Breast cancer (BC) is the most common cancer in women, highly heterogeneous at both the clinical and molecular level. Radiation therapy (RT) represents an efficient modality to treat localized tumor in BC care, although the choice of a unique treatment plan for all BC patients, including RT, may not be the best option. Technological advances in RT are evolving with the use of charged particle beams (i.e. protons) which, due to a more localized delivery of the radiation dose, reduce the dose administered to the heart compared with conventional RT. However, few data regarding proton-induced molecular changes are currently available. The aim of this study was to investigate and describe the production of immunological molecules and gene expression profiles induced by proton irradiation. We performed Luminex assay and cDNA microarray analyses to study the biological processes activated following irradiation with proton beams, both in the non-tumorigenic MCF10A cell line and in two tumorigenic BC cell lines, MCF7 and MDA-MB-231. The immunological signatures were dose dependent in MCF10A and MCF7 cell lines, whereas MDA-MB-231 cells show a strong pro-inflammatory profile regardless of the dose delivered. Clonogenic assay revealed different surviving fractions according to the breast cell lines analyzed. We found the involvement of genes related to cell response to proton irradiation and reported specific cell line- and dose-dependent gene signatures, able to drive cell fate after radiation exposure. Our data could represent a useful tool to better understand the molecular mechanisms elicited by proton irradiation and to predict treatment outcome The idea that genes may function as biomarkers of disease response provides the rationale for the development of molecularly based signatures to predict response to radiation treatment in cancer, including breast cancer (BC). Moreover, microarray-based expression studies have demonstrated their relevance in the comprehension of cancer behavior as well as in cancer care. It is well known that BC is the most common cancer in women, highly heterogeneous at both the clinical and molecular level and showing distinct subtypes associated with different clinical outcomes; hence the need to develop targeted therapeutic strategies , 2. GeneIonizing radiation (IR) delivered during radiation therapy (RT) plays a key role in the therapeutic treatments for many types of cancer, including BC. Although technological advances in radiation delivery have decisively enhanced treatment efficacy, the current scenario still presents standard RT schedules that do not take into account the specific molecular subtypes of patients with cancer in the same anatomic position  . Howeverhttps://www.ptcog.ch/index.php/facilities-in-operation, accessed June 2018].Thus, the choice of a unique RT plan, common to all BC patients, may not be the best option. In addition, the advent of hadrontherapy, particularly with the use of accelerated proton beams, has led to a number of potential advantages over conventional (photon/electron-based) RT for cancer, the most important of which is arguably a more localized delivery of the radiation dose with the consequent sparing of healthy tissues and/or organs at risk –13. In BHowever, few data are available regarding proton-induced molecular changes, particularly in breast cells, a topic that therefore deserves to be accurately described.In addition, based on our previous findings, IR exposures could stimulate the secretion of numerous inflammatory factors, which can affect cell fate via multiple pathways and may thus influence tumor progression control and the overall therapy outcome –20. ThesIn this scenario, the aim of this study was to describe the dose–response effects on cell survival induced by proton beam irradiation and, for the first time to our knowledge, the radiation-induced GEPs and immunological molecules profiles produced by the MCF10A mammary non-tumorigenic cell line, and MCF7  and MDA-MB-231  BC cell lines with different aggressive phenotypes , after g–1.Proton irradiations were performed using the 62 MeV proton beam generated by the superconducting cyclotron clinically used at the CATANA (Centro di AdroTerapia ed Applicazioni Nucleari Avanzate) eye proton therapy facility of the Italian Institute for Nuclear Physics in Catania, Italy –22. The 2 incubator; 2 days before irradiation they were seeded in T25 tissue culture flasks at 3–5×105 cells per flask. Cell survival was evaluated by clonogenic assay performed as previously described [x-axis for the dosimetric error and on the y-axis for the error of the SFs calculated with the error propagation method.The human non-tumorigenic breast epithelial MCF10A cell line and the human breast adenocarcinoma MCF7 and MDA-MB-231 cell lines were purchased from the American Type Culture Collection (ATCC) and cultured according to the manufacturer’s instructions  as previously reported . Cells wescribed , 23, 28.Cells were collected at 24, 48 and 72 h post-irradiation. Twenty-four hours before irradiation, the growth medium was replaced with fresh medium. At the above times after exposure, irradiated conditioned medium (ICMs) was collected. For each cell line, one flask with the same cell number compared with treated samples was seeded and grown under the same experimental conditions. Thus, their complete conditioned medium (CM) was collected and used as control . Complete media without cells were incubated under the same experimental conditions and used as the blank controls. CM and ICM were stored at −80°C until use. Immediately before the cytokine assay, thawed samples were centrifuged at 12 000 rpm for 5–10 min to allow precipitation of any lipid excess and tested for the following panel of 17 cytokines, chemokines and growth factors: interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12(p70), IL-13, IL-17, interferon (IFN)-γ, tumor necrosis factor (TNF)-α, monocyte chemoattractant protein-1 (MCP-1), macrophage inflammatory protein-1β (MIP-1β), granulocyte–macrophage colony-stimulating factor (GM-CSF) and granulocyte colony-stimulating factor (G-CSF). The assay was carried out using the Luminex system  according to the manufacturer’s instructions, as previously described [t-test, and the significance level was set at P < 0.05. The false discovery rate (FDR) was used as a multiple test correction method. Genes were identified as being differentially expressed if they showed a fold change (FC) of at least 2 with a P-value < 0.05 compared with untreated MCF10A, MCF7 and MDA-MB-231 cell lines used as reference samples. As described above, the same experimental approach was performed for both cell lines used in this project. The data discussed in this publication have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) [To study molecular pathways and cell networks activated at the transcriptional level following proton beam irradiations, gene expression experiments by cDNA microarray were conducted in order to select potential new biomarkers of radiosensitivity and radioresistance as previously described , 23. Tweus (GEO)  and are https://reactome.org/). One of its main priorities is to provide easy and efficient access to its high-quality curated data and to provide intuitive bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge. It also provides a rapid means to reduce large lists of genes into functionally related groups of genes to help unravel the biological content captured by high-throughput technologies such as microarray analyses [Differentially expressed gene lists obtained by GEP analysis were analyzed using Reactome, a tool that provides a comprehensive set of functional annotation for investigators to understand biological meaning behind a large list of genes. Reactome is a free, open-source, open-data, curated and peer-reviewed knowledge base of biomolecular pathways ; MCF10A 2 Gy, 881 DEGs (224 down-regulated and 657 up-regulated); and MCF10A 9 Gy, 929 DEGs (319 down-regulated and 610-up regulated) ; MCF7 2 Gy, 506 DEGs (134 down-regulated and 291 up-regulated); and MCF7 9 Gy, 604 DEGs (158 down-regulated and 446 up-regulated) ; MDA-MB-231 2 Gy, 1105 DEGs (328 down-regulated and 777 up-regulated); and MDA-MB-231 9 Gy, 1429 DEGs (389 down-regulated and 1040 up-regulated) , as a function of the delivered dose, we produced Venn diagrams as displayed in Fig. Figure Moreover, the 27, 51–70 and 8 gene signatures, were loaded into the Reactome tool in order to select the related pathways as shown in Table in silico approach by PubMatrix, as described in the literature and also by our group [Finally, we decided to confirm and validate the involvement of genes belonging to the above-mentioned signatures, using an ur group . In thisThere is a substantial lack of data regarding cancer cell and molecular responses induced by proton irradiation, which prompted this work. Moreover, the possibility that proton therapy may be used to treat BC led us to study this topic in breast cell lines. The identification of gene signatures linked to specific radiation regimens, as reported in this work, could be helpful for the understanding of the molecular mechanisms linked with the treatment efficacy, allowing the introduction of biological features in RT planning. In this sense, for the first time to our knowledge, we have performed molecular analyses of BC cells exposed to different doses of proton irradiation, highlighting gene expression signatures related to specific cell lines and proton irradiation configurations. Data collected here confirm that radiation effects on cells are heterogeneous and appear to act in a cell line-dependent manner. This may in turn lead to an individualized form of treatment with a significant predictive value.Dose–response effects were analyzed on the non-tumorigenic MCF10A) and tumorigenic BC (MCF7 and MDA-MB-231) cell lines with different aggressive phenotypes. The SFs obtained showed different cellular radiosensitivity; in particular, the MCF7 cells revealed greater sensitivity to proton irradiation than MDA-MB-231 and MCF10A cells, which were more radioresistant  on the three breast cell lines. Considering that IR induces significant effects on immune system modulation able to drive the survival/cell death balance and senescence process, we first studied the cytokine, chemokine and growth factor profiles induced in MCF10A, MCF7 and MDA-MB-231 cell lines by proton irradiation Tables . These mOverall, we investigated the time and dose dependence of cytokine signatures for the three cell lines tested, highlighting some differences that we think may also be justified by the different aggressive phenotypes of the three BC cell lines. Indeed, while MCF10A cells are strongly affected by the dose effect, since strong quantitative differences are observable between low (0.5 and 2 Gy) and high (9 Gy) doses for most of the molecules assayed see , the MDAThe immune response profile of MDA-MB-231 cells to irradiation was characterized by a stronger activation, in a time-dependent manner and regardless of dose delivered, of 11 out 17 of the immunological factors analyzed, compared with to MCF10A and MCF7 cells. Summarizing, MDA-MB-231 cells showed the strongest potentially pro-inflammatory profile compared with the other cell lines analyzed. The inflammatory network seems to be unbalanced as IL-10, the main anti-inflammatory cytokine, is not expressed and the IL-4/IL-13 up-regulation is rather limited. Moreover, the observed strong IFN-γ up-regulation at low and high doses denotes a TH1-type response activation.Considering the three immunological profiles, IL-6, IL-8 and MCP-1 could be considered candidate biomarkers of proton irradiation of mammary epithelial cells.in vitro experiment. On the other hand, it could be speculated that the release of these molecules can play a different and specific paracrine role in each patient’s tumor microenvironment, generating individual long-term effects on tumor progression and post-treatment sequelae, e.g. local or systemic inflammation, fibrosis or cell senescence.Interestingly, these results are comparable with those obtained by our group for the same cell lines following electron beams . HoweverMoreover, as graphically displayed, some genes were specifically deregulated according to the dose delivered (dose-dependent gene lists), whereas other genes (cell-dependent gene list) were commonly deregulated after all the three doses delivered and were probably linked to BC-specific features Figs , 3 and 4In MCF10A cells, a 92 common gene list . Interestingly, we selected 27, 51 and 70 genes that were activated in BC cells after 0.5, 2 and 9 Gy, respectively Table . MoreoveAll genes belonging to these signatures were also analyzed by the PubMatrix tool, in order to search for biological interpretation through NCBI literature of data obtained from microarray analysis.In particular, we speculate that these genes could represent specific biomarkers of proton breast cell response. Among these genes, no information about the involvement of C9orf131, MGC16142, FAM13A and C8orf3 genes in the radiation cell response (proton or other types) is now available in the literature, which requires further investigationOn the other hand, limited but promising data were collected for the other genes of the 8 gene signature. In particular, the ABCA10 gene codes for a member of the superfamily of ATP-binding cassette (ABC) transporters, often overexpressed in several tumors and responsible for the traffic of a wide variety of xenobiotics, lipids and metabolic products across the cell membranes. These proteins were also described as implicated in multidrug resistance, but no information is available regarding their hypothetical role in radioresistance processes , 33.The INPP5D gene is a member of the inositol polyphosphate-5-phosphatase (INPP5) family regulated by AKT signaling and recently described as involved in the apoptotic process, also during hypoxia conditions independently of p53 activation . As has The EN1 gene is involved in the development of the central nervous system, and recently was described as a prosurvival transcription factor in the basal-like BC type. It is also described as exclusively overexpressed in some extremely aggressive cancers, also associated with hypoxia, inflammation and high leukocyte infiltration . HoweverThe SLC6A13 gene encodes a neurotransmitter transporter involved in the uptake of δ-aminolevulinic acid (ALA). ALA-induced protoporphyrin accumulation is a strategy widely used in cancer treatment. Its expression was found in some cancer cell lines and can play a role in enhancing the accumulation of ALA-induced protoporphyrin . AdditioOverall, data from immunological signatures and the top five lists of GEP profiling seem to intersect at some points; nonetheless, transcriptional and translational levels are not always coincident and the time windows analyzed are different. In particular, immunological pathways which varied following radiation among the top five lists analyzed are: (i) Interleukin 4 and 13 signaling with 23 genes varying in MCF10 exposed to 2 Gy; and (ii) the two anti-inflammatory pathways: Interleukin 10 and Interleukin 4 and 13 signaling, with 15 and 28 genes varying in MDA-MB-231 exposed to 9 Gy. In addition, in MDA-MB-231, the ‘Gene and protein expression by Jak–STAT signalling after IL-12 stimulation’ is commonly different among all the dose configurations analyzed.In MCF7 cells, the GEP profiling does not involve immunological pathways, justifying the poor secretion levels of immunological proteins, whereas in MCF10 and MDA-MB-231 cells the transcriptional activation of an anti-inflammatory response sustained by the IL-4/IL-13 loop is still observable 24 h post-treatment, to balance inflammation generated by IR. Furthermore, the conspicuous IFN-γ release 48–72 h post-treatment by MDA-MB-231 cells exposed to all the doses is fully explained by the activation of ‘Jak–STAT signalling after IL-12 stimulation’ 24 h post-irradiation.In conclusion, as previously described by several authors and also by our group , 13, 38,This work highlighted the molecular response to proton irradiation of three breast cell lines (the tumorigenic MCF7 and MDA-MB-231 and the non tumorigenic MCF10A), in terms of GEP and secretion of immunological molecules. The three cell lines studied demonstrated differential GEP/cytokine profiles after proton irradiation, showing a cell line- and dose-dependent type of response to radiation. The immunological signatures are dependent on the dose delivered (low doses of 0.5 and 2 Gy vs a high dose of 9 Gy) in MCF10A and MCF7 cell lines, whereas MDA-MB-231 cells show a strong pro-inflammatory expression profile regardless of the dose. We selected a specific 8 gene signature which represents a list of common genes deregulated in response to the doses used and in all the cell lines analyzed in this work that in our opinion need more investigation. It can be envisaged that our data could be useful in the definition of personalized proton therapy protocols in combination with targeted therapies dedicated to breast cancer.Supplementary DataClick here for additional data file.Supplementary DataClick here for additional data file.Supplementary DataClick here for additional data file.Supplementary DataClick here for additional data file.Supplementary DataClick here for additional data file.Supplementary DataClick here for additional data file.


While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9963797 With the discovery of molecular mechanisms driving cancer, a new generation of molecularly targeted drugs has entered the market over the last two decades. A remaining challenge in the envisaged transition from ‘one-size-fits all’ therapeutic approach to personalised treatment is the development of resistance to single-targeted drug treatment. Combinatorial therapy may be able to overcome this by co-targeting multiple mechanisms involved in cancer cell growth and survival5 and lung adenocarcinoma6. This has sparked a wide interest in identifying novel efficient pairs of small molecule targeted drugs. However, our knowledge about beneficial targeted drug combinations is still limited, partly due to the combinatorial complexity.Already compelling results have been achieved with some combinations of cancer signalling inhibitors, e.g. the synergistic combination of MAP2K1/2 (MEK) and BRAF inhibitors for malignant melanoma7, melanoma cell lines8, sarcoma cell lines9 and lung-cancer-patient-derived cell culture models10. In a study performed by O’Neil et al., combinations of around 12 targeted drugs were screened across a panel of 39 cancer cell lines. In total 22 experimental drugs were tested in all possible combinations and in combination with 16 approved drugs11. The National Cancer Institute (NCI) screened pairs of 104 FDA-approved cancer drugs against the NCI-60 cell line panel. Around 30 of the tested compounds can be classified as small molecule targeted therapies12. In the AstraZeneca’s drug combination data set of the latest DREAM challenge, 118 drugs, including 59 targeted therapies, were tested in 910 pairwise combinations against 85 cancer cell lines13.Few high-throughput screens testing multiple targeted drugs in combinations have been published with open access data. Examples include combinatorial screens on ovarian cancer cell lineshttps://www.ppu.mrc.ac.uk/)14 for 15 of 19 drugs. Eight cancer cell lines were treated with single inhibitors and 171 pairwise combinations at equimolar concentrations from 10–0.16 µM. Cell viability was determined after 48 hours from ATP content using CellTiter-Glo 2.0 (Promega). Several of the synergistic combinations involved the PI-103 inhibitor. Six of these combinations were investigated in a secondary screen. We here applied a matrix design allowing for synergy assessment outside the equimolar range and determined inhibitor effects from both viability (ATP content) and cell confluency (brightfield imaging). Figure In this paper, we report results from a high-throughput screen testing the effects of combining 19 small-molecule inhibitors on cancer cell viability. The drugs target signalling proteins involved in several well described signalling pathways, including PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Inhibitors were selected for high specificity and a minimum of known off-target activities, as judged from characterizations available from the MRC PPU lab  have suffered from toxic effects24. Thus, identification of compounds that increase the therapeutic index for PI3K inhibitors could enable new uses for PI3K inhibition in oncology.In the here presented dataset we rediscovered previously reported synergistic combinations and discovered several new synergies. The synergistic reduction of cell viability resulting from jointly targeting PI3K/mTOR and TAK1, previously reported by us for the AGS cell lineThe outcomes of our work contribute to expanding the available data sets on novel combinations of targeted drugs across a variety of cancer cell lines and can benefit the fields of cancer therapeutics, molecular signalling interventions and molecular cancer biology.The following eight cancer cell lines were used in this study: A498 (kidney cancer), AGS (gastric adenocarcinoma), COLO 205 , DU-145 (prostate cancer), MDA-MB-468 (breast cancer), SF-295 (glioblastoma), SW-620 , and UACC-62 (melanoma). All cell lines used in the study are of human origin. Seven of the cell lines were obtained from the NCI-DCTD Repository, Frederick MD, and are part of the DTP 60 cell line panel (NCI-60 collection). The AGS cell line was obtained from ATCC. AGS cells were cultured in HAMS’s F12  supplemented with 10% FBS , 10 µg/ml penicillin-streptomycin  and 1 µg/ml fungizone . MDA-MB-468 were cultured in RPMI 1640  supplemented with 5% FBS , 2 mM L-Glutamine  and 100 U/ml penicillin-streptomycin . All other cells were cultured in RPMI 1640  supplemented with 10% FBS , 2 mM L-Glutamine  and 100 U/ml penicillin-streptomycin . Cells were cultured for approximately two weeks before assay and discarded after a maximum of two months in culture. The day before seeding out cells for the high throughput assay, cells were sub-cultured 1:2. Due to technical reasons, this was not performed for biological replicate 2 and 3 of the follow-up secondary screen.Cell lines were treated for 48 hours with 19 small-molecule inhibitors in single and pairwise applications, comprising a total of 171 drug combinations. All drugs were pre-diluted in DMSO to a stock concentration of 20 mM. Drugs were applied in an equimolar ray design from 10 µM to 0.016 µM using the following procedure: First, 2.0 mM solutions of each inhibitor and inhibitor combination were prepared. These were then serially diluted in DMSO . The solutions were further diluted in medium , of which 5 µl were added to each cell culture well. Total cell culture volume per well was 35 µl. Highest final concentrations were thus 10 µM in each well. All dilutions and liquid handling were performed with robotic equipment using sterile and pyrogen free pipette tips. The inhibitors are listed in Online-only Table 1. Final DMSO concentration in assay wells was 0.5%.In the secondary screen six combinations were screened in a matrix designed across all cell lines. As the PI inhibitor was identified as one of the top drugs involved in synergistic combinations, it was chosen as the anchor drug. The inhibitor was combined with BI, D1, JN, G2, PD, and 5Z. Cell lines were treated for 48 hours with the seven small-molecule inhibitors in single application and six drug combinations in all possible combinations of doses 10, 2, 0.4, 0.08 and 0.02 µM. Drugs, prepared as stocks of 20 mM in DMSO, were first diluted to 400x final concentration in DMSO. The solutions were further manually mixed 1:1 with either DMSO, for single drugs, or combination drug and applied to a 96 well plate. Further liquid handling was performed with robotic equipment. The solutions were diluted in medium (28.5x), of which 5 µl was added to each cell culture well leading to a total volume of 35 µl. We note that a new set of inhibitors was ordered for this screen.Cells were seeded in 384-well plates  at densities shown in Table For the first two biological replicates of the primary screen, two technical replicates are reported. Two of the four technical replicates assayed were excluded due to insufficient mixing of drug dilutions for these replicates. For the third biological replicate all four technical replicates assayed are included. For MDA-MB-468 and AGS cells, one or two biological replicates were performed, respectively. Some data points for the AGS cell line had to be excluded due to erroneous drug addition within the robotic script.For the first biological replicate in the secondary screen, some technical replicates had to be excluded for the SW-620 cell line as too little drug volume was added. For the MDA-MB-468 cell line the first biological replicate was excluded due to low cell viability in the control wells. For the first, second and third biological replicate some technical replicates had to be excluded due to non-efficient cell attachment caused by an air bubble below the cell suspension. Additionally, some wells were excluded in the cell confluency data due to condensation of water on the bottom of the plate.25: Drug_cell_response.pdf). Drug combination plots were generated from average viability scores with standard deviations using “R-script-plotting-drug-combination.R” (figshare deposit25: Combination_plots_primary_screen.pdf). Data points with missing values (14%) are represented as NA values.For each 384-well plate, viability data of treated cells was normalised to the DMSO control cells measured in the same plate. Normalised viability data for each biological replicate where processed using “R-script-average-of-biological-replicates.R”. The average viability score for each technical replicate as well as the average viability score across the biological replicates were calculated and exported as tab-separated values (tsv) files. Standard deviation across biological replicates was calculated using “R-script-calculate-mean-STDV.R”. Dose response curves were generated using “R-script-sensitivity-of-drugs-across-cell-lines-plot.R” . The same script was used to generate drug combination plots for confluency data (figshare deposit25: Combination_plots_secondary_screen_confluency.pdf).For confluency data brightfield images were analysed using the SoftMax® Pro Software field analysis setting. For this, the region of the image covering the well was selected and confluency was reported. We note that the absolute values for confluency for very flat and long cells  may be underestimated while absolute confluency for round cells (e.g. MDA-MB-498) may be overestimated. We further want to indicate that COLO 205 are growing sub-confluent. For completeness of the data set, confluency is reported for this cell line. For each 384-well plate, viability and confluency data of treated cells was normalised to the plate-internal DMSO control. Technical replicates and averages of biological replicates are reported as comma-separated files (csv). These were further processed using “Biological_Average_secondary_screen.R” to calculate the biological average, standard deviation and coefficient of variation. Drug combination plots were generation from average viability scores with standard deviations using “Combination_plots_secondary_screen.R”  available in our figshare deposit25.Online-only Table 1 lists inhibitor abbreviation, name and unique identifiers (InChiKey and PubChem CID) along with the provider and primary targets as given by provider. CHEBI IDs are provided when available. We provide extensive target profile information in the form of bioassay information extracted from PubChem (last accessed on 24-06-2019) and kinase profiling data from the MRC PPU International Centre for Kinase Profiling - Kinase Profiling Inhibitor Database . For these inhibitors, we performed an exhaustive literature search to provide annotated information25, 10.6084/m9.figshare.9810719. Below, the file names for the different data sets are listed.All data is available at figshare25. Relative viability data per biological replicate is available in [Cell line].NTNU_HTS_Repl[1-3]_processed.tsv25. Mean viability data of technical and biological replicates are depicted in [Cell line].tech-mean_Effect.tsv25 and [Cell line].mean_Effect.tsv25. Example plots for drug sensitivity across the eight cell lines are shown in Fig. 25. In Fig. Calculation of synergy and statistical significance of quantified synergy, Fig. 25 for the primary screen and in Combination_plots_secondary_screen_viability.pdf and Combination_plots_secondary_screen_confluency.pdf for the secondary screen for viability and confluency response, respectively25.The raw luminescence data from the three biological replicates from the primary screen can be found in NTNU_HTS_Repl1-3]_raw.txt within figshare_raw.txt 25. Raw as well as relative confluency data can be found in NTNU_HTS_secondary_confluency_REPL[1-3].csv25. Mean viability and confluency across the three biological replicates are depicted in NTNU_HTS_secondary_viability_Biological_Average.tsv25 and NTNU_HTS_secondary_confluency_Biological_Average.tsv25, respectively.The raw luminescence data and relative viability data from the three biological replicates from the secondary screen can be found in NTNU_HTS_secondary_viability_REPL[1-3].csv25. Synergistic drug combinations are further presented in bliss_synergies.tsv25. A visual representation of Bliss excess is depicted in Fig. The Bliss excess values for each combination tested in the primary screen across the eight cell lines are represented in bliss_significance.tsv25 and bliss_synergies_secondary_screen_confluency.tsv25 from viability and confluency data, respectively. A visual representation of Bliss excess ± standard deviation from the viability data is shown in bliss_synergies_secondary_screen_plots_viability.pdf25 and in bliss_synergies_secondary_screen_plots_confluency.pdf25 from the confluency data. Cumulative bliss scores for the viability data can be found in Mean.Bliss.excess.matrix.tsv25.Bliss excess values for each combination per dose tested in the secondary across eight cell lines are represented in bliss_synergies_secondary_screen_viability.tsvhttps://web.expasy.org/cellosaurus). All 7 cell lines were found to be identical or similar to their database profiles with percent matches at 95–100%. The AGS cell line was validated by STR profiling performed by the in-house Genomics Core Facility. Eight core STR loci  plus amelogenin had a match of 81% to the ATCC profile (CRL-1739).The seven NCI-cell lines used were received from the NCI-DCTD Repository and cultivated no more than 25 passages after reception in the lab. All cell lines were validated by STR profiling performed by Cell Line Authentication Service at ATCC. Eight core STR loci  plus amelogenin were matched to database profiles obtained from ATCC and Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSMZ). For two of the cell lines, SF-295 and UACC-62, no database profile was available for comparison from ATCC and DSMZ. However, both cell lines were found to match their STR profile listed on the ExPASy website  in the ray designed screen. Overall, we observed a mean coefficient of variation of 8% across all biological replicates in the primary screen and 7% in the secondary screen for both the viability and confluency data.26.To assess reproducibility between biological replicates, we calculated Pearson correlation coefficients for all measured data points . We observe a high correlation between all comparisons with a correlation of R = 0.89 between Replicate 1 vs Replicate 2, R = 0.88 between Replicate 1 vs Replicate 3 and R = 0.87 between Replicate 2 vs Replicate 3 of the primary screen See Fig. . In the The primary screen led to the identification of 55 synergies according to the chosen cut-off from 1368 total combinations for all cell lines. An analysis of the list showed that 23 unique combinations were classified as synergistic across seven unique cell lines, with no synergistic combination in the MDA-MB-468 cell line. The inhibitors PI  and SF (target PTEN) were identified to be involved in the majority of synergistic combination. The PI inhibitor was identified as synergistic with BI (target MAPK14) and with D1 (target RPS6KA1/2/3/6) in more than half of the tested cell lines. Further synergistic drug pairs sorted by occurrence across cell lines are G2 (target PDPK1), JN (targets MAPK8/9/10), P5 (target SYK) and AK .19. Additionally, we tested PI in combination with 5Z a combination previously identified as synergistic in the AGS cell line in a previous study by our group15 and Morris et al.16.As the PI inhibitor was identified as one of the top drugs involved in synergistic combinations, it was chosen as the anchor drug for the secondary screen. We combined the PI inhibitor with the top four synergistic drug pairs judged by occurrence across cell lines. We also included PI in combination with PD (targets MAP2K1/2), to investigate synergistic behaviour of drug concentrations outside the concentration ratios tested in the primary screen. This was motivated by the fact that drugs targeting PI3K and MAP2K1/2 have been previously reported to be synergistic in several cell lines16. The PI-PD combination  was found to be synergistic in five cell lines and non-synergistic in three cell lines including the two colorectal cancer cell lines SW-620 and COLO 205. While colorectal cancer cell lines have been frequently observed to display synergistic response to combined inhibition of MAP2K1/2 (MEK1/2) and PI3K30, weaker synergy has previously been observed in SW-620 and COLO 205 cells28.Results from the secondary screen confirmed combinations as being synergistic or antagonistic in most cell lines. We confirmed the synergistic effect of PI-G2 in SF-295, SW-620 and UACC-62 cells. Additionally, we observed a synergistic response in A498 and AGS cells in the secondary screen. The combination PI-BI was also redetected as synergistic in five of the cell lines, while for two cell lines the combination did not show a greater effect compared to single drug treatment in the secondary screen. For two combinations, that were not classified as synergistic in the primary screen, PI-5Z and PI-PD, we could observe synergistic responses in the secondary matrix design screen. The PI-5Z combination was synergistic in two cell lines including AGS cells, confirming previous findingshttp://rbbt.bsc.es/CImbinator/CombinationIndex) from which several synergy metrics can be computed and visualised31.Other users may investigate alternative methods for synergy quantification or another threshold using the provided data. To assist other researchers in visualising and analysing the dataset from the primary screen described herein we have prepared files that can be uploaded to the web-service CImbinator (


C. albicans, including resistant isolates, as well as C. glabrata, and C. tropicalis with IC50 values of 4–29 μM. Cytotoxicity assays using human THP-1, HepG2, and A549 human cell lines showed selective toxicity toward fungal cells. The mode of action for SH1009 was characterized using chemical-genetic interaction via haploinsufficiency (HIP) and homozygous (HOP) profiling of a uniquely barcoded Saccharomyces cerevisiae mutant collection. Approximately 5300 mutants were competitively treated with SH1009 followed by DNA extraction, amplification of unique barcodes, and quantification of each mutant using multiplexed next-generation sequencing. Barcode post-sequencing analysis revealed 238 sensitive and resistant mutants that significantly  responded to aurone SH1009. The enrichment analysis of KEGG pathways and gene ontology demonstrated the cell cycle pathway as the most significantly enriched pathway along with DNA replication, cell division, actin cytoskeleton organization, and endocytosis. Phenotypic studies of these significantly enriched responses were validated in C. albicans. Flow cytometric analysis of SH1009-treated C. albicans revealed a significant accumulation of cells in G1 phase, indicating cell cycle arrest. Fluorescence microscopy detected abnormally interrupted actin dynamics, resulting in enlarged, unbudded cells. RT-qPCR confirmed the effects of SH1009 in differentially expressed cell cycle, actin polymerization, and signal transduction genes. These findings indicate the target of SH1009 as a cell cycle-dependent organization of the actin cytoskeleton, suggesting a novel mode of action of the aurone compound as an antifungal inhibitor.Every year, more than 250,000 invasive candidiasis infections are reported with 50,000 deaths worldwide. The limited number of antifungal agents necessitates the need for alternative antifungals with potential novel targets. The 2-benzylidenebenzofuran-3-(2H)-ones have become an attractive scaffold for antifungal drug design. This study aimed to determine the antifungal activity of a synthetic aurone compound and characterize its mode of action. Using the broth microdilution method, aurone SH1009 exhibited inhibition against  Candida albicans is the most frequently isolated opportunistic fungal pathogen and is implicated in superficial mucosal infections, or candidiasis of the oral cavity and genitalia of humans, particularly in immunocompromised patients /negative control value. The assay for each cell line was performed in triplicate. The CC50 values were calculated using GraphPad Prism .The A549 human lung carcinoma epithelial cell line and human monocytic THP-1 cell line were grown in RPMI-1640 culture medium, while HepG2 human liver carcinoma epithelial cells were grown in DMEM culture medium. Both media were supplemented with 10% FBS and 1% penicillin-streptomycin antibiotics. After maintaining the cell growth at 37°C with 5% CO× g. Once the concentration was adjusted, sterile glycerol was added to 15% (vol/vol) and 200 μL aliquots of the pool were stored in PCR strip tubes at −80°C.The yeast deletion collections were obtained as individual mutants in 96-well plates that had been stored at −80°C. Both the HIP deletion pool and HOP deletion pool were created separately as previously described , 93. TheS. cerevisiae S288C wild type parent strain of these mutants was determined (500 μM) as previously described Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The authors used a standard microbiological techniques to assess the antifungal activity of aurone SH1009 and also implemented chemogenomic profiling in order to understand the mechanism of the antifungal activity of Aurone SH1009.Major Comment: Antifungal drug discovery is immensely hampered by the scarcity of selective fungal targets due to the significant resemblance between mammalian and fungal cells. The chemogenomic profiling used in this study was not able to identify a molecular target that is specific to fungi, instead a plethora of metabolic and biological pathways were identified as the most affected. Unfortunately, these affected pathways are not specific to fungi and are shared with mammalian cells and hence their identification as a potential target for aurone doesn’t support the authors’ claims of potential novel target. In fact, the concluded mechanism of action is anticipated and typical for an anticancer compound. However, in order to consider this study as a significant contribution to the field of antifungal drug discovery, the authors should perform toxicity studies to demonstrate that aurone SH1009 is safe to mammalian cells at the tested concentrations. It would be important to see that aurone has a selective toxicity toward fungal cells and doesn’t produce similar effect on mammalian cell cycle, at least at the tested concentrations.Minor Comments:Abstract.The epidemiological data provided in line 33 and 34 should be included in the introduction and be supported with references.IntroductionLine 68: remove comma after candidiasisLine 79: better replace the word invade with colonizeLine 87: the mode of action of amphotericin does not alter ergosterol, instead it bind to ergosterol and form pores in the cytoplasmic membrane … please modify the sentenceParagraph 2: Why picking few resistance mechanisms adopted by fungi and ignoring other mechanisms without justification. For example there is at least four mechanisms of azole resistance and the authors just mention one mechanism (hyperexpression of efflux pumps). Same thing applies for amphotericin and echinocandin. I prefer to remove the mechanisms of resistance from the introduction part.Result:• Table 1: Please be consistent in your presentation of MIC data. Either include the standard error in both IC50 or MIC90 or remove it.• Define MIC 90 in the title of table 1It is better to stick to CLSI appreviation. Use MIC 50 instead of IC 50Line 237, aurone at 400 uM represent 25 x MIC not 5 x MIC, based on the provided MIC data (16 uM)ST1. Please replace the phrase (resistant to everything) and be specific, such as resistant to amphotericin B, fluconazole, itraconazole…..etcM&M:The author should follow the most recent vision of CLSI when performing the antifungal susceptibility testing. The referenced protocol M27A3 (2008) is old and the 2017 newer version is available.DiscussionLine 736, I believe the author means ERG3 not ERG2. Please check.https://doi.org/10.3389/fmicb.2016.02173) for more information.Line 740, please remove the word  as the original study by Pfaller 2004 followed an older version of CLSI where the MIC data were recorded after 48h of incubation, hence most C. glabrata isolates appeared resistant to azoles. However recent reports indicate that Azole resistance in C. glabrata do not exceed 10 %. So, I think the word intrinsically is inaccurate description for the azole resistance in C. glabrata. Please review the article  specifies the same is equivalent to 500µM. Please explain why these values are different. Also, lines 257-260 indicate that even fluconazole and amphotericin B were used at a concentration which is equivalent to 5 times the concentration that reduces the growth of C. albicans by 50%. In this case, please specify the IC50 for fluconazole and amphotericin B in the figure legend. Also indicate how 5 times the IC50 for SH1009  is 400µM when IC50 is only 16.28µM.3. Line 415 is incorrect. Fig. ‘7’ and not ‘6’ depicts the growth curves of Sachharomyces mutants.4. Line 413: What dilutions of SH1009 were used in testing the 12 mutants individually for differences in growth to S. cerevisiae? Please indicate.5. Why is the growth of each mutant in Fig. 7 not tested in the presence of the same concentration (500 µM) as the one used in the HIP/HOP screens? Also, this reviewer thinks that this figure should include graphs showing the growth of mutants in the presence of 0 µM, 16 µM as well as 500 µM concentration of SH1009 to ensure comparable analysis.6. Figure 9 should include panels with one sensitive (ARK1Δ) and one resistant (END3Δ) endocytic mutants identified through the HIP/HOP screening to show their effects on actin polarization.7. Lines 604-605: Figure 7A shows the growth of mutants exposed to SH1009 at a concentration of 16 µM. Please explain how does the figure demonstrate that CDC42 deletion mutant is hypersensitive to aurone treatment at concentration of 3 µM?Minor comments:1. In Table 1, please write the names of Candida albicans strains M1-M7 in an increasing order  to enable easy analysis.2. The replicates performed in the cell viability assay have not been specified in the figure 2. Please indicate the number of replicates and indicate the standard deviations for the different treatments in the dot plots itself. Also, please indicate the treatments above each dot plot in the figure so that that it is self explanatory.3. Line 252: please rewrite the dilutions of SH1009 as “3.125 µM-200 µM”, the same as is written in the methods (line 964).4. Label the figure 4A and 4B to indicate HIP or/ HIP profile so that the figure is self-explanatory.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  3 Nov 2019RESPONSE TO REVIEWERS: Note that the editor and reviewer comments appear as a numbered comment and in brackets.Our responses appear below each comment.Page and line numbers refer to the newly submitted “Manuscript” document.Response to Academic Editor comments on Journal Requirements: http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf]Comment 1:  Response 2a: There are no ethical or legal restrictions on our sharing a de-identified data set.http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.]Response 3: We have, accordingly, provided the sources of the mammalian cell lines and fungal strains. Materials and Methods, Materials and Reagents sub-section, Page 40, Lines 922 – 926. Comment 4: Response 4: The correction to appropriate limits has been made in the following 6 positions: Results, Page 9, Line 204Results, Page 14, Line 321Results, Page 15, Lines 339, 343, and 347Results, Page 16, Line 372Results, Page 19, Line 424Results, Page 22, Lines 507-508Results, Page 26, Line 599https://collections.plos.org/s/antimicrobial-resistance. If you would like your manuscript to be considered for this collection, please let us know in your cover letter and we will ensure that your paper is treated as if you were responding to this call. If you would prefer to remove your manuscript from collection consideration, please specify this in the cover letter.]Comment 5: Response 6: We have removed the funding-related text from the Acknowledgments section and would like to alter our Financial Disclosure statement to reflect the funding.Acknowledgements, Page 51, Lines 1160 - 1162. We would like the Financial Disclosure to be re-worded as follows:This research was supported in part by the Tennessee Center for Botanical Medicine Research and the Molecular Biosciences Program at Middle Tennessee State University. The funders had not role in the study design, data collection and analysis, the decision to publish, or manuscript preparation.Response to Reviewers' comments: Reviewer #1: Major Comment: Response: We agree with the reviewer and thank them for bringing up this aspect of our study. Therefore, cytotoxicity assays for aurone SH1009 were performed using three different types of human cell lines: HepG2, A549 and THP-1 cells. Accordingly:The main results of cytotoxicity assay have been added to the abstract. Abstract, Page 2, Lines 41 - 42 Note that in order to add this wording, a part of an introductory sentence was deleted to maintain the 300 word limit.Cytotoxicity results have been added to the Results in the “Aurone SH1009 is selectively inhibitory for Candida spp.” sub-section. Results, Pages 11 – 12, Lines 241 - 262.Table 2 has been added to the Results section, Page 12. Panel 3D, which is dose-response curve of human cell lines to SH1009, has been added to Figure 3 Figure 3 legend, Pages 12-13, Lines 269 - 285.Additional wording has been added to the Conclusion section.Conclusion, Page 39, Lines 902-903 Additional reagents have been added to the “Materials and reagents” sectionMaterials and Methods, Page 40, Lines 924 – 932 Methodology for the cytotoxicity testing has been added to the Materials and Methods as subsection “Cytotoxicity Assay" - Materials and Methods, Pages 44 – 45, Lines 1015 – 1039Reviewer #1 Minor Comments: Abstract: Comment: [The epidemiological data provided in line 33 and 34 should be included in the introduction and be supported with references.]Response: As suggested by the reviewer, we have included epidemiological data in the introduction with its supported reference. Introduction, Page 5, Lines 101 -103. Introduction:Comment 1: [Line 68: remove comma after candidiasis]Response 1: The comma has been removed. Page 3, Line 68Comment 2: [Line 79: better replace the word invade with colonize]Response 2: As suggested by the reviewer, this word has been replaced. Page 4, Line 79Comment 3: Response 3: As suggested by the reviewer this sentence has been modified. Page 4, Lines 87 – 88Comment 4: [Paragraph 2: Why picking few resistance mechanisms adopted by fungi and ignoring other mechanisms without justification? For example there is at least four mechanisms of azole resistance and the authors just mention one mechanism (hyperexpression of efflux pumps). Same thing applies for amphotericin and echinocandin. I prefer to remove the mechanisms of resistance from the introduction part.]Response 4: These mechanisms were chosen to be included because they pertain to the antifungal testing results. However, we agree with the reviewer that is unclear as to why only particular mechanisms of resistance were chosen. We have removed the specific resistance mechanisms from this section, but have retained the references for these and other mechanisms.Page 4, Lines 87 – 98Results:Comment 1: [Table 1: Please be consistent in your presentation of MIC data. Either include the standard error in both IC50 or MIC90 or remove it.]Response 1: The standard error values have been added to IC50 values. Page 8, Table 1 Comment 2: [Define MIC 90 in the title of table 1]Response 2: The MIC90 has been defined in the Table 1 legend. Page 8, Line 175 Comment 3: [It is better to stick to CLSI abbreviation. Use MIC50 instead of IC50]Response 3: We thank the reviewer for this suggestion, but we prefer to use IC50 because several multidisciplinary fields are more familiar with the IC50 term. Comment 4: Response 4: We apologize for this confusing error and thank the reviewer for their careful reading of the manuscript. We meant a five-fold increased concentration (~500 uM) relative to the IC50 concentration (16.25 uM) as calculated from the serial two-fold dilution to determine the IC50. This concentration was written correctly in the Materials and Methods section , but we made the mistake in the Results section. We have tried to revise the above and related statements using language that clarifies the chosen concentration as follows: Results Pages 10 -11, Lines 229 - 231: “Treatment with SH1009 at a concentration representing an approximate five-fold increase of the IC50 calculated from the two-fold dilution series (500 uM)…”Figure 3 legend, Pages 12 - 13, Lines 275 - 278: “…treated with concentrations five-fold higher than the IC50 concentrations of SH1009 (16.28 µM), fluconazole (0.5 µg/mL) and amphotericin B (0.25 μg/mL)”Materials and Methods, Page 43, Lines 1006 - 1007:  “…was treated with an aurone concentration approximately five-fold higher (500 µM) than the dilution producing the IC50 of SH1009 (16.25 µM) in the two-fold dilution series.”In Figure 3C, the concentration of aurone SH1009 has been changed from (400 uM) to (500 uM) as was correctly stated in the Materials and Methods section.Comment 5: Response 5: We have revised the sentence and specified the resistance in Table 1 and in Supporting Materials, S1 Table.Results, Page 8, Lines 178 – 179, Supporting Materials, S1 TableMaterials and Methods:Comment 1: [The author should follow the most recent vision of CLSI when performing the antifungal susceptibility testing. The referenced protocol M27A3 (2008) is old and the 2017 newer version is available.]Response 1: The 2017 newer version of CLSI protocol has been cited as follows: Results, Page 7, Line 151Discussion, Page 30, Line 312Materials and Methods, Page 41, Lines 954 and 959Discussion:Comment 1: Response 1: We can understand why the reviewer would ask us to check again. However, the study mentioned a mutation in ERG2 gene as a reason for polyene resistance. Jensen RH, Astvad KMT, Silva LV, Sanglard D, Jørgensen R, Nielsen KF, et al. Stepwise emergence of azole, echinocandin and amphotericin B multidrug resistance in vivo in Candida albicans orchestrated by multiple genetic alterations. J. Antimicrob. Chemother. 2015;70(9):2551-5.https://doi.org/10.3389/fmicb.2016.02173) for more information.]Comment 2: Response 2a: Line 237 specifies that 5 times the IC50 for SH1009 is 400μM while line 976 (time-kill assay in methods) specifies the same is equivalent to 500μM. Please explain why these values are different. Also indicate how 5 times the IC50 for SH1009  is 400μM when IC50 is only 16.28μM.We apologize for this confusing error and thank the reviewer for their careful reading of the manuscript. We meant a five-fold increased concentration (~500 uM) relative to the IC50 concentration (16.25 uM) as calculated from the serial two-fold dilution to determine the IC50. This concentration was written correctly in the Materials and Methods section , but we made the mistake in the Results section. We have tried to revise the above and related statements using language that clarifies the chosen concentration as follows:  Results Pages 10 -11, Lines 229 - 231: “Treatment with SH1009 at a concentration representing an approximate five-fold increase of the IC50 calculated from the two-fold dilution series (500 uM)…”Figure 3 legend, Pages 12 - 13, Lines 275 - 278:  “…treated with concentrations five-fold higher than the IC50 concentrations of SH1009 (16.28 µM), fluconazole (0.5 µg/mL) and amphotericin B (0.25 μg/mL)”Materials and Methods, Page 43, Lines 1006 - 1007:  “…was treated with an aurone concentration approximately five-fold higher (500 µM) than the dilution producing the IC50 of SH1009 (16.25 µM) in the two-fold dilution series.”In Figure 3C, the concentration of aurone SH1009 has been changed from (400 uM) to (500 uM) as was correctly stated in the Materials and Methods section .Response 2b: "Also, lines 257-260 indicate that even fluconazole and amphotericin B were used at a concentration which is equivalent to 5 times the concentration that reduces the growth of C. albicans by 50%. In this case, please specify the IC50 for fluconazole and amphotericin B in the figure legend."We have specified the IC50 concentrations of fluconazole and amphotericin B in the legend for Figure 3.Results, Page 13, Line 278Comment 3: [Line 415 is incorrect. Fig. ‘7’ and not ‘6’ depicts the growth curves of Saccharomyces mutants.]Response 3: We have revised the number of figure to correctly be Figure 7. Results, Page 19, Line 438 Comment 4: [Line 413: What dilutions of SH1009 were used in testing the 12 mutants individually for differences in growth to S. cerevisiae? Please indicate.]Response 4: We have indicated the dilutions of SH1009 in the text.Results, Page 19, Lines 436 – 437Comment 5: Response 5a: "Why is the growth of each mutant in Fig. 7 not tested in the presence of the same concentration (500 μM) as the one used in the HIP/HOP screens?"We appreciate the reviewer’s question. For individual mutant screening, the final concentration of cells of each S. cerevisiae mutant was only 2.5-0.5�103 cells/mL; hence, we used 2-fold serial dilutions (3.125 µM –200 µM) of SH1009. In the HIP/HOP screen, the cell concentration was much higher (1.25� 109 cells/mL) in order to accommodate all the mutant in the population (125-250 cells/mutant/µL). Therefore, the concentration of SH1009 that caused only 20% inhibition to this higher cell concentration was 500 µM. Response 5b: "Also, this reviewer thinks that this figure should include graphs showing the growth of mutants in the presence of 0 μM, 16 μM as well as 500 μM concentration of SH1009 to ensure comparable analysis."We agree with the reviewer and did, in fact, test at additional concentrations. We have now included multi-panel graphs as supplemental Fig. S1, which show the growth of S. cerevisiae WT and mutants in the presence of 0 μM, 16 μM, and 200 μM of aurone SH1009. Figure S1 is also included below for your reference. Results, Page 19, Line 439Supporting Information, Page 56, line 1435 (reference to Supporting Information)Comment 6: [Figure 9 should include panels with one sensitive (ARK1Δ) and one resistant (END3Δ) endocytic mutants identified through the HIP/HOP screening to show their effects on actin polarization.]Response 6: Thank you for this suggestion. It would have been interesting to further explore actin polarization in these S. cerevisiae mutants (ARK1Δ and END3Δ). However, the focus of this study was Candida albicans, and it seems slightly out of the scope of this paper. The main goal of this procedure was to show the effect of bioactive aurone SH1009 on actin polarization in C. albicans. However, the using of S. cerevisiae CDC42∆ mutant is because this mutant was hypersensitive to SH1009 and harbors a deletion for an actin-regulatory gene; hence, used as a control. The actin polarization of S. cerevisiae mutants (ARK1Δ and END3Δ) have been reported previously in the literature.Ayscough KR. Coupling actin dynamics to the endocytic process in Saccharomyces cerevisiae. Protoplasma. 2005;226(1-2):81-8.Gourlay CW, Ayscough KR. Identification of an upstream regulatory pathway controlling actin-mediated apoptosis in yeast. J Cell Sci. 2005;118(Pt 10):2119-32.Comment 7: [Lines 604-605: Figure 7A shows the growth of mutants exposed to SH1009 at a concentration of 16 μM. Please explain how does the figure demonstrate that CDC42 deletion mutant is hypersensitive to aurone treatment at concentration of 3 μM?]Response 7: Thank you for bringing this to our attention. Although we do have data for CDC42Δ deletion mutant growth at 3 μM, showing that the mutant was hypersensitive even at this concentration, the sentence has been revised to 16 μM to reflect the results as shown in Figure 7A. Results section, Page 27, Line 628 Reivewer #2 Minor Comments: Comment 1: Response 1: We have written the names of Candida albicans strains in increasing order in Table 1. Results section, Page 8, Lines 174 – 179Comment 2: Response 2: We have modified Figure 2 in order to include the number of replicates, the standard deviation, and the name of treatments above each dot plot. In the legend for Figure 2, we have included the number of replicates and standard deviations (n=3±SD) for panel 2D. Results section, Page 9, Lines 195 – 204 Comment 3: Response 3: We have revised the dilutions of SH1009 as the reviewer suggested. Results section, Page 12, Line 286. Comment 4: [Label the figure 4A and 4B to indicate HIP or/ HIP profile so that the figure is self-explanatory.]Response 4: We have labeled Figures 4A and 4B as the reviewer suggested. The authors would like to thank the reviewers again for their thorough and insightful review of the manuscript. All of the comments were much appreciated.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 20 Nov 2019Chemogenomic profiling to understand the antifungal action of a bioactive aurone compoundPONE-D-19-18335R1Dear Dr. Farone,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. 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For more information, please contact With kind regards,Shankar Thangamani, DVM, PhDAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 3 Dec 2019PONE-D-19-18335R1 Chemogenomic profiling to understand the antifungal action of a bioactive aurone compound Dear Dr. Farone:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. For any other questions or concerns, please email Thank you for submitting your work to PLOS ONE.With kind regards,PLOS ONE Editorial Office Staffon behalf ofDr. Shankar Thangamani Academic EditorPLOS ONE


Genetic deletion of BFL-1 in cell lines that express it at relatively high levels only had minor impact on BH3-mimetic drug sensitivity, suggesting it is not a critical pro-survival protein in melanoma. Combinations of MCL-1 inhibitors with BRAF inhibitors also caused only minimal additional melanoma cell killing over each drug alone, whilst combinations with the proteasome inhibitor bortezomib was more effective in multiple cell lines. Our data show for the first time that therapies targeting specific combinations of BCL-2 pro-survival proteins, namely MCL-1 plus BCL-XL and MCL-1 plus BCL-2, could have significant benefit for the treatment of melanoma.Malignant melanoma is one of the most difficult cancers to treat due to its resistance to chemotherapy. Despite recent successes with BRAF inhibitors and immune checkpoint inhibitors, many patients do not respond or become resistant to these drugs. Hence, alternative treatments are still required. Due to the importance of the BCL-2-regulated apoptosis pathway in cancer development and drug resistance, it is of interest to establish which proteins are most important for melanoma cell survival, though the outcomes of previous studies have been conflicting. To conclusively address this question, we tested a panel of established and early passage patient-derived cell lines against several BH3-mimetic drugs designed to target individual or subsets of pro-survival BCL-2 proteins, alone and in combination, in both 2D and 3D cell cultures. None of the drugs demonstrated significant activity as single agents, though combinations targeting MCL-1 plus BCL-XL, and to a lesser extent BCL-2, showed considerable synergistic killing activity that was elicited  Patient outcomes have improved with the development of inhibitors targeting mutant BRAF found at high frequency in melanoma3. However, essentially all patients with BRAF mutations relapse, despite initially responding, as resistance occurs within 12 months5. Immunotherapy has also shown promise in melanoma patients. However, not all patients respond, side effects can be severe and acquired resistance remains a barrier to improving patient outcomes6. Hence, alternative treatments for melanoma are still required.Malignant melanoma is a difficult-to-treat cancer due to its capacity to metastasize and resist chemotherapy7, and contributes to therapeutic resistance. Intrinsic apoptosis is regulated by the BCL-2 protein family which includes pro-survival and pro-apoptotic subgroups8. The pro-survival members, BCL-2, BCL-XL, BCL-W, MCL-1 and BFL-1, have all been implicated in melanoma survival and chemoresistance.Defective apoptotic signaling is a hallmark of most cancers, including melanoma9. These “BH3-mimetic” compounds engage their targets similarly to the natural pro-apoptotic ligands, the BH3-only proteins. The first-in-class BH3-mimetic was ABT-737 that binds BCL-2, BCL-XL and BCL-W with high affinity10. This compound, and its orally bioavailable analog ABT-26311, have been tested on melanoma cell lines in vitro and in vivo. Generally, their efficacy is poor18, implicating the pro-survival proteins not targeted  in melanoma cell survival. Indeed, several studies showed improved cell killing when ABT-737 was combined with RNAi to reduce Mcl-1 levels19, enforced expression of peptides that target MCL-1 , or treatment with drugs that reduce MCL-1 and/or induce NOXA22. Similarly, co-targeting Mcl-1 and Bcl-xL by RNAi leads to greater killing than targeting either alone19. BFL-1 expression has been shown to be relatively high in melanoma25 and implicated in melanoma cell survival23 as Bfl-1 knockdown enhances sensitivity to ABT-737 and other anti-cancer agents, though the effect is cell line dependent and in some cases minor24.Small molecule antagonists of the BCL-2 pro-survival proteins have now been developed27, BCL-2 (ABT-199/“Venetoclax”)28 and MCL-1 31 were developed. Despite their high target affinities, these compounds generally have weak single agent efficacy in most tumors, except those of hematological origin. Of these compounds, only the MCL-1 inhibitor S63845 has been tested on melanoma cells and was generally ineffective as a single agent29. There are no small molecules targeting BFL-1, though a cell-penetrating BFL-1-selective peptide showed some activity in BFL-1-expressing melanoma cells32.Recently, specific inhibitors of BCL-XL 35. In this paper, we tested the most potent BH3-mimetics in a panel of melanoma cell lines. Our studies showed that MCL-1 and BCL-XL must be co-targeted to achieve the most effective melanoma cell killing, though co-operativity was also observed by co-targeting MCL-1 and BCL-2. Using genetic approaches, we also demonstrated only a minor role for BFL-1 in melanoma cell responses to BH3-mimetics.More recently, BH3-mimetic combinations were shown to act synergistically in hematological and some solid tumorsBH3-mimetic drugs , PLX4032 and bortezomib were purchased from Selleckchem. Q-VD-OPh was from MP Biomedicals Australasia.2.Melanoma cell lines were cultured in RPMI medium (Gibco) supplemented with 10% (v/v) heat-inactivated fetal bovine serum (Moregate), 2 mM Glutamax (Gibco), 100 U/ml penicillin/streptomycin (Gibco) at 37 °C in a humidified incubator with 5% CO50 values were calculated using nonlinear regression algorithms in Prism software (GraphPad) from the combined data of multiple separate assays. Synergy analysis was performed using Combenefit software with Bliss, Loewe and HSA models36.Cells (1000 per well) were seeded into white 96-well plates, then 4 h later treated with dilutions of drugs alone or in combination. Cell viability was assessed after 24 or 72 h treatment using the CellTiter-Glo assay (Promega) according to the manufacturer’s instructions with luminescence measured on an Ensight Multimode plate reader (PerkinElmer). Results were normalized to the viability of cells treated with the highest % (v/v) of vehicle. EC37. Antigen retrieval for BCL-XL and MCL-1 was performed by boiling in a microwave for 15 min in citrate buffer (pH 6.0) or for BCL-2, with EDTA buffer (pH 8.0). After blocking with Background Sniper blocking reagent , sections were incubated with antibodies to BCL-XL, MCL-1 or BCL-2  overnight at 4 °C. After incubation with secondary antibodies , signals were detected using the DAB Chromogen signal staining kit . Positive control tissues included testes (for BCL-XL) and tonsil (for MCL-1 and BCL-2).Tissue microarrays were prepared as described previouslyTo evaluate antibody staining, cancer cells with positive staining in the cytoplasm were counted and the percentage of positive tumor cells graded as: 0: none; 1: 1–5%; 2: 6–25%; 3: 26–50%; 4: 51–75% and 5: 76–100%. The intensity of staining was rated as: 0: none; 1: weak; 2: moderate; 3: intense. An H-score for each sample was calculated by multiplying the grading and intensity scores (range 0–15), then categorized as high (9–15) or low (as 0–8).Cells  were seeded in a 24-well plate then 24 h later treated with drugs alone or in combination (±50 μM Q-VD-OPh) for 24 h. Cells were then washed with binding buffer before staining with Annexin V-APC (BD Biosciences) and propidium iodide (Sigma). Cells were analyzed on a BD FACSCanto II flow cytometer (BD Biosciences). Data were analyzed using FlowJo software with the % viable cells (Annexin V negative/ propidium iodide negative) calculated relative to % viable cells cultured in vehicle.40. Briefly, 200 μl of melanoma cell suspension  was overlaid on top of hard (1.5% w/v) agarose. Plates were incubated for 72 h until 3D spheroids had formed. For the A02 cell lines, spheroids were then embedded into a gel of 40% (w/v) bovine collagen I (Cultrex® Bovine Collagen I) and allowed to grow and invade into the collagen matrix. Spheroids (either on agarose without embedding or after embedding) were treated with drugs at the concentrations and times indicated. To stain dead cells, DRAQ7 (Beckman Coulter Life Sciences) was added to a final concentration of 1.5 μM. Spheroids were imaged using an IN Cell Analyzer 2200 /Nikon ×4/0.20, Plan Apo, CFI/60 or an Olympus FV3000 Laser Scanning Confocal Microscope/Olympus UPLSAPO ×4 or ×20, as indicated.Spheroids were prepared as described2, 1 mM EGTA, 10% (v/v) glycerol and 1% (v/v) Triton-X-100; Sigma-Aldrich) with protease inhibitor  for 1 h at 4 °C. Lysate supernatants equivalent to 200,000 cells were separated by SDS-PAGE  before transferring to nitrocellulose and probing with antibodies to: MCL-1 , BCL-XL (BD Transduction Laboratory), BCL-2 (BD Biosciences), BFL-1 , BAK (Sigma), BAX (BD Pharmingen), BIM , NOXA (Abcam) and β-actin (Sigma).Total protein extracts were prepared by lysing cells in lysis buffer , subsequently re-infected with lentiviruses expressing sgRNA, then re-sorted for mCherry+/CFP+ cells. Expression of sgRNA was induced by treatment with doxycycline  for at least 3 days prior to Western blotting to determine gene deletion efficiency, and treatment with BH3-mimetic drugs.Lentiviral particle production for Cas9 (with mCherry) and sgRNA (with CFP) expression was performed as described previously42. These included wild-type and mutant BRAF and NRAS lines  panel derived from patients at the Austin Hospital To confirm that key pro-survival proteins targeted by BH3-mimetics  were expressed in more clinically-relevant patient-derived tissue, we performed immunohistochemistry on 127 biopsies from melanoma patients with Stage III/IV disease Fig. . High-le50’s > 5 μM in CellTiter-Glo assays that measure ATP levels reflecting metabolically active cells and overall live cell numbers , ABT-199 (targeting BCL-2), A1331852 (targeting BCL-XL) and S63845 (targeting MCL-1). Essentially all cell lines were resistant to the individual drugs, with most displaying EC50’s for S63845 decreasing from ~10 μM to <20 nM in most lines for the ABT-263 and A1331852 combinations  of ABT-263, ABT-199 or A1331852, with EC50 > 2 μM) to single-agent treatment  enhanced melanoma cell killing. We examined the S63845/ABT-263 combination on a subset of melanoma cell lines to target the greatest number of pro-survival proteins and thereby provide the best opportunity of an enhanced response. Increased (2–9-fold) killing was observed for each drug alone in some lines, though most remained resistant , and in combinations with each drug at 1 μM, and there was close correlation with the CellTiter-Glo assays. Significant effects with individual drugs were only observed at high concentrations (10 μM)  Fig. , whilst μM) Fig. . Hence, 45. Of the three cell lines tested, A02 formed solid 3D spheroids while A375 and LM-MEL-28 formed more loosely organized clusters. Like the 2D cultures, treatment with 1 μM of each drug alone over 24 h resulted in little cell death (based on DRAQ7 staining intensity and distribution) in any cell line  of ABT-263, ABT-199 or A1331852  than parental LM-MEL-28 cells for most drug combinations. However, Bax deletion had greater impact compared to Bak deletion, reducing sensitivity by 10- > 90-fold, and had greater effect at the higher drug doses 23. To further investigate how BFL-1 levels influence melanoma cell sensitivity to BH3-mimetic combinations, we used validated sgRNAs46 to effectively delete Bfl-1 in LM-MEL-28 and M14 cells . These lines remained relatively resistant to each BH3-mimetic alone, and Bfl-1 deletion only had minor impact on sensitivity to various combinations . Hence, BFL-1 provides only a minor barrier to BH3-mimetic responses, even when relatively highly expressed.We next examined whether ons Fig. . For M1447 or proteasome inhibitors in melanoma14. These combinations increased killing over treatment with either drug alone, though relatively high doses of the BH3-mimetics (>1 μM) were required for significant effects. Only one study has combined S63845 with a BRAF inhibitor, where enhanced sensitivity to a single combination dose was observed with one cell line compared to treatment with either drug alone29. To our knowledge no studies have examined S63845 and proteasomal inhibitor combinations.Several studies have examined the effect of combining ABT-737/ABT-263 with BRAF inhibitors50 was reduced in all cell lines with 20 nM and 100 nM bortezomib  that must be targeted to achieve maximal cell killing was unknown. Moreover, no study has yet exploited the full suite of the most clinically relevant and well-characterized BH3-mimetics to ascertain the survival safeguards of melanoma cells.The well-documented chemoresistance of melanoma has spurred multiple studies to identify the critical factor(s) that enable(s) tumor survival. These typically have focussed on the BCL-2 pro-survival proteins that are often over-expressed in tumor cells, and contribute to tumorigenesis and drug resistanceHere, we showed that melanoma cells are protected by several BCL-2 pro-survival proteins, consistent with previous reports. More specifically, rigorous drug combination testing in both 2D and 3D cultures showed that maximal killing activity was achieved through co-targeting of MCL-1 and BCL-XL using combinations of S63845 with either A1331852 or ABT-263. This is consistent with the high-level expression of these proteins across essentially all of the cell lines examined.28. Interestingly, high BCL-2 expression was observed in relatively more patient biopsies analyzed by immunohistochemistry compared to cell lines, suggesting that co-targeting of BCL-2 with MCL-1 could be more beneficial in patients than predicted by our cell line data alone. Similarly, the low MCL-1 levels in a significant proportion of patient samples could indicate patients might be more responsive to BCL-XL or BCL-2 inhibition alone than observed in cell lines. In some cell lines, longer treatment time (72 h vs. 24 h) was not as effective as shorter-term treatment at lower dose combinations. This also has clinically important implications as it suggests that resistant cell subpopulations could emerge leading to poor patient responses. Hence, further investigation into the basis of tumor cell heterogeneity and resistance mechanisms is warranted. Although BFL-1 has been implicated in melanoma cell survival, the results have varied between studies as to whether it is essential25. Our data demonstrated that cell lines expressing relatively high levels of BFL-1 can survive in its absence, and Bfl-1 gene deletion only marginally influences BH3-mimetic sensitivity. This is consistent with other reports showing melanoma cell sensitivity to ABT-737 only increased ~2-fold following Bfl-1 knock-down19.Synergistic responses, albeit less pronounced, were also observed when MCL-1 and BCL-2 were co-targeted though this was more variable between cell lines. Notably, BCL-2 expression was also more variable Fig. , with weeither BCL-XL or BCL-2 , our data support a model whereby a threshold level of pro-survival proteins must be antagonized to unleash sufficient pro-apoptotic molecules to activate apoptosis. Excess pro-survival proteins act as potential “sinks” for released pro-apoptotic molecules, hence, the need to inhibit multiple pro-survival proteins for potent killing in some tumors34. BCL-XL and MCL-1 are the critical guardians of BAK, whilst a wider complement of pro-survival proteins, including BCL-2 itself, restrain BAX52. Hence, it might be predicted that Bax deletion would impact more on ABT-199-mediated killing, whilst Bak-deleted cells would be most insensitive to the S63845/A1331852 combination. However, our knock-out studies showed that both BAX and BAK levels influence sensitivity to all BH3-mimetic combinations. A likely explanation for this is that both ABT-199 and A1331852 (as well as S63845 and ABT-263) can release pro-survival protein-bound BH3-only proteins, such as BIM, from their major direct targets29. As BIM, and some other BH3-only proteins, can directly activate both BAX and BAK, this could explain the involvement of both these proteins in responses to each drug combination. The less efficient deletion of Bak vs. Bax in our cells also should be considered, as complete deletion would likely have indicated a more important role for BAK than we demonstrated. Unfortunately, difficulty in deriving BAK-deleted clones prevented such assessment. Of note, the involvement of both BAX and BAK we observed contrasts with that observed in HCT116 colorectal cancer cells where the S63845/A1331852 combination results in BAX-dependent, but BAK-independent apoptosis53.As synergy was observed with combinations targeting MCL-1 plus 35 and lung cancer34. Hence, a pattern is emerging that these are the more important pro-survival proteins in solid tumors. From a clinical viewpoint, co-targeting of MCL-1 plus BCL-XL is likely to be problematic due to on-target toxicities. Antagonism of BCL-XL alone results in thrombocytopenia54 whilst a recent report indicated that co-treatment of mice with S63845 plus A1331852 resulted in acute liver toxicity34.Recently, studies have shown that co-targeting of MCL-1 and BCL-XL could be effective in the treatment of breastvs. normal cells could also be effective. Similarly, co-treatment with drugs that induce BH3-only proteins  to antagonize the non-targeted pro-survival BCL-2 family member, or directly activate BAX/BAK, could also have a similar effect. However, our studies combining S63845 with a BRAF inhibitor (that upregulates BIM and PUMA in melanoma cells18) only enhanced killing in one of four melanoma cell lines tested. Poor responses in the other cell lines could be due to multiple reasons, including insufficient BIM/PUMA upregulation to overcome the non-targeted pro-survival proteins present. Indeed, the cells that responded best to the S63845/PLX4032 combination were UACC 257 that had relatively lower BCL-2 and BCL-XL levels. We also tested S63845 with the proteasomal inhibitor bortezomib that has previously been shown to synergize with ABT-73714, probably due to its ability to upregulate NOXA. Interestingly, the S63845/bortezomib combination also enhanced melanoma cell killing over either drug alone in two cell lines, even at low drug concentrations, and the S63845 EC50 was reduced by bortezomib co-treatment in all cell lines. This enhanced killing activity correlated with increased NOXA expression. One possible reason for this synergy is that NOXA upregulation reduces the amount of “free” MCL-1 available to inhibit BAX/BAK, lowering the threshold for S63845 to antagonize additional MCL-1 molecules, thereby more efficiently inducing apoptosis. Recent studies have also shown that NOXA can directly activate BAK55, hence, MCL-1 inhibition by S63845 could make more of the upregulated NOXA available to induce apoptosis by this mechanism.Strategies to overcome these toxicities can be envisaged. For example, antibody-targeted nanoparticles allowing delivery of BH3-mimetics directly into tumor cells should be beneficial. Combinations of S63845 or A1331852 with other anti-cancer agents that downregulate BCL-XL or MCl-1 respectively in tumor 57. Due to the synergy we observe with this combination in vitro in several melanoma cell lines, (and the apparent high BCL-2 expression in melanoma patient tissue), this could provide the most attractive avenue for future investigation of BH3-mimetics in melanoma.As the BH3-mimetic combinations with PLX-4032 and bortezomib only provided an advantage over both drugs alone in a subset of cell lines, such combinations may not be universally applicable, unlike pure BH3-mimetic-only combinations that synergize in all cell lines tested. Hence, one possible strategy to overcoming toxicities associated with targeting MCL-1 plus BCL-XL would be to exploit this potent synergy, and identify a therapeutic window where lower doses or treatment schedules might still be effective for killing tumor cells but sparing normal tissue. Moreover, reports showing MCL-1 and BCL-2 co-inhibition is tolerated in mice, and is synergistic in hematological malignancies, are starting to emergeDespite advances with BRAF inhibitors and immunotherapy, many patients still succumb to metastatic melanoma. Hence, alternative treatments are required. Here, we have conclusively identified MCL-1 and BCL-XL as the pro-survival proteins most critical for melanoma cell survival and co-targeting them results in synergistic killing. This provides a basis for future therapeutic approaches to melanoma treatment by directly targeting apoptotic pathways.Supplementary Material


The tumor cell growth inhibitory effect of 27 compounds was first analysed in different human tumor cell lines, allowing the identification of a hit compound, TXA1. Its hydrochloride salt TXA1·HCl was then synthesized, to improve solubility and bioavailability. Both TXA1 and TXA1·HCl inhibited the growth of MCF-7, NCI-H460, A375-C5, HeLa, 786-O, Caki-2 and AGS cell lines. The effect of TXA1·HCl in MCF-7 cells was found to be irreversible and was associated, at least in part, with an increase in cellular apoptosis.Our previous work has described a library of thioxanthones designed to have dual activity as P-glycoprotein modulators and antitumor agents. Some of these compounds had shown a significant cell growth inhibitory activity towards leukemia cell lines, without affecting the growth of non-tumor human fibroblasts. However, their effect in cell lines derived from solid tumors has not been previously studied. The present work aimed at: (i) screening this small series of compounds from an in-house library, for their


Mesua beccariana, Mesua ferrea and Mesua congestiflora were studied. Eleven xanthone derivatives identified as mesuarianone (1), mesuasinone (2), mesuaferrin A (3), mesuaferrin B (4), mesuaferrin C (5), 6-deoxyjacareubin (6), caloxanthone C (7), macluraxanthone (8), 1,5-dihydroxyxanthone (9), tovopyrifolin C (10) and α-mangostin (11) were isolated from the three Mesua species. The human cancer cell lines tested were Raji, SNU-1, K562, LS-174T, SK-MEL-28, IMR-32, HeLa, Hep G2 and NCI-H23. Mesuaferrin A (3), macluraxanthone (8) and α-mangostin (11) showed strong cytotoxicities as they possess significant inhibitory effects against all the cell lines. The structure-activity relationship (SAR) study revealed that the diprenyl, dipyrano and prenylated pyrano substituent groups of the xanthone derivatives contributed towards the cytotoxicities.The cytotoxic structure-activity relationships among a series of xanthone derivatives from  Mesua, from the Clusiaceae family was selected for preliminary pharmacognosy analysis as some previous chemical investigations on this genus have revealed their activities to be due to the phloroglucinols, xanthones and neoflavonoids present in these plants [6) has been reported for its growth inhibitory effect against HL-60 (leukemia) cells [7) and macluraxanthone (8) having good activities on Plasmodium falciparum [8) has also been reported to exhibit potent cholinesterase inhibitory [9) has been shown to display moderate cytotoxicity towards KB cells [α-Mangostin (11) however, showed anti-proliferative and pro-apoptotic effects on chronic B leukemia cells [1–5 and 10 have not been reported. We report here the biological activities for compounds 1–11  cells , antioxia) cells  as well a) cells . There axanthone  having ghibitory , strong hibitory . On the KB cells . α-Mangoia cells  and was ia cells . The bionds 1–11  as well M. beccariana, M. ferrea and M. congestiflora are listed in 50 values of tested cancer cells treated with the isolated xanthone derivatives are summarized in Cytotoxicities of all the isolated xanthone derivatives were evaluated against a panel of cancer cell lines which included Raji (human B lymphocyte), SNU-1 (human gastric carcinoma), K562 (human erythroleukemia cells), LS-174T , HeLa , SK-MEL-28 , NCI-H23 (human lung adenocarcinoma), IMR-32 (human neuroblastoma) and Hep G2 (human hepatocellular liver carcinoma). All the xanthone derivatives isolated from 3), caloxanthone C (7) and macluraxanthone (8), or diprenylated like α-mangostin (11) or geranylated as in mesuasinone (2) and have a fused pyrano ring showed prominent inhibitory effects towards Raji cells. Interestingly, the hydroxylation of the xanthone nucleus at C-6 of mesuaferrin A (3) and macluraxanthone (8) led to a significant increase in the cytotoxic effect, as shown in 1) was probably due to the indirectly attached prenyl group at the pyrano moiety. Xanthone derivatives such as 6-deoxyjacareubin (6) and mesuaferrin B (4) with no prenyl chain and mesuaferrin C (5) with a furano ring have their cytotoxic effects further reduced. On the other hand, the simple xanthone, 1,5-dihydroxyxanthone (9) as well as tovopyrifolin C (10) which is methoxylated at C-2 of the xanthone nucleus did not inhibit the growth of the Raji cells or any of the other cancer cell lines tested.Xanthones with a prenyl group such as mesuaferrin A  and the dipyrano group in mesuaferrin B (4) induce inhibitory activity towards K562 cells. Moreover, the presence of the prenylated pyrano with an oxygen atom at R3, as in macluraxanthone (8) (Differences in the activity of compounds bserved. . Apparenhone (8)  reduces hone (8) A.α-mangostin (11), dipyrano rings as in mesuaferrin B (4), and for prenylated tri-hydroxylated xanthones with a pyrano ring like mesuaferrin A (3) and macluraxanthone (8). The prenylated dihydroxylated monopyranoxanthone, caloxanthone C (7) showed a significant cytotoxicity towards Hep G2. However, the cytotoxicity towards SNU-1 cells was reduced due to the absence of one OH group. The methoxylated diprenylated xanthone derivative, α-mangostin (11) revealed significant anti-proliferative effects against both SNU-1 and NCI-H23 while the inhibitory effect towards Hep G2 was reduced due to the presence of its diprenyl groups. On the other hand, derivatives having a fused furano ring, mesuaferrin C (5), indirectly prenylated dipyrano ring, mesuarianone (1), monopyrano ring, 6-deoxyjacareubin (6), geranyl group with pyrano ring, mesuasinone (2), as well as dihydroxylated xanthones, 1,5-dihydroxyxanthone (9) have their cytotoxicities towards SNU-1 cells reduced. Meanwhile, the monopyrano derivative, 6-deoxyjacareubin (6) and furano derivative, mesuaferrin C (5) have reduced inhibitory effects against NCI-H23 cells. The latter furano derivative also possesses weak cytotoxicity towards Hep G2 cells. Both NCI-H23 and Hep G2 cells were less vulnerable to the rest of the xanthone derivatives , macluraxanthone (8), α-mangostin (11) and mesuaferrin B (4) demonstrated significant cytotoxic activity towards LS-174T cells. This could be due to the prenylated pyrano moiety together with dihydroxylation at C-5 and C-6 or the presence of diprenyl or dipyrano moieties. However, caloxanthone C (7) and mesuasinone (2) which are prenylated or geranylated dihydroxy pyranoxanthone derivatives showed a decrease in inhibitory activity against the LS-174T cells. 6-Deoxyjacareubin (6), with a fused monopyrano ring and mesuaferrin C (5) with a fused furano ring, had their inhibitory activity towards LS-174T cell line diminished. The non-prenylated 1,5-dihydroxyxanthone (9) along with mesuarianone (1) with a prenylated methyl group at the pyrano ring also had no inhibitory activity towards LS-174T cell line (Mesuaferrin A (ell line C.6) and a furano ring in mesuaferrin C (5) when compared to the LS-174T cells. The replacement of OH by H in caloxanthone C (7) as compared in macluraxanthone (8) astonishingly reduces the activity of the xanthone. On the other hand, the indirectly-attached prenylated dipyrano ring in mesuarianone (1), the geranylated pyrano ring in mesuasinone (2) and the dihydroxyl group in 1,5-dihydroxyxanthone (9) were inactive towards SK-MEL-28 cells  and caloxanthone C (7). The inhibitory potency of the dipyrano derivative, mesuaferrin B (4) towards IMR-32 cells remarkably decline compared to SK-MEL-28 while the absence of the OH group at C-6 of caloxanthone C (7) was associated with a higher cytotoxicity compared to that on SK-MEL-28 cells (The difference in the structure-activity relationship between SK-MEL-28 and IMR-32 cell lines was noticed for mesuaferrin B (28 cells C.1), dipyrano-mesuaferrin B (4), monopyrano-6-deoxyjacareubin (6), dihydroxylated 1,5-dihydroxyxanthone (9) and diprenylated α-mangostin (11) which did not show significant inhibitory growth of the cells. Meanwhile, the presence of a furano moiety in mesuaferrin C (5) further diminished remarkably its inhibitory activity towards HeLa cells . Cell stocks were placed in a −86 °C ultra- low temperature freezer  and preserved in a liquid nitrogen tank .All the apparatus used for cell culture were sterilized and decontaminated using a Hirayama HICLAVE HVE-50 . Cell culture handling was carried out in an ESCO Class II BSC Biosafety Cabinet . The healthy cells were spun down, adherent together and separated from unhealthy and dead cells by using Thermo Scientific Sorvall ST 16R centrifuge . All cultures were incubated in 5% COM. beccariana, root bark of M. ferrea and roots of M. congestiflora were collected from the Sri Aman district in Sarawak, Malaysia. All the plant materials were identified by Associate Professor Dr. Rusea Go, Department of Biology, Faculty of Science, Universiti Putra Malaysia and deposited in the herbarium of the Department of Biology, Faculty of Science, Universiti Putra Malaysia.The stem bark of M. beccariana stem bark (3 Kg) was extracted successively with n-hexane, dichloromethane, ethyl acetate and methanol . The extracts were dried under reduced pressure using a rotary evaporator to obtain the hexane (15.6 g), dichloromethane (21.2 g), ethyl acetate (15.8 g) and methanol (80.5 g) extracts, respectively. Column chromatographic purification  of the hexane extract gave two xanthones, mesuarianone (1) and mesuasinone (2) [6) [M. ferrea afforded hexane (49.6 g), dichloromethane (19.5 g), ethyl acetate (16.7 g) and methanol (62.2 g) extracts. Column chromatography purification of the hexane extract yielded four compounds which were identified as mesuaferrin A (3) [5) [7) [8) [4) [9) [10) [M. congestiflora roots were macerated consecutively with hexane, ethyl acetate and methanol at room temperature. The extracts were desiccated under reduced pressure using a rotary evaporator to yield hexane (5.5 g), ethyl acetate (61.0 g) and methanol (120.5 g) extracts. Column chromatographic separation of the hexane extract afforded a xanthone α-mangostin (11) [1–11 are in agreement with literature data [The air-dried and powdered none (2) . Meanwhie hexane .6 g, dic acetate .7 g and  (3) [5) , caloxane hexane .6 g, dic [7) [8) . Meanwhi acetate .7 g and omethane .5 g, eth[9) [10) . Furtheromethane .2 g, ethe hexane .6 g, dic acetate .7 g and ure data ,19,20,2150 values ranging from 6.89–72.45 µM except for LS-174T and IMR-32 cell lines.The cytotoxic assays were performed by the MTT assay as described by Mosmann . In thisPreliminary insights towards the structure-activity relationships among a series of xanthone derivatives are proposed. The xanthone derivatives bearing substituent groups such as diprenyl, dipyrano and prenyl-pyrano displayed cytotoxicity towards almost all the tested cancer cell lines.