N-acetyltransferase 1 (NAT1) is associated with invasive and lobular breast carcinomas as well as with bone metastasis following an epithelial-to-mesenchymal transition. We investigated the effect of NAT1 gene deletion in three different human breast cancer cell lines, MDA-MB-231, MCF-7, and ZR-75-1. Human NAT1 was knocked out using CRISPR/Cas9 technology and two different guide RNAs. None of the NAT1 knockout (KO) cell lines exhibited detectable NAT1 activity when measured using its selective substrate p-aminobenzoic acid (PABA). Endogenous acetyl coenzyme A levels (cofactor for acetylation pathways) in NAT1 KO cell lines were significantly elevated in the MDA-MB-231 (p < 0.001) and MCF-7 (p=0.0127) but not the ZR-75-1 (p > 0.05). Although the effects of NAT1 KO on cell-doubling time were inconsistent across the three breast cancer cell lines, the ability of the NAT1 KO cell lines to form anchorage-independent colonies in soft agar was dramatically and consistently reduced in each of the breast cancer cell lines. The NAT1 KO clones for MDA-MB-231, MCF-7, and ZR-75-1 had a reduction greater than 20-, 6-, and 7- folds in anchorage-independent cell growth, respectively, compared to their parental cell lines . The results indicate that NAT1 may be an important regulator of cellular acetyl coenzyme A levels and strongly suggest that elevated NAT1 expression in breast cancers contribute to their anchorage-independent growth properties and ultimately metastatic potential.Elevated expression of  N-acetyltransferase 1 (NAT1) catalyzes the transfer of an acetyl group from acetyl coenzyme A (AcCoA) to arylamine and hydrazine substrates [Human arylamine bstrates . Human Nbstrates , 4. NAT1bstrates . Elevatebstrates . Additiobstrates –9 as welbstrates , 11.N-acetyltransferase 2  activity exhibited more mammary tumors, and this finding was independent of carcinogen metabolism [A recent report demonstrated that congenic rats expressing high levels of rat tabolism . The efftabolism –17. Intetabolism  and its In the present study, we utilized CRISPR/Cas9 to investigate the effects of NAT1 knockout (KO) on endogenous AcCoA levels and the cell growth properties in three human breast cancer cell lines that originate from separate pleural effusions of different malignant breast cancer patients frequently used in breast cancer research.2. Horizon Discovery Group  designed 5 different gRNAs for NAT1, and DNA 2.0 Inc  cloned the gRNAs into a Cas9 expressing vector expressing a dasher-GFP tag. Initially, each of the 5 gRNA/Cas9 vectors was transiently transfected in each cell line using the Amaxa Nucleofector II . After forty-eight hours, transfection cells were harvested and DNA isolated. The SURVEYOR Mutation Detection Kit  was used to determine the effectiveness of each gRNA's ability to induce DNA strand breaks effectively. The gRNAs #2 and #5 were the most effective at inducing DNA strand breaks and were chosen to separately KO NAT1 as described below. The selected gRNA sequences were the following: gRNA #2, CCA GATCCGAGCTGTTCCCTTTG  or gRNA #5, GAAAGAATTGGCTATAAGAAGTCTAGG .MDA-MB-231, MCF-7, and ZR-75-1 breast cancer cell lines were obtained from ATCC . MDA-MB-231 is estrogen receptor-negative, progesterone receptor-negative, and HER2-negative. MCF-7 is estrogen receptor-positive, progesterone receptor-positive, and HER2-negative. ZR-75-1 is estrogen receptor-positive, progesterone receptor-positive, and HER2-positive. A MDA-MB-231 breast cancer cell line with a single FRT site  and a nonspecific scrambled shRNA inserted in the FRT site was used as the parent MDA-MB-231 cell line. The construction of this cell line was described previously . MDA-MB-The parent MDA-MB-231 cell line described above was transfected with either #2 or #5 gRNA/Cas9 vectors separately as above and 48 hr after transfection cells were sorted for GFP fluorescence . MCF-7 and ZR-75-1 cells were transfected with #2 or #5 gRNA/Cas9 separately with Lipofectamine 3000 , and 48 hr after transfection cells were sorted for GFP fluorescence as previously described. The GFP-positive cells were collected and plated at a low cell density so that individual unique clones could be isolated. After several weeks, individual cells grew into large enough colonies to utilize cloning cylinders to trypsin cells off the plate and transfer to a 96-well culture plate. Approximately 25 to 50 separate clones, chosen at random, for each cell gRNA, were passaged until nearly confluent in a 6-well plate and then were tested for PABA NAT1 activity. GFP-positive clones with undetectable PABA NAT1 activity were selected for further characterization. The NAT1 open reading frame was sequenced. We chose transient transfection of the gRNA/Cas9 protein to minimize off-target effects; thus; the gRNA/Cas9 plasmid was only present in the cell for a short time (48–96 hr) as opposed to stable long-term expression of gRNA/Cas9 where the editing machinery would be present indefinitely.E. coli. For each NAT1 KO cell line, five transformed E. coli colonies were selected and grown overnight. Cultures of bacteria were then harvested for plasmid purification. Purified plasmids and primers were sent for DNA sequencing  to determine base changes caused by gRNA/Cas9.Genomic DNA was isolated from MDA-MB-231, MCF-7, and ZR-75-1 NAT1 KO cell lines. The NAT1 open reading frame was amplified by PCR and cloned into pcDNA™3.1/V5-His-TOPO®  following manufacturer's recommendations. TOPO cloning reaction for the individual cell lines was transformed into One Shot TOP10 chemically competent The genetically engineered MDA-MB-231 MCF-7 and ZR-75-1 cell lines described above were authenticated by the ATCC Short Tandem Repeat (STR) profiling authentication service.In vitro N-acetylation assays using the NAT1-selective substrate PABA were conducted, and N-acetyl-PABA was separated and quantitated by high-performance liquid chromatography (HPLC) as previously described [μL suitably diluted cell lysate, PABA (300 μM), and AcCoA (1 mM) were incubated at 37°C for 10 min. Three independent measurements (n = 3) performed in triplicate were completed for each cell line. In vitro N-acetylation assays using the NAT2-selective substrate sulfamethazine enzymatic assays were conducted as described previously [μM sulfamethazine, and 1 mM AcCoA were incubated at 37°C for 120 min. Reactions were terminated by the addition of 1/10 volume of 1 M acetic acid. The reaction tubes were centrifuged to remove precipitated protein. Sulfamethazine and N-acetyl-sulfamethazine were separated and quantified by reverse-phase HPLC. Three independent measurements (n = 3) performed in triplicate were completed for each cell line. Under the conditions of this assay, the limit of detection was 0.005 nmoles/min/mg protein.escribed . Brieflyeviously . BrieflyN-acetylation in situ was determined by spiking media with a known concentration of PABA as previously described [μM PABA. N-acetyl-PABA was separated and quantitated by HPLC as previously described [Measurement of NAT1-catalyzed escribed . Brieflyescribed . The num5) were plated into 96-well black/clear bottom plates  and incubated overnight at 37°C and 5% CO2. Once attached to the plate, cells were washed with PBS and then fixed to the plate with 3.7% formaldehyde in PBS for 20 min at room temperature. After fixing, cells were permeabilized using 0.1% Triton-X100 in PBS for 5 min with constant agitation, and the process was repeated 4 times. Cells were blocked with Odyssey® Blocking Buffer in PBS  for 1.5 hours with constant agitation. After blocking, cells were incubated with rabbit anti-NAT1 (ab109114 (1:200), Abcam, Cambridge, UK) or rabbit NAT2 (ab194114 (1:100) Abcam) and β-actin (A2228 (1:200), Sigma-Aldrich, St. Louis, MO, USA) overnight at 4°C with constant agitation. Due to the high similarity between human NAT1 and NAT2, we evaluated the specificity of the primary antibodies against human NAT1 and NAT2. The specificity of ab109114 was about 4-fold greater for human NAT1 than NAT2, and the specificity of ab194114 was about 7-fold greater for human NAT2 than NAT1 (manuscript in preparation). After primary antibodies incubation, plates were washed 5 times with 0.1% Tween 20 in PBS for 5 min. Secondary detection was carried out using IRDye® 800CW Goat anti-Rabbit IgG (1:1200) or IRDye® 680RD Goat anti-Mouse IgG (1:1200),  by incubation for 60 min. Finally, cells were washed with 0.1% Tween 20 in PBS. NAT1 or NAT2 and β-actin were simultaneously visualized using an Odyssey infrared imaging Scanner (LI-COR Biosciences) using the 680 nm channel and 800 nm channel. Relative fluorescence units (RFUs) allowed a quantitative analysis. Relative protein expression was calculated by dividing RFU for NAT1 or NAT2 (800 nm channel) by the RFU of β-actin (680 nm channel). Protein expression in NAT1 KO cells was divided by the protein expression in the parental cell line to determine fold change. The data was generated from 4 independent measurements for MDA-MB-231, MCF-7, and ZR-75-1 parental and their NAT1 KO cell lines.Cells  . HPLC separation and quantitation of AcCoA were achieved as previously described [Endogenous AcCoA levels within MDA-MB-231, MCF-7, and ZR-75-1 parental and NAT1 KO cell lines were measured by HPLC as previously described  with minescribed . The dathttp://www.doubling-time.com/compute.php). The number of separate doubling time determinations for MDA-MB-231, MCF-7, and ZR-75-1 cells was 3, 3, and 4, respectively.Doubling time for each parental and NAT1 KO cell line was determined by plating each cell line to a confluence level that would give the cell lines ample room to grow for at least 7 days or 168 hr. The same number of cells were plated for parental and NAT1 KO cell lines for each cell line. Cells were plated in 6-well plates in triplicate and allowed to grow for 7 days (168 hr). Cells were counted and doubling time calculated using the online calculator . The results are expressed as the mean ± the standard error of the mean (SEM). Values of p < 0.05 were considered statistically significant.Differences between the MDA-MB-231 and MCF-7 parental and NAT1 KO cell lines were analyzed for significance by ANOVA followed by Bonferroni post hoc test. Differences between the ZR-75-1 parental and NAT1 KO cell lines were analyzed for significance by Student's Sequencing the NAT1 gene of MDA-MB-231 gRNA #2 (clone 2–19) KO cell line revealed a deletion of a single cytosine at 96 bases (bp) from the translation start codon . This siSequencing the NAT1 gene of MCF-7 gRNA #2 (clone 2–4) KO cell line showed a 34 bp deletion in the open reading frame, which spans from 95 to 129 bp . This deSequencing the NAT1 gene of ZR-75-1 gRNA #2 (clone 2–10) KO cell line showed a single adenosine insertion at 95 bp in the open reading frame . This inin vitro N-acetylation of PABA in the parental cell line was 14.4 ± 2.8, 39.0 ± 5.9, and 121 ± 19 nmoles/min/mg for MDA-MB-231, MCF-7, and ZR-75-1 cell lines, respectively  decreased in the MDA-MB-231 gRNA #2 and gRNA #5, the MCF-7 gRNA #2 and gRNA #5, and the ZR-75-1 gRNA #2 NAT1 KO cells compared to their respective parental cells (p < 0.0001) compared to their respective parental cell line whereas no significant changes (p > 0.05) in NAT2 protein expression were observed in ZR-75-1 gRNA #2 NAT1 KO cells compared to the parental   .n = 12; ANOVA, p < 0.05)  .n = 3; Student's t-test, p > 0.05)  .n = 3). Both MDA-MB-231 NAT1 KO cell lines had a significant  increase in doubling time compared to the parental MDA-MB-231 cell line , which did not differ significantly  compared to the parental MCF-7 cell line . The doubling time of the ZR-75-1 gRNA #2 NAT1 KO cell line was significantly  elevated compared to the parental ZR-75-1 cell line   .dependent colony formation was 68.2 ± 8.6, 86.2 ± 9.9, and 96.7 ± 9.1 colonies, respectively, which did not differ statistically from each other   .dependent colony formation was 102 ± 5, and 39.4 ± 6.5 colonies, which differed statistically from each other   .independent colony formation assays  allows the determination of cancer cell ability to form colonies in the absence of cellular attachment to a surface. The MDA-MB-231 parental cell line formed anchorage-independent colonies at markedly higher levels than the NAT1 KO clones . The number of colonies formed by the two NAT1 KO cell lines were not statistically different from each other (p > 0.05).Anchorage-O clones . The numindependent colonies at a higher level than the MCF-7 NAT1 KO clones . The MCF-7 gRNA #2 and #5 NAT1 KO clones were not statistically (p > 0.05) different from each other.The MCF-7 parental cell line formed anchorage-O clones . The numindependent colonies at a higher level than the NAT1 KO cell line clone .The ZR-75-1 parental cell line formed anchorage-ne clone . The nump < 0.001) and MCF-7 (p=0.0127) but not the ZR-75-1 (p > 0.05). Although the effects of NAT1 KO on cell doubling time were inconsistent across the three breast cancer cell lines, the ability of the NAT1 KO cell lines to form anchorage-independent colonies in soft agar was dramatically and consistently reduced in each of the breast cancer cell lines. The NAT1 KO clones for MDA-MB-231, MCF-7, and ZR-75-1 had a reduction greater than 20-, 6-, and 7-folds in anchorage-independent cell growth, respectively, compared to their parental cell lines .We investigated the effect of NAT1 gene deletion in three different human breast cancer cell lines, MDA-MB-231, MCF-7, and ZR-75-1. Human NAT1 was knocked out using CRISPR/Cas9 technology and two different guide RNAs. None of the NAT1 KO cell lines exhibited detectable NAT1 activity when measured using their selective substrate PABA. Endogenous AcCoA levels (cofactor for acetylation pathways) in NAT1 KO cell lines were significantly elevated in the MDA-MB-231 . We also used a MDA-MB-231 breast cancer cell line with a single FRT site and a nonspecific scrambled shRNA inserted in the FRT site as the MDA-MB-231 parental cell line. This cell line was transfected with gRNA #2 and #5 to facilitate comparison of the results of NAT1 KO with those previously described for NAT1 knockdown .We isolated single clones from MDA-MB-231 and MCF-7 cells lines with both gRNA #2 and #5. We were not able to isolate a NAT1 KO clone using gRNA #5 in the ZR-75-1 cell line, likely due to reduced growth rate of ZR-75-1 NAT1 KO cell lines. Other groups have also investigated NAT1 KO in MDA-MB-231 and other cell lines by CRISPR/Cas9 , 22. TheN-acetylation below limits of detection both in vitro and in situ. Despite this functional validation of the NAT1 KO, the effects on endogenous levels of AcCoA and cancer cell growth were not completely consistent across different cell lines. The KO of NAT1 activity by both gRNA #2 and gRNA #5 in MDA-MB-231 breast cancer cells caused a modest but significant (p < 0.0001) elevation in doubling time but neither gRNA #2 or gRNA #5 caused significant (p > 0.05) elevation in doubling time for the MCF-7 breast cancer cell line. NAT1 KO in ZR-75-1 cells using gRNA #2 resulted in a 1.7-fold (p < 0.001) elevation in doubling time. Previous studies in our laboratory found that knockdown of NAT1 in MDA-MB-231 by approximately 40% did not significantly change the doubling time [NAT1 KO in MDA-MB-231, MCF-7, and ZR-75-1 reduced levels of PABA ing time . Knockdoing time . With NAing time . Wang eting time . We perfp < 0.0001) reduced in each of the NAT1 KO cells lines. In MDA-MB-231 and MCF-7 cells, NAT1 KO was associated with a significant increase (p < 0.0001) in NAT2 protein. However, NAT2 enzymatic activity in the parental or NAT1 KO cell lines was below the limit of detection.The level of NAT1 was substantially and significantly (p < 0.05) increases in AcCoA levels relative to their respective parental cell lines. However, the ZR-75-1 NAT1 KO cell line did not show the same increase in AcCoA compared to the parental cell line as did the MDA-MB-231 and MCF-7 cell lines. The results for MDA-MB-231 and MCF-7 NAT1 KO cell lines are similar to what was observed when NAT1 was knocked down by shRNA in MDA-MB-231 cells [Human NAT1 has the capacity to hydrolyze AcCoA , 4 and p31 cells .Nε amino group of lysine residues can be posttranslationally modified via acetylation, the process by which numerous key cellular processes, including energy metabolism, mitosis, and autophagy, are known to be regulated [KD (low affinity) for AcCoA [N-acetylation of endogenous and exogenous substrates, but also catalyzes the hydrolysis of AcCoA to CoA in the presence of folate [AcCoA is considered a central metabolic intermediate whose level reflects the general energetic state of the cell . In addiegulated , 24. Notor AcCoA , and thuor AcCoA . NAT1 usf folate , 4. It ip > 0.05) between parental and NAT1 KO cell lines. These results agree with previous studies where knockdown of NAT1 in MDA-MB-231 cells by shRNA did not alter the ability of the cells to form anchorage-dependent colonies [Anchorage-dependent colony formation in human MDA-MB-231 and MCF-7 did not show a significant difference (colonies . AlthougThe ability of the NAT1 KO cell lines to form anchorage-independent colonies in soft agar was dramatically and consistently reduced in each of the MDA-MB-231, MCF-7, and ZR-75-1 breast cancer cell lines. This data is consistent with previous results following knockdown of NAT1 by shRNA in MDA-MB-231  and HT-2in vitro and in vivo. Importantly, NAT1 knockdown in MDA-MB-231 cells resulted in a significant reduction in their ability to metastasize to and colonize in the lungs when injected into nude mice [in vivo.Anchorage-independent growth is one of the hallmarks of metastatic tumors. Tumor cells often lose epithelial features and acquire mesenchymal properties via a complex and dynamic EMT. Through EMT, the tumor cells are believed to acquire increased motility and resistance to apoptosis, ultimately leading to metastasis . Savci-Hude mice , suggestN-acetylation in vitro and in situ and measurement of NAT1-specific immunoreactive protein. KO of NAT1 caused a significant decrease in cell growth for MDA-MB-231 and ZR-75-1, but not for MCF-7 NAT1 KO cells relative to their respective parental cell lines. NAT1 KO caused a significant increase in cellular AcCoA levels in MDA-MB-231 and MCF-7 cells but not for ZR-75-1 cells relative to the parental cell lines. Each NAT1 KO cell line showed a dramatic decrease in the number of colonies that formed in an anchorage-independent manner relative to their respective parental cell line. Although it appears that NAT1 KO can influence the cell morphology and cell-cell interactions in cancer cell lines, further investigation is needed into whether or not NAT1 depletion ultimately alters metastatic potential in vivo.In conclusion, we knocked out human NAT1 with CRISPR/Cas9 technology using two different gRNA's in three different breast cancer cells lines. We verified complete NAT1 KO by measurement of PABA


Cell cytotoxicity assays, such as cell viability and lactate dehydrogenase (LDH) activity assays, play an important role in toxicological studies of pharmaceutical compounds. However, precise modeling for cytotoxicity studies is essential for successful drug discovery. The aim of our study was to develop a computational modeling that is capable of performing precise prediction, processing, and data representation of cell cytotoxicity. For this, we investigated protective effect of quercetin against various mycotoxins (MTXs), including citrinin (CTN), patulin (PAT), and zearalenol (ZEAR) in four different human cancer cell lines  in vitro. In addition, the protective effect of quercetin (QCT) against various MTXs was verified via modeling of their nonlinear protective functions using artificial neural networks. The protective model of QCT is built precisely via learning of sparsely measured experimental data by the artificial neural networks (ANNs). The neuromodel revealed that QCT pretreatment at doses of 7.5 to 20 μg/mL significantly attenuated MTX-induced alteration of the cell viability and the LDH activity on HeLa, PC-3, Hep G2, and SK-N-MC cell lines. It has shown that the neuromodel can be used to predict the protective effect of QCT against MTX-induced cytotoxicity for the measurement of percentage (%) of inhibition, cell viability, and LDH activity of MTXs. Cell cytotoxicity assays have a central role in toxicology studies in the assessment of the in vivo toxic potential of pharmaceutical chemical agents based on in vitro cell cytotoxicity studies . In vitrMycotoxins (MTXs) are toxic developing in many foodstuffs which are responsible for creating a worldwide problem in various agricultural commodities ,7,8. FinQuercetin (QCT)  is a natural dietary compound that possess strong anti-oxidant and exhibited beneficial effect against major classes of MTXs in both in vitro and in vivo experiments . It has Artificial neural networks (ANNs) belong to a class of Artificial Intelligence (AI) tools which are inspired by neuroscience and the architecture of human brain. Similar to the biological neural networks, ANNs are capable of parallel processing using large number of neurons and the learning is conducted based on the given data ,15,16,17Neuromodeling is a methodology for building models of systems using ANN via learning the relationship between the inputs and their corresponding outputs. This modeling method is especially useful for applications where the relationship between the system input and output is unknown and/or highly nonlinear. The behavior of the original system can be reproduced via an ANN-based model learned using sparsely measured local data of the system. This process is also called ANN-based system identification .The aim of our study is to develop a precise model for the protective effect of QCT against MTX-induced cytotoxicity in four different human cell lines  using the ANN-based system identification method. Toward this end, two ANN models were designed to predict the protective effect of QCT against MTX-induced cytotoxicity that was evaluated with the measurement of percentage (%) of inhibition, cell viability, and LDH activity of MTXs  in HeLa, PC-3, Hep G2, and SK-N-MC.System identification is a methodology for building models of a dynamic system using measurements of the system’s input and output signals. It also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction. In this study, a neural network approach has been used for system identification. Specifically, multilayer neural networks and back propagation  learningx is given to both the system and the neural network and the output of the system is taken as the desired response d for training the neural network. The objective of system identification is then to build a neural network whose response o matches to the response y of the system for a given set of inputs x. To achieve this objective input signals are presented repeatedly to the network and the norm of the error vector The system to be modeled is placed in parallel with a neural network with nonlinear learning capability. That input signal x from the external world. This data is weighted with different synaptic weights {jiw} and feed-forwarded to the hidden layer. Each neuron in the hidden layer sums the weighted inputs it receives from its preceding layer and applies a nonlinear transfer function also called “activation function” before passing them as inputs to the output layer. The output layer neurons perform similar computation and yield the output values y of the neural network. The output of the hidden and output neurons can be expressed as follows.hf and of are the activation functions, and jb and kb are the bias of the hidden and output layer, respectively. In this study the sigmoid activation function is used:A multilayer neural network (MNN), as shown in The multiple layers and nonlinear activation enable the MNN to learn a mapping of any complexity. The network is learned based on repeated presentations of the training samples and iterative adjustments of the weights using back-propagation algorithm.y is the network output and t is the desired target and the error is computed over all the data points p. Using (1) and (2), (4) can be written asAssume that the error function of the network shown in W are chosen appropriately for all the patterns, then the error E will approach close to zero. At this situation, the system can produce output values close to the target values for all the inputs. At this state the network is regarded as completely learned and we can say that the function If all W. This goal is achieved via learning which is performed by iteratively updating W such that the error E, at the output, is reduced. Different optimization techniques, like gradient-based back-propagation, genetic algorithm, and simulated annealing, are used for training neural networks. However, back-propagation [So the goal is to find the appropriate value of the network weights pagation  is the mThe aim of our study is to model the protective effect of QCT against MTX-induced cytotoxicity in four different human cell lines  using the above-mentioned multilayer neural network based system identification method. Toward this end, two ANN models are designed and trained to predict the protective effect of QCT against MTX-induced cytotoxicity.The experimental results revealed that % of inhibition increased with increasing dose of MTX, while QCT pretreatment significantly decreased % of inhibition in HeLa, PC-3, Hep G2, and SK-N-MC cell lines . An incrThe neuromodeling of protective effect of QCT against MTX  in different cell lines  has been performed via learning of neural networks. For this end, determination of % of inhibition and cell viability using crystal violet assay and LDH activity using LDH assay were performed.The cytotoxicity was evaluated by determination of % of inhibition on HeLa, PC-3, Hep G2, and SK-N-MC cells that were pretreated with QCT (0–20 μg/mL) for 6 h, followed by the incubation with MTX (0–200 µM) for 24 h. The % of inhibition was measured for discrete amounts of MTX  at every QCT dose of 0.0, 5.0, 7.5, 10, 15, and 20 µg/mL for each cell lines and MTX. A total 720 data points were measured, of which 480 data points corresponding to the % inhibition for QCT values of 0.0, 5.0, 10, and 20 µg/mL, as listed in The doses of MTX and QCT are the primary inputs to the neural network, and the target to be learned is the corresponding % of inhibition. As the % inhibition of QCT on different cell lines and different MTXs is different, a naïve way would be to learn 12 different neural network models corresponding the four different cell lines and three different MTXs. However, in this study we aim to design a single neural network that models the overall behavior on the above mentioned cell lines and MTXs. To distinguish the % inhibition corresponding to the four cell lines and the three MTXs, additional binary codes are used as input to the network. The four cell lines HeLa, PC-3, Hep G2, and SK-N-MC are encoded as 1000, 0100, 0010, and 0001, respectively, and, the three MTXs  are encoded as 100, 010, and 001, respectively. For example, the input to the neural network corresponding to 10 µg/mL QCT on HeLa cell lines for 0.78125 µM CTN is . The doses of QCT and MTXs are normalized using their corresponding maximum doses used in this study, i.e., . The output of the network, i.e., the % of inhibition, is expressed a real number between zero and one [N is the number of validation data points. The results of the empirical method are presented in So the neural network to be designed should have nine input and one output nodes. The appropriate number of hidden nodes required to learn the system was determined empirically. Starting with an initial five hidden nodes in a single hidden layer, the number of hidden nodes were incremented gradually. The network was trained using the back-propagation algorithm with a learning rate of 0.05 on the training dataset for a fixed 10,000 epochs, and its performance was evaluated on the randomly selected validation dataset. The root mean square errorFrom After the learning was completed, the network was evaluated on the test set and the % of inhibition values were obtained from the output of the ANN. The % of inhibition surface obtained from the trained neural network for Hep G2 cell lines with MTX CTN ranging from 0 to 200 µM for different doses of QCT ranging from 0 to 20 µg/mL is presented in The results of ANN were compared with that of linear regression method which is commonly used for a rough estimation of experimental results. R = 0.999) with the experimentally measured data, which is substantially higher than the correlation obtained from other statistical method such as partial least squares (PLS) regression (R = 0.90). Also note that separate models of linear regression or the partial least squares regression have to be built for each of the cell line and MTX combination, whereas the proposed ANN models all the cell line MTX combinations in as single model. The plot of the error between the measured % of inhibition and the predicted values using the ANN model and PLS is as shown in Similar figures for other MTXs, such as PAT and ZEAR, are presented in Similar experiments, as those done for modeling the % of inhibition of QCT, were also conducted to model the cell viability and LDH activity. As in the case of % of inhibition, variation of cell viability and LDH activity was measured on cells treated with QCT  and incubated with MTXs: CTN (100 µM), PAT (50 µM), and ZEAR (100 µM). As shown in The aim of neural network in this case is to predict the cell viability and LDH activity given the doses of MTXs and QCT for all the combination of MTXs and cell lines. The architecture of the ANN for modeling this system is different from the case of % of inhibition model as the network needs to output two values. The output of the network, i.e., the cell viability and LDH activity, are normalized to real numbers between zero and one by dividing with their corresponding maximum values encountered in this study, i.e., 100 and 350, respectively. The binary encoding scheme as described in A network with nine input nodes, 10 hidden nodes, 10 hidden nodes, and two output nodes, namely, a 9-10-10-2 network, shown in The trained network was evaluated on the test set and the cell viability and LDH activity values were obtained from the output of the ANN. The cell viability and the LDH activity curve obtained from the trained neural network for different cell lines pretreated with different doses of QCT ranging from 0 to 20 µg/mL and incubated with MTXs: CTN (100 µM), PAT (50 µM), and ZEAR (100 µM) is presented in Rcell_viability = 0.995 and RLDH_activity = 0.997) with the experimentally measured data. As seen from The plot of the error between the measured cell viability and LDH activity and the corresponding values predicted using the ANN model is as shown in The main objective of this paper was to model the protective effect of QCT against MTXs using ANNs, and to verify the ability of the model in estimating the protective effect of QCT. Specifically, the protective effect of QCT against three different MTXs  on four different cell lines  was measured experimentally, and the data were used to model their nonlinear protective functions  using multilayer neural networks.The experimental measurements revealed that treatment with MTX significantly decreased cell viability and increased LDH activity. However, the % of inhibition of four different cells pretreated with the three MTXs was consistently decreased with the dose of QCT. Also, pretreatment with QCT attenuated MTX-induced alteration of cell viability and LDH activity that it could protect the cell lines from cytotoxicity. The effects of QCT against the MTX-induced cytotoxicity were conducted via cell viability and LDH release assays in Hela, PC-3, Hep G2, and SK-N-MC cell lines. The experimental results showed that treatment with MTX significantly decreased cell viability and increased LDH activity. However, pretreatment with QCT significantly attenuated MTX-induced alteration of cell viability and LDH activity, which suggests that it could protect the cell lines from cytotoxicity. Therefore, these results suggest that QCT may inhibit MTX-induced diseases in humans.Two different ANN models with sizes of 9-20-15-1 and 9-10-10-2, determined empirically, were used to model the % of inhibition, the cell viability, and LDH activity, respectively. For both tasks, the experimentally measured data for the protective effects of QCT for three different MTXs in four different cells was used for training the neural network. As a result, twelve and twenty-four different models of precise protective effects of QCT were built on the two ANNs, respectively. Unlike the commonly used statistical methods, like linear regression or partial least square regression, which require separate models to be computed for each MTX and cell line combination, a single neural network was designed to model the different combinations using a special binary encoding scheme for each MTX and cell line combination. Moreover, quantitative evaluations of the network’s performance on the test sets exhibited high correlation with experimentally measured data which was substantially higher than that of individual models computed using other statistical methods.It was observed that the additional burden for the neural network to discriminate between the different input combinations, expressed as binary codes, demands comparatively larger networks, which require more number of iterations for network convergence and hence longer training times. However, it was shown that single model for different input combination provides a unified and elegant solution with the ability to precisely model the protective effects of QCT against MTXs.d-glucose, 2 mmol/L l-glutamine, 110 mg/L sodium pyruvate, 100 U/mL penicillin, and 100 μg/mL streptomycin at 37°C in a humidified atmosphere containing 95% air and 5% CO2.Citrinin (CTN), patulin (PAT), zearalenol (ZEAR), and quercetin (QCT) were purchased from Sigma-Aldrich . HeLa , PC-3 (prostate cancer cell), Hep G2 (liver cancer cell), and SK-N-MC (brain cancer cell) human cell lines were obtained from the American type culture collection . The cells were grown in Dulbecco’s Modified Eagle’s Medium (DMEM) and Roswell Park Memorial Institute (RPMI) 1640 supplemented with 10% fetal bovine serum (FBS), 4.5 g/L To prevent mycoplasma effectively we followed a procedure described previously . BrieflyThe MTX-induced cytotoxicity in different cell lines  was measured by determination of percentage (%) of inhibition, cell viability, and LDH activity.4 cells per well in culture media and allowed to attach overnight; cells were pretreated with QCT (0–20 μg/mL) at 37 °C in a humidified atmosphere of 5% CO2/95% air for 6 h followed by the incubation with mycotoxins (0–200 µM) for 24 h.At first, we measured percentage (%) of inhibition to observe the cytotoxic effect of the MTXs  and MTX + QCT on HeLa, PC-3, Hep G2, and SK-N-MC. Briefly, cells were seeded in 24-well plates with 5 × 104 cells per well in culture media and allowed to attach overnight. The cells were pretreated with the doses of QCT at 5, 10, and 20 μg/mL at 37 °C in a humidified atmosphere of 5% CO2/95% air for 6 h, followed by the incubation with CTN (100 µM), PAT (50 µM), and ZEAR (100 µM) for 24 h. After 24 h of incubation, removed medium and washed the cells with phosphate buffer solution (PBS) and 0.2% crystal violet solution was added to each well. After 10 min of incubation, the crystal violet solution was removed carefully by washing with water. Finally, added 100 μL 1% sodium dodecyl sulfate (SDS) to solubilize the color solution until the color is uniform and no areas of dense coloration in bottom of wells. The samples were read at 590 nm in a microplate reader . The cell viability is expressed as the percentage of absorbance of control.Crystal violet assay was used to determine MTX-induced cell death. Briefly, cells were seeded in 24-well plates with 5 × 104 cells/well) and cultured in 24-well culture plates. The cells were then preincubated with or without different concentrations of QCT  at 37 °C for 6 h followed by incubation with CTN (100 µM), PAT (50 µM), and ZEAR (100 µM) for 24 h. After treatment was over, cells were centrifuged at 240 × g for 4 min and the culture supernatant was transferred in a new plate. The assay mixture was prepared and added to each well and the plate incubated wrapped in foil at room temperature for 30 min. Reaction was terminated by adding the stop solution to each well. The plate was read at 490 nm at a reference wavelength of 690 nm. The extent of cytotoxicity is expressed as the percentage of absorbance of control.Lactate dehydrogenase (LDH) activity assay was used to determine MTX-induced cytotoxicity. LDH release into the media was taken as an indicator of cell damage and the assay is based on the principle of reduction of nicotinamide adenine dinucleotide (NAD) by LDH. The reduced NAD (NADH) is utilized in the stoichiometric conversion of a tetrazolium dye which is measured spectrophotometrically using an LDH assay kit . Briefly, cells were seeded   on a standard computer station (Intel(R) Core(TM) i7-6700k 4.00 GHz, 8 cores, 8 GB RAM) machine, whereas the partial least squares method was implemented using MATLAB’s built-in plsregress function.All the data were expressed as mean ± SD and one-way ANOVA  followed by Dunnett’s test was used for the statistical analysis using SPSS software . *


ADAR or ADAR1) as an essential gene for the survival of a subset of cancer cell lines. ADAR1-dependent cell lines display increased expression of interferon-stimulated genes. Activation of type I interferon signaling in the context of ADAR1 deficiency can induce cell lethality in non-ADAR1-dependent cell lines. ADAR deletion causes activation of the double-stranded RNA sensor, protein kinase R (PKR). Disruption of PKR signaling, through inactivation of PKR or overexpression of either a wildtype or catalytically inactive mutant version of the p150 isoform of ADAR1, partially rescues cell lethality after ADAR1 loss, suggesting that both catalytic and non-enzymatic functions of ADAR1 may contribute to preventing PKR-mediated cell lethality. Together, these data nominate ADAR1 as a potential therapeutic target in a subset of cancers.Systematic exploration of cancer cell vulnerabilities can inform the development of novel cancer therapeutics. Here, through analysis of genome-scale loss-of-function datasets, we identify adenosine deaminase acting on RNA ( Specific cancer cell vulnerabilities can provide an opportunity for the development of novel cancer therapeutics. In this study the authors demonstrate that targeting ADAR1 represents a potential therapeutic vulnerability in cancers with activated interferon response signatures. Importantly, most lung cancer patients are not eligible for targeted therapies because their tumors lack a targetable genomic alteration. Moreover, a substantial proportion of lung cancer patients treated with immune checkpoint inhibitors do not achieve an objective response6. Thus, the discovery of novel therapeutic modalities remains critical to improving outcomes in lung cancer care.Despite the discovery and widespread use of novel targeted therapies that inhibit the activity of mutant oncogene products, such as EGFR and ALK8. Identification of these synthetic lethal interactions may offer an opportunity for the development of novel classes of therapies for lung cancer. In this study, we utilize genome-scale loss-of-function datasets to uncover genetic dependencies in lung cancer cell lines. We find that lung cancer cell lines expressing high levels of interferon-stimulated genes (ISGs) are vulnerable to deletion of the RNA adenosine deaminase, ADAR or ADAR1. ADAR deletion induces phosphorylation of the cytoplasmic double-stranded RNA (dsRNA) sensor PKR, leading to downstream signaling. Deletion of PKR can partially rescue cell lethality after ADAR1 loss, indicating that ADAR genetic dependency is at least partly mediated by PKR signaling. Overexpression studies demonstrate that both the catalytic and non-enzymatic functions of ADAR1 may restrain PKR-mediated cell lethality in ADAR1-dependent lung cancer cell lines. Taken together, our data suggest that ADAR1 may represent a potential therapeutic target in cancers displaying activation of interferon response pathways.Lung cancer cells may harbor specific genomic or functional alterations that render them vulnerable to particular genetic perturbations9 in search of novel genetic dependencies in lung cancer. Based on previously described criteria9, we identified 11 genes that are potentially required for the survival of subsets of lung cancer cell lines  to inosine (I) in RNA. This editing function is thought to destabilize regions of RNA duplexes that are formed by inverted repetitive elements within RNAs15. In this manner, A-to-I editing has been proposed to prevent cytoplasmic RNA sensors of the innate immune system, such as MDA5 and PKR, from erroneously recognizing endogenous dsRNA as foreign15. Indeed, germline mutations in human ADAR cause Aicardi-Goutières syndrome, which is characterized by constitutive activation of type I interferon (IFN-I) signaling and widespread autoinflammation16, highlighting the role of ADAR1 in curbing aberrant IFN-I responses.-dependent lung cancer cell lines, we performed differential gene expression analysis on publicly available RNA sequencing (RNA-seq) data derived from cancer cell lines in the Cancer Cell Line Encyclopedia (CCLE)17. Consistent with the known role of ADAR1 in IFN-I signaling, we found higher expression of ISGs in ADAR1-dependent versus non-ADAR1-dependent lung cancer cell lines  or interferon-β (IFN-β) caused increased cell lethality in ADAR1-deficient A549 and NCI-H1437 cells as compared to vehicle-treated controls  and the induction of cell lethality (MDA5/MAVS-independent) through distinct upstream mechanisms in cancer cell lines.Consistent with the known role of ADAR1 in suppressing IFN-β expressionion Fig. , whereaslls Fig. , suggestlls Fig.  without lls Fig. . TogetheADAR genetic dependency, we next examined whether the cGAS/STING cytosolic DNA sensing pathway may mediate the cell lethality induced by ADAR1 loss. Indeed, we observed that ADAR KO-sensitive cell lines produced higher levels of the adaptor protein STING as compared to ADAR KO-insensitive cell lines , we observed increased levels of PKR phosphorylation only after the combination of ADAR1 depletion and IFN-β treatment  p150 isoform of ADAR1 prior to deletion of endogenous ADAR1 prevented PKR phosphorylation and rescued cell lethality, overexpression of the WT p110 isoform had minimal effects on these phenotypes lls Fig. , c, e. WADAR loss as a genetic dependency in a subset of lung cancer cell lines which possess a high interferon gene expression signature. Although an elevated interferon gene expression signature is predictive of ADAR1-dependence, the mechanisms underlying this sensitivity to ADAR knockout will require further investigation. The p150 isoform of ADAR1 appears to prevent lethality in cancer cell lines at least partly through inhibition of the cytoplasmic RNA sensor PKR .Our study suggests several possible approaches to disrupt the function of ADAR1 in cancer cells: direct enzymatic inhibition of its adenosine deaminase activity and/or inactivation of non-enzymatic functions unique to the p150 isoform, such as direct PKR binding. Further experiments will be required to determine the utility of each of these approaches for cancer therapeutics.l-glutamine. The following cancer cell lines were obtained from the CCLE: A549, NCI-H460, NCI-H1299, NCI-H1437, RERFLCAI, HCC366, HCC1438, NCI-H196, NCI-H596, NCI-H1650, and SW900 (lung); PATU-8902 (pancreas); RKO ; AGS (stomach); BT20 (breast); and RKN (soft tissue). Prior to shipping each cell line, the CCLE performed cell line authentication with DNA fingerprinting and mycoplasma testing.Cancer cell lines were grown and maintained in RPMI media supplemented with 10% fetal bovine serum (FBS), and 1% penicillin, streptomycin, and http://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design). sgRNA sequences are displayed in Supplementary Table http://www.genome-engineering.org/crispr). For lentivirus production, individual CRISPRv2 vectors were introduced along with packaging vectors into 293 T cells via calcium phosphate transfection according to the manufacturer’s instructions (Clontech). Lentivirus was harvested at 48 and 72 h after transfection in RPMI media supplemented with 10% FBS and filtered with 45 μm filters before transduction of cancer cell lines. Transduced cell lines were selected in 2 μg/mL puromycin and/or 10 μg/mL blasticidin for at least 5 days prior to use in assays. Thereafter, protein lysates were collected from the transduced cells and protein levels of the targeted gene(s) were assessed by immunoblotting. For cell lines in which two genes were knocked out, stable single KO cell lines maintained under 2 μg/mL puromycin selection were transduced with lentivirus expressing Cas9, a second guide RNA, and a blasticidin resistance marker. Double KO cells were selected with both 2 μg/ml puromycin and 10 μg/ml blasticidin for at least 5 days prior to use in assays.Single guide RNA sequences were designed using the sgRNA Designer tool on The RNAi Consortium (TRC) portal (Cell counting was performed using a Vi-Cell XR Cell Counter (Beckman-Coulter). For ATP bioluminescence experiments, cells were plated at a density of 3000 cells per well in 96-well assay plates (Corning). ATP bioluminescence was assessed at 3 and 6 days after plating with the CellTiter-Glo Luminescent Cell Viability Assay (Promega). For crystal violet staining, cells were plated at a density ranging from 25,000 to 100,000 cells per well in 12-well tissue culture plates. Once the GFP control cells grew to near confluency, each well was washed twice with ice cold PBS, fixed with ice cold methanol for 10 min on ice, stained with 0.5% crystal violet solution (made in 25% methanol) for 10 min at room temperature, and washed at least four times with water. To quantify cell viability in crystal violet stained plates, images were obtained from five fields of each well at ×40 magnification with an Olympus inverted microscope. The number of cell nuclei in each image was counted using ImageJ and averaged for each well. All cell viability assays were performed in triplicate.Cells were lysed in RIPA lysis buffer (Thermo Fisher Scientific) supplemented with 1× protease and phosphatase inhibitor cocktails (Roche). Protein concentrations were obtained using the BCA Protein Assay Kit (Pierce) and normalized between all samples. Protein extracts were analyzed by standard immunoblotting with the following primary antibodies: ADAR1 (#14175), ISG15 #2758), MAVS (#3993), MDA5 (#5321), STING (#13647), total PKR (#3072), and phospho-STAT1 Tyr701 (#9167) from Cell Signaling; phospho-PKR Thr-446 (ab32036) from Abcam; IFNAR1 (A304-290A) and total STAT1 (A302-752A) from Bethyl; and β-actin (sc-47778) from Santa Cruz. All antibodies were used at a dilution of 1:1000 except β-actin which was used at a dilution of 1:4000 or 1:5000. The following secondary antibodies were used at a 1:5000 or 1:10,000 dilution: Goat anti-Rabbit IRDye 800CW  and Goat anti-Mouse IRDye 680LT  from LI-COR Biosciences. Immunoblots for phospho-PKR and phospho-STAT1 were stripped with Restore Western Blot Stripping buffer  prior to immunoblotting with total PKR and total STAT1, respectively. Immunoblots were imaged using the LI-COR digital imaging system and ImageJ. Quantitation of band intensities was performed with ImageJ. All immunoblots were cropped to optimize clarity and presentation. Uncropped scans of the immunoblots presented in the main figures of this manuscript are shown in Supplementary Fig. , MAVS . On day 3, the culture media was replaced with 1.5 mL fresh media for each well. On day 4, the enzyme-linked immunosorbent assay was performed on the conditioned media as described above.IFN-β detection was performed with the VeriKine-HS Human IFN Beta Serum ELISA Kit (PBL Assay Science) according to the manufacturer’s instructions. To detect spontaneous IFN-β production, 4 × 10For interferon treatment assays, cells were plated at a density of 3000 cells per well in a 96-well assay plate (Corning). The following day, cells were treated with human interferon-alpha 1  or recombinant human IFN-beta 1a  protein . Control wells were treated with sterile water (vehicle). Cell viability was assayed 3 days after IFN-I treatment with CellTiter-Glo Luminescent Cell Viability Assay (Promega). The ATP bioluminescence values of the treated wells were normalized to those of the vehicle-treated controls. Apoptosis was assayed 3 days after IFN-I treatment with the Caspase-Glo 3/7 Assay (Promega). Dose curves were obtained using least-squares nonlinear regression on a standard four-parameter logistic model using GraphPad Prism 7 software.GFP or ADAR. RNA was isolated using the RNeasy Kit (Qiagen) with on-column DNase I treatment followed by ribosomal RNA depletion using the NEBNext rRNA Depletion Kit (E6310). RNA sequencing libraries were prepared using the NEBNext Ultra Directional RNA Library Prep Kit (E7420S) and sequenced on the Illumina HiSeq instrument (150-bp paired-end reads). Alignment against the human genome (hg19) was performed using the STAR aligner32. Reads were quantified using HTSeq33 and each gene was then fit with a generalized linear model using DESeq2 (ref. 34).For GFP control or ADAR1-deficient A549 cell lines, cells were treated with 10 ng/mL IFN-β or sterile water (vehicle) for 24 h before RNA isolation. For HCC366, NCI-H1650, and NCI-H196 cells, RNA was isolated 5 days after transduction with lentivirus co-expressing Cas9 and sgRNAs targeting t-statistics (Tij) of normalized expression values for each sample per gene were calculated using Tij = (xij–xj)/(sj/√n), where xij = normalized expression value for sample i and gene j, xj = sample mean for normalized expression of gene j, sj = sample standard deviation for normalized expression of gene j, n = number of samples.Heat maps showing standardized ADAR KO-sensitive and KO-insensitive lung cancer cell lines was compiled from Supplementary Data x + 1) transformed RPKM expression values across all 27 genes in the signature. These raw sums were standardized and the z-scores were reported as the final interferon gene expression signature scores for each of the CCLE cell lines (Supplementary Data To generate interferon gene expression signature scores for cancer cell lines in the CCLE (Supplementary Data ADAR open-reading frame (ORF) clone was obtained from GeneCopoeia (C0744). The entire ORF was sequenced to confirm fidelity to the NCBI Reference Sequence NM_001111.4. Entry clones for both ADAR1 p110 and p150 were obtained through PCR-amplification of the ADAR ORF and were sub-cloned into a Gateway donor vector. Using the QuikChange Lightning Site-Directed Mutagenesis Kit (Agilent), silent mutations were introduced separately into the protospacer adjacent motif (PAM) sequences targeted by ADAR sgRNA 1 and sgRNA 2 to render the constructs resistant to CRISPR-Cas9 editing by these sgRNAs. Subsequently, the E912A mutation was engineered into each CRISPR-Cas9-resistant ADAR1 construct separately using site-directed mutagenesis. The resulting CRISPR-Cas9-resistant ADAR1 constructs and a GFP control construct were sub-cloned into the pLX307 lentiviral expression vector (Addgene) under the control of an EF-1α promoter. Each expression vector was then transfected into 293 T cells to generate lentivirus. Lentiviral transduction of target cell lines was performed as described above.An R2) was calculated by linear regression analysis in Supplementary Fig. ADAR KO-sensitive and KO-insensitive cell lines were made with Mann–Whitney U tests in Supplementary Data p-values less than 0.05 were considered to be statistically significant. All statistical tests were performed using R version 3.4.1 or GraphPad Prism 7 software.No statistical methods were used to predetermine sample size. Investigators were not blinded to sample allocation for any of the experiments. Data in all graphs are presented as the mean of either independent biological or technical replicates, as indicated in the figure legends, with all error bars representing standard deviation. The Krusal–Wallis test was utilized in Fig. Supplementary Data 1Supplementary Data 2Supplementary Data 3Reporting SummarySupplementary InformationDescription of Additional Supplementary Files


Mechanisms of treatment resistance in head and neck squamous cell carcinoma (HNSCC) are not well characterized. In this study, HNSCC tumors from a cohort of prospectively enrolled subjects on an ongoing tissue banking study were divided into those that persisted or recurred locoregionally (n=23) and those that responded without recurrence (n=35). Gene expression was evaluated using llumina HumanHT-12-v3 Expression BeadChip microarrays. Sparse Partial Least Squares – Discriminant Analysis (sPLS-DA) identified 135 genes discriminating treatment-resistant from treatment-sensitive tumors. BCL-xL was identified among 23% of canonical pathways derived from this set of genes using Ingenuity Pathway analysis. The BCL-xL protein was expressed in 8 HNSCC cell lines examined. Cells were treated with the BCL-xL inhibitor, ABT-263 (navitoclax): the average half maximal inhibitory concentration (IC50) was 8.9μM (range 6.6μM – 13.9μM). Combining ABT-263 did not significantly increase responses to 2 Gy radiation or cisplatin in the majority of cell lines. MCL-1, a potential mediator of resistance to ABT-263, was expressed in all cell lines and HNSCC patient tumors, in addition to BCL-xL. Treatment with the MCL-1 inhibitor, A-1210477, in HNSCC cell lines showed an average IC50 of 10.7μM . Adding A-1210477 to ABT-263 (navitoclax) treatment resulted in an average 7-fold reduction in the required lethal dose of ABT-263 (navitoclax) when measured across all 8 cell lines. Synergistic activity was confirmed in PCI15B, Detroit 562, MDA686LN, and HN30 based on Bliss Independence analysis. This study demonstrates that targeting both BCL-xL and MCL-1 is required to optimally inhibit BCL-family pro-survival molecules in HNSCC, and co-inhibition is synergistic in HNSCC cancer cells. Head and neck squamous cell carcinoma (HNSCC) is diagnosed in approximately 55,000 patients in the United States each year , and it Evasion of apoptosis is a ‘hallmark of cancer’ . OncogenThe process of apoptosis is controlled by a balance among several initiators, mediators, and inhibitors. The BCL-2 family proteins work in concert to regulate the initiation of the intrinsic apoptosis pathway. Pro-survival BCL-2 proteins, such as BCL-2, BCL-xL, and MCL-1, inhibit BCL-2 pro-apoptotic effectors, such as BAX and BAK. The pro-apoptotic effectors promote apoptosis by forming pore complexes in the mitochondrial membrane leading to release of oxygen free-radicals and cytochrome c, which in turn activate the caspase cascade leading to programmed cell death . UpregulIn the current study, we compare HNSCC tumors that were eradicated by chemoradiation to those that persisted or recurred locoregionally after treatment. Gene expression data were analyzed to identify transcripts that could differentiate these two groups, and BCL-xL was noted to be involved in several key networks among the genes identified. This led us to examine the efficacy of small-molecule targeting of BCL-xL in HNSCC. Treatment with ABT-263 (navitoclax), a small molecule inhibitor of BCL-2/BCL-xL, was effective in killing HNSCC cells at high doses, but had limited additive effect in combination with radiation or cisplatin. This led to examination of factors limiting the efficacy of BCL-xL/BCL-2 inhibition, specifically MCL-1 activity. MCL-1 expression was associated with resistance to radiation in HNSCC cells and found to increase in expression after treatment with ABT-263 (navitoclax). Co-inhibition of MCL-1 was required to optimize approaches targeting BCL-2/BCL-xL in HNSCC, resulting in synergistic activity.A cohort of patients with HNSCC were selected for the study based on the inclusion and exclusion criteria described in the methods section. The characteristics of the patient cohort are presented in Table The gene expression profiles of tumors from patients with locoregional failure were compared to the profiles of tumors from responders using sPLS-DA Figure . The optEight HNSCC cell lines  were used to examine responses to radiation and cisplatin. Surviving fraction of cells after exposure to 2Gy, 4Gy, and 6Gy radiation were examined in clonogenic survival assays performed in triplicate. HNSCC cell lines demonstrated a range of survival to 2Gy radiation - from HN30: 55.1% (±10.7% Standard Error (SEM)) to Detroit562: 89.2% (±4.6% SEM). Data for more radiation sensitive cell lines  are presented in Figure Cell viability assays using MTT -2,5-diphenyltetrazolium bromide) were also used to assess response of the 8 cell lines to cisplatin treatment. The half maximal inhibitory concentration (IC50) for cisplatin ranged from PCI15A: 2.81μM (standard deviation (SD) ±0.99) μM to UMSCC6: 13.29μM (SD ±0.88μM). Representative cell viability curves are presented in Figure In vitro studies were carried out to determine if BCL-xL, and related anti-apoptosis family members were associated with radiation and cisplatin response. The efficacy of targeting BCL-xL therapeutically was also examined. Baseline protein expression of BCL-2 and BCL-xL were assessed using Western Blot. BCL-xL was strongly and consistently expressed in all cell lines, while conversely, BCL-2 showed very weak or no expression in 7 cell lines: only HN5 demonstrated consistent high expression of BCL-2  is a potent inhibitor of BCL-xL, with activity against BCL-2 and BCL-w . The HNSThe efficacy of ABT-263 in combination with 2Gy radiation was evaluated in clonogenic survival assays examining the panel of 8 HNSCC cell lines, performed in duplicate. A third trial was not performed because combination treatment was generally ineffective. 2μM and 4μM doses of ABT-263 were studied Figure . In geneThe effect of combining cisplatin with ABT-263 was also studied using cell viability assays with MTT. We examined whether 2μM and 4μM doses of ABT-263 reduced the IC50 of cisplatin when given in combination. ABT-263 did decrease the IC50 of cisplatin in most cell lines Figure , howeverOverall, the effect of inhibition of BCL-xL/BCL-2/BCL-w with ABT-263 in combination with standard therapy for HNSCC was not robust, which led to an exploration of possible mechanisms of resistance to ABT-263.ABT-263 targets BCL-xL and BCL-2, but does not target the MCL-1 protein, another BCL-2 family pro-survival molecule. We therefore examined MCL-1 expression across the panel of 8 HNSCC cell lines using Western Blot Figure . Notablyt-test comparing MCL-1 levels between untreated and treated samples showed that MCL-1 was significantly increased after treatment with ABT-263 (p=0.04). BCL-2 and BCL-xL levels were also examined on western blot before and after treatment of 4 μM ABT-263. BCL-2 was not expressed in several lines, both before and after treatment, and no consistent increase in expression was observed. BCL-xL was significantly increased after treatment with ABT-263 (p=0.01).It was also hypothesized that treatment with ABT-263 (navitoclax) would result in upregulation of MCL-1 expression. MCL-1 expression was evaluated in the panel of 8 cell lines 24 hours after treatment with 4μM of ABT-263. MCL-1 was indeed noted to increase in all cell lines after ABT-263 (navitoclax) treatment Figure . Paired In addition, we tested the response of the HNSCC cell lines to a recently-described  selectivNext, the efficacy of A-1210477 was examined in combination with ABT-263 using MTT assays. HNSCC cell lines were treated with 2.5μM and 5μM doses of A-1210477 to determine if this resulted in a dose reduction required to achieve the IC50 with ABT-263. The assays were performed in triplicate. The IC50 of ABT-263 was substantially reduced in all cell lines tested Figure . The IC5It was suspected that the drug combination of ABT-263 and A-1210477 was synergistic based on results of the 8 cell line panel. Four of the cell lines  were examined using cell viability assays, with expanded dose ranges to allow for Bliss Independence synergy analysis. These were performed in duplicate as they were confirmatory to the MTT assays. Bliss Independence analysis showed there was synergy observed for the drug combination in each line  is associated with treatment response. Our in vitro data also suggest that MCL-1 expression may be an important factor associated with resistance to radiation. Large prospective studies are required to validate whether immunohistochemical expression of BCL-xL, BCL-2, and MCL-1 in pre-treatment biopsy specimens on patients with HNSCC is associated with response to radiation/chemoradiation.Our investigation led us to focus on BCL-xL and other BCL-2 family proteins in HNSCC. Several other groups have examined the relationship between BCL-xL and BCL-2 in HNSCC and responses to treatment. Work by Bauer and colleagues demonstrated that patients with laryngeal SCC who express low levels of BCL-xL demonstrate better responses to chemotherapy and chemoradiation compared to patients with high BCL-xL expression, which was recapitulated in a HNSCC cell line model examining cisplatin-resistant cells . In a siin vitro and in vivo models, and also enhanced the effects of both cytotoxic chemotherapeutics and radiation [in vitro findings. The experience treating patients with HNSCC across all of these early studies has been very limited.ABT-737, A BH3-mimetic small molecule that targets the BCL-2 family pro-survival proteins BCL-2, BCL-xL, and BCL-w, was first described in 2005. This drug showed anti-tumor activity in both adiation . ABT-263adiation . ABT-263adiation , 24 and adiation –28. Thesin vitro efficacy of BCL-2/BCL-xL inhibitors in HNSCC, both as single agents and in combination with chemotherapy, our study presents the examination of ABT-263 (navitoclax) alone and in combination with cisplatin in the largest number of HNSCC cell lines reported to date. Our study is also the first to examine the combination of ABT-263 (navitoclax) with radiation therapy in HNSCC, and only the second report we could find in any tumor type. Our comprehensive evaluation led us to draw generalizable conclusions for HNSCC, specifically that targeting BCL-xL and BCL-2 alone was effective, but did not result in consistent synergy when combined with radiation and/or cisplatin.Therapeutics targeting BCL-2 pro-survival proteins have been tested preclinically in HNSCC. (-)-Gossypol, a BH3-mimetic which inhibits BCL-xL and BCL-2, has been studied in HNSCC cell lines, and (-)-gossypol demonstrated improved efficacy compared to cisplatin and induced apoptosis in cisplatin resistant HNSCC lines . A studyin vitro seemed to correlate with radiation resistance. It should be noted however, that it was BCL-xL that was identified in our gene expression evaluation, and not MCL-1, that appeared to be associated with locoregional control. It is not surprising that certain gene expression transcripts are useful biomarkers because of their consistent association with certain disease phenotypes (e.g. radiation resistance) and prognosis, while other molecules are important for treatment selection, but not necessarily predictive biomarkers.The limited activity of BH3-mimetics in solid tumors, as well as our data showing that high doses of ABT-263 were necessary to achieve a response in HNSCC cells, suggest that there are redundant mechanisms that inhibit apoptosis in cancer cells. Because BCL-2 and BCL-xL inhibitors fail to target MCL-1, MCL-1 expression is an obvious potential mechanism by which cancer cells resist treatment with ABT-263, and this mechanism has been demonstrated in some cancer models , 36. MCLin vitro activity as a single agent, and synergistic effect when combined with ABT-263 when applied to several cancer cell lines. Phillips and colleagues demonstrated that resistance to BCL-2 inhibition with venetoclax in non-Hodgkin's lymphoma cells could be overcome with co-treatment with A-1210477, resulting in synergy between these two drugs [MCL-1 specific inhibitors have been discovered very recently. The first report on A-1210477 was published in 2015 by Leverson and colleagues  in a stuwo drugs . The intin vivo model would best support our findings, and these studies are planned. To our knowledge, this study is the first to examine the combination of ABT-263 with an MCL-1 inhibitor in HNSCC. A-1210477 was selected as it was the earliest available MCL-1 inhibitor available for the studies in this report. This agent cannot be used in vivo due to the pharmacokinetic profile of this molecule [in vitro and in vivo efficacy against MCL-1 dependent myeloma, leukemia, and lymphoma cells [in vivo studies of HNSCC.Our study represents a preliminary look at the complex interplay between apoptosis signaling molecules in HNSCC, and the authors recognize several limitations of our report. First, we acknowledge that the cohort of patients examined in our study is heterogenous, and that the definitions we applied for failure and response to treatment is perhaps unconventional. However, we placed careful restrictions and definitions to the discovery set in order to focus on the biology of resistance to non-surgical treatment. We also recognize that evidence of treatment responses in an molecule , but othmolecule . Very rema cells . This reIn conclusion, expression of the BCL-2 family prosurvival molecule BCL-xL was a component of several gene networks identified after profiling treatment-resistant and treatment-sensitive HNSCC tumors. Inhibition of BCL-xL with ABT-263 (navitoclax) demonstrated consistent efficacy in HNSCC cells, but at relatively high doses. MCL-1 expression correlated with radiation resistance in HNSCC cells, and inhibition of MCL-1 with A-1210477 enhanced response to ABT-263 (navitoclax). Increasing clinical experience with BH3-mimetic agents, and newly characterized agents targeting MCL-1 may lead to novel and effective strategies targeting HNSCC.Patients with newly diagnosed HNSCC were enrolled on an ongoing IRB-approved prospective cohort study and tumor banking program at our institution, as previously described , 44. Fortreatment failures if they received radiation or chemoradiation to treat their disease, and subsequently failed treatment with persistent or recurrent local (primary site) or regional (neck lymph node) disease in the treated field. Patients who also received surgery with subsequent radiation or chemoradiation were included among the treatment failure group, as locoregional failure in these cases were considered disease that resisted non-surgical therapy. Patients were defined as treatment responders if they received primary radiation or chemoradiation and experienced a complete response, with no documented local or regional recurrence at the last follow up at the time of analysis.Patients were required to have been diagnosed with squamous cell carcinoma with a primary tumor site in the upper aerodigestive tract. All patients were required to have undergone diagnostic biopsy and/or initial treatment for HNSCC at Montefiore Medical Center. Cases were defined as not included in the treatment responder group, in order to enrich this group for those tumors with complete response to and no recurrence after non-surgical treatment.Of note, subjects who received surgical resection as a component of their primary treatment were Patients with non-squamous cell cancer histology , and patients with primary tumors located in sites other than the upper aerodigestive tract (eg. cutaneous cancers) were excluded. Patients with primary cancer of the nasopharynx and paranasal sinuses were also excluded. Also, patients who underwent treatment for a previous head and neck cancer (ie. enrolled at diagnosis of a second or subsequent primary HNSCC), and those who were enrolled at the time of HNSCC recurrence were also excluded.Figure ® TotalPrep™ RNA Amplification Kit . Global expression was analyzed by RNA hybridization to the Illumina® HumanHT-12-v3 Expression BeadChip . Probes were matched to known genes and alternative splice variants using the RefSeq database release 17 (https://www.ncbi.nlm.nih.gov/refseq/) and UniGene build 188 (https://www.ncbi.nlm.nih.gov/unigene). Controls for each RNA sample were used to confirm RNA quality, biotin labeling success, hybridization stringency, and signal levels.Total RNA was extracted from tumor tissue using TRIzol™ by a standardized protocol . RNA was collected by alcohol precipitation and quantitated for microarray analysis. Quality control was performed on selected samples, checking for integrity of RNAs using an Agilent 2100 bioanalyzer  and RNA pico chips as described by the manufacturer. Total RNA (500 ng) was amplified and biotin labeled with the Illumina®) prior to analysis. Expression data were batch corrected using the ComBat function from the sva R package [® Pathway Analysis (IPA) .Microarray expression values were quantile normalized within BeadStudio  genotyping. All experiments were performed within 20 or less passages for each line.HNSCC cell lines HN30, HN31, PCI15A, PCI15B, UMSCC6, MDA686LN, and HN5, were obtained from a repository maintained by Dr. Jeffrey N. Myers, MD, PhD at the University of Texas, MD Anderson Cancer Center, with some cell lines requiring the following permissions . The Detroit562 cell line was acquired from the American Type Culture Collection. All cells were maintained in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% Fetal Bovine Serum (FBS), nonessential amino acids, sodium pryuvate and 1% antibiotic — penicillin/strepomycin. Cells were incubated at 37°C, 5% COCells were seeded onto 6-well plates at densities of 800–1600 cells/well, 24 hours before treatment. At 24 hrs cells were treated with γ-irradiation  at indicated doses and returned to 37°C incubation. After 5 days media was replaced with fresh media, and cells were allowed to continue growth. Colony growth continued to be monitored up to 5-14 days post γ-irradiation. At the time of harvest, media was removed and cell clones were washed, fixed, and stained with 0.25% Cresyl Violet. The results were quantified with ImageJ software . PlatingCells were seeded into 6-well plates as described above. Cells were pre-treated with ABT-263 for 4 hours before γ-irradiation . Harvest and survival calculations were carried out as described above. Experiments were performed in duplicate and not repeated a third time after preliminary results demonstrated little benefit with combination treatment.in vitro were washed with PBS, scraped from cell culture plates, and lysed using standard RIPA buffer. Protein concentrations of cell lysates were determined by Lowry Protein Assay . Equal amounts of proteins were loaded onto SDS-PAGE gels, separated by electrophoresis, and transferred onto PVDF membranes. Membranes were washed in TBS-T and blocked in 1% BSA at room temperature for 1 hour or in 4°C overnight. The membrane was probed overnight at 4°C with primary antibody. Then it was washed and incubated with appropriate secondary antibody. Membranes were probed using the following antibodies: BCL-2, BCL-xL, MCL-1 , each at 1:500 dilution and Beta-Actin as a loading control protein  at 1:10,000 dilution. Following fluorescent secondary antibody incubation at 1:2500 dilution, immunoreactive proteins were visualized with the LI-COR® Odyssey® FC Imaging System. Protein band quantification was determined with image studio software . All protein quantification experiments were carried out on a minimum of three representative western blots.Cells grown 10 concentration. IC50 values were calculated by non-linear regression four-parameter logistics curve analysis with GraphPad Prism . All MTT assays were repeated in triplicate.All cell lines were plated on 96-well plates at 1,500 cells/well . Cells were incubated for 24 hours before drug addition. Incubation time was maintained between 48 - 72 hours for each condition. Conditions were optimized for each condition. Cell death was generally more rapid when cisplatin treatment was included. Specifically, incubation time for Cisplatin alone was 48hrs, ABT-263 (navitoclax) alone was 72 hrs, Cisplatin and ABT-263 (navitoclax) was 48 hrs, A-1210477 was 72 hrs and ABT-263 (navitoclax) plus A-1210477 was 72 hrs All drugs were initially dissolved in dimethyl sulfoxide (DMSO), and thus control (untreated) wells were maintained in corresponding dilutions of dissolvent DMSO. After drug incubation, MTT 3--2,5-diphenyltetrazolium bromide) was added until visualization of formazan crystals, approximately 180 minutes. All media was then aspirated from wells, and the cells were lysed in 100 μL of DMSO on an orbital pulse shaker. Approximate viability was determined with Benchmark Plus™ Microplate Spectrophotometer System  at 570 nm. The percentage of growth rates were calculated relative to the untreated control and were graphed as a function of the logHNCC cells (1500 cells/well) were seeded in 96-well white plates. Cells were incubated with serial dilutions of ABT-263 (20 mM to 0.078 mM) and co-treated with A1210477 at the indicated concentrations. Control (untreated) wells were maintained in corresponding dilutions of dissolvent DMSO. Cell viability was assayed after 72 hours treatment using the Cell Titer-Glo , according to the manufacturer's protocol. Luminescence was measured using the TECAN M200 microplate reader. Viability assays were performed in duplicates and the data normalized to vehicle-treated control wells. IC50 values were determined by nonlinear regression analysis using Prism software . The Bliss Independence combination synergy was assessed using the Combenefit platform as previously described in Veroli et al. Bioinformatics 2016. U tests. Paired T-tests were used to compare expression of BCL-xL and MCL-1 in HNSCC cell lines before and after treatment with ABT-263 (navitoclax). A two-sided p-value < 0.05 was considered statistically significant for all statistical calculations.Bioinformatic analysis carried out on the patient cohort is described in a preceding section. Descriptions and references for calculations for plating efficiency/surviving fraction, IC50 calculations, and Bliss Independence analysis are described in their respective sections above. Mean averages and standard deviation (SD) or standard error of the mean (SEM) were used to present normally distributed data, where appropriate . Median average and interquartile ranges  were calculated for gene expression levels of BCL-xL, MCL-1 and BCL2, and comparisons between responders and failure subjects were carried out using Mann-Whitney


Correction to: BMC Cancerhttps://doi.org/10.1186/s12885-019-6453-zFollowing publication of the original article , the autIn Fig. 1 C and D, the cell lines are different, it should be as follow:c) Western blotting analysis of TOPK expression in different ESCC cell lines. d) Relative expression of TOPK in different ESCC cell lines compared to the KYSE450 cell line.The updated Fig. 1 C and D is supplied below. In Fig. 2, Fig. 4 B and C, Fig. 6 D and E, the cell lines were mislabeled. EC9706 should be instead of KYSE510, TE13 should be instead of KYSE30.


The most active compound was 3’,4’,5-trihydroxyflavone 7, especially against A549 and MCF-7 cell lines. The correlation between anti-proliferative and antioxidant activity was only moderate, and it was determined for A549 and U87 cancer cell lines. The most important fragment for those two effects is the ortho-dihydroxy group in ring B. Conclusions. Trihydroxyflavones demonstrated anticancer activity. Further and more detailed studies should to be carried out to estimate the structure–activity relationship of these compounds.Plant polyphenols have been highlighted not only as chemopreventive, but also as potential anticancer substances. Flavones are a subclass of natural flavonoids reported to have an antioxidant and anticancer activity. The aim of our study was to evaluate antioxidant and anticancer activity of seventeen trihydroxyflavone derivatives, including apigenin (API) and baicalein (BCL). Also, we wanted to find out if there is a correlation between those two effects. Cell growth inhibition testing was carried out using MTT assay in three different human cancer cell lines: lung (A549), breast (MCF-7) and brain epithelial (U87). Antioxidant activity was determined by the DPPH radical scavenging method. Thirteen trihydroxyflavones possessed anticancer activity against at least one tested cancer cell line. They were more active against the MCF-7 cell line, and the lowest activity was determined against the U87 cell line. The majority of compounds inhibited cancer cell growth at EC Cancer is one of the major causes of mortality worldwide. Despite tremendous efforts to create effective chemotherapy drugs, there is still a huge toxicity and selectivity issue. The toxicity of modern chemotherapy and cancer cell resistance to anticancer agents leads us to seek new treatments and prevention methods of this insidious disease ,2. The iHowever, regardless of the intensive research on plant material, the biological activity is known for less than 15% of plants, and they remain an attractive source for scientists to find new molecules. Natural anticancer drugs have a low cost and possible several mechanisms of action , and theFlavonoids are one of the most tested and widely distributed substances of plant origin ,9. They Flavones are one of the largest flavonoid subclasses. They contain a double bond between C2 and C3 in heterocycles, as well as a C4 carbonyl group. Benzene ring B is connected to the C2 atom in central chromone cycles. Usually, flavones do not have substituents in the C3 position ,18,19. MFlavones possess a wide range of different biological activities. They act as antioxidants, inhibit cell proliferation, and have antimicrobial, estrogenic, anti-inflammatory effects . FlavoneAPI and baicalein (BCL) are the most widely studied trihydroxyflavones. It was established that those compounds may inhibit cancer cell proliferation ,12. BCL ortho-catechol group and C3 hydroxyl group are important for anti-proliferative activity. Surprisingly, all tested flavonoids did not possess any effect on normal human cell viability.It is supposed that flavone cytotoxicity is related to the hydroxyl group number and position in their structure . Pouget The antioxidant activity of flavonoids is studied quite widely. They disrupt the electron transport chain due to their radical scavenging properties and also chelate metal ions . The antortho-dihydroxy group, are strong antioxidants. Trihydroxyflavones BCL and galangine lack this structural element and have 2.5–5 times less activity. API does not possess DPPH radical-scavenging properties. Unsubstituted flavone and monohydroxylated flavones do not possess antioxidant properties . Howeverortho hydroxyl groups. Other structural elements are important as well: C2–C3 double bond and C4 keto group, hydroxyl groups in positions 3 and 5 [ortho-dihydroxy group is required for free radical scavenging activity.Hyun et al.  found th 3 and 5 ,32. Gome 3 and 5  comparedDespite the numerous studies of separate flavonoids and plant extracts enriched by those compounds, the relationship between anticancer and antioxidant activity remains unclear and still has to be studied more thoroughly.Hydroxyflavones are very attractive compounds for anticancer activity studies since they have low toxicity and may interact with DNA . FlavopiThe anti-proliferative activity of trihydroxyflavones  possessi1 and 3) showed anti-proliferative effect against glioblastoma cells at lower than 25 µM concentration. Most trihydroxyflavones inhibited cancer cell proliferation at concentrations in the range from 10 to 50 µM.The trihydroxyflavone effect on cancer cell viability was variable . Thirtee50 = 25 µM). Shukla [50 for BCL against the MCF-7 cell line as 66.3 ± 5.9 µM, and Lee [API did not possess high activity against all tested cell lines, whereas BCL was moderately active against the MCF-7 breast cancer cell line. Scherbakov et al.  establis. Shukla  suggests. Shukla  discover. Shukla  establis and Lee  found th7 which contains two neighbouring hydroxyl groups in ring B. Compound 1 containing hydroxyl groups in the same positions of ring B but lacking hydroxyl groups in ring A, possessed 2.9 times lower activity. Similar activity was determined for compound 9. Compound 10 differs from compound 9 only in the different position of one hydroxyl group in ring B, and this could determine its lower activity. Compounds 3 and 5 contain hydroxyl groups in the same positions of rings B and C; only the hydroxyl group position in ring A is different, and the EC50 value is twofold different .Ten trihydroxyflavones inhibited proliferation of non-small cell lung cancer cells at lower than 100 µM concentrations a. The mo7 appeared to be the most active among all trihydroxyflavones. It reduced MCF-7 cell viability twofold more than that of BCL and sixfold more than that of API. The EC50 values of most compounds varied between 12 µM and 24 µM.Almost all tested trihydroxyflavones (13 out of 17) inhibited the proliferation of MCF-7 cancer cells b. The sa50 values lower than 100 µM , also did not show high antioxidant activity.Park et al.  studied 50 was 12.7 ± 0.25 μM. BCL antioxidant capacity does not coincide with the value described in this work (EC50 was found to be 80 μM). This can be explained by the influence of pH on the antioxidant activity of hydroxyflavones. It is known that [Wei et al.  determinown that  hydroxyfBased on the calculated Pearson coefficient, no strong correlation was found between the anticancer and antioxidant activity of tested trihydroxyflavones .p > 0.05).A moderate correlation was found between the trihydroxyflavone anticancer activity against the A549 cell line and the free radical scavenging properties. Also, there was a moderate correlation between the anti-proliferative effect on glioblastoma cells and antioxidant activity. However, in both cases, those correlations were not proved to be statistically significant  contain two ortho hydroxyl groups (compounds 11 and 13), and compounds 1 and 10 have a catechol structural element. The compounds that were not active against the A549 cell line also did not affect the viability of breast cancer cells, with the exception of compounds 8 and 13 (both having hydroxyl groups at positions 2’ and 7).Compound 1 and 7. Compound 3 showed a similar anti-proliferative activity as compound 1, and was two times more active than compound 7. It contains all three hydroxyl groups in different positions. Compounds 1 and 3 contain a hydroxyl group in the same position in ring C, and compound 7 contains the third hydroxyl group in ring A, possibly contributing to its lower activity.The glioblastoma was the least susceptible to the effect of trihydroxyflavones. The importance of the catechol group B ring in the anti-proliferative effect was determined again. This group was presented in compounds 1 and 9. Compound 1 contains a catechol group, and compound 9 contains two ortho hydroxyl groups in ring B. Moderately active antioxidants were compounds 14 and 15, containing two hydroxyl groups in ring A, and compounds 7 and 10 containing hydroxyl groups in ring B.The substituents in ring B were the most important for the antioxidant activity of trihydroxyflavones. The strongest antioxidants were compounds 7  was shown to have the highest potency for non-small cell lung cancer (A549) and breast cancer (MCF-7). The viability of glioblastoma cells was most inhibited by compound 3 . The most active free radical binding was compound 1 .The most active trihydroxyflavones against all cancer cell lines and the strongest antioxidants are shown in ortho-dihydroxy group in ring C is very important for both antioxidant and anticancer activity. However, compound 3 lacking this group and not possessing free radical scavenging properties was also one of the most active anticancer agents. Its anti-proliferation activity could be related to different mechanisms of action.Our results showed that the Trihydroxyflavones possessing hydroxyl groups in different positions of 2-phenyl-1,4-benzopyrone (at least 99% pure) were kindly provided by Dr. Vytautas Smirnovas, and originally purchased from Indofine Chemical Company, Inc. . The solutions of trihydroxyflavones were made in dimethylsulfoxide (DMSO) directly before experiments. DMSO  was obtained from Sigma-Aldrich . 6-Hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid  was purchased from Sigma-Aldrich. 2,2-Diphenyl-1-picrylhydrazyl  was purchased from Alfa Aesar . 3--2,5-diphenyltetrazolium bromide  was purchased from Sigma-Aldrich. Ethanol (96.6%) was obtained from Stumbras, LLC .TM Express, Dulbecc’o modified Eagle high glucose medium (DMEM Glutamax), fetal bovine serum (FBS), penicillin/streptomycin solution , phosphate buffered saline (PBS) were obtained from Thermo Fisher Scientific.All cell culture plastic ware was purchased from Thermo Fisher Scientific , Corning  and Techno Plastic Products . TrypLE2. All cell cultures routinely were grown to 70% confluence and trypsinized with 0.125% TrypLE™ Express solution before passage. They were used until passage 20.Human non-small cell lung carcinoma cell line A549, human breast adenocarcinoma cell line MCF-7 and human glioblastoma cell line U87 ) were grown in DMEM Glutamax medium supplemented with 10% FBS and 1% antibiotics at 37 °C in a humidified atmosphere containing 5% CO3 cells/well) and incubated at 37 °C for 24 h. Then, serial dilutions of tested compounds (from 100 µM to 3.125 µM) were made in microplates. Cells treated only with medium containing 0.25% DMSO served as a negative control. Free medium without cells was used as a positive control. After 72 h incubation at 37 °C, 20 μL of MTT 0.5 mg/mL solution in sterile water was added into each well. After 4 hours, the liquid was aspirated from the wells and discarded. Formazan crystals were dissolved in 100 μL of DMSO, and absorbance was measured at a test wavelength of 490 nm and a reference wavelength of 630 nm using a multi-detection microplate reader. The experiments were repeated three times independently, and the results are presented as the means ± SD.Cell viability was studied using the method of MTT. Cells (100 μL) were seeded in 96-well plates in triplicate (5 × 1050) values, reducing cell viability by 50%, were calculated.Applying Hill fit to compound dose–cell metabolic activity (absorbance) curves, the effective concentration  radical scavenging method. DPPH 60 µM solution was prepared in 96.6% of alcohol immediately before each experiment and protected from light. In a 1-cm diameter quartz cell, 1 mL of ethanolic DPPH solution was mixed with 5 µL of tested trihydroxyflavone solution in DMSO. DPPH ethanolic solution (1 mL) mixed with 5 µL of DMSO served as a negative control. Also, the antioxidant activity of Trolox was established in parallel, and this substance served as a positive control.Light absorbance was measured at a wavelength of 515 nm using a spectrophotometer: Cary 8454 UV-Vis (Agilent Technologies). Ethanol (96.6%) was used as a blank solution. Experimentally, it has been determined that absorbance constantly decreases and stabilizes after 30 min.; thus, the absorbance of all tested samples was measured 30 min. after the preparation of sample of tested compounds.50) at which the tested compound reduced the free radical induced oxidation by 50%. Hill fit to compound dose–antioxidant activity (absorbance) was used.Antioxidant activity was evaluated by calculating the effective concentration  or −0.99–(−0.90) (negative); strong when r = 0.70–0.89 (positive) or −0.89–(−0.70) (negative); and moderate when r = 0.40–0.69 (positive) or −0.69–(−0.40) (negative). Student's t-test was used for comparing two groups. The level of statistical significance was set at p < 0.05.Data are presented as the mean ± standard error (S.E.) of at least three independent experiments. The correlation between antioxidant and anticancer activity was assessed by calculating Pearson’s coefficient Ortho-dihydroxyl structural fragment in ring B is very important for both anticancer and antioxidant activity of trihydroxyflavones. The anticancer effect of trihydroxyflavones against A549 and U87 cells could be related to their antioxidant activity: anti-proliferative effect directly correlates with DPPH radical scavenging activity. 3,3′,6-trihydroxyflavone (contains hydroxyl groups attached to the different rings) does not possess antioxidant activity but is a highly active anticancer compound. It could have different mechanisms of action.Trihydroxyflavones are more active against human breast and non-small cell lung cancer cell lines and show lower activity against glioblastoma cells. The majority of trihydroxyflavones show a free radical scavenging effect; some of them are 3.5 times more active than Trolox. The