High-throughput drug screening in advanced pre-clinical 3D melanoma models identifies potential first-line therapies for NRAS-mutated melanoma

High-throughput drug screening in advanced pre-clinical 3D melanoma models identifies potential first-line therapies for NRAS-mutated melanoma | 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 High-throughput drug screening in advanced pre-clinical 3D melanoma models identifies potential first-line therapies for NRAS-mutated melanoma Cristian Angeli, Demetra Philippidou, Eliane Klein, Christiane Margue, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6594118/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 Background Despite significant advances in targeted (BRAFi + MEKi) and immune (anti-PD1/PD-L1, anti-CTLA4, and anti-LAG3) therapies, treatment options for NRAS mut melanoma remain limited. Currently, NRAS mut patients rely on immune checkpoint inhibitors, classical chemotherapy, and off-label MEK inhibitors, with over 50% experiencing rapid disease progression. One of the key challenges in developing effective targeted therapies is the lack of preclinical models that accurately recapitulate the tumor microenvironment (TME) and the intrinsic resistance of melanoma cells bearing NRAS mutation. Methods To address this, we performed high-throughput screening (HTS) of over 1,300 compounds in 3D NRAS mut melanoma spheroids. A multi-step analysis was performed to identify hits, which were further tested by performing drug-response curve (DRC) analysis. Most promising compounds were further validated using mono- and co-culture 3D in vitro models that mimic three main metastatic sites in melanoma, such as skin/dermal, lung, and liver, utilizing spheroid and hydrogel systems. Ultimately, validation was conducted using zebrafish xenograft models to enable a more refined and accurate assessment of drug response. Results High-throughput drug screening of NRAS-mutant melanoma spheroids identified 17 candidate compounds, which were subsequently validated through DRC analyses. Among the most promising drugs, Daunorubicin HCl (DH) and Pyrvinium Pamoate (PP) were selected for further investigation, demonstrating potent anti-melanoma activity in advanced 3D co-culture systems and zebrafish xenograft models. Notably, PP demonstrated higher cytotoxicity compared to Trametinib, the off-label MEK inhibitor, with an inhibitory effect on AKT and invasive behavior in the primary metastatic melanoma cell lines. Additionally, combinatorial treatment with Trametinib resulted in additive effects on cell proliferation and viability. Importantly, both compounds showed similar efficacy in NRAS mut and BRAF wt /NRAS wt melanoma cell lines that were resistant to MEK inhibitors. Conclusions Using advanced 3D melanoma models that incorporate key TME elements and zebrafish xenograft models, this study highlights the potential of Daunorubicin HCl and Pyrvinium Pamoate as novel first-line therapies for NRAS mut melanoma, with a noteworthy effect also on MEKi-resistant cells. These findings support drug repurposing strategies and underscore the importance of physiologically relevant preclinical models in identifying effective therapies. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Cutaneous melanoma is an aggressive cancer with rising incidence rates ( 1 ). Its progression is largely driven by MAPK pathway activation through mutations in BRAF (~ 50%) and NRAS (~ 25%) ( 2 , 3 ). While BRAF mut melanoma patients benefit from BRAFi/MEKi and immune-checkpoint inhibitor (ICIs) therapies ( 4 , 5 ), targeting NRAS mut remains challenging ( 6 ). Current strategies focus on MAPK inhibition or alternative pathways but have limited success. ICIs are the first-line treatment for NRAS mut melanoma ( 7 ), yet response rates are poorer than in BRAF mut patients ( 8 ). With no approved targeted therapies, novel treatments are urgently needed. While cost-effective and straightforward in high-throughput screening (HTS) campaigns, two-dimensional (2D) cell culture models lack the complexity of in vivo tissues or tumors, such as complex architecture and cell-extracellular matrix (ECM) interactions, nutrient and waste exchange, or the O 2 -CO 2 gradient among others. These features are present in 3D culture systems such as spheroids, which offer a more accurate representation of tissue architecture and cell interactions, facilitating a more physiologically relevant assessment of potential therapeutic compounds ( 9 , 10 ). We have recently developed multicomponent 3D melanoma models for preclinical drug testing ( 11 ). Drug discovery via HTS traditionally requires a multi-phase process involving specialized expertise, advanced technology such as lab automation, and substantial time and economic investments, as large numbers of compounds need to be analyzed. Drug repurposing, which involves identifying new therapeutic avenues for existing or investigational drugs beyond their original indication, offers an interesting alternative to the identification of de-novo drugs, a process that is time-consuming and expensive ( 12 , 13 ). This approach reduces the risk of safety-related failures, as these drugs have already undergone safety trials, thereby potentially shortening the time required for approval. In the present study, we applied a drug repurposing approach to conduct HTS on NRAS mut melanoma cells cultured as 3D spheroids, leading to the identification of two highly effective compounds: Daunorubicin HCl and Pyrvinium Pamoate. Additionally, we evaluated the combination of these compounds with the MEKi Trametinib used off-label for NRAS mut patients. Both monotherapy and combination treatments were tested in advanced pre-clinical models, including in vitro 3D melanoma co-cultures and in vivo zebrafish models showing promising effects of the repurposed compounds for the treatment of NRAS mut melanoma patients. MATERIALS AND METHODS Cells and reagents NRAS mut human melanoma cell lines SKmel147 (Prof. Dr. Jochen Utikal, University Medical Center Mannheim, Germany), SKmel30 and MelJuso (DSMZ, Leibniz Institut, Germany), and the BRAF wt /NRAS wt human melanoma cell line WM3918 (Rockland, USA) were cultured in RPMI 1640 enriched with GlutaMAX (Gibco Thermo Fisher Scientific, USA), supplemented with 10% FCS (Fetal Calf Serum, Gibco Thermo Fisher Scientific, USA) and 0.1 mg/mL Normocin (InvivoGen, USA). Primary human melanoma cell lines M160915 and M161022 (Prof. Mitchell Levesque, University of Zurich Hospital, Switzerland) were cultured in RPMI 1640 (Gibco Thermo Fisher Scientific, USA), supplemented with 10% FCS, 1mM Sodium Pyruvate (Gibco Thermo Fisher Scientific, USA), 4mM L-Glutamine (Gibco Thermo Fisher Scientific, USA), and 0.1 mg/mL Normocin. NHDF (normal human dermal fibroblasts) (Promocell, C-12300), MRC-5 (human lung fibroblasts) (ATCC, CCL-171), and LX-2 cells (human hepatic stellate cells) (Merk, SCC064) were cultured in DMEM enriched with GlutaMAX (Gibco Thermo Fisher Scientific, USA), supplemented with 10% FCS, 2.5% HEPES buffer 1M (Gibco Thermo Fisher Scientific, USA), and 0.1 mg/mL Normocin. HMEC-1 (human endothelial cells) (ATCC, CRL-3243) were cultured in MCDB131 (Gibco Thermo Fisher Scientific, USA), supplemented with 10% FCS, 1 µg/mL Hydrocortisone (Sigma-Aldrich, USA), 10mM L-Glutamine, 0.1 mg/mL Normocin, and 10 ng/mL recombinant human EGF (PeproTech, USA). Trametinib (MEKi)-resistant SKmel30 and WM3918 cell lines were generated by continuous drug exposure of parental drug-sensitive cell lines to 5xIC50 and 1xIC50 concentrations, respectively, for approximately 3 months. The Binimetinib (MEKi)-resistant MelJuso cell line was generated by continuous drug exposure of the parental drug-sensitive cell line to 10xIC50 concentration of Binimetinib. All cell lines were transduced with Multiplicity of Infection (MOI) 3 of lentiviral vectors carrying reporter genes, for stable fluorescent protein expression. SKmel147, SKmel30, and M161022 were transduced with rLV.EF1.mCherry-9; NHDF, MRC-5 and LX-2 were transduced with pLenti-C-mGFP-P2A-Puro.; HMEC-1 were transduced with pLV-Bsd-CMV > tagBFP. After transduction, cells were subjected to antibiotic selection (either Puromycin or Blasticidin) and FACS-sorted using a BD FACSMelody™ Cell Sorter (BD Bioscences, USA). Cell growth was maintained at 37°C in a humidified atmosphere comprising 5% CO2. All cell lines were regularly examined for mycoplasma contamination. Cell Line authentication was performed at Luxgen (Luxembourg). The compound libraries Prestwick Chemical library® (PCL, Prestwick Chemicals, USA) is composed of 1267 mainly FDA-approved compounds supplied at 10mM concentration in DMSO. The in-house “Melanoma drug library” (MDL) was generated based on literature for their effect on the different melanoma genomic subtypes. It is composed of 61 compounds supplied at 10 mM concentration in DMSO, purchased from Selleckchem. Selected hit drugs were purchased individually from Prestwick Chemicals and dispensed in a specific ready-to-use source plate. For cell treatments outside the HTS workflow: Trametinib (#S2673), Daunorubicin HCl (#S3035), and Pyrvinium Pamoate (#S5816) were purchased from Selleckchem (Germany). Staurosporine (#CAYM81590-1) was purchased by Cayman Chemical (USA). 3D High-throughput screening HTS assays were performed using the HTS platform, “Disease Modelling and Screening Platform” (DMSP) of LIH/LCSB, Luxembourg. The platform is equipped with two liquid handler workstations (Biomek NXp and Biomek FXp; Beckman Coulter), two integrated incubators (Cytomat 24-C; Thermofisher), an acoustic droplet ejector (Echo 550; Labcyte), a multimode plate reader (SpectraMax i3;Molecular Devices), a confocal high-content microscope (CV8000; Yokogawa) equipped with solid lasers (wavelengths: 405/488/561 nm) and emission filters (445/45 nm, 525/50 nm, 600/37 nm), and an integrated robotic arm on rail (SCARA; Beckman Coulter). Cells were seeded in 384-well U-bottom ULA black plates (Corning®, 4516, USA) at a density of 5 x 10 3 cells/well in 20 µL/well, centrifuged at 500 x g for 5 minutes, and incubated for 72 hours at 37°C and 5% CO 2 to allow for spheroid formation. After the compounds from the PCL and MDL libraries were dispensed (one compound per well) at nanoliter range using the acoustic droplet ejector, a further 40 µL of fresh culture medium were added and spheroids were incubated for 5 days at 37°C and 5% CO 2 . Every compound was dispensed at a final concentration of either 1 µM or 10 µM, each of them in duplicate (on two separate plates), with a final DMSO concentration of 0.1%. Side wells were dedicated to a pre-selected positive control compound for the screening (Foretinib 30 µM) and negative controls (DMSO 0.1%), and the first and last rows and columns of the plate were excluded to reduce edge effects. Additional plates were added to the screen with the compounds falling into the edge effect area. To detect and quantify the spheroid response to drugs we extracted the maximum intensity projection (MIP) area for each spheroid by applying high-content image analysis (see below). The MIPs were obtained from the Calcein AM (Cayman Chemical, USA) signal, thus representing a surrogate measure of cell viability, and informing on the size or growth of spheroids exposed to the drugs. At the end of the drug treatment, Calcein AM was added 4 X concentrated as 20 µL/well to reach a final concentration of 4 µM (80 µL final volume in each well) and incubated for 2 hours at 37°C. We initially used different Calcein AM concentrations and incubation times to optimize the ratio signal-to-noise to give us the most robust signal for imaging of spheroids. Confocal images were acquired using a 10x objective, 488 nm laser 525/50 nm emission filter, Z-stack acquisition (e.g. the Z-stack consisted of 40 slices taken sequentially with 10 µm step size for a total span of 390 µm) and on-the-fly generation of MIP images mode. A mock test was run before each HTS campaign to check the quality of the cells and assay, following the same seeding and timing procedures and including a drug response performed using a 3-fold dilution series of Foretinib starting at 10 µM. Hit drug identification CellPathFinder® was used to analyze MIP images and extract the total spheroid area in each well. In brief, a segmentation mask was created on the Calcein AM green-fluorescent signal, which allowed for the calculation of the radius and area of the spheroid MIP. The software summed all the area’s segments outputting a total Calcein AM area per well (µm 2 ). The application of a statistical test (Grubb’s test), followed by visual inspection, removed outliers (such as failure of segmentation) from the set of data. The Z’-factor was calculated for each plate of the primary screening as a quality control step. The raw MIP measures were used to mathematically set a plate-specific cut-off for determining hit drugs, by applying the following formula: averageDMSO − (3 x standard_deviationDMSO) ( 25 ). Drugs were taken into consideration if the raw MIP area values were below the cut-off in both duplicate plates. Data was normalized for the corresponding DMSO controls in the plate and expressed as percentage of residual MIP. Only drugs below 50% of this residual MIP in both duplicates from the set of values below the acceptable SD cut-off, were processed into the final selection step. Finally, we visually examined the MIP images to confirm the drug's effect and rule out false positives. Additionally, we applied extra criteria, such as reviewing existing literature, to compile a final list of effective drugs. Rstudio was used for the analysis and the creation of relative plots. Drug-response curve analysis in HTS fashion for hit validation Drug-response curves (DRC) to determine the relative half-maximal inhibitory concentration (IC50) values were generated for the 17 selected hits using the same approach as described for the primary screening. Drugs, including the positive control Foretinib, were dispensed in duplicate using a 3-fold dilution series from the dedicated source plate, starting from 10µM with 10 dilutions. Cell viability was assessed using Calcein AM (as previously described). Data were normalized by the DMSO control within each plate. GraphPad 10.3.1 software (GraphPad, USA) software and non-linear regression (four parameters) analysis were used to extrapolate IC50 and R 2 values for each tested compound. 3D Mono- and Multi-component spheroid generation Mono-component spheroids were generated in 384 well ULA U-bottom plates (S-Bio®, MS-9384UZ, Japan) as follows: melanoma cells were seeded at a density of 0.5-1 × 10 3 cells/well in 80 µL of RPMI. The plate was centrifuged 500 x g for 5 minutes and incubated at 37°C and 5% CO 2 for 96 hours. Multi-component spheroids were generated as described before ( 11 ). Melanoma cells, fibroblasts or hepatic stellate cells, and endothelial cells were seeded at a cellular ratio of 1:3:3 in 384-well black/clear round bottom ultra-low attachment spheroid microplates (Corning®, 4516, USA). Melanoma cells and HMEC-1 were seeded together at densities of 0.5 × 10 3 cells/well and 1.5 × 10 3 cells/well, respectively, in 40 µL of RPMI. The plate was centrifuged 500 x g for 5 minutes and incubated. After 24 hours of incubation, either NHDF, MRC-5, or LX-2 were seeded at densities of 1.5 × 10 3 cells/well in a further 40 µL of RPMI, on top of the preformed spheroids, the plate was then centrifuged 500 x g for 5 minutes and incubated at 37°C and 5% CO 2 for 72 hours. 2D and 3D DRC and IC50 determination Generation of DRCs and determination of IC50 values of drugs in 2D tested in non-cancerous cells (NHDF, MRC-5, LX-2, and HMEC-1) were performed as follows: cells were seeded in a 96-well black plate (µClear Greiner®, Belgium) at a density of 5 × 10 3 cells/well in 100 µL of cell line-specific medium. Drugs were diluted in a 3-fold dilution series for 8 dilutions, with starting concentrations of Daunorubicin HCl and Pyrvinium Pamoate of 10 µM. Cell viability was determined with the CellTiter-Glo® 3D Cell Viability Assay (Promega, USA). Upon 5 days of treatment, a microplate reader Cytation 5 Cell Imaging Multi-Mode Reader (Agilent BioTek, USA) was used for luminescence measurements. The IC50 experiments were performed in technical and biological triplicates. Dose-response curves and IC50 values were generated with GraphPad 10.3.1 software (GraphPad, USA) and determined with the non-linear log (inhibitor) vs response-variable slope (four parameters) equation. For selected melanoma cells the determination of IC50 values of drugs tested was performed in 3D as follows: cells were seeded in 384-well U-bottom ULA plates (S-Bio®, MS-9384UZ, Japan) at densities of 0.5-1 x 10 3 cells/well in 80 µL/well, centrifuged at 500 x g for 5 minutes, and incubated for 4 days at 37°C and 5% CO 2 . Drugs were diluted in a 3-fold dilution series for 10 dilutions, with starting concentrations of Daunorubicin HCl of 10 µM and Pyrvinium Pamoate of 1 µM. Before drug and cell viability reagent were added, spheroids were visually inspected utilizing a bench-top microscope as a quality control step. After 5 days of treatment, cell viability was determined with the CellTiter-Glo® 3D Cell Viability Assay (Promega, USA). A microplate reader Cytation 5 Cell Imaging Multi-Mode Reader (Agilent BioTek, USA) was used for luminescence measurements. The IC50 experiments were performed in technical and biological triplicates. Dose-response curves and IC50 values were generated with GraphPad 10.3.1 software (GraphPad, USA) and determined with the non-linear log (inhibitor) vs response-variable slope (four parameters) equation. 3D Synergy Assay SKmel30 and SKmel147 cells were seeded at a density of 0.5 x 10 3 cells/well in 384-well ULA plates (S-Bio®, MS-9384UZ, Japan) and spheres were allowed to form for 4 days before addition of drugs. They were treated for 5 days with either single drugs or combinations of Trametinib and either Pyrvinium Pamoate or Daunorubicin HCl in a matrix format at a fixed 1:2 dilution range. Drug concentrations were pre-determined based on each inhibitor’s IC50 value. Cell viability was assessed with the CellTiter-Glo® 3D Cell Viability Assay (Promega, USA). Synergy scoring was determined using the “inhibition readout” (calculated as “100 - Cell Viability”) of the online SynergyFinder software version 3.0 ( https://synergyfinder.fimm.fi ) and implementing the ZIP calculation method, as published before ( 26 ). Zero Interaction Potency (ZIP) scores 10 correspond to antagonist and synergistic effects, respectively. 3D proliferation kinetic and end-point assay Kinetic (time-lapse microscopy) cell proliferation and endpoint cell viability, under drug treatments, were evaluated as described before ( 11 ). In brief, either mono- or -multicomponent spheroids were generated as previously described using labeled cells to allow the tracking of the different cell types. After spheroid generation, 40 µL medium were removed from each well and replaced with 40 µL medium supplemented with 2 times concentrated compounds and controls. The plate was centrifuged at 500 x g for 5 minutes and placed in an incubator (BioSpa8, Agilent BioTek, USA) connected to an automated live-cell imaging system (Cytation 10, Agilent BioTek, USA). Images were acquired every 12 hours for 5 days using a 10X magnification objective and 590 nm LED and a Texas Red filter cube (Excitation 586/15 nm, Emission 647/57 nm) to track melanoma fluorescence signal over time. On day 5, spheroid cell viability was determined using the CellTiter-Glo® 3D Cell Viability Assay (Promega, USA). A microplate reader Cytation 5 Cell Imaging Multi-Mode Reader (Agilent BioTek, USA) was used for luminescence measurements. Kinetic and end-point cell proliferation data were analyzed and plotted with GraphPad 10.3.1 software (GraphPad, USA). Confocal microscopy of 3D multi-component spheroids Confocal images of 3D multi-component spheroids were acquired using the Cytation 10 (Agilent BioTek, USA) confocal microscope with spinning disk technology. The instrument is equipped with a laser combiner (spectral range 398–643 nm) and a DAPI filter cube (Excitation 390/40 nm, Emission 442/42 nm), a GFP filter cube (Excitation 472/ 30 nm, Emission 520/35 nm), and a TRITC filter cube (Excitation 556/20 nm, Emission 600/37 nm). Pictures were acquired using a 20x magnification objective. 3D apoptosis and cell death assays using confocal microscopy Melanoma cells were seeded in 384-well black U-bottom ULA microplates (Corning®, USA) at densities of 0.5 x 10 3 cells/well in 80 µL/well of medium, centrifuged at 500 x g for 5 minutes, and incubated for 2 days at 37°C and 5% CO2. Upon removal of 40 µL/well of medium, drugs were dispensed 2 times concentrated in 40 µL/well of medium, centrifuged at 500 x g for 5 minutes, and incubated for 5 days at 37°C and 5% CO2. The positive control, Staurosporine at 1µM concentration was added 24 hours previous the end of the assay, for strong induction of apoptosis and cell death. CellEvent™ Caspase-3/7 Detection Reagent (Invitrogen, Thermo, USA) and SYTOX™ Blue Dead Cell Stain (Invitrogen, Thermo, USA) were added and incubated at 37°C for at least 2 hours. Cytation 10 was used to acquire multiple images in z-stacking using DAPI, GFP, and TRITC filter cubes and a 20X magnification objective. Brightfield pictures were also acquired at 20x magnification. Maximum intensity projected (MIP) images were generated using Gen5 (Agilent BioTek, USA). For mCherry-expressing melanoma cell lines, the mCherry signal was used to visualize the total spheroid mass, while for non-labeled melanoma cells, brightfield images were used to visualize the total spheroid mass. 3D invasion assay Melanoma cell lines SKmel147 and M160915 were seeded in ultra-low attachment BIOFLOAT™ 96-well plates (Facellitate, Germany) in densities of 2,5 x 10 3 and 5 x 10 3 , respectively. After 3 days of spheroid formation, they were embedded between two layers of collagen type I, containing 2mg/ml Collagen type I (MercMillipore, Germany), 1% FCS (Gibco Thermo Fisher Scientific, Waltham, USA) in RPMI (Gibco Thermo Fisher Scientific, USA). The pH of the collagen solution was adjusted to 7.4 using 1M NaOH. 50 µl per well of collagen I solution was pipetted into an optically clear, black-walled 96-well plate (µClear Greiner®, Belgium) and left to polymerize for 5 minutes at 37°C. Next, one spheroid per well was transferred on top of the collagen layer and immediately covered with 50 µL of collagen solution and polymerized for 15 minutes at 37°C. Next, 100 µl of medium containing either 0,5% DMSO (negative control) or 2 times IC50 concentration of the drug was added on top of the collagen layer. For each experimental condition, 8 spheroids were used. Pictures were taken on day 0 (immediately after embedding) and after 3 days collagen embedding, using Cytation 10 (Agilent BioTek, USA) manual imaging mode and 4x magnification. The area of cellular invasion was analyzed using ImageJ software (Fiji). Statistical analysis was performed using GraphPad 10.3.1 software (GraphPad, USA). Western blot analysis Cells were seeded in 6-well Aggrewell plates (StemCell, USA) at densities of 0.5-1 x 10 3 cells/well in 5 mL of medium, centrifuged at 100 x g for 5 minutes, and incubated for 4 days. Drugs were dispensed and cells were incubated for 3 and 5 days. Cell lysis was performed on ice with cold lysis buffer (RIPA 1X containing cOmplete phosphatase inhibitor, Roche, Switzerland), protein concentration was determined using Pierce™ BCA Protein Assay Kit (Thermo, USA), and protein lysates were further analyzed by SDS-PAGE ad Western Blot. The detection of enhanced chemiluminescence signals was performed as previously described ( 27 ). Primary antibodies used in the study were: GAPDH (1:5000, polyclonal, #G9545, Rabbit, Sigma, USA), ERK (1:1000, Rabbit, L34F12, #CST4696S, CellSignaling, USA), pERK (1:1000, Rabbit, D13.14.4E, #CST4370S, CellSignalling, USA), AKT (1:1000, Mouse, 4OD4, #CST2920S, CellSignalling, USA), pAKT (Ser473) (1:1000, Rabbit, D9E, #CST4060S, CellSignalling, USA). All primary and HRP-conjugated secondary antibodies were purchased from Cell Signalling Technology (Boston, USA). Hydrogel-embedded melanoma co-culture Melanoma-TME hydrogel encapsulation co-cultures were generated using transglutaminase cross-linkable poly(ethylene glycol) (PEG) hydrogels previously described ( 28 ). A ready-to-use kit consisting of frozen aliquots of the 3% PEG precursor solution (8-arm 40kDa PEG macromers bioconjugate with RGD adhesion and MMP-cleavable peptide motives) and of the activated Human Factor XIII (FXIIIa) were purchased (Ectica Technologies, Switzerland). The cell suspension was created by mixing mCherry-expressing melanoma cells at a density of 2–4 x 10 4 /100µL with HMEC-1 expressing BFP at a density of 20 x 10 4 /100µL, and with either NHDF, or MRC-5, or LX-2 expressing GFP a density of 20 x 10 4 /100µL, centrifuged at 300 x g for 3 minutes and supernatant was removed, and 45 µL of complete RPMI were added. Afterwards, 43 µL of PEG precursor solutions were added and gently mixed to dissolve the cellular pellet. Then, 12 µL of FXIIIa was added, and the solution was gently mixed without introducing bubbles. 5 µL of solution was dispensed in each well in a black 96-well plate (µClear Greiner®, Belgium) to create homogeneous domes and incubated at RT for 5 minutes until to reach polymerization. 200 µL/well of RPMI supplemented with 10ng/mL of VEGF (Peprotech, USA) was dispensed in each well and incubated for 3 days at 37°C and 5% CO2. 2 times concentrated drugs were added in 100µL/well of fresh medium, upon removal of 100 µL/well of the old medium, and further incubated for 5 days at 37°C and 5% CO2. Confocal microscope Cytation 10 (Agilent, BioTek, USA) was used to acquire multiple images in z-stack modality using DAPI, GFP, and TRITC filter cubes and 20X magnification object, selecting 4 ROIs per well. Maximum intensity projected images were analyzed using ImageJ (Fiji). Zebrafish husbandry, determination of maximum tolerated concentrations of drugs, and xenografts Zebrafish experiments were performed in two different institutions, the Zebrafish Facility of the University of Padova (under Italian Ministry of Health Authorization n. 1111/2024-PR (OPBA prot. D2784.185)) and the Aquatic Platform of the Luxembourg Centre for Systems Biomedicine at the University of Luxembourg (RRID:SCR_025429), in collaboration with Professor Natascia Tiso and Dr. Maria Lorena Cordero-Maldonado, respectively. Adult nacre zebrafish lines were housed in each facility according to standard protocols ( 29 , 30 ). Embryos were obtained by natural spawning and reared until the experiments at 2 dpf in E3 medium at 28° C. First, to determine the maximum tolerated concentration (MTC) of the drugs to be tested in the xenografts (Trametinib, DH and PP), we first treated non-injected naïve 2 dpf nacre larvae with serial dilutions of drugs of interest to determine the highest tolerated and non-toxic concentration until 5 dpf. Larvae viability and development were monitored daily during drug treatment. The cut-off of 20% mortality and no developmental defect was set to determine the MTC. Second, for the performance of the cell transplantations, on the day of the injections, the 2 dpf embryos were manually dechorionated and anesthetized with buffered tricaine (80 mg/l, Sigma-Aldrich). SKmel147-mCherry and MelJuso-RES-mCherry cell lines were detached using phenol red-free TryplE reagent (Gibco Thermo Fisher Scientific) and resuspended in PBS at a concentration of 2 x 10 5 cells/ µL. The cells were injected into the yolk as a single droplet (around 100 cells per embryo) using a World Precision Instrument (Sarasota, USA) or FemtoJet 4X (Eppendorf, Germany) microinjectors. PBS with phenol red was injected as a vehicle control. After 24h, the larvae were fluorescently assessed for successful cell implantation and subjected to drug treatment with 12 nM Trametinib, 1 µM DH, 111 nM of PP, and their combinations for 3 days at 32°C. Larvae viability was monitored daily. After 3 days, larvae were anesthetized as described above, and photos of xenografts were taken using an M165 FC microscope with DFC7000T camera (Leica Camera, Germany) or Nikon SMZ25 fluorescent stereomicroscope (Nikon Instruments, Japan). Data was analyzed based on fluorescence intensity to measure xenograft area and number of cells using the “Measurements” tool of the Volocity 6.0 software (Perkin Elmer, Italy). Statistical Analysis All experiments represent at least 3 biological replicates. Statistical analysis was performed using GraphPad 10.3.1 software (GraphPad, USA). The Gaussian distribution of data was assessed with Shapiro-Wilk normality test. Data following Gaussian distribution was analyzed using Ordinary one-way ANOVA with Dunett’s multiple comparison test. Data not following Gaussian distribution was analyzed using ordinary Kruskal-Wallis with Dunn’s multiple comparison test. One sample t-test was used to analyze data expressed as a percentage of the untreated control (normalized to 100%). RESULTS High-throughput drug screening and dose-response assays identify promising novel compounds for NRAS mut melanoma We evaluated the effects of two drug libraries, the commercial Prestwick Chemical Library® (1267 compounds) and an in-house Melanoma Drug Library (61 compounds) selected based on literature and previous data, on SKmel147 NRAS mut melanoma cells cultured as 3D spheroids. Drugs were tested at 10 µM and 1 µM to minimize off-target effects. A HTS workflow was developed using a fully automated platform (Fig. 1 A), with individual drugs dispensed in a specific plate layout (Fig. 1 B). The negative control was 0.1% DMSO, while Foretinib at a concentration of 30 µM was used as the positive control for inducing cell death. To account for the edge effect, additional plates were included to test drugs dispensed onto spheroids located in edge-affected wells. Cell viability was assessed via Calcein AM signal-based spheroid area segmentation and area analysis (Fig. 1 C). Standard quality control (QC) parameters, including Z’-factor (> 0.5) (Fig. 1 D) and coefficient of variation (< 10%) (Fig. 1 E), were assessed across all plates, ensuring robust data for hit identification along our HTS campaign. Hits identification involved multiple steps (Fig. 2 A). First, a statistical model analyzed the Maximum Intensity Projection (MIP) spheroid area values that were obtained by automated image segmentation using the Calcein AM signal. Only the measurements that deviated by at least three standard deviations from the mean of the DMSO control (cut-off) ( 14 ) in both duplicates were considered for the next selection step (see Materials & Methods for details) (Fig. 2 B). Next, all spheroid MIP data were normalized to the average MIP measured in spheroids treated with DMSO. The hits were selected based on values below 50% of residual MIP, following exposure to drugs. The spheroid shrinkage effects were confirmed by manual visual inspection, and additional information, such as FDA status and pathway relevance, was reviewed. Seventeen promising drugs (Table 1 ) were selected based on well-established roles in targeting pathways critical to melanoma progression (Fig. 2 C), including DNA synthesis and damage, epigenetic regulation, and apoptosis. Table 1 Important features of the 17 selected hit drugs. target, FDA status, therapeutic effect and targeted pathway. Highlighted are Daunorubicin HCl (brown) and Pyrvinium Pamoate (purple), subsequently selected for further validations Compound Target FDA approved Therapeutic effect Targeted pathway AZD6738 ATR NO Antineoplastic DNA synthesis Camptothecine Topoisomerase I NO Antineoplastic DNA synthesis CHIR-124 CHK1 NO Antineoplastic Cell Cycle Cladribine Ribonucleotide reductase YES Antineoplastic DNA synthesis Daunorubicin HCl Topoisomerase II YES Antibacterial DNA synthesis Entinostat HDAC NO Antineoplastic Epigenetic Epirubicin HCl Topoisomerase II YES Antineoplastic DNA synthesis Irinotecan HCl Trihydrate Topoisomerase I YES Antineoplastic DNA synthesis Lanatoside C Plasma membrane Na+/K + ATPase NO Cardiotonic Molecular Pump Obatoclax Bcl-2 NO Antineoplastic Apoptosis PD0325901 MEK NO Antineoplastic MAPK Proscillaridin A Plasma membrane Na+/K + ATPase NO Antiarrhythmics Molecular Pump Pyrvinium Pamoate CK1α YES Anthelmintic WNT TAK-733 MEK NO Antineoplastic MAPK Topotecan Topoisomerase I YES Antineoplastic DNA synthesis Ulixertinib ERK NO Antineoplastic MAPK XL888 HSP90 NO Antineoplastic Epigenetic Notable compounds included topoisomerase inhibitors and poisons that target DNA stability (such as Daunorubicin hydrochloride), a checkpoint kinase 1 (CHK1) inhibitor (CHIR-124), and epigenetic modulators like Entinostat (HDAC inhibitor), for example. Additionally, heat-shock protein 90 (HSP90) and BCL-2 inhibitors (e.g. XL888 and Obatoclax) were selected alongside cardiac glycosides (such as Lanatoside C, Proscillaridin A), inhibiting the Na+/K + ATPase pump, which represent an emerging therapeutic target in cancer ( 14 , 15 ). MAPK pathway inhibitors (such as PD0325901, TAK-733, or Ulixertinib) and casein kinase 1 α (CK1α) agonist Pyrvinium Pamoate, targeting the WNT-β-Catenin pathway, were also included. Validation of hits was performed via high-throughput dose-response curve (DRC) analysis (Supplementary Fig. 1A). DRC generation confirmed the inhibitory effects of individual drugs, displaying IC50 values ranging from 2 nM to > 1 µM, with only a few compounds unable to derive IC50 values. Compounds targeting DNA stability (such as Cladribine, Topotecan, Irinotecan, and Daunorubicin HCl) showed IC50 values below 50 nM, demonstrating strong effect of the compounds as well as indicating the sensitivity of our HTS assay. Other efficient compounds included Lanatoside C, Proscillaridin A, Pyrvinium Pamoate, XL888, and PD0325901, and generated IC50 values below 200 nM. Surprisingly, potent MAPK inhibitors (such as TAK-733 and Ulixertinib) or cell cycle inhibitors (CHIR-124) exhibited limited inhibitory effects. Collectively, these HTS data identified promising compounds for further validation as potential candidates for the treatment of NRAS mut melanoma. NRAS mut melanoma cells are sensitive to Daunorubicin HCl and Pyrvinium Pamoate Further investigation primarily focused on two compounds, DH and PP, as potential first-line treatments for NRAS mut melanoma. In addition to their strong inhibitory effects on SKmel147 melanoma cells observed during the HTS and DRC campaign, DH was selected from the major targeted pathway category, ‘DNA synthesis’, while PP was chosen for its notable impact on the WNT-β-Catenin pathway, which plays a critical role in melanoma progression ( 16 , 17 ). Furthermore, both compounds were selected based on their FDA approval status, underscoring a drug repurposing approach. Both drugs showed strong efficacy across four treatment-naive melanoma cell lines cultured as spheroids (SKmel147, SKmel30, M160915) or 3D aggregates (M161022) (Fig. 2 D-E). Despite their FDA-approved status, additional testing on non-cancerous cells (Supplementary Fig. 1B-C) revealed higher IC50 values for PP compared to melanoma cells (“Dermal Fibroblasts”-NHDF = 998 nM; “Lung Fibroblasts”-MRC5 = 252.3 nM; “Hepatic Stellate Cells”-LX-2 = 209.9 nM; “Endothelial Cells”-HMEC-1 = 505.6 nM), indicating therapeutic safety. Meanwhile LX-2 and HMEC-1 cell lines displayed sensitivity to DH compared to PP (Supplementary Fig. 1D-E). In conclusion, DH and PP demonstrated very good activity against NRAS mut melanoma cells grown as spheroids with minimal effects on most of the non-cancerous cells, underscoring their potential for drug screening and repurposing in melanoma therapy. Daunorubicin HCl and Pyrvinium Pamoate inhibit proliferation and viability of NRAS mut melanoma cells cultured in 3D To further elucidate the action of DH and PP on NRAS mut melanoma cells, proliferation assay was performed using time-lapse microscopy by tracking over time the proliferation of three NRAS mut melanoma cell lines, cultured as spheroids and constitutively expressing the mCherry fluorescent protein. The MEKi Trametinib (T) and Staurosporine (STAU), a well-characterized apoptosis inducer, served as positive controls. The fluorescent signal emitted by melanoma cells was used to follow spheroid sizes dynamically, enabling the evaluation of the compounds’ effect on cellular proliferation. Previously determined IC50 values for DH, PP, and Trametinib were used to assess the drug efficacy across the different assays. PP and Trametinib induced a pronounced reduction in melanoma spheroid/3D aggregate proliferation across all tested cell lines (Fig. 3 A, Supplementary Fig. 2A & 3A). DH exhibited a similar inhibitory effect in SKmel147 and M161022 cells (Fig. 3 A, Supplementary Fig. 3A), however, with weaker effects on SKmel30 cells (Supplementary Fig. 2A). This reduced efficacy of DH on SKmel30 may be explained by the cell line’s unique capacity to respond to DNA damage, possibly due to a TP53 gene deletion (Cellosaurus SK-MEL-30; CVCL_0039). All 3 cell lines demonstrated significant reduction in cell viability of DH and PP-treated spheroids/aggregates compared to untreated controls (Fig. 3 B, Supplementary Fig. 2B & 3B). Consistent with the proliferation assay results, SKmel30 exhibited a reduced sensitivity to DH, although the reduction in viability remained significant (Supplementary Fig. 2B). Interestingly, in M161022 cells, DH and PP showed an even more pronounced inhibitory effect than Trametinib (Supplementary Fig. 3B). Drug effects were also evaluated in NRAS mut melanoma cells embedded in a hydrogel matrix, to mimic the extracellular matrix (ECM) within the tumor microenvironment. Consistent with observations from scaffold-free spheroid and 3D aggregate cultures, DH and PP significantly inhibited the growth of SKmel147 (Supplementary Fig. 4A) and of the primary cell line M161022 (Supplementary Fig. 4B). Collectively, these findings demonstrate that DH and PP effectively suppress proliferation and reduce cell viability in a panel of NRAS mut melanoma cell lines cultured under various 3D conditions. Pyrvinium Pamoate has a strong cytotoxic effect on NRAS mut melanoma spheroids Next, we evaluated whether the arrest of spheroid growth in response to DH or PP exposure was caused by cytotoxic effects of these compounds. After 5 days of treatment, spheroids and 3D aggregates were stained to assess the levels of activated executioner caspases 3 and 7 (CellEvent Caspase 3/7) and dead cells (SytoxBlue). Additionally, the expression of mCherry fluorescent protein in the transduced cells was used to identify viable tumor mass. The level of apoptosis and cell death was linked to the tumor mass of the spheroid or cell aggregate on day 5 of treatment. Intrinsic apoptosis and cell death were observed in the inner core of untreated (UT) spheroids, aligning with the innate in vivo of 3D tumor formations ( 18 ). Trametinib and DH exhibited low levels of cytotoxicity in SKmel147 (Fig. 3 C) and SKmel30 cells (Supplementary Fig. 2C), suggesting a predominantly cytostatic effect. In contrast, PP induced apoptosis and cell death significantly in both cell lines, also indicated by a markedly reduced mCherry signal. Notably, in SKmel30, this reduction was even more pronounced than with STAU treatment (Supplementary Fig. 2C). Primary metastatic melanoma cell lines exhibited similar cytotoxic responses across all treatments; with PP resulting in the greatest tumor mass reduction in spheroids and aggregates (Fig. 3 C, Supplementary Fig. 3C & Fig. 5 A). In conclusion, our findings demonstrate that PP exerts a potent cytotoxic effect in a panel of established and primary metastatic treatment-naive NRAS mut melanoma cell lines cultured under 3D conditions. We evaluated the effects of the drugs on key proliferation and survival pathways in NRAS mut melanoma, specifically ERK (MAPK pathway) and AKT (AKT pathway). ERK levels remained unchanged up to 5 days in SKmel147 (Fig. 3 D-E) and SKmel30 cells (Supplementary Fig. 2D-E) across all treatments, indicating that DH and PP do not interfere with ERK expression. Similar findings were observed in the primary melanoma cell lines M161022 (Supplementary Fig. 3D-E) and M160915 (Supplementary Fig. 5D-E). However, 5 days of PP treatment significantly affected the survival of primary melanoma cells, resulting in insufficient lysate collection, underscoring the strong cytotoxic effect of PP in these cells. As expected, phosphorylated ERK (pERK; active form) was reduced by Trametinib in all cell lines (Fig. 3 F, Supplementary Fig. 2F & 3F & 5F), consistent with its high specificity for MEK inhibition. However, treatment with DH and PP did not consistently alter pERK levels. For example, PP inhibited pERK in a time-dependent manner in SKmel147 (Fig. 3 F) and M161022 (Supplementary Fig. 3F), but not in SKmel30 (Supplementary Fig. 2F) or M160915 (Supplementary Fig. 5F). Neither Trametinib nor DH altered basal AKT expression across all cell lines, and phosphorylated AKT (pAKT, Ser473) exhibited inconsistent regulation in response to the drugs (Fig. 3 G, Supplementary Fig. 2G & 3G & Fig. 5 G). Notably, PP treatment consistently reduced total AKT levels in SKmel147 (Fig. 3 D-E), M161022 (Supplementary Fig. 3D-E), and M160915 (Supplementary Fig. 5D-E). Due to this strong reduction in AKT expression, pAKT quantification was not feasible. In contrast, SKmel30 displayed a different response, with an increase in pAKT levels following PP treatment (Supplementary Fig. 2D-E). Overall, PP exerts an inhibitory effect on AKT protein levels, predominantly in primary metastatic melanoma cell lines. Daunorubicin HCl and Pyrvinium Pamoate inhibit primary melanoma cell invasion The inhibitory effects of DH, PP, and Trametinib on the invasive abilities of melanoma cells were evaluated in SKmel147 and M160916 spheroids embedded in a type I Collagen matrix after 3 days of drug treatment. SKmel30 and M161022 were excluded from this assay due to non-invasive phenotype and failure to form compact spheroids, respectively. In SKmel147, significant invasion inhibition occurred only with Trametinib, whereas DH and PP led to only minor reductions in cell motility (Supplementary Fig. 6A). In contrast, in the primary melanoma cell line M160916, all three compounds significantly suppressed invasive activity (Supplementary Fig. 6B). Although PP and DH did not reduce invasion in the established SKmel147 cell line, both compounds effectively inhibited the invasion of the primary metastatic melanoma cell line. Combinatorial treatment with Trametinib (MEKi) shows additive effects After assessing the effect of DH and PP as monotherapy on a panel of NRAS mut melanoma cell lines, we explored their potential in combined treatments with Trametinib. This strategy was prompted by the well-studied ability of melanoma to develop resistance to monotherapies ( 19 , 20 ). We performed a 3D synergy assay on spheroids of 2 NRAS mut melanoma cell lines, SKmel147 and SKmel30, to investigate potential synergistic effects between Trametinib and DH, and Trametinib and PP (Supplementary Fig. 7A). The range of concentrations of compounds (1:2 dilution ratio) were selected based on cell line-specific IC50 concentrations previously generated. Zero interaction potency (ZIP) synergy score analysis did not reveal overall synergism (defined as ZIP synergy score > 10); however, an additive effect (defined as ZIP synergy score >-10 and < 10) was consistently observed across all conditions. For the following experiments, we selected drug concentrations determining the regions of maximum synergistic effects: Trametinib at 0.06 nM and DH and PP at 45 nM. We subsequently assessed the impact of these combinations on SKmel147 and SKmel30 spheroid proliferation and cell viability in parallel with single synergy concentration treatments. Despite using drug concentrations belonging to the region with the highest synergism, the results remained additive, showing only a slight reduction in proliferation (Supplementary Fig. 7B-D) and cell viability (Supplementary Fig. 7C-E) compared to the respective single treatments in both cell lines. The drug combinations exhibited good effects on SKmel147 cell proliferation (Supplementary Fig. 7B) and spheroid viability (Supplementary Fig. 7C) but did not affect SKmel30 growth (Supplementary Fig. 7D-E), consistent with previous observations on the intrinsic resistance of this cell line. Based on these results, we further concentrated on the characterization of the monotherapies (based on the cell line-specific IC50 values) using advanced in vitro pre-clinical models. Advanced in vitro 3D co-culture models reveal melanoma-specific effects and low toxicity of Daunorubicin HCl and Pyrvinium Pamoate The role of non-cancerous cells in the tumor microenvironment (TME) in supporting cancer survival and drug resistance is well established ( 10 , 21 , 22 ). Co-culture models are valuable for assessing drug efficacy by capturing cancer cell–TME interactions and evaluating toxicity on non-cancerous cells. Using our previously established Multicomponent Melanoma Spheroid (MMS) models ( 11 ), which mimic key metastatic sites such as “skin/dermal” (HMEC-1 + NHDF), “lung” (HMEC-1 + MRC-5), and “liver” (HMEC-1 + LX-2), we assessed the effects of DH and PP. mCherry fluorescent labeling allowed real-time visualization of cell populations by extracting the residual MIP spheroid area. Time-lapse microscopy showed reduced SKmel147 proliferation in all MMS models compared to untreated controls (Fig. 4 A-C). To a similar extent, DH, PP, and Trametinib reduced total co-culture viability (Fig. 4 D-F), with 30–40% residual viability attributed to melanoma while TME normal cells survived (Fig. 4 A-C). SKmel30 showed similar proliferation inhibition in the “dermal” (Supplementary Fig. 8A) and “lung” (Supplementary Fig. 8B) models but less in the “liver” models (Supplementary Fig. 8C) compared to monocomponent spheroids (Supplementary Fig. 2A). In line with the monocomponent data, DH had a weaker inhibitory effect on SKmel30 proliferation than Trametinib and PP. Corresponding viability assays showed significant reductions, with DH presenting the lowest efficacy compared to Trametinib and PP (Supplementary Fig. 8D-F), with residual viability due to melanoma and TME cells (Supplementary Fig. 8A-C). We next investigated the effects of DH and PP in complex models incorporating an extracellular matrix (ECM), a critical factor in melanoma progression and drug resistance ( 23 , 24 ). Using hydrogel-embedded melanoma-TME co-culture models, we evaluated the efficacy of these drugs alongside Trametinib while also assessing their effects on non-cancerous cells. Fluorescent labeling enabled clear visualization of melanoma and TME cell populations. In these models, SKmel147 showed significant growth reductions across all conditions (Fig. 4 G-J). In the “dermal” (Fig. 4 H) and “lung” (Fig. 4 I) models, all three drugs reduced the melanoma population by over 50% compared to controls. The “liver” (Fig. 4 J) model revealed increased sensitivity of SKmel147 to Trametinib, compared to the other models, and to the monoculture (Supplementary Fig. 4A). Interestingly, DH and especially PP significantly inhibited M161022 growth across all three models (Supplementary Fig. 9A-D), in line with what was observed in monoculture (Supplementary Fig. 4B). The survival of non-cancerous TME cells was also evaluated. Importantly, drug concentrations effective against melanoma cells had a low impact on the survival of non-cancerous cells in co-culture, highlighting the potent melanoma inhibitory effects and safety profile of these compounds. MEK inhibitor-resistant melanoma cells are sensitive to Daunorubicin HCl and Pyrvinium Pamoate Given melanoma's rapid development of resistance to current therapies, we have generated NRAS mut (SKmel30) and WT (WM3918) melanoma cell lines resistant to Trametinib (Tres) and evaluated the efficacy of DH and PP in resistant cells. Of note, SKmel147 has not developed resistance, even after prolonged drug exposure (approximately 6 months), illustrating the high heterogeneity between melanoma cells and has therefore not been included in the following experiments. DRCs of sensitive and resistant cell lines cultured as spheroids and relative IC50 values were generated for DH, PP, and Trametinib after 5 days of treatment. Resistance to Trametinib was confirmed by increased IC50 values in both SKmel30-Tres (Fig. 5 A) and WM3918-Tres cells (Supplementary Fig. 10A) in comparison to the sensitive counterparts. A 3D apoptosis/cell death assay further showed the low cytotoxic effect of Trametinib in sensitive SKmel30 cells (Fig. 5 D), consistent with previous findings (Supplementary Fig. 2C), as well as in SKmel30-Tres cells (Fig. 5 E). A similar effect was observed in WM3918-Tres cells (Supplementary Fig. 10E), whereas Trametinib treatment exhibited cytotoxicity on sensitive WM3918 cells (Supplementary Fig. 10D). Although DH appeared to be more effective in SKmel30-Tres cells compared to their sensitive counterparts, as indicated by a lower IC50 value (Fig. 5 B), it did not induce substantial levels of apoptosis or cell death after 5 days of treatment (Fig. 5 E). In contrast, WM3918 cells displayed a significant increase in sensitivity to DH, as demonstrated by a reduction in IC50 values in WM3918-Tres cells (116.6 nM) compared to their sensitive counterparts (743.6 nM) (Supplementary Fig. 10B). This was further supported by the strong cytotoxic effect of DH observed in both WM3918 sensitive (Supplementary Fig. 10D) and WM3918-Tres cells (Supplementary Fig. 10E). Consistent with previous findings, PP induced the high levels of apoptosis and cell death following 5 days of treatment in both sensitive SKmel30 cells (Fig. 5 D) and SKmel30-Tres cells (Fig. 5 E) compared to Trametinib and DH. Images revealed a reduced spheroid size and lower cytotoxic activity of PP in SKmel30-Tres cells compared to SKmel30 sensitive cells, which was further corroborated by IC50 values, where SKmel30-Tres cells exhibited a higher IC50 value (66.2 nM) than SKmel30 sensitive cells (23 nM) (Fig. 5 C). Despite comparable growth inhibition of WM3918 sensitive and WM3918-Tres cells by PP (Supplementary Fig. 10C), PP exhibited a pronounced cytotoxic effect in WM3918-Tres cells (Supplementary Fig. 10E), which was not observed in their sensitive counterparts (Supplementary Fig. 10D). These findings suggest that DH and PP hold potential as second-line therapeutic agents for targeting melanoma cells that have developed resistance to targeted therapies such as MEKi. Daunorubicin HCl and Pyrvinium Pamoate show strong inhibitory effects in zebrafish xenografts melanoma models To assess the in vivo efficacy of the drugs, SKmel147-mCherry and MelJuso-RES-mCherry cell lines were injected into the yolks of 2-day post-fertilization nacre zebrafish larvae. As SKmel30 Tres cells used in in vitro experiments failed to form proper tumors post-injections, MelJuso-RES-mCherry has been chosen to represent resistant melanoma phenotype. At 24 hours post-injection, larvae were randomized into groups of 30–40 individuals and treated with the drugs as monotherapy or in combination treatments at doses previously established as the maximum tolerated dose. After 72 hours of treatment, larval survival, metastasis status and xenograft size were evaluated. Despite the highly invasive and motile phenotype of the melanoma cells, no increase in migration from the initial injection site was detected (Fig. 6 A-B). Consistent with in vitro findings, a significant reduction in xenograft area was observed in both sensitive and resistant cell lines. Notably, this effect was evident not only in monotherapy groups (Trametinib, DH, and PP) but also in combination treatments (Fig. 6 C-D). The number of injected cells in the untreated control remained stable over the 72-hour period, whereas a significant reduction was observed in the treated groups, indicating strong cytotoxic effects of the tested drugs in vivo (Fig. 6 E-F). While no increased mortality was noted in MelJuso-RES-mCherry injected larvae, some mortality was observed in SKmel147 injected larvae, particularly in groups treated with PP and its combinations, despite the doses being within the previously established safe range for larval survival and development (Fig. 6 G-H). DISCUSSION Systemic therapy for melanoma has advanced significantly, with targeted and immune therapies primarily benefiting BRAF mut patients ( 31 ). While NRAS mut melanoma patients rely on ICIs as a first-line treatment, with response rates below 50% ( 4 , 7 ), and off-label MEKi, like Trametinib, as a second-line treatment if ICI must be discontinued. Hence, additional novel therapeutic options for NRAS mut patients are urgently needed. To address this, we conducted high-throughput screening (HTS) to evaluate more than 1300 compounds. Unlike traditional HTS on adherent cell cultures, we used 3D melanoma spheroids to improve physiological relevance and mimic patient tumor responses more accurately ( 32 ). Hit identification followed a rigorous multi-step process, which led to the identification of 17 promising compounds with strong inhibitory effects on NRAS mut melanoma cells in 3D spheroids, warranting their further investigation as potential melanoma therapies. A limitation of this study is the use of only one cell line for screening. However, given the increased complexity of the 3D culture model, using a single cell line served as a practical and reasonable foundation for identifying potential candidate compounds. Among the identified hits, DH and PP emerged as the most promising candidates. DH, an anthracycline antibiotic ( 33 ), is approved for acute myeloid leukemia (AML) ( 34 , 35 ). It acts as a topoisomerase II (TOP2) poison, inducing DNA damage and apoptosis ( 34 , 36 , 37 ). In melanoma, Mu et al. demonstrated that TOP2α is significantly overexpressed compared to benign nevi ( 38 ). PP is a cyanine dye approved as an anthelmintic drug ( 39 ). Its mechanism of action is not yet fully elucidated, however some studies on cancer describe PP as a CK1α agonist that promotes β-Catenin degradation ( 39 , 40 ), a mechanism also observed in uveal melanoma ( 41 ). DH and PP were tested in dose-response assays in NRAS mut melanoma cell lines, showing high efficacy in 3D spheroid cultures. Both demonstrated a favorable safety profile in non-cancerous cells, particularly PP, consistent with their FDA approval status. Despite significant viability reduction, their effects appeared largely cytostatic, as proliferation decreased but remained above baseline. All melanoma cell lines responded similarly, except SKmel30, which exhibited greater resistance to DH, likely due to a TP53 deletion (Cellosaurus.org). This aligns with findings that mutant p53 reduces the efficacy of TOP1 and TOP2 inhibitors ( 42 ). Furthermore, Dunsche et al. demonstrated that a rare TP53 mutation (R285K) confers increased resistance to cisplatin treatment in metastatic melanoma cells. This resistance can be overcome through ferroptosis induction ( 43 ). On the other hand, we showed that PP induced apoptosis and cell death across all cell lines. In contrast, Trametinib and DH triggered lower cytotoxic effects, particularly in SKmel30, which exhibited general resistance, as observed in the Staurosporine-treated spheroids. Apoptosis and cell death were evaluated after five days of treatment; therefore, DH and T may have induced cytotoxicity at an earlier time point or activated alternative programmed cell death pathways, such as necroptosis ( 44 , 45 ). However, translational cancer therapies primarily depend on prolonged treatment regimens rather than short-term interventions. Mechanistically, we focused on the deregulation of key pathways involved in NRAS mut melanoma survival, which are often rewired in response to targeted therapy treatments, leading to drug resistance. These pathways include the MAPK and AKT pathways ( 46 – 48 ). As expected, Trametinib induced a general reduction in ERK activation due to its inhibitory action on MEK, the upstream kinase of ERK. In contrast, DH exhibited inconsistent effects on the deregulation of pERK and pAKT across the cell lines, suggesting the involvement of other major targets. Notably, PP reduced AKT levels in SKmel147 and primary cell lines, positioning it as a potential AKT regulator. PP's role in AKT inhibition in cancer has been reported ( 49 – 51 ), also in uveal melanoma ( 41 ). Given PP’s apoptotic effects, AKT may contribute to this by downregulating anti-apoptotic molecules like BCL-2 and BCL-xL and affecting mitochondrial stability ( 48 , 52 ). Conversely, PP increased pAKT in SKmel30, potentially upregulating β-catenin via GSK3β inhibition, warranting further investigation. Overall, additional studies are needed to fully understand DH and PP’s effects on melanoma. We evaluated DH and PP for their ability to inhibit cell invasion using spheroids embedded in a type I collagen matrix. Trametinib suppressed invasion in both melanoma cell lines, contrasting a study by Vultur et al., who found that MEKi increased motility in metastatic but not non-metastatic melanoma ( 53 ). Notably, DH and PP significantly reduced invasive behavior only in the primary metastatic cell line (M160915), suggesting their effects may be phenotype-specific in NRAS mut melanoma. To evaluate compound effects within the tumor microenvironment, DH and PP were tested in co-culture systems, including Melanoma Multicomponent Spheroids (MMS) ( 11 ) and a hydrogel-based melanoma-TME system. NRAS mut melanoma cell lines were co-cultured with endothelial cells and fibroblasts (NHDF for skin/dermal, MRC-5 for lung, LX-2 for liver) and treated with Trametinib, DH, and PP at IC50 concentrations. While SKmel147 and SKmel30 responded similarly to treatments in skin/dermal and lung models, SKmel30 exhibited increased resistance to PP in the liver model. A more advanced 3D hydrogel system revealed that DH and PP selectively inhibited melanoma cell growth with minimal impact on non-cancerous cells. This effect was stronger than Trametinib in the M161022 model. Notably, SKmel147 responded differently in the liver model, highlighting treatment response variations based on metastatic sites. This aligns with Forschner et al.'s findings that response to targeted therapies and immune checkpoint inhibitors varies by metastatic site ( 54 ). It is well known that melanoma develops resistance to treatments in the majority of cases, leading to relapses and leaving patients, especially NRAS mut , without efficient treatment options. To overcome this, we evaluated DH and PP in MEKi-resistant NRAS mut and BRAF wt /NRAS wt melanoma cell lines. Importantly, PP effectively inhibited MEKi-resistant melanoma growth, while DH showed an even greater effect than in non-resistant cells. Although preliminary, our results support the potential of both compounds as a second- or third-line treatment for MEKi-resistant patients, and we are currently following this up by generating more melanoma cell lines resistant to targeted therapies and eventually to ICIs. Final validation of DH and PP anti-melanoma efficacy was performed in a zebrafish xenograft model. Recently, such models have been established and proved to be valid for melanoma development, drug screening and resistance mechanism in vivo studies. It provides a relevant physiological background and offers ethical advantages over mice adhering to the 3R principles (replacement, reduction, refinement) ( 55 – 57 ). This validation confirmed DH and PP anti-tumor effects in sensitive and MEKi-resistant NRAS mut melanoma models with low toxicity, enhancing their translational effect. Overall, given its role in AKT inhibition, apoptosis, and cell death induction, PP stands as a promising first-line therapy for NRAS mut melanoma, warranting further clinical investigation. One key challenge is PP’s current tablet formulation, which limits systemic absorption. However, Esumi et al. reported intestinal absorption of PP, leading to reduced pancreatic tumor size in mice ( 58 ). Furthermore, we hypothesize that DH could be effectively utilized in an immunotherapy-rechallenging setting, given its potential as a chemotherapeutic anthracycline compound capable of inducing immunogenic cell death ( 44 ), thereby enhancing the anti-cancer immune response ( 59 ). This hypothesis is further supported by the findings of Gebhardt et al., who demonstrated that low doses of paclitaxel increased the presence of functional cytotoxic T-cells while reducing tumor-suppressive MDSCs (myeloid-derived suppressor cells), ultimately resensitizing patients resistant to immune checkpoint inhibitors ( 60 ). CONCLUSIONS In this study, we performed high-throughput drug screening using 3D NRAS mut melanoma spheroid cultures, improving the translational relevance of drug responses over traditional 2D models. Among the over 1300 compounds tested, Daunorubicin HCl and Pyrvinium Pamoate emerged as effective against NRAS mut melanoma, demonstrating growth inhibition in advanced 3D in vitro models and zebrafish xenografts, paving the way for their potential consideration either alone or in combination with other therapies. Finally, we also introduced that the strategic combination of repurposed, clinically approved drugs with advanced human 3D models holds considerable potential to both accelerate the drug discovery process and improve drug approval success rates. Abbreviations NRAS Neuroblastoma RAS viral oncogene homolog BRAF V-Raf Murine Sarcoma Viral Oncogene Homolog B WT Wild Type TME Tumor Microenvironment ECM Extracellular Matrix HTS High-Throughput Screening MIP Maximum Intensity Projection ULA Ultra Low Attachment ZIP Zero Interaction Potency DRC Drug-Response Curve IC50 Half-maximal inhibitory concentration MMS Melanoma Multicomponent Spheroid DH Daunorubicin HCl PP Pyrvinium Pamoate MEK Mitogen-activated protein kinase kinase ERK Extracellular signal-regulated kinase AKT Protein Kinase B or PKB or Ak strain transforming Declarations Acknowledgments We thank Prof. Dagmar Kulms (University of Dresden) for her valuable discussions on 3D models and cell death, and Prof. Mitchell Levesque (University of Zurich) for sharing M161022 and M160915 cell lines. We thank Dr. Benjamin Simona (ECTICA) and Dr. Riccardo Urbanet (ECTICA) for their valuable discussions on 3D models and hydrogel optimization. We thank the staff of the LCSB Aquatic Platform, particularly the animal care technicians, for their valuable daily work and support of zebrafish experiments. Consent for publication All the authors read and approved the final manuscript. Conflict of interest statement The authors declare no conflict of interest. Author contributions CA, DP, EK, JW, SG, and CM designed and performed the cell-based experiments. CA established the hydrogel-embedded co-culture models, designed, and performed the experiments utilizing these models. FI contributed to data analysis. MC and BS performed and acquired the HTS data. JW, LCM, NT, and GR established the zebrafish models, designed, and performed the experiments. CA, JW, CM, and SK wrote, edited, and reviewed the manuscript. SK conceived the project and supervised the research. Ethic statement All primary melanoma cell lines derived from metastatic melanoma tumors (fresh or slow frozen) were obtained from the repository of Prof. Mitchell Levesque of the Department of Dermatology, University Hospital Zurich, Switzerland. The repository was established for the approved clinical study: BASEC: 2018-02050, 2018-02052, 2019-01326. The Zebrafish Facility at the University of Padova holds the authorization 407/2015-PR (OPBA), and the Zebrafish Core Facility at the Luxembourg Centre for Systems Biomedicine at the University of Luxembourg is registered as an authorized breeder, supplier, and user of zebrafish with Grand-Ducal Decree of 26 January 2023. All practices involving zebrafish complied with the European Legislation for the Protection of Animals used for Scientific Purposes (Directive 2010/63/EU) and following the principles of the 3Rs. Funding Statement This work was supported by ‘‘MelCol’’ and ‘‘MultiMel’’ grants from the Vera Nijs & Jens Erik Rosborg Foundation (FVNER) under the aegis of the Fondation de Luxembourg and the ‘‘SecMelPro’’ grant from the Fondation Cancer, Luxembourg. Data availability statement The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Uncropped Western Blots are available as Supplementary Data. References Long GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet. 2023;402(10400):485–502. Shain AH, Bastian BC. From melanocytes to melanomas. Nature Reviews Cancer. Volume 16. Nature Publishing Group; 2016. pp. 345–58. Akbani R, Akdemir KC, Aksoy BA, Albert M, Ally A, Amin SB et al. Genomic Classification of Cutaneous Melanoma. Cell [Internet]. 2015;161(7):1681–96. 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A chemical screen in zebrafish embryonic cells establishes that Akt activation is required for neural crest development. Elife. 2017;6. Esumi H, Lu J, Kurashima Y, Hanaoka T. Antitumor activity of pyrvinium pamoate, 6-(dimethylamino)‐2‐[2‐(2,5‐dimethyl‐1‐phenyl‐1 H ‐pyrrol‐3‐yl)ethenyl]‐1‐methyl‐quinolinium pamoate salt, showing preferential cytotoxicity during glucose starvation. Cancer Sci. 2004;95(8):685–90. Haggerty TJ, Dunn IS, Rose LB, Newton EE, Martin S, Riley JL, et al. Topoisomerase inhibitors modulate expression of melanocytic antigens and enhance T cell recognition of tumor cells. Cancer Immunol Immunother. 2011;60(1):133–44. Gebhardt C, Simon SCS, Weber R, Gries M, Mun DH, Reinhard R et al. Potential therapeutic effect of low-dose paclitaxel in melanoma patients resistant to immune checkpoint blockade: A pilot study. Cell Immunol. 2021;360. Additional Declarations No competing interests reported. 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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-6594118","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453784103,"identity":"7095194c-eba9-4931-9fd8-02a3e3f95f77","order_by":0,"name":"Cristian Angeli","email":"","orcid":"","institution":"University of Luxembourg","correspondingAuthor":false,"prefix":"","firstName":"Cristian","middleName":"","lastName":"Angeli","suffix":""},{"id":453784104,"identity":"d304883c-0e0b-45b1-bfcd-b842aab049a6","order_by":1,"name":"Demetra Philippidou","email":"","orcid":"","institution":"University of 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11:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6594118/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6594118/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82758886,"identity":"cb56e17e-849f-4d9c-bcbd-e9be7a5c0936","added_by":"auto","created_at":"2025-05-15 02:24:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":629442,"visible":true,"origin":"","legend":"\u003cp\u003eHits identification involved multiple steps (Fig. 2A). First, a statistical model analyzed the Maximum Intensity Projection (MIP) spheroid area values that were obtained by automated image segmentation using the Calcein AM signal. Only the measurements that deviated by at least three standard deviations from the mean of the DMSO control (cut-off) (14) in both duplicates were considered for the next selection step (see Materials \u0026amp; Methods for details) (Fig. 2B). Next, all spheroid MIP data were normalized to the average MIP measured in spheroids treated with DMSO. The hits were selected based on values below 50% of residual MIP, following exposure to drugs. The spheroid shrinkage effects were confirmed by manual visual inspection, and additional information, such as FDA status and pathway relevance, was reviewed.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6594118/v1/e5d42cd13c882475b2449695.png"},{"id":82758885,"identity":"4e2b36eb-71c6-4598-996d-bd11edd16d48","added_by":"auto","created_at":"2025-05-15 02:24:14","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":455238,"visible":true,"origin":"","legend":"\u003cp\u003eA multistep selection process identifies novel compounds for potential treatment of NRASmut melanoma. A) Schematic representation of the multistep process of drug selection for subsequent drug-response curve (DRC) validation. DMSO-3STD: deviation of at least 3 standard deviations from the mean of DMSO control. B) Scatter plots (left panel) represent the total MIP Calcein AM area of a series of compounds screened. The upper panel represents the first replicate, and the lower panel represents the second replicate. The red dashed line indicates the plate-specific cut-off: only compounds that were below the cut-off in both replicates were selected (compound 1 here). Right panel: confocal images of melanoma spheroids treated with either compound 1 or 2 in both replicates. C) The pie chart represents the pathways targeted by the 17 selected hit compounds. D and E) Drug-response curves of Daunorubicin HCl and Pyrvinium Pamoate generated on four NRASmut melanoma cell lines (SKmel147, SKmel30, M160915 and M161022), utilizing CellTiter-Glo® 3D Cell Viability Assay as readout after 5 days of treatment. Reported IC50 values in the tables are mean (±SD) of 3 independent biological replicates.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6594118/v1/1c89aa1d551bc044da873729.jpeg"},{"id":82758888,"identity":"11a389a3-60ad-430e-8bba-2fc2b261d457","added_by":"auto","created_at":"2025-05-15 02:24:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":777777,"visible":true,"origin":"","legend":"\u003cp\u003eDaunorubicin HCl and Pyrvinium Pamoate induce inhibition of proliferation and viability and trigger cytotoxic effects in SKmel147 spheroids. A) Proliferation of SKmel147-mCherry spheroids treated for 5 days with indicated drugs. Fluorescent images depicting the spheroid area were acquired every 12 hours. (n=3, mean±SD). B) Cell viability of SKmel147-mCherry spheroids was assessed after 5 days of drug treatment. Data are normalized to untreated control. Staurosporine was used as positive control at 200 nM in A and B. One sample T-test was used in B for statistical significance testing. (n=3, mean±SD; **p≤0.01, ***p≤0.001, ****p≤0.0001) C) Representative pictures of apoptosis and cell death detection in SKmel147-mCherry spheroids that were treated with different compounds for 5 days. Staurosporine was used as positive control at 1µM. Apoptosis (green) and cell death (blue) were measured upon the addition of the CellEvent Caspase-3/7 and Sytox Blue detection reagents, respectively. Confocal images (20x magnification) of single spheroids are shown. Scale bar = 200 µm (n=3). D) Western blot of whole cell lysates from SKmel147 spheroids treated for 3 and 5 days with either Daunorubicin HCl (DH), Pyrvinium Pamoate (PP) or Trametinib (T). E) Quantification of the total AKT and ERK protein levels in SKmel147, normalized to GAPDH and to untreated control. F-G) pERK/ERK and pAKT/AKT ratios respectively in SKmel147 spheroids. GAPDH was used as a loading control; representative blots of three biological replicates are shown. Cell-line specific IC50 drug concentrations were used for spheroid stimulations in the different assays. UT: untreated, STAU: Staurosporine.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6594118/v1/6dad383dc1b52f86aa911d9c.png"},{"id":82759330,"identity":"086b81e0-0866-4649-97bb-2fdae070bc8c","added_by":"auto","created_at":"2025-05-15 02:32:14","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1332150,"visible":true,"origin":"","legend":"\u003cp\u003eSKmel147-TME co-culture models show inhibitory melanoma-specific effect and low toxicity on non-cancerous cells. A-C) Upper panels: Kinetic response of SKmel147-mCherry co-cultures to 5-day drug treatment in 3 different Melanoma Multicomponent Spheroid (MMS) models: “Skin/Dermal” (A), “Lung” (B), and Liver” (C). Images of mCherry fluorescence were acquired every 12 hours. The spheroid area was determined and plotted. Lower panels: corresponding confocal images (20x magnification) of the different cell populations after 5 days of drug treatment. Scale bar = 200 µm. (n=3. mean±SD) D-F) Cell viability of the 3 SKmel147-MMS models after 5 days of drug treatment. Data are normalized to untreated control. Staurosporine was used as positive control at 200 nM in A-F. (n=3. mean±SD). G) Representative confocal pictures (20x magnification) of 3 hydrogel-embedded SKmel147-TME co-culture models (“Dermal”, “Lung”, and Liver”) after 5-day treatment: SKmel147-mCherry (red), NHDF/MRC-5/LX-2 (green), HMEC-1 (blue). Scale bar = 200 µm. H-J) Plots representing the percentage of fluorescent area of the different cell populations in the 3 hydrogel co-culture models. Data are normalized to the untreated control of each specific cell population. One sample T-test was used for H-J for statistical significance testing (n=3. mean±SD; *p≤0.05, **p≤0.01). Melanoma cell line-specific IC50 concentrations of Daunorubicin HCl (DH), Pyrvinium Pamoate (PP) and Trametinib (T) were used.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6594118/v1/0d161660935479770af2da5a.jpeg"},{"id":82759333,"identity":"cb62b432-57bf-4abb-9035-47aaa8a9ed8b","added_by":"auto","created_at":"2025-05-15 02:32:15","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1166193,"visible":true,"origin":"","legend":"\u003cp\u003eDaunorubicin HCl and Pyrvinium Pamoate exert inhibitory and cytotoxic effect on MEKi-resistant NRASmut melanoma cell lines. A-C) Representative drug response curves for Trametinib (MEKi) (A), Daunorubicin HCl (B) and Pyrvinium Pamoate (C) in Trametinib-sensitive (green) and -resistant (red) SKmel30 cells cultured as spheroids, utilizing CellTiter-Glo® 3D Cell Viability Assay as readout after 5 days of treatment. Reported IC50 values in the tables are mean±SD of 3 independent biological replicates. Tres: Trametinib-resistant. D-E) Representative photos of apoptosis and cell death detection in SKmel30 (D) and SKmel30 T-res (E) spheroids after 5 days of treatment. Staurosporine was used as positive control at 1µM. Apoptosis (green) and cell death (blue) were measured upon the addition of the CellEvent Caspase-3/7 and Sytox Blue detection reagents, respectively. Confocal images (20x magnification) of single spheroids are shown. Scale bar = 200 µm (n=3).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6594118/v1/6eaf2a6e186123ebc91df27d.jpeg"},{"id":82758896,"identity":"51f5f858-74a1-4709-88b6-dd18ee0af3de","added_by":"auto","created_at":"2025-05-15 02:24:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":716864,"visible":true,"origin":"","legend":"\u003cp\u003eDaunorubicin HCl and Pyrvinium Pamoate show strong melanoma inhibitory effects in zebrafish xenografts models. Left panel: SKmel147-mCherry xenografts. Right panel: MelJuso-RES-mCherry xenografts. Skmel147-mCherry (A) and MelJuso-RES-mCherry cells (B) were injected into the yolk of 2dpf zebrafish and subjected to mono- or combinatory treatments. Scale bar: 500 µm. The xenograft area (C-D) and the number of cells per xenograft (E-F) were evaluated after 3 days of treatment based on the mCherry signal by two independent investigators. Graphs represent the mean ± SD of normalized data. Statistical significance was assessed with Shapiro-Wilk normality test followed by Kruskal Wallis test with Dunn’s multiple comparisons:\u0026nbsp; ∗p \u0026lt;0.05; ∗∗p \u0026lt; 0.01; ∗∗∗p \u0026lt; 0.001; ∗∗∗∗p \u0026lt; 0.00001.\u0026nbsp; (G-H) Larvae viability was monitored daily over the course of the treatment. MelJuso-RES-mCherry cells: MEKi-resistant NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cells.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6594118/v1/61bc4cd271caaeaf244540d5.png"},{"id":83363538,"identity":"aa08e510-4523-4723-8f72-6d6aba6e8ae2","added_by":"auto","created_at":"2025-05-23 18:01:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6564943,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6594118/v1/14e646b2-58a1-40ad-b087-404e5f32057e.pdf"},{"id":82758903,"identity":"36dfe023-1f0a-463e-9b1d-f696fd2b9bdc","added_by":"auto","created_at":"2025-05-15 02:24:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":9853296,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1Angelietal05May2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-6594118/v1/aa524b9df28608e844269d47.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"High-throughput drug screening in advanced pre-clinical 3D melanoma models identifies potential first-line therapies for NRAS-mutated melanoma","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCutaneous melanoma is an aggressive cancer with rising incidence rates (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Its progression is largely driven by MAPK pathway activation through mutations in BRAF (~\u0026thinsp;50%) and NRAS (~\u0026thinsp;25%) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). While BRAF\u003csup\u003emut\u003c/sup\u003e melanoma patients benefit from BRAFi/MEKi and immune-checkpoint inhibitor (ICIs) therapies (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), targeting NRAS\u003csup\u003emut\u003c/sup\u003e remains challenging (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Current strategies focus on MAPK inhibition or alternative pathways but have limited success. ICIs are the first-line treatment for NRAS\u003csup\u003emut\u003c/sup\u003e melanoma (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), yet response rates are poorer than in BRAF\u003csup\u003emut\u003c/sup\u003e patients (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). With no approved targeted therapies, novel treatments are urgently needed.\u003c/p\u003e \u003cp\u003eWhile cost-effective and straightforward in high-throughput screening (HTS) campaigns, two-dimensional (2D) cell culture models lack the complexity of \u003cem\u003ein vivo\u003c/em\u003e tissues or tumors, such as complex architecture and cell-extracellular matrix (ECM) interactions, nutrient and waste exchange, or the O\u003csub\u003e2\u003c/sub\u003e-CO\u003csub\u003e2\u003c/sub\u003e gradient among others. These features are present in 3D culture systems such as spheroids, which offer a more accurate representation of tissue architecture and cell interactions, facilitating a more physiologically relevant assessment of potential therapeutic compounds (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). We have recently developed multicomponent 3D melanoma models for preclinical drug testing (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Drug discovery via HTS traditionally requires a multi-phase process involving specialized expertise, advanced technology such as lab automation, and substantial time and economic investments, as large numbers of compounds need to be analyzed. Drug repurposing, which involves identifying new therapeutic avenues for existing or investigational drugs beyond their original indication, offers an interesting alternative to the identification of de-novo drugs, a process that is time-consuming and expensive (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This approach reduces the risk of safety-related failures, as these drugs have already undergone safety trials, thereby potentially shortening the time required for approval.\u003c/p\u003e \u003cp\u003eIn the present study, we applied a drug repurposing approach to conduct HTS on NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cells cultured as 3D spheroids, leading to the identification of two highly effective compounds: Daunorubicin HCl and Pyrvinium Pamoate. Additionally, we evaluated the combination of these compounds with the MEKi Trametinib used off-label for NRAS\u003csup\u003emut\u003c/sup\u003e patients. Both monotherapy and combination treatments were tested in advanced pre-clinical models, including \u003cem\u003ein vitro\u003c/em\u003e 3D melanoma co-cultures and \u003cem\u003ein vivo\u003c/em\u003e zebrafish models showing promising effects of the repurposed compounds for the treatment of NRAS\u003csup\u003emut\u003c/sup\u003e melanoma patients.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCells and reagents\u003c/h2\u003e \u003cp\u003eNRAS\u003csup\u003emut\u003c/sup\u003e human melanoma cell lines SKmel147 (Prof. Dr. Jochen Utikal, University Medical Center Mannheim, Germany), SKmel30 and MelJuso (DSMZ, Leibniz Institut, Germany), and the BRAF\u003csup\u003ewt\u003c/sup\u003e/NRAS\u003csup\u003ewt\u003c/sup\u003e human melanoma cell line WM3918 (Rockland, USA) were cultured in RPMI 1640 enriched with GlutaMAX (Gibco Thermo Fisher Scientific, USA), supplemented with 10% FCS (Fetal Calf Serum, Gibco Thermo Fisher Scientific, USA) and 0.1 mg/mL Normocin (InvivoGen, USA). Primary human melanoma cell lines M160915 and M161022 (Prof. Mitchell Levesque, University of Zurich Hospital, Switzerland) were cultured in RPMI 1640 (Gibco Thermo Fisher Scientific, USA), supplemented with 10% FCS, 1mM Sodium Pyruvate (Gibco Thermo Fisher Scientific, USA), 4mM L-Glutamine (Gibco Thermo Fisher Scientific, USA), and 0.1 mg/mL Normocin. NHDF (normal human dermal fibroblasts) (Promocell, C-12300), MRC-5 (human lung fibroblasts) (ATCC, CCL-171), and LX-2 cells (human hepatic stellate cells) (Merk, SCC064) were cultured in DMEM enriched with GlutaMAX (Gibco Thermo Fisher Scientific, USA), supplemented with 10% FCS, 2.5% HEPES buffer 1M (Gibco Thermo Fisher Scientific, USA), and 0.1 mg/mL Normocin. HMEC-1 (human endothelial cells) (ATCC, CRL-3243) were cultured in MCDB131 (Gibco Thermo Fisher Scientific, USA), supplemented with 10% FCS, 1 \u0026micro;g/mL Hydrocortisone (Sigma-Aldrich, USA), 10mM L-Glutamine, 0.1 mg/mL Normocin, and 10 ng/mL recombinant human EGF (PeproTech, USA). Trametinib (MEKi)-resistant SKmel30 and WM3918 cell lines were generated by continuous drug exposure of parental drug-sensitive cell lines to 5xIC50 and 1xIC50 concentrations, respectively, for approximately 3 months. The Binimetinib (MEKi)-resistant MelJuso cell line was generated by continuous drug exposure of the parental drug-sensitive cell line to 10xIC50 concentration of Binimetinib. All cell lines were transduced with Multiplicity of Infection (MOI) 3 of lentiviral vectors carrying reporter genes, for stable fluorescent protein expression. SKmel147, SKmel30, and M161022 were transduced with rLV.EF1.mCherry-9; NHDF, MRC-5 and LX-2 were transduced with pLenti-C-mGFP-P2A-Puro.; HMEC-1 were transduced with pLV-Bsd-CMV\u0026thinsp;\u0026gt;\u0026thinsp;tagBFP. After transduction, cells were subjected to antibiotic selection (either Puromycin or Blasticidin) and FACS-sorted using a BD FACSMelody\u0026trade; Cell Sorter (BD Bioscences, USA). Cell growth was maintained at 37\u0026deg;C in a humidified atmosphere comprising 5% CO2. All cell lines were regularly examined for mycoplasma contamination. Cell Line authentication was performed at Luxgen (Luxembourg).\u003c/p\u003e \u003cp\u003eThe compound libraries Prestwick Chemical library\u0026reg; (PCL, Prestwick Chemicals, USA) is composed of 1267 mainly FDA-approved compounds supplied at 10mM concentration in DMSO. The in-house \u0026ldquo;Melanoma drug library\u0026rdquo; (MDL) was generated based on literature for their effect on the different melanoma genomic subtypes. It is composed of 61 compounds supplied at 10 mM concentration in DMSO, purchased from Selleckchem. Selected hit drugs were purchased individually from Prestwick Chemicals and dispensed in a specific ready-to-use source plate. For cell treatments outside the HTS workflow: Trametinib (#S2673), Daunorubicin HCl (#S3035), and Pyrvinium Pamoate (#S5816) were purchased from Selleckchem (Germany). Staurosporine (#CAYM81590-1) was purchased by Cayman Chemical (USA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3D High-throughput screening\u003c/h3\u003e\n\u003cp\u003eHTS assays were performed using the HTS platform, \u0026ldquo;Disease Modelling and Screening Platform\u0026rdquo; (DMSP) of LIH/LCSB, Luxembourg. The platform is equipped with two liquid handler workstations (Biomek NXp and Biomek FXp; Beckman Coulter), two integrated incubators (Cytomat 24-C; Thermofisher), an acoustic droplet ejector (Echo 550; Labcyte), a multimode plate reader (SpectraMax i3;Molecular Devices), a confocal high-content microscope (CV8000; Yokogawa) equipped with solid lasers (wavelengths: 405/488/561 nm) and emission filters (445/45 nm, 525/50 nm, 600/37 nm), and an integrated robotic arm on rail (SCARA; Beckman Coulter). Cells were seeded in 384-well U-bottom ULA black plates (Corning\u0026reg;, 4516, USA) at a density of 5 x 10\u003csup\u003e3\u003c/sup\u003e cells/well in 20 \u0026micro;L/well, centrifuged at 500 x g for 5 minutes, and incubated for 72 hours at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e to allow for spheroid formation. After the compounds from the PCL and MDL libraries were dispensed (one compound per well) at nanoliter range using the acoustic droplet ejector, a further 40 \u0026micro;L of fresh culture medium were added and spheroids were incubated for 5 days at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. Every compound was dispensed at a final concentration of either 1 \u0026micro;M or 10 \u0026micro;M, each of them in duplicate (on two separate plates), with a final DMSO concentration of 0.1%. Side wells were dedicated to a pre-selected positive control compound for the screening (Foretinib 30 \u0026micro;M) and negative controls (DMSO 0.1%), and the first and last rows and columns of the plate were excluded to reduce edge effects. Additional plates were added to the screen with the compounds falling into the edge effect area. To detect and quantify the spheroid response to drugs we extracted the maximum intensity projection (MIP) area for each spheroid by applying high-content image analysis (see below). The MIPs were obtained from the Calcein AM (Cayman Chemical, USA) signal, thus representing a surrogate measure of cell viability, and informing on the size or growth of spheroids exposed to the drugs. At the end of the drug treatment, Calcein AM was added 4 X concentrated as 20 \u0026micro;L/well to reach a final concentration of 4 \u0026micro;M (80 \u0026micro;L final volume in each well) and incubated for 2 hours at 37\u0026deg;C. We initially used different Calcein AM concentrations and incubation times to optimize the ratio signal-to-noise to give us the most robust signal for imaging of spheroids. Confocal images were acquired using a 10x objective, 488 nm laser 525/50 nm emission filter, Z-stack acquisition (e.g. the Z-stack consisted of 40 slices taken sequentially with 10 \u0026micro;m step size for a total span of 390 \u0026micro;m) and on-the-fly generation of MIP images mode. A mock test was run before each HTS campaign to check the quality of the cells and assay, following the same seeding and timing procedures and including a drug response performed using a 3-fold dilution series of Foretinib starting at 10 \u0026micro;M.\u003c/p\u003e\n\u003ch3\u003eHit drug identification\u003c/h3\u003e\n\u003cp\u003eCellPathFinder\u0026reg; was used to analyze MIP images and extract the total spheroid area in each well. In brief, a segmentation mask was created on the Calcein AM green-fluorescent signal, which allowed for the calculation of the radius and area of the spheroid MIP. The software summed all the area\u0026rsquo;s segments outputting a total Calcein AM area per well (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e). The application of a statistical test (Grubb\u0026rsquo;s test), followed by visual inspection, removed outliers (such as failure of segmentation) from the set of data. The Z\u0026rsquo;-factor was calculated for each plate of the primary screening as a quality control step. The raw MIP measures were used to mathematically set a plate-specific cut-off for determining hit drugs, by applying the following formula: averageDMSO \u0026minus;\u0026thinsp;(3 x standard_deviationDMSO) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Drugs were taken into consideration if the raw MIP area values were below the cut-off in both duplicate plates. Data was normalized for the corresponding DMSO controls in the plate and expressed as percentage of residual MIP. Only drugs below 50% of this residual MIP in both duplicates from the set of values below the acceptable SD cut-off, were processed into the final selection step. Finally, we visually examined the MIP images to confirm the drug's effect and rule out false positives. Additionally, we applied extra criteria, such as reviewing existing literature, to compile a final list of effective drugs. Rstudio was used for the analysis and the creation of relative plots.\u003c/p\u003e\n\u003ch3\u003eDrug-response curve analysis in HTS fashion for hit validation\u003c/h3\u003e\n\u003cp\u003eDrug-response curves (DRC) to determine the relative half-maximal inhibitory concentration (IC50) values were generated for the 17 selected hits using the same approach as described for the primary screening. Drugs, including the positive control Foretinib, were dispensed in duplicate using a 3-fold dilution series from the dedicated source plate, starting from 10\u0026micro;M with 10 dilutions. Cell viability was assessed using Calcein AM (as previously described). Data were normalized by the DMSO control within each plate. GraphPad 10.3.1 software (GraphPad, USA) software and non-linear regression (four parameters) analysis were used to extrapolate IC50 and R\u003csup\u003e2\u003c/sup\u003e values for each tested compound.\u003c/p\u003e\n\u003ch3\u003e3D Mono- and Multi-component spheroid generation\u003c/h3\u003e\n\u003cp\u003eMono-component spheroids were generated in 384 well ULA U-bottom plates (S-Bio\u0026reg;, MS-9384UZ, Japan) as follows: melanoma cells were seeded at a density of 0.5-1 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well in 80 \u0026micro;L of RPMI. The plate was centrifuged 500 \u003cem\u003ex g\u003c/em\u003e for 5 minutes and incubated at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e for 96 hours.\u003c/p\u003e \u003cp\u003eMulti-component spheroids were generated as described before (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Melanoma cells, fibroblasts or hepatic stellate cells, and endothelial cells were seeded at a cellular ratio of 1:3:3 in 384-well black/clear round bottom ultra-low attachment spheroid microplates (Corning\u0026reg;, 4516, USA). Melanoma cells and HMEC-1 were seeded together at densities of 0.5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well and 1.5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well, respectively, in 40 \u0026micro;L of RPMI. The plate was centrifuged 500 \u003cem\u003ex g\u003c/em\u003e for 5 minutes and incubated. After 24 hours of incubation, either NHDF, MRC-5, or LX-2 were seeded at densities of 1.5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well in a further 40 \u0026micro;L of RPMI, on top of the preformed spheroids, the plate was then centrifuged 500 \u003cem\u003ex g\u003c/em\u003e for 5 minutes and incubated at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e for 72 hours.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2D and 3D DRC and IC50 determination\u003c/h2\u003e \u003cp\u003eGeneration of DRCs and determination of IC50 values of drugs in 2D tested in non-cancerous cells (NHDF, MRC-5, LX-2, and HMEC-1) were performed as follows: cells were seeded in a 96-well black plate (\u0026micro;Clear Greiner\u0026reg;, Belgium) at a density of 5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well in 100 \u0026micro;L of cell line-specific medium. Drugs were diluted in a 3-fold dilution series for 8 dilutions, with starting concentrations of Daunorubicin HCl and Pyrvinium Pamoate of 10 \u0026micro;M. Cell viability was determined with the CellTiter-Glo\u0026reg; 3D Cell Viability Assay (Promega, USA). Upon 5 days of treatment, a microplate reader Cytation 5 Cell Imaging Multi-Mode Reader (Agilent BioTek, USA) was used for luminescence measurements. The IC50 experiments were performed in technical and biological triplicates. Dose-response curves and IC50 values were generated with GraphPad 10.3.1 software (GraphPad, USA) and determined with the non-linear log (inhibitor) vs response-variable slope (four parameters) equation. For selected melanoma cells the determination of IC50 values of drugs tested was performed in 3D as follows: cells were seeded in 384-well U-bottom ULA plates (S-Bio\u0026reg;, MS-9384UZ, Japan) at densities of 0.5-1 x 10\u003csup\u003e3\u003c/sup\u003e cells/well in 80 \u0026micro;L/well, centrifuged at 500 \u003cem\u003ex g\u003c/em\u003e for 5 minutes, and incubated for 4 days at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. Drugs were diluted in a 3-fold dilution series for 10 dilutions, with starting concentrations of Daunorubicin HCl of 10 \u0026micro;M and Pyrvinium Pamoate of 1 \u0026micro;M. Before drug and cell viability reagent were added, spheroids were visually inspected utilizing a bench-top microscope as a quality control step. After 5 days of treatment, cell viability was determined with the CellTiter-Glo\u0026reg; 3D Cell Viability Assay (Promega, USA). A microplate reader Cytation 5 Cell Imaging Multi-Mode Reader (Agilent BioTek, USA) was used for luminescence measurements. The IC50 experiments were performed in technical and biological triplicates. Dose-response curves and IC50 values were generated with GraphPad 10.3.1 software (GraphPad, USA) and determined with the non-linear log (inhibitor) vs response-variable slope (four parameters) equation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3D Synergy Assay\u003c/h3\u003e\n\u003cp\u003eSKmel30 and SKmel147 cells were seeded at a density of 0.5 x 10\u003csup\u003e3\u003c/sup\u003e cells/well in 384-well ULA plates (S-Bio\u0026reg;, MS-9384UZ, Japan) and spheres were allowed to form for 4 days before addition of drugs. They were treated for 5 days with either single drugs or combinations of Trametinib and either Pyrvinium Pamoate or Daunorubicin HCl in a matrix format at a fixed 1:2 dilution range. Drug concentrations were pre-determined based on each inhibitor\u0026rsquo;s IC50 value. Cell viability was assessed with the CellTiter-Glo\u0026reg; 3D Cell Viability Assay (Promega, USA). Synergy scoring was determined using the \u0026ldquo;inhibition readout\u0026rdquo; (calculated as \u0026ldquo;100 - Cell Viability\u0026rdquo;) of the online SynergyFinder software version 3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://synergyfinder.fimm.fi\u003c/span\u003e\u003cspan address=\"https://synergyfinder.fimm.fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and implementing the ZIP calculation method, as published before (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Zero Interaction Potency (ZIP) scores\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;10 and \u0026gt;\u0026thinsp;10 correspond to antagonist and synergistic effects, respectively.\u003c/p\u003e\n\u003ch3\u003e3D proliferation kinetic and end-point assay\u003c/h3\u003e\n\u003cp\u003eKinetic (time-lapse microscopy) cell proliferation and endpoint cell viability, under drug treatments, were evaluated as described before (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In brief, either mono- or -multicomponent spheroids were generated as previously described using labeled cells to allow the tracking of the different cell types. After spheroid generation, 40 \u0026micro;L medium were removed from each well and replaced with 40 \u0026micro;L medium supplemented with 2 times concentrated compounds and controls. The plate was centrifuged at 500 x g for 5 minutes and placed in an incubator (BioSpa8, Agilent BioTek, USA) connected to an automated live-cell imaging system (Cytation 10, Agilent BioTek, USA). Images were acquired every 12 hours for 5 days using a 10X magnification objective and 590 nm LED and a Texas Red filter cube (Excitation 586/15 nm, Emission 647/57 nm) to track melanoma fluorescence signal over time. On day 5, spheroid cell viability was determined using the CellTiter-Glo\u0026reg; 3D Cell Viability Assay (Promega, USA). A microplate reader Cytation 5 Cell Imaging Multi-Mode Reader (Agilent BioTek, USA) was used for luminescence measurements. Kinetic and end-point cell proliferation data were analyzed and plotted with GraphPad 10.3.1 software (GraphPad, USA).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConfocal microscopy of 3D multi-component spheroids\u003c/h2\u003e \u003cp\u003eConfocal images of 3D multi-component spheroids were acquired using the Cytation 10 (Agilent BioTek, USA) confocal microscope with spinning disk technology. The instrument is equipped with a laser combiner (spectral range 398\u0026ndash;643 nm) and a DAPI filter cube (Excitation 390/40 nm, Emission 442/42 nm), a GFP filter cube (Excitation 472/ 30 nm, Emission 520/35 nm), and a TRITC filter cube (Excitation 556/20 nm, Emission 600/37 nm). Pictures were acquired using a 20x magnification objective.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3D apoptosis and cell death assays using confocal microscopy\u003c/h2\u003e \u003cp\u003eMelanoma cells were seeded in 384-well black U-bottom ULA microplates (Corning\u0026reg;, USA) at densities of 0.5 x 10\u003csup\u003e3\u003c/sup\u003e cells/well in 80 \u0026micro;L/well of medium, centrifuged at 500 x g for 5 minutes, and incubated for 2 days at 37\u0026deg;C and 5% CO2. Upon removal of 40 \u0026micro;L/well of medium, drugs were dispensed 2 times concentrated in 40 \u0026micro;L/well of medium, centrifuged at 500 x g for 5 minutes, and incubated for 5 days at 37\u0026deg;C and 5% CO2. The positive control, Staurosporine at 1\u0026micro;M concentration was added 24 hours previous the end of the assay, for strong induction of apoptosis and cell death. CellEvent\u0026trade; Caspase-3/7 Detection Reagent (Invitrogen, Thermo, USA) and SYTOX\u0026trade; Blue Dead Cell Stain (Invitrogen, Thermo, USA) were added and incubated at 37\u0026deg;C for at least 2 hours. Cytation 10 was used to acquire multiple images in z-stacking using DAPI, GFP, and TRITC filter cubes and a 20X magnification objective. Brightfield pictures were also acquired at 20x magnification. Maximum intensity projected (MIP) images were generated using Gen5 (Agilent BioTek, USA). For mCherry-expressing melanoma cell lines, the mCherry signal was used to visualize the total spheroid mass, while for non-labeled melanoma cells, brightfield images were used to visualize the total spheroid mass.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3D invasion assay\u003c/h2\u003e \u003cp\u003eMelanoma cell lines SKmel147 and M160915 were seeded in ultra-low attachment BIOFLOAT\u0026trade; 96-well plates (Facellitate, Germany) in densities of 2,5 x 10\u003csup\u003e3\u003c/sup\u003e and 5 x 10\u003csup\u003e3\u003c/sup\u003e, respectively. After 3 days of spheroid formation, they were embedded between two layers of collagen type I, containing 2mg/ml Collagen type I (MercMillipore, Germany), 1% FCS (Gibco Thermo Fisher Scientific, Waltham, USA) in RPMI (Gibco Thermo Fisher Scientific, USA). The pH of the collagen solution was adjusted to 7.4 using 1M NaOH. 50 \u0026micro;l per well of collagen I solution was pipetted into an optically clear, black-walled 96-well plate (\u0026micro;Clear Greiner\u0026reg;, Belgium) and left to polymerize for 5 minutes at 37\u0026deg;C. Next, one spheroid per well was transferred on top of the collagen layer and immediately covered with 50 \u0026micro;L of collagen solution and polymerized for 15 minutes at 37\u0026deg;C. Next, 100 \u0026micro;l of medium containing either 0,5% DMSO (negative control) or 2 times IC50 concentration of the drug was added on top of the collagen layer. For each experimental condition, 8 spheroids were used. Pictures were taken on day 0 (immediately after embedding) and after 3 days collagen embedding, using Cytation 10 (Agilent BioTek, USA) manual imaging mode and 4x magnification. The area of cellular invasion was analyzed using ImageJ software (Fiji). Statistical analysis was performed using GraphPad 10.3.1 software (GraphPad, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eCells were seeded in 6-well Aggrewell plates (StemCell, USA) at densities of 0.5-1 x 10\u003csup\u003e3\u003c/sup\u003e cells/well in 5 mL of medium, centrifuged at 100 x g for 5 minutes, and incubated for 4 days. Drugs were dispensed and cells were incubated for 3 and 5 days. Cell lysis was performed on ice with cold lysis buffer (RIPA 1X containing cOmplete phosphatase inhibitor, Roche, Switzerland), protein concentration was determined using Pierce\u0026trade; BCA Protein Assay Kit (Thermo, USA), and protein lysates were further analyzed by SDS-PAGE ad Western Blot. The detection of enhanced chemiluminescence signals was performed as previously described (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Primary antibodies used in the study were: GAPDH (1:5000, polyclonal, #G9545, Rabbit, Sigma, USA), ERK (1:1000, Rabbit, L34F12, #CST4696S, CellSignaling, USA), pERK (1:1000, Rabbit, D13.14.4E, #CST4370S, CellSignalling, USA), AKT (1:1000, Mouse, 4OD4, #CST2920S, CellSignalling, USA), pAKT (Ser473) (1:1000, Rabbit, D9E, #CST4060S, CellSignalling, USA). All primary and HRP-conjugated secondary antibodies were purchased from Cell Signalling Technology (Boston, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHydrogel-embedded melanoma co-culture\u003c/h2\u003e \u003cp\u003eMelanoma-TME hydrogel encapsulation co-cultures were generated using transglutaminase cross-linkable poly(ethylene glycol) (PEG) hydrogels previously described (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). A ready-to-use kit consisting of frozen aliquots of the 3% PEG precursor solution (8-arm 40kDa PEG macromers bioconjugate with RGD adhesion and MMP-cleavable peptide motives) and of the activated Human Factor XIII (FXIIIa) were purchased (Ectica Technologies, Switzerland). The cell suspension was created by mixing mCherry-expressing melanoma cells at a density of 2\u0026ndash;4 x 10\u003csup\u003e4\u003c/sup\u003e/100\u0026micro;L with HMEC-1 expressing BFP at a density of 20 x 10\u003csup\u003e4\u003c/sup\u003e/100\u0026micro;L, and with either NHDF, or MRC-5, or LX-2 expressing GFP a density of 20 x 10\u003csup\u003e4\u003c/sup\u003e/100\u0026micro;L, centrifuged at 300 x g for 3 minutes and supernatant was removed, and 45 \u0026micro;L of complete RPMI were added. Afterwards, 43 \u0026micro;L of PEG precursor solutions were added and gently mixed to dissolve the cellular pellet. Then, 12 \u0026micro;L of FXIIIa was added, and the solution was gently mixed without introducing bubbles. 5 \u0026micro;L of solution was dispensed in each well in a black 96-well plate (\u0026micro;Clear Greiner\u0026reg;, Belgium) to create homogeneous domes and incubated at RT for 5 minutes until to reach polymerization. 200 \u0026micro;L/well of RPMI supplemented with 10ng/mL of VEGF (Peprotech, USA) was dispensed in each well and incubated for 3 days at 37\u0026deg;C and 5% CO2. 2 times concentrated drugs were added in 100\u0026micro;L/well of fresh medium, upon removal of 100 \u0026micro;L/well of the old medium, and further incubated for 5 days at 37\u0026deg;C and 5% CO2. Confocal microscope Cytation 10 (Agilent, BioTek, USA) was used to acquire multiple images in z-stack modality using DAPI, GFP, and TRITC filter cubes and 20X magnification object, selecting 4 ROIs per well. Maximum intensity projected images were analyzed using ImageJ (Fiji).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eZebrafish husbandry, determination of maximum tolerated concentrations of drugs, and xenografts\u003c/h2\u003e \u003cp\u003eZebrafish experiments were performed in two different institutions, the Zebrafish Facility of the University of Padova (under Italian Ministry of Health Authorization n. 1111/2024-PR (OPBA prot. D2784.185)) and the Aquatic Platform of the Luxembourg Centre for Systems Biomedicine at the University of Luxembourg (RRID:SCR_025429), in collaboration with Professor Natascia Tiso and Dr. Maria Lorena Cordero-Maldonado, respectively. Adult \u003cem\u003enacre\u003c/em\u003e zebrafish lines were housed in each facility according to standard protocols (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Embryos were obtained by natural spawning and reared until the experiments at 2 dpf in E3 medium at 28\u0026deg; C. First, to determine the maximum tolerated concentration (MTC) of the drugs to be tested in the xenografts (Trametinib, DH and PP), we first treated non-injected na\u0026iuml;ve 2 dpf nacre larvae with serial dilutions of drugs of interest to determine the highest tolerated and non-toxic concentration until 5 dpf. Larvae viability and development were monitored daily during drug treatment. The cut-off of 20% mortality and no developmental defect was set to determine the MTC. Second, for the performance of the cell transplantations, on the day of the injections, the 2 dpf embryos were manually dechorionated and anesthetized with buffered tricaine (80 mg/l, Sigma-Aldrich). SKmel147-mCherry and MelJuso-RES-mCherry cell lines were detached using phenol red-free TryplE reagent (Gibco Thermo Fisher Scientific) and resuspended in PBS at a concentration of 2 x 10\u003csup\u003e5\u003c/sup\u003ecells/ \u0026micro;L. The cells were injected into the yolk as a single droplet (around 100 cells per embryo) using a World Precision Instrument (Sarasota, USA) or FemtoJet 4X (Eppendorf, Germany) microinjectors. PBS with phenol red was injected as a vehicle control. After 24h, the larvae were fluorescently assessed for successful cell implantation and subjected to drug treatment with 12 nM Trametinib, 1 \u0026micro;M DH, 111 nM of PP, and their combinations for 3 days at 32\u0026deg;C. Larvae viability was monitored daily. After 3 days, larvae were anesthetized as described above, and photos of xenografts were taken using an M165 FC microscope with DFC7000T camera (Leica Camera, Germany) or Nikon SMZ25 fluorescent stereomicroscope (Nikon Instruments, Japan). Data was analyzed based on fluorescence intensity to measure xenograft area and number of cells using the \u0026ldquo;Measurements\u0026rdquo; tool of the Volocity 6.0 software (Perkin Elmer, Italy).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll experiments represent at least 3 biological replicates. Statistical analysis was performed using GraphPad 10.3.1 software (GraphPad, USA). The Gaussian distribution of data was assessed with Shapiro-Wilk normality test. Data following Gaussian distribution was analyzed using Ordinary one-way ANOVA with Dunett\u0026rsquo;s multiple comparison test. Data not following Gaussian distribution was analyzed using ordinary Kruskal-Wallis with Dunn\u0026rsquo;s multiple comparison test. One sample t-test was used to analyze data expressed as a percentage of the untreated control (normalized to 100%).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eHigh-throughput drug screening and dose-response assays identify promising novel compounds for NRAS\u003csup\u003emut\u003c/sup\u003e melanoma\u003c/h2\u003e \u003cp\u003eWe evaluated the effects of two drug libraries, the commercial Prestwick Chemical Library\u0026reg; (1267 compounds) and an in-house Melanoma Drug Library (61 compounds) selected based on literature and previous data, on SKmel147 NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cells cultured as 3D spheroids. Drugs were tested at 10 \u0026micro;M and 1 \u0026micro;M to minimize off-target effects. A HTS workflow was developed using a fully automated platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), with individual drugs dispensed in a specific plate layout (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The negative control was 0.1% DMSO, while Foretinib at a concentration of 30 \u0026micro;M was used as the positive control for inducing cell death. To account for the edge effect, additional plates were included to test drugs dispensed onto spheroids located in edge-affected wells. Cell viability was assessed via Calcein AM signal-based spheroid area segmentation and area analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eStandard quality control (QC) parameters, including Z\u0026rsquo;-factor (\u0026gt;\u0026thinsp;0.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) and coefficient of variation (\u0026lt;\u0026thinsp;10%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), were assessed across all plates, ensuring robust data for hit identification along our HTS campaign.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHits identification involved multiple steps (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). First, a statistical model analyzed the Maximum Intensity Projection (MIP) spheroid area values that were obtained by automated image segmentation using the Calcein AM signal. Only the measurements that deviated by at least three standard deviations from the mean of the DMSO control (cut-off) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) in both duplicates were considered for the next selection step (see Materials \u0026amp; Methods for details) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, all spheroid MIP data were normalized to the average MIP measured in spheroids treated with DMSO. The hits were selected based on values below 50% of residual MIP, following exposure to drugs. The spheroid shrinkage effects were confirmed by manual visual inspection, and additional information, such as FDA status and pathway relevance, was reviewed.\u003c/p\u003e \u003cp\u003eSeventeen promising drugs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were selected based on well-established roles in targeting pathways critical to melanoma progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), including DNA synthesis and damage, epigenetic regulation, and apoptosis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImportant features of the 17 selected hit drugs. target, FDA status, therapeutic effect and targeted pathway. Highlighted are Daunorubicin HCl (brown) and Pyrvinium Pamoate (purple), subsequently selected for further validations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFDA approved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTherapeutic effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTargeted pathway\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAZD6738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA synthesis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamptothecine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopoisomerase I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA synthesis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHIR-124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCell Cycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCladribine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRibonucleotide reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA synthesis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaunorubicin HCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopoisomerase II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntibacterial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA synthesis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntinostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEpigenetic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpirubicin HCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopoisomerase II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA synthesis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrinotecan HCl Trihydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopoisomerase I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA synthesis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanatoside C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlasma membrane Na+/K\u0026thinsp;+\u0026thinsp;ATPase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCardiotonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMolecular Pump\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObatoclax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBcl-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApoptosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD0325901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMEK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMAPK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProscillaridin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlasma membrane Na+/K\u0026thinsp;+\u0026thinsp;ATPase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntiarrhythmics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMolecular Pump\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyrvinium Pamoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK1α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnthelmintic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWNT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAK-733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMEK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMAPK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopotecan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopoisomerase I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNA synthesis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUlixertinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMAPK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXL888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSP90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntineoplastic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEpigenetic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotable compounds included topoisomerase inhibitors and poisons that target DNA stability (such as Daunorubicin hydrochloride), a checkpoint kinase 1 (CHK1) inhibitor (CHIR-124), and epigenetic modulators like Entinostat (HDAC inhibitor), for example. Additionally, heat-shock protein 90 (HSP90) and BCL-2 inhibitors (e.g. XL888 and Obatoclax) were selected alongside cardiac glycosides (such as Lanatoside C, Proscillaridin A), inhibiting the Na+/K\u0026thinsp;+\u0026thinsp;ATPase pump, which represent an emerging therapeutic target in cancer (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). MAPK pathway inhibitors (such as PD0325901, TAK-733, or Ulixertinib) and casein kinase 1 α (CK1α) agonist Pyrvinium Pamoate, targeting the WNT-β-Catenin pathway, were also included.\u003c/p\u003e \u003cp\u003eValidation of hits was performed via high-throughput dose-response curve (DRC) analysis (Supplementary Fig.\u0026nbsp;1A). DRC generation confirmed the inhibitory effects of individual drugs, displaying IC50 values ranging from 2 nM to \u0026gt;\u0026thinsp;1 \u0026micro;M, with only a few compounds unable to derive IC50 values. Compounds targeting DNA stability (such as Cladribine, Topotecan, Irinotecan, and Daunorubicin HCl) showed IC50 values below 50 nM, demonstrating strong effect of the compounds as well as indicating the sensitivity of our HTS assay. Other efficient compounds included Lanatoside C, Proscillaridin A, Pyrvinium Pamoate, XL888, and PD0325901, and generated IC50 values below 200 nM. Surprisingly, potent MAPK inhibitors (such as TAK-733 and Ulixertinib) or cell cycle inhibitors (CHIR-124) exhibited limited inhibitory effects.\u003c/p\u003e \u003cp\u003eCollectively, these HTS data identified promising compounds for further validation as potential candidates for the treatment of NRAS\u003csup\u003emut\u003c/sup\u003e melanoma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eNRAS\u003csup\u003emut\u003c/sup\u003e melanoma cells are sensitive to Daunorubicin HCl and Pyrvinium Pamoate\u003c/h2\u003e \u003cp\u003eFurther investigation primarily focused on two compounds, DH and PP, as potential first-line treatments for NRAS\u003csup\u003emut\u003c/sup\u003e melanoma. In addition to their strong inhibitory effects on SKmel147 melanoma cells observed during the HTS and DRC campaign, DH was selected from the major targeted pathway category, \u0026lsquo;DNA synthesis\u0026rsquo;, while PP was chosen for its notable impact on the WNT-β-Catenin pathway, which plays a critical role in melanoma progression (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Furthermore, both compounds were selected based on their FDA approval status, underscoring a drug repurposing approach. Both drugs showed strong efficacy across four treatment-naive melanoma cell lines cultured as spheroids (SKmel147, SKmel30, M160915) or 3D aggregates (M161022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E).\u003c/p\u003e \u003cp\u003eDespite their FDA-approved status, additional testing on non-cancerous cells (Supplementary Fig.\u0026nbsp;1B-C) revealed higher IC50 values for PP compared to melanoma cells (\u0026ldquo;Dermal Fibroblasts\u0026rdquo;-NHDF\u0026thinsp;=\u0026thinsp;998 nM; \u0026ldquo;Lung Fibroblasts\u0026rdquo;-MRC5\u0026thinsp;=\u0026thinsp;252.3 nM; \u0026ldquo;Hepatic Stellate Cells\u0026rdquo;-LX-2\u0026thinsp;=\u0026thinsp;209.9 nM; \u0026ldquo;Endothelial Cells\u0026rdquo;-HMEC-1\u0026thinsp;=\u0026thinsp;505.6 nM), indicating therapeutic safety. Meanwhile LX-2 and HMEC-1 cell lines displayed sensitivity to DH compared to PP (Supplementary Fig.\u0026nbsp;1D-E).\u003c/p\u003e \u003cp\u003eIn conclusion, DH and PP demonstrated very good activity against NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cells grown as spheroids with minimal effects on most of the non-cancerous cells, underscoring their potential for drug screening and repurposing in melanoma therapy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDaunorubicin HCl and Pyrvinium Pamoate inhibit proliferation and viability of NRAS\u003c/b\u003e \u003csup\u003e \u003cb\u003emut\u003c/b\u003e \u003c/sup\u003e \u003cb\u003emelanoma cells cultured in 3D\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo further elucidate the action of DH and PP on NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cells, proliferation assay was performed using time-lapse microscopy by tracking over time the proliferation of three NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cell lines, cultured as spheroids and constitutively expressing the mCherry fluorescent protein. The MEKi Trametinib (T) and Staurosporine (STAU), a well-characterized apoptosis inducer, served as positive controls. The fluorescent signal emitted by melanoma cells was used to follow spheroid sizes dynamically, enabling the evaluation of the compounds\u0026rsquo; effect on cellular proliferation. Previously determined IC50 values for DH, PP, and Trametinib were used to assess the drug efficacy across the different assays. PP and Trametinib induced a pronounced reduction in melanoma spheroid/3D aggregate proliferation across all tested cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Fig.\u0026nbsp;2A \u0026amp; 3A).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDH exhibited a similar inhibitory effect in SKmel147 and M161022 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Fig.\u0026nbsp;3A), however, with weaker effects on SKmel30 cells (Supplementary Fig.\u0026nbsp;2A). This reduced efficacy of DH on SKmel30 may be explained by the cell line\u0026rsquo;s unique capacity to respond to DNA damage, possibly due to a TP53 gene deletion (Cellosaurus SK-MEL-30; CVCL_0039). All 3 cell lines demonstrated significant reduction in cell viability of DH and PP-treated spheroids/aggregates compared to untreated controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Supplementary Fig.\u0026nbsp;2B \u0026amp; 3B). Consistent with the proliferation assay results, SKmel30 exhibited a reduced sensitivity to DH, although the reduction in viability remained significant (Supplementary Fig.\u0026nbsp;2B). Interestingly, in M161022 cells, DH and PP showed an even more pronounced inhibitory effect than Trametinib (Supplementary Fig.\u0026nbsp;3B). Drug effects were also evaluated in NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cells embedded in a hydrogel matrix, to mimic the extracellular matrix (ECM) within the tumor microenvironment. Consistent with observations from scaffold-free spheroid and 3D aggregate cultures, DH and PP significantly inhibited the growth of SKmel147 (Supplementary Fig.\u0026nbsp;4A) and of the primary cell line M161022 (Supplementary Fig.\u0026nbsp;4B).\u003c/p\u003e \u003cp\u003eCollectively, these findings demonstrate that DH and PP effectively suppress proliferation and reduce cell viability in a panel of NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cell lines cultured under various 3D conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePyrvinium Pamoate has a strong cytotoxic effect on NRAS\u003csup\u003emut\u003c/sup\u003e melanoma spheroids\u003c/h2\u003e \u003cp\u003eNext, we evaluated whether the arrest of spheroid growth in response to DH or PP exposure was caused by cytotoxic effects of these compounds. After 5 days of treatment, spheroids and 3D aggregates were stained to assess the levels of activated executioner caspases 3 and 7 (CellEvent Caspase 3/7) and dead cells (SytoxBlue). Additionally, the expression of mCherry fluorescent protein in the transduced cells was used to identify viable tumor mass.\u003c/p\u003e \u003cp\u003eThe level of apoptosis and cell death was linked to the tumor mass of the spheroid or cell aggregate on day 5 of treatment. Intrinsic apoptosis and cell death were observed in the inner core of untreated (UT) spheroids, aligning with the innate \u003cem\u003ein vivo\u003c/em\u003e of 3D tumor formations (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Trametinib and DH exhibited low levels of cytotoxicity in SKmel147 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) and SKmel30 cells (Supplementary Fig.\u0026nbsp;2C), suggesting a predominantly cytostatic effect. In contrast, PP induced apoptosis and cell death significantly in both cell lines, also indicated by a markedly reduced mCherry signal. Notably, in SKmel30, this reduction was even more pronounced than with STAU treatment (Supplementary Fig.\u0026nbsp;2C). Primary metastatic melanoma cell lines exhibited similar cytotoxic responses across all treatments; with PP resulting in the greatest tumor mass reduction in spheroids and aggregates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Supplementary Fig.\u0026nbsp;3C \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eIn conclusion, our findings demonstrate that PP exerts a potent cytotoxic effect in a panel of established and primary metastatic treatment-naive NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cell lines cultured under 3D conditions.\u003c/p\u003e \u003cp\u003eWe evaluated the effects of the drugs on key proliferation and survival pathways in NRAS\u003csup\u003emut\u003c/sup\u003e melanoma, specifically ERK (MAPK pathway) and AKT (AKT pathway). ERK levels remained unchanged up to 5 days in SKmel147 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E) and SKmel30 cells (Supplementary Fig.\u0026nbsp;2D-E) across all treatments, indicating that DH and PP do not interfere with ERK expression. Similar findings were observed in the primary melanoma cell lines M161022 (Supplementary Fig.\u0026nbsp;3D-E) and M160915 (Supplementary Fig.\u0026nbsp;5D-E). However, 5 days of PP treatment significantly affected the survival of primary melanoma cells, resulting in insufficient lysate collection, underscoring the strong cytotoxic effect of PP in these cells.\u003c/p\u003e \u003cp\u003eAs expected, phosphorylated ERK (pERK; active form) was reduced by Trametinib in all cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, Supplementary Fig.\u0026nbsp;2F \u0026amp; 3F \u0026amp; 5F), consistent with its high specificity for MEK inhibition. However, treatment with DH and PP did not consistently alter pERK levels. For example, PP inhibited pERK in a time-dependent manner in SKmel147 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF) and M161022 (Supplementary Fig.\u0026nbsp;3F), but not in SKmel30 (Supplementary Fig.\u0026nbsp;2F) or M160915 (Supplementary Fig.\u0026nbsp;5F).\u003c/p\u003e \u003cp\u003eNeither Trametinib nor DH altered basal AKT expression across all cell lines, and phosphorylated AKT (pAKT, Ser473) exhibited inconsistent regulation in response to the drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, Supplementary Fig.\u0026nbsp;2G \u0026amp; 3G \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). Notably, PP treatment consistently reduced total AKT levels in SKmel147 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E), M161022 (Supplementary Fig.\u0026nbsp;3D-E), and M160915 (Supplementary Fig.\u0026nbsp;5D-E). Due to this strong reduction in AKT expression, pAKT quantification was not feasible. In contrast, SKmel30 displayed a different response, with an increase in pAKT levels following PP treatment (Supplementary Fig.\u0026nbsp;2D-E).\u003c/p\u003e \u003cp\u003eOverall, PP exerts an inhibitory effect on AKT protein levels, predominantly in primary metastatic melanoma cell lines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDaunorubicin HCl and Pyrvinium Pamoate inhibit primary melanoma cell invasion\u003c/h2\u003e \u003cp\u003eThe inhibitory effects of DH, PP, and Trametinib on the invasive abilities of melanoma cells were evaluated in SKmel147 and M160916 spheroids embedded in a type I Collagen matrix after 3 days of drug treatment. SKmel30 and M161022 were excluded from this assay due to non-invasive phenotype and failure to form compact spheroids, respectively. In SKmel147, significant invasion inhibition occurred only with Trametinib, whereas DH and PP led to only minor reductions in cell motility (Supplementary Fig.\u0026nbsp;6A). In contrast, in the primary melanoma cell line M160916, all three compounds significantly suppressed invasive activity (Supplementary Fig.\u0026nbsp;6B).\u003c/p\u003e \u003cp\u003eAlthough PP and DH did not reduce invasion in the established SKmel147 cell line, both compounds effectively inhibited the invasion of the primary metastatic melanoma cell line.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eCombinatorial treatment with Trametinib (MEKi) shows additive effects\u003c/h2\u003e \u003cp\u003eAfter assessing the effect of DH and PP as monotherapy on a panel of NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cell lines, we explored their potential in combined treatments with Trametinib. This strategy was prompted by the well-studied ability of melanoma to develop resistance to monotherapies (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). We performed a 3D synergy assay on spheroids of 2 NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cell lines, SKmel147 and SKmel30, to investigate potential synergistic effects between Trametinib and DH, and Trametinib and PP (Supplementary Fig.\u0026nbsp;7A). The range of concentrations of compounds (1:2 dilution ratio) were selected based on cell line-specific IC50 concentrations previously generated. Zero interaction potency (ZIP) synergy score analysis did not reveal overall synergism (defined as ZIP synergy score\u0026thinsp;\u0026gt;\u0026thinsp;10); however, an additive effect (defined as ZIP synergy score \u0026gt;-10 and \u0026lt;\u0026thinsp;10) was consistently observed across all conditions. For the following experiments, we selected drug concentrations determining the regions of maximum synergistic effects: Trametinib at 0.06 nM and DH and PP at 45 nM.\u003c/p\u003e \u003cp\u003eWe subsequently assessed the impact of these combinations on SKmel147 and SKmel30 spheroid proliferation and cell viability in parallel with single synergy concentration treatments. Despite using drug concentrations belonging to the region with the highest synergism, the results remained additive, showing only a slight reduction in proliferation (Supplementary Fig.\u0026nbsp;7B-D) and cell viability (Supplementary Fig.\u0026nbsp;7C-E) compared to the respective single treatments in both cell lines. The drug combinations exhibited good effects on SKmel147 cell proliferation (Supplementary Fig.\u0026nbsp;7B) and spheroid viability (Supplementary Fig.\u0026nbsp;7C) but did not affect SKmel30 growth (Supplementary Fig.\u0026nbsp;7D-E), consistent with previous observations on the intrinsic resistance of this cell line.\u003c/p\u003e \u003cp\u003eBased on these results, we further concentrated on the characterization of the monotherapies (based on the cell line-specific IC50 values) using advanced \u003cem\u003ein vitro\u003c/em\u003e pre-clinical models.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdvanced\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003cb\u003e3D co-culture models reveal melanoma-specific effects and low toxicity of Daunorubicin HCl and Pyrvinium Pamoate\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe role of non-cancerous cells in the tumor microenvironment (TME) in supporting cancer survival and drug resistance is well established (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Co-culture models are valuable for assessing drug efficacy by capturing cancer cell\u0026ndash;TME interactions and evaluating toxicity on non-cancerous cells. Using our previously established Multicomponent Melanoma Spheroid (MMS) models (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), which mimic key metastatic sites such as \u0026ldquo;skin/dermal\u0026rdquo; (HMEC-1\u0026thinsp;+\u0026thinsp;NHDF), \u0026ldquo;lung\u0026rdquo; (HMEC-1\u0026thinsp;+\u0026thinsp;MRC-5), and \u0026ldquo;liver\u0026rdquo; (HMEC-1\u0026thinsp;+\u0026thinsp;LX-2), we assessed the effects of DH and PP. mCherry fluorescent labeling allowed real-time visualization of cell populations by extracting the residual MIP spheroid area. Time-lapse microscopy showed reduced SKmel147 proliferation in all MMS models compared to untreated controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). To a similar extent, DH, PP, and Trametinib reduced total co-culture viability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-F), with 30\u0026ndash;40% residual viability attributed to melanoma while TME normal cells survived (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). SKmel30 showed similar proliferation inhibition in the \u0026ldquo;dermal\u0026rdquo; (Supplementary Fig.\u0026nbsp;8A) and \u0026ldquo;lung\u0026rdquo; (Supplementary Fig.\u0026nbsp;8B) models but less in the \u0026ldquo;liver\u0026rdquo; models (Supplementary Fig.\u0026nbsp;8C) compared to monocomponent spheroids (Supplementary Fig.\u0026nbsp;2A). In line with the monocomponent data, DH had a weaker inhibitory effect on SKmel30 proliferation than Trametinib and PP. Corresponding viability assays showed significant reductions, with DH presenting the lowest efficacy compared to Trametinib and PP (Supplementary Fig.\u0026nbsp;8D-F), with residual viability due to melanoma and TME cells (Supplementary Fig.\u0026nbsp;8A-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next investigated the effects of DH and PP in complex models incorporating an extracellular matrix (ECM), a critical factor in melanoma progression and drug resistance (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Using hydrogel-embedded melanoma-TME co-culture models, we evaluated the efficacy of these drugs alongside Trametinib while also assessing their effects on non-cancerous cells. Fluorescent labeling enabled clear visualization of melanoma and TME cell populations. In these models, SKmel147 showed significant growth reductions across all conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-J). In the \u0026ldquo;dermal\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH) and \u0026ldquo;lung\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI) models, all three drugs reduced the melanoma population by over 50% compared to controls. The \u0026ldquo;liver\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ) model revealed increased sensitivity of SKmel147 to Trametinib, compared to the other models, and to the monoculture (Supplementary Fig.\u0026nbsp;4A). Interestingly, DH and especially PP significantly inhibited M161022 growth across all three models (Supplementary Fig.\u0026nbsp;9A-D), in line with what was observed in monoculture (Supplementary Fig.\u0026nbsp;4B). The survival of non-cancerous TME cells was also evaluated. Importantly, drug concentrations effective against melanoma cells had a low impact on the survival of non-cancerous cells in co-culture, highlighting the potent melanoma inhibitory effects and safety profile of these compounds.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMEK inhibitor-resistant melanoma cells are sensitive to Daunorubicin HCl and Pyrvinium Pamoate\u003c/h2\u003e \u003cp\u003eGiven melanoma's rapid development of resistance to current therapies, we have generated NRAS\u003csup\u003emut\u003c/sup\u003e (SKmel30) and WT (WM3918) melanoma cell lines resistant to Trametinib (Tres) and evaluated the efficacy of DH and PP in resistant cells. Of note, SKmel147 has not developed resistance, even after prolonged drug exposure (approximately 6 months), illustrating the high heterogeneity between melanoma cells and has therefore not been included in the following experiments.\u003c/p\u003e \u003cp\u003eDRCs of sensitive and resistant cell lines cultured as spheroids and relative IC50 values were generated for DH, PP, and Trametinib after 5 days of treatment. Resistance to Trametinib was confirmed by increased IC50 values in both SKmel30-Tres (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and WM3918-Tres cells (Supplementary Fig.\u0026nbsp;10A) in comparison to the sensitive counterparts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA 3D apoptosis/cell death assay further showed the low cytotoxic effect of Trametinib in sensitive SKmel30 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), consistent with previous findings (Supplementary Fig.\u0026nbsp;2C), as well as in SKmel30-Tres cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). A similar effect was observed in WM3918-Tres cells (Supplementary Fig.\u0026nbsp;10E), whereas Trametinib treatment exhibited cytotoxicity on sensitive WM3918 cells (Supplementary Fig.\u0026nbsp;10D). Although DH appeared to be more effective in SKmel30-Tres cells compared to their sensitive counterparts, as indicated by a lower IC50 value (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), it did not induce substantial levels of apoptosis or cell death after 5 days of treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eIn contrast, WM3918 cells displayed a significant increase in sensitivity to DH, as demonstrated by a reduction in IC50 values in WM3918-Tres cells (116.6 nM) compared to their sensitive counterparts (743.6 nM) (Supplementary Fig.\u0026nbsp;10B). This was further supported by the strong cytotoxic effect of DH observed in both WM3918 sensitive (Supplementary Fig.\u0026nbsp;10D) and WM3918-Tres cells (Supplementary Fig.\u0026nbsp;10E). Consistent with previous findings, PP induced the high levels of apoptosis and cell death following 5 days of treatment in both sensitive SKmel30 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) and SKmel30-Tres cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) compared to Trametinib and DH. Images revealed a reduced spheroid size and lower cytotoxic activity of PP in SKmel30-Tres cells compared to SKmel30 sensitive cells, which was further corroborated by IC50 values, where SKmel30-Tres cells exhibited a higher IC50 value (66.2 nM) than SKmel30 sensitive cells (23 nM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eDespite comparable growth inhibition of WM3918 sensitive and WM3918-Tres cells by PP (Supplementary Fig.\u0026nbsp;10C), PP exhibited a pronounced cytotoxic effect in WM3918-Tres cells (Supplementary Fig.\u0026nbsp;10E), which was not observed in their sensitive counterparts (Supplementary Fig.\u0026nbsp;10D).\u003c/p\u003e \u003cp\u003eThese findings suggest that DH and PP hold potential as second-line therapeutic agents for targeting melanoma cells that have developed resistance to targeted therapies such as MEKi.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eDaunorubicin HCl and Pyrvinium Pamoate show strong inhibitory effects in zebrafish xenografts melanoma models\u003c/h2\u003e \u003cp\u003eTo assess the \u003cem\u003ein vivo\u003c/em\u003e efficacy of the drugs, SKmel147-mCherry and MelJuso-RES-mCherry cell lines were injected into the yolks of 2-day post-fertilization \u003cem\u003enacre\u003c/em\u003e zebrafish larvae. As SKmel30 Tres cells used in \u003cem\u003ein vitro\u003c/em\u003e experiments failed to form proper tumors post-injections, MelJuso-RES-mCherry has been chosen to represent resistant melanoma phenotype. At 24 hours post-injection, larvae were randomized into groups of 30\u0026ndash;40 individuals and treated with the drugs as monotherapy or in combination treatments at doses previously established as the maximum tolerated dose. After 72 hours of treatment, larval survival, metastasis status and xenograft size were evaluated. Despite the highly invasive and motile phenotype of the melanoma cells, no increase in migration from the initial injection site was detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Consistent with \u003cem\u003ein vitro\u003c/em\u003e findings, a significant reduction in xenograft area was observed in both sensitive and resistant cell lines. Notably, this effect was evident not only in monotherapy groups (Trametinib, DH, and PP) but also in combination treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). The number of injected cells in the untreated control remained stable over the 72-hour period, whereas a significant reduction was observed in the treated groups, indicating strong cytotoxic effects of the tested drugs \u003cem\u003ein vivo\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-F). While no increased mortality was noted in MelJuso-RES-mCherry injected larvae, some mortality was observed in SKmel147 injected larvae, particularly in groups treated with PP and its combinations, despite the doses being within the previously established safe range for larval survival and development (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eSystemic therapy for melanoma has advanced significantly, with targeted and immune therapies primarily benefiting BRAF\u003csup\u003emut\u003c/sup\u003e patients (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). While NRAS\u003csup\u003emut\u003c/sup\u003e melanoma patients rely on ICIs as a first-line treatment, with response rates below 50% (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), and off-label MEKi, like Trametinib, as a second-line treatment if ICI must be discontinued. Hence, additional novel therapeutic options for NRAS\u003csup\u003emut\u003c/sup\u003e patients are urgently needed.\u003c/p\u003e \u003cp\u003eTo address this, we conducted high-throughput screening (HTS) to evaluate more than 1300 compounds. Unlike traditional HTS on adherent cell cultures, we used 3D melanoma spheroids to improve physiological relevance and mimic patient tumor responses more accurately (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHit identification followed a rigorous multi-step process, which led to the identification of 17 promising compounds with strong inhibitory effects on NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cells in 3D spheroids, warranting their further investigation as potential melanoma therapies. A limitation of this study is the use of only one cell line for screening. However, given the increased complexity of the 3D culture model, using a single cell line served as a practical and reasonable foundation for identifying potential candidate compounds.\u003c/p\u003e \u003cp\u003eAmong the identified hits, DH and PP emerged as the most promising candidates. DH, an anthracycline antibiotic (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), is approved for acute myeloid leukemia (AML) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). It acts as a topoisomerase II (TOP2) poison, inducing DNA damage and apoptosis (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In melanoma, Mu et al. demonstrated that TOP2α is significantly overexpressed compared to benign nevi (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). PP is a cyanine dye approved as an anthelmintic drug (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Its mechanism of action is not yet fully elucidated, however some studies on cancer describe PP as a CK1α agonist that promotes β-Catenin degradation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), a mechanism also observed in uveal melanoma (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDH and PP were tested in dose-response assays in NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cell lines, showing high efficacy in 3D spheroid cultures. Both demonstrated a favorable safety profile in non-cancerous cells, particularly PP, consistent with their FDA approval status. Despite significant viability reduction, their effects appeared largely cytostatic, as proliferation decreased but remained above baseline. All melanoma cell lines responded similarly, except SKmel30, which exhibited greater resistance to DH, likely due to a \u003cem\u003eTP53\u003c/em\u003e deletion (Cellosaurus.org). This aligns with findings that mutant p53 reduces the efficacy of TOP1 and TOP2 inhibitors (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Furthermore, Dunsche et al. demonstrated that a rare \u003cem\u003eTP53\u003c/em\u003e mutation (R285K) confers increased resistance to cisplatin treatment in metastatic melanoma cells. This resistance can be overcome through ferroptosis induction (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). On the other hand, we showed that PP induced apoptosis and cell death across all cell lines. In contrast, Trametinib and DH triggered lower cytotoxic effects, particularly in SKmel30, which exhibited general resistance, as observed in the Staurosporine-treated spheroids. Apoptosis and cell death were evaluated after five days of treatment; therefore, DH and T may have induced cytotoxicity at an earlier time point or activated alternative programmed cell death pathways, such as necroptosis (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). However, translational cancer therapies primarily depend on prolonged treatment regimens rather than short-term interventions.\u003c/p\u003e \u003cp\u003eMechanistically, we focused on the deregulation of key pathways involved in NRAS\u003csup\u003emut\u003c/sup\u003e melanoma survival, which are often rewired in response to targeted therapy treatments, leading to drug resistance. These pathways include the MAPK and AKT pathways (\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). As expected, Trametinib induced a general reduction in ERK activation due to its inhibitory action on MEK, the upstream kinase of ERK. In contrast, DH exhibited inconsistent effects on the deregulation of pERK and pAKT across the cell lines, suggesting the involvement of other major targets. Notably, PP reduced AKT levels in SKmel147 and primary cell lines, positioning it as a potential AKT regulator. PP's role in AKT inhibition in cancer has been reported (\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), also in uveal melanoma (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Given PP\u0026rsquo;s apoptotic effects, AKT may contribute to this by downregulating anti-apoptotic molecules like BCL-2 and BCL-xL and affecting mitochondrial stability (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Conversely, PP increased pAKT in SKmel30, potentially upregulating β-catenin via GSK3β inhibition, warranting further investigation. Overall, additional studies are needed to fully understand DH and PP\u0026rsquo;s effects on melanoma.\u003c/p\u003e \u003cp\u003eWe evaluated DH and PP for their ability to inhibit cell invasion using spheroids embedded in a type I collagen matrix. Trametinib suppressed invasion in both melanoma cell lines, contrasting a study by Vultur et al., who found that MEKi increased motility in metastatic but not non-metastatic melanoma (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Notably, DH and PP significantly reduced invasive behavior only in the primary metastatic cell line (M160915), suggesting their effects may be phenotype-specific in NRAS\u003csup\u003emut\u003c/sup\u003e melanoma.\u003c/p\u003e \u003cp\u003eTo evaluate compound effects within the tumor microenvironment, DH and PP were tested in co-culture systems, including Melanoma Multicomponent Spheroids (MMS) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and a hydrogel-based melanoma-TME system. NRAS\u003csup\u003emut\u003c/sup\u003e melanoma cell lines were co-cultured with endothelial cells and fibroblasts (NHDF for skin/dermal, MRC-5 for lung, LX-2 for liver) and treated with Trametinib, DH, and PP at IC50 concentrations. While SKmel147 and SKmel30 responded similarly to treatments in skin/dermal and lung models, SKmel30 exhibited increased resistance to PP in the liver model. A more advanced 3D hydrogel system revealed that DH and PP selectively inhibited melanoma cell growth with minimal impact on non-cancerous cells. This effect was stronger than Trametinib in the M161022 model. Notably, SKmel147 responded differently in the liver model, highlighting treatment response variations based on metastatic sites. This aligns with Forschner et al.'s findings that response to targeted therapies and immune checkpoint inhibitors varies by metastatic site (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is well known that melanoma develops resistance to treatments in the majority of cases, leading to relapses and leaving patients, especially NRAS\u003csup\u003emut\u003c/sup\u003e, without efficient treatment options. To overcome this, we evaluated DH and PP in MEKi-resistant NRAS\u003csup\u003emut\u003c/sup\u003e and BRAF\u003csup\u003ewt\u003c/sup\u003e/NRAS\u003csup\u003ewt\u003c/sup\u003e melanoma cell lines. Importantly, PP effectively inhibited MEKi-resistant melanoma growth, while DH showed an even greater effect than in non-resistant cells. Although preliminary, our results support the potential of both compounds as a second- or third-line treatment for MEKi-resistant patients, and we are currently following this up by generating more melanoma cell lines resistant to targeted therapies and eventually to ICIs.\u003c/p\u003e \u003cp\u003eFinal validation of DH and PP anti-melanoma efficacy was performed in a zebrafish xenograft model. Recently, such models have been established and proved to be valid for melanoma development, drug screening and resistance mechanism \u003cem\u003ein vivo\u003c/em\u003e studies. It provides a relevant physiological background and offers ethical advantages over mice adhering to the 3R principles (replacement, reduction, refinement) (\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). This validation confirmed DH and PP anti-tumor effects in sensitive and MEKi-resistant NRAS\u003csup\u003emut\u003c/sup\u003e melanoma models with low toxicity, enhancing their translational effect.\u003c/p\u003e \u003cp\u003eOverall, given its role in AKT inhibition, apoptosis, and cell death induction, PP stands as a promising first-line therapy for NRAS\u003csup\u003emut\u003c/sup\u003e melanoma, warranting further clinical investigation.\u003c/p\u003e \u003cp\u003eOne key challenge is PP\u0026rsquo;s current tablet formulation, which limits systemic absorption. However, Esumi et al. reported intestinal absorption of PP, leading to reduced pancreatic tumor size in mice (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Furthermore, we hypothesize that DH could be effectively utilized in an immunotherapy-rechallenging setting, given its potential as a chemotherapeutic anthracycline compound capable of inducing immunogenic cell death (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), thereby enhancing the anti-cancer immune response (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). This hypothesis is further supported by the findings of Gebhardt et al., who demonstrated that low doses of paclitaxel increased the presence of functional cytotoxic T-cells while reducing tumor-suppressive MDSCs (myeloid-derived suppressor cells), ultimately resensitizing patients resistant to immune checkpoint inhibitors (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn this study, we performed high-throughput drug screening using 3D NRAS\u003csup\u003emut\u003c/sup\u003e melanoma spheroid cultures, improving the translational relevance of drug responses over traditional 2D models. Among the over 1300 compounds tested, Daunorubicin HCl and Pyrvinium Pamoate emerged as effective against NRAS\u003csup\u003emut\u003c/sup\u003e melanoma, demonstrating growth inhibition in advanced 3D \u003cem\u003ein vitro\u003c/em\u003e models and zebrafish xenografts, paving the way for their potential consideration either alone or in combination with other therapies. Finally, we also introduced that the strategic combination of repurposed, clinically approved drugs with advanced human 3D models holds considerable potential to both accelerate the drug discovery process and improve drug approval success rates.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNRAS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeuroblastoma RAS viral oncogene homolog\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBRAF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eV-Raf Murine Sarcoma Viral Oncogene Homolog B\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWild Type\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTME\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular Matrix\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHTS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Throughput Screening\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMIP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum Intensity Projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eULA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUltra Low Attachment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eZIP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZero Interaction Potency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDRC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDrug-Response Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIC50\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHalf-maximal inhibitory concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMMS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMelanoma Multicomponent Spheroid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDaunorubicin HCl\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePyrvinium Pamoate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMEK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitogen-activated protein kinase kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eERK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular signal-regulated kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAKT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein Kinase B or PKB or Ak strain transforming\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Prof. Dagmar Kulms (University of Dresden) for her valuable discussions on 3D models and cell death, and Prof. Mitchell Levesque (University of Zurich) for sharing M161022 and M160915 cell lines. We thank Dr. Benjamin Simona (ECTICA) and Dr. Riccardo Urbanet (ECTICA) for their valuable discussions on 3D models and hydrogel optimization. We thank the staff of the LCSB Aquatic Platform, particularly the animal care technicians, for their valuable daily work and support of zebrafish experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCA, DP, EK, JW, SG, and CM designed and performed the cell-based experiments. CA established the hydrogel-embedded co-culture models, designed, and performed the experiments utilizing these models. FI contributed to data analysis. MC and BS performed and acquired the HTS data. JW, LCM, NT, and GR established the zebrafish models, designed, and performed the experiments. CA, JW, CM, and SK wrote, edited, and reviewed the manuscript. SK conceived the project and supervised the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthic statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll primary melanoma cell lines derived from metastatic melanoma tumors (fresh or slow frozen) were obtained from the repository of Prof. Mitchell Levesque of the Department of Dermatology, University Hospital Zurich, Switzerland. The repository was established for the approved clinical study: BASEC: 2018-02050, 2018-02052, 2019-01326.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Zebrafish Facility at the University of Padova holds the authorization 407/2015-PR (OPBA), and the Zebrafish Core Facility at the Luxembourg Centre for Systems Biomedicine at the University of Luxembourg is registered as an authorized breeder, supplier, and user of zebrafish with Grand-Ducal Decree of 26 January 2023. All practices involving zebrafish complied with the European Legislation for the Protection of Animals used for Scientific Purposes (Directive 2010/63/EU) and following the principles of the 3Rs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by ‘‘MelCol’’ and ‘‘MultiMel’’ grants from the Vera Nijs \u0026amp; Jens Erik Rosborg Foundation (FVNER) under the aegis of the Fondation de Luxembourg and the ‘‘SecMelPro’’ grant from the Fondation Cancer, Luxembourg.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Uncropped Western Blots are available as Supplementary Data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLong GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet. 2023;402(10400):485\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShain AH, Bastian BC. From melanocytes to melanomas. Nature Reviews Cancer. Volume 16. Nature Publishing Group; 2016. pp. 345\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkbani R, Akdemir KC, Aksoy BA, Albert M, Ally A, Amin SB et al. Genomic Classification of Cutaneous Melanoma. Cell [Internet]. 2015;161(7):1681\u0026ndash;96. 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Cell Immunol. 2021;360.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-6594118/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6594118/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite significant advances in targeted (BRAFi\u0026thinsp;+\u0026thinsp;MEKi) and immune (anti-PD1/PD-L1, anti-CTLA4, and anti-LAG3) therapies, treatment options for NRAS\u003csup\u003emut\u003c/sup\u003e melanoma remain limited. Currently, NRAS\u003csup\u003emut\u003c/sup\u003e patients rely on immune checkpoint inhibitors, classical chemotherapy, and off-label MEK inhibitors, with over 50% experiencing rapid disease progression. One of the key challenges in developing effective targeted therapies is the lack of preclinical models that accurately recapitulate the tumor microenvironment (TME) and the intrinsic resistance of melanoma cells bearing NRAS mutation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo address this, we performed high-throughput screening (HTS) of over 1,300 compounds in 3D NRAS\u003csup\u003emut\u003c/sup\u003e melanoma spheroids. A multi-step analysis was performed to identify hits, which were further tested by performing drug-response curve (DRC) analysis. Most promising compounds were further validated using mono- and co-culture 3D \u003cem\u003ein vitro\u003c/em\u003e models that mimic three main metastatic sites in melanoma, such as skin/dermal, lung, and liver, utilizing spheroid and hydrogel systems. Ultimately, validation was conducted using zebrafish xenograft models to enable a more refined and accurate assessment of drug response.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigh-throughput drug screening of NRAS-mutant melanoma spheroids identified 17 candidate compounds, which were subsequently validated through DRC analyses. Among the most promising drugs, Daunorubicin HCl (DH) and Pyrvinium Pamoate (PP) were selected for further investigation, demonstrating potent anti-melanoma activity in advanced 3D co-culture systems and zebrafish xenograft models. Notably, PP demonstrated higher cytotoxicity compared to Trametinib, the off-label MEK inhibitor, with an inhibitory effect on AKT and invasive behavior in the primary metastatic melanoma cell lines. Additionally, combinatorial treatment with Trametinib resulted in additive effects on cell proliferation and viability. Importantly, both compounds showed similar efficacy in NRAS\u003csup\u003emut\u003c/sup\u003e and BRAF\u003csup\u003ewt\u003c/sup\u003e/NRAS\u003csup\u003ewt\u003c/sup\u003e melanoma cell lines that were resistant to MEK inhibitors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eUsing advanced 3D melanoma models that incorporate key TME elements and zebrafish xenograft models, this study highlights the potential of Daunorubicin HCl and Pyrvinium Pamoate as novel first-line therapies for NRAS\u003csup\u003emut\u003c/sup\u003e melanoma, with a noteworthy effect also on MEKi-resistant cells. These findings support drug repurposing strategies and underscore the importance of physiologically relevant preclinical models in identifying effective therapies.\u003c/p\u003e","manuscriptTitle":"High-throughput drug screening in advanced pre-clinical 3D melanoma models identifies potential first-line therapies for NRAS-mutated melanoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 02:24:09","doi":"10.21203/rs.3.rs-6594118/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":"47495930-2fc1-42fe-ab87-83d4f1150b6e","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-29T09:53:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 02:24:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6594118","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6594118","identity":"rs-6594118","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}