The causative agent, CCHF virus (CCHFV), has been characterized for years; however, its tropism in cell lines of different host and tissue origins remains unclear. This study characterized the susceptibility of 16 human and 6 animal cell lines to CCHFV. Increased viral load and viral nucleoprotein expression, and productive CCHFV replication were detected in human vascular (HUVEC), renal (SW-13 and HEK-293), hepatic (Huh7), and cerebral (U-87 MG) cell lines, which were considered CCHFV-highly permissive cell lines. Renal cell lines derived from monkey and dog could also support CCHFV replication. This study evaluated the susceptibility of different cell lines to CCHFV and identified CCHFV-permissive cell lines. Our findings raise concerns regarding the use of cell lines in  Orthonairovirus of family Nairoviridae. CCHF severity and CCHFV infection/exposure in humans have been described based on clinical findings and epidemiological studies (cynomolgus macaque model (Crimean-Congo hemorrhagic fever (CCHF) is a severe infectious disease across a vast geographic area in Asia, Africa, and Europe with fatality rates of 5–30% in humans . The aet studies ; however studies . To dateue model . The limin vitro models for investigating CCHFV infection and replication may promote defining of the molecular mechanisms of pathogenesis and benefit the assessing of drug candidates for antiviral effects. Monocytes, endothelial cells, hepatocytes, and renal cells have been shown to be permissive to CCHFV  cells, human umbilical vein endothelial cells (HUVEC), human hepatocarcinoma (Huh7) cells, and epidermal carcinoma (A431) cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum . Baby hamster kidney (BHK-21) cells, human embryonic kidney (HEK-293) cells, human cervix adenocarcinoma (HeLa) cells, African green monkey kidney (Vero) cells, African green monkey kidney, clone E6 (Vero E6) cells, Madin-Darby canine kidney (MDCK) cells, U-87 MG, HMC3, HepG2, SH-SY5Y, MRC-5, PK-15, and DH82 cells were cultured in Eagle’s minimum essential medium (EMEM) supplemented with 10% FBS. Human lung carcinoma (A549) cells were cultured in F12 medium supplemented with 10% FBS. SW-13 cells were cultured in L-15 medium supplemented with 10% FBS. HULEC-5a cells were cultured in MCDB131 supplemented with 10% FBS. THP-1 and Raji cells were cultured in RPMI-1640 medium supplemented with 10% FBS. Huh7, HUVEC, A549, MRC-5, and PK-15 cell lines were obtained from the National Virus Resource Center . RD, A431, BHK-21, HEK-293, HeLa, Vero, Vero E6, U-87 MG, HMC3, HepG2, SH-SY5Y, MDCK, DH82, SW-13, HULEC-5a, THP-1, and Raji cell lines were obtained from the American Type Culture Collection . All cell lines were tested negative for mycoplasma using EZ-PCR Mycoplasma test kit . All cell line details are summarized in Hyalomma asiaticum ticks by inoculating suckling mice with tick homogenates via both intracranial and intraperitoneal routes as previously described (Crimean-Congo hemorrhagic fever virus (CCHFV) strain YL16070 (GenBank accession: KY354082) used in this study was originally isolated from escribed . A batch50/mL). Viral titer endpoints were calculated using the Reed–Muench method.Viral titer of CCHFV in filtered supernatant from mouse brain homogenates was determined using end-point dilution assay as described previously . Briefly5 cells per well) were seeded in 6-well plates one night before the infection assay. Briefly, the growth medium was discarded and the cells infected with 10 μL cleared supernatant from mouse brain homogenates (diluted to 500 μL/well with appropriate medium containing 2% FBS) at a multiplicity of infection (MOI) of 0.01. After 1 h of viral adsorption at 37°C, the cells were supplemented with 1500 μL/well of appropriate medium containing 2% FBS and then incubated at 37°C in a 5% CO2 atmosphere. This infection was designated as the first round of infection. Supernatants of the first round of infection were collected at 4 d p.i. based on the growth property of CCHFV on Vero cells in the previous study  analysis was performed to determine viral loads. Total RNA was extracted from culture supernatants of all 22 cell lines infected by CCHFV at 4 d p.i. using TRIzol reagent  according to the manufacturer’s instructions. One-step qRT-PCR was used to determine the viral loads using a One Step PrimeScript™RT-PCR Kit (Takara) and a StepOnePlus Real-Time PCR System . The primers, probes, and RNA standards were prepared as previously described . The virThe expression of CCHFV NP in cells at 4 d p.i. was detected by IFA as previously described . Based oFor some cells, the expression of NP was further quantified by flow cytometry using a BD LSRFortessa flow cytometer (BD Biosciences). Briefly, infected or mock-infected cells were detached with trypsin at 2 d p.i., and fixed with 4% paraformaldehyde in phosphate-buffered saline (PBS). The fixed cells were incubated in 0.2% Triton X-100-PBS for permeabilization and then blocked with 5% bovine serum albumin (BSA). The cells were then stained with fluorescein isothiocyanate (FITC)-conjugated CCHFV NP monoclonal antibody  at room temperature for 1 h. The cells were washed three times with PBS containing 0.5% BSA prior to analysis.5) were seeded in the 6-well plates overnight and were then incubated with the filtered supernatant from mouse brain homogenates at an MOI of 0.01. After 1 h of adsorption at 37°C, the cells were washed three times with PBS and the appropriate medium was added. Cells were then incubated for the indicated time at 37°C. Supernatants (50 μL) were harvested from each sample at the indicated time points and used to determine the viral titers by end-point dilution assays as described above.Viral growth properties of CCHFV in different cell lines were characterized by growth curve analysis. Briefly, cells (5 × 10Vero E6 cells were infected with CCHFV at an MOI of 0.01. The culture supernatants (20 mL) were collected at 4 d p.i., inactivated with β-propiolactone (1:2000 v/v), cleared of cell debris and concentrated to 1 mL using Amicon Ultra concentrators . The clarified supernatants were then centrifuged at 13000 × g for 30 min at 4°C. The pellets were resuspended in 10 μL PBS. Virus suspension were adsorbed onto formvar-coated copper grids for 5 min, negatively stained using 2% phosphotungstic acid (PTA) for 1 min and then examined with a transmission electron microscope .CCHFV infection in each cell line was characterized by IFA. During the first round of infection, 14 of the 16 human cell lines were infected with CCHFV at different efficiencies, including HUVEC, HULEC-5a, Raji, Huh7, HepG2, SW-13, HEK-293, A549, MRC-5, U-87 MG, HMC3, SH-SY5Y, HeLa and RD Table 1.9 to 1.4 × 1011 copies/mL were detected in culture supernatants of all the 16 human cell lines in the first round of infection. These levels were 50–4,000 times higher than the baseline viral load of 3.2 × 107 copies/mL (4 TCID50/mL) than those from HeLa (3.2 × 103 TCID50/mL) and HepG2 (4 × 102 TCID50/mL). When the supernatant was used to inoculate fresh cells for the second round of infection, CCHFV RNA was detected in supernatants from all cell lines, but the levels were significantly reduced except for HepG2 cells having comparable virus loads between the first and second rounds  generated in Huh7 cells (5 TCID50/mL) generated in U-87 MG cells. However, viral titers in supernatants from RD and HepG2 cells still increased slightly from 72 h p.i. to 96 h p.i. Virus titers in supernatants from CCHFV-non permissive cell line HepG2 was only around 3.2 × 102 TCID50/mL, but that from CCHFV-highly permissive cell lines  could reach at least 104 TCID50/mL and from CCHFV-permissive cell lines  could reach at least 103 TCID50/mL . In addition, the different IFN-competence of each cell line may also be involved. Previous studies have shown that CCHFV is a potent inducer of the interferon response and upregulate the interferon signaling pathway in A549, Huh7, HepG2, Vero cells and primary macrophages . Viral titers generated from highly-permissive cell lines decreased significantly after 24 h, which may be related to IFN level and virus degradation over time. Decrease of viral titers was also observed in the permissive cell lines after 72 h, but was not significant probably due to the low yields of infectious viruses. Therefore, the IFN production and competence of these cell lines in response to CCHFV infection need to be further evaluated systematically so as to clarify the effect on the amplification of CCHFV. Meanwhile, the decrease of virus titers from the cell lines after 72 h would also explain the reduced infection as evidenced by IFA assays in the second round of infection. Therefore, we recommend harvesting virus culture supernatants at 24 or 48 h post infection for better use of the viruses in further in vitro assay with the highly-permissive cell lines like SW13, HEK293, and Huh7.CCHFV growth kinetics were characterized and are distinguishable between the CCHFV-highly permissive cell lines  and CCHFV-permissive cell lines  as well as CCHFV-non permissive cell line (HepG2). Virus titers from all tested cell lines increased rapidly at the initial 24 hours of infection. The highly-permissive cell lines had comparatively titers of 1.8 × 10Hyalomma ticks, including cattle, sheep, and camels were not investigated. Further studies on cell lines derived from these and other animals may shed light on whether these animals serve as potential reservoirs, and provide an ex vivo approach for investigating the molecular mechanisms of infection in animal hosts.CCHFV infects various domestic livestock and wild animals. Most remain asymptomatic, even though they may develop severe viraemia . CCHFV iHyalomma asiaticum ticks in China. It is phylogenetically divergent from the prototype strain IbAr10200 which was isolated from Hyalomma excavatum ticks in Nigeria (5 TCID50/mL) in the mouse brain homogenate was insufficient to support the infection assays with high dose of viruses. In the growth curves, CCHFV titer was measured with TCID50, but not PFU, which may also cause different understanding of infectivity. Third, despite the cell lines were obtained from ATCC, the different batches of the cell lines in individual laboratories may also affect the efficiency of detection.There are limitations in this study and factors that may affect the results. First, the CCHFV strain YL16070 used in our study was isolated from  Nigeria . Based o Nigeria . The evo Nigeria . Howeverex vivo studies and highlight the need for careful consideration based on their host and tissue origin, necessity of CPE observation, and other characteristics that may be involved. Taken together, cell lines of vascular, hepatic, renal, and cerebral origins, which represent the targeting tissues of CCHFV invasion in vivo, exhibited high viral load in supernatants. Based on our findings, we suggest that U-87 MG, SW-13, HEK293, Huh7, and HUVEC are useful for investigating CCHFV pathogenesis and evaluating the effects of antiviral drugs specific to respective tissues, and Vero E6 is applicable for virus isolation and culture as sustainability of CCHFV proliferation. In summary, our findings provide information to be considered for optimizing ex vivo experiments for fundamental studies on CCHFV and developing CCHF disease control and prevention strategies.This study compared the susceptibility to CCHFV in human and animal cell lines derived from different tissues. The findings provide an important reference for the use of cell lines in The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.SS and FD conceived and designed the study. SD, QW, XW, and CP performed the experiments. SD, QW, and SS analyzed the data. SD drafted the manuscript. SS, FD, JL, ST, and TZ reviewed and edited the manuscript. SS, FD, and ST acquired the funding. All authors contributed to the article and approved the submitted version.This work was supported by the National Program on Key Research Project of China , the National Science and Technology Major Project on Important Infectious Diseases Prevention and Control (2018ZX10734-404), and the Strategic Biological Resources Capacity Building Project of Chinese Academy of Sciences (KFJ-BRP-017-06).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


MGMT methylation level, COX-2 and PGE2 EP4 protein levels were also determined in order to better understand the molecular mechanisms exerted by these compounds and to find out which of them can serve best in GBM therapy.Glioblastoma (GBM) is the deadliest and the most common primary brain tumor in adults. The invasiveness and proliferation of GBM cells can be decreased through the inhibition of Wnt/β-catenin pathway. In this regard, celecoxib is a promising agent, but other COXIBs and 2,5-dimethylcelecoxib  await elucidation. Thus, the aim of this study was to analyze the impact of celecoxib, 2,5-DMC, etori-, rofe-, and valdecoxib on GBM cell viability and the activity of Wnt/β-catenin pathway. In addition, the combination of the compounds with temozolomide (TMZ) was also evaluated. Cell cycle distribution and apoptosis, MGMT methylation level were determined using MS-HRM method. The impact of COXIBs, in the presence and absence of TMZ, on Wnt pathway was measured on the basis of the expression of β-catenin target genes. Cell cycle distribution and apoptosis analysis were performed using flow cytometry. COX-2 and PGE2 EP4 receptor expression were evaluated using Western blot analysis.Celecoxib, 2,5-DMC, etori-, rofe- and valdecoxib were evaluated using three commercially available and two patient-derived GBM cell lines. Cell viability was analyzed using MTT assay, whereas alterations in MGMT methylation status, or radio/chemoresistance. Celecoxib and 2,5-DMC were the most cytotoxic. Cell cycle distribution was altered, and apoptosis was induced after the treatment with celecoxib, 2,5-DMC, etori- and valdecoxib in T98G cell line. COXIBs and 2,5-DMC did not influence MGMT methylation status, but inhibited COX-2/PGE2/EP4 pathway.Wnt/β-catenin pathway was attenuated by COXIBs and 2,5-DMC irrespective of the COX-2 expression profile of the treated cells, their Not only celecoxib, but also 2,5-DMC, etori-, rofe- and valdecoxib should be further investigated as potential good anti-GBM therapeutics.The online version contains supplementary material available at 10.1186/s12885-021-08164-1. GBM represents the most aggressive and the most common primary brain tumor in adults. It is highly heterogeneous and driven by diverse genetic, epigenetic, and developmental programs . DespiteMGMT (the O6-methylguanine-DNA methyltransferase) promoter methylation. This is beneficial in the context of temozolomide (TMZ) response [One of the most critical oncogenic drivers of GBM is the Wnt/β-catenin pathway . As we aresponse .MGMT methylation status also requires further investigation.Several analogs of selective COX-2 inhibitors (COXIBs) exist . CelecoxMGMT methylation level, COX-2 and PGE2 EP4 protein levels were also determined in order to better understand the molecular mechanisms exerted by these compounds and to find out which of them can serve best in GBM therapy.Thus, the aim of this study was to analyze the impact of celecoxib, 2,5-DMC, etori-, rofe-, and valdecoxib on GBM cell viability and the activity of Wnt/β-catenin pathway. In addition, the combination of the compounds with TMZ was also evaluated. Cell cycle distribution and apoptosis, MGMT and COX-2 in A-172, T98G, and U-138 MG cell lines under normal conditions compared to a housekeeping gene (TBP) are presented in Fig. MGMT promoter methylation with no MGMT expression. It is radiosensitive and TMZ sensitive [MGMT promoter, expressing high MGMT protein level. This cell line is TMZ resistant [MGMT promoter and high MGMT expression. COX-2 is also expressed in this cell line. It is also regarded as TMZ resistant [In this study, three commercially available GBM cell lines, A-172 (ATCC-CRL-1620), T98G 92090213) and U-138 MG (ATCC-HTB-16) and two patient-derived primary GBM cell lines, P1 and P6 were used. A-172 and U-138 MG cell lines were purchased from American Type Culture Collection (ATCC), whereas T98G cell line was obtained from the European Collection of Authenticated Cell Cultures (ECACC). The basic expression levels of 3 and U-1ensitive . T98G ceesistant . Eventuaesistant .Fig. 1TMGMT is expressed only in P6 cell line, while COX-2 in both of the cell lines. The basic expression levels of MGMT and COX-2 in P1 and P6 cell lines under normal conditions compared to a housekeeping gene (HPRT) are presented in Fig. P1 patient-derived cell line was obtained from 64-year-old female, whereas P6 from 68-year-old male patient. Tissue samples were provided by Chair and Department of Neurosurgery and Neurotraumatology of Poznan University of Medical Sciences based on the ethical approval (KB 534/180). Cells were authenticated by a standard histopathological and molecular analysis done routinely at the Department of Neurosurgery and Neurotraumatology, Heliodor Swiecicki Clinical Hospital, Poznań, Poland. Cells were derived from tumor mass of GBM patients by dissociation using Brain Tissue Dissociation Kit . Our qPCR analysis revealed that 2 in a humidified incubator . The passage number was kept low (maximum number 25). Cell morphology and proliferation rate was constantly monitored during the experimental part of the project and never showed any abnormalities. Standard safety procedures were used to avoid cell line contamination with other cell lines and microbes. The LookOut® Mycoplasma PCR Detection Kit  was used every two weeks to verify if the cells were mycoplasma free.A-172, P1, and P6 cells were grown in ATCC-formulated Dulbecco’s modified Eagle’s medium (DMEM) , whereas T98G and U-138 MG cell lines were grown in ATCC-formulated Eagle’s Minimum Essential Medium (EMEM) , respectively. These media were supplemented with FBS  to a final concentration of 10%, as well as antibiotics (penicillin and streptomycin)  to the final concentrations of 1%. For the experiments, the amount of FBS was reduced to 5%. Regarding T98G cell line, the medium was also supplemented with 2 mM glutamine, 1% non-essential amino acids, and 1% sodium pyruvate . Cells were incubated at 37 °C in an atmosphere consisting of 95% air and 5% CO50 values are presented in Table Four COXIBs were used in this experiment, namely: celecoxib, etori-, rofe- and valdecoxib as well as dimethyl derivative of celecoxib – 2,5-DMC. Their chemical structures and ICThe effect of selected COXIBs, 2,5-DMC and PKF118–310 on the viability of A-172, T98G and U-138 MG cells was assessed by the MTT assay. Briefly, 10,000 cells/well were seeded on 96-well plates and incubated for 24 h. Afterward, tested compounds in various concentrations were added; the controls with DMSO were also included. The test was performed for 48 h, and afterward, the cells were exposed for 3 h to MTT solution dissolved in 10% FBS medium (0.5 mg/ml). After the above-mentioned incubation time, MTT solution was removed, and formazan crystals were dissolved in acidic isopropanol. The absorbance was measured at λ = 570 nm and λ = 690 nm on Tecan Infinite M200 microplate reader (Austria). All the experiments were repeated at least three times with four measurements per assay. Based on MTT assay, the concentrations that allow survival of more than 70% of cells were chosen for further analysis.NEDD9 were the following: forward 5′-GAACAAGAGGTATATCAGGTG-3′, and reverse 5′-TTGAGTGGTATGAGAAGGAG-3′. The mean expression of TBP (TATA-box-binding protein) and PBGD (porphobilinogen deaminase) in the analysis of A-172, T98G, and U-138 MG cell lines transcripts and HPRT1 (hypoxanthine phosphoribosyltransferase 1) in the analysis of P1 and P6 cell lines transcripts were used for data normalization. Primer sequences for HPRT qPCR analysis were as follows: forward 5′-CCTGGCGTCGTGATTAGTGAT-3′, and reverse 5′- AGACGTTCAGTCCTGTCCATAA-3′. The ΔΔCt method was used for fold-change calculation. All samples were analyzed in triplicate, and the reaction was repeated twice.Genomic DNA and total RNA were isolated using GeneMATRIX UNIVERSAL DNA/RNA/protein kit  after 48 h of treatment. The isolations were repeated three times. For cDNA synthesis, RevertAid First Strand cDNA Synthesis Kit  was used, according to the protocol provided by the manufacturer. Next, qPCR for genes of interest was performed using Hot FIREPol EvaGreen qPCR Mix Plus  and Chromo4 thermal cycler . The oligonucleotides were synthesized at the Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, and their sequences were published before , 25. PriMuse® Caspase-3/7 Assay Kit  was used in order to verify if the analyzed compounds are able to induce apoptosis in GBM cells. This fluorescent-based assay relies on the quantitative measurements of apoptotic status based on caspase-3/7 activation analyzed simultaneously with cellular plasma membrane permeabilization and cell death.Briefly, cells from all three cell lines  were seeded on 6-well plates and incubated for 24 h. Afterward, the analyzed compounds were added in concentrations based on MTT results, and the cells were further incubated for 48 h. 0.5% or 1% DMSO and 100 nM topotecan were used as negative and positive controls, respectively. The subsequent analysis was performed on Muse™ Cell Analyzer according to the manufacturer’s recommendations .The Muse™ Cell Cycle Kit and Muse™ Cell Analyzer  was used in order to check the number of cells in each phase of the cell cycle. The assay utilizes propidium iodide-based staining of DNA content to discriminate and measure the percentage of cells in each cell cycle phase . The analysis was performed after 48 h incubation with the analyzed compounds, according to the protocol provided by the manufacturer.MGMT promoter was determined by MS-HRM on Light Cycler®96  in T98G cell line. First, 500 ng of genomic DNA was bisulfite converted using EZ DNA Methylation Kit . As positive/methylated and negative/unmethylated controls, we used CpG Methylated HeLa Genomic DNA  and CpGenome Universal Unmethylated DNA Set , respectively. Standardized solutions with a given DNA methylation percentage  were prepared. MS-HRM was performed using 5 X Hot FIREPol EvaGreen HRM Mix  under default cycling conditions and with primers previously published by Wojdacz & Dobrovic [The methylation level of Dobrovic . After aCells were lysed with RIPA buffer containing protease inhibitory cocktail. Protein concentration was measured using Spectrophotometer , and 50 μg of denaturized protein samples were separated on SDS-PAGE. Proteins were then transferred to activated with methanol polyvinylidene difluoride (PVDF) membrane using wet tank blotting. Unspecific binding was reduced by 5% w/v non-fat dry milk in PBS-T and hCOX2- , hPGE2 EP4- (SantaCruz Biotechnology), specific antibody incubations were carried out overnight at 4 °C. As loading control hGAPDH-  specific antibody conjugated to HRP was employed and was incubated for 1 h at RT. Secondary antibodies were directed against the host of primary antibodies and incubated for 2 h at RT. Membranes were developed using SuperSignal™ West Pico PLUS Chemiluminescent Substrate , and signals were read on G-box . Densitometric data generated for Western blots were obtained using GelPro Analyzer 3.1.p < 0.05 considered as significant.Student’s t test  was used for analysis of the significance of differences between experimental groups and their respective controls, with MGMT promoter methylation and does not correlate with TMZ resistance. The highest cytotoxicity was observed after the treatment with 2,5-DMC, followed by celecoxib  were used.Axin-2, c-MYC, BIRC5, CCND1, and NEDD9 after 48 h of treatment with the analyzed compounds are presented in Fig. The mRNA level of β-catenin and its target genes, CTNNB1 encoding β-catenin was downregulated by all the analyzed compounds. PKF118–310 additionally reduced the mRNA level of Axin2 and surprisingly upregulated CCND1. The most effective Wnt pathway inhibitor was however rofecoxib, downregulating not only CTNNB1, but also of its target genes: Axin2, c-MYC, BIRC5 and NEDD9. The second most effective Wnt pathway inhibitor was celecoxib. Still it has to be mentioned that also undesirable up-regulation of c-MYC and CCND1 was observed after the treatment with this compound. 2,5-DMC was also exerting a similar effect concerning c-MYC. Etori- and valdecoxib showed moderate effects – downregulated both CTNNB1 and Axin2, as well as BIRC5 (etoricoxib), but up-regulated CCND1 .As far as A-172 cell line is concerned, c-MYC) were downregulated after the treatment with this compound. It has to be however mentioned that in this experiment, PKF118–310 was used in the highest concentration (0.5 μM) as compared to two other cell lines. Next, the most significant effects were observed after the treatment with etoricoxib, which downregulated CTNNB1 and two of its target genes, c-MYC and BIRC5. Celecoxib and its methylated analog showed similar effects as described in A-172 cell line - we observed both desired effects , but also potentially dangerous up-regulation of CCND1 oncogene (after the treatment with celecoxib). Rofecoxib was able to downregulate CCND1 and BIRC5, whereas valdecoxib CTNNB1 and c-MYC.Regarding T98G cell line, PKF118–310 Wnt pathway inhibitory effect was the most profoundly seen. All of the analyzed genes , whereas valdecoxib was not influencing the Wnt pathway target genes expression in this cell line.Regarding U-138 MG cell line, PKF118–310 downregulated β-catenin and its two target genes: Wnt/β-catenin pathway and apoptosis are interconnected; thus our next goal was to analyze if COXIBs/2,5-DMC induce apoptosis in GBM cell lines. Figure The results of the cell cycle distribution analysis illustrate that again only T98G cell line is susceptible to COXIBs treatment Fig. b. In thiMGMT methylation is an important determinant of GBM patients response to TMZ [MGMT methylation level was analyzed in T98G cell line. This cell line was chosen for this experiment since the above-mentioned tests showed the highest sensitivity to the COXIBs treatment compared to the other two cell lines. Figure MGMT methylation level is not altered, as compared to the results for 0.5% DMSO (control). In all cases, the resulting methylation level was close to 50% methylation.e to TMZ . Thus, tc-MYC and NEDD9 in P1 cell line, and also Axin2, c-MYC, CCND1, and BIRC5 in P6 cell line. Its dimethyl derivative, 2,5-DMC was slightly less effective – in P1 cell line it downregulated c-MYC expression, but also upregulated BIRC5. Moreover, the mRNA levels of Axin2 and CCND1 were reduced after the treatment with this compound in P6 cell line. On the other hand, rofecoxib was effective only in P1 cell line – it reduced the expression of c-MYC and NEDD9, but increased BIRC5.Next, based on the qPCR analysis of β-catenin target genes expression using commercial GBM cell lines, we chose the three most effective compounds - celecoxib, 2,5-DMC and rofecoxib, and verified their impact on two patient-derived primary GBM cell lines – P1 and P6 after 48 h of treatment . However, similarly to A-172 cell line, CCND1 was upregulated after the treatment with both combinations . On the other hand, the combination of TMZ and rofecoxib downregulated the mRNA levels of two genes: c-MYC and BIRC5.Similar results were obtained for U-138 MG cell line. In this case, TMZ combined with either celecoxib or 2,5-DMC downregulated Axin2, but upregulated BIRC5, CCND1, and NEDD9. Noteworthy, the latter was up-regulated also when the combinations of TMZ with celecoxib, 2,5-DMC and rofecoxib were evaluated. Furthermore, besides previously mentioned NEDD9 up-regulation no gene expression changes were observed when TMZ was combined with celecoxib in this cell line. Similar results were observed in P6 cells. As far as TMZ and 2,5-DMC combination is concerned, the beneficial downregulation of BIRC5 and CCND1 was observed in P6 cells. The latter gene, as well as CTNNB1 were however up-regulated in the same experimental setting in P1 cells. TMZ and rofecoxib was also inducing β-catenin up-regulation in P6 cells, but downregulated Axin2 in P1 cell line.Next, we wanted to verify if Wnt/β-catenin pathway is inhibited in patient-derived primary P1 and P6 cell lines, when 30 μM TMZ is combined with 25 μM celecoxib, 10 μM 2,5-DMC or 100 μM rofecoxib Fig. . TMZ treRecent literature points out that Wnt/β-catenin pathway hyperactivation is one of the most important features of GBM –30. UpreThus, we focused on the impact of celecoxib, 2,5-DMC, etori-, rofe-, and valdecoxib, on GBM cells. The results of this study show that not only celecoxib, but also 2,5-DMC (lacking COX-2 inhibitory activity) as well as etori-, rofe- and valdecoxib can be regarded as promising compounds with anti-GBM properties. Their mechanism of action, determined based on the results of this study, is shown in Fig. MGMT promoter methylation status and does not correlate with the TMZ resistance. In this regard, A-172, T98G, and U-138 MG cell lines reacted in a similar way to the treatment with the analyzed compounds. The two most cytotoxic compounds were 2,5-DMC and celecoxib. Similar results were published regarding celecoxib’s treatment of GBM cell lines U87 and U251 – in this case celecoxib reduced not only cell proliferation, but also colony formation, and cell invasion [We found that, in regard to the impact of COXIBs on GBM cell viability, the sensitivity of GBM cell lines to COXIBs does not depend on the invasion . The facinvasion , 36. ThaWe found that tested cells showed different sensitivities to Wnt pathway inhibitor PKF118–310 - the most sensitive was U-138 MG. Our study demonstrates that also COXIBs are effective Wnt pathway inhibitors, but their influence is highly dependent on the cell line used. In this regard, rofecoxib was most effectively inhibiting β-catenin and its target gene expression in A-172 cell line, etoricoxib in T98G cell line, whereas 2,5-DMC in U-138 MG cell line. On the other hand, celecoxib was the most efficient, as compared to 2,5-DMC and rofecoxib, when tested on patient-derived primary cell lines.BIRC5, encoding survivin was observed after the treatment with 2,5-DMC, etori- and rofecoxib. These findings also confirm the importance of the correlation between apoptosis induction and the reduction of survivin levels via Wnt/β-catenin pathway inhibition in GBM cells [Furthermore, Wnt/β-catenin signaling pathway regulates apoptosis through many different mechanisms, including Wnt/BMP (bone morphogenic protein) interaction, SFRP2 (secreted Frizzled-related protein-2) expression, and β-catenin . In thisBM cells , 39.CCND1 gene expression, since cyclin D1, encoded by this gene is necessary for G1/S transition.Moreover, the effect of selected compounds on apoptosis may be correlated to their impact on cell cycle distribution. A significant similarity between these two phenomena was observed in the case of T98G cell line. The same compounds - celecoxib, 2,5-DMC, etori-, and valdecoxib reduced the number of cells in G0/G1 and increased the fraction of cells in G2/M phases similarly to the effect of a positive control of the assay – topotecan and also PKF118–310. In regard to rofecoxib, the induced changes in cell cycle distribution can be attributed to its effect on MGMT promoter methylation [MGMT methylation status after 48 h of treatment. Perhaps a longer incubation time is required to observe this phenomenon. Such an epigenetic change would be beneficial in regard to TMZ treatment. Interestingly, Wickström et al. [MGMT through direct interaction with TCF/LEF transcription factor binding sites located in the 5′-upstream regulatory region of the MGMT gene. Moreover, in this study celecoxib treatment reduced the levels of MGMT in a mouse model [It has recently been established that in gastric cancer cells, PGE2 induces DNMT3B expression and activity, which in turn results in a higher level of hylation . Howeverm et al.  have shose model .Elevated levels of COX-2 in GBM are highly correlated with many aggressive aspects of the disease, including a high rate of GBM cell proliferation, higher tumor grade, and poor prognosis . Thus, oRecently, PGE2 has emerged as a principal mediator for COX-2 cascade-driven gliomagenesis . PGE2 meTMZ is the most common alkylating agent used in GBM treatment . Thus, wMGMT methylation status of the treated cells. Moreover, we found that β-catenin target genes were down-regulated by COXIBs and 2,5-DMC also when these drugs were combined with TMZ. In regard to the potential cardiotoxicity of rofe- and valdecoxib, their administration into the tumor niche should be a solution. Novel drug delivery systems could limit the effects on the entire organism, enhancing the effect on the remaining glioma stem cells after the resection of this diffusely growing tumor. In our study, the T98G cell line was the most prone to apoptosis induction and cell cycle arrest, indicating that COXIBs and 2,5-DMC could potentially be valuable drugs for radio- and chemoresistant GBM patients. Our study also demonstrates that the COX2/PGE2/EP4 signaling can be attenuated by COXIBs and 2,5-DMC. Therefore it needs to be emphasized that more pre-clinical and clinical studies addressing combinational COXIBs/2,5-DMC and chemotherapy treatment, as well as targeted brain delivery are required to fully establish the role of these compounds in anti-GBM treatment.To conclude, the results of our study show that COXIBs, as a class of drugs, produce variable, but beneficial effects and can be regarded as good anti-GBM therapeutics. Celecoxib and 2,5-DMC were the most cytotoxic, but other COXIBs also showed good antitumor activities, including Wnt/β-catenin pathway target genes inhibition. Our findings also support the theory that the Wnt/β-catenin pathway is attenuated by COXIBs and 2,5-DMC irrespective of the COX-2 expression profile or Additional file 1: Supplementary Fig. 1. The original images for all relevant western blot analysis of COX-2 and PGE2 EP4 expression in different cell lines. A-172 (A), T98G (B), U-138 MG (C), P1 (D) and P6 (E) cells were treated with celecoxib, 2,5-DMC and rofecoxib for 48 and 72 h. The concentration of compounds used in this assay provided at least 70% of cell viability. PGE2 EP4 and COX-2 protein levels were evaluated by western blot. DMSO was used as a treatment control. GAPDH was used as loading control. The closest protein standard marker band for respected analyzed protein is marked with expected size in kDa. Mostly the protein standard marker is added as separate picture taken. Black boxes represent the cropped bands taken for the analysis and presented in the main body of the manuscript. These were taken into densitometric analyses.


While LOH rates varied among MA lines from different ancestors, there was no apparent correlation between the genetic divergence of the parental haploid genomes within ancestral clones and LOH rates. Together, our results suggest that hybrids between diverse lineages of the human pathogenic Cryptococcus can generate significant genotypic and phenotypic diversities during asexual reproduction.To better understand the potential factors contributing to genome instability and phenotypic diversity, we conducted mutation accumulation (MA) experiments for 120 days for 7 diploid cryptococcal hybrids under fluconazole (10 MA lines each) and non-fluconazole conditions (10 MA lines each). The genomic DNA content, loss of heterozygosity (LOH) rate, growth ability, and fluconazole susceptibility were determined for all 140 evolved cultures. Compared to that of their ancestral clones, the evolved clones showed: (i) genomic DNA content changes ranging from ~22% less to ~27% more, and (ii) reduced, similar, and increased phenotypic values for each tested trait, with most evolved clones displaying increased growth at 40 °C and increased fluconazole resistance. Aside from the ancestral multi-locus genotypes (MLGs) and heterozygosity patterns (MHPs), 77 unique MLGs and 70 unique MPHs were identified among the 140 evolved cultures at day 120. The average LOH rates of the MA lines in the absence and presence of fluconazole were similar at 1.27 × 10 Saccharomyces cerevisiae and Candida albicans [C. albicans, resulting in aneuploidy and greater resistance to fluconazole [A stable genome allows faithful transmission of genetic information from parent to progeny, ensuring genotypic and phenotypic stability within individual organisms or populations. However, genome instability is common and has been found in most organisms. Genome instability can be manifested in multiple forms and caused by various factors, such as genome doubling, chromosomal rearrangement, mitotic recombination, gene duplication, gene deletion, gene conversion, and transposition. Many of these changes involve double-strand DNA breaks, followed by repair through either the synthesis-dependent strand annealing using homologous sequences or non-homologous end joining ,2. Amongalbicans ,6,7,8,9.conazole . Throughconazole ,12,13. ACryptococcus (HPC) is a group of basidiomycete yeasts and an excellent model for studying the genetic stability of hybrids and investigating how genome sequence divergence between homologous chromosomes can impact the LOH rates. HPC consists of two species complexes, the Cryptococcus neoformans species complex (CNSC) and the Cryptococcus gattii species complex (CGSC). Most strains of CNSC and CGSC are haploid, existing in one of two mating types, MATα and MATa. These haploid yeast cells typically propagate asexually by budding until strains of the opposite mating-type (a-α) or the same mating-type (α-α) come into contact on substrates conducive for mating. The mating products are initially in a dikaryotic hyphal form that transitions into a transient diploid phase before meiosis to produce sexual spores . However, the diploid status can be induced and maintained through the hypha-to-yeast transition at 37 °C. In nature, most haploid strains are MATα [MATa strains are relatively rare, heterozygous diploid hybrids from a-α mating of HPC were frequently reported from environmental and clinical settings [Cryptococcus neoformans (serotype A) and Cryptococcus deneoformans (serotype D) are the most prevalent [The human pathogenic are MATα . Althougsettings ,10,11,12settings ,17,18,19revalent . Serotyprevalent ,21. Yet,S. cerevisiae, the induced chromosomal instability is often regulated by interactions between chromosomes leading to aneuploidy in cells [Previous investigations of serotype AD hybrids showed that both meiosis and mitosis could generate diploid or aneuploid progeny, along with some loci being heterozygous while others being homozygous ,21,22,23in cells . Ploidy in cells .C. albicans, including LOH, aneuploidy, gene duplication, and novel mutations [ERG11 (the fluconazole target gene) and AFR1 (the transporter for triazoles), are associated with fluconazole resistance [Experimental evolution is a valuable tool for studying the mechanisms of adaptation to specific environmental conditions. For example, experimental evolution revealed the genetic mechanisms for fluconazole resistance and adaptations to new environments in utations ,29,30. Tutations . Researcsistance ,32.In this study, we aimed to investigate the genome instability of inter-lineage hybrids of HPC, including hybrids between lineages within CGSC and hybrids between CNSC and CGSC. Specifically, we were interested in whether genomes of hybrids from evolutionarily more divergent parental strains were more stable than those from evolutionarily more similar parents. On the one hand, the increased sequence divergence between more divergent homologous chromosomes could reduce the rate of homologous recombination, resulting in more stable genomes . On the Cryptococcus hybrids from our previous work were used as the starting clones to initiate the MA experiments [Seven diploid eriments . These o6 cells takes three days under our experimental conditions, representing ~20 ± 1 mitotic divisions over three days of growth [It was estimated that the growth of one cell to ~10f growth . In the The genomic DNA contents of the D120 cultures of all 140 MA lines and their respective ancestral clones were determined by fluorescence-activated cell sorting (FACS), using the protocol described previously . In thisThirteen nuclear genetic markers located on ten chromosomes and two mitochondrial markers were used to analyze the genotypes of the evolved clones in this study  broth, YEPD broth, and YEPD-FLC broth . These cells were inoculated in 96-well microtiter plates and incubated at three different temperatures  for 3 days. OD600 values were measured on day 0 and day 3. Three biological replicates of each sample under each treatment were performed with three experimental repeats.The growth abilities of haploid parental strains, diploid ancestral clones, and the D120 cultures of all 140 MA lines were quantified in nine conditions. Briefly, fresh cells of these cultures were collected and adjusted to a final concentration of ~10For all D120 cultures of the 140 MA lines, the fluconazole minimal inhibitory concentration (MIC) was determined following the CLSI broth microdilution method (M27-A2) . We examWe evaluated the genotypic diversity of the evolved clones using the Shannon-Weiner Diversity index (H). Pearson correlation tests were used to determine the relationships between the parental genetic divergence of the hybrids and LOH rates, ploidy changes, multi-locus genotypic diversity, growth rates under nine conditions, and fluconazole susceptibility of evolved clones. We examined the effects of parental genetic divergence on the observed genetic and phenotypic changes. The effects of fluconazole stress on these genetic and phenotypic changes were estimated by comparing the observed changes between F lines and Y lines. In addition, we investigated the potential impacts of ancestral clones’ fluconazole susceptibility on the changes of MICs of the evolved clones. The interaction effects of parental genetic divergence, fluconazole stress, and ancestral fluconazole susceptibility were evaluated. Generalized linear models were used to evaluate the relationships among the above factors using the R package ‘lme4’ . The mulIn this study, the ploidy changes, genotypic diversity, LOH, growth rates under nine conditions, and fluconazole MIC changes of all D120 cultures were determined. The multi-locus heterozygosity patterns and LOH rates were estimated based on multi-locus genotype data. We also estimated the effects of factors  on the observed genetic and phenotypic changes, as well as the correlations between them. Below we describe the findings.p < 0.001).Over 800 mitotic generations, more than 70% of D120 cultures showed different genomic DNA contents from their respective ancestral clones  at the two assayed mitochondrial loci . In addition, the D120 cultures from the seven ancestral clones showed different genotypic diversities , including six ancestral MLGs and 77 MATa or MATα haploid parental genotypes . Among the remaining 108 MA lines, we found a total of 70 de novo MHPs, including eight MHPs shared between Y lines and F lines , 34 MHPs were only found among Y lines, and 28 unique MHPs were found only in F lines.According to their multilocus heterozygosity patterns (MHPs), these seven ancestral clones were assigned to two MHPs, MHP.16 and MHP.74  accumulated over 800 mitotic generations among the 140 MA lines, corresponding to 1.27 × 10p values < 0.001). The only exceptions were D120 cultures from the ancestral clone of YMD88 showing decreased growth in the YEPD-FLC medium at 40 °C. The findings suggest that the MA temperature at 37 °C can increase the growth ability of the evolved clones at a higher temperature .The D120 cultures showed diverse growth rates under the nine tested environmental conditions  and the F lines . In contrast, it was significantly negatively correlated with ploidy changes of the Y lines  and the F lines . We also observed negative correlations between parental genetic distance and growth rates of the Y lines under all nine conditions and those of the F lines under six conditions . Overall, our data indicate that parental genetic divergence significantly affected ploidy changes, fluconazole MIC changes, and growth rates under most tested conditions of both the Y lines and the F lines, as well as LOH rates of the F lines.The genetic distance between parental genomes within hybrids showed several significant correlations with genetic and phenotypic changes of evolved hybrid MA clones . In contrast, D120 cultures from the Y lines had on average higher growth rates than those from F lines under 7 of the 9 conditions, except for the YEPD-FLC medium at 37 °C and 40 °C. These results indicate that fluconazole stress could affect the ploidy levels and fluconazole MIC values of the evolved clones during mitotic divisions.Pairwise comparisons of D120 cultures between Y lines and F lines were used to evaluate the potential effects of fluconazole stress on the observed genetic and phenotypic changes. Overall, we found that D120 cultures from the Y lines and the F lines showed similar genetic diversity. There was no statistically significant difference in LOH rates between F lines and Y lines. Interestingly, D120 cultures from F lines showed greater increases in ploidy and fluconazole MIC than those from Y lines (p = 0.013), while negatively correlated with growth rates at 40 °C in the YEPD-FLC medium . However, for both F lines and Y lines, diverse correlations were observed when the analyses were conducted for the lines derived from individual strains (as illustrated in p < 0.001), while positively correlated with those of Y lines from YMD135 . Interestingly, for MA lines derived from YMD88, LOH rates were significantly positively correlated with fluconazole MIC changes of Y lines  but negatively correlated with those of F lines . We found similarly diverse correlations between LOH rates and growth rates, with ancestral clones, MA conditions , and growth conditions contributing to the positive and negative correlations. For example, the D120 cultures of the Y lines from YMD165 showed a statistically significant positive correlation between their LOH rates and growth rates in the YEPD-FLC medium at 37 °C  and phenotypic changes  among the D120 cultures. For example, LOH rates of F lines were significantly positively correlated with fluconazole MIC changes  but not in F lines. The D120 cultures from both F lines and Y lines from some ancestral clones showed positive correlations between ploidy changes and fluconazole MIC changes and growth rates, while those of others were negatively correlated or showed no association (as illustrated in p < 0.001) but negatively associated with that of F lines . For growth rates in different media, ploidy changes were only negatively associated with the growth rates of D120 cultures in Y lines from YMD72 in the YEPD medium at 30 °C  and of Y lines from YMD89 in the YEPD-FLC medium at 30 °C . Overall, the relationships between the observed genetic changes and phenotypic changes varied among Y lines and F lines from different ancestral clones.Similar to the observed relationships between LOH rates and phenotypic changes among MA lines, a diversity of relationships between ploidy changes and phenotypic changes were also found. For example, we observed overall positive correlations between ploidy changes and growth rates for D120 cultures in Y lines in the RPMI medium  contributed significantly to the ploidy status of MA lines. The evolved clones under fluconazole stress (F lines) had higher ploidies on average than those under non-fluconazole stress lines (Y lines). Evidence that fluconazole stress can induce ploidy changes  was reported in many species, such as oformans ,56,57. Aoformans . Fluconaalbicans . In C. nIn this study, we focused on the impacts of two factors separately on the genotypic and phenotypic changes of these hybrids during mutation accumulation. The results demonstrated that: (i) parental genetic distance can influence ploidy changes and growth rates among the D120 cultures of the MA lines under several conditions, and (ii) fluconazole stress can impact ploidy changes and fluconazole MIC changes. Interestingly, the analyses also indicated interaction effects of these two factors, with certain correlations found only in one of the two MA conditions. Indeed, our analyses showed interactions between parental genetic divergence and MA conditions on most observed phenotypic and genetic changes among the D120 cultures, including LOH rates, ploidy changes, genetic diversity, fluconazole MIC changes, and growth rates at 30 °C and 37 °C in the RPMI medium and at 40 °C in the YEPD-FLC medium. Because natural environments are heterogenous and cryptococcal hybrids can disperse across geographic regions and ecological niches, divergent genotypes with different phenotypic properties could be evolved and selected from single hybrids through sexual reproduction, which could bring negative impacts to patients and our healthcare system.C. albicans, monosomy of chromosome 5 increases resistance to several antifungal drugs [Aneuploidy refers to gains and/or losses of individual chromosome (s) or chromosomal segment (s) from the normal chromosome set. The ploidy changes in aneuploid strains could directly alter the transcription level of many genes on the affected chromosome (s) and indirectly change the expressions of genes on other chromosomes. This is because most genetic and metabolic pathways contain genes from multiple chromosomes, and when some components of a pathway in one chromosome are activated or inhibited, other components in other chromosomes could also be affected. In the present study, we found that most MA lines deviated from the genomic DNA contents of their ancestral clones after ~800 mitotic generations, with both increased and decreased ploidy levels compared to their ancestral clones  transporter-encoding gene [ERG11 [ERG11 locus among D120 cultures of 140 MA lines, including three in Y lines and three in F lines. The reason for the different LOH rates of ERG11 between these two studies was likely due to the different fluconazole concentrations used. In the current study, we used 4 µg/mL of fluconazole (vs. 32 µg/mL in the previous study), and the parental haploid strains could all grow on the agar medium containing 4 µg/mL of fluconazole. Another study found that chromosome 4 is the second most frequently formed disomy at high concentrations of fluconazole [Two genes on chromosome 1 were confirmed to be associated with fluconazole resistance in ing gene ,32,38. Ae [ERG11 . Differeconazole .p < 0.001) among D120 cultures of F lines . In contrast, no correlation was found between them among the Y lines. In addition, the nuclear locus CNI01350 had the highest LOH rate, and it is a QTL (quantitative trait locus) associated with cell size in Cryptococcus neoformans [In this study, we did not find any relationship between LOH at any specific locus/loci and the increased fluconazole MIC values. However, by comparing heterozygous rates of the examined 13 nuclear loci together to fluconazole MIC changes, we found a significantly negative correlation between heterozygous rates and fluconazole MIC changes  could be used to predict all the observed variations. Instead, both the parental genetic divergence and the MA conditions, as well as their interactions, contributed to the observed genotypic and phenotypic changes among the MA lines. Interestingly, significant correlations among MA lines from individual ancestors were frequently observed between certain genetic changes and phenotypic changes. In addition, most evolved lines showed increased fluconazole resistance, especially those maintained on medium containing fluconazole, suggesting that the treatment of infection by cryptococcal hybrids could present additional challenges over those by non-hybrids. For example, the presence of sublethal dose antifungals for the treatment of infections by hybrids might not kill these pathogens but instead facilitate the evolution of diverse genotypes and increased drug resistance. Together, our findings indicate the enormous potential of cryptococcal hybrids to generate genotypic and phenotypic diversities and that multiple factors can impact the production of such diversities. Future analyses of the genomes and transcriptomes of the evolved clones could help reveal their detailed genetic changes and the potential mechanisms contributing to the observed phenotypic differences among these clones.


BAX, BCL2, TP53, and CDKN1 expression in tested cell lines following incubation with ciprofloxacin and levofloxacin. The downregulation of topoisomerase II genes (TOP2A and TOP2B) was noticed. Three-dimensional (3D) cell culture analysis confirmed the higher cytotoxic effect of tested fluoroquinolone against cancer cell lines. Conclusions: Our results suggest that both ciprofloxacin and levofloxacin may have great potential, especially in the supportive therapy of bladder cancer treatment. Taking into account the low costs of such therapy, fluoroquinolones seem to be ideal candidates for repositioning into bladder cancer therapeutics.Introduction: Introducing new drugs for clinical application is a very difficult, long, drawn-out, and costly process, which is why drug repositioning is increasingly gaining in importance. The aim of this study was to analyze the cytotoxic properties of ciprofloxacin and levofloxacin on bladder and prostate cell lines in vitro. Methods: Bladder and prostate cancer cell lines together with their non-malignant counterparts were used in this study. In order to evaluate the cytotoxic effect of both drugs on tested cell lines, MTT assay, real-time cell growth analysis, apoptosis detection, cell cycle changes, molecular analysis, and 3D cultures were examined. Results: Both fluoroquinolones exhibited a toxic effect on all of the tested cell lines. In the case of non-malignant cell lines, the cytotoxic effect was weaker, which was especially pronounced in the bladder cell line. A comparison of both fluoroquinolones showed the advantage of ciprofloxacin (lower doses of drug caused a stronger cytotoxic effect). Both fluoroquinolones led to an increase in late apoptotic cells and an inhibition of cell cycle mainly in the S phase. Molecular analysis showed changes in  Quinolones are a group of antibiotics with anticancer properties. They are divided into four groups; a fluoride atom was added to drug molecules in second quinolone group, leading to the formation of fluoroquinolones. Their mechanism of action is based on an inhibition of bacterial gyrase ,2. They We decided to analyze ciprofloxacin and levofloxacin because they are the most commonly used fluoroquinolones in urology . CiproflUrinary bladder and prostate cancers are respectively the fourth and the first most common malignancies among males in Europe . TransurIntroducing new drugs for clinical application is a long, drawn-out, and costly process. For these reasons, in recent years, fewer and fewer new drugs were registered for clinical use. Drug repositioning (searching for the new target for drugs currently registered for use) allows introducing a medicament for new clinical indications without such difficult procedures . CurrentThe aim of this study was to compare properties of two fluoroquinolones, ciprofloxacin and levofloxacin, as potential drugs for genitourinary cancer treatment. This is the first study concerning the direct comparison of these two drugs. Very important aspect in testing potential anticancer drugs is their influence on surrounding healthy tissues, which is why in this study, we compared the effect of both drugs between cancer cell lines and their non-malignant counterparts. In previous studies analyzing fluoroquinolones, such a comparison was performed very rarely. Most of the studies were focused on one type of disease or, when analyzing fluoroquinolones modification, on the panel of cancer cell lines. We decide to analyze two malignancies: prostate and bladder cancer. The reason of choosing these conditions was that both drugs are often used in prostate and urinary bladder infections treatment. That is why the repositioning of ciprofloxacin and levofloxacin for these two new clinical indication seems to be reasonable.2 T-flasks  at 37 °C and 5% CO2.SV-HUC-1 , T24 (human bladder cancer), DU-145 (human prostate cancer), and RWPE-1  cell lines were purchased from the American Type Culture Collection . T24 and DU-145 were cultured in DMEM/Ham’s F-12 medium  containing 10% fetal bovine serum  supplemented with 5 μg/mL of amphotericin B , 100 μg/mL of streptomycin, and 100 U/mL of penicillin . SV-HUC-1 was cultured in F-12K medium  with the same supplementation. RWPE-1 was cultured in KBM-Gold™ Keratinocyte Cell Basic Medium  supplemented with a KGM-Gold SingleQuot Kit . All cell lines were grown in a plastic tissue culture 75 cmCiprofloxacin and Levofloxacin working solutions  were prepared by dilution of ready to use for intravenous infusions of ciprofloxacin  and levofloxacin  solutions in media used for cell culture.Cell viability after drug treatment was analyzed using MTT assay. For this purpose, cells were seeded in 24-well plates and treated with different concentrations  of ciprofloxacin and levofloxacin for 24 and 48 h. Photographic documentation of cells after incubation with drugs was performed using a light-inverted microscope Leica DMi1 . The morphometric analysis of cells by measuring their area was performed using EPview 1.3 software . Post treatment, MTT solution  was added to each well and incubated for 2 h at 37 °C in dark. Formazan crystals were dissolved in dimethyl sulfoxide , and the absorbance was measured at 570 nm (characteristic length) and 655 nm (reference length) using the Varioskan LUX plate reader . Each experiment was performed at least in triplicate. The results of the MTT assay, collected from all performed repetitions, were used to calculate Lethal Concentrations (LC) causing death of 10%, 50%, and 90% of cells—LC10, LC50, and LC90. The reduction of cell viability was calculated by comparison of the absorbance in drug treated cells to untreated cells as a control. On the basis of the obtained results, logarithmic trend lines were fitted and a curve equation was generated, which allowed for appropriate LC value calculation.In order to confirm the calculations after MTT assay, LC10, LC50, and LC90, real-time cell growth analysis using the xCELLigence RTCA DP  was performed. Cells were seeded in an E-Plate 16  and incubated in appropriate cell culture medium for 24 h. In the next step, ciprofloxacin or levofloxacin in concentrations corresponding to LC values were added. For representation of changes in cell growth, an electrical impedance was measured every 30 min for 24 or 48 h and presented as a cell index (CI). The cell index values can be used to monitor the cell number, viability, morphology, and adhesion degree. A well with medium without cells served as the background, and cells cultured with medium without drugs were used as a control. Each experiment was performed in triplicate.® Cell Cycle Kit  was performed. This method allows for cell cycle phase determination by quantifying the amount of DNA in the cell. Propidium iodide (PI) and RNAse mix were used for DNA staining. Detached cells were fixed in 70% ethanol at −20 °C and kept in these conditions for at least 24 h before staining. After washing from ethanol, cells were stained with PI for 30 min at room temperature. Stained cells were analyzed by flow cytometry with BD FACSCanto II   . The obtThe level of apoptosis was assessed based on the quantification of phosphatidylserine (PS) level on the outer cell membrane. After exposition to LC values calculated after 24 h incubation with ciprofloxacin or levofloxacin, cells were detached and stained with the FITC Annexin V Apoptosis Detection Kit II . The staining procedure was performed according to the manufacturer’s instructions. Cells stained with annexin V (AnV) and propidium iodide (PI) were analyzed by flow cytometry with a BD FACSCanto II  . The obt®3/7 and Caspase-Glo®9  assays according to the manufacturer’s protocol. Briefly, cells were seeded in proper density on a 96-well black plate with a clear bottom . Cells were allowed to attach to the growth surface for 24 h, and next, both tested drugs in LC50 concentration were added for 24 h incubation. After the preparation of both reagents , reagents were brought to room temperature. Next, an equal volume of reagent was added directly to cells in culture medium. After that, the plate was placed in a Varioskan LUX plate reader , shaken for 30 s at 300 rpm, and incubated in the dark for 30 min, after which the luminescence signal was measured. Experiment was performed in triplicate.The caspase activity was measured using Caspace-GloBAX, BCL2, TOP2A, TOP2B, CDKN1A, and TP53 genes was analyzed by real-time PCR. The RNA was extracted using the RNeasy Mini Kit according to the manufacturer’s protocol . cDNA was synthesized from 500 ng of total RNA with the use of a Transcriptor High Fidelity cDNA Synthesis System . Quantitative real-time PCR was performed using PrimePCR™ primers  and a LightCycler 480 SYBR Green I Master . NormFinder was used to analyze the expression stability of reference genes and to select the combination of the most stable genes (SDHA and TBP). The Roche LightCycler 480 software 1.5  was used to perform advanced relative quantification analysis.The expression of TMSpheraTM plates . Spheroids were grown for 3 to 4 days, after which analysis was performed. For analysis of cell viability, a CellTiter-Glo® 3D assay was used . Both tested drugs in calculated LC concentrations were added to the generated spheroids and incubated for 24 and 48 h. The day before analysis, the reagent was thawed at 4 °C; directly before use, it was placed in a water bath (22 °C) for 30 min. Spheroids were transferred to a low-attachment, 96-well, U-shaped PrimeSurface® white plate , and next, an equal volume of reagent was added directly to cells in the culture medium. After that, the plate was placed in a Varioskan LUX plate reader , shaken for 5 min at 420 rpm, and incubated in the dark for 30 min, after which the luminescence signal was measured. In the case of caspase activity measurement, a Caspase-Glo®3/7 3D assay  was used using similar protocol. Before use, the substrate was combined with buffer, and the obtained reagent was brought to room temperature. The difference in protocol compared to the CellTiter-Glo® 3D assay was the shaking parameters (30 s at 500 rpm). The experiment was performed in triplicate. Photographic documentation of spheroids after incubation with drugs was performed using a light-inverted microscope Leica DMi1 . Morphometric analysis of spheroids by measuring their area was performed using EPview 1.3 software .Spheroids were generated from T24 and SV-HUC-1 cell lines. For this purpose, cells were seeded at a density of 25,000 cells per well in low-attachment 96-well U-shaped NunclonEach experiment was performed at least in triplicate. The average cell viability was expressed as a percentage relative to the control. All data were presented as means ± SD. Normal distribution of data was analyzed using the Shapiro–Wilk test. Parametric analysis was performed with one-way ANOVA with Tukey post hoc (for cell viability) or two-way ANOVA with Sidak post hoc . Non-parametric analysis was performed using the Kruskal–Wallis test. In order to compare the cytotoxicity profiles of both tested drugs on all tested cell lines, Pearson comparison was performed GraphPad Software 8.4 .Morphological changes in the bladder cell lines  including cell shrinkage, rounding, and detachment were visible after ciprofloxacin treatment, especially in higher concentrations A. TwentyMorphological changes of prostate cell lines (RWPE-1 and DU-145) were also observed after ciprofloxacin treatment, especially in higher concentrations. Cell shrinking, rounding, and detachment that led to a decrease in cell number with increasing drug concentration were visible. Changes in cell morphology were more visible in the case of cancer DU-145 cells, in which detachment and shape change, into a more rounded shape, were observed already in 100 µg/mL concentration, especially after 48 h incubation A. MorphoCiprofloxacin acted cytotoxic against all of the tested bladder and prostate cell lines in a dose and time-dependent manner. A lack of significance was observed in the case of 25 µg/mL and 50 µg/mL for SV-HUC-1 and 25 µg/mL for T24 . CiproflMTT assay enabled for lethal concentration (LC) calculation causing death of 10%, 50%, and 90% cells in vitro . These values were used in further analysis . Real-tiThe results of cell cycle analysis revealed that ciprofloxacin caused a rise of the cell number in the S phase, especially after treatment with LC90 concentrations. In the case of bladder cell lines, this effect was especially visible in cancer cells, while in non-malignant urothelium, only slight changes were observed. However, statistically significant results were observed only in the case of the T24 cell line treated with ciprofloxacin (decrease in cell number in the G1/G0 phase compared to control after LC90 treatment). The effect of ciprofloxacin on prostate cell lines was the opposite: larger differences were observed in the non-malignant epithelium compared to the cancer prostate epithelium. In this case, a lack of statistically differences were observed .p < 0.005). The obtained results were similar to cell cycle analysis; in bladder cell lines, an increased number of AnV+/PI+ cells was especially visible in cancer cells, while in prostate cell lines, the situation was the opposite . In both bladder cell lines, a decrease in the CDKN1 gene was observed, while in the case of the prostate cell line, a decrease was observed in LC90 in RWPE-1 and LC10 and LC90 in DU-145 cell lines. In the case of the DU-145 cell line, an increase in CDKN1 expression was observed after treatment with LC50 concentration  calculation causing the death of 10%, 50%, and 90% cells in vitro . These values were used in further analysis . Real-tiThe results of cell cycle analysis revealed that levofloxacin caused a rise of cells number in the S phase, especially after treatment with LC90 concentrations. Levofloxacin increased the number of cells in the S phase in both bladder cell lines; in bladder cancer cells, an increased number of cells in the G2/M phase was also observed. In the case of prostate cell lines, a similar increase in cell number in the S phase was observed; this effect was more pronounced in cancer cells, but the results compared to control were not statistically significant .p < 0.005). Levofloxacin induced an increase in the number of AnV+/PI+ cells in all tested cell lines (p < 0.005); in the case of cancer cells, this effect was more visible  and LC50 and LC90 (RWPE-1). In the case of cancer cell lines, a decrease in CDKN1 expression was observed in the T24 cell line, while in the case of DU-145, a lack of significant changes was noticed  ,15. TakiConcentrations of fluoroquinolones in prostate tissue are 2.45-fold higher in the case of ciprofloxacin and 2.96-fold higher in the case of levofloxacin compared to serum. However, the penetration of drugs in such a way enables to obtain concentrations of 5.81 µg/g and 4.49 µg/g after administration of respectively 200 mg and 1000 mg ciprofloxacin and 20.8 µg/g after 500 mg of levofloxacin ,22,23. TCiprofloxacin was more cytotoxic than levofloxacin for all tested cell lines. The highest differences were observed in the case of the bladder cancer cell line, in which a lack of difference between ciprofloxacin and levofloxacin cytotoxicity was observed only in the case of the highest concentration after 48 h incubation A. In theCrystal formation in higher concentrations of ciprofloxacin was observed in all tested cell lines. In our opinion, crystal formation did not significantly affect the concentration of ciprofloxacin in cell culture. In the case of levofloxacin, crystals did not appear, and cell viability was higher compared to these same concentrations of ciprofloxacin. Differences in crystal formation between cell lines (higher number in SV-HUC-1 and DU-145) were caused probably by the different compositions of culture medium. Fluroquinolones dissolve better in acidic pH. Despite that fact, that pH of culture medium is generally neutral; it changes into more acidic, during the culture, when cells grow and consume nutrients. High drug concentrations do not allow for proper cell proliferation, which is why the pH of the medium is not lowered. This property of ciprofloxacin can be dangerous; application of this drug can cause crystal nephropathy in patients with alkaline urine . Such a CDKN1A expression (coding p21 protein) was also decreased together with the upregulation of the pro-apoptotic gene BAX, which indicates an inhibition of cell cycle in the S phase and induction of apoptosis. However, an increase in BCL2 expression was also noticed . The influence of levofloxacin at the molecular level has not been analyzed so far. Similarly, as in the case of ciprofloxacin, BAX and BCL2 genes were upregulated except in the T24 cell line, in which the BCL2 gene was downregulated. TP53 and CDKN1A genes were downregulated except in the SV-HUC-1 cell line in LC50 concentration, in which the CDKN1A gene was overexpressed. The TP53 gene was not detected in the T24 cell line, which is similar to the case of ciprofloxacin, while in the case of non-malignant uroepithelium, decreased expression was observed  ,31,32,33 noticed A. We addell line . Levofloell line . In thisobserved A.WAF1, and NF-κB inactivation [BAX expression with a lack of significant changes in the BCL2 gene in LC10 and LC50 concentrations, which suggests apoptosis induction. In LC90, a lack of changes compared to control or a decrease in BAX expression was observed (in RWPE-1 and DU-145 respectively), which can indicate the advantage of the necrotic pathway. CDKN1A and TP53 genes were downregulated in L50 and LC90 concentrations except for DU-145, in which an overexpression of the TP53 gene and CDKN1A gene was observed in LC50 concentration, which could suggest that in this case, the pro-survival pathways were involved ; that is why an active form of caspases could not be detected in analyzed cells. Cell death can be also induced by other mechanisms such as the TNF signaling pathway, which could lead to caspase 3/7 activation. Other possible mechanisms are the activation of caspase 3/7 omitting caspase 9 by DIABLO, ARTS, and HTRA2 activation. Apoptosis can be also induced independently for caspase by ENDO-G and AIF activation [Summarizing the molecular analysis and caspases activity, the mechanism of tested fluoroquinolones was dependent on the type of cell line and tested drug concentration. Generally, in lower concentrations, we observed pro-survival pathways activation, while in the highest (LC90) concentration, the tivation . Taking Our results showed that both non-malignant and cancer prostate cell lines were comparably sensitive to both drugs in 2D culture. These results suggest that fluoroquinolones in high concentrations can damage heathy tissue. Available data showed that ciprofloxacin can be administered in a high dose (1000 mg) without any drug-related adverse events . HoweverTOP2A and TOPO2B genes expression, two isoforms of eucaryotic topoisomerase II (α and β), showed their downregulation in almost all tested cell lines (beside levofloxacin in the RWPE-1 cell line on the TOP2A gene) by both fluoroquinolones. To our knowledge, this is the first study confirming the inhibition of topoisomerase II by fluoroquinolones in human cancer and non-malignant cell lines . This effect is probably caused by a lack of serine and acidic amino acid residues in topoisomerase molecules, which in procaryotic cells are responsible for interaction of the drug with the enzyme . That isll lines . These rThe advantage of our study is its comparison of the cytotoxic effect of both drugs on cancer and non-malignant bladder and prostate epithelial cells. Such a comparison has not been performed before, which is why in our study, we proved that both tested fluoroquinolones were more effective against bladder cancer cell lines A,C. ThisThe results obtained in this study indicate that ciprofloxacin and levofloxacin can be use as potential drugs, especially in the treatment of bladder cancer. An ideal indication for the utilization of this drug is condition after TURBT. This surgical procedure is related with a high percentage of relapses, probably by implantation of the remaining cancer cells after resection of the bladder tumor ,43. The The use of both fluoroquinolones in cancer treatment will require a prolonged use of these drugs. The long-term use of ciprofloxacin and levofloxacin is well tolerated, and they are generally considered as safe and efficacious antimicrobial agents . UnfortuProperties of ciprofloxacin and levofloxacin create opportunities for their use as anticancer agents. They have confirmed in vitro anticancer properties and can achieve higher concentrations in different organs than in serum after oral and intravenous administration. Our results suggest that both tested drugs can be potentially used in supportive therapy of bladder cancer treatment. From both tested fluoroquinolones, ciprofloxacin has better cytotoxic properties. Taking into account the low costs of such therapy, fluoroquinolones seem to be ideal candidates for repositioning into bladder cancer therapeutics.


Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: The online version contains supplementary material available at 10.1186/s13073-021-01001-x. Tumour heterogeneity (TH) refers to genetic and epigenetic differences of the same tumour type between patients, between tumours in a single patient, and within the cells of a tumour. It contributes to the medical complexity of cancer treatment and can lead to drug resistance, therapeutic failure and higher lethal outcome . TH is cSingle-cell RNA sequencing (scRNA-seq) has become an established technology to dissect TH at the transcriptional level, revealing high-resolution cellular composition and dynamics and offering an unprecedented opportunity to address TH therapeutically –7. For ihttps://gitlab.com/bu_cnio/beyondcell) [In this situation, it is reasonable to hypothesise that drugs (or drug combinations) capable of targeting TH at single-cell resolution can be identified by integrating drug response profiles and scRNA-seq data in order to discover therapeutic clusters (TCs), which we have defined as groups of cells with a similar drug response. This would help to address the tumour therapeutic complexity, revealing the impact of TH in response to drugs and the scope of tumour cells whose therapeutic approach could be managed with approved drugs, clinical trials or drug repositioning strategies. However, there are no current computational methodologies capable of relating single-cell gene expression and high-throughput drug screening datasets to suggest knowledge-driven treatments. To address these challenges, we developed Beyondcell (ondcell) , a novelondcell) –18, primondcell)  and mousondcell) . Our reshttps://gitlab.com/bu_cnio/bollito) [A re-analysis of the samples was applied using the bollito pipeline (bollito) , which ebollito) .https://gitlab.com/bu_cnio/beyondcell) [C) and the drug sensitivity (SSC) collections containing 4690 and 819 signatures respectively [C captures the transcriptional changes induced by a drug, while SSC contains signatures reflecting the transcriptional status of sensitivity or resistance prior to drug treatment   starts wectively –14. PSC C) or the predicted sensitivity to a given drug (using the SSC). The BCS can also evaluate the cells’ functional status using functional gene sets such as molecular pathways, or cancer hallmarks. The calculated BCS matrix allows the determination of therapeutic clusters (TCs) within cellular populations defined as ‘a set of cells that share a common response to a set of drugs’. In order to find potential TCs, a clustering analysis is applied to the BCS matrix where cells are grouped by their differential response to the selected drugs. For each drug, Beyondcell also calculates the switch point (SP), which reflects the homogeneity of the drug response throughout the single-cell dataset  which estimates, for each cell in the preprocessed single-cell expression matrix, the enrichment in every drug signature in the specified collections. The BCS ranges from 0 to 1 and measures the cell perturbation susceptibility (using the PShttp://www.lincsproject.org) [https://portals.broadinstitute.org/ccle/home). In the CTRP [https://ocg.cancer.gov/programs/ctd2/data-portal). The gene expression data used in order to obtain the CTRP expression signatures were those of the CCLE. Of the 887 cell lines, 805 were present in the CCLE dataset. In the GDSC data [http://www.cancerrxgene.org/downloads). The release 21Q1 of the Achilles dataset was downloaded from the DepMap portal [The Library of Integrated Network-based Cellular Signatures   is a catect.org) . From thect.org) , 28 drugthe CTRP , 545 druDSC data , 265 comDSC data . Drug rep portal .C captures the transcriptional changes induced by a drug; while the SSC captures the sensitivity to the drug effect. In both cases, these expression signatures are obtained from a differential expression analysis, although using different designs. Despite its different biological interpretations, both types of signature were represented as the two gene sets formed by the N most upregulated and downregulated genes (the UP and DN genesets). Several N were tested , and no significant disagreement was found between the top synergistic and antagonistic interactions (data not shown). Thus, following Iorio et al. rationale [N was taken as 250.A gene expression signature is a general model for the representation of the transcriptional changes associated to a given biological phenotype or perturbation. In this study, we have considered two expression signature collections: the PS50, 100, 0 and 500signature reversion principle, which aims at the identification of drugs inducing a transcriptional response complementary to that of the disease. In order to obtain consensus expression signatures for each drug, a differential expression analysis was performed on control vs treated cells using limma [C signature collection are available at Additional file Drug-induced expression signatures were obtained from experiments in which the transcriptional state of the cell is measured before and after treatment with the drug. This makes it possible to study the transcriptional effect of the drug, that is, it informs on whether the transcriptional state of each cell is more similar to that of untreated or treated cells. But it also allows us to predict sensitivity under the ng limma . Full delimma R package. The area under the curve (AUC) was used as the measure of drug response; as contrary to IC50, it can always be estimated without extrapolation from the dose-response curve and also because it has shown more accuracy in the prediction of drug response [Site.Primary and the Histology information of the cell line. In the GDSC 2.0, the variable Site and in the CTRP, the ‘CCLE primary Site’ was used.Drug-induced expression signatures were obtained from pharmacogenomics experiments in which the pharmacological response to a drug and the transcriptional state before treatment were considered. In order to obtain the expression signature, we considered the area under the curve (AUC) as a measure of drug response. For each drug, we performed a differential expression analysis between AUC high cell lines and AUC low cell lines by considering the AUC as a continuous variable using the response . In all C signature collection are available at Additional file Further details of SSThe Beyondcell package  also incswitch point (SP). In cases where the gene signature is unidirectional, all steps remain the same, although the rescaling will only be applied to one set of normalised scores , the Beyondcell score is independently obtained for each signature mode. The individual sum of the expression is calculated and divided by the number of genes in the given signature that are present in the single-cell expression matrix. The obtained raw scores are normalised in order to penalise cells with a great number of zeroes and/or with outliers. The individual up and down normalised scores are summed and rescaled between 0 and 1 by calculating their C collection has been calculated and the first and last decile have been selected. Here, the rationale is the following: for each drug, the DSS score quantifies the similarity between the induced expression patterns across cell types. And while some drugs induce similar patterns across distinct cell types, the majority of them have different effects across all of them [In order to obtain TCs, the Beyondcell methodology can optionally generate a background score matrix. The background score matrix allows the user to reduce the computation time. It aims to characterise the heterogeneity of the drug responses in the whole dataset, by using a reduced collection of drugs that is able to capture the main differences between individual cells. To generate this background selection, the drug specificity score (DSS) of the whole PS of them .bcRegressOut removes the effect of all unwanted sources of variation from the scoring matrix in two steps: first, it imputes missing data (if necessary) using a k-nearest neighbours (KNN) algorithm; second, it obtains the residuals derived from the regression model via QR decomposition. The obtained residuals can then be used for the downstream dimensional reduction steps.After obtaining the normalised and scaled Beyondcell scores, we observed that the normalisation step was not sufficient to avoid the correlation between sample-level metrics  and the calculated scores. To correct for this, we have implemented a regression function. The BCS matrix, including the computed scores for all drugs of interest, can be used as an input for a dimensionality reduction and clustering analysis. In this step, cells are grouped by their differential response to the analysed drugs. A Uniform Manifold Approximation and Projection (UMAP) allows the visualisation of the identified TCs. With this analysis, cells can be classified into distinct TCs, which represent sets of cells sharing a common response to a particular drug exposure.bcRank function computes the BCS matrix statistics. In particular, it calculates the switch point, mean, median, standard deviation, variance, minimum, maximum, proportion of NaN and residuals’ mean of each signature. We recommend prioritising drugs taking into account both the switch point and the residuals. The Beyondcell package includes the function bc4Squares, which helps to visualise this prioritisation. A 4 squares plot consists in a scatter plot of the residuals' means (x-axis) vs the switch points (y-axis) of a specified cluster . The 4 quadrants are highlighted: the top left and bottom right corners contain the drugs to which these cells are least/most sensitive, respectively. The centre quadrants show the drugs to which the selected cells are differentially insensitive or sensitive when compared to the other clusters.The We have applied Beyondcell to 5 studies featuring sc-RNASeq: (i) the Ben-David et al. study  focused We applied Beyondcell to the Ben-David et al. dataset  to demonC drug signatures to the collected MCF7-AA cells at t0. We found that median BCS obtained with SSC signatures for cells at t0 correlate significantly  with MCF7-AA cell viability measures reported by Ben-David et al. [C signatures, drugs were then classified in three groups: chemotherapy, targeted therapy and others . The BCS for targeted therapies showed a significant correlation with MCF7-AA cell viability   included both parental and resistant cells. A small fraction of the parental cells in TC5 (15%) expressed BRAFi-resistance markers like AXL, NRG1, DCT and FGFR1 that are also expressed in BRAFi-resistant cells, showing that they were clonally selected from the parental population contributing to the resistance , we evaluated Beyondcell’s ability to identify drug-resistant cellular populations and to propose drug treatments . BeyondcC drug signatures, showing a higher BCS score (higher sensitivity) in TC5, and therefore, it could be proposed to target BRAFi-unresponsive cells. In fact, MEKi is a standard treatment in advanced melanoma patients in combination with BRAFi (2(FC)| > 2, FDR < 0.05) . In addition, vemurafenib and dabrafenib BCSs obtained using SSC showed that TC5 had lower sensitivity to RAF inhibitors than the rest of the TCs, confirming that TC5 cells are pre-resistant to BRAFi  and unresponsive to dinaciclib (CDKi) and gemcitabine. On the other hand, cells in the resistant condition are differentially sensitive to bardoxolone methyl (NF-kBi) and unresponsive to AZD6482 PIK3i)  and endometrial/uterine cancer cell lines, while TC1 included cells from bladder, gallbladder and pancreatic cancer cell lines. TC3 was enriched in breast and colon/colorectal cancer, while gliomas were exclusively located in TC2 together with kidney and thyroid cancer  and the genes identified as essential for proliferation. These analyses showed that lung cancer cell lines did not cluster together; therefore, TCs detected by Beyondcell are not driven by mutational and essentiality events , which were spread across the different TCs  presented higher drug response heterogeneity than RERFLCAD1 , evidencing a more variable drug-response behaviour in the metastatic cell line. We found that 48% of cancer cell lines show > 60% of drug homogeneity (SP = 0 or SP = 1) suggesting low cell variability in drug response. A total of 88% of the cell lines have a median SP > 0.7, meaning that they contain a greater number of insensitive than sensitive cells, and 17% of cell lines have a median SP > 0.9, indicating that none of the cells would exhibit sensitivity against half of the therapeutic options  with high sensitivity to EGFRi, in agreement with experimental validations performed by Kinker and colleagues [p = 8.3e−13) [To explore the functional properties of the TCs, we correlated BCS and 12 expression programs known to be recurrently heterogeneous within cancer cell lines (RHPs). Cells enriched in the epithelial senescence-associated (EpiSen) program correlated  . This reWe also employed our method to study more heterogeneous samples such as primary tumour samples. First, we used an scRNA-seq dataset from 16 melanoma patients treated with immune checkpoint inhibitors (ICIs) and 15 untreated patients. This prospective study aimed to detect transcriptional cell states related to responsiveness to ICIs and identify a transcriptional ICI-resistance program expressed by malignant cells associated with T cell exclusion and immune evasion which predicts clinical responses to immunotherapy in melanoma patients . BeyondcIn a second study, we wanted to dissect therapeutic heterogeneity and propose novel therapeutic strategies in small-cell lung cancer patients (SCLC) patients treated with cisplatin . In geneAddressing TH is a critical factor for the design of effective treatments in cancer and a current challenge for precision oncology . ConsequAs a proof of concept, we applied Beyondcell in five independent datasets comprising cancer cell lines –18 and pNevertheless, the therapeutic heterogeneity revealed in our analysis pinpoints where the integration of new information is most needed. Other single-cell measurements such as cell imaging, genetic and epigenetic state or measuring gene expression at various time points could reveal differences in the drug responses of tumour cell populations . Our resThe tumour microenvironment represents an additional layer in cancer therapeutics. Immunoediting processes—that is, the dynamic interactions between tumour cells and the immune system—drive tumour evolution and contribute to therapeutic failure . SeveralIn summary, understanding how TH leads drug response in patients will help to design precise therapeutic regimens  anticipating the appearance of relapse, managing drug resistance mechanisms, delaying tumour growth or even inducing complete tumour regression. Therefore, there is an urgent need to develop methodologies to directly address TH in the design of anticancer treatment regimens. Beyondcell provides a valuable resource for better understanding of the biological and therapeutic impact of TH. Our results highlight the utility of Beyondcell to reveal, from single-cell transcriptomics, the cellular heterogeneity in drug response in cancer cell lines and patients, identifying resistant and sensitive cellular subpopulations and proposing drugs to target them. Finally, Beyondcell has been implemented as an open-source software package that can be easily combined with scRNA-seq gold-standard methodologies in custom automated analysis pipelines. Our software is complementary to current single-cell analysis approaches, opening up a discovery space to support the design of more effective lines of therapy.Additional file 1: Supplementary Methods and Figures. Supplementary Methods and Supplementary Figures S1–S16.Additional file 2: Table S1. Beyondcell results for clusters and conditions with Ho et al. dataset.Additional file 3: Table S2. Genes differentially expressed in cluster 5 for Ho et al. dataset.Additional file 4: Table S3. Cell distribution in clusters, cancer types and cell lines for Kinker et al. dataset.Additional file 5: Table S4. Differential sensitivity drugs in cancer type and clusters for Kinker et al. dataset.Additional file 6: Table S5. Beyondcell drug scores correlation modules for Kinker et al. dataset.Additional file 7: Table S6. Differential drug vulnerability analysis for TCs for Kinker et al. dataset using PRISM dataset.Additional file 8: Table S7. Differential EGFRi vulnerability analysis for cell lines in TC1 in comparison to TC2 for Kinker et al. dataset using PRISM dataset.Additional file 9: Table S8. Cell lines drug homogeneity for Kinker et al. dataset.Additional file 10: Table S9. Correlation of drug sensitivity and Recurrent Heterogeneous Programs (RHP) Beyondcell scores.Additional file 11: Table S10. Differential gene expression analysis of TC2 cells vs rest of TCs.Additional file 12: Table S11. Differential EGFRi vulnerability analysis for undifferentiated cell lines in comparison to differentiated cells.Additional file 13: Table S12. Differential sensitivity drugs for platinum-resistant patients for Stewart et al. dataset.