Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.https://github.com/gambalab/DREEP.DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at The online version contains supplementary material available at 10.1186/s12916-023-03182-1. Intra-tumour heterogeneity (ITH) is a recurrent issue in designing effective treatment strategies in clinical practice. It refers to heterogeneous cancer cell populations present in the same tumour that exhibit high cellular plasticity and phenotypic heterogeneity. Accumulating evidence from independent studies shows that ITH gives rise to rare sub-populations of malignant cells characterized by a different epigenetic and transcriptional state that renders them refractory to a variety of anticancer drugs, protecting the whole population from complete eradication –4.Single-cell RNA sequencing (scRNA-seq) has now become a powerful tool to explore ITH at the transcriptional level  and thusTo address these challenges, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a novel bioinformatics tool designed to augment the precision and efficacy of therapeutic strategies by addressing the innate heterogeneity of cancer cell populations. DREEP’s primary function is to assess the complexity of tumour therapeutics and recommend specific drugs tailored to target distinct cell subpopulations. DREEP can predict a cell’s vulnerability to more than 2000 drugs by simplifying the whole process with enrichment analysis, eliminating the need for complex parameter adjustments and thus making this type of analysis accessible to a broad audience.https://github.com/gambalab/DREEP.We validate DREEP performance extensively using in vitro drug response data of over 200 cancer cell lines covering 22 distinct cancer types from publicly available single-cell and drug viability datasets. We show that our method can effectively identify recurring drug vulnerabilities across cells of diverse tumour types and establishes a clear relationship between drug response and the functional status of the cells. We show that DREEP can detect changes in drug sensitivity over time in the breast cancer cell line MCF7 and predict the emergence of drug resistance. Additionally, using molecular barcoded MDA-MB-468 cells . In conchttps://github.com/gambalab/gficf) [https://github.com/gambalab/DREEP. Enrichment Scores (ES) and associated p-values were computed using the fgsea package [fgsea package utilizes an adaptive and efficient multi-level split Monte Carlo approach [ p-value of an ES. Subsequently, the p-values for each drug across the cells are corrected for the false discovery rate (FDR) using the Benjamini–Hochberg correction method, implemented within the function p.adjust in the base package of the R statistical environment. Finally, only ES values with an FDR < 0.1 were deemed significant (unless otherwise stated) and used to predict the effect of a drug on a single cell. The percentage of sensitive and resistant cells in a sample is finally computed using only significant ES scores.To predict drug response at the single-cell level, we first used gf-icf normalization (b/gficf)  to extrab/gficf)  against  package . The fgsapproach  to estimhttps://depmap.org). Cell lines associated with haematopoietic tumours were excluded. Raw counts were normalized with edgeR [entropy. After these filtering steps, the final expression matrix comprised 1117 cell lines and 13,849 genes.Raw counts and associated metadata of the bulk RNA-seq data of cancer cell lines used to build GPDS signature were obtained from the depmap portal (th edgeR  and tranGenomic Profiles of Drug Sensitivity (GPDS) ranked lists were built by computing the Pearson correlation coefficient (PCC) between the expression of each gene and the potency of a drug across the cell lines for which drug potency was measured. At the end of this process, each GPDS is a ranked list of 13,849 expression-based biomarker genes whose degree of PCC reflects their importance in predicting the effect of a small molecule. This is because the AUC value of a drug reflects its cell line growth inhibition, with lower values indicating sensitivity and higher values implying tolerance to the drug. Thus, a gene whose expression positively correlates with the AUC of a drug is predictive of resistance to that drug; conversely, a negatively correlated gene is predictive of sensitivity to the drug.z-score percentiles from the AUC of each drug across the cell lines for which AUC was available and then defined a cell line sensitive to a drug only if its z-score was in the 5% percentile. Next, we applied DREEP to scRNA-seq data of each cell line and computed its median enrichment score for each drug. Finally, precision-recall and ROC curves on each cell-viability screening dataset were computed with the precrec R package  using the corresponding “gold standard” and sorting DREEP drug/cell line pairs according to the estimated median cell line enrichment score multiplied by minus one in order to have drugs predicted to be sensitive at the top.To estimate DREEP’s precision and sensitivity in predicting drug response from scRNA-seq data of cancer cell lines, we used the three cell-viability screening datasets from CTRP2, GDSC, and PRISM described above. For each cell-viability screening dataset, the corresponding “gold standard” is composed of pairs of cell lines and drugs for which the corresponding cell line is sensitive. To build the “gold standard” of each cell-viability screening dataset, we first computed the Fuzzy Jaccard Distance defined as follows:DREEP output can be arranged in a drug response matrix umap function of the uwot R package (https://github.com/jlmelville/uwot). Cell embedding from DREEP predictions has been implemented in the function runDrugReduction of the DREEP package.https://doi.org/10.6084/m9.figshare.15022698. scRNA-seq data of barcoded MDA-MB-468 cells were obtained from GEO (GSE228154). Raw UMI count data of nutlin treated and untreated cells was downloaded from figshare at https://figshare.com/s/139f64b495dea9d88c70 and only cells with at least 5000 UMI and with less than 10% of UMI counts in mitochondrial genes were used. PDCs TPM counts were instead retrieved from supplementary data of the original work [The scRNA-seq data of the cell line pan-cancer dataset from Kinker et al.  was obtanal work .https://depmap.org). Only drugs for which the in vitro response was available for at least 100 cell lines out of the 1117 for which gene expression data was available were retained. After this filtering, the final drug sensitivity dataset comprised 478 drugs from the GDSC dataset, 511 drugs from the CTRP2 dataset, and 1445 drugs from the PRISM dataset. For all three cell viability datasets, drug potency was expressed in terms of AUC (area under the curve) of the corresponding dose–response graph.The drug sensitivity dataset of CTRP2 , GDSC (Genomics of Drug Sensitivity in Cancer), and PRISM (Profiling Relative Inhibition in Mixtures) were all obtained from the depmap portal (P < 0.05). At the end of this process, we obtained a matrix dist function of the R statistical environment. Clustering of both, cell lines and drugs, was performed using the hclust function and resulting dendrograms cut with the cutree function of R statistical environment.We first used DREEP to predict the effect of the 2434 drugs on each of the 53,513 single-cell transcriptional profiles of the Kinker et al. dataset . Then, wp-values using the Benjamini–Hochberg method to identify drugs with differential sensitivity between afatinib-sensitive and afatinib-tolerant subpopulations. Finally, a drug was considered specific for a subpopulation if its FDR was less than 0.1 and the median enrichment score across the other subpopulations was greater than zero. This analysis has been implemented in the runDiffDrugAnalysis function of the DREEP package.We used DREEP to predict the effects of 2434 drugs on 1541 sequenced cells of the barcoded MDA-MB-468 cell line [4 MDA-MB-468 cells were seeded in a 96-well plate and incubated overnight at 37 °C. The two drugs to test were prepared in different dilutions and then combined in all possible drug pairs to generate a 5 × 5 combination matrix. Then, cells were exposed to either single agent drug or to the drug pairs of the drug combination matrix, while negative controls were treated with DMSO (each treatment was performed in triplicate). Following 72-h incubation at 37 °C, cell viability was measured with CellTiter (Promega) and the absorbance was read at 490 nm with the plate reader GloMax® Discover instrument. The drug interactions and expected drug responses were calculated with the Combenefit tool [4 × 10fit tool  using thDSEA was employed by using the online drugEnrichr tool  using thAll methods were run using default parameters and when required single-cell expression was normalized using the Seurat package. Common drugs between DREEP and each method were found by simply intersecting the drug names.PDCs drug response data were retrieved from supplementary data of the original work . Specifipre-defined collection of Genomic Profiles of Drug Sensitivity (GPDS); these are lists of genes ranked according to their contribution in correctly predicting the effect of a small molecule. To build GPDS, we integrated several publicly available RNA-seq datasets and cell-line viability screens, including the following datasets: GDSC [DREEP (Drug Response Estimation from single-cell Expression Profiles) is a bioinformatics tool that leverages results from large-scale cell-line viability screens and enrichment analysis to predict drug vulnerability from the transcriptional state of a cell. It requires a ts: GDSC , CTRP2 [ts: GDSC , 33, andts: GDSC   across all the cancer cell lines for which the drug potency was measured see Fig. ” sectionUsing this approach, we generated a collection of 2434 GPDS signatures  and used them to predict the single-cell-level drug response with the strategy depicted in Fig. We found 90 small molecules shared among GDSC, CTRP2, and PRISM datasets. To assess the consistency of GPDS ranked lists, we calculated Spearman correlation coefficients (SCC) between GPDS representations of the same drug but constructed from distinct drug viability datasets. Figure S1  . Indeed,Cluster C3 includes 69% of ovarian cancer cell lines (9 out of 13) and 70% of endometrial/uterine ones (7 out of 10). Cell lines in this cluster are predicted to be sensitive to drugs in the D2 cluster, which is enriched for topoisomerase, AURORA, HDAC, and CDK inhibitors. Interestingly, ovarian and uterine cancers are usually due to inefficient DNA repair . The staCluster C4 includes all skin cancer cell lines (16 out of 16), all kidney (6 out of 6), all brain (12 out of 12), and 13 lung cancer cell lines. Cell lines in cluster C4 are predicted to be sensitive to multiple families of drugs, including RAF, KIT, PDGFR, and PI3K/mTOR inhibitors. This agrees with current clinical practice which uses mTOR and BRAF inhibitors as first-line therapy for kidney cancer and melanoma, respectively , 64, sinTo further demonstrate DREEP’s ability to identify recurring drug vulnerabilities across cancer cells of different tumour types with shared genetic backgrounds, we applied our method to the dataset from McFarland et al. . This daAltogether, these results show that DREEP can find recurrent drug sensitivity patterns shared by cell lines from multiple cancer types, as well as the relationship between drug response and functional cell status.Next, to evaluate DREEP’s ability to recognize adaptation to drug treatment over time, we applied it to the Ben-David et al. dataset . As Fig.As shown in Fig. In summary, these analyses demonstrate DREEP’s ability to recapitulate drug response and variability over time.Next, we evaluated DREEP’s ability to identify innate drug-resistant cell populations and to suggest drug co-treatments. To this end, we used single-cell data of the untreated barcoded MDA-MB-468 breast cancer cell line that we recently generated [We applied DREEP to each of the two untreated MDA-MB-468 cell subpopulations (i.e. afatinib-tolerant and afatinib-sensitive) and predicted drugs able to selectively inhibit the growth of either. We thus found 156 drugs predicted to preferentially inhibit the afatinib-tolerant subpopulation and 80 drugs  and three AKT/MTOR/PI3K inhibitors  from the list of drugs predicted to selectively inhibit the afatinib-tolerant subpopulation. We then measured experimentally if these drugs had an additive, synergistic, or antagonistic effect on MDA-MB-468 cells in combination with afatinib  from both primary and metastatic sites of individuals suffering from head and neck squamous cell carcinoma . These mAs illustrated in Fig. p-value = 3.6e − 09) between DREEP predictions and the experimentally observed drug responses for five specific drugs: Docetaxel, Doxorubicin, Epothilone B, Gefitinib, and Vorinostat , followed by CTRP2 with an SCC of 0.591 , and PRISM with an SCC of 0.512 .Next, to assess the predictive performance of DREEP on this dataset, we first applied it to the single-cell profiles of each patient and then converted these predictions from the single-cell level to the patient level by computing the median enrichment score for each drug across the cells within each individual patient. As depicted in Fig. Intra-tumour heterogeneity (ITH) presents a significant challenge in developing effective cancer treatments and achieving precision oncology. ITH refers to the variation of tumour cells both within and between tumours. As such, it is crucial to have methods to accurately identify the drug sensitivities of different tumour clones and determine the most effective treatment for each cancer type and the individual patient. To address this challenge, we introduced DREEP, a novel computational method that predicts drug sensitivity at the single-cell level and can propose cell population-specific treatments. To achieve this, DREEP uses a set of drug-sensitivity signatures obtained by correlating gene expression and drug sensitivity profiles across hundreds of tumorigenic cell lines. It then uses these signatures to compute an enrichment score reflecting the degree of sensitivity of a cell to a specific drug.https://github.com/gambalab/DREEP.To test the predictive performance and reliability of DREEP, we applied it to two independent datasets: (i) the pan-cancer cell line dataset from Kinker et al. comprising 198 tumorigenic cell lines from 22 distinct cancer types  and (ii)Our tool relies heavily on the quality and predictive power of GPDS, which were trained by linking gene expression to drug response data from pharmacogenomic screens. However, these screens may possess inherent biases and limitations in covering the broad spectrum of cancer types. We applied several evaluation metrics on multiple showcases to assess DREEP’s predictive accuracy and ability to generalize across different cancer types, encompassing approximately one hundred thousand cells originating from more than 20 distinct cancer types.While our results have demonstrated DREEP’s ability to generalize across various cancer types effectively, we recognize that potential biases in our training data could limit DREEP’s applicability to underrepresented cancer types. Therefore, it is crucial to interpret DREEP’s performance within the context of the available training data. We advocate for ongoing refinement and expansion of pharmacogenomic screens to improve the diversity and coverage of cancer types. Addressing these biases in future data collection efforts can enhance DREEP’s utility in advancing personalized cancer treatment strategies. Moreover, in the future, it would be crucial to evaluate how the integration of other single-cell -omics measurements, such as genomic, proteomic, and epigenetic cell states, can improve the identification of variations in drug responses among cancer cell populations. Finally, since tumour heterogeneity is linked to clonal evolution processes, it would be also important to consider the dynamics of clonal populations under therapy pressure to improve the performance of future computational methods that will be developed for personalized diagnosis and treatment of cancer patients.Project name: DREEP.https://github.com/gambalab/DREEPProject home page: Operating system(s): Platform independent.Programming language: R and C +  + Other requirements: R version ≥ 4.3Licence: MIT License.Any restrictions to use by non-academics: Licence needed.Additional file 1: Figure S1. GPDS consistency across viability datasets. Figure S2. DREEP’s ROC AUC distribution across cancer types. Figure S3. DREEP Method Performance on Pan-Cancer Dataset using IC50-Derived GPDS. Figure S4. Drug prediction performance using absolute expression to extract relevant genes of a cell. Figure S5. Performance comparison between DREEP and other single cell drug prediction tool. Figure S6. DREEP's predictions for McFarland et al. Dataset.Additional file 2: Table S1. Accurately and erroneously predicted drugs. Table S2. MoA enrichment analysis of accurately and erroneously predicted drugs. Table S3. Cell-lines therapeutic clusters. Table S4. MoA enrichment analysis of drug clusters. Table S5. Differential Drug Analysis of MDAMB468 cells. Table S6. Drug Combination results. Table S7. Cell Viability of patient-derived cultures (PDCs). Table S8. AUC of ROC and PPV curves for the comparison of DREEP vs BeyonCell (Figure S Table S9. AUC of ROC and PPV curves for the comparison of DREEP vs scDEAL (Figure S Table S10. AUC of ROC and PPV curves for the comparison of DREEP vs scDRUG (Figure S


Both flavonoids, as well as cisplatin as a reference anticancer drug, were tested in vivo against ten human cancer cell lines , colon cancer , prostate cancer , lung cancer (A549) and leukemia (MV-4-11) and two normal cell lines ) and murine embryonic fibroblasts (BALB/3T3). Chalcone 1 and aurone 2 demonstrated potent to moderate anticancer activity against nine tested cancer cell lines (including drug-resistant ones). The antiproliferative activity of all the tested compounds against cancer and the normal cell lines was compared to determine their selectivity of action. Prenylated flavonoids, especially the semisynthetic derivative of xanthohumol (1), aurone 2, were found as selective antiproliferative agents in most of the used cancer cell lines, whereas the reference drug, cisplatin, acted non-selectively. Our findings suggest that the tested flavonoids can be considered strong potential candidates for further studies in the search for effective anticancer drugs.The antiproliferative activity of xanthohumol ( Cancer is a serious group of diseases involving uncontrolled cell proliferation, which causes millions of deaths every year, making it the second leading cause of death in low- and middle-income countries . Therefo1).Flavonoids, such as chalcones, exhibit numerous biological properties that are beneficial to human health, and most importantly, they are relatively non-toxic. One of the most biologically active natural chalcones is xanthohumol (1) , the major prenylflavonoid found in the hop (Humulus lupulus L.), is known for its various biological properties, such as anti-inflammatory, antioxidant, antibacterial, antifungal, antiplasmodial and antiviral properties [1) significantly suppresses the development of a wide variety of human cancer cells in vitro during the initiation, promotion and progression of carcinogenesis. Hence, it has been characterized as a “broad spectrum” cancer chemopreventive agent [1), only a few papers concerning the antiproliferative activity of xanthohumol derivatives, metabolites or analogues have been published so far [Xanthohumol  and its aurone derivative (2), as well as cisplatin as a reference anticancer drug, were examined against ten human cancer and two normal cell lines and then compared to determine their selectivity of action. The studies demonstrate how the modification of a prenylated chalcone into its aurone derivative may affect anticancer activity and selectivity.In continuation of our research on xanthohumol derivatives as potential therapeutic agents, in our previous studies, we synthesized an “aurone type” derivative, which was the starting material for obtaining glycosylated derivatives, and then the influence of the skeleton type of the prenylated flavonoids and the presence of sugar moiety in the flavonoid structure on the binding to human serum albumin and the inhibition of the cyclooxygenases COX-1 and COX-2 were investigated . In this2), towards human cancers as well as normal cells have not been published so far.To the best of our knowledge, the antiproliferative activities of xanthohumol, an aurone-type derivative (1), its aurone (2) (Xanthohumol (rone (2)  and cispAdditionally, the cytotoxicity of these compounds was also tested against normal cell lines: murine embryonic fibroblasts (BALB/3T3) and human lung microvascular endothelium (HLMEC). One of the used cancer cell lines was resistant to doxorubicin (LoVo/Dx). The concentrations of the tested compounds (µM) at which 50% cell growth inhibition was observed are summarized in 1) and its aurone (2) showed potent  to moderate  antiproliferative activity against the examined cancer cell lines, except colon cancer cells (HT-29), in which case the compounds were rather weakly active (IC50 > 50 µM). Xanthohumol (1) and aurone (2), as well as cisplatin, displayed no significant differences in their antiproliferative effect towards the MCF-7, SK-BR-3, T47D, LoVo, LoVo/Dx, PC-3 and A549 cancer cell lines (p > 0.05). Among all the used cancer cell lines, cisplatin, which is commonly used as a drug in anticancer therapy, showed significantly higher activity (p < 0.05) only in the case of two cancer cell lines: colon cancer (HT-29) and leukemia (MV-4-11). In the case of the colon cancer cell line, both flavonoids showed relatively low antiproliferative activity (91.31 ± 8.92 µM and 62.09 ± 16.52 µM), whereas cisplatin was 4.8-fold and 3.3-fold more active, respectively. In the case of the MV-4-11 leukemia cancer cell line, both flavonoids displayed high antiproliferative activities . However, cisplatin was significantly more active . The significant differences in the antiproliferative activities between all the tested compounds (p < 0.05) were recorded. In the case of the prostate cancer cell line (Du145), cisplatin was the most active, xanthohumol (1) was only slightly less active and aurone (2), with a value of IC50 = 14.71 ± 4.42 µM, was the least effective at inhibiting the growth of the prostate cancer line. The obtained results of the antiproliferative activity of the tested flavonoids against the human cancer cell lines often revealed no statistical difference in activity compared to cisplatin ), which may indicate that both xanthohumol 1 and its aurone 2 may be potential candidates as compounds for use in anticancer therapy.Both xanthohumol  to the IC50 value for the corresponding tumor cell line using the following equation:The selective effect of a drug can be expressed by the selectivity index (SI), which is determined by comparing the cytotoxic activity of each compound against each cancer cell line with the activity against a normal cell line. The SI was calculated as the ICThe SI values higher than 1.00 indicate that the tested compound exhibited higher selectivity towards cancer cells than normal ones . The selB, whereas the selective indexes for human lung microvascular endothelium (HLMEC), SIB, shows nonselective action of cisplatin against all the tested human cancer cell lines. These data confirm the well-known fact that many of the commonly used chemotherapeutics, including cisplatin, are toxic not only to malignant cells but also to normal cells.Due to the different antiproliferative activities of the tested compounds against two normal cell lines, human (HLMEC) and murine (BALB/3T3), the values of the selectivity indexes SIA and SIB for a given compound against a respective cancer line differ. However, without a doubt, the calculated selectivity indexes of the tested compounds clearly indicate that both tested flavonoids show high selectivity of action against all the tested cancer cell lines except colon cancer (HT-29), while cisplatin was selective only for colon cancer (LoVo cell line) and leukemia (MV-4-11 cell line). However, this result is not clear, since the selective action against the above-mentioned cancers is confirmed only by the selectivity indexes calculated for the normal murine embryonic fibroblast (BALB3/T3), SI2), which exhibited very high selectivity towards all the breast cancer cell lines tested  and the colon cancer lines LoVo and LoVo/Dx . These results may be very interesting in the context of possible future applications of this compound, due to the resistance of the LoVo/Dx line to the commonly used anticancer drug doxorubicin. In the case of both prostate cancers , as well as lung cancer (A549), aurone 2 was as selective as xanthohumol (1) considering the values of SIA and slightly more selective when comparing the antiproliferative activity against cancer cell lines to the activity towards normal murine embryonic fibroblasts cells (SIB). The selectivity of both flavonoids against the above-mentioned cancer cell lines can be considered high . Very high and high selectivity of the tested prenylated flavonoids towards most of the cancer lines used indicates that both compounds can be recognized as potent and selective potential anticancer agents. Moreover, the higher selectivity of aurone (2) in comparison to xanthohumol (1) clearly shows that the modification of the flavonoid skeleton, prenylated chalcone, into its aurone derivative significantly increases the selectivity against almost all of the used cancer cell lines. The most selective among of all the investigated compounds was aurone , a prenylated chalcone naturally occurring in hops, is known for its various biological activities, including anticancer activity [1) inhibited the proliferation, migration and invasion, as well as induced apoptosis and cell cycle arrest, in many types of cancer-inhibiting carcinogenesis and metastasis [1), including Akt, NF-kB, ROS and ERK1/2 [1) can also enhance apoptosis induced by TRAIL in cancer cells [1) raises concerns due to its conversion into the most potent phytoestrogen known, 8-prenylnaringenin (8-PN), which may promote the growth of estrogen-dependent cancers [Natural dietary compounds, such as flavonoids and synthetic pharmacological agents, can arrest and/or reverse carcinogenesis—this process is often called chemoprevention. Recently, there has been much research focused on the biological activities of prenylated flavonoids, which very often exhibit stronger properties than those of non-prenyl parent flavonoids ,28,29,30activity ,31. Resetastasis ,6,9. Muld ERK1/2 ,6. Studier cells ,33,34. D cancers .1) to isoxanthohumol (3) and O-demethylation, catalyzed by the host’s cytochrome P450 enzyme (CYP1A2) and gut microbiota, resulting in the formation of 8-prenylnaringenin (4) (4), are strongly sought. This two-step reaction involves the spontaneous isomerization of xanthohumol (enin (4)  [36,37,31) to α,β-dihydroxanthohumol (5) prevents isomerization to isoxanthohumol (3) and, therefore, conversion to the potentially dangerous 8-prenylnaringenin (5) might be as effective an antiproliferative agent against human breast cancer cell lines  and an ovarian cancer cell line (A2780) as xanthohumol (1). Both prenylated flavonoids exhibited a comparable growth-inhibiting effect, whereas structural modifications, such as the isomerization of xanthohumol (1) to isoxanthohumol (3) or the demethylation of isoxanthohumol (3) to 8-prenylnaringenin (4), led to a reduction in the cytostatic activity towards the cancer cell lines tested [4) in the body, can affect the growth inhibition and selectivity against human cancer cell lines in vitro. Therefore, the antiproliferative properties and selectivity of the aurone derivative of xanthohumol (2) against ten human cancer cell lines in comparison to xanthohumol (1) and a reference, cisplatin (a drug commonly used in anticancer treatment), was investigated. Our studies have shown that the modification of xanthohumol (1) to an aurone derivative does not adversely affect the antiproliferative activity against the human cancer cell lines tested. Both xanthohumol (1) and its aurone (2) showed potent to moderate antiproliferative activity against the examined cancer cell lines. This is a remarkable finding since modifications of xanthohumol (1) to other analogous flavonoids with a flavanone or flavone skeleton reduce its potent anticancer activity [1) into an aurone derivative (2) significantly (p < 0.05) reduced the cytotoxic effect against normal cells: 1.5-fold in the case of the human lung microvascular endothelial (HLMEC) cell line and 2.3-fold in the case of the murine embryonic fibroblasts (BALB/3T3) cell line, respectively. The comparable antiproliferative activities of flavonoids 1 and 2, as well as the significantly lower cytotoxicity of aurone 2 towards normal cells lines, makes this compound a more selective anticancer agent than xanthohumol (1) against breast cancers , doxorubicin-resistant colon cancer (LoVo/Dx) and leukemia (MV-4-11), whereas in the case of prostate cancer  and lung cancer (A549), aurone 2 exhibited a comparable selectivity of action as xanthohumol (1). Since aurone 2 is not able to be transformed into potentially dangerous 8-prenylnaringenin, as happens in the case of xanthohumol (1), the obtained results may be very interesting in the context of possible future applications of this compound as a potential anticancer drug, including estrogen-dependent ones.Modification of the chalcone to the dihydrochalcone skeleton by the hydrogenation of the α,β-olefin bond counteracts spontaneous chalcone—flavanone isomerization. Thus, the saturation of the double bond at the α,β-position in xanthohumol , RPMI 1640 and Opti-MEM media were purchased from Gibco, Scotland, UK. All the solvents used for the syntheses and purification were of analytical grade, and those used for HPLC analysis were of HPLC grade .Mercury (II) acetate , sodium bicarbonate, magnesium sulfate, L-glutamine, sodium pyruvate, amino acids, doxorubicin, insulin, sulforhodamine B, 3--2,5-diphenyl tetrazolium bromide, Tris base, hydrochloric acid, acetic acid, formic acid, dimethylformamide, sodium dodecyl sulfate and dimethyl sulfoxide-dv/v), 3–32 min linear gradient from A:B (55:45 v/v) to A:B (5:95 v/v), 32–37 min isocratic A:B (5:95 v/v), 37–39 min linear gradient from A:B (5:95 v/v) to A:B (55:45 v/v), with 10 min of re-equilibration of the column before the next run. High-resolution ESI-MS spectra were obtained on a Bruker micrOTOF-Q spectrometer. The direct infusion ESI-MS parameters were as follows: The mass spectrometer was operated in negative ion mode with the potential between the spray needle and the orifice at 4.5 kV, a nebulizer pressure of 0.4 bar and a drying gas flow rate of 4 L/min at 200 °C. The sample flow rate was 180 μL/min. Ionization mass spectra were collected at the range m/z 150–3000. The instrument was calibrated with an Agilent electrospray calibration solution  that was diluted with acetonitrile. NMR spectra , 1H–13C NMR (HSQC)) were recorded at 600 and 151 MHz on a DRX Bruker Avance instrument using DMSO-d6 as a solvent. The chemical shifts are provided in parts per million (ppm) . UV spectra were collected on a Cintra 303 Spectrophotometer (GBC) in methanol.Column chromatography was performed using Sephadex LH-20  or silica gel 60  with the solvent mixtures specified in each experiment. The HPLC analyses were performed on a Waters 2695 Alliance instrument equipped with a Waters 2996 photodiode array detector (detection at wavelengths within the range of 200 to 600 nm) and using an Agilent Zorbax XDB C-18 analytical HPLC column . The elution was achieved at a flow rate of 1 mL/min; eluent A: 0.05% formic acid in redistilled water; eluent B: 0.05% formic acid in methanol, using the following elution program: 0–3 min isocratic A:B (55:45 1) was isolated from supercritical CO2 extracted hops  obtained from Łukasiewicz—Institute of New Chemical Syntheses . The method of isolation is described in the Xanthohumol  (Z)-6,4′-dihydroxy-4-methoxy-7-prenylaurone (2) was prepared from xanthohumol (1) according to the method described in our previous studies using mercury (II) acetate as a catalyst of the reaction [(reaction . The met1) and (Z)-6,4′-dihydroxy-4-methoxy-7-prenylaurone (2) are available in the The NMR, mass spectroscopy and spectrophotometry data of xanthohumol  medium was supplemented with 2 mM L-glutamine, 5% fetal bovine serum  and 0.8 mg L−1 insulin (T47D) or 1 mM sodium pyruvate  and 0.1 ug/mL doxorubicin (LoVo/Dx). The media contained antibiotics: 100 µg mL−1 streptomycin and 100 U mL−1 penicillin. All the cell lines were incubated at 37 ºC in a humid atmosphere saturated with 5% CO2. The following established in vitro cell lines were applied: human breast adenocarcinoma (MCF-7 and SK-BR-3), human breast ductal carcinoma (T47D), human prostate adenocarcinoma (PC-3), human prostate carcinoma (Du145), human colorectal adenocarcinoma (HT-29 and LoVo), doxorubicin-resistant human colorectal adenocarcinoma (LoVo/Dx), human myelomonocytic leukemia (MV-4-11), human lung adenocarcinoma (A549), normal murine embryonic fibroblasts (BALB/3T3) and human lung microvascular endothelial (HLMEC). All the cell lines were obtained from the American Type Culture Collection  except for LoVo and LoVo/DX, which were received from the Technical University of Gdańsk, Poland, courtesy of Prof. E. Borowski. All the cell lines were maintained in the Institute of Immunology and Experimental Therapy, Wroclaw, Poland. The cells were cultured in the following media: DMEM medium (BALB/3T3), Eagle medium , RPMI medium  or RPMI 1640 + Opti-MEM (1:1) medium . The DMEM medium was supplemented with 2 mM L-glutamine and 10% fetal bovine serum. The Eagle medium was supplemented with 4 mM L-glutamine, 10% fetal bovine serum, 1 mM sodium pyruvate for Du145 cells or 2 mM L-glutamine, 1% amino acids and 0.8 mg LThe details of this technique were described by Skehan et al. . After 7The details of this technique were described by Antoszczak et al. . The pro4 cells per well (103 cells per well for HLMEC) in 100 µL of culture medium. The analysis of the antiproliferative activity of the compounds was performed using sulforhothamine B (SRB)  or 3--2,5-diphenyl tetrazolium bromide (MTT) (for the leukemia cells—MV-4-11). The details of this technique have been previously described by Skehan et al. [−1) of xanthohumol (1), aurone (2) and the reference drug cisplatin. Each compound at each concentration was tested in a single experiment, which was repeated at least 3 times. DMSO, which was used as a solvent in a dilution corresponding to its highest concentration applied to the tested compounds, did not exert any inhibitory effect on cell proliferation. The results were calculated as the IC50 (μM) , i.e., the concentration of the tested agent that inhibits the proliferation of the cancer cell population by half [50 values and the mean values ± SD calculated for each experiment separately using GraphPad Prism software  are presented in Twenty-four hours before incubation with the tested compounds, the cells were seeded in 96-well plates  at a density of 10n et al.  and Anton et al. . An assa by half . The IC5p value < 0.05.The data are presented as the mean value ± standard error of the mean (SD). Statistical analysis was performed with the one-way ANOVA with Tukey’s post hoc test . The significance was accepted at a 1), has remarkable biological properties. The anticancer activity of xanthohumol (1) has been the subject of studies for more than 20 years. It is known that xanthohumol (1) may induce cell cycle arrest and apoptosis in cancer cells. Numerous cell signaling pathways, such as NF-κB, Akt, ERK, ROS, STAT3, Notch and AMPK, are involved in the anticancer activity of this prenylated chalcone [1) an excellent starting material for obtaining other biologically active compounds. Our studies have shown that both xanthohumol (1) and its semisynthetic aurone derivative (2) have significant antiproliferative activity against nine out of ten cancer cell lines, including doxorubicin-resistant human colon cancer (LoVo/Dx). Moreover, they displayed much higher cytotoxicity against almost all the tested cancer cell lines compared to the normal cell lines. Plants are a great source of many natural compounds that have potential in the prevention and treatment of numerous diseases, including cancer. The major prenylated flavonoid of hops, xanthohumol  in comparison to xanthohumol (1) clearly shows that the chalcone–aurone type modification of the flavonoid skeleton in prenylated flavonoids may significantly increase the selectivity of action against tumor cell lines in vitro. In addition, aurone 2 is unable to convert into the potentially dangerous 8-prenylnaringenin (4) in the body, which could be a major advantage for its use over xanthohumol (1) in the context of possible future applications as a potential anticancer drug, including estrogen-dependent ones. Nevertheless, it is crucial to conduct additional research into the mechanisms of action of aurone 2 to confirm its therapeutic potential and possible use in future cancer treatment.The higher selectivity of aurone (1) and its aurone derivative (2) are potent and selective antiproliferative agents. However, further research is required to determine the molecular mechanisms of action and confirm the obtained results using in vivo cancer models.Our studies have shown that xanthohumol (


Cytidine analogs, such as decitabine (DAC) and cytarabine (ara-C), have been widely used in the clinical treatment for several cancer types, including myelodysplastic syndrome and acute myeloid leukemia AML; . HoweverCDA in these cell lines, we found that CDA is expressed in only two 5hmdC-sensitive and 14 5fdC-sensitive cell lines. Interestingly, a subset of cell lines lacking CDA expression, including SEM (a human acute lymphoblastic leukemia cell line) and Raji (a human B lymphoblastoid cell line), was strongly inhibited by either or both oxidized methylcytidines  of 5hmdC and 5fdC in 45 human cancer cell lines . The surytidines , suggestytidines , and thaytidines . 5hmdC- ytidines , indicatytidines  and an iytidines  of SEM cEµ-Myc p19Arf−/− mouse B-cell lymphoma cell line, which was shown to be an ideal model system for analyzing gene–drug interactions  and 2ʹ-deoxynucleoside 5ʹ-phosphate N-hydrolase 1 (Dnph1), a nucleotide salvage factor recently found to be responsible for removing 5hmdUMP (Due to the inability of cytidine monophosphate kinase 1 (CMPK1) to phosphorylate modified cytidine monophosphates, the direct incorporation of 5mdC, 5hmdC and 5fdC into the genome is prevented . A deamiractions  and doesractions . Cas9-ex library , followe library . Bioinfoand Dctd , suggest 5hmdUMP . Thus, win vitro deamination assay indicated that dCMP could be converted to dUMP by DCTD and CDA (CDA than not (14/19), while most 5hmdC-sensitive cells have no CDA expression (8/10)  .DCTD KO SEM and Raji cells , reaching about 600 μmol/L in 10 min and being eliminated from peripheral blood within 4 h  for 11 days. Xenograft tumor growth of wild-type Raji cells was significantly restricted by 5hmdC treatment while that of DCTD-deficient cells was not affected (DCTD KO tumors (DCTD KO tumors (Wild-type and affected . ConsistO tumors  and S8C.O tumors . SignifiO tumors .−Sca-1+c-Kit+ (LSK) hematopoietic stem/progenitor cells and Gr-1+Mac-1+ myeloid cells in the bone marrow of 5hmdC-treated mice were not changed either  and spleen size in 5hmdC-treated animals . The perd either . The cond either . Taken tDck, Dctd, Cda, and Dnph1  derived from normal cord blood using colony formation assay. Treating hematopoietic stem cells (CD34+) with up to 100 μmol/L 5hmdC exerted little inhibitory effect on their colony forming abilities  derived from all patient samples except patients 5 and 6 due to insufficient cell numbers. Importantly, DCK was found to be highly expressed only in the bone marrow cells of patients 1–4 but not in those of patients 7–10, whereas no significant difference was observed in the expression of DCTD, CDA and DNPH1 between patients 1–4 and patients 7–10  and nucleoside diphosphate kinases (NDPKs), generating 5hmdUTP and 5fdUTP that can be incorporated into the genome by DNA polymerase and eventually result in DNA damage and cell death . Accordihttps://doi.org/10.1093/procel/pwac028.The online version contains supplementary material available at pwac028_suppl_Supplementary_Material_S1Click here for additional data file.pwac028_suppl_Supplementary_Material_S2Click here for additional data file.


Nanomolar concentrations of geldanamycin significantly reduced cell growth, invasion, and migration in vitro. However, administration of geldanamycin in vivo did not result in significant anti-cancer activity. Our findings show that myogenesis and muscle differentiation pathways are upregulated in pleural mesothelioma which may be related to the invasive behaviour. However, geldanamycin as a single agent does not appear to be a viable treatment for mesothelioma.Mesothelioma is characterised by its aggressive invasive behaviour, affecting the surrounding tissues of the pleura or peritoneum. We compared an invasive pleural model with a non-invasive subcutaneous model of mesothelioma and performed transcriptomic analyses on the tumour samples. Invasive pleural tumours were characterised by a transcriptomic signature enriched for genes associated with MEF2C and MYOCD signaling, muscle differentiation and myogenesis. Further analysis using the CMap and LINCS databases identified geldanamycin as a potential antagonist of this signature, so we evaluated its potential  Mesothelioma is a cancer that usually arises in the pleura, occasionally developing from other serous membranes . Its devFor many mesothelioma patients, palliative chemotherapy with cisplatin/pemetrexed is the first-line therapy . RecentlRecent developments in systems biology have allowed gene expression profiling to be used not only for comparing specific pathology but also to unravel drug-disease associations . The conHere, using transcriptomic data from invasive and non-invasive models of murine mesothelioma, we aimed to map an invasive signature of mesothelioma and identify drug repurposing candidates with potential anti-mesothelioma activity.−8, z-score = 2.78) and myocardin (MYOCD), a co-transcriptional activator of serum response factor that inhibits the cell cycle and induces smooth muscle differentiation Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: Yes**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Morales et al investigate geldanamycin as a treatment for mesothelioma. They implant two murine mesothelioma cells subcutaneously and orthotopically into mice and identify differentially expressed genes between the invasive growth in the orthotopic location and the non-invasive growth of the sucutaneously implanted tumors. They perform pathway and database analyses with the differentially expressed genes to identify a gene expression signature associated with invasive growth and geldanamycin as a potential antagonist of this signature. They subsequently test geldanamycin in vitro and in vivo and find reduction of cell growth, migration and invasion in vitro but no inhibition of tumor growth or invasion in vivo.The experiments appear technically well performed and the manuscript is well written.I have two main concerns:1) The difference in growth characteristics between the models is dictated by the location and may thus not reflect the aggressiveness of the tumor cells. I would be more interested in a difference between cell models that do and do not grow invasively in the pleura.2) The authors do not explore the difference between their in vitro and in vivo findings. According to the literature HSP90 is the target of geldanamycin. Is this target present in their models? How is it affected by geldanamycin treatment in vitro and in vivo? Have others found efficacy of geldanamycin in vivo in other cancers?Reviewer #2: The Authors compared an invasive pleural model with a non-invasivesubcutaneous model of mesothelioma. They performed transcriptomic analyses on the tumour samples and used differentially expressed genes between pleural and subcutaneous mesothelioma to interrogate platforms allowing to identify drugs able to produce a similar transcriptional difference. Within the list of drugs, the Authors select geldanamycin, an hsp90 inhibitor. This drug has already been shown to inhibit mesothelioma cell growth in vitro and the Authors confirm that nanomolar concentrations of geldanamycin inhibit cancer cell growth, invasion, and migration in vitro in the four mesothelioma cell lines tested. However, they observed no effect in vivo on growth or invasion in an orthopic model using cells labeled with a luciferase reporter.This study includes some interesting observations, however, several issues have to be addressed before this manuscript can be considered for publication.Major:1. If the Authors select log fold change >1, and exclude Ig genes, where differential expression might result from the tumor microenvironment, the remaining 99 genes allow to identify “mesothelial cell signature” in the enrichr plateform that they used. This means that the pleural environment is more favorable to mesothelial differentiation program commitment. This is an important information that the Authors should add for the benefit of the community. It also provides a slightly different perspective compared to what the Authors have proposed as data “suggesting that muscle development signatures were related to the invasive phenotype of pleural mesothelioma”. Indeed, it adds more emphasis on the fact that orthotopic environment favors the maintenance of mesothelial differentiation. This does not preclude the selection of geldanamycin for further investigations.2. The Authors should provide evidence whether the AB1-luciferase cell line used in the orthotopic model has the same profile as parental cells. For example they could test whether they maintain the high expression of UPK3B and LRRN4 as parental cells.3. The Authors could compare whether AB1 and AB1-luc show the same sensitivity to geldanamycin growth inhibition effects.4. The Authors could also test whether there are different levels of HSP90 and a different ratio of HSP90 alpha vs beta isoforms between AB1 cells used for the transcriptomic analysis and the AB1-luciferase cells, which may help explain difference in sensitivity.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoYes: Emanuela Felley-BoscoReviewer #2: **********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  21 Mar 2023PONE-D-22-23207R1Geldanamycin treatment does not result in anti-cancer activity in a preclinical model of orthotopic mesotheliomaOrozco Morales et al. Point-by-point reply to reviewers’ comments. We thank the reviewers for their constructive comments, which have helped us to improve our manuscript. Here, we present a point-by-point reply addressing all comments.Reviewer #1: Morales et al investigate geldanamycin as a treatment for mesothelioma. They implant two murine mesothelioma cells subcutaneously and orthotopically into mice and identify differentially expressed genes between the invasive growth in the orthotopic location and the non-invasive growth of the sucutaneously implanted tumors. They perform pathway and database analyses with the differentially expressed genes to identify a gene expression signature associated with invasive growth and geldanamycin as a potential antagonist of this signature. They subsequently test geldanamycin in vitro and in vivo and find reduction of cell growth, migration and invasion in vitro but no inhibition of tumor growth or invasion in vivo.The experiments appear technically well performed and the manuscript is well written.I have two main concerns:1) The difference in growth characteristics between the models is dictated by the location and may thus not reflect the aggressiveness of the tumor cells. I would be more interested in a difference between cell models that do and do not grow invasively in the pleura.We agree with the reviewer that having a cell line that does not grow invasively in the pleural cavity would be of great interest, but the two different cell lines that we tested (AB1 and AE17), in two different mouse strains (C57BL/6 and BALB/c), showed invasive and fast-proliferative behaviour in the pleural space, but not in the subcutaneous space, which is why we compared the transcriptomes of the tumours derived from the same cell line, yet growing in different anatomical compartments.2) The authors do not explore the difference between their in vitro and in vivo findings. According to the literature HSP90 is the target of geldanamycin. Is this target present in their models? How is it affected by geldanamycin treatment in vitro and in vivo? Have others found efficacy of geldanamycin in vivo in other cancers?We chose geldanamycin using the LINCS drug repurposing database as it was predicted to antagonise the invasion-associated gene expression profile. The source data in the LINCS database are derived from cell lines treated with thousands of compounds. The gene expression profile induced by geldanamycin can be HSP90-independent. Indeed, geldanamycin was initially discovered as an antibiotic, and only decades later was to be shown to bind to HSP90. However, to address the presence of HSP90 present in our models and to observe if geldanamycin downregulates HSP90 in vitro, we now added western blots in Supplementary figure 2  from both cell lines that were used for in vitro and in vivo experiments, AB1 and AB1-Luc respectively. These data show the same basal expression of HSP90 in both cell lines, as well as an unchanged expression after addition of geldanamycin at 500 nM, a concentration which in our migration experiments reduced cell migration. These data agree with others that have shown that geldanamycin treatment does not downregulate HSP90 protein expression levels in urinary bladder cancer cell lines RT4 and T24 , and breast cancer cell line MCF-7 , but rather inhibits HSP90’s downstream targets  , suggesting that geldanamycin’s mechanism of action is either HSP90-independent or works through inhibition of its downstream targets as per the literature. We have added the following sentences to the Discussion section : “In our study, we observed that addition of geldanamycin did not affect the expression of HSP90 itself. These data agree with others that have shown that geldanamycin treatment does not downregulate HSP90 protein expression levels as such (49) (50), but rather inhibits HSP90’s downstream targets (49) (50). Another explanation for geldanamycin’s mechanism of action as observed in our in vitro experiments is that it is HSP90-independent.”Supplementary Fig S2. Luciferase transduction does not affect the AB1 cell line phenotype (H) Western blot for cell lines AB1 and AB1-Luc shows housekeeping protein GAPDH at 37 kDa, and HSP90 protein at 90 kDa.Reviewer #2: The Authors compared an invasive pleural model with a non-invasive subcutaneous model of mesothelioma. They performed transcriptomic analyses on the tumour samples and used differentially expressed genes between pleural and subcutaneous mesothelioma to interrogate platforms allowing to identify drugs able to produce a similar transcriptional difference. Within the list of drugs, the Authors select geldanamycin, an hsp90 inhibitor. This drug has already been shown to inhibit mesothelioma cell growth in vitro and the Authors confirm that nanomolar concentrations of geldanamycin inhibit cancer cell growth, invasion, and migration in vitro in the four mesothelioma cell lines tested. However, they observed no effect in vivo on growth or invasion in an orthopic model using cells labeled with a luciferase reporter.This study includes some interesting observations, however, several issues have to be addressed before this manuscript can be considered for publication.Major:1. If the Authors select log fold change >1, and exclude Ig genes, where differential expression might result from the tumor microenvironment, the remaining 99 genes allow to identify “mesothelial cell signature” in the enrichr plateform that they used. This means that the pleural environment is more favorable to mesothelial differentiation program commitment. This is an important information that the Authors should add for the benefit of the community. It also provides a slightly different perspective compared to what the Authors have proposed as data “suggesting that muscle development signatures were related to the invasive phenotype of pleural mesothelioma”. Indeed, it adds more emphasis on the fact that orthotopic environment favors the maintenance of mesothelial differentiation. This does not preclude the selection of geldanamycin for further investigations.We thank the reviewer for this observation. We have performed this analysis ourselves and have indeed observed a mesothelial cell signature for different cell types. We added the data as Supplementary figure 1  and discussed in the Results section : “Further analysis with the Descartes dataset in Enrichr (24) (25) (26) of the IPL differentially expressed genes (log fold change >1) while excluding immunoglobulin-related genes resulted in a mesothelial cell signature , suggesting that environmental cues from the pleural space drive this mesothelial program more than the subcutaneous space”.Figure S1A. Enrichment analysis of IPL (log fold change >1) differentially expressed genes using the Descartes dataset in Enrichr, p values 0.05 and 0.01 are represented with dotted line on –log10 = 1.3 and 2, respectively.2. The Authors should provide evidence whether the AB1-luciferase cell line used in the orthotopic model has the same profile as parental cells. For example they could test whether they maintain the high expression of UPK3B and LRRN4 as parental cells.3. The Authors could compare whether AB1 and AB1-luc show the same sensitivity to geldanamycin growth inhibition effects.To address questions 2 and 3 from Reviewer #2, we looked at the expression of the surface markers PD-L1, MHC-I and podoplanin with flow cytometry, and performed in vitro scratch migration and cytotoxicity assays with geldanamycin in both cell lines. We did not observe any difference between AB1 and AB1-Luc. These results in Supplementary figure 1  under the section: Geldanamycin treatment does not have anti-mesothelioma activity in vivo , in page 7, lines 120 – 125: “To assess any therapeutic effect of geldanamycin on mesothelioma invasion and growth in vivo, we used our optimised model of orthotopic invasive mesothelioma (14) with AB1 cells expressing luciferase (AB1-Luc). The luciferase transduction had not changed the phenotype and geldanamycin sensitivity of the AB1 cell line, in terms of its expression of PD-L1, MHC-I and podoplanin, nor its in vitro invasive potential . In addition, the basal levels of HSP90 did not change between cell lines, with or without geldanamycin .”Supplementary Fig S2. Luciferase transduction does not affect the AB1 cell line phenotype (A - C) Flow cytometry for surface markers (A) PD-L1, (B) MHC-I, and (C) Podoplanin in AB1 and AB1-Luc cell lines. Data is represented as MFI (Median). N = 5. (D) MTT assay in AB1 and AB1-Luc cell lines. The curve is presented as a non-linear regression; log(geldanamycin) versus response. IC50 values are in nM. Data are presented as means ± SD of 3 replicates. (E - G) Scratch assay for migration in (E) AB1 and (F) AB1-Luc cell lines. Geldanamycin was serial diluted from 400 nM to 1.25 nM, with 0 nM as control. (G) AB1 and AB1-Luc side by side comparison. (H) Western blot for cell lines AB1 and AB1-Luc shows housekeeping protein GAPDH at 37 kDa, and HSP90 protein at 90 kDa.4. The Authors could also test whether there are different levels of HSP90 and a different ratio of HSP90 alpha vs beta isoforms between AB1 cells used for the transcriptomic analysis and the AB1-luciferase cells, which may help explain difference in sensitivity.To address question 4, as previously mentioned in question 2 from Reviewer #1, we performed western blot on both cell lines to determine the basal levels of HSP90 and after geldanamycin treatment at 500 nM. These results are similar to what others have found when using geldanamycin at similar concentrations in vitro. This could indicate that geldanamycin does not directly downregulate HSP90 and instead reduces is action, affecting its downstream targets. These results are now discussed in the Discussion section  .AttachmentResponse to reviewers - PONE-D-22-23207R1.docxSubmitted filename: Click here for additional data file. 27 Mar 2023Geldanamycin treatment does not result in anti-cancer activity in a preclinical model of orthotopic mesotheliomaPONE-D-22-23207R1Dear Dr. Orozco Morales,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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For more information, please contact Kind regards,Luka BrcicAcademic EditorPLOS ONEAdditional Editor Comments :Thank you fro additional analyses and changes according to reviewers´ comments, which improved the quality and clarity of your manuscript.Reviewers' comments: 25 Apr 2023PONE-D-22-23207R1 Geldanamycin treatment does not result in anti-cancer activity in a preclinical model of orthotopic mesothelioma Dear Dr. Lesterhuis:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. 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Drug resistance continues to be a significant problem in cancer therapy, leading to relapse and associated mortality. Although substantial progress has been made in understanding drug resistance, significant knowledge gaps remain concerning the molecular underpinnings that drive drug resistance and which processes are unique to certain drug classes. The NCI-60 cell line panel program has evaluated the activity of numerous anticancer agents against many common cancer cell line models and represents a highly valuable resource to study intrinsic drug resistance. Furthermore, great efforts have been undertaken to collect high-quality omics datasets to characterize these cell lines. The current study takes these two sources of data—drug response and omics profiles—and uses a multi-omics investigation to uncover molecular networks that differentiate cancer cells that are sensitive or resistant to antifolates, which is a commonly used class of anticancer drugs. Results from a combination of univariate and multivariate analyses showed numerous metabolic processes that differentiate sensitive and resistant cells, including differences in glycolysis and gluconeogenesis, arginine and proline metabolism, beta-alanine metabolism, purine metabolism, and pyrimidine metabolism. Further analysis using multivariate and integrated pathway analysis indicated purine metabolism as the major metabolic process separating cancer cells sensitive or resistant to antifolates. Additional pathways differentiating sensitive and resistant cells included autophagy-related processes  and adhesion/cytoskeleton-related pathways . Volcano plot analysis and the receiver operating characteristic (ROC) curves of top selected variables differentiating Q1 and Q4 revealed the importance of genes involved in the regulation of the cytoskeleton and extracellular matrix (ECM). These results provide novel insights toward mechanisms of intrinsic antifolate resistance as it relates to interactions between nucleotide metabolism, autophagy, and the cytoskeleton. These processes should be evaluated in future studies to potentially derive novel therapeutic strategies and personalized treatment approaches to improve antifolate response. Cancer drug resistance represents a formidable obstacle that severely impacts patient outcomes. The emergence of intrinsic resistance, preexisting prior to treatment initiation, as well as acquired resistance, acquired during or after therapy, significantly diminishes the efficacy of not only conventional chemotherapeutic agents but also novel tumor-targeted and immunotargeted therapies . The curOne of the most commonly used chemotherapeutic drug classes is antifolates. Antifolate drugs play a crucial role in cancer therapy by inhibiting folate metabolism, which is essential for DNA synthesis and cell proliferation. These drugs target enzymes involved in the folate pathway, such as dihydrofolate reductase (DHFR) and thymidylate synthase (TS), thereby disrupting nucleotide production and impairing cancer cell growth . HoweverThe advent of high-throughput omics technologies, encompassing epigenomics, genomics, proteomics, and metabolomics, and others, has provided unprecedented opportunities to unravel the complex molecular landscape associated with drug resistance in cancer. Utilizing these multi-omics approaches allows for the identification of key molecular signatures and networks implicated in drug resistance, thereby enabling the development of personalized treatment modalities tailored to individual patients . Additiohttps://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS4296, and replicate measurements were averaged [https://www.ebi.ac.uk/pride/archive/projects/PXD013615) (accessed on 25 May 2023) [https://dtp.cancer.gov) (accessed on 24 May 2023). Lastly, copy number variant (CNV) data and DNA methylation data (averaged by gene) were downloaded from CellMiner (accessed on 21 July 2023) [All baseline omics datasets of NCI-60 cell lines were acquired from publicly available datasets. Transcriptomics data were obtained from averaged ,14 (accely 2023) ,17. Two 50 values for each compound for each cell line [Drug response data for all compounds screened in the NCI-60 cell line program were downloaded from CellMiner in the form of z-scores which are autoscaled GIell line . Mechaniell line . For eacp-values were calculated for metabolites, proteins, and transcripts between cell lines in the top quartile  and the bottom quartile . Fold changes were calculated using average values in Q4 and average values in Q1. p-values were calculated using Students’ t-test (two-tailed). Metabolites (as KEGG IDs), proteins (as Uniprot IDs), and transcripts  with a p < 0.1 were input into OmicsNet for integrated network analysis [Fold changes and analysis . KEGG waanalysis .Relative abundance values for metabolomics, transcriptomics, proteomics, CNVs, and DNA methylation datasets were imported into Omicsanalyst for integrated multivariate analysis . Quartilhttps://pathview.uncc.edu/overview was utilized, incorporating data from metabolomics, proteomics, and transcriptomics datasets [p-value less than 0.1 were included in the analysis. For the species database, hsa-Homo sapiens was selected. Positive fold changes were indicative of an increase in Q4 of the total rank order of z-scores, representing variables that increased in cells more resistant to antifolate treatment.In order to visualize pathway perturbations using multi-omic data, the Pathview software datasets ,24. The 50 response data were sixteen entries for compounds that were defined by CellMiner as having an antifolate mechanism of action. These were broadly defined as antifols as well as inhibitors of specific enzymes including dihydrofolate reductase (DHFR), thymidylate synthetase (TYMS) inhibitors, and phosphoribosylglycinamide formyltransferase (GART)  . The dislyst 5.0 . Unsuperp-values for all metabolites, transcripts, proteins, CNVs, and DNA methylation values were calculated between Q1 and Q4  represented the cell lines with the highest z-score rank, indicating greater resistance to antifolate agents, while the first quartile (Q1) represented the cell lines with the lowest z-score rank, indicating higher sensitivity to antifolate agents . Using t1 and Q4 .p < 0.1 between Q1 and Q4 to determine the overall differentiating cellular processes between the two groups. Joint pathway analysis with metabolites and transcripts revealed ribosome (p = 2.51 × 10−16), splicosome (p = 5.60 × 10−13), endocytosis (p = 2.06 × 10−10), RNA transport (p = 6.17 × 10−10), and RNA degradation (p = 6.17 × 10−10) as among the most significant pathways differentiating Q1 and Q4 (p = 1.21 × 10−21), ribosome biogenesis (p = 4.60 × 10−19), splicosome (p = 1.42 × 10−15), DNA replication (p = 1.65 × 10−14), and RNA polymerase (p = 2.13 × 10−12) as among the most significant pathways differentiating Q1 and Q4  was analyzed separately using PCA. This analysis revealed the separation of Q1 and Q4 in each dataset in an unsupervised manner A–E. ResuAdditional analyses were performed on DIABLO-selected variables. Firstly, volcano plot analysis was performed to identify molecules most significantly increased or decreased in Q4 cells. Results showed that transcripts overall had the largest fold changes between Q1 and Q4, with many genes related to adhesion/cytoskeletal structure  and other notable differences in nuclear functions  in calcium regulation  . Receivep < 0.1 between Q1 and Q4 to understand the overall increases or decreases in the activities of these pathways in Q4 samples  and network analysis approaches in the multi-omics analysis. These orthogonal approaches allowed for the identification of common key pathways and molecules, highlighting the importance of autophagy, cytoskeleton, and nucleotide metabolism-related pathways as key for differentiating cells sensitive or resistant to antifolates. Interestingly, the current study did not identify DHFR or TS as major differentiators of sensitive and resistant cells, but these have been primarily studied in acquired resistance settings. As our previous work has shown with alkylating agents, intrinsic and acquired resistance seem to be defined by distinct metabolic mechanisms, which may explain this difference ,27,28.The cytoskeleton and autophagy are two essential cellular processes that play critical roles in maintaining cell structure, function, and overall homeostasis. The cytoskeleton is a dynamic network of protein filaments found in the cytoplasm of eukaryotic cells, comprising microtubules, actin filaments (microfilaments), and intermediate filaments . These fThe connection between cytoskeleton activity and autophagy becomes evident when considering the process of autophagosome formation and intracellular transport. Autophagosome formation relies on certain components of the cytoskeleton, particularly microtubules, which provide the necessary tracks for the movement of motor proteins to transport vesicles and organelles throughout the cell . The proThe connection between autophagy and cancer drug resistance is a multifaceted and dynamic field of study within cancer biology. The role of autophagy in cancer is complex and context-dependent, as it can have both pro-survival and pro-death functions, depending on various factors including the stage of cancer, the specific tumor microenvironment, and the type of cancer treatment being administered . AutophaAside from metabolic changes related to autophagy, the current study also identified purine metabolism as a major differentiator between sensitive and resistant cells. Cancer cells exhibit various mechanisms to rewire nucleotide metabolism and develop resistance to drug treatments . By blocIn conclusion, this study employed a comprehensive approach to categorize cancer cell lines in the NCI 60-cell line panel as sensitive or resistant to antifolates based on drug response data and identified molecular networks that differentiate between these two groups. The analysis revealed significant associations between antifolate response and core molecular processes, particularly nucleotide metabolism and autophagy-related pathways, as well as changes in cell structure-related pathways, including cytoskeletal pathways. Understanding these resistance mechanisms is critical for developing effective strategies to combat drug resistance and enhance the efficacy of antifolate-based therapies. The findings of this study contribute valuable insights into the molecular underpinnings of antifolate response and open new avenues for exploring potential targets to overcome drug resistance in cancer treatment.


Monepantel is an anti‐helminthic drug that also has anti‐cancer properties. Despite several studies over the years, the molecular target of monepantel in mammalian cells is still unknown, and its mechanism‐of‐action is not fully understood, though effects on cell cycle, mTOR signalling and autophagy have been implicated.Viability assays were performed on >20 solid cancer cell cells, and apoptosis assays were performed on a subset of these, including 3D cultures. Genetic deletion of BAX/BAK and ATG were used to establish roles of apoptosis and autophagy in killing activity. RNA‐sequencing was performed on four cell lines after monepantel treatment, and differentially regulated genes were confirmed by Western blotting.We showed that monepantel has anti‐proliferative activity on a broad range of cancer cell lines. In some, this was associated with induction of apoptosis which was confirmed using a BAX/BAK‐deficient cell line. However, proliferation is still inhibited in these cells following monepantel treatment, indicating cell‐cycle disruption as the major anti‐cancer effect. Previous studies have also indicated autophagic cell death occurs following monepantel treatment. We showed autophagy induction in multiple cell lines; however, deletion of a key autophagy regulator ATG7 had minimal impact on monepantel’s anti‐proliferative activity, suggesting autophagy is associated with, but not required for its anti‐tumour effects. Transcriptomic analysis of four cell lines treated with monepantel revealed downregulation of many genes involved in the cell cycle, and upregulation of genes linked to ATF4‐mediated ER stress responses, especially those involved in amino‐acid metabolism and protein synthesis.As these outcomes are all associated with mTOR signalling, cell cycle and autophagy, we now provide a likely triggering mechanism for the anti‐cancer activity of monepantel. We recently reported similar (micromolar) activity to that seen in ovarian cancer cell lines on a small number of cell lines reflecting the cancer types studied in the Phase I trial, though this was not wide‐ranging.22.12 except U‐87 MG and SW620 which were grown in 5% CO2. Cell lines were cultured in the following media with 10% (v/v) FBS  and 1% (v/v) penicillin/streptomycin : HOSE 6‐3, HOSE 17‐1, HCT 116: DMEM/F12 (Gibco); SW620: DMEM/F12, 1% (v/v) HEPES (Gibco); all melanoma lines, MDA‐MB‐435, SW480: RPMI 1640(−Glutamine) (Gibco), 2 mM GlutaMAX (Gibco); MDA‐MB‐231: RPMI 1640(+Glutamine) (Gibco); OVCAR‐3, OVCAR‐5: DMEM high glucose (Gibco); A549, U‐87 MG, COLO 320: DMEM/F12 (Gibco), 2 mM GlutaMAX; DMS53: Waymouth (Gibco); PC‐3: RPMI 1640(−Glutamine) (Gibco); Fibroblasts: DMEM high glucose (Gibco), 50 μM 2‐mercaptoethanol, 250 μM L‐Asparagine (Gibco), 1 mM HEPES (Gibco).Monepantel was provided by PharmAust and a stock solution prepared at 10 mM in 100% ethanol and stored at −20°C. Vorinostat was purchased from Selleck (Cat#A1047) and 5FU from Sigma (Cat#F6627‐1G). Both were prepared as 10 mM stock solutions in phosphate‐buffered saline (5FU) or DMSO (Vorinostat). Cell lines were originally obtained from ATCC except HCT 116 which was gift from Associate Professor Grant Dewson (WEHI) with identities confirmed by STR profiling and shown to be mycoplasma negative based on in‐house MycoAlert assays (Lonza). All were maintained at 37°C with 10% CO2.2The 5FU‐resistant SW620 colon cancer cells were generated by continuous culture in the presence of increasing concentrations of 5FU. Exponentially growing SW620 cells were initially treated with 1 μM 5FU and when they reached confluence passaged 1:10 into a fresh flask with increasing concentrations of 5FU on each passage as follows: 4, 6, 8, 10, 12, 15, 30, 50, 100 μM. The same process was followed for the Vorinostat‐resistant cells with cells initially treated with 1 μM Vorinostat which was increased on each passage with the following doses: 4, 6, 8, 10, 12, 15 μM.2.3CellTiter‐Glo‐based viability assays were performed as previously described.2.4Cells  were seeded into 24‐well plates, then 16–24 h later treated with monepantel or vehicle prepared in media used for each cell line. Live and dead cells were harvested and pelleted by centrifugation then incubated with Annexin V‐APC (BD Biosciences) and propidium iodide (PI) (Sigma Aldrich) in Annexin V binding buffer (BD Biosciences). FACS analysis was performed on a BD FACSCanto II flow cytometer (BD Biosciences). Data were analyzed using FlowJo Software Version 10 (FlowJo‐LLC) with the number of viable cells (Annexin V negative/PI negative) normalized relative to the number of viable cells cultured in the vehicle control.2.55; LM‐MEL‐28, A549) from cultured cell lines were harvested, pelleted, and resuspended in 100 μL of Cultrex RGF BME, Type 2 (R&D Systems). Drops (4–8) of the suspension were then added to each well of a pre‐warmed 12‐well plate, then incubated inverted at 37°C for 30 min before media was added. Cells were then incubated at 37°C in 10% CO2 for 2–4 weeks. Media was then removed before cold TryPLE was added and cells resuspended by pipetting, then incubated for 10 min at 37°C before further shearing by pipetting. This resuspension process was then repeated before the wells are examined under a microscope to ensure a single‐cell suspension is obtained. Cells were then centrifuged at 320 g for 5 min, resuspended in Cultrex and split between two wells of a 12‐well plate with 3–4 domes per well. Spheroids were allowed to grow for ~1 week before media was changed into media containing the vehicle or 25 μM monepantel. Spheroids were imaged at Day 0 and 168 h posttreatment before FACS analysis.Cells  and propidium iodide (PI) (Sigma Aldrich) in Annexin V binding buffer (BD Biosciences). FACS analysis was performed on a BD FACSCanto II flow cytometer (BD Biosciences) and data were analyzed as described above.2.7Cell cycle analysis was performed similarly to the apoptosis assays. After drug treatment, cells were harvested, pelleted, and resuspended in PI buffer  Triton x‐100) prior to FACS analysis on a BD FACSCanto II flow cytometer using appropriate gating parameters for cell cycle. Data were analyzed using FlowJo Software Version 10 cell cycle platform (FlowJo‐LLC).2.86) for 1 h at 4°C in Onyx lysis buffer  glycerol and 1% (v/v) Triton‐x‐100; Sigma‐Aldrich) for all blots except those probed with antibodies to CHOP and BiP where enhanced RIPA  Igepal, 0.3% (w/v) sodium deoxycholate, 1% (w/v) SDS) or for 4EBP1 where Igepal buffer  Igepal) was used instead. Lysis buffers were supplemented with protease inhibitors ). Lysate supernatants  were separated by SDS‐PAGE  before transferring to nitrocellulose and probing with antibodies to: BAK , BAX , ATG7 , BiP , CHOP , actin , p62 , LC3 , 4EBP1 , NOXA .Total protein extracts were prepared by lysing cells  and allowed to adhere overnight. The media was then replaced with media containing 25 μM monepantel or an equivalent volume of vehicle (ethanol) and incubated at 37°C for 48 h. Cells were collected and counted and pellets containing 1 × 105 cells snap‐frozen prior to total RNA extraction performed using the miRNeasy micro kit (Qiagen) following the manufacturer's instructions. The RNA concentration was determined using RNA ScreenTape on the TapeStation instrument (Agilent).Cell lines  were seeded into 6‐well plates . RNAseq libraries were pooled prior to Next Gen Sequencing on an Illumina NextSeq500 platform performed at the Walter and Eliza Hall Institute .2.10Subread aligner in the Rsubread package.featureCounts program.voom function in limma package.t‐statistic was used to assess differential expression of genes.goana and kegga functionslimma were used to test the enrichment of GO (Gene Ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, respectively, in cell lines treated with monepantel compared to vehicle controls. The barcodeplot function in limma was used to generate barcode plots displaying enrichment of KEGG pathways of particular interest. Statistical significance of enrichment of these pathways was tested using the limma function roast.Sequencing reads were mapped to the GRCh38/hg38 reference genome using the 2.11portals.broadinstitute.org/goox/crispick/public) providing the sequences: (1) 5′‐GAAGCTGAACGAGTATCGGC‐3′ and (2) 5′‐GCTGCCAGCTCGCTTAACAT‐3′. Expression of sgRNA was induced by treatment with doxycycline  for at least 3 days prior to Western blotting to determine gene deletion efficiency.Deletion of ATG7 was performed using a lentiviral vector system2.12Bak−/−Bak−/− or Atg7−/− cells) with Prism 8 (GraphPad) software using an unpaired t‐test with Welch's correction. In the case of titrations, the same test was applied to data corresponding to any individual doses where there was an obvious difference in response between cell lines, or using Wilcoxon matched‐paired signed rank test to compare entire dose–response curves.For all apoptosis assays where treatments resulted in >10% change in cell viability, statistical analysis was performed to compare vehicle versus treatments or corresponding treatments between cell genotypes , DMS 53 (lung), OVCAR‐3 (ovarian), HCT 116 (colon)) but not other lines (OVCAR‐5 (ovarian), A549 (lung), U‐87 MG (brain), MEF (mouse embryonic fibroblast)), including the HOSE 6‐3 line that showed only a weak response in the CTG assay Figure . In all We also extended our studies to more physiologically relevant 3D cultures. Here, we generated spheroids of LM‐MEL‐28 cells that undergo significant apoptosis in 2D culture, as well as A549 cells that only showed decreased proliferation with minimal apoptosis. Both 3D cultures responded to monepantel very similarly to how they responded in the 2D cultures; the LM‐MEL‐28 cells underwent apoptosis, while the A549 monepantel‐treated spheroids remained as viable as those treated with vehicle Figures  but grewBAX−/−BAK−/− cells)  for BAX and BAK  Figure . BAX and) Figure . Consist) Figure . This su3.3required for monepantel activity, we used CRISPR‐Cas9 gene editing to generate OVCAR‐3 and MEF cells that were deficient in ATG7 (ATG7−/−), an enzyme required for lipidation of LC3B‐I, converting it to LC3B‐II, an essential step in autophagosome maturation. Both ATG7−/− lines showed significantly decreased ATG7 protein levels , prior to RNA isolation and sequencing.Multi‐dimensional scaling of the data revealed clustering of treated and untreated samples from the same cell lines Figure . DiffereATF4, a master transcription factor associated with ER stress responses, EIF1, EIF4EPB1 and six different amino acid tRNA synthetase 1 genes which are associated with regulation of protein synthesis, and DDIT4 and SESN2 which are associated with mTOR signaling  analysis Table . Of the g Figure . Combineg Figure .The eight commonly downregulated genes were predominantly associated with cell cycle regulation consistent with our data that monepantel impacts cell proliferation. Moreover, barcode plots generated for individual cell lines following enrichment analysis showed strong negative enrichment for genes associated with cell cycle following monepantel treatment Figure .To gain potential insights into the molecular basis for the differential sensitivity of cell lines to monepantel, we next identified genes differentially regulated in the most responsive cell lines  but not in the HOSE 6‐3 cell line Figure . This siHence, the RNAseq data strongly points to two major cellular processes with monepantel treatment—the ER stress pathway and cell cycle regulation.3.5To functionally validate the downregulation of cell cycle genes, particularly in the most responsive cell lines, we next examined the effect of monepantel on cell cycle kinetics. Consistent with the gene expression changes, monepantel induced an increase in the percentage of cells in G0/G1 and a reduction in the percentage of cells in S phase in sensitive OVCAR‐3 cells. While a similar trend was observed in HOSE 6‐3 cells, effects were less pronounced Figure . As thisPMAIP which encodes the pro‐apoptotic BH3‐only protein NOXA, which was upregulated by monepantel in all cell lines irrespective of apoptotic response (Table We next examined induction of se Table . Consistse Table . It shouAtf4 was deleted (Atf4−/−) with monepantel  and time‐dependent (48–120 h) effects on cell viability across the cell lines tested. As previously reported, non‐malignant cell lines tested were generally less sensitive compared to the majority of cancer lines examined. Interestingly, we observed evidence of apoptosis induction in approximately 50% of a representative subset of cell lines examined which is inconsistent with previous studies that concluded apoptosis is not induced by monepantel. The use of BAX/BAK‐deficient cells has become the gold standard for confirming whether drugs are acting through the BCL‐2‐regulated pathway.We first showed that monepantel has broad anti‐cancer effects across multiple cancer types  with similar dose  metabolism. At a protein level, we observed further evidence of an ER‐stress response including increased levels of the ER chaperone protein BiP and the major transcription factor acting downstream of ATF4, CHOP.Notably, deletion of ATF4 increased the sensitivity of MEFs to monepantel, strongly suggestive of ER stress being induced as a survival mechanism, presumably elicited by the unfolded protein response (UPR) associated with ER stress.EIF4EBP gene was transcriptionally upregulated, as was the encoded 4E‐BP1 protein, in response to monepantel treatment. Other reports on the anti‐cancer effects of monepantel place 4E‐BP1 along the mTOR signaling axis and induction of autophagy as critical processes underlying drug activity. Consistent with this, our data show upregulation of two critical negative regulators of mTORC1, DDIT4,SESN2One consequence of the UPR is the global suppression of protein translation which is mediated by the 4E‐BPs (direct targets of ATF4) that inhibit eIF4F assembly required for translation initiation.As in previous reports, we showed evidence of autophagy induction triggered in response to monepantel treatment. However, those previous studies did not provide any details on why or how it was induced. The compelling evidence reported here on the induction of ER stress responses likely fills that gap. Indeed, autophagy induction and mTOR signaling pathways are major consequences of ER stress.In summary, our data provide compelling evidence for the induction of ER stress and associated UPR as being involved in the mechanism‐of‐action of monepantel, and strongly suggests that cell cycle arrest is the major consequence of treatment, although apoptosis and autophagy can both occur downstream of the stress response Figure . What isTiffany J. Harris: Data curation ; formal analysis ; investigation (lead); writing – review and editing (supporting). Yang Liao: Formal analysis (supporting); visualization (supporting); writing – original draft (supporting); writing – review and editing (supporting). Wei Shi: Formal analysis (supporting); visualization (supporting); writing – original draft (supporting); writing – review and editing (supporting). Marco Evangelista: Investigation (supporting); writing – review and editing (supporting). Bhupinder Pal: Formal analysis (supporting); writing – original draft (supporting); writing – review and editing (supporting). Hamsa Puthalakath: Resources ; writing – original draft (supporting); writing – review and editing (supporting). Roger Aston: Resources (supporting); writing – review and editing (supporting). Richard Mollard: Funding acquisition (supporting); resources (supporting); writing – original draft (supporting); writing – review and editing (supporting). John M. Mariadason: Resources (supporting); writing – original draft (supporting); writing – review and editing (supporting). Erinna F. Lee: Conceptualization (lead); data curation (lead); formal analysis (lead); funding acquisition (lead); methodology (lead); project administration (lead); resources (lead); supervision (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead). Walter D. Fairlie: Conceptualization (lead); data curation (lead); formal analysis (lead); funding acquisition (lead); investigation ; methodology ; project administration (lead); resources (lead); supervision (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead).Figure S1‐S7Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.