\u003e for 24 hours. Enrichment was performed using\u0026nbsp;fgseaMultilevel\u0026nbsp;on log₂ fold-change–ranked genes with mouse GO gene sets. Nodes represent significantly enriched pathways (padj \u0026lt; 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\u0026nbsp;GO Biological Process\u0026nbsp;(GOBP) pathways for CuSO₄ (B), Cu(DDC)₂ (D), and Cu(CQ)₂ (F), ranked by NES. Dot size indicates gene count and color reflects adjusted p-value (padj).\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/c9901cf794dabc00be7d1612.jpeg"},{"id":89304819,"identity":"b3fd27e5-e6b2-44dd-b3cc-b523abf473d1","added_by":"auto","created_at":"2025-08-18 14:57:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1107752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscript-level characterization of copper-responsive and immunogenic cell death (ICD) genes in CT26 cells. \u003c/strong\u003e(A–B) Heatmaps of log₂ fold changes (Log₂FC) for genes annotated in the Gene Ontology (GO) Biological Process term “response to copper ion” (A) and a curated ICD signature (B), following treatment with CuSO₄, Cu(DDC)₂, and Cu(CQ)₂. Hierarchical clustering was applied to genes (rows), with treatments (columns) ordered by condition. Red indicates upregulation; blue indicates downregulation. (C–E) Volcano plots for CuSO₄ (C), Cu(DDC)₂ (D), and Cu(CQ)₂ (E) treatments, with genes from the ICD signature labeled and highlighted in yellow. Vertical dashed lines denote the absolute log₂ fold change threshold, and the horizontal line indicates the adjusted p-value cutoff (padj \u0026lt; 0.05). Points are colored by direction of change: red (upregulated), blue (downregulated), and gray (non-significant). (F–H) Running enrichment score (RES) plots show the enrichment profiles for the GO “response to copper ion” (blue) and ICD (red) gene sets under CuSO₄ (F), Cu(DDC)₂ (G), and Cu(CQ)₂ (H) treatments. The x-axis represents the ranked gene list (ordered by log₂ 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.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/1f5ca63e54075bf8c6d46d3d.png"},{"id":89304820,"identity":"7475fc54-44fd-44e7-890c-ff8f65a3b9a6","added_by":"auto","created_at":"2025-08-18 14:57:07","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":345578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistinct patterns of copper accumulation and immune activation in CT26 and MC38 tumor models \u003c/strong\u003e(A) Female BALB/c and C57BL/6 mice were inoculated subcutaneously (SC) with 5 × 10⁵ CT26 or MC38 cells. Subcutaneous (s.c.) tumors were collected at two time points, when tumors were approximately 100 mg or 800 mg. (B) Tumor copper was measured by ICP-MS. (C) Tumor δ⁶⁵Cu values were normalized to 100 mg CT26 tumors. (D) Tumor cytokines were quantified using the Meso Scale Discovery Proinflammatory Panel. (E) Immune cell populations were assessed by IHC; CD4⁺ T cells were inferred from CD8⁻FoxP3⁻CD3⁺ cells. (F–G) Plasma copper (F) and δ⁶⁵Cu (G). (H) Plasma cytokine levels. (I) Mice bearing CT26 or MC38 tumors were treated intraperitoneally with Cu(DDC)₂ liposomes (1 mg/kg; 0.25 mg/kg copper) or Cu(CQ)₂ liposomes (8.4 mg/kg; 0.80 mg/kg copper) and tumors were collected 24 hours later. (J–K) Surface calreticulin (CRT) levels assessed by IHC in CT26 (J) and MC38 (K) tumors. Data are presented as mean ± SD. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ns = not significant.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/9bf52023d4345bab2149d901.jpeg"},{"id":89302691,"identity":"6acc737e-a91d-477a-9ab4-ef11f33ee5d6","added_by":"auto","created_at":"2025-08-18 14:41:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":678409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCopper ionophore monotherapy modestly alters tumor growth without impacting body weight in CT26 and MC38 models. \u003c/strong\u003e(A) Female BALB/c and C57BL/6 mice were inoculated subcutaneously with 5 × 10⁵ CT26 or MC38 cells, respectively. Mice were treated intraperitoneally with Cu(DDC)₂ liposomes (1 mg/kg; 0.25 mg/kg copper) or Cu(CQ)₂ liposomes (8.4 mg/kg; 0.80 mg/kg copper) from day 3 post-inoculation. The agents were administered Monday through Friday for two weeks. (B, E) Tumor growth curves over time in CT26 (B) and MC38 (E) models. (C, F) Tumor volumes and (D, G) body weights at day 19 post-inoculation. Data are presented as mean ± SEM.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/51cc67061f434bcfb0ba41fa.png"},{"id":105460698,"identity":"ff70bdce-7455-40a8-b0fb-7d030d2e6bff","added_by":"auto","created_at":"2026-03-26 09:58:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5261437,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/a05bdcf2-5269-4a9e-8ced-8335ab62554c.pdf"},{"id":89302659,"identity":"ae090f11-8601-4d1d-aaaa-d79a8b80ed54","added_by":"auto","created_at":"2025-08-18 14:41:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1782001,"visible":true,"origin":"","legend":"","description":"","filename":"ApoptosisSupFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/d59b2d9a36b6ebd7981eb452.docx"},{"id":89304816,"identity":"c8625360-f9f5-456a-bba0-1d26cd5ef390","added_by":"auto","created_at":"2025-08-18 14:57:07","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1192135,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract. Ionophore-mediated copper delivery enhances immunogenic cell death in copper-dysregulated colorectal cancer models.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Agents known to complex copper and act like ionophores to shuttle copper across cell membranes were tested for their ability to enhance ICD marker stimulation in combination with copper. (B) RNAseq analysis revealed copper ionophore-induced activation of ICD gene signatures with up to 1000-fold lower concentrations compared to CuSO₄. (C) Tumor copper content was analyzed in high-copper CT26 and low-copper MC38 models, showing differences in the copper isotopic composition. (D) Calreticulin exposure was assessed \u003cem\u003ein vivo\u003c/em\u003e, demonstrating model-specific responses to copper ionophores. (E) Copper formulations were well tolerated with modest single-agent activity, supporting their potential for safe copper delivery during ICD induction.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7322091/v1/77500693c4632d7f56ff59ff.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immunogenic cell death in colorectal cancer models is modulated by baseline and ionophore-induced copper accumulation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCopper (Cu) is an essential transition metal that plays a significant role in numerous biological processes, including oxidative phosphorylation, redox homeostasis, and immune function[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. 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 copper levels through depletion strategies, such as copper chelation, aims to restrict cuproplasia, a phenomenon in which copper availability promotes tumor growth[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Conversely, copper overload, driven by compounds known as copper ionophores, has been explored as a strategy to disrupt cellular copper homeostasis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], triggering cell death through mechanisms distinct from those of traditional apoptosis or ferroptosis. Recently, a unique copper-dependent cell death modality termed cuproptosis, characterized primarily by mitochondrial protein aggregation induced by lipoylation stress, culminating in metabolic collapse and mitochondrial dysfunction, was identified[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, understanding the complexities underlying copper-induced cell death pathways is critical for the therapeutic exploitation of copper.\u003c/p\u003e\u003cp\u003eBeyond the induction of direct cytotoxic effects, Cu also exerts a considerable influence on immune modulation within the tumor microenvironment. It has been shown that copper can regulate cytokine production, promote macrophage polarization towards an M1-like pro-inflammatory phenotype, and facilitate T-cell activation and proliferation[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These immunomodulatory properties position Cu modulation as a potentially effective adjunctive strategy to augment conventional immunotherapies, including immune checkpoint inhibitors (ICIs). However, a significant gap in our understanding remains regarding whether immunogenic cell death (ICD), marked by the release of damage-associated molecular patterns (DAMPs) such as ATP, HMGB1, and calreticulin (CRT), represents a generalized response to elevated copper or is uniquely influenced by specific copper ionophore chemistries.\u003c/p\u003e\u003cp\u003eTranslating copper modulation into effective cancer therapies remains challenging due to its narrow therapeutic window and the potential for toxicity from both deficiency and excess[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The distribution and availability of Cu are tightly regulated, making targeted accumulation in tumors difficult without affecting healthy tissues. Emerging technologies, including nanocarriers and metal\u0026ndash;organic frameworks, are being developed to improve the specificity, safety, and pharmacokinetic profiles of targeted copper delivery[\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Among these, small-molecule copper ionophores, such as elesclomol, diethyldithiocarbamate (DDC), and clioquinol (CQ) facilitate intracellular copper accumulation and trigger oxidative stress mechanisms linked to immunogenic cell death (ICD)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These compounds form lipophilic copper complexes that enter cells directly, increasing intracellular copper levels through transporter-independent uptake. DDC, a major metabolite of disulfiram (DSF), and CQ, an antifungal agent, have both been shown to enhance copper uptake and induce cytotoxic stress in cancer cells through redox disruption and mitochondrial dysfunction[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Although these and other ionophores can increase intracellular Cu levels, their immunogenic effects vary and are not well defined. Differences in structure, copper-binding affinity, and cellular targeting likely contribute to the distinct patterns of ICD activation. Additionally, the role of baseline copper levels in modulating these responses has not been fully elucidated and may vary according to the tumor type or treatment context. It remains unclear how differences in ionophore chemistry and tumor Cu levels translate into distinct therapeutic effects.\u003c/p\u003e\u003cp\u003eThis study examined how copper delivery strategies and intracellular copper homeostasis influence the induction of immunogenic cell death. We assessed a panel of copper-binding compounds to define the relationship between ionophore activity, cytotoxicity, and ICD induction and to determine whether copper alone is sufficient to trigger DAMP exposure. To evaluate how baseline copper levels affect susceptibility to copper delivery, we used liposomal formulations of copper ionophores in tumor cells with distinct copper phenotypes. We also analyzed transcriptional responses across different Cu carriers to identify shared and distinct immunogenic programs. Finally, we assessed \u003cem\u003ein vivo\u003c/em\u003e ICD by measuring calreticulin surface exposure following ionophore treatment in CT26 and MC38 tumor models, which were identified to contain high and low basal copper content. Together, these experiments offer insights into how copper levels and delivery mechanisms contribute to ICD induction in vivo.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Materials\u003c/h2\u003e\u003cp\u003e1,2-Distearoyl-sn-glycero-3-phosphocholine (DSPC) and cholesterol (Chol) were obtained from Avanti Polar Lipids Inc. (Alabaster, AL). \u003csup\u003e3\u003c/sup\u003e[H]-cholesteryl hexadecyl ether (CHE) and picofluor-15 scintillation fluid were purchased from PerkinElmer, Inc. (Boston, MA). Roswell Park Memorial Institute (RPMI) 1640 medium, Dulbecco\u0026rsquo;s Modified Eagle\u0026rsquo;s medium (DMEM), Eagle's Minimum Essential Medium (EMEM), Ham's F-12 medium, 0.25% (w/v) trypsin-EDTA, Hank\u0026rsquo;s balanced salt solution (HBSS) without calcium and magnesium, primary antibody against CRT (product number PA3-900), secondary antibody conjugated to DyLight\u0026reg; 488 (product number 35552), Hoechst 33342, and CellMask\u0026trade; Deep Red were obtained from Thermo Fisher Scientific (Waltham, MA). Fetal bovine serum (FBS) and l-glutamine (Life Technologies) were obtained from Life Technologies (Carlsbad, CA, USA). Ethidium homodimer I was obtained from Biotium (Fremont, CA). Sucrose and chloroform were obtained from EMD Chemicals Inc. (Gibbstown, NJ, USA). Copper-binding ligands, including sodium diethyldithiocarbamate trihydrate (DDC), clioquinol (CQ), 2,2\u0026prime;:6\u0026prime;,2\u0026Prime;-terpyridine (TER), 4\u0026prime;-(4-chlorophenyl)-2,2\u0026prime;:6\u0026prime;,2\u0026Prime;-terpyridine (CPT), and 4,4\u0026prime;,4\u0026Prime;-tri-tert-butyl-2,2\u0026prime;:6\u0026prime;,2\u0026Prime;-terpyridine (TTBT) were obtained from Sigma-Aldrich (Oakville, ON, Canada) as analytical grade reagents.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Analytical assays\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Atomic absorption spectroscopy\u003c/h2\u003e\u003cp\u003eThe copper content in the in vitro samples was assessed using flameless atomic absorption spectroscopy (AAS). Samples (media or cell lysates) were first digested overnight in 20% nitric acid at room temperature to ensure complete dissolution of the copper species. Following digestion, the samples were centrifuged to remove debris and the supernatants were diluted in 0.1% nitric acid. Measurements were performed using a PerkinElmer AAnalyst 600 instrument (Waltham, MA, USA). The thermal protocol included drying at 110\u0026deg;C for 30 s, heating to 130\u0026deg;C for 45 s, ashing at 1200\u0026deg;C for 30 s, atomization at 2000\u0026deg;C for 5 s, and a final cleaning phase at 2450\u0026deg;C for 4 s.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Inductively coupled plasma mass spectrometry (ICP-MS)\u003c/h2\u003e\u003cp\u003eThe total copper levels and copper isotopic compositions in the in vivo samples were analyzed using a Thermo Scientific iCAP Qc ICP-MS system fitted with an autosampler for aqueous sample handling. The instrument was operated in kinetic energy discrimination (KED) mode, using helium as the collision gas and an argon flow of 0.3 mL/min. A sample volume of 1 mL was injected during each run. Copper isotopes ⁶\u0026sup3;Cu and ⁶⁵Cu were measured simultaneously, with indium and rhodium (10 ppb, Thermo Fisher) included as internal standards to ensure analytical accuracy. The quantification was performed using a calibration series prepared from copper standards (Sigma-Aldrich) at concentrations of 0.1, 1, 10, 100, and 500 ppb in 2% ultrapure nitric acid.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Liposomal Cu(DDC)₂ and Cu(CQ)₂ formulations\u003c/h2\u003e\u003cp\u003eTo prepare DSPC/Chol liposomes, lipids were dissolved in chloroform at a 55:45 molar ratio of DSPC to cholesterol. The radiolabeled lipid tracer \u003csup\u003e3\u003c/sup\u003e[H]-CHE was added prior to solvent evaporation. Specific activity (approximately 20,000\u0026ndash;25,000 DPM/\u0026micro;mol total lipid) was assessed by transferring 10 \u0026micro;L of the lipid solution to a scintillation vial, evaporating chloroform, and adding 5 mL of Picofluor scintillation fluid. Radioactivity was quantified in quadruplicate using a Packard 1900 TR Liquid Scintillation Analyzer.\u003c/p\u003e\u003cp\u003eLiposomal Cu(DDC)₂ was prepared according to a previously described method[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] with several modifications. The formulation involved \u003cem\u003ein situ\u003c/em\u003e complexation of CuSO₄ and diethyldithiocarbamate (DDC) within preformed DSPC/cholesterol (55:45 mol%) liposomes. Liposomes were hydrated in 300 mM sucrose and 10 mM HEPES buffer (pH 7.4), and initially formed as multilamellar vesicles via thin-film hydration. Vesicles were then sequentially extruded through two stacked 80 nm polycarbonate membranes (Avanti Polar Lipids) using a 10 mL mini-extruder (15 passes) to obtain unilamellar liposomes. DDC and CuSO\u003csub\u003e4\u003c/sub\u003e were added stepwise to the suspension at a final drug-to-lipid molar ratio of 0.2, followed by gentle stirring at room temperature to promote Cu(DDC)₂ formation and incorporation into the lipid bilayer. The final formulation was sterilized by filtration through 0.2 \u0026micro;m syringe filters (Pall Corporation) to eliminate aggregates and unencapsulated material.\u003c/p\u003e\u003cp\u003eLiposomal Cu(CQ)₂ was prepared according to a previously described method[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] with slight modifications. DSPC/cholesterol (55:45 mol%) lipid films were hydrated with 300 mM CuSO₄, followed by extrusion through two stacked 80 nm polycarbonate membranes, as described above. External CuSO\u003csub\u003e4\u003c/sub\u003e was removed and exchanged for HEPES-buffered saline (pH 7.4) using tangential flow 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)₂ complexation and bilayer association. The formulation was then filtered through 0.2 \u0026micro;m syringe filters prior to use.\u003c/p\u003e\u003cp\u003eFor both formulations, copper complex formation was confirmed by UV\u0026ndash;visible spectrophotometry at 435 nm after dilution in 100% HPLC-grade methanol. Liposomal lipid concentrations were quantified by liquid scintillation counting as \u003csup\u003e3\u003c/sup\u003e[H]-cholesteryl hexadecyl ether (CHE) was incorporated in all liposomal formulations. All the sterile filtered formulations were used directly for cytotoxicity, RNA sequencing, and \u003cem\u003ein vivo\u003c/em\u003e experiments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.4 \u003cem\u003eIn vitro\u003c/em\u003e characterization assays\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Tissue culture\u003c/h2\u003e\u003cp\u003eAll cell lines were maintained in their respective media supplemented with 10% FBS and 2 mM l-glutamine, unless otherwise noted. The CT26 murine colon carcinoma cell line was obtained from the American Type Culture Collection (ATCC) (Manassas, VA, USA) (CRL-2638) and cultured in DMEM. The MDA-MB-231 human breast adenocarcinoma cell line was obtained from ATCC (Cat # HTB-26) and cultured in RPMI 1640. The A549 human lung adenocarcinoma cell line was obtained from ATCC (Cat # CCL-815) and cultured in RPMI 1640 medium. The SKOV3 human ovarian adenocarcinoma cell line was obtained from ATCC (Cat # HTB-77) and was cultured in McCoy\u0026rsquo;s 5A medium. The MCF7 human breast adenocarcinoma cell line was obtained from ATCC (Cat # HTB-22) and cultured in RPMI 1640. The BxPC3 human pancreatic adenocarcinoma cell line was obtained from ATCC (Cat # CRL-1687) and cultured in DMEM. The Capan-1 human pancreatic adenocarcinoma cell line was obtained from ATCC (Cat # HTB-79) and cultured in IMDM medium with 20% FBS and 4 mM L-glutamine. All the cells were grown and maintained in a humidified incubator at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. When cells reached 90% confluence (70% for Capan-1), they were washed with HBSS without calcium and magnesium, detached with 0.25% (w/v) trypsin-EDTA, and passaged into a new cell culture flask. Cells were subcultured at a ratio of 1:4 to 1:10 depending on the desired cell density.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2 Cytotoxicity assays\u003c/h2\u003e\u003cp\u003eCells were seeded in 384-well black-walled, clear-bottom plates (Greiner Bio-One) in 50 \u0026micro;L of growth medium per well at densities optimized for each cell line (typically 1500\u0026ndash;2000 cells/well). Following overnight attachment, cells were treated with the compounds at the indicated concentrations. After 72 hours, cell viability was assessed by staining with Hoechst 33342 (4.87 \u0026micro;M) and ethidium homodimer I (312.5 nM) to distinguish viable (Hoechst-positive, ethidium-negative) from non-viable (Hoechst-positive, ethidium-positive) cells. Plates were incubated with dyes for 20 min at 37\u0026deg;C, imaged using the IN Cell Analyzer 2200 (GE Healthcare), and analyzed using Developer Toolbox 1.9 software to quantify viable and dead cells. Viability was expressed as the fraction of live cells normalized to that of vehicle-treated controls. Graphs were generated using GraphPad Prism 10.0 (GraphPad Software, La Jolla, CA, USA).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.6. In vitro assessment of immunogenic cell death (ICD)\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1 ATP release assay:\u003c/h2\u003e\u003cp\u003eCT26 cells were seeded at a density of 200,000 cells/well in 24-well plates (Corning, Cat. no. 353047) with 300 \u0026micro;L of culture medium and allowed to adhere for 24 h. Next, 100 \u0026micro;L of treatment solution or control medium was added per well, and the cells were incubated for another 24 h without disturbing the plate to prevent non-specific ATP release. At the end of the treatment, 50 \u0026micro;L of supernatant was carefully transferred from each well\u0026mdash;avoiding cell contact\u0026mdash;to a 96-well white-walled, clear-bottom plate that contained 50 \u0026micro;L of CellTiter-Glo\u0026reg; 2.0 reagent (Promega). An ATP standard curve (10\u0026ndash;1000 nM prepared in serum-free medium) was used. The plates were shaken at room temperature for 2 min and incubated for 15 min. Luminescence was measured using a FLUOstar OPTIMA plate reader and used to quantify extracellular ATP levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2 HMGB1 quantification by ELISA:\u003c/h2\u003e\u003cp\u003eTo evaluate HMGB1 release, CT26 cells (200,000 cells/well) were plated in 300 \u0026micro;L of medium in 24-well plates and cultured for 24 h. The cells were then treated with 100 \u0026micro;L of drugs or control medium and incubated for another 24 h. After treatment, 5 \u0026micro;L of the supernatant was transferred to a pre-coated HMGB1 ELISA plate ( Cat. No. ST51011) containing 105 \u0026micro;L of diluent. The standards, controls, and samples were prepared according to the manufacturer\u0026rsquo;s protocol. After shaking for 30 s, the plates were incubated at 37\u0026deg;C for 24 h. Subsequent steps, including antibody addition and substrate development, were performed in accordance with the manufacturer\u0026rsquo;s instructions. The absorbance was recorded at 450 nm (reference at 600 nm) using a Multiskan\u0026reg; Spectrum reader (Thermo Fisher). HMGB1 concentrations were determined using a standard curve ranging from 2.5\u0026ndash;80 ng/mL.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e2.6.3 Surface calreticulin (CRT) detection:\u003c/h2\u003e\u003cp\u003eA 96-well black-walled, clear-bottom plate was first coated with 50 \u0026micro;L of 25 \u0026micro;g/mL poly-d-lysine to enhance cell attachment and incubated for 1 h at room temperature. After removing the coating solution, 20,000 CT26 cells were seeded per well in 100 \u0026micro;L of the medium and allowed to adhere for 24 h. Cells were then treated with 100 \u0026micro;L of drug or control medium and incubated for 24 h. Following treatment, the cells were washed three times with HBSS (with Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺, without phenol red), fixed with 4% methanol-free formaldehyde in PBS for 20 min, and washed again. A primary anti-CRT antibody (1:200 in staining buffer) was applied for 1 h on ice, followed by three washes and incubation with DyLight\u0026reg; 488-conjugated secondary antibody (1:500) for 30 min on ice in the dark. After additional washes, the cells were counterstained with Hoechst 33342 (1:2000 in PBS) and CellMask\u0026trade; Deep Red (1:1000) for 20 min, washed again, and imaged in PBS using the IN Cell Analyzer 2200. Twenty fields per well were captured using flat-field correction. Image analysis was conducted using IN Cell Workstation 3.7 software (GE Healthcare) to quantify CRT fluorescence intensity on the cell membrane. Mean intensities were averaged across all cells per condition and normalized to untreated controls to express CRT exposure as fold change.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.7. RNA Sequencing and Transcriptomic Analysis\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e2.7.1 Bulk mRNA sequencing\u003c/h2\u003e\u003cp\u003eSample quality control was performed using an Agilent 2100 Bioanalyzer or an Agilent 4200 TapeStation. Qualifying samples were then prepared following the standard protocol for Illumina Stranded mRNA prep (Illumina). Sequencing was performed on the Illumina NextSeq2000 with Paired End 59bp \u0026times; 59bp reads. Sequencing data were demultiplexed using Illumina's BCL Convert. De-multiplexed read sequences were then aligned to the Homo sapiens (hg38 no Alts, with decoys) /Mus musculus (mm10) reference sequence using the DRAGEN RNA app on Basespace Sequence Hub.\u003c/p\u003e\u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://supportdocs.illumina.com/SW/DRAGEN_v41/Content/SW/DRAGEN/TPipelineIntro_fDG.htm\u003c/span\u003e\u003cspan address=\"https://supportdocs.illumina.com/SW/DRAGEN_v41/Content/SW/DRAGEN/TPipelineIntro_fDG.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Differential expression analysis was performed using DESeq2.\u003c/p\u003e\u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/release/bioc/html/DESeq2.html\u003c/span\u003e\u003cspan address=\"https://bioconductor.org/packages/release/bioc/html/DESeq2.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using DRAGEN software.\u003c/p\u003e\u003cp\u003eDifferential Expression app on Basespace\u003c/p\u003e\u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://basespace.illumina.com/apps/14229215/DRAGEN-Differential-Expression\u003c/span\u003e\u003cspan address=\"https://basespace.illumina.com/apps/14229215/DRAGEN-Differential-Expression\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e2.7.2 Gene Set Enrichment Analysis (GSEA)\u003c/h2\u003e\u003cp\u003eRanked gene lists based on log2 fold change values were subjected to Gene Set Enrichment Analysis (GSEA) using the clusterProfiler package in R. Background gene sets included [gene set sources, for example, Molecular Signatures Database (MSigDB)] and a custom-curated copper/ICD gene set. Enrichment was visualized using dot plots, ridge plots, and enrichment maps, highlighting the pathways associated with immune responses, oxidative stress, and metal ion homeostasis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e2.7.3 Gene Ontology and Pathway Analysis\u003c/h2\u003e\u003cp\u003eGene Ontology (GO) enrichment analysis was performed using clusterProfiler to identify the biological processes and pathways enriched among the differentially expressed genes. Pathways of interest, including the immune response, apoptosis, and stress response pathways, were specifically investigated. Enriched pathways were visualized as bubble plots, and GO terms were prioritized based on adjusted p-values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e2.7.4 Copper-responsive and ICD gene signature analysis\u003c/h2\u003e\u003cp\u003eGene sets specific to copper response and immunogenic cell death (ICD) were used to assess treatment-induced transcriptional changes. The copper-responsive set was derived from the Gene Ontology Biological Process term \u0026ldquo;response to copper ion,\u0026rdquo; while the ICD gene set consisted of 34 curated genes previously identified by Zhou et al. (2023).\u003csup\u003e34\u003c/sup\u003e Ranked gene lists (log₂ fold change) for CuSO₄, Cu(DDC)₂, and Cu(CQ)₂ treatments were analyzed using GSEA implemented in clusterProfiler. Running enrichment score (RES) plots and heatmaps were generated to visualize the magnitude and direction of pathway engagement across conditions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Murine tumor studies\u003c/h2\u003e\u003cp\u003e All animal experiments were approved by the University of British Columbia Animal Care Committee (protocol A22-0274) and performed in female BALB/c and C57BL/6 mice bearing subcutaneous CT26 and MC38 tumors, respectively. Tumors were initiated by injecting 5 \u0026times; 10⁵ CT26 or MC38 cells subcutaneously into the right flank.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e2.8.1 Efficacy studies\u003c/h2\u003e\u003cp\u003eTo assess tumor growth inhibition, mice were treated with intraperitoneal injections of Cu(DDC)₂ liposomes (1 mg/kg; 0.25 mg/kg copper) or Cu(CQ)₂ liposomes (8.4 mg/kg; 0.80 mg/kg copper) beginning on day 3 post-inoculation. Treatments were administered once daily, Monday through Friday, for two consecutive weeks (10 total doses). Tumor volumes were measured three times per week using digital calipers and were calculated as (length \u0026times; width\u0026sup2;)/2. Body weight was recorded throughout the treatment period. Tumor growth was compared on day 19.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e2.8.2 CRT exposure studies\u003c/h2\u003e\u003cp\u003eTo evaluate in vivo ICD responses, mice with CT26 or MC38 tumors were treated with a single intraperitoneal dose of Cu(DDC)₂ or Cu(CQ)₂ liposomes when tumors reached\u0026thinsp;~\u0026thinsp;200 mm\u0026sup3;. Tumors were collected 24 h later and processed for immunohistochemical (IHC) analysis of calreticulin (CRT) surface exposure, described below.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e2.8.3 IHC analysis\u003c/h2\u003e\u003cp\u003e\u003cem\u003eTumor Harvesting and Tissue Processing\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTumors were harvested 72 h after final treatment for immune infiltration. Tumors were processed through graded reagent alcohols and xylene (Fisher) using a TP1020 Carousel tissue processor (Leica), and then used to construct tissue microarrays with a Beecher Instruments manual tissue microarrayer before being sectioned at 4 \u0026micro;m on a Microm HM355 microtome. The slides were deparaffinized using xylene and graded reagent alcohols, followed by antigen retrieval using a rodent decloaking solution in a decloaking chamber (Biocare).\u003c/p\u003e\u003cp\u003e\u003cem\u003eMulticolor Immunofluorescent Staining\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe slides were loaded into an Intellipath FLX autostainer (Biocare) for multicolor immunofluorescence staining. The first round of staining started with blocking endogenous peroxidases and non-specific binding using Peroxidased-1 for 5 min and Rodent Block M for 30 min (Biocare). CD3 (clone SP7, Abcam) was applied for 30 min, followed by MACH2 Rabbit-HRP polymer (Biocare) for 10 min, and OPAL520 (Akoya) for 10 min. The slides were then removed from the stainer and microwaved in AR9 (Akoya) to denature the reagents from the previous round, leaving the floor in place. Round 2 proceeded similarly using FoxP3 (clone FJK-16s, Fisher), rat\u0026ndash;mouse HRP polymer (Biocare), and OPAL620, followed by AR6 (Akoya). Round 3 used PAX5 (clone EPR3770(2), Abcam), MACH2 Rabbit-HRP, and OPAL650, followed by AR6. Round 4 included CD8 (clone D4W2Z, Cell Signaling Technology), MACH2 rabbit-HRP, and OPAL690, followed by AR6. Round 5 used a background sniper (Biocare) for 10 min in place of rodent block M, followed by pan-keratin (clone E6S1S; Cell Signaling Technology), OPAL570, and AR6. The final round included F4/80 (clone D2S9R, Cell Signaling Technology), MACH2 rabbit-HRP, and OPAL540. Slides were then removed from the Intellipath FLX and coverslipped with a ProLong Diamond antifade mountant (Fisher).\u003c/p\u003e\u003cp\u003e\u003cem\u003eImage Acquisition and Analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSlides were imaged using the Vectra 3 multispectral imaging system (Akoya), capturing 1 \u0026times; 1 images per core for analysis using the inForm image analysis software (Akoya). A base algorithm was written to segment the tissue based on keratin, DAPI, and autofluorescence into the epithelium, stroma, and other regions (e.g., blank space, folds, and advanced necrosis). Individual phenotyping algorithms have been developed to identify mutually exclusive markers (e.g., F4/80-CD3, CD8-PAX5, and FoxP3). The results from the inForm were combined in Phenopreports (Akoya) to overlay markers and define phenotypes. This process was performed in triplicates.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Statistical analysis\u003c/h2\u003e\u003cp\u003eGraphPad Prism 10.0 software was used for all statistical analyses. \u003cem\u003eIn vitro\u003c/em\u003e data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and \u003cem\u003ein vivo\u003c/em\u003e data as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM), as noted in figure legends. Group comparisons were performed using unpaired two-tailed t-tests. This included comparisons of tumor volume at day 19, plasma and tumor copper levels, cytokine levels, and calreticulin staining. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eDiethyldithiocarbamate (DDC) is a well-characterized copper-binding compound that has been historically used for heavy metal chelation in both industrial and clinical settings, with increasing interest in its anticancer potential when complexed with copper[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Given recent evidence that copper complexes of DDC can induce immunogenic cell death (ICD)[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], we first evaluated whether DDC promotes ATP release, a hallmark of ICD, when paired with transition metals (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;C). DDC alone had a minimal effect, but in combination with various copper 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 (~\u0026thinsp;8-fold), with the copper combination further enhancing this to 30- to 40-fold. In contrast to copper, most other metals suppressed TTBT\u0026apos;s ATP-releasing activity of TTBT, with the exception of cobalt, which had no effect. Transition metals alone had a limited effect on ATP release (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). Dose-response analysis confirmed that ATP release by CuDDC was concentration-dependent and detectable at concentrations as low as 3.1 \u0026micro;M in B16-F10 cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD\u0026ndash;E).\u003c/p\u003e\n\u003cp\u003eTo better understand whether this ATP induction was primarily due to copper transport or the intrinsic biological effects of the ligands, a panel of copper-binding compounds from several drug classes was profiled (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF). Cytotoxicity was assessed with and without CuSO₄ at 24 and 72 hours. Although the IC₅₀ values varied widely, copper potentiation, defined as a shift in IC₅₀, was only observed for DDC, 8-hydroxyquinoline (8HQ), and pyrithione, which are known ionophore-like compounds[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. To assess whether this potentiation correlated with copper transport, cells were pulsed for 4 hours with each compound and CuSO₄ and the intracellular copper levels were measured (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG). Ionophore-like compounds significantly increased intracellular copper levels (2- to 8-fold). The terpyridine analogs TTBT and CPT also increased the copper content by 3- to 5-fold, respectively. Other compounds, including several polyphenols and chemotherapies known to bind divalent metals, had minimal effects or slightly decreased copper accumulation.\u003c/p\u003e\n\u003cp\u003eThese findings indicate that copper ionophore-like compounds significantly enhance intracellular copper accumulation and potentiate cytotoxicity in the presence of extracellular copper. While some compounds such as TTBT, CPT, and clioquinol increased copper levels without corresponding increases in cytotoxicity, others such as DDC showed time-dependent potentiation, with effects observed at 24 h but not at 72 h.\u003c/p\u003e\n\u003cp\u003eTo evaluate whether copper-ligand combinations promote immunogenic signaling, three damage-associated molecular patterns (DAMPs)\u0026ndash;ATP release, HMGB1 release, and calreticulin (CRT) exposure\u0026ndash;were measured in CT26 cells following 24-hour treatment (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C). The compounds were applied at doses optimized for ATP induction as single agents (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), either alone, with CuSO₄, or CuSO₄ alone at the same concentration. The known ionophore group (brown bar) showed minimal activity across DAMPs when used alone, but the addition of copper 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 a single agent with little further enhancement upon copper addition. TTBT was the only terpyridine compound that increased all three DAMPs in the presence of copper. The other compound classes showed minimal effects regardless of the addition of copper.\u003c/p\u003e\n\u003cp\u003eTo determine whether intracellular copper levels alone accounted for DAMP induction, internalized copper from Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG was compared to the DAMP responses following CuSO₄ treatment alone (CuSO₄ columns in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C). There was a strong positive correlation between the intracellular copper content and the release of ATP, HMGB1, and CRT (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD), suggesting that copper accumulation can directly stimulate ICD-associated pathways. The ionophore-like activity, defined as the fold increase in intracellular copper over CuSO₄ alone, was assessed to determine whether copper-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. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). However, when analyzing all treatments, there was no consistent correlation between total intracellular copper levels and DAMP release, likely because of copper-independent effects in some compound classes. Together, these results support a model in which copper ionophore-like activity drives ICD-like responses by increasing intracellular copper concentrations beyond the threshold achieved with soluble copper alone.\u003c/p\u003e\n\u003cp\u003eWith intracellular copper delivery emerging as a potential strategy to activate ICD and enhance immune responses, copper uptake following addition of CuSO₄, Cu(DDC)₂, and Cu(CQ)₂ was assessed across a panel of human and murine cell lines (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;C). Baseline intracellular copper levels were measured, followed by a 4-hour treatment with each compound. Addition of Cu(DDC)₂ and Cu(CQ)₂ resulted in increases in intracellular copper when compared to cells incubated with 100 \u0026micro;M CuSO₄. The copper doses for the Cu(DDC)₂ and Cu(CQ)₂ studies were 20- and 4 fold less, respectively. To determine whether basal copper levels were predictive of treatment sensitivity, eight cancer cell lines were assessed, and cytotoxicity after 72-hour exposure to Cu(DDC)₂ and Cu(CQ)₂ was determined (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE\u0026ndash;F). A heatmap of cuproptosis-related gene expression revealed notable heterogeneity in core regulators such as FDX1, DLAT, and PDHA1 across cell lines (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD), with MDA-MB-231 cells displaying both high expression of multiple cuproptosis-associated genes and the greatest sensitivity to Cu(CQ)₂, consistent with its elevated basal copper levels. When IC₅₀ values were plotted against baseline copper levels, no correlation was observed for Cu(DDC)₂ (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eH), whereas Cu(CQ)₂ cytotoxicity showed a significant association with basal copper content (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eI).\u003c/p\u003e\n\u003cp\u003eTo examine the transcriptomic effects of copper treatments, gene ontology enrichment analysis was performed with CT26 cells treated with CuSO₄, Cu(DDC)₂, or Cu(CQ)₂ at IC₇₀ for 24 hours, a dose selected to capture death-related signaling while limiting confounding from growth inhibition. Gene set enrichment was performed using log₂ fold-change\u0026ndash;ranked genes and mouse GO gene sets (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Enrichment maps revealed that CuSO₄ primarily induced pathways related to ion transport along with smaller clusters associated with cell motility and keratinization (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Cu(DDC)₂ and Cu(CQ)₂ also showed enrichment in ion transport-related terms, likely reflecting their role in facilitating copper uptake; however, these agents had broader transcriptional responses that suggested changes in immune and cellular regulatory processes. Cu(DDC)₂ treatment led to enrichment of pathways related to membrane organization, vesicle trafficking, and hormone transport (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;D), potentially reflecting intracellular responses to altered metal homeostasis. Cu(CQ)₂, 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. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE\u0026ndash;F). Some 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 the activation of both immunostimulatory and regulatory programs. These results indicate that, while the core transcriptomic effects of CuSO₄ and both ionophores involve metal ion processing, Cu(DDC)₂ and Cu(CQ)₂ engage distinct gene expression programs that reflect different cellular responses to copper delivery.\u003c/p\u003e\n\u003cp\u003eTo determine whether transcriptional responses to copper accumulation corresponded with immunogenic stress signaling, the expression changes in Cu-responsive and ICD-related genes were profiled in CT26 cells. All three treatments activated genes associated with the Gene Ontology category \u0026quot;response to copper ion,\u0026quot; with the most pronounced expression in CuSO₄ and Cu(DDC)₂, despite Cu(DDC)₂ being administered at an 800-fold lower molar dose compared to CuSO₄ (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). This suggests that intracellular delivery mechanisms may be more influential than the total amount of copper administered to drive the transcriptional response. Under the same conditions, the expression of a curated 31-gene immunogenic cell death (ICD) signature (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB) was examined. Each treatment upregulated subsets of ICD genes but with differing breadths and intensities. Volcano plots showed that CuSO₄ induced widespread upregulation and downregulation of genes, which was consistent with a broad stress response (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). In contrast, Cu(DDC)₂ triggered more selective transcriptional activation, including strong upregulation of NLRP3 and TNF (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD). Cu(CQ)₂ had fewer differentially expressed genes overall but included increased expression of P2RX7 and NLRP3 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE). These patterns suggest that CuSO₄ induces a broader transcriptional response, whereas ionophore-based treatments result in a more selective activation of immune-related gene pathways (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eGene set enrichment analysis (GSEA) plots visualized the overall enrichment of the copper response and ICD pathways (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF\u0026ndash;H). Although CuSO₄ induced the most differentially expressed genes, its enrichment for the copper ion response set was the weakest, likely because of widespread transcriptional changes that dilute pathway-specific signals. Cu(CQ)₂ yielded the strongest and most selective enrichment of both Cu-responsive and ICD-associated genes, despite lower overall transcriptional activation. Cu(DDC)₂ showed intermediate behavior, with strong activation of ICD components and robust copper-responsive transcription. These findings support a model in which ionophore-based copper delivery can modulate gene expression in a more targeted manner than extracellular copper alone, potentially shaping.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, CT26 cells \u003cem\u003ein vitro\u003c/em\u003e had significantly higher intracellular copper levels than MC38 cells. Since this may affect the therapeutic response to copper delivery, copper and immune-related features were characterized in syngeneic murine tumor models following s.c. injection of the two cell lines (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Copper levels in CT26 cells were approximately twice the level in MC38 cells, but this difference was less pronounced in larger tumors (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). The common isotopes of copper are ⁶\u0026sup3;Cu and ⁶⁵Cu, and copper isotopic fractionation is thought to reflect cellular metabolism and transporter activity[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. While altered ⁶⁵Cu/⁶\u0026sup3;Cu 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 tumor tissue[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. In this study, MC38 tumors showed higher ⁶⁵Cu enrichment than CT26 tumors, particularly smaller tumors (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). Plasma levels of total and isotopic copper were not significantly different when plasma was obtained from tumor-bearing mice (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF\u0026ndash;G).\u003c/p\u003e\n\u003cp\u003eLarge MC38 tumors have higher TNF\u0026alpha; and IL-12 cytokine levels, which may reflect immune activation. In addition, elevated IL-10 levels were observed, which may be associated with immunosuppression. Interestingly, plasma cytokine levels showed an opposite trend, with higher TNF\u0026alpha; and IL-12 levels in CT26 tumors and lower levels in MC38 tumors (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD, H). CT26 tumors also showed higher CD8\u0026thinsp;+\u0026thinsp;infiltration than MC38 tumors, suggesting greater immune priming in this model. Similar levels of other lymphocyte populations and macrophages were observed in these two tumor models (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eTo determine the \u003cem\u003ein vivo\u003c/em\u003e susceptibility of copper 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)₂ or Cu(CQ)₂. CRT exposure was assessed 24 h later using IHC (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eI). In the CT26 tumor model, Cu(CQ)₂, 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)₂ was given (delivering 0.25 mg/kg Cu; Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eJ). MC38 tumors had higher baseline CRT levels, but treatment with both ionophore-like compounds led to decreased CRT expression, including a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) 7-fold reduction when the mice were injected with Cu(DDC)₂ and a 2-fold decrease (p\u0026thinsp;=\u0026thinsp;0.051) when the mice were injected with Cu(CQ)₂ (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eK). These findings indicate important differences in copper accumulation and immune-related responses between the two tumor models, with implications for how Cu-based therapeutics might differentially modulate ICD in distinct tumor contexts.\u003c/p\u003e\n\u003cp\u003eThe activity of monotherapies with Cu(DDC)₂ and Cu(CQ)₂ 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. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). Both agents produced modest growth inhibition compared to the vehicle controls, with Cu(CQ)₂ showing a slightly greater effect in the CT26 model (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB\u0026ndash;C, E\u0026ndash;F). Body weight was unaffected by this dose schedule (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD, G), and no significant adverse events were observed. These data suggest that copper ionophore monotherapy is well tolerated but modestly suppresses tumor growth. These results suggest that these agents may be better suited for use in combination regimens aimed at enhancing immunotherapy through copper-dependent or other immunomodulatory mechanisms.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study investigated the relationship between intracellular copper and immunogenic cell death (ICD) with a focus on enhancing cellular copper delivery using ionophore-like compounds. Although prior studies have demonstrated that copper-containing treatments can stimulate ICD-related responses such as calreticulin (CRT) exposure and DAMP release[19,25,26,45], the specific contributions of intracellular copper levels, both at baseline and following treatment, remain largely uncharacterized. Here, we show that intracellular copper accumulation correlates with the activation of ICD signatures, and that the ionophore-like activity of a compound significantly influences the magnitude of this response. Compounds that induced greater intracellular copper uptake more robustly activated ICD-associated pathways, including those related to TNF\u0026alpha; signaling and antigen presentation.\u003c/p\u003e\n\u003cp\u003eInterestingly, several copper-binding compounds, including TTBT, CPT, and clioquinol, increased intracellular copper levels but failed to enhance cytotoxicity or CRT expression. These findings suggest that copper uptake alone is insufficient to induce cell death or ICD, and that compound-specific factors, such as subcellular localization, redox potential, or engagement of parallel stress pathways, likely modulate biological outcomes[46]. Among the tested ionophores, diethyldithiocarbamate (DDC) has emerged as the most potent. DDC activated ICD-related transcriptional programs at doses nearly 800-fold lower than CuSO₄ and induced additional gene pathways related to vesicle trafficking and immune-cell recruitment. This broad activity profile may contribute to enhanced therapeutic efficacy but also introduces complexity in distinguishing specific immunogenic signals from generalized stress responses. Notably, the cytotoxic enhancement observed with DDC was time-dependent, evident at 24 h, but diminished by 72 h, possibly due to cumulative copper equilibration from the culture media.\u003c/p\u003e\n\u003cp\u003eClioquinol (CQ), which was historically used as an antimicrobial agent and was withdrawn following an outbreak of subacute myelo-optic neuropathy (SMON) in Japan[47], displayed distinct biological behavior. Subsequent evidence suggests that SMON may be linked to genetic susceptibility to ABC transporter variants[48]. CQ has since re-emerged as a candidate therapeutic agent in oncology and neurodegeneration[33,49]. In this study, Cu(CQ)₂ exhibited moderate ionophore-like activity, with cytotoxicity significantly correlated with basal intracellular copper levels. This correlation supports a model in which endogenous copper homeostasis influences the sensitivity of cancer cells to Cu(CQ)₂. Furthermore, Cu(CQ)₂ treatment upregulated pro-inflammatory cytokine transcripts, including TNF\u0026alpha; and NLRP3, and led to a two-fold increase in CRT exposure in CT26 tumors, although this difference was not statistically significant (p = 0.09). These findings indicate that, although CQ is a weaker ionophore than DDC, it may retain immunomodulatory potential in settings where tumors exhibit elevated basal copper levels.\u003c/p\u003e\n\u003cp\u003eTranscriptomic profiling revealed that while CuSO\u003csub\u003e4\u003c/sub\u003e, Cu(DDC)₂, and Cu(CQ)₂ all activated metal ion-related pathways, they diverged substantially in their immune signaling signatures. CuSO₄ induced a broad, stress-associated transcriptional response consistent with high extracellular copper exposure, while Cu(DDC)₂ and Cu(CQ)₂ each activated more selective subsets of immune-related genes. Gene ontology analysis confirmed that Cu(CQ)₂ preferentially engaged in cytokine production and adaptive immune pathways, whereas Cu(DDC)₂ was more strongly associated with vesicle organization and trafficking. These findings support the notion that although all three compounds target core copper-handling mechanisms, the downstream biological consequences vary considerably based on the chemical properties and intracellular behavior of the compound.\u003c/p\u003e\n\u003cp\u003eIn vivo, the relationship between intracellular copper and ICD marker expression was variable. In CT26 tumors, both Cu(DDC)₂ and Cu(CQ)₂ treatments modestly increased surface calreticulin exposure. In contrast, MC38 tumors, which had lower baseline copper levels and distinct isotopic signatures, showed reduced CRT expression following treatment. These results suggest that differences in tumor-intrinsic copper metabolism may influence the effectiveness of copper-based ICD induction. While the in vitro data revealed compound-specific activation of immune-related genes, the in vivo outcomes highlight the need to consider tumor copper handling and the metabolic state when evaluating therapeutic responses.\u003c/p\u003e\n\u003cp\u003eAn additional insight from this study is the potential relevance of stable copper isotope profiling as a biomarker for tumor copper metabolism. Previous studies have linked shifts in \u003csup\u003e65\u003c/sup\u003eCu/\u003csup\u003e63\u003c/sup\u003eCu ratios to cancer severity, with lower serum \u003csup\u003e65\u003c/sup\u003eCu/\u003csup\u003e63\u003c/sup\u003eCu observed in patients with colon, breast, and liver cancer, and higher \u003csup\u003e65\u003c/sup\u003eCu abundance detected in liver tumor tissue[41,43,50,51]. These isotopic shifts were initially attributed to the selective binding of \u003csup\u003e65\u003c/sup\u003eCu by lactate, but more recent work suggests that hepatic fractionation is a more likely explanation[44]. In our study, CT26 tumors exhibited higher total copper levels than MC38 tumors, while MC38 tumors showed an increased relative abundance of \u003csup\u003e65\u003c/sup\u003eCu despite similar plasma copper content. This tumor-specific fractionation may reflect differences in copper transporter expression or metabolic states, and supports further investigation into the role of isotopic profiling as a functional readout of tumor copper biology.\u003c/p\u003e\n\u003cp\u003eAlthough both Cu(DDC)₂ and Cu(CQ)₂ demonstrated only modest single-agent efficacy in vivo, their distinct immune and transcriptional signatures suggest their therapeutic potential in combination with other treatments. Their ability to modulate immune-relevant pathways and influence ICD markers, even modestly, indicates their suitability as partners in combination regimens. These include checkpoint inhibitors, T cell\u0026ndash;engaging therapies, or other immune-enhancing agents that rely on increased antigen presentation. The partial immunogenic responses observed in vivo are consistent with previous reports of copper-induced tumor regression and further support the strategy of rational combination therapy.\u003c/p\u003e\n\u003cp\u003eTaken together, these findings highlight the complexity of copper-mediated ICD and the need for precision in therapeutic design. The biological activity of copper-based treatments depends not only on compound chemistry, but also on tumor-specific factors, including basal copper levels, isotopic distribution, and copper-handling capacity. Future efforts should focus on developing targeted delivery systems, such as nanocarriers, that can selectively increase copper levels in tumors while minimizing systemic toxicity. Stratifying tumors based on Cu content and isotopic profiles may help identify patients most likely to benefit from copper-based therapies. To advance the clinical utility of copper-enhanced immunotherapy, future strategies will need to align compound properties with tumor-specific copper biology and the immune context, using targeted delivery systems and biomarker-driven patient selection to guide treatment decisions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eCu\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eCopper\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eDDC\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ediethyldithiocarbamate\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eCu(DDC)\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ecopper diethydithiocarbamate\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eCQ\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eclioquinol\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eCu(CQ)\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ecopper clioquinol\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eDSF\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003edisulfiram\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eCHE\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e[H]-Cholesteryl Hexadecyl Ether\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eDSPC\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003e1\u003c/em\u003e,\u003cem\u003e2-Distearoyl-sn-Glycero-3-Phosphocoline\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eIC\u003c/em\u003e\u003csub\u003e\u003cem\u003e50\u003c/em\u003e\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003einhibitory concentration of 50\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003ei.p\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eintraperitoneal\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003estandard deviation\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eSEM\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003estandard error of the mean\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eTFF\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003etangential flow filtration\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eATP\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eadenosine triphosphate\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eHMGB1\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ehigh mobility group box 1\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eCRT\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ecalreticulin\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eDAMP\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003edamage-associated molecular patterns\u003c/em\u003e,\u003cem\u003eRES\u003c/em\u003e,\u003cem\u003erunning enrichment score\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eGO\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003egene ontology\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eTCA\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003etricarboxylic acid\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eIHC\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eimmunohistochemistry\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Statement\u003c/p\u003e\n\u003cp\u003eAll animal procedures were conducted in accordance with the Canadian Council on Animal Care (CCAC) guidelines and approved by the University of British Columbia Institutional Animal Care Committee under Animal Care Certificate A14-0290. Human-derived cell lines were obtained from established commercial sources and used in accordance with the institutional biosafety guidelines. No primary human tissue or patient data were used.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by grants from the Canadian Institutes of Health Research (153132), Canadian Cancer Society (705290), and the NanoMedicines Innovation Network (DRG 03190). Devon Heroux acknowledges additional support from the NanoMedicines Innovation Network Doctoral Award. We gratefully acknowledge the additional support from the BC Cancer Foundation.\u003c/p\u003e\n\u003cp\u003eCredit authorship contribution statement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevon Heroux:\u003c/strong\u003e Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing \u0026ndash; original draft, writing \u0026ndash; review and editing, Visualization, Project administration. \u003cstrong\u003eAda W.Y. Leung:\u003c/strong\u003e Conceptualization, Methodology, Formal Analysis, Investigation. \u003cstrong\u003eRoger Gilabert-Oriel:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Formal Analysis, Investigation. \u003cstrong\u003eSaeid Farzaneh:\u0026nbsp;\u003c/strong\u003eMethodology, Formal Analysis, Investigation. \u003cstrong\u003eKaty Milne:\u0026nbsp;\u003c/strong\u003eMethodology, Formal Analysis, Investigation. \u003cstrong\u003eMarina Wolf:\u0026nbsp;\u003c/strong\u003eMethodology, Investigation. \u003cstrong\u003eSana\u003c/strong\u003e \u003cstrong\u003eAlayoubi:\u003c/strong\u003e Investigation. \u003cstrong\u003eHimmeshwar Singh:\u0026nbsp;\u003c/strong\u003eInvestigation. \u003cstrong\u003eTrevor MacFarlane:\u0026nbsp;\u003c/strong\u003eInvestigation\u003cstrong\u003e. Chantal Di Vito:\u0026nbsp;\u003c/strong\u003eInvestigation\u003cstrong\u003e. Brad H. Nelson.\u0026nbsp;\u003c/strong\u003eResources and supervision\u003cstrong\u003e. Charles J. Walsby:\u003c/strong\u003e Resources, Supervision. \u003cstrong\u003eJessica Kalra:\u0026nbsp;\u003c/strong\u003eConceptualization, Resources, Supervision. \u003cstrong\u003eMarcel B. Bally:\u0026nbsp;\u003c/strong\u003eConceptualization, Resources, Writing \u0026ndash; review and editing, Supervision, Funding acquisition.\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interests\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Investigational Drug Program/PharmaCore at the BC Cancer Research Institute for their contribution to the \u003cem\u003ein vivo\u003c/em\u003e studies and Norman Chow for his technical assistance. Figures were created using BioRender.com.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSolomon EI, Heppner DE, Johnston EM, Ginsbach JW, Cirera J, Qayyum M et al (2014) Copper active sites in biology. Chem Rev 114:3659\u0026ndash;3853\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBertini I, Cavallaro G, McGreevy KS (2010) Cellular copper management\u0026mdash;a draft user\u0026rsquo;s guide. Coord Chem Rev 254:506\u0026ndash;524\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKramer TR, Johnson WT (2020) Copper and immunity. Nutrient Modulation of the Immune Response. CRC, pp 239\u0026ndash;254\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCamphausen K, Sproull M, Tantama S, Venditto V, Sankineni S, Scott T et al (2004) Evaluation of chelating agents as anti-angiogenic therapy through copper chelation. 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Metallomics 7:299\u0026ndash;308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/C4MT00269E\u003c/span\u003e\u003cspan address=\"10.1039/C4MT00269E\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Copper ionophores, immunogenic cell death, cuproptosis, clioquinol, diethyldithiocarbamate, damage-associated molecular patterns","lastPublishedDoi":"10.21203/rs.3.rs-7322091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7322091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCopper ionophores represent a promising therapeutic strategy for disrupting copper homeostasis in tumors and triggering immunogenic forms of cell death. However, the extent to which these agents induce immunogenic stress varies with ionophore and tumor-intrinsic factors that remain poorly defined. Here, a panel of copper-binding compounds was evaluated to determine how copper delivery, intracellular copper accumulation, and tumor copper biology influence immunogenic cell death (ICD). This study focused on colorectal cancer models with divergent copper phenotypes. The results showed that ICD marker induction, including ATP release, HMGB1 release, and calreticulin exposure, correlated strongly with intracellular copper accumulation and was significantly enhanced by the ionophore used. Using transcriptomic analysis in CT26 cells, this study provides evidence that copper ionophores activate shared ICD-associated gene signatures while also engaging distinct transcriptional programs based on the ionophore structure. In vivo, CT26 and MC38 tumors exhibited contrasting copper levels, isotopic signatures, and ICD marker responses following treatment. These findings suggest that baseline copper metabolism may influence both the magnitude and nature of immune stress responses in treated tumors. Together, the results highlight the importance of tumor copper biology and ionophore identity in shaping immunogenic outcomes and provide a framework for the rational design of copper-based therapeutics.\u003c/p\u003e","manuscriptTitle":"Immunogenic cell death in colorectal cancer models is modulated by baseline and ionophore-induced copper accumulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 14:41:02","doi":"10.21203/rs.3.rs-7322091/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2f169ba-0e18-4424-8799-c0a94f40c6d2","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T09:57:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-18 14:41:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7322091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7322091","identity":"rs-7322091","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}