Temporal signaling dynamics are important for controlling the fate decisions of mammalian cells. In this study, we developed BioMASS, a computational platform for prediction and analysis of signaling dynamics using RNA-sequencing gene expression data. We first constructed a detailed mechanistic model of early transcriptional regulation mediated by ErbB receptor signaling pathway. After training the model parameters against phosphoprotein time-course datasets obtained from breast cancer cell lines, the model successfully predicted signaling activities of another untrained cell line. The result indicates that the parameters of molecular interactions in these different cell types are not particularly unique to the cell type, and the expression levels of the components of the signaling network are sufficient to explain the complex dynamics of the networks. Our method can be further expanded to predict signaling activity from clinical gene expression data for in silico drug screening for personalized medicine.A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate. Cancer is considered a genetic and lifestyle disease. Signaling-related genes are often altered in a genetic and epigenetic manner, and those changes are encoded in the spatiotemporal dynamics of a signal network . To infeThe ErbB receptor tyrosine kinase family is composed of four members, ErbB1  receptor), ErbB2, ErbB3, and ErbB4, and there are several ligands known to bind to these receptors. Upon ligand binding, the receptors form dimers, activate their intrinsic tyrosine kinases and stimulate multiple signaling pathways . With thIn this study, we constructed a comprehensive mathematical model of the ErbB receptor to c-Fos activation signaling cascade by integrating two previously developed independent mathematical models describing the processes of ErbB receptor signaling  and c-FoWe utilized BioMASS for parameter training on cancer cell lines and hypothesis generation concerning the regulatory principles of the ErbB signaling network. We first trained the kinetic parameters of the ErbB network against phosphorylation data from multiple breast cancer cell lines together with their autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) to infer the initial protein level of the model species. Then, the model proceeded to prediction in another cell line on which it was not trained using its own phosphorylation data. As a result, the model learned the parameters from other cells lines and was able to accurately predict the quantitative behavior of the untrained cell line solely from its autologous gene expression data. This result indicates a possibility that cell-type specific ErbB signaling dynamics can be explained by gene expression levels, not by differences in the kinetic parameters in the network. Accordingly, it should be possible to predict the dynamic properties of a signaling network only from public transcriptome data, and our computational framework will be useful to perform the analysis for identification of the key regulators and for assessment of therapeutics targeting the network.Mechanistic modeling is a powerful method to investigate complex biological systems ,28 sincehttps://github.com/okadalabipr/biomass), a Python framework for Modeling and Analysis of Signaling Systems  se Systems d and (3) Systems f,g.In the modeling of biological systems, parameter estimation is one of the most critical steps . NeverthIn a previous study, a mathematical model accounting for c-Fos regulation in response to ErbB receptor activation was developed to understand how different types of growth factors give rise to unique gene expression patterns and cell fates . The modIn this study, to investigate the common regulatory mechanisms of ErbB receptor signaling on transcriptional control in multiple breast cancer cell lines, we integrated and constructed a comprehensive model of ErbB signal transduction  and the cis or trans-acting genes and have been named Expression Quantitative Trait Loci (eQTL) [For construction of a cell-specific model for each cell line, we incorporated gene expression data from the Cancer Cell Line Encyclopedia (CCLE)  to interi (eQTL) . We assuhttps://github.com/okadalabipr/ccle_extractor). To estimate parameters using the training datasets, we minimized the residual sum of squares to obtain common parameter sets that can reproduce ErbB signaling dynamics in these three cell lines. We ran optimization simultaneously and finally obtained 30 good-fitting parameters  and autologous CCLE RNA-seq expression data to train the model parameters . To extrrameters .To test our hypothesis that the gene expression level is the major factor that determines the network dynamics, we attempted to predict signaling dynamics in another breast cancer cell line subtype, SK-BR-3, from its gene expression data. Since we assumed that parameter values are identical across cell lines, we applied the trained model parameters constrained by time series data of pAkt, pERK, and pc-Fos in the MCF-7, BT-474 and MDA-MB-231 cell lines to the SK-BR-3 cell line model a. By usiTo examine how a change in the amount of an ErbB network component  affects cellular output, we performed sensitivity analysis  for initWe visualized sensitivities for each parameter set, not the averaged sensitivities. Akt activity, which is often associated with drug resistances in breast cancer cells , is stroIn this study, we presented a computational platform for model building and numerical analysis of cancer signaling networks. We used RNA-seq data to infer the initial values of model components and numerous time course phosphorylation data  for training the model parameters. Using this platform, we constructed a comprehensive model of ErbB receptor signaling-early transcriptional regulation by integrating two models independently developed in previous studies ,20. SeveThe methods developed by Hidalgo et al.  and CubuUsing the CCLE RNA-seq data as the initial values for each cell line, the model, which is jointly parameterized by training data sets of several cell lines, gained an additional predictive ability to infer signaling dynamics in a different cell line. This result indicates that gene expression levels of ErbB network components play an important role in the control of signal transduction dynamics. It also indicates that gene expression data rather than mutation data, which often indicate changes in enzyme activity of kinases and phosphatase or protein binding affinity, may be suitable for inferring signaling dynamics and activities. Previous studies have shown that signaling dynamics control cell fate ,46, but In this report, we showed an example of sensitivity analysis for a particular cell line SK-BR-3. The sensitive process, which is usually a bottleneck in the network, is cell-specific. Therefore, comparison of the sensitive regions among different cell lines will be necessary to generate additional information about network homeostasis. It has been reported that a particular gene expression profile could improve the classification of breast cancer and prediction of therapeutic responses . We antiWe constructed the mathematical model linking ErbB receptor to c-Fos regulation by integrating two previously developed mathematical models. The Birtwistle model  and the https://data.broadinstitute.org/ccle/CCLE_RNAseq_rsem_genes_tpm_20180929.txt.gz. Initial protein levels of the model species  were inferred from gene expression data , a Python script for processing CCLE datasets.CCLE RNA-seq gene expression data used for the model individualization were downloaded from MCF-7, BT-474, SK-BR-3 and MDA-MB-231 cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS). Before treatment with 10 nM EGF or HRG, the cells were synchronized by serum starvation for 16 h. Cells were lysed with BioPlex Lysis buffer, cell lysates were cleared by centrifugation and total protein concentration in supernatants was determined using a protein assay reagent from Bio-Rad Laboratories, CA, USA. For Western blotting, anti-phospho-Akt  and anti-phospho-ERK  were purchased from Cell Signaling Technology . Anti-phospho-Fos  was purchased from Abcam . We adopted the transfer and normalization methods described in previous studies to minimize transfer errors and variability between the blots . Proteinhttps://github.com/okadalabipr/Imoto_Cancers_2020. BioMASS is available from https://github.com/okadalabipr/biomass.All data and code for model simulation, parameter estimation and prediction are available from the GitHub website: We constructed a comprehensive model of the immediate-early gene response triggered by ErbB receptor activation and predicted signaling and transcriptional activities based on RNA-seq data of breast cancer cell lines. We developed a computational framework for model construction, parameterization and sensitivity analysis of the model. Our study indicates that gene expression levels in the ErbB network may govern the cell-specific signaling dynamics across breast cancer cell lines and therefore play an important role in regulation of cell fate.


Studies in recent years have shown that academic procrastination in postgraduates is very common and has a negative impact on their mental health. Therefore, we conducted this study to explore the influencing mechanism of postgraduate academic procrastination. In this study, based on the Temporal Decision Model (TDM) of procrastination and the strength model of self-control, we administered a questionnaire survey to 577 full-time postgraduates  to explore the influence mechanisms and gender differences of motivational and volitional factors on academic procrastination. Our results indicated significant differences in academic self-efficacy between females and males. Academic self-efficacy was positively correlated with academic self-control and negatively correlated with academic procrastination; academic self-control was negatively correlated with academic procrastination. Academic self-control had a completely mediating effect in the influence of academic self-efficacy on academic procrastination. Gender variables moderated the influence of academic self-efficacy on academic self-control and thus significantly moderated the mediating effect of academic self-control. Specifically, academic self-control had a stronger mediating effect between academic self-efficacy and academic procrastination for female postgraduates. Our findings may provide guidance for postgraduates who exhibit academic procrastination and extend the theory of academic procrastination. Procrastination means that, in spite of their knowledge of negative effects, people choose to delay their schedules . BecauseEarly procrastination theories, such as the expected value theory , the TemAccording to Bandura, self-efficacy refers to individuals’ beliefs about whether they can finish a certain task. Academic self-efficacy is a special category of self-efficacy and refers to learners’ judgment of their own ability and the action ability of the set learning goals to be implemented and achieved . Self-efThe procrastination decision-making model holds that the core process of procrastination is the decision-making process of “doing now or doing in the future” and that self-control is the key factor affecting this decision-making process . The infOn the other hand, people with high academic self-efficacy have a high degree of “persistence and effort” in the implementation of the whole learning plan . This “pn = 68,429), Previous studies have shown significant gender differences in academic self-efficacy, with male students often showing higher academic self-efficacy . In a laSome studies showed a significant difference in the degree of academic procrastination between genders  and someMost previous studies discussed the motivational (academic self-efficacy) and volitional (academic self-control) causes of procrastination, specifically the relationship between academic self-efficacy, academic self-control, and academic procrastination, as well as gender differences. There is a scarcity of comprehensive research on the interaction mechanism between these variables, however, especially for graduate students, who work more independently than younger students. This study investigated the mediating effect of academic self-control between academic self-efficacy and academic procrastination. Based on the significant gender differences in academic self-efficacy, self-control, and procrastination found in previous studies, we investigated whether gender variables could moderate the influence of academic self-efficacy on academic self-control and the influence of academic self-efficacy on academic self-control. We created four research hypotheses :(1)Academic self-efficacy has a negative and significant impact on academic procrastination.(2)Academic self-efficacy affects academic procrastination through academic self-control.(3)Gender variables play a moderating role in the direct effect of academic self-efficacy on academic procrastination.(4)Gender variables can moderate the influence of academic self-efficacy on academic self-control, thus significantly moderating the mediating effect of academic self-control.We used a random cluster sampling of full-time graduate students in a university town of a Chinese city. Professors distributed 650 questionnaires in classes, and participants’ informed consent was obtained before the 577 valid questionnaires were collected. The effective recovery rate was 88.8%. Among them, 222 were in their first year, 188 in their second year, and 167 in their third year; 351 were women, and 226 were men. The average age was 23.93 ± 1.67 years.2/df = 2.33, CFI = 0.95, TLI = 0.91, SRMR = 0.06, RMSEA = 0.05).2/df = 4.46, CFI = 0.88, TLI = 0.91, SRMR = 0.07, RMSEA = 0.07). A multivariate Lagrange multiplier test identified modifications to improve the model fit. The modified index showed the highest MI between item 3 and item 13. Because there are enough items (12) in this dimension (a sense of self-control), we deleted the two items according to some researchers’ advise , so 36 items were retained in the final questionnaire.The scale used in this study was revised by ’ advise , ran aga2/df = 2.32, CFI = 0.91, TLI = 0.92, SRMR = 0.07, RMSEA = 0.05).We used the study procrastination questionnaire for Chinese master’s students compiled by 2, have not been developed for LMS models. Alternatively, by referring to 2/df<5, CFI > 0.9, TLI > 0.9, SRMR < 0.08, RMSEA < 0.08 , indicating that the common method bias in this study was not significant . Then, the moderated mediation model  was examined. The test results showed that the AIC value of Model 1 was 7.87 lower than that of Model 0 , and the BIC value decreased by 0.69 , which indicates that Model 1 was a better fit than Model 0 (p < 0.01); thus, Model 1 had better fit (According to the LMS procedure recommended by  Model 0 . In additter fit  and the p < 0.001), while gender had no significant predictive effect on academic self-control . The interaction between academic self-efficacy and gender had a significant predictive effect on academic self-control . (2) The direct prediction effect of academic self-efficacy on academic procrastination was not significant , the effect of gender on academic procrastination was not significant , and the interaction between gender and academic self-efficacy was not significant in predicting academic procrastination , but the predictive effect of academic self-control on academic procrastination was significant . (3) The mediating effect was significantly different between male and female groups , and the mediating effect of academic self-control was significant in both male and female groups. To be specific, the mediating effect of academic self-control in female students  was significantly stronger than that in male students . (4) The moderating effect of gender on academic self-efficacy and academic self-control was further analyzed by simple slope test. The results showed that compared with male students, female students’ academic self-efficacy had a more significant predictive effect on academic self-control; Simple slope  = 0.75, p < 0.001, Simple slope  = 0.35, p < 0.001 . To be specific, academic self-control played a significant mediating role in both male and female groups, but female postgraduate students were stronger than male postgraduate students in the influence of academic self-efficacy on academic self-control and the mediating role of academic self-control. According to our data analysis, this difference between male and female groups was mainly caused by the significant difference of academic self-efficacy between male and female groups. The academic self-efficacy of male students was significantly higher than that of female students, which is consistent with the conclusions of most studies . That isOn the other hand, some studies found that groups of females performed better on self-control . In addiIn sum, although the academic self-efficacy of male students was significantly higher than that of female students, the effect of academic self-efficacy on academic self-control was significantly lower than that of female students. The mechanism of academic self-efficacy affecting academic procrastination via academic self-control was significantly different between male and female postgraduate students.The measurements of variables in this study were cross-sectional self-reports. Follow-up and experimental methods should be considered to make the measurements of variables more objective and accurate.It should be pointed out that Bandura’s self-efficacy theory and the strength model of self-control only involve “general self-efficacy” and “general self-control theory,” and this study only proves the applicability of these two theories in the specific behavior of “learning.” In addition, because this study investigated several variables , all belonging to special psychological traits, the process of motivation, the emotion process, and the selection process mentioned in the self-control strength model were not investigated in this study. The effect of situational self-efficacy on situational procrastination should be examined in future studies.On the other hand, compared with other student groups, the relationship between postgraduate students’ academic procrastination, self-efficacy, and self-control was more explicit and objective, because postgraduate students were less affected by external variables and their learning was more independent. For example, the academic procrastination behavior of middle school students was related to perceived support from teachers . In the As indicated in this study, to reduce the procrastination behavior of postgraduate students, we can enhance their sense of academic self-efficacy and cultivate academic self-control, keeping in mind that improving men’s sense of academic self-efficacy will be less effective than it is for women. For male postgraduate students, more effort should be made to reduce their academic procrastination by directly cultivating strategies for and methods of academic self-control.The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by the Research Ethics Committee in the School of Psychology in Guizhou Normal University. The patients/participants provided their written informed consent to participate in this study.SZ conceived the experimental design. GL contributed to the data collection, preparation of the statistical analyses, and drafted the manuscript. JH was mainly responsible for the language polishing and proofreading. GC and YP were dedicated to refining the manuscript. All authors have read and approved the final draft.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.


In spite of substantial progress made toward understanding cancer pathogenesis, this disease remains one of the leading causes of mortality. Thus, there is an urgent need to develop novel, more effective anticancer therapeutics. Thiadiazole ring is a versatile scaffold widely studied in medicinal chemistry. Mesoionic character of this ring allows thiadiazole-containing compounds to cross cellular membrane and interact strongly with biological targets. Consequently, these compounds exert a broad spectrum of biological activities. This review presents the current state of knowledge on thiadiazole derivatives that demonstrate in vitro and/or in vivo efficacy across the cancer models with an emphasis on targets of action. The influence of the substituent on the compounds’ activity is depicted. Furthermore, the results from clinical trials assessing thiadiazole-containing drugs in cancer patients are summarized. According to the most recent data provided by the International Agency for Research on Cancer (IARC), 18.1 million new cases and 9.6 million cancer deaths were registered worldwide in 2018  thiadiazole linked with a polymethylene spacer to α-bromoacryloyl amido benzoheterocycles are another example of compounds carrying 1,2,4-thiadiazole moiety with an anticancer activity. These compounds showed a considerable activity in human myeloid leukemia cell lines HL-60 and U937 , as well as in melanoma SK-MEL-1 cell line (IC50 from 2.09 to 8.95 μM). The most active were compounds 1 and 2 containing N-unsubstituted indole in the benzoheterocycle. Comparing these two derivatives, the anticancer activity decreased on increasing the length of the chain from three (compound 1) to six (compound 2) methylene units. Among the derivatives characterized by the same alkyl chain (6 methylene units), the greatest potency was exhibited by compound 2 bearing α-bromoacrylamidoindole moiety . A replacement of indole (compound 2) with N-methyl indole (compound 3) decreased an anticancer activity. Further decrease was observed when indole was replaced with benzofuran (compound 4) and benzothiophene (compound 5). Treatment with each of compounds induced apoptosis in HL-60 and U937 cells, although most effective anticancer compound 1 appeared to be a less potent apoptosis inducer compared to the rest of derivatives, suggesting that this compound may trigger also some other mechanisms underlying cell death imidazothiadiazole-6,11-dione, further termed as NSC745885, turned out to be the most potent among a series of anthraimidazole-6,11-dione tetracyclic analogues with different side chain. Out of 60 cancer cell lines of different origin, the most remarkable sensitivity to NSC745885 displayed leukemia, melanoma, ovarian, breast, prostate cancer as well as glioma cell lines . The relatively lower, yet significant sensitivity to NSC745885 presented colon and non-small cell lung cancer cell lines  .Fig.Fig16].FEZH2 is observed in numerous cancer of different origin and its inactivation was therapeutically effective in several cancer models thiadiazole-6,11-dione, termed NSC763968, appeared to be most active. Leukemia and prostate cancer cell lines were particularly sensitive to this compound . Slightly lower toxicity of NSC763968 was observed in ovarian cancer, breast cancer, melanoma, renal cancer, glioma and non-small cell lung cancer cell lines . Colon cancer cell lines were the least sensitive to NSC763968 . Of note, 10 μM concentration of this compound was relatively not toxic towards human non-cancerous cell lines, as it reduced the viability of urothelial SV-HUC-1 cells to 80%, prostatic stromal myofibroblasts WMPY-1 to 60%, and prostate epithelial cells RWPE-1 to 90%. At the same time, 10 μM doxorubicin decreased the viability of SV-HUC-1, WMOY-1 and RWPE-1 cells to 50%, 20%, and 40%, respectively. In prostate cancer DU-145 cells, NSC763968 induced apoptosis and inhibited phosphorylation of ERK and p38 kinases  thiadiazole-6,11-dione derivative, RV-59, turned out to be particularly toxic to human colon cancer HCT116 cells with high cytoplasmic level of a transcription factor Nrf2 (cNrf2). A significant contribution of a cytoplasmic, but not nuclear, localization of Nrf2 to aggressive phenotype of colorectal cancer cells has previously been documented by the same group -ethyl}-thiadiazol-2-yl)-2-phenyl-acetamide, referred to as compound 6, exhibited potency similar to that of BPTES, but much better aqueous solubility of 13 μg/mL. Treatment with 20 μM concentration of this compound significantly attenuated the growth of lymphoma B P493 cells in vitro as well as in a mouse xenograft model while injected daily in doses of 12.5 mg/kg acetyl]amino]-3-pyridazinyl]butyl]-1,3,4-thiadiazol-2-yl]-2-pyridineacetamide, referred to as CB-839 , but had no effect on the viability of the breast cancer ER+/HER2− cell line T47D. The rates of Gln consumption were reduced for HCC1806 and MDA-MB-231 cells treated with CB-839, indicating that the antitumor activity of this compound was indeed linked to the diminished Gln metabolism. The in vivo efficacy of CB-839 was further examined in two breast cancer xenograft models, a primary patient-derived TNBC xenograft and an HER2+ basal-like cell line JIMT-1-based xenograft. In either model, oral dosing of 200 mg/kg of CB-839 twice daily remarkably inhibited xenografts’ growth and was well tolerated, with no overt signs of toxicity methyl]-5-phenyl-1,3,4-thiadiazol-2-yl]-2,2-dimethylpropanamide, referred to as litronesib or LY2523355 , the recommended dose of litronesib was determined to be 5 mg/mis study . In moreis study . Phase I358019, tb]thiadiazolindolin‐2‐ones showed considerable cytotoxicity, with IC50 values ranging from 1.1 to 8.9 μM against human lung A549, cervical HeLa, breast MCF‐7, and colon HCT-116 cancer cell lines. Among them, compounds 7 ((E)‐5‐fluoro‐3‐‐imidazothiadiazol‐5‐yl)methylene)indolin‐2-one) and 11 ((E)‐3‐imidazothiadiazol‐5‐yl)methylene)indolin‐2‐one) appeared to be most potent with IC50 values ranging from 1.1 to 1.6 μM and from 2.5 to 2.9 μM, respectively. Compounds 7 and 11 presented anti-tubulin polymerization activity with IC50 of 0.15 and 1.23 μM, respectively -3-((2-cyclopropyl-6-(4-methoxyphenyl)imidazo thiadiazol-5-yl)methylene)indolin-2-one), 14 (((E)-3-((6-(4-chlorophenyl)-2-cyclopropylimidazothiadiazol-5-yl)methylene)-5-methoxyindolin-2-one) and 15 ((E)-5-chloro-3-((6-(4-chlorophenyl)-2-cyclopropylimidazothiadiazol-5-yl)methylene)indolin-2-one) exhibited cytotoxicity towards the cell lines used in the previous study with GI50 values from 0.13 to 3.8 μΜ. They also displayed the anti-tubulin activity (IC50 between 2.8 and 5.6 μM). Treatment with these compounds decreased the level of polymerized fraction of tubulin, induced the cell cycle arrest in the G2/M phase and apoptosis. Similarly to the compounds described in the earlier report, also conjugates 7, 14 and 15 of this study bound in the colchicine-binding site of the tubulin -2-[(4-fluorobenzyl)thio]-1,3,4-thiadiazole, referred to as compound 6a, significantly reduced the clonogenic activity (LD50 2.2 μM) of distinct clones of myeloid progenitors, varying with respect to sensitivity to Gleevec. Compound 6a diminished proliferation of leukemia HL-60 cells and induced their differentiation [Radi and co-workers synthesized a series of substituted benzoylamino-2-[(4-benzyl)thio]-1,3,4-thiadiazole which turned out to inhibit either both Abl and Src kinases, or only one of them. The Abl inhibitory activity of those compounds ranged between 0.044 and 1.26 μM, and their Src inhibitory activity was between 0.064 and 1.137 μM -2-((5-((4-(trifluoromethyl)phenyl)amino)-1,3,4-thiadiazol-2-yl)thio)acetamide, termed as compound 2, turned out to inhibit Abl kinase with an IC50 value of 7.4 µM. Docking studies revealed the role of the distal nitro group in the formation of a crucial hydrogen bond with the key amino acid residues of Abl protein. Treatment with this compound inhibited the viability of human leukemia K562, MT-2, Jurkat and cervical carcinoma HeLa cells with IC50 values of 33.0, 166.8, 17.9 and 12.4 μM, respectively. In the same assay, imatinib displayed IC50 values of 5.0, 9.7, 6.7 and 15.2 μM, respectively. Compound 2 turned out to be 5 times less toxic towards peripheral blood mononuclear cells (PBMC) (IC50 141.3 μM) than imatinib (IC50 28.3 μM). Aside from the inhibitory effect on Abl kinase, compound 2 diminished to the lesser extent activity of BTK, CSK, FYN A, and LCK kinases  thiadiazol-2-yl]-1H-indole hydrobromide) and 12b (1-Methyl-3- thiadiazol-2-yl]-1H-indole hydrobromide), exhibited a remarkable antiproliferative activity in SUIT-2, Capan-1 and Panc-1 cell lines with IC50 values ranging from 0.85 to 1.70 μM. Moreover, both compounds significantly inhibited the growth and migration of primary patient-derived adenocarcinoma cells and resistant to gemcitabine Panc-1R cells. Treatment with either compound decreased proteolytic activity of matrix metalloproteinases MMP2 and MMP9 and compound 12b diminished the phosphorylation of focal adhesion kinase (FAK), a non-receptor tyrosine kinase which regulates cell proliferation and motility thiadiazol-2-yl]-1citabine , confirmThe anticancer potential of numerous thiadiazole derivatives is undeniable. However, the studies reviewed above have several limitations. First, caution needs to be taken when comparing compounds’ efficacy determined by different assay methods. Second, the activity of the majority of thiadiazole derivatives was evaluated in cancer cells, while their activity in non-cancerous cells remains unknown. Third, in most of the cited studies, the compounds’ efficacy was evaluated in models in vitro. Finally, the molecular targets of the majority of thiadiazole derivatives have not been identified so far. Further in-depth analyses are needed to determine compounds’ selectivity, bioavailability and mode of action. Nevertheless, data depicted above clearly indicate that thiadiazole moiety may be valuable lead structure in the development of new compounds with improved anticancer activity.


We extend this paradigm to measuring proteome activity landscapes by acquiring and integrating quantitative data for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 CCLs. These data are used to contextualize proteins and p-sites and predict drug sensitivity. For example, we find that Progesterone Receptor (PGR) phosphorylation is associated with sensitivity to drugs modulating estrogen signaling such as Raloxifene. We also demonstrate that Adenylate kinase isoenzyme 1 (AK1) inactivates antimetabolites like Cytarabine. Consequently, high AK1 levels correlate with poor survival of Cytarabine-treated acute myeloid leukemia patients, qualifying AK1 as a patient stratification marker and possibly as a drug target. We provide an interactive web application termed ATLANTiC ( Proteome activity has a major role in cancer progression and response to drugs. Here, the authors use comprehensive proteomic and phosphoproteomic data, in conjunction with drug-sensitivity screens, to generate a community resource consisting of landscapes of pathway and kinase activity across different cell lines However, post-translational modifications (PTMs) have been rarely investigated systematically in the context of drug sensitivity3, even though it is well-established that proteome activity regulated by e.g., dynamic phosphorylation plays a major role in cancer initiation, progression, and response to drugs. Consequently, many kinase inhibitors that target phosphorylation-regulated signaling pathways have been developed as cancer drugs, and some have transformed the clinical management of several cancer entities5.Integration of genomic, transcriptomic, and proteomic profiles of tumor cell lines with phenotypic drug response data has extended our understanding of tumor biology and helped to delineate the mechanisms of action (MoA) of several drugs6 to a depth of >10,000 proteins. The newly generated proteomic and phosphoproteomic data is integrated with our previously published CRC65 data7, as well as with published phenotypic drug sensitivity (~900 drugs) and published molecular drug target selectivity information (224 drugs) from our laboratory8 , which allows users to visualize activity landscapes and protein/p-site abundance information across the entire dataset, to query the results of all drug response modeling analyses and to deconvolute the target space of kinase inhibitors in specific cell lines. We envision that ATLANTiC will enable the research community to perform many additional investigations based on the tens of thousands of observations in this study, only few of which can be highlighted in this report. For example, we reveal that PGR phosphorylation is correlated with sensitivity to endocrine therapy in PGR-positive breast cancer. We also show that AK1 is capable of inactivating antimetabolites like Cytarabine and that AK1 protein levels correlate with poor survival of Cytarabine-treated acute myeloid leukemia patients. This qualifies AK1 as a patient stratification marker and possibly as a drug target.Here, we profile the baseline phosphoproteomes of the NCI60 and CRC65 cell line panels Fig.  to an ovry8 Fig. . Based oFigure 11 based on the proteins and p-sites of the NCI60 panel confirms the previous notion6 that cell lines from certain tumor entities are molecularly similar to each other , whereas other entities are highly heterogeneous . We then focused on functional associations between any two proteins recorded in pathway databases and compared the abundance correlation of these two proteins across the NCI60 and CRC65 cell line panels at the protein and phosphoprotein level (Supplementary Methods). Interestingly, only 32 and 51% of the functional associations recapitulated at the phosphoprotein level are also recapitulated at the protein level in the NCI60 and CRC65 dataset, respectively proteome data, we computed activity landscapes in order to assess the relative activity of signaling pathways (by integrating information on proteins and p-sites involved in a pathway) and kinases  in cell lines Fig.  feature lls Fig.  and C10 lls Fig.  that car17 and gene set variation analysis18 to place proteins and p-sites into new functional and phenotypic contexts simultaneously using a guilt-by-association approach (Supplementary Methods). Figure t-statistic) in MSI− cells (red nodes and edges) compared to MSI+ cells are involved in DNA mismatch repair (MMR), while proteins/p-sites with significantly higher abundance  in MSI+ cells (blue nodes and edges) compared to MSI− cells are primarily involved in transcriptional processes. Focusing on proteins/p-sites with significantly higher abundance in MSI− cells compared to MSI+ cells highlights the DNA helicases MRE11A and RAD50 (involved in double-strand break repair) and the putative RNA helicase DDX27 as associated with MMR proficiency alongside the known MMR proteins MSH2 and PMS219  on a protein previously shown to be part of the PR-DUB complex22 promoting the deubiquitination of histone H2A at lysine 119 and regulating DNA double-strand break repair is significantly higher abundant  in MSI− cells compared to MSI+ cells. These data, together with the fact that proteins involved in DNA double-strand break repair are frequently mutated in MSI+ colorectal cancers23 suggest that ASXL2 may also be mutated in MSI+ colorectal cancers. A similar analysis was performed for the NCI60 cell lines proteomic data can be generated efficiently today, the challenge arises that not all proteins and only very few p-sites are functionally annotated. We, therefore, developed a new method incorporating weighted gene correlation network analysis219 Fig. . MRE11A 24. This provides an additional way of functionalizing p-sites, because p-sites with similar outlier behavior might represent novel pathway members of hyperactive kinases in the respective cell lines.Next, we explored the possibility of using protein and p-site abundance to explain drug sensitivity and first focused on outliers . Outliers are particularly attractive because of their potential ease of interpretation. Clustering outlier p-site abundance across cell lines reveals hundreds of strong phosphorylation events from the same pathway context in particular cell lines Fig. . Example16. Another example is the sensitivity of SR cells to Crizotinib and Ceritinib due to high abundance of ALK phosphorylation , likely caused by constitutive kinase activity of the NPM1-ALK fusion protein15  and MK-2461 (an MST1R inhibitor) in in vitro experiments , which explains their exquisite sensitivity towards the BCR-ABL inhibitors Imatinib and Nilotinib Fig. . Similarn15 Fig. . This ou27 is only detected in MOLT4 cells (GluC data), which are sensitive to the DNA alkylating agent Bendamustine. Given that Bendamustine is an aromatic acid, we hypothesize that SLC16A10 may be responsible for the sensitivity of MOLT4 cells to this drug  is highly correlated with topoisomerase inhibitors including Irinotecan , confirming prior observations at the mRNA level28, as well as with response to DNA synthesis inhibitors such as Gemcitabine  and Triethylenemelamine  drug dataset Fig. . Its pros32 Fig. . Elastic7.04 to the highest = 1 × 108.39 value), which is downstream of MEK-MAPK signaling  is a resistance marker for Selumetinib. We hypothesize that WDR24_pT581 may inhibit the GATOR1 sub-complex, which suppresses the responsiveness to MEK inhibition via activating mTORC1 and the TORC1 signaling pathway34. Similarly, MAPK3_pY204, MAPK1_pY187, as well as RPS6KA1_pT368 are associated with resistance to Perifosine  show high phosphorylation of RPS6KA3_pS715  of drugs.To study the potential of combining kinase inhibitors with other drugs systematically in silico, we correlated the abundance of activity-enhancing or inhibitory p-sites on kinases with the response to all drugs  was 17 times more sensitive to 5FU than CCRFCEM cells , suggesting that modeling drug response as a function of proteins involved in a drugs’ metabolism can be a powerful tool for predicting drug sensitivity.Next, we asked the question whether the sensitivity of cell lines to drugs can be explained by the levels of proteins that are involved in their metabolism. As an example, we trained random forest models (Supplementary Methods) on the CRC65 dataset using all combinations of 14 proteins involved in the metabolism of the antimetabolite drug 5-fluorouracil  in order to capture non-linear relationships between protein abundance and drug sensitivity. Models using 3–7 proteins achieve good prediction accuracy in colorectal cancer cell lines of the NCI60 panel  multiple phosphoproteins and/or p-sites that show divergent abundance patterns with these drugs (Supplementary Methods). This way, we identified for example high relative abundance of multiple p-sites on BCL3 , a regulator of cell proliferation and apoptosis40, as sensitivity markers for topoisomerase II inhibitors, such as Teniposide, Etoposide, and Doxorubicin , the primary mediator of EGF-stimulated activation of PI3K/AKT41, and PTPN11_pY62 , a phosphatase that dephosphorylates GAB1 and EGFR42. Several kinase inhibitors form a strong cluster that is enriched in drugs targeting the BRAF-MEK-ERK1/2 axis, including Vemurafenib (BRAF), Selumetinib (MEK), PD98059 (MEK), and Hypothemycin  is associated with sensitivity to BRAF, MEK, and ERK inhibitors. These results underscore the importance of understanding the signaling network of kinases in detail, since inhibiting an upstream kinase might result in pathway rewiring, which could be avoided by targeting the downstream protein on which these different pathways converge. Additional clusters are driven by other MoAs proteomic markers for drug response. Based on the hypothesis that the responses to drugs targeting the same pathways might be explained by the same group of molecular phosphoprotein/p-site markers, we applied an extended sparse multiblock partial least square regression (SMBPLSR) algorithmcin Fig. . Similarcin Fig.  is assoc44. Progesterone receptor (PGR) phosphoprotein abundance as well as several p-sites  are associated with this cluster of drugs, as well as GREB1_pS1193 . Cox proportional-hazards models were used to evaluate potential confounding factors, revealing that pPGR is confounded by age  and the T grading scale  but independent of tumor grade  and HER2 status . This observation is driven by two leukemia cell lines  is Adenylate kinase isoenzyme 1 (AK1). AK1 acts as a balancing enzyme for cellular nucleotide ratios by transferring the terminal phosphate of NTPs to AMP or dAMPnce Fig.  are Zalcnes Fig.  and led AK1 Fig.  in CytarAK1 Fig. . To testAK1 Fig. . High abTaken together, our study shows that activity landscapes of tumor cell lines as measured by quantitative (phospho)proteomics can make considerable contributions to understanding and predicting the response of cancer cell lines to a wide variety of cancer drugs. In prototypical examples, we show the potential of the data and of this approach for basic science and translational research, albeit stressing again that the findings on the breast cancer and AML patient cohorts need further validation in independent cohorts before they may be translated into clinical practice. We also anticipate that many more such opportunities may be identified when engaging the scientific community via ATLANTiC and ProteomicsDB. This work also highlights the pressing need for the functionalization of the tens of thousands of p-sites that can nowadays be measured relatively easily. While we demonstrate some ways in which this may be done using bioinformatics, further mechanistic insights into how phosphorylation regulates molecular processes in cancer cells and how this explains or influences the action of drugs will require additional efforts in the future. In particular, the glaring lack of systematic information on kinase-substrate relationships needs addressing with great urgency.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting Summary


Cell cultures derived from strawberry fruit at different developmental stages have been obtained to evaluate their potential use to study different aspects of strawberry ripening. Callus from leaf and cortical tissue of unripe-green, white, and mature-red strawberry fruits were induced in a medium supplemented with 11.3 µM 2,4-dichlorophenoxyacetic acid  under darkness. The transfer of the established callus from darkness to light induced the production of anthocyanin. The replacement of 2,4-D by abscisic acid (ABA) noticeably increased anthocyanin accumulation in green-fruit callus. Cell walls were isolated from the different fruit cell lines and from fruit receptacles at equivalent developmental stages and sequentially fractionated to obtain fractions enriched in soluble pectins, ester bound pectins, xyloglucans (XG), and matrix glycans tightly associated with cellulose microfibrils. These fractions were analyzed by cell wall carbohydrate microarrays. In fruit receptacle samples, pectins were abundant in all fractions, including those enriched in matrix glycans. The amount of pectin increased from green to white stage, and later these carbohydrates were solubilized in red fruit. Apparently, XG content was similar in white and red fruit, but the proportion of galactosylated XG increased in red fruit. Cell wall fractions from callus cultures were enriched in extensin and displayed a minor amount of pectins. Stronger signals of extensin Abs were detected in sodium carbonate fraction, suggesting that these proteins could be linked to pectins. Overall, the results obtained suggest that fruit cell lines could be used to analyze hormonal regulation of color development in strawberry but that the cell wall remodeling process associated with fruit softening might be masked by the high presence of extensin in callus cultures. Ripening of fleshy fruits is a complex developmental process involving changes in color, flavor, and texture that make the tissue edible to seed-dispersing animals . From anIt is generally accepted that the modification of the mechanical properties of the primary cell walls due to cell wall disassembly, the reduction of intercellular adhesion as a result of middle lamella dissolution, and the reduction in cell turgor are the major causes of fruit softening ,7,8,9. AAn additional area of active research in strawberry is the genetic regulation of fruit color. Anthocyanin is the principal pigment leading to the red color of ripe strawberry fruit ,13. SomeIn many cases, cell cultures can be used as simple model systems to study developmental processes, such as stem cell regulation, mineral deficiency, or disease and stress responses ,18,19,2030K medium supplemented with a combination of benzyladenine (BA) and 2,4-Dichlorophenoxyacetic acid  at different concentrations. The presence of 2,4-D in the medium was needed to induce the formation of friable callus; by contrast, BA diminished callus production. After 8 weeks of culture, the highest amount of callus was formed in leaf explants cultured in media only supplemented with 2,4-D , several callus lines from fruit at each developmental stage could be recovered. One cell line was selected for each explant type and further multiplied  from dark to light conditions induced the production of anthocyanin, especially in leaf and red-fruit cell lines (To determine if abscisic acid (ABA) enhanced the production of anthocyanin in strawberry calli cultured in the presence of light, cell lines were incubated in Nll lines . The subll lines ; howeverCell walls from leaf and fruit cell cultures were extracted and treated with different solvents to yield fractions enriched in soluble and ester-bound pectins , xyloglucans (4M KOH), and matrix polysaccharides tightly linked into cellulose microfibrils (cadoxen). Cell walls from fruits at green, white, and red stages were subjected to the same fractionation process for comparison. Polysaccharide profiles of cell walls from callus cultures were quite different from the one described for fruits. In all cell wall fractions, HG and RGI pectin mAbs displayed a very low signal. The amounts of XG and xylan in KOH and cadoxen fractions were also lower than in fruit fractions. By contrast, the three mAbs against extensin, LM1, JIM11, and JIM20 showed strong signals in cell culture fractions and were highest in the sodium carbonate fraction. Extensin epitopes were not detected in fruit samples or in the water fraction from callus cultures. LM23 also showed a different profile in fruit and cell cultures. This mAb binds to xylogalacturonan, and it was detected in all fractions from callus lines except water fraction but was absent in fruit cell walls.A principal component analysis (PCA) was performed to provide an overview of sample group separation . In the The whole set of microarray data was subjected to hierarchical clustering . Samples30K, a mineral formulation with lower ionic strength than MS, was more suitable for micropropagation and regeneration of strawberry cv. ‘Chandler’ [30K medium supplemented with a low 2,4-D concentration was also suitable for isolation and proliferation of cell cultures from fruit receptacle at different developmental stages. Explants from red fruit showed the lowest capacity to develop callus in this medium. Similarly, the proliferation rate of the cell lines obtained diminished accordingly to the developmental age of the initial explant, being higher in green immature fruits and lower in ripe fruits. As observed in this research, Hong et al. [Callus cultures have been induced in strawberry from leaf , petiolehandler’ ,35. In thandler’ . The addhandler’  also foug et al.  reportedg et al. ; to the FaMYB10 expression and therefore anthocyanin production in strawberry fruit, their effect being additive. The replacement of 2,4-D by ABA at low concentrations notably increased anthocyanin production in callus from immature green fruit but did not significantly affect that of the other lines. ABA and auxins play antagonistic effects on strawberry fruit ripening. Decreasing ABA biosynthesis in green fruit by virus induced gene silencing of FaNCDE1, a key gene in ABA biosynthesis, or injection of the ABA inhibitor fluridone impaired fruit development, obtaining a colorless fruit phenotype [Light slightly increased the proliferation rate of green and white callus cultures, and it induced the accumulation of red pigmentation, especially in leaf and ripe-fruit lines. A similar observation was made in cv. ‘Brighton’ . In strahenotype . A similhenotype . Contrarhenotype , and thehenotype . The resThe pectin profile in cell wall fractions isolated from fruits showed that the amounts of HG and RGI epitopes increased from green to white fruit and then declined in red ripe fruit to very low levels. This pattern was observed in all fractions analyzed except in the water fraction from ripe fruit that contained significant amounts of low methyl ester HG recognized by JIM5. These findings support previous observations of pectin solubilization, i.e., a reduction in the amount of pectins tightly bound to the cell wall concomitant to an increase in water-soluble pectins as a typical feature of strawberry remodeling during fruit ripening ,44,45,46Carbohydrate profiles from cell cultures were quite different from fruit. In callus cell walls, pectin antibodies showed a very weak signal. By contrast, callus cell wall extracts were enriched in extensin epitopes associated with tightly bound pectins (sodium carbonate fraction) and matrix glycans. Extensins are one of the main classes of hydroxyproline-rich glycoproteins (HRGPs) that contain multiple Ser-(Pro)3–5 repeats and Tyr motifs acting as cross-link sites ,54. ThesCarbohydrate microarrays provide information about the relative levels of glycan epitopes but do not allow quantifying absolute levels of cell wall components . In callFragaria × ananassa Duch.) plants, cv. ‘Chandler’, micropropagated in N30K mineral formulation [Strawberry  and 2,4-D . Fruits, 10–20 fruits per developmental stage, were washed with water and sterilized in a 10% commercial bleach solution with 100 µL of Tween-20 for 15 min. Then, fruits were peeled to remove achenes, sterilized again as previously described, and washed three times with sterile water. Sections of cortical tissue, 0.5 × 0.5 cm, were dissected and cultured in 25 × 150 mm test tubes containing N30K medium supplemented with 11.3 µM 2,4-D. In all cases, culture media were solidified with 8 g/L agar and autoclaved for 15 min at 121 °C and 1.05 kg/cm2. Explants were cultured in the dark at 25 ± 2 °C and subcultured to fresh medium every two weeks. Once induced, leaf and fruit calli were isolated from the explant and cultured in N30K medium supplemented with 11.3 µM 2,4-D, with subculturing at 4 week intervals. Afterwards, a single cell line from each explant type was selected and multiplied for further studies.For callus induction, twenty leaf disks from in vitro plants were cultured in Petri dishes containing N30K medium supplemented with 11.3 µM 2,4-D. Cultures were incubated either in darkness or under a 16 h photoperiod at 40 µmol·m−2·s−1 provided by Sylvania Gro-lux lamps, and the fresh weight was recorded after 4 weeks of culture. Five tubes per treatment and cell line were employed, and the experiment was repeated twice.The effect of darkness vs. light incubation on the growth of the different cell lines was analyzed. Callus tissues, 0.3 g, were inoculated in test tubes containing 20 mL of N30K medium supplemented with ABA at different concentrations  and incubated in light (16 h photoperiod of 40 µmol·m−2·s−1) for 14 days. Two control treatments consisting of calli cultured in N30K medium either supplemented with 11.3 µM 2.4-D or without growth regulators were also employed. After 14 days, the calli were visually inspected for the presence of red coloration, frozen in liquid nitrogen, and stored at −80 °C until used. Five tubes per treatment were employed, and the experiment was repeated twice.Three hundred mg aliquots of each cell line were transferred to test tubes containing Nv/v) for 4 h at 4 °C in darkness. The homogenate was centrifuged at 10,000× g for 15 min, and the absorbance at 515 nm of the supernatant was measured. As pelargonidin 3-glucoside is the main anthocyanin compound in strawberry fruit, anthocyanin concentration was expressed as mg/100 g of callus of pelargonidin 3-glucoside using an extinction coefficient of 17,333 L·mol−1·cm−1 [For anthocyanin extraction, frozen callus was pulverized with a mortar and pestle in liquid nitrogen. Then, 0.3 g were incubated in 3 mL MeOH-HCl  as reported by [g, and the pellets were de-starched by aqueous DMSO 90% treatment. The final residue, cell wall extract, was lyophilized prior to fractionation. Three independent extractions per fruit stage and cell line were performed.Fruit receptacle and callus from cell cultures grown in Norted by . Briefly2CO3, 4 M KOH, and cadoxen ) using a tissue lyser . Both alkaline fractions included 0.1% NaBH4 freshly added just before use. For the first water-soluble fraction, 10 mg of cell wall extract were homogenized in tissue lyser with 500 µL of water at 30 Hz shaking for 20 min, followed by gentle rocking 1 h at RT. After centrifugation at 2700× g for 15 min, supernatants were saved in a fresh tube and stored as water-fraction, while pellets were further extracted with the next solvent following the same extraction steps. Supernatants of each fraction were diluted four times (first dilution 1:1 and five-fold for the following dilutions) and printed as four technical replicates, giving a 16-spots sub-array per sample. All samples were printed simultaneously on the same sheet of nitrocellulose as adjacent arrays. Printing onto nitrocellulose was performed by an ArrayJet Sprint  and quantified as previously described [w/v milk powder (MPBS). Secondary anti-rat or anti-mouse antibodies conjugated to alkaline phosphatase  were diluted (1/5000) in MPBS. Primary mAbs used in this study  using IBM SPSS Statistics software, version 25. Levene test for homogeneity of variance was performed prior to ANOVA. Tukey and Kruskal–Wallis tests were used for mean separation in the case of homogeneous and non-homogeneous variances, respectively. Normalized values obtained in the carbohydrate microarray experiment were subjected to principal component analysis using the R package FactoMineR. All tests were performed at The results obtained in this research suggest that cell cultures obtained from strawberry receptacle at different developmental stages could be a useful model system to achieve a better understanding of the ripening process in strawberry. Metabolic pathways involved in anthocyanin production or hormonal regulation of ripening could be investigated using this system. Despite the differences observed in cell wall composition in fruits and callus cultures, some aspects of cell wall disassembly during fruit development, e.g., pectin remodeling, could also be addressed using this system.


Doxorubicin is one of the most potent broad-spectrum antitumor and chemotherapeutic agents. However, it produces cardiotoxicity.in vivo and in vitro.To investigate whether R-(-)-carvone exerts a cardioprotective effect against doxorubicin toxicity Cell culture and animal experiment.in vivo. Furthermore, the mechanism of its cardioprotective effect was studied.The synergistic effect of R-(-)-carvone with doxorubicin was evaluated in the MCF 7 cancer cell line while its protective effect against doxorubicin toxicity was evaluated in the normal heart cell line (H9C2) and 50 value of 14.22 μM and potentiated the cytotoxic action of doxorubicin, while it decreased the toxicity of doxorubicin on a normal heart cell line. In BALB/c mice, R-(-)-carvone protected the heart from the toxic action of doxorubicin, as was evident by biochemical and histological studies. The protective effect of R-(-)-carvone on the H9C2 heart cell line and on heart in vivo was due to an increase in catalase activity.R-(-)-carvone exerted cytotoxic action on the MCF 7 cancer cell line with an ICR-(-)-carvone has synergistic anticancer action with doxorubicin on the MCF 7 cell line while decreasing its cardiotoxicity. Doxorubicin (DOX), an anthracycline antibiotic, is one of the most potent broad-spectrum antitumor and chemotherapeutic agents used since late 1960s .  A prev(Carum carvi), dill (Anethum graveolens) and spearmint (Mentha spicata) seed oils -ceed oils . Also, 4eed oils .in vitro and BALB/c mice in vivo. Also, the synergistic cytotoxic effect of R-(-)-carvone with DOX on the MCF 7 cell line was investigated.The protective effect of carvone against paclitaxel-induced retinal and optic nerve cytotoxicity was reported recently . HoweverThe study was conducted in the Experimental Animal Laboratory of the Faculty of Pharmacy, Al-Ahliyya Amman University. The ethics committee approved all submitted for this study  at Al-Ahlyyia Amman University. For acclimatization purposes, mice were kept one week before the experiments under the standard environmental conditions . Male BALB/c  mice were divided into four groups, each containing eight animals weighing between 23 and 28 g. Group I was the negative control group that received vehicle (2% Tween 20) only. Group II was the positive control that received vehicle and DOX  dissolved in sterile normal saline. Groups III and IV were treatment groups that received R-(-)-carvone  (75 and 150 mg/kg suspended in 2% Tween 20), respectively. Vehicle and R-(-)-carvone were given intraperitoneally (i.p.) once daily for five consecutive days . DOX 20. The DOXAfter the designated period, blood samples were obtained from the retro-orbital plexus using glass heparinized capillary tubes. Sera were separated and stored at -20 °C until biochemical analyses. Hearts were obtained immediately after sacrifice and washed several times with ice-cold saline. Sections of the heart were fixed in 10% buffered formalin for histopathological study, and the remaining heart tissue was stored at -80 °C until homogenization.About 100 mg of heart tissue was homogenized in ice-cold (1:9 wt/vol) cell lysis buffer  with a Teflon homogenizer (Potter-Elvehjem). The homogenate was centrifuged at 12,000 g for 10 min at 4 °C. The collected supernatants were collected and kept at -80 °C until they were analyzed for catalase activity. Protein concentrations of supernatants were assayed by the Lowry method .The formalin-fixed tissues were embedded in paraffin, sectioned at 5 µm and the sections, and then stained with eosin/hematoxylin. Sections of the heart from each mouse in all groups were examined and photographed using a light microscope MC 170 HD Leica Camera, Switzerland. All the qualitative histological analysis was performed by one of the authors (M.A.A.)Total creatine kinase (CK), (Biosystems Cat. #: 11790) and lactate dehydrogenase (LDH)  activities in serum were assayed spectrophotometrically according to the manufacturer’s guidelines.The MCF 7  and H9C2  cell lines were obtained from The European Collection of Authenticated Cell Cultures (UK). The cell lines were grown in a humidified 5% CO2 atmosphere incubator at 37 °C and in DMEM high glucose  containing 10% fetal bovine serum, 10 g/L penicillin-streptomycin and 10 g/L L-glutamine. A stock solution of R-(-)-carvone and DOX (1:1 ratio) was freshly prepared in 2-fold serial dilutions. A stock solution of each agent was prepared in dimethyl sulfoxide (0.5%). The effect of R-(-)-carvone and DOX combination on the MCF 7 and H9C2 cell lines was evaluated by MTT -2,5-diphenyl-tetrazolium bromide) assay . Cells were seeded into 96-well plates at a density of 7000 cells/well in a suitable medium. After 24 h incubation at 37 °C, cells were treated with R-(-)-carvone, DOX, or their (1:1) combination in serial dilution ranging from 100 µM to 6.25 µM for 48 h. MTT was then performed using an MTT kit where 15 µL of the staining reagent was added to each well, and incubated for four h at 37 °C followed by the addition of 100 µL of the solubilization stop/mix solution and incubation for 1 h before measuring the absorbance using microplate reader  at 590 nm.Synergy evaluation was performed for R-(-)-carvone in combination with the anticancer drug (DOX). It was performed against the MCF 7 and H9C2 cell lines using the MTT test as described above. The degree of synergism was determined by calculating the Combination index (CI) using Compusyn software (version 1.0) based on the median-effect analysis of Chou and Talalay given that CI<1, CI=1, and CI>1 indicates synergy, additive, and antagonism, respectively .For cultured cells, the cells were harvested and homogenized with cell lysing buffer  and sonicated in ice three cycles, 90 s/cycle using a sonicator . The catalase  test was performed according to the manufacturer’s directions.All data passed the normality test (Shapiro-Wilk). Values were presented as mean ± standard error except for IC50 experiment where values were represented as mean + standard deviation. Differences between group means in CK, LDH, and catalase were analyzed by one-way analysis of variance and post-hoc Turkey’s test using GraphPad Prism version 6 . Results were considered statistically significant when p<0.05.On histopathological analysis, the heart tissue of mice that received DOX at a dose of 20 mg/kg showed disorganization and degeneration of the myocardium. Concomitant treatment with R-(-)-carvone revealed normal muscle fiber architecture . DOX tre50) values of R-(-)-carvone, DOX, and the DOX and R-(-)-carvone combination (1:1) in H9C2 and MCF 7 cell lines are illustrated in 50 values of 14.22 µM. Using CompuSyn software, R-(-)-carvone/DOX exerted antagonistic effect on H9C2 cells, but a synergistic effect on the MCF 7 cell line -cell line .in vivo -carvone alone or by R-(-)-carvone/DOX combination in normal heart in vivo . Similarin vivo .In the present investigation, the synergistic effect of R-(-)-carvone on DOX cytotoxicity was investigated in the MCF 7 breast cancer cell line. The MCF 7 cell line was chosen because DOX is widely used for treatment of breast cancer. However, the use of DOX is limited by its cardiotoxicity . Therefo50 value of 14.22 µM. Other studies reported IC50 values of 166.2 µM -carvone had cytotoxic effect on the breast adenocarcinoma cell line with an IC166.2 µM , and 0.6in vitro. Similarly, the protective effect of R-(-)-carvone in vivo was evident on the heart by lowering the elevated activities in CK and LDH caused by DOX treatment. Also, the histological degenerative changes produced by DOX were less in R-(-)-carvone-treated groups. It should be noted that only males were used in this study because the male sex hormone level is almost constant, while that of female sex hormones vary along the estrous cycle. The results of the present work may differ if females were used -carvone exerted a synergistic cytotoxic effect when administered with DOX on MCF 7, whereas its effect was antagonistic on the H9C2. This indicates that R-(-)-carvone can decrease the toxicity on normal heart cells while increasing it in the tested cancer cell line ere used . TherefoThe findings of this study raise a question: Why did R-(-)-carvone exert a synergistic effect with DOX on the MCF 7 cell line while it produced an antagonistic effect on the normal heart cell line? The mechanism of the R-(-)-carvone antagonistic effect was not investigated herein. However, previous study have reported that R-(-)-carvone induces p53, caspase 3-mediated apoptosis in a breast cancer cell line, and arrests MCF 7 cells in the S phase of the cell cycle . AnotherDifferent mechanisms were proposed to explain DOX anticancer action. These include targeting the enzyme topoisomerase II, which is essential in DNA replication and iron chelation . High leThe ROS elevation is a therapeutic approach used in cancer treatment. Increasing the endogenous ROS threshold level in cancer cells might also put healthy cells of some organs that are vulnerable to oxidative toxicity, such as kidney liver and heart . The mosin vitro as well as in the hearts of mice in vivo. Catalase is considered one of the major enzymes involved in the detoxification of hydrogen peroxide (H2O2). Overexpression of catalase in the heart of transgenic mice suppressed DOX cardiotoxicity -carvone exerted its protective effect by increasing catalase activity in the H9C2 toxicity . The antin vitro ,23, wher-carvone exerted a cardioprotective effect through increasing catalase activity while enhancing the cytotoxicity of DOX on the breast cancer cell line. Future investigations are needed to explore the mechanism of the synergistic effect of R-(-)-carvone with DOX on breast cancer and other different cancer cell lines. Also, clinical studies are required to determine the efficacy of R-(-)-carvone as an adjuvant in cancer therapy, its optimal dose, and effectiveness in a specific cancer types .


In 2019, the Paris Fire Brigade described the chemical, biological, radiological, and nuclear chain of survival (CBRN-CS) to point out five essential tasks that first responders should perform in managing individuals in a CBRN situation [In CBRN events, we face multiple challenges. Despite the indisputable, constant threat of real CBRN situations, fortunately, they are rare. Thus, most first responders have never been exposed to real CBRN situations. Moreover, first responders often have insufficient levels of training and knowledge, and few have the opportunity to attend CBRN training refresher courses or simulation courses . We predIt is well known that stress affects cognitive performance , 6. SimuIn January 2020, we initiated a survey to evaluate the CBRN-CS as a cognitive tool in training first responders in Paris. We asked all participants to place the CBRN-CS items in the correct order, immediately after the course, and 6 months later. Our results from the first 74 participants, which included exclusively medical practitioners and nurses, showed that 84.7% could place all the CBRN-CS items in the correct order immediately after the course. However, 6 months later, only 55.5% of respondents  could reconstitute the CBRN-CS, with items in the correct order. Despite the small size of this test population, we concluded that we needed to improve our strategy for teaching the CBRN-CS. We aimed to make the CBRN-CS more accessible and easier to remember, especially under stressful and uncommon situations, which are rarely practiced.Cognitive aids, like checklists or emergency manuals, can support clinicians in optimizing their actions during critical events . In emerThus, based on this approach, we developed a French mnemonic device to make the CBRN-CS easier to remember. This device is called “I AM THOR”, and its French translation is “DUST DAHO” Table . It is cOur ability to recall learned information is often impaired, when acute stress, time pressure, and unfamiliarity are associated with the management of rare, high-acuity critical events, like CBRN situations. During the first few hours, whether they are on the scene or in front of the hospital, first responders must remember the appropriate response, share the same objectives, and coordinate their actions, regardless of their profession and the CBRN situation.In CBRN events, appropriate, rapid actions are critical to curb disorganization and to optimize the management of injured individuals. Adding the mnemonic device “I AM THOR” to the CBRN-CS training could increase long-term memorization by first responders and help them initiate the main tasks required in any given CBRN situation.


Esophageal adenocarcinoma (EAC) is a highly heterogeneous disease, dominated bylarge-scale genomic rearrangements and copy number alterations. Suchcharacteristics have hampered conventional target-directed drug discovery andpersonalized medicine strategies, contributing to poor outcomes for patients. Wedescribe the application of a high-content Cell Painting assay to profile thephenotypic response of 19,555 compounds across a panel of six EAC cell lines andtwo tissue-matched control lines. We built an automated high-content imageanalysis pipeline to identify compounds that selectively modified the phenotypeof EAC cell lines. We further trained a machine-learning model to predict themechanism of action of EAC selective compounds using phenotypic fingerprintsfrom a library of reference compounds. We identified a number of phenotypicclusters enriched with similar pharmacological classes, including methotrexateand three other antimetabolites that are highly selective for EAC cell lines. Wefurther identify a small number of hits from our diverse chemical library thatshow potent and selective activity for EAC cell lines and that do not clusterwith the reference library of compounds, indicating they may be selectivelytargeting novel esophageal cancer biology. Overall, our results demonstrate thatour EAC phenotypic screening platform can identify existing pharmacologicclasses and novel compounds with selective activity for EAC cell phenotypes. Clinical trials with new molecular targeted agents havepredominantly been directed toward epidermal growth factor receptor and humanepidermal growth factor receptor 2 (HER2) receptors912 but thus far have provenunsuccessful. A potential explanation is the almost ubiquitous coamplification ofalternative receptor tyrosine kinases and downstream pathways leading to redundancyand drug resistance.14 An alternative to target-based drug discovery, and increasingin popularity with technological advances, is phenotypic drug discovery, defined asthe identification of novel compounds or other types of therapeutic agents with noprior knowledge of the drug target. Recent advances in phenotypic screening includeautomated high-content profiling.16 This approach involvesquantifying a large number of morphological features from cell or small-modelorganism assays in an unbiased way to identify changes and phenotypes of interest.One benefit to this method is that a target does not need to be predefined, but themechanism of action (MoA) of hit compounds can be inferred by reference to knowncompound sets using multivariate statistics and machine-learning approaches. Thus,this may prove a beneficial strategy for complex, heterogeneous diseases in whichtarget biology is poorly understood and modern, target directed drug discoverystrategies have made little impact on patient care, as exemplified by EAC.EAC is a highly heterogeneous disease, dominated by large-scale genomicrearrangements and copy number alterations.18 Therefore, upon chemicalperturbation, we can detect changes in a subset of profiled features, allowing aphenotypic fingerprint to be assigned to a particular perturbation orcompound.1921 Thesefingerprints can then be used to identify specific phenotypic changes of interest,identify compounds that cause strong alterations in cell morphology suggestingchanges in cellular state or stress, or predict MoA by similarity comparison toreference libraries of well-annotated compound mechanisms.21 However, this type of analysisis typically performed in a single “model” cell line, chosen for its suitability forimage analysis. As a proof of principle that high-content phenotypic profiling couldbe applied to a panel of morphologically distinct EAC and tissue-matched controlcell lines, we iteratively optimized cell culture conditions, cell-platingdensities, and the Cell Painting assay staining protocol across our cell panel.Assay performance in terms of distinguishing distinct compound MoA for each celltype was evaluated by testing a small reference set of well-annotated compoundsrepresenting eight distinct mechanistic classes and performing principal componentanalysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to visualizeclustering of distinct mechanistic classes. We further developed a machine-learningmodel capable of predicting MoA across the panel of heterogeneous EAC cell lines.Following assay validation, we subsequently screened a library of 19,555 smallmolecules comprising target annotated probe compounds, approved drug libraries, andtwo diverse chemical sets with unknown MoA. PCA clustering of compound fingerprintsdistinguished a number of phenotypic clusters composed of similar pharmacologicclasses active in the EAC cell lines. We also applied a Mahalanobis distancethreshold and differential Z-score on our phenotypic data to identify compounds fromour screen that were selectively active in EAC versus tissue-matched control cells.For prioritized hits, we have selected a subset and validated EAC selectivity withfollow-up dose-response testing and performed transcriptomic pathway analysis pre-and posttreatment on sensitive and insensitive cell lines to further elucidate MoA.We further applied PCA and machine-learning analysis to phenotypic fingerprints fromour diverse chemical set to identify compounds that exhibit selective activity onEAC cell phenotypes by a mechanism distinct from our reference set, indicating theymay exhibit novel MoA.Taking an unbiased, profiling approach to phenotypic screening, we chose to apply theCell Painting assay to capture large amounts of information on cellular andsubcellular morphology to quantify the cellular state across a panel of geneticallydistinct EAC cell lines. Cell Painting is an assay developed to capture as manybiologically relevant morphological features in a single assay so as not toconstrain discovery to what we think we already know.Herein we describe the development and validation of a high-content phenotypicprofiling assay and associated image informatics and machine-learning toolbox toclassify the MoA of phenotypic screening hits across a panel of EAC andtissue-matched control cell lines. This approach has enabled the identification ofchemical and target classes, including histone deacetylase (HDAC) inhibitors, whichconsistently cause the same cellular response across the panel of EAC lines,demonstrating efficacy against the heterogeneity of the disease. In addition, weidentify pharmacologic classes such as the antimetabolites and new chemical entitieswith high selectivity for some EAC cell lines relative to tissue-matched controls.We propose that applying high-content multiparametric phenotypic profiling to apanel of genetically annotated EAC cell lines may stimulate new drug discovery anddrug development programs for EAC through the identification of drug-repurposingopportunities and novel chemical starting points with selective activity forspecific EAC genotypes.22EPC2-hTERT cells were a kind donation from Anil Rustgis’s Lab, University of Pennsylvania.Cell line identification  was confirmed by short tandem repeat genotyping .The cell lines were confirmed to be mycoplasma negative using the VenorGeMmycoplasma detection PCR kit .2). TheBarrett’s esophagus line (CP-A) and the esophageal epithelial line (EPC2-hTERT)were grown in KSFM  supplemented with humanrecombinant epidermal growth factor (5 g/L) and bovine pituitary extract (50mg/L). Soybean trypsin inhibitor  was used to neutralizetrypsin.EAC lines were grown in RPMI supplemented with fetal bovine serum (10%) and L-glutamine (2 mM) and incubatedunder standard tissue culture conditions (37 °C and 5% COCells were seeded (50 µL per well) into 384-well, CELLSTAR Cell CultureMicroplates  and incubatedunder standard tissue culture conditions for 24 h before the addition ofcompounds. CP-A cells were seeded at 800 cells per well, SK-GT-4 cells wereseeded at 1000 cells per well, and the remaining cell lines were all seeded at1500 cells per well.Supplementary Table S1.Compound source plates were made at 1000-fold assay concentration and added tothe cells with an overall dilution in media of 1:1000 from source to assayplate. Library concentrations are shown in The primary screen was carried out as a single replicate, and the validationdose-response study was in triplicate.After 48 h of incubation in the presence of the compounds, cells were fixed bythe addition of an equal volume of formaldehyde  to the existing media, incubated at room temperature(20 min), and washed twice in phosphate-buffered saline (PBS). Cells were thenpermeabilized in Triton-X100  and incubated at room temperature (20min) followed by two more washes with PBS.The staining solution Table 1 Plates were imaged on an ImageXpress micro XLS equipped with a robotic plate loader . Four fields of view werecaptured per well using a 20× objective and five filters  were removed. Finally, the image-level data were aggregated tothe well (compound) level, and this was used in the analysis.The random forest classifier was implemented using R’s Random Forest packagewith the following specified parameters: ntree = 500, data stratified byclass, and sample size set to the smallest class size for balance. Theimages from three concentrations for each compound were pooled and treatedas a single class. Two different analyses were run: first, MoA predictionwas implemented for each cell line individually, and second, usingleave-one-out cross-validation, one EAC cell line was left out of thetraining set at a time and that line was run as a test set.PCA and t-SNE were implemented using the built-in R functions prcomp andRTSNE, respectively, to visualize the clustering of the compounds for eachcell line.Z-scores and Mahalanobis scores were centered and scaled for each compoundacross the panel of cell lines. Spearman correlation was then used togenerate a distance matrix, and hierarchical clustering was determined usingcomplete linkage.Cells were seeded in six-well plates and incubated for 24 h. Media were thenremoved and replaced with DMSO (0.1%) or methotrexate (5 µM) in DMSO andincubated for 6 h. Cells were scraped and lysed using QIAshredders , and RNA was extracted by means of the QiagenRNeasy Mini kit  according to themanufacturer’s instructions and included a DNase digestion step .Of the purified RNA, 100 ng was used as input for amplification-free RNAquantification by the NanoString nCounter Analysis System with the HumanPanCancer Pathways and Metabolic Pathways panels. Raw counts were normalizedto the internal positive controls and housekeeping genes using the nSolver4.0 software.Because EAC is such a heterogeneous disease, we chose to develop a high-contentphenotypic screening assay composed of a panel of EAC and tissue-matchednontransformed cell lines that captured this heterogeneity and thus provides adiscovery platform for identification of novel targets and drug MoA thatselectively target EAC. We assessed the amenability of 12 cell lines tohigh-content profiling, 10 EAC lines , and two tissue-matched nontransformedlines; a Barrett’s esophagus line CP-A, and a normal esophageal squamous lineimmortalized by expression of telomerase EPC2-hTERT. We assessed each cell lineagainst a list of criteria that indicated high performance for high-contentscreening, including cell adhesion quality, cellular morphology, proliferationin 384-well plates, image segmentation, and MoA prediction accuracy. Thesecriteria ensure image quality/information content, high-throughput screeningcompatibility, and image segmentation accuracy for downstream analysispipelines. Based on suitable cell adhesion and morphological properties, we tookforward the following eight cell lines for high-content assay development,including image segmentation and machine learning analysis: CP-A, EPC2-hTERT,FLO-1, JH-EsoAD1, MFD-1, OAC-P4C, OE33, and SK-GT-4 . For this, we trained a random forest classifier using theCellProfiler extracted phenotypic information from the images of cells treatedwith the reference set of compounds.Standard assay quality control metrics such as Z′Factor are unsuitable formultiparametric assays, particularly cell-based phenotypic profiling assays inwhich a desired phenotype is unknown and/or there is a lack of positivecontrols.Accuracy in the ability to predict MoA was used to assess whether the EAC andtissue-matched control cell lines were amenable to the phenotypic profilingassay, further validate whether image segmentation was accurate, and ensure thatthe phenotypic information extracted was relevant and broad enough to allowaccurate prediction of MoA. To robustly evaluate compound selectivity and MoAacross our heterogeneous panel of genetically distinct EAC cells, it wasparticularly important to assess the performance of each individual cell lineand ensure that one cell line did not perform significantly better or worse thanthe others. A characteristic of EAC cell lines  is the migration and formation of cell clumps, which are challengingto segment accurately by automated image analysis. Here we wanted to confirmthat they were equal to the rest of the panel and suitable for the assaypipeline. OAC-P4C is a particularly morphologically heterogeneous line, so itwas also important to ensure that image-level data can be used for phenotypiccompound profiling in these types of cell lines.29 t-SNE is a nonlinear technique and is capable of capturinglocal as well as global structure.30 Input feature importance can also be examined when using PCA but nott-SNE. Given the complexity of the relationship between the features extractedfrom the images using both techniques allowed us to gauge feature importance,assess both a linear and nonlinear technique as there were no prior assumptionsabout the relationships between features, and look at both local and globaltrends in the data set. The results demonstrate that distinct compound classesgenerally cluster together. However, the strength of the phenotypic responsevaried across compound classes with some, such as the statins, producing a lessdistinct response than others. PCA clustering shows the statins are much closerto the DMSO controls .To visualize the phenotypic information extracted, we performed two datareduction methods, PCA and T-SNE, on the well-level data for the small referencelibrary of compounds and plotted the first two components, colored bymechanistic class. PCA is a linear feature extraction technique, projecting thedata in a lower-dimensional space while preserving the global structure of thehigher-dimensional data.We next optimized a random forest classifier to test the MoA prediction on ourreference library of well-annotated compounds. The extracted features from threeconcentrations of each compound were pooled and used to train the classifier. Wechose 0.1, 1, and 10 µM, because using a broad range of concentrations meansthat each compound does not need to be optimized individually across each cellline.32 was 20.38% across the entire panel of cell lines, rangingfrom 12% to 27%. The overall prediction accuracy for each cell line ranged from73% to 88% across all eight compound classes, demonstrating the assay was welloptimized across the panel. The weakest cell line was the SK-GT-4.When trained and tested on each individual cell line the average out-of-bagerror34 We implementedleave-one-cell-line-out and trained it on five of the EAC lines, testing on theremaining line. Here, as expected, it performed less well overall. However, theaccuracy for each cell line ranged from 58% to 71% , indicating the ability of this classifier to be transferred to newcell lines despite having no prior training on them and thus the potential forthe application of the classifier across a broader panel of cell lines withoutthe need to train each cell line individually.To confirm that the classifier was not overfitting, we used leave-one-outcross-validation.Overall, the accuracy of the machine learning demonstrates that the phenotypicprofiling assay is of high quality across all eight cell lines, includingmorphologically heterogeneous cells, and feature extraction produces meaningfuldata for phenotypic analysis. The phenotypic profiling assay can therefore beapplied to provide an initial evaluation of MoA of hit compounds influencing EACcell proliferation, survival, and morphology. As such, our multiparametrichigh-content phenotypic profiling assay may prove useful in the prioritizationof compound hits, which represent novel MoAs, and the deprioritization ofcompounds, which represent undesirable MoAs for subsequent medicinal chemistryand target deconvolution investments. We therefore prioritized the full panel ofeight lines that passed our quality control criteria  for a phenotypic screen of 19,555 small molecules.A total of 19,555 small molecules, including approved drugs, were profiledagainst our panel of eight cell lines using the ImageXpress microXL high-contentimaging platform. Cells were treated with the commercially available PrestwickChemical Library of 1280 mostly off-patent drugs, the LOPAC library ofpharmacologically active compounds (1280 compounds), a proprietary diversechemical library provided by CRUK Therapeutics Discovery Laboratories, the BioAscent library of 3200 compounds, andbespoke libraries of 387 target-annotated compounds and chemical probes. Theprimary phenotypic screen across all eight cell lines encompassed 512 × 384 wellplates, 3.9 million images, and 36 TB of data in total. Image analysis wasperformed using CellProfiler across a computer cluster.Using a panel of cell lines better represents a heterogeneous disease and allowedus to identify compounds that demonstrated selective activity across multipleEAC lines and not in the tissue-matched control. We ran two parallel analysesfor primary hit selection against the EAC lines: one based on broad,morphological, phenotypic changes and the other on cell growth and survivalusing nuclei count. At cytotoxic concentrations, there are few attached cells,and these are often rounded up, leading to a lack of information in the images.Therefore, images with 20 or fewer cells were removed from the morphologicalanalysis.We began our analysis with a subset of 3000 annotated compounds (excluding theCRUK Therapeutics Discovery Laboratories and BioAscent lead-like molecules).35 It therefore addresses problems of both scale and correlation of thevariables and is particularly beneficial for large multivariate data sets. Thisleads to elliptic rather than circular decision boundaries, as is the case forEuclidean. We therefore chose to use it as an unbiased metric of compoundactivity upon each cell line in the screen.To identify compounds inducing strong phenotypic changes, we used PCA on thefeature data to reduce the dimensions and then calculated the Mahalanobisdistance to the DMSO controls for the first 15 principal components, whichexplain approximately 90% of the variation in the data across each cell line.The Mahalanobis distance measures the distance of each point from the datadistribution . The data distribution takes intoaccount the mean and the spread of the data points using the covariance matrixas a normalization factor.Suppl. Fig. S3).Phenotypic analysis identified 62 compounds that selectively target two or moreof the EAC lines over the nontransformed esophageal cells. Clustering the cellpanel’s responses to these molecules showed a number of phenotypic clustersenriched with similar pharmacologic classes, including HDAC inhibitors,microtubule disruptors, and antimetabolites, suggesting that hits have clusteredmechanistically , we identified 27compounds that were selectively active in two or more of our EAC lines. Here,hits were defined as having a z-score of −3 or greater in the EAC lines and adifference of at least 2 in one or both of the control cell lines . This comparison was made between each EAC lineand the control lines to define hits and then selected if they were selectivelyactive in at least two EAC lines across the panel .Compounds from the growth and survival analysis cluster into several therapeuticclasses, suggesting mechanistic pathways that may be selective for EAC cellgrowth and survival. Classes include antimetabolites and HDAC inhibitors. Theseclasses were also identified in the morphometric phenotypic analysis  and thez-scores .From the subset of 3000 annotated compounds, we identified the drug methotrexateand three other structurally related antimetabolites, pemetrexed, raltitrexed,and aminopterin, as highly selective for EAC cell lines relative totissue-matched control CP-A and EPC2-hTERT cells in both the nuclei count andmorphological phenotypic analyses. We therefore validated this class of compoundfor dose-dependent activity. Aminopterin was removed from further analysisbecause of its toxicity profile in the clinicSuppl. Table S3), validating our hit selection criteria.Nuclei count dose responses for methotrexate, pemetrexed, and raltitrexeddemonstrated strong selectivity against the EAC lines and showed minimalcytotoxic or phenotypic activity in either the CP-A or the EPC2-hTERT line evenat 10 µM (Suppl. Table S2) showed strong dose-dependent phenotypic changes, moving fromphenotypically inactive (clustering with DMSO controls) to clustering with theDNA-damaging agents at active concentrations  in all but the JH-EsoAD1 and MFD-1 lines. All three compounds alsoshowed little or no effect in the control lines EPC2-hTERT and CP-A, clusteringclosely with the DMSO controls at all concentrations tested.Multiparametric phenotypic dose-response profiles of the antimetabolites overlaidon the reference library of annotated compounds . Several other genes changed with methotrexate treatment, but nonewere significant. Further mechanistic studies are required to further elucidatehow and if such expression changes confer selectivity to methotrexate.NanoString differential expression analysisSuppl. Fig. S5), and machine-learning probabilities for all classes were low . Machine learning predicted it to be DNA damaging (91% probability)in the OAC-P4C cells; however, its clustering was distinct, and themachine-learning probability that it is DNA damaging in the OE33 cell line wasonly 52%  as well as a tool for MoAdeconvolution of hit compounds. Our results demonstrate that this can bestandardized across heterogeneous panels of cells.39 These results, together withour identification of hit compounds from our diverse chemical set, which are notclassified by our reference set of known MoAs, demonstrates the impact of phenotypicscreening in combination with machine learning for MoA studies. This strategy willbe used to assess and prioritize novel small-molecule hits from the diverse chemicallibrary screen for further mechanistic studies. From our primary phenotypic screen,we have identified in total 75 compounds that match our hit selection criteria forselective activity across the EAC panel. These 75 hits are an accumulation of the 62compounds defined by cell morphometric phenotypic analysis and 27 compounds definedby cell proliferation and survival (nuclei count) analysis, with 14 compoundsoverlapping. The 75 hits shall be further progressed through dose-response studiesand secondary assays to confirm and prioritize classes of selective compounds forsubsequent drug repurposing and or drug discovery studies.Following one class of compounds identified in our primary phenotypic screen of19,555 small molecules tested across all eight esophageal cell lines, we validatedantimetabolites as selectively lethal to the EAC lines in vitro followingdose-response studies. Using the multiparametric phenotypic information to generatephenotypic dose responses, combined with a reference library of compounds, machinelearning, and clustering techniques, we demonstrated the ability to study/predictthe MoA of hits from the screen. Here we validated this technique using theantimetabolite hit compounds , showingDNA damage as a likely MoA for the selectivity of these compounds, which isconsistent with the literature.In addition, using bioinformatic approaches, we hope that integration of phenotypicdata with genetic data across our panel of diverse cell lines may provide insightinto the selective activity of phenotypic hits and generate the basis for futuregenetic biomarker–based clinical trials in EAC.Overall, our high-content EAC assay has proven effective in the identification andmechanistic characterization of hit compounds, demonstrating its utility as a novelempirical strategy for the discovery of new therapeutic targets, chemical startingpoints, and repurposing of existing drug classes to reignite drug discovery anddevelopment in EAC.Click here for additional data file.Supplemental material,Supplemental_Material_for__high_content_phenotypic_profiling_by_Hughes,_et_alfor High-Content Phenotypic Profiling in Esophageal Adenocarcinoma IdentifiesSelectively Active Pharmacological Classes of Drugs for Repurposing and ChemicalStarting Points for Novel Drug Discovery by Rebecca E. Hughes, Richard J. R.Elliott, Alison F. Munro, Ashraff Makda, J. Robert O’Neill, Ted Hupp and Neil O.Carragher in SLAS Discovery