Most in vitro cancer cell experiments have been performed using 2D models. However, 3D spheroid cultures are increasingly favored for being more representative of in vivo tumor conditions. To overcome the translational challenges with 2D cell cultures, 3D systems better model more complex cell-to-cell contact and nutrient levels present in a tumor, improving our understanding of cancer complexity. Despite this need, there are few reports on how 3D cultures differ metabolically from 2D cultures.Well-described cell lines from colorectal cancer (HCT116 and SW948) and pancreatic ductal adenocarcinoma (Panc-1 and MIA-Pa-Ca-2) were used to investigate metabolism in 3D spheroid models. The metabolic variation under normal glucose conditions were investigated comparing 2D and 3D cultures by metabolic flux analysis and expression of key metabolic proteins.p = 0.0096). Mitochondrial transport protein, TOMM20, expression decreases in all spheroid models compared to 2D, and monocarboxylate transporter (MCT) expression increases in 3 of the 4 spheroid models.We find significant differences in glucose metabolism of 3D cultures compared to 2D cultures, both related to glycolysis and oxidative phosphorylation. Spheroids have higher ATP-linked respiration in standard nutrient conditions and higher non-aerobic ATP production in the absence of supplemented glucose. In addition, ATP-linked respiration is significantly inversely correlated with OCR/ECAR (In this study of CRC and PDAC cell lines, we demonstrate that glucose metabolism in 3D spheroids differs significantly from 2D cultures, both in terms of glycolytic and oxidative phosphorylation metrics. The metabolic phenotype shift from 2D to 3D culture in one cell line is greater than the phenotypic differences between each cell line and tumor source. The results herein emphasize the need to use 3D cell models for investigating nutrient utilization and metabolic flux for a better understanding of tumor metabolism and potential metabolic therapeutic targets.The online version contains supplementary material available at 10.1186/s40170-022-00285-w. Cellular metabolism is tightly controlled and serves an essential function for normal growth and survival. Since the identification of an altered metabolic state in cancer by Warburg almost 100 years ago  and its Notably, the majority of cancer drug testing has been done in 2D cultures . HoweverWhereas cancer spheroid metabolism has largely been studied using omics , 15 and To better understand how 3D spheroid growth and metabolism differ from corresponding 2D-grown cells, we performed parallel studies evaluating a range of metabolic flux parameters and related metabolic protein markers in the colon cancer cell lines SW948 and HCT116 and the pancreatic cancer cell lines Panc1 and MIA-Pa-Ca-2. To maintain the 3D cell cultures close to the glucose levels found in vivo , all expCell lines were chosen based on published results on metabolism , 21, andl-glutamine , penicillin (100 U/ml), streptomycin (100 μg/ml)  in a humidified incubator at 37 °C with 5% CO2 infusion. Cells were grown in 2D adherent culture conditions, from which spheroids were prepared before each experiment. Spheroids were formed from a 40-μl volume of detached single-cell suspensions with 5000 cells, either in hanging drops in a dish, or in CELLSTAR cell repellent U-bottom plates . Spheroids were grown for 3 days (CRC) or 4 days (PDAC) before conducting metabolic assays.SW948 and MIA Pa-Ca-2 were purchased from European Collection of Authenticated Cell Cultures (ECACC), Panc1 and HCT116 cell lines were generously provided by collaborators at the Stavanger University Hospital Molecular Biology Lab. All cell lines were cultured in DMEM  supplemented with 10% fetal bovine serum (FBS) , 5 mM glucose , 2 mM Cells were seeded in flat-bottom 96-well plate at a density of 5000 cells/well. At each timepoint, 3 wells were stained with Hoechst and the entire well imaged using Leica SP8 confocal microscope  for direct cell detection and counting in the LASX software.Spheroids were cultured in U-bottom plates as described above, but in densities from 200 cells/well to 10,000 cells/well. Transmitted light images of the spheroids were captured on days 3, 4, 6, and 8 using the Leica SP8 confocal microscope. Images were analyzed in ImageJ to obtain the cross-sectional area of the spheroid.l-glutamine, and 5 mM glucose adjusted pH to 7.4 before running the experiment. Before running mitochondrial and glycolysis assays, titration of CCCP over a range of concentrations was performed as per the Seahorse cell characterization procedure. The CCCP concentration chosen was that which yielded the maximum OCR value, for each cell line and culture method . For mitochondrial oxygen measurements, assay media consisted of unbuffered, serum-free DMEM 8.3 g/L , NaCl 1.85 g/L, 2 mM od Table .2-free incubator for 45 min–1 h prior to running the assay. Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured over 90 min (15 mix and measure cycles), with compounds being injected every 3 cycles. For the mitochondrial respiration assays, the following compounds were injected sequentially : oligomycin (3 μM), CCCP (0.5 μM), rotenone (1 μM), and antimycin A  . For the glycolysis assays, the assay media was not supplemented with glucose and the following compounds were injected sequentially : glucose (10 mM), oligomycin (3 μM), and 2-deoxy-D-glucose (100 mM). Protein concentration was measured in each well for normalization using standard BCA assay  according to manufacturer’s instructions.One day prior to assay analysis, 10,000 cells were seeded in each well of a XF96e cell culture plate using culture media as described above. Approximately 1 h before the assays, culture media was exchanged for 180 μl assay media. The plates were then incubated at 37 °C in a CO2-free incubator for 45 min–1 h prior to running the assay. OCR and ECAR were measured over 150 min (23 mix and measure cycles). For the mitochondrial respiration assays, the following compounds were injected sequentially : oligomycin after cycle 3 (3 μM), CCCP after cycle 9 : glucose after cycle 3 (10 mM), oligomycin after cycle 9 (3 μM), and 2-deoxy-D-glucose after cycle 15 (100 mM). To use the data for comparison of metabolic phenotypes across cell models regardless of absolute metabolic activity, OCR and ECAR were normalized to basal OCR levels (MST) and ECAR level after glucose injection (GST). Statistical significance was determined in GraphPad using ordinary one-way ANOVA, correction for multiple comparisons by the Sidak method, with alpha = 0.05.Spheroid cell culture plates were used for the XF96e assays. Each well was coated with 25 μl CellTak  at a concentration of 33 μg/ml. Spheroids were transferred from hanging drops and placed in the centers of wells in the spheroid assay, which contained 160 μl assay media. Plates were then incubated at 37 °C in a COBuffer Factor of assay media was calculated using the Agilent Seahorse XF Buffer Factor Protocol. This number (1.70 mM/pH) was then used for the calculation of the ATP production rate after each injection following the information contained in the Agilent White Paper: “Quantifying Cellular ATP Production Rate Using Agilent Seahorse XF Technology” and Mookerjee et al ., accordiresp and PPRglyc, with the PPRresp being from all mitochondrial oxygen consumption   of 0.258 mpH/pmol H+ and combined with data from the glycolysis assays and residual OCR after AMA from mitochondrial stress test, run in parallel to GST. The total proton production rate (PPR) is found by dividing ECAR by BP Eq.  and can glyc Eq.  that ince assays  due to te assays .7\documet tests, correction for multiple comparisons by the Holm-Sidak method, with alpha = 0.05.The Glycolytic Index (GI) is a way to normalize between samples, allowing comparison of phenotype and contribution of glycolysis to ATP production regardless of changes in absolute ATP production rates Eq. . The Cral-Lactate Assay Kit  according to the manufacturer’s instructions. Glutamine was assayed using the Glutamine/Glutamate Determination Kit , adjusted for a low volume assay in a microplate. These results include both glutamine and endogenous glutamate. All values are presented normalized to surface area of cultures grown for equivalent period as that in the. Surface area in 2D was obtained from area measured in images on day 4 from the proliferation experiments. Surface area in 3D was estimated from area of spheroids in growth experiments after the same time in culture and extrapolated to the surface area of sphere, A = 4·π·r2.Media was collected from spheroids grown in ULA round-bottom 96-well plates and 2D cultures from flat 96-well plates, at days 0, 2, and 4, with no refeeding of media during this period. Day 0 for spheroids is culture day 3 for CRC spheroids and day 4 for PDAC spheroids and on this day they received full media exchange. The media from each timepoint was then used in the metabolite assays. Glucose concentration was assayed using the GlucCell glucose monitoring system  according to the manufacturer’s instructions. Lactate was assayed using the n = 600) comprised of 10,000 cells were produced and grown for 3–4 days before collection and dissociation using Accutase . The spheroids were collected by rinsing plates with PBS and placed in a centrifuge tube. After centrifuging 5 min at 100 RCF, the supernatant was discarded and the pellet was resuspended in 5 ml Accutase. The tubes were placed on a rocker at room temperature and resuspended every 10 min using a P1000 pipet to gently disrupt the spheroids. Adherent cells were collected on the same day as the spheroids, also detached using Accutase. The cell lines varied considerably in incubation time needed for complete detachment and dissociation. Cell suspensions were counted using a Muse cell analyzer and Muse count and viability assay . The cell suspensions were then fixed with 3.7% PFA for 30 min. Cells were kept in PBS until staining. 5 × 105 cells were used for each staining reaction (at 1 × 106 cells/ml). Each tube was incubated in blocking/permeabilization buffer  for 1 h at room temp, rinsed with PBS before adding primary antibodies (single stain per tube) and incubated overnight at 4 °C, rocking. The next day, they were rinsed with PBS and the secondary antibodies were added, with the exception of unstained control and the tubes stained with conjugated TOMM20 antibody. Finally, cells were rinsed and resuspended in PBS containing 0.5% BSA for analysis in Bio-Rad S3e   and CytoFlex  . Primary antibodies: Rabbit Anti-Glucose Transporter GLUT1 antibody, EPR3915 ; Mouse MCT1 Antibody SC-365501 and Mouse MCT4 Antibody SC-376140 ; Rabbit UCP2 antibody ; Llama anti-rabbit IgG polyclonal antibody, CF™ 488A and anti-mouse IgG polyclonal antibody, CF™ 488A . Datasets were analyzed using FCS Express , gated to singlet populations, and the median values were compared in ratios of 3D to 2D.Expression of metabolic proteins  was measured by flow cytometry. Spheroids  over the 5 days whereas the highest cell density is nearly quadruples in size to 2.87 mm2 in the same time period  to 0.76 mm2  in area and grow to 0.23 mm2 (200 cells) and 1.04 mm2 . MIA-Pa-Ca-2 form large spheroids that are easily dissociated and with an irregular shape  to 2.08 mm2 , and exhibit less growth compared to the other cell lines over the 8 days of size measurements , but respiratory capacity is 32 pp lower, p < 0.00001  .Panc1 spheroids increase in respiratory metrics but this change (16.5 pp) is only significant for ATP-linked respiration, .05 Fig. E. Like H001 Fig. C from 52001 Fig. D, but nop = 0.0096), so the lower the OCR/ECAR, the more respiration is linked to ATP production.Except for SW948, the other cell lines all trend to higher respiratory activity in 3D culture . ATP-linked respiration in 3D is significantly higher in all cell lines. Changes in total glycolytic capacity between 2D and 3D are more varied. HCT116 and Panc1 exhibit changes in the same direction for both respiration and glycolysis, but not on the same scale. However, the 3D models of all the cell lines demonstrate lower OCR/ECAR than their 2D counterparts, reflecting a more glycolytic phenotype. When combining 2D and 3D samples, ATP-linked respiration is negatively correlated to basal OCR/ECAR  and p < 0.0001 (oligo)  was calculated using results from the Seahorse assays Eqs. –6, as dego) Fig. A. SW948 The expression levels of relevant metabolic proteins were analyzed using flow cytometry to investigate their potential role as markers to support or explain the results of the metabolic flux assays. This includes the major membrane proteins involved in nutrient transport represented by glucose transporter 1 (GLUT1), monocarboxylate transporter 1 (MCT1), and monocarboxylate transporter 4 (MCT4) and two mitochondria-associated proteins, translocase of outer mitochondrial membrane 20 (TOMM20) and uncoupling protein 2 (UCP2). Median protein expression is plotted in Fig. 2 than in 2D, but the other cell lines are quite similar between 2D and 3D cultures. The increase in HCT116 in 3D is also present for glutamine consumption and lactate production. MIA-Pa-Ca-2 in 3D consumes more glutamine per mm2 than in 2D and Panc1 in 3D produces almost twice as much lactate as in 2D. This is especially large considering there is no increase in glucose consumption in 3D. Finally, despite being cultured in physiological glucose, the cells are not being starved of glucose, even over 4 days without media exchange  expression increases in spheroids of HCT116, SW948, and Panc1 that differ significantly from 2D in basal glycolysis. The change in TOMM20 could be related to the increase in mitochondrial ATP-coupling as TOMM20 overexpression has shown to affect proliferation of colorectal cancer, impacting ATP production and mito-potential . FurtherDespite the significant shift to glycolysis in the spheroid cultures shown in the present study, the expression of GLUT1 did not show the same significant change between 2D and 3D cultures. Increased GLUT1 expression has been associated with cancer aggressiveness –52, and This study is primarily limited by the few cell lines included. Studying more cell lines and including tumor-derived organoids can provide a more complete view of the variation among 3D models and correlation with markers. Other improvements include sectioning spheroids and performing immunohistochemistry for localization of protein expression , 59. AnoWe find that metabolism changes significantly when cells are cultured in 3D, compared to 2D. In our data, spheroids demonstrate an increase in glycolytic activity over monolayer cultures, and the complexity of the 3D culture environment allows for improved utilization of other nutrient sources than glucose for ATP production. Spheroids have higher ATP-linked respiration in standard nutrient conditions and higher non-aerobic ATP production in the absence of supplemented glucose. Mitochondrial transport protein, TOMM20, expression decreases in all spheroid models compared to 2D, and monocarboxylate transporter expression increases in 3 of the 4 spheroid models. To our knowledge, the presented study is the most robust analysis comparing 2D and 3D spheroid metabolism using live metabolic flux measurements. Our results show that investigation of cancer metabolism should focus on using more complex 3D in vitro cell models to expand our knowledge within this field and gain a better understanding of the applicability to tumor biology. Even more importantly, the recent surge in screening and repurposing established metabolic drugs for cancer treatment should be done in 3D cell models to improve the translation into in vivo tumor settings.Additional file 1: Figure S1. Morphology of cell lines. 2D adherent (top) and 3D spheroid (bottom) cultures. . Figure S2. Spheroid growth over several days and with varying starting cell number, shown from day 3 after spheroid preparation. Transmitted light confocal images, 5X objective (each image is 750x750 um). Figure S3. Seahorse XFp is a viable option for low replicate studies of spheroid metabolism, depending on spheroid morphology. OCR data from Mito Stress Test of spheroids analyzed in Seahorse XFp are shown compared to data from Seahorse XF96. The OCR values relative to basal do not differ greatly in the CRC models, (A) HCT116 (n=4) and (B) SW948 (n=5), however PDAC models were more challenging. (C) Signal from Panc1 (n=4) may be increased by using larger spheroids or longer culture of the same starting cell number. (D) MIA-Pa-Ca-2 (n=3) exhibit a flatter spheroid morphology which presents an issue in the normal Seahorse culture plates, as they lack the special machining of the XF96 spheroid plates that help capture the spheroids and flow fluid around and up the sides of the wells. The movement of these spheroids may be avoided with different coating prior to transfer of the spheroids. Figure S4. Protein-normalized OCR and ECAR values in 2D and 3D. (A) OCR from MST in 2D. (B) OCR from MST in 3D. (C) ECAR from GST in 2D. (D) ECAR from GST in 3D. Figure S5. Overlaid histograms of protein expression. By cell line, all runs, to show relative magnitudes and expression distribution of the cell populations. Showing normalized peak values to 1000 events. Figure S6. Metabolite levels relative to starting amount. (A) Glucose levels in CRC cell lines. (B) Lactate levels in CRC cell lines. (C) Glutamine levels in CRC cell lines. (D) Glucose levels in PDAC cell lines. (B) Lactate levels in PDAC cell lines. (C) Glutamine levels in PDAC cell lines. Blue is 2D, Red is 3D. Error bars represent standard deviation. Table S1. Forward scatter intensity from flow cytometry experiments. Cells from spheroids exhibit lower intensity of forward scattering and a higher coefficient of variation. Experimental replicates 1 and 2 were performed on BioRad S3e and replicate 3 on CytoFlex. The large difference in forward scattering magnitudes between these runs are due to the difference in detector sensitivities between the instruments. Table S2. Seahorse CCCP concentrations. Final concentration of CCCP in the wells during Seahorse assays for each cell line in 2D and 3D, based on titration over a range of concentrations yielding the maximum OCR values.


Knowing the responses of a patient to drugs is essential to make personalized medicine practical. Since the current clinical drug response experiments are time-consuming and expensive, utilizing human genomic information and drug molecular characteristics to predict drug responses is of urgent importance. Although a variety of computational drug response prediction methods have been proposed, their effectiveness is still not satisfying.k predicted sensitive cell lines.In this study, we propose a method called LGRDRP (Learning Graph Representation for Drug Response Prediction) to predict cell line-drug responses. At first, LGRDRP constructs a heterogeneous network integrating multiple kinds of information: cell line miRNA expression profiles, drug chemical structure similarity, gene-gene interaction, cell line-gene interaction and known cell line-drug responses. Then, for each cell line, learning graph representation and Laplacian feature selection are combined to obtain network topology features related to the cell line. The learning graph representation method learns network topology structure features, and the Laplacian feature selection method further selects out some most important ones from them. Finally, LGRDRP trains an SVM model to predict drug responses based on the selected features of the known cell line-drug responses. Our five-fold cross-validation results show that LGRDRP is significantly superior to the art-of-the-state methods in the measures of the average area under the receiver operating characteristics curve, the average area under the precision-recall curve and the recall rate of top-Our results demonstrated that the usage of multiple types of information about cell lines and drugs, the learning graph representation method, and the Laplacian feature selection is useful to the improvement of performance in predicting drug responses. We believe that such an approach would be easily extended to similar problems such as miRNA-disease relationship inference. Personalized medicine focuses on finding appropriate drugs for individual patients. Since the same drugs have different effects on different patients, knowing the responses to drugs for each individual is a prerequisite of personalized medicine . Since cBased on cell line gene expression data, Torkamani et al.  used PCABased on the gene expression features of cell lines and the chemical features of drugs, Li et al.  developeBased on the assumption that similar cell-lines tend to respond similarly to similar drugs, a lot of network-based drug response prediction methods have been proposed recently. For example, after integrating different kinds of information such asgene mutation, DNA copy number and mRNA expression data of cell lines and compound molecular properties, ATC-codes and side-effects of drugs, Wang et al.  built siIn most network based drug response prediction methods, heterogeneous data are integrated using a weighted graph , how to capture useful topological information of a graph is important for the efficiency of drug response prediction. Recently many learning graph representation methods have been introduced, and GraRep  is a staIn the paper, we formulate the drug response prediction problem as a classification task as most of the existing methods: for each drug, classify cell lines into two groups: sensitive and resistant according to the cell lines’ features. To improve drug response prediction performance, we integrate several available related data such as known cell line-drug associations, miRNA expression profiles of cell lines, chemical structures of drugs, PPIs, cell line gene sequence variations and hypermethylation informtion into a heterogeneous network. Then GraRep  and LaplWe conducted a series of 5-fold cross-validation experiments to test the performance of LGRDRP and some other state-of-the-art drug response prediction methods. The cross-validations were done for each drug. When a query drug was selected, all the cell lines with known responses (sensitive or resistant) to the drug were randomly divided into 5 groups. We randomly select one group as the test data and the other four as the training data. The heterogeneous network of the train data was obtained by removing the edges between the query drug vertex and the test cell line vertices.For each cell line, a drug response prediction method calculated a score, and the test cell lines were sorted according to their scores. With a fixed threshold, if the score of one cell line is below the threshold, it is labeled as negative (resistant), and if it is known sensitive to the query drug, it is a false negative; if it is known resistant to the drug, it is a true negative. When the prediction score of a cell line is equal to or above the threshold, it is viewed as positive, and if it is known sensitive to the query drug, it is a true positive; if it is known resistant to the drug, it is a false positive. The true positive rate (TPR), the false positive rate (FPR), the precision ratio (Prec) and the recall ratio (Rec) can be computed as follows: TPR = TP/(TP + FN), FPR = FP/(FP + TN), Prec = TP/(TP + FP), Rec = TP/(TP + FN), where TP, FN, FP and TN are the numbers of cell lines that are true positive, false negative, false positive and true negative, respectively.With the threshold increases from the smallest score to the highest score, a receiver operating characteristic (ROC) curve is drawn according to the varying TPRs and FPRs as X-axis values and Y-axis values respectively.The area under the ROC curve (AUC) is calculated to evaluate the prediction performance. Since in our data set, the number of resistant responses is much larger than the sensitive responses. To better measure the prediction performance, we also used another metric: the area under the precision-recall (PR) curve (AUPR). A PR curve is a trajectory of the performance at a plane with the precision ratio as Y-axis value and recall ratio as X-axis value when the threshold changes.Furthermore we may be more interested in the cell lines at the top of the sorted list. Therefore, the percentages of the true sensitive cell lines in the top 10, 20, 50, 100 of the sorted cell lines according to their response scores to the query drug were also used to evaluate the prediction performance.K, d and t need to set for LGRDRP, where K denotes the maximal transit steps, d determines the dimension of representation vectors and t is the number of features remained after the feature selection procedure. We tested the performance of LGRDRP with different K, d and t. Figure K, d and t. In the left panel, t was set 64. Please note, when the length K increased, the average AUC of LGRDRP increased accordingly. However, when K increased from 6 to 7, the performance of LGRDRP didn’t improve too much, but the computing time increased significantly. When d increased from 16 to 256, AUC increased accordingly. However, when d is set 512, AUC begins to decrease. The reason may be that the overfitting model results in poor generalization.There are three parameters t, and the results are shown in the right panel of Fig. t is too small, the left features can’t capture enough structure information, but when t is too large, too many features may include some unimportant information which may disturb the prediction ability [With entclass1pt{minimaIn the following tests, We compared the prediction performance of LGRDRP with three other art-of-the-state methods: HNMDRP , SVMDRP Figure Figure In this paper, we propose a drug responses prediction method called LGRDRP. It first uses the cell line miRNA expression profile to build a cell line similarity network, drug chemical structures to build drug similarity network, cell line gene variations and methylation data to build cell line-gene interaction network. By integrating the known cell line-drug responses into the above networks, LGRDRP constructs a heterogeneous network. Then LGRDRP uses a learning graph representation method GraRep to obtain the representation vectors as the topology structure features of vetices in the network. To avoid overfitting causing by using too many features, a Laplacian score method is adopted to pick out some important features. Finally, LGRDRP learns an SVM model which is used to predict drug responses. Extensive 5-fold cross-validation experiments showed that LGRDRP was generally superior to three art-of-the-state methods HNMDRP, SVMDRP and Stanfield’s method. The success of our method is based on the effective integration of diverse biological information, the good graph representation of the topology structure of the network, and the effective feature selection. After minor modifications or simple extensions, LGRDRP can also be employed in other biological predictions such as gene-disease , drug-taThe heterogeneous network consists of a drug similarity network, a cell line similarity network, a gene similarity network, a cell line-drug interaction network, and a cell line-gene interaction network, as shown in Fig. http://pubchem.ncbi.nlm.nih.gov/). To avoid disturbing from noises and make sure the network has a clear biological meaning, the elements smaller than 0.2 in the similarity matrix were set as 0. The drug similarity network consists of 226 vertices and 24456 edges, where each vertex denotes a drug and each edge has a similarity score weight . The Pearson correlation coefficient of the miRNA expressions of two cell lines is regarded as the similarity between them, and is used as the weight of the corresponding edge in the network. Protein-protein interactions (PPIs) have been extensively studied, and we used the interactions between the proteins to represent the interactions between the genes coding the proteins. The gene-gene similarity network is based on the PPI data from iRefIndex [miRNA expression information of cell lines could be used to classify cancer cell lines into subtypes , and ourRefIndex , which chttp://www.cancerrxgene.org/). The responses have been divided into two types sensitive and resistant according to the log-normalized IC50 threshold, and there are 20346 sensitive responses and 155277 resistant responses. Accordingly, there are 20346 interaction edges with weight 1 in the cell line-drug interaction network.The cell line-drug interaction network is based on the drug response data of the 968 cell lines and the 226 drugs from GDSC . The CFE data of the cell lines have been downloaded from GDSC and were used to build the cell line-gene interaction network. Similar to previous literature , we clasG is G is Finally, we constructed a heterogeneous network including 226 drugs vertices in the drug similarity network, 2981 gene vertices in the gene similarity network, and 968 cell line vertices in the cell line similarity network. The network is represented as a weighted graph As most similar works, we assume that similar cell lines tend to have similar responses to the same drug, and predict the response of a query cell line to a certain drug by utilizing the similarity between the query cell line and other cell lines with known responses to the drug. Since the heterogeneous network has integrated multiple types of information related with cell-lines and drugs, the neighbourhood of cell-lines in the network could be used to measure the similarity between cell-lines. Based on the idea, we use the learning graph representation method GraRep  to obtaiG, Learning Graph Representation (LGR) aims to learn a feature vector Given a graph M to represent the heterogeneous network G, and element M, a weighted degree matrix W is calculated according to Eq.  as follows:c is resistant to d (i.e. there is no edge between the vertices d and c in G) and For a drug d, c) in G while maximizing d and c. Therefore, the following log-likelihood G is our global objective function.E indicates whether there is an edge between the drug vertex d and the cell line vertex c: N is the number of resistant responses, and d to the cell vertex c.Our objective is to maximize x be 0, therefore Eq. ,,k-step r on Eqs. .9\documek-step representations with K could capture more distant similarity information but also introduce more noise.As we have assumed before, cell lines with similar topology structures in the network tend to have similar responses to the same drug. Since cell lines with similar topology structures are more similar with respect to the representation vectors, we construct a binary classification model using the representation vectors of the cell lines as the input features and output their responses (sensitive or resistant) to a query drug. To integrate comprehensive similarity information between cell lines, we consider all K and d are large numbers, the number of features  method could effectively deal with the imbalance problem by assigning different weights to positive and negative samples, we chose SVM to conduct the classification task. In the following experiments, we employed LIBSVM  to do thand the order of the polynomial kernel took the default value 3, the weights for the positive class and the negative class were set as the number of negative samples, and positive samples, respectively, and the other options were left default. For each drug, LIBSVM can learn an SVC model on the training data, and for each cell line, the model outputs a decision score. If the decision score is larger than 0, the cell line is predicted sensitive to the drug, and a larger score indicates that the prediction is more convinced.


Indian natural products have been anecdotally used for cancer treatment but with limited efficacy. To better understand their mechanism, we examined the publicly available data for the activity of Indian natural products in the NCI-60 cell line panel.We examined associations of molecular genomic features in the well-characterized NCI-60 cancer cell line panel with in vitro response to treatment with 75 compounds derived from Indian plant-based natural products. We analyzed expression measures for annotated transcripts, lncRNAs, and miRNAs, and protein-changing single nucleotide variants in cancer-related genes. We also examined the similarities between cancer cell line response to Indian natural products and response to reference anti-tumor compounds recorded in a U.S. National Cancer Institute (NCI) Developmental Therapeutics Program database.Phyllanthus and cucurbitacin products with known anti-tumor agents with anti-mitotic mechanisms of action. Curcumin and curcuminoids mostly clustered together. We found associations of response to Indian natural products with expression of multiple genes, notably including SLC7A11 involved in solute transport and ATAD3A and ATAD3B encoding mitochondrial ATPase proteins, as well as significant associations with functional single nucleotide variants, including BRAF V600E.Hierarchical clustering based on cell line response measures identified clustering of These findings suggest potential mechanisms of action and novel associations of in vitro response with gene expression and some cancer-related mutations that increase our understanding of these Indian natural products.The online version contains supplementary material available at 10.1186/s12885-022-09580-7. Ayurveda is a traditional system of medicine that originated around 3000–4000 BCE, which utilizes Indian natural products (INP) derived mainly from plants to treat “imbalances” in the body aiming to cure a variety of diseases, including cancer . In the Historical references in Ayurvedic text contain some of the first descriptions of cancer (blood and soft tissue) and their successful treatment with a combination of INPs administered via oral and topical routes . HoweverCurcuma longa (turmeric). Curcumin has generated a lot of interest as an INP with possible chemo-preventative, anticancer, and anti-inflammatory properties, highlighting the difficulty of defining a specific indication due to its description as a panacea [NCT02064673, NCT02944578, NCT02782949) exploring the role of curcumin as a chemo-preventative agent in preventing gastric cancer, cervical intraepithelial neoplasia, and the recurrence of prostate cancer.Curcumin is a bioactive polyphenol that is the most common curcuminoid, a group of compounds that impart a yellow color to  panacea . Some re panacea , 8. ThesAzadirachta indica) is another commonly used herbal product that has several component INPs with reported anticancer properties, which highlights the difficulty in isolating active INP compounds. Nimbolide is a terpenoid lactone derived from Neem that induces apoptosis in pancreatic cancer cells through reactive oxygen species (ROS) generation and upregulation of pro-apoptotic proteins [Neem (proteins . Geduninproteins . These mPhyllanthus emblica), a.k.a. Indian gooseberry, is part of the genus Phyllanthus, which has been used in traditional herbal medicine to treat multiple ailments. The Phyllanthus genus includes several species  which have been used to treat a wide range of ailments from diabetes to renal calculi [P. emblica, has been shown to attenuate tumor growth in breast and pancreatic cancer models through multiple mechanisms including growth signal inhibition of the PI3K pathway and tyrosine kinase inhibition [Amla  and bitter gourd (Momordica charantia) [Cucurbitacins are a group of compounds characterized by a tripterpene hydrocarbon. which are found in over 40 species, including Indian plants such as Brahmi (arantia) . These parantia) .. Data regarding rarer INPs including plumbagin (Plumbago zeylanica), alizarin (Rubia cordifolia), and Achilleol A (Achillea odorata) are limited or have not yet been reported [While putative anti-cancer mechanisms of action have been suggested for commonly used INPs as detailed above, these data are often limited to in vitro response in one or a few cell linesreported . AnalysiOur overall strategy to explore the possible mechanisms of action of INPs was to compare patterns of cell line response to each INP with publicly available data to those for standard reference anticancer compounds and to identify clusters (subtrees) of INPs with similar patterns of response across the NCI-60 cell lines. Next, we examined the association of gene expression levels and of clinically or biologically important single nucleotide variants (SNVs) with response to individual INPs. We also examined how the molecular features associated with tumor cell line responses to individual INPs were distributed among the INP subtrees that had similar patterns of response. Lastly, we investigated the biological pathways representing the gene expression patterns that were associated with different INP subtrees. These analyses provided new insights into potential mechanisms of actions of the INPs.To examine the activity of INPs in tumor cells, we analyzed publicly available data from the NCI-60 cancer cell line panel. The NCI-60 initiative was started by the U.S. National Cancer Institute (NCI) in 1989 with the purpose of screening candidate anti-cancer compounds on 60 cancer cell lines representing 10 different tumor types. Over 100,000 compounds have been screened to date, including INPs and well-characterized reference compounds approved for clinical use  –18. The , molecular features of the NCI-60 cell lines have been extensively characterized. Their gene expression, whole exome sequencing, and other molecular data have been made publicly available [Additionallyvailable , 21. Thevailable , 23. MeaFigure A biomedical literature search in PubMed at the National Center for Biotechnology Information (NCBI) using keywords “Ayurveda” AND “cancer” AND “review” was conducted to identify Ayurvedic herbs of interest, with a total of 170 publications found. Each publication was manually reviewed. Among them, 25 publications contained a comprehensive description of one or more Ayurvedic herbs and their specific INPs that are commonly used by Ayurvedic practitioners in cancer treatment. These INPs were included in subsequent searches. All INPs identified in our manual curation were then searched in PubMed for evidence of any activity in cancer cell lines and were compiled, resulting in the total of 258 INPs.n = 66) were excluded from further analysis. The unique NSC numbers for the remaining INPs (n = 192) identified from biomedical literature were interrogated using the NCI PUBLIC COMPARE portal for available GI50 data (https://dtp.cancer.gov/public_compare) [n = 117) were excluded. The remaining INPs (n = 75) were used as input for separate queries in the NCI PUBLIC COMPARE portal. The public version of the NCI PUBLIC COMPARE database does not store the taxonomy and global locations of the original source products for the database compounds. The queries use Pearson correlation analysis to compare the vector of GI50 values across the NCI-60 cell line panel for each input INP to the vector of GI50 values for available COMPARE reference antitumor agents . We used a cutoff of the absolute value for a pairwise Pearson correlation coefficient |r|> 0.5 to select the reference compounds with similar GI50 response profiles to each input INP.The NCI DTP screening program uses a special identifier, called an NSC number, for each compound screened in the NCI-60 cell line panel. Those INPs obtained from our literature search that did not have NSC numbers (compare) , 25. Eaccompare) . Those I10 GI50 data  from the static public release at the DTP website NCI-60 Growth Inhibition data repository (https://wiki.nci.nih.gov/display/NCIDTPdata/NCI-60+Growth+Inhibition+Data). This dataset is currently available under previous releases . Details of the sample handling, preparation and cell line testing methods followed to generate the data in this repository are described elsewhere [10GI50, a measure of cell line response to treatment. Here and below, we refer to these measures as logGI50. All logGI50 values that were not available were set to missing. The term “compound” is used to describe the INPs and reference compounds with available logGI50 data. As multiple experiments had been run for each compound, the median logGI50 was calculated, using replicate experiments, for each cell line-compound pair. These median logGI50 values for each NCI-60 cell line were computed for all 132 compounds using 15,199 experiment records. The majority of the data were screened in molar units, except for the product of Ricinus communis (NSC 15384), which had the units in μg/ml and was not included in the clustering analysis for that reason. A more detailed description of the public COMPARE algorithm and the NCI-60 cell line panel can be found elsewhere [The NSC numbers of the 75 INPs and the 57 reference compounds that were correlated with at least one of those 75 INPs with |r|> 0.5 Table  were uselsewhere . The NLOIn order to identify groups of INPs with similar patterns of activity in the NCI-60 cell line panel, we employed hierarchical clustering of the INPs. The initial clustering to identify groups of compounds with similar response patterns was based on the logGI50 values Fig. . Referen10 scale) for the total inhibition of growth, respectively[https://wiki.nci.nih.gov/NCIDTPdata/NCI-60+Growth+Inhibition+Data). Values for all INPs and reference compounds were extracted, and median values were computed as detailed above. Pairwise Euclidean distances were calculated, and unrooted radial hierarchical trees were generated using the methodology described above. These trees were visualized and compared to the tree inferred using logGI50 values . For each gene-INP pair, Spearman correlation was computed to evaluate the association between pre-treatment gene expression and logGI50 in 58 cell lines. Benjamini–Hochberg procedure was applied to control the false discovery rate (FDR) across the 23,059 gene × 75 INP pairs. Gene-INP pairs with FDR-adjusted p < 0.05 were considered significant. A positive value of the Spearman correlation coefficient ρ indicated an association of higher gene expression with higher logGI50 values of an INP, i.e., with increased resistance to that INP. Similarly, negative values of ρ showed an association of higher gene expression with lower logGI50 values, i.e., with increased sensitivity to that INP. Here and below, the terms sensitivity and resistance were used to define the direction of the associations, as the analyses of logGI50 values were performed on the continuous scale. All genes with significant Spearman correlations were investigated to determine whether the gene involved in the gene-INP pair was associated with a known molecular mechanism of action of reference compounds that clustered in the same subtree with that INP.To examine how NCI-60 cell line response to INPs may be influenced by molecular genetic features, we analyzed the association of median logGI50 values with NCI-60 molecular data. Pre-treatment gene expression data for the NCI-60 cell lines was downloaded from the CellMinerCDB resource , 28. A ment team . Cell lihttps://biit.cs.ut.ee/gprofiler/gost), which is a regularly updated web-based utility that includes annotated pathway gene sets from KEGG, Reactome, and WikiPathways [p < 0.1) in each cluster were stratified to negatively and positively correlated groups (Supplementary Tables p < 0.05 (FDR adjusted).Gene set enrichment analysis was performed using g:Profiler  data were downloaded from the CellMiner data download portal , 31. Onet-test was used to compare logGI50 values between groups of NCI-60 cell lines defined by variant status, for each SNV-INP pair. A positive value of the t-statistic indicated an association of higher gene expression with higher logGI50 values of an INP, i.e. increased resistance to that INP, whereas a negative value of the t-statistic showed an association of higher gene expression with lower logGI50 values, i.e. increased sensitivity to that INP. All analyses of associations between response to the natural products and sequence variants were performed using the RStudio v1.0.153. Biological interpretation of significant SNV-response associations was based on SNV annotation in OncoKB, using its updated annotation of levels of functional and oncogenic SNV effects as of 03/25/2021, and on published reports in biomedical literature.The data were filtered using a list of candidate genes and functionally relevant SNVs from OncoKB v. 1.17, a curated precision oncology knowledge base . As outlp < 0.05) of logGI50 with gene expression and with single nucleotide variants, and of association of significantly upregulated and downregulated cellular pathways with INP subtrees was performed using Cytoscape v. 3.9.1 [Visualization of significant associations ; however, they also included some agents that act as DNA intercalators (doxorubicin) and anti-metabolites (methotrexate). Some INPs of the cucurbitacin family and its derivatives  affect mitotic spindles and delay mitoses leading to a G2/M phase cell cycle arrest of cancer cells , 14. PhyPhyllanthus INPs.The 22 reference compounds in this subtree had many different mechanisms of action; however, the majority fit into either alkylators , ribonucleoide reductase inhibitors , and broad inhibitors of RNA synthesis . The 34 INPs included in this cluster consisted of a large group of cinnamon-based INPs and some Calendula products.The 17 reference compounds in this subtree consisted of a variety of alkylators , anti-metabolites , and DNA-crosslinking agents (carboxy-platinum). The 25 INPs included in Subtree 3 consisted of curcumin, curcuminoids, neem, and Supplementary Figs. p value < 0.05). All significant results are listed in Table Using pre-treatment gene expression data of 23,059 transcripts and the median logGI50 values of the 75 INPs, we conducted a Spearman correlation analysis that identified 204 natural product-gene pairs (including 190 unique genes and 28 unique INPs) that were statistically significant after adjusting for multiple testing  demonstrates increased resistance of tumor cell lines to plumbagin associated with increased gene expression of SLC7A11, which is consistent with the previous findings by our group and other authors about the potential role of this transporter in resistance to multiple antitumor agents and natural products [SLC7A11 (solute carrier family 7 member 11) has recently been suggested as potential drug target in pancreatic adenocarcinoma . It playproducts , 42, 44.ρ = -0.57, unadjusted p value = 3.68 × 10–6, FDR adjusted p value = 0.04) and ATAD3B  genes demonstrates that increased sensitivity of cell lines to curcumin  was associated with increased expression of the ATAD3A and ATAD3B genes.ATAD3A and ATAD3B are mitochondrial ATPase proteins expressed in embryogenesis. ATAD3B has been shown to be over-expressed in head and neck cancer and hepatocellular carcinoma , 46. Curρ = -0.66, unadjusted p value = 1.69 × 10–8, FDR adjusted p value = 3.84 × 10–3) demonstrates an association between increased sensitivity of cell lines to phyllanthoside and increased expression of the MYB gene. This suggests a potential role of MYB-mediated transcriptional regulation in response to this INP.MYB, a transcriptional activator, is a proto-oncogene that has been shown to be over-expressed in hematologic, colorectal, and breast cancer . The negThe results of pathway analysis using g:Profiler are presented in Supplementary Tables Biological pathway analysis using g:Profiler identified several biological pathways and functions which may be associated with increased sensitivity or resistance to INPs. Among the INPs in Subtree 1, resistance to NSC number 328426 (phyllanthoside), 342443 (S3’-desacetyl-phyllanthoside), 94743 (cucurbitacin A), 143925 (pekilocerin A), 112167 (elatericin B) was associated with pathways related to mineral homeostasis (Supplementary Table Andrographis paniculata) was associated with expression of genes involved in several molecular pathways , 643023 , 365798 (piceatannol), 112166 (cucurbitacin K) and sensitivity to 32982 (curcumin), 309909 (nimbolide), 87868 (phenethyl mustard oil), 742021 (curcumin tri adamantylaminoethylcarbonate), 742019 (ethoxycurcumin trithiadiazolaminomethylcarbonte), 705537 (daturaolone), 643769 , 383468 , suggesting that overexpression of those genes may confer increased sensitivity to these products. This association indicates that such INPs could be explored to target this pathway. Curcumin and its related analogues have been demonstrated to also have a demethylating effect [While the Slit/Robo pathway mainly involves functions to promote axon branching and neuronal migration, it is also involved in other physiological processes including angiogenesis and apoptosis . Promoteg effect .t-test to analyze the differences between logGI50 values comparing cell lines with and without individual protein-changing single nucleotide variants in each of the 107 genes listed in OncoKB. After FDR adjustment, 13 SNV-INP pairs satisfied the FDR adjusted p value < 0.05, including 4 unique genes and 10 unique natural products. Below we discuss examples of associations of functionally important variants and likely oncogenic variants from OncoKB . This association suggests that cucurbitacin D may have a role in targeting cancers with BRAF mutations or having an effect on BRAF [OncoKB lists BRAF V600E as a level 1 actionable variant, which was present in 9 cell lines  in the NCI-60 dataset. Tumors with this variant are responsive to treatment with BRAF inhibitors  and in combination with MEK inhibitors this has been shown to be an effective treatment strategy for melanoma . Consist on BRAF . AlternaMultiple INPs were significantly associated with likely oncogenic individual variants listed in OncoKB in the KDR and KNSTRN genes  and the likely gain of function variant T992I in MET.The receptor tyrosine kinase MET gene variant T992I was associated with sensitivity to multiple INPs, including products from the cucurbitacin family  and the Tylophorine family  and resistance to other products .Calendula family  and the Phyllanthus family  and resistance to achilleol A (NSC 710351).The likely oncogenic, likely gain of function KDR gene variant C482R was associated with sensitivity to two INPs from the The likely oncogenic, likely gain of function kinetochore KNSTRN gene variant A40E was associated with sensitivity to three INPs .In this study, we used in vitro data to examine the associations of variation in gene expression and deleterious mutations with tumor cell response to INPs. We also compared response patterns to those of reference compounds as a preliminary investigation of the possible mechanisms of action of these products at the cellular level. We reported the findings that were highly significant after the correction for multiple comparisons. We compared publicly available cancer cell line response data in the NCI-60 panel for 75 INPs to data for standard reference antitumor compounds. Our joint analysis of molecular data and measures of cell line response to INPs and the comparison of the cytotoxic effects of INPs to those of established antitumor reference compounds allowed us to quantitively assess the potential involvement of individual genes and molecular pathways in tumor cell response to INPs. In Supplementary Figs. Subtree 1 from the clustering of logGI50 values of INPs and reference compounds consisted of many products with anti-mitotic mechanisms of action, confirming previously reporting anti-mitotic activity of some INPs including phyllanthoside, S3’-desacetyl-phyllanthoside and the cucurbitacin family, 34. Ove/ ATAD3B expression and sensitivity to curcumin has not been previously reported. The products of these genes, ATPase family AAA domain containing 3A and 3B proteins, are involved in multi-protein complexes associated with mtDNA that are important for regulation of mitochondrial biogenesis and lipogenesis. Curcumin has been reported to regulate expression of enzymes involved in mitochondrial biogenesis and mitochondrial oxidative stress, to increase apoptosis and autophagic cell death, and to reduce cellular proliferation [Our analysis found multiple novel associations between gene expression and logGI50 values of INPs, including a highly significant association between increased levels of SLC7A11 expression and resistance to plumbagin. This resistance may involve increased SLC7A11 expression inhibiting ferroptosis, a distinct form of cell death due to excessive lipid peroxidation . To our feration –62. The feration , 63, 64.feration .Cucurbitaceae family, which include cucurbitacin D, are found in many gourd species. While they have demonstrated cytotoxicity in many cell lines, our finding of increased sensitivity in BRAF V600E mutated cell lines which includes almost all the melanoma cell lines in our dataset may warrant further investigation.Our analysis of SNV variants demonstrated a statistically significant association of BRAF V600E with logGI50 measure of response to cucurbitacin D. The triterpene compounds from the Triphala, Momordica charantia, and Withania somnifera. Additional open-source natural products databases [Paucity of INPs available in the public domain and consequently their underrepresentation in the NCI-60 cell line database limited our ability to evaluate some of the more commonly used Ayurvedic concoctions and herbs of interest including atabases –67 contaWe used logGI50 values as the primary response endpoint because many previous studies have shown these measures to be a relevant outcome to study associations with molecular targets. When using logGI50 values, clusters of compounds derived from logGI50 values have been shown to correlate well both with potential mechanism of cell line response and with similarities among compound structures , 68–70.We used median logGI50 derived from the five-range dose screen as our measure of cell line response for the analysis of associations with molecular features of tumor cell lines. While this single logGI50 measure is informative in characterizing the cytotoxic effect of individual products, it may not reflect the cytotoxicity of the compound if it fell outside the pre-defined range of activity, in which case this measure would not reflect low levels of activity of some of the compounds we analyzed. As we analyzed pre-treatment gene expression levels for each cancer cell line, our findings cannot characterize the association between cell line response and post-treatment gene expression changes in response to each INP or reference compound. Such analyses may be of potential benefit in the future if post-treatment response data for Indian natural products become available. As the NCI-60 panel does not include normal cell lines for comparison, we did not focus on toxicity of these compounds and further studies will need to examine the side effects of these INPs.As a note of caution, our findings do not indicate clinical efficacy but rather our study is an attempt to characterize available INPs and identify possible mechanisms of action for further study. In this analysis, utilization of the in vitro molecular screening data from the NCI-60 allowed us to identify molecular features of tumor cells associated with response to INPs. As Ayurvedic products are often used in specific combinations, our analysis would not be able to evaluate their clinical and immunomodulatory features associated with response to the combinations of such agents. Additionally, due to the limited representation of tumors and mutational features in the NCI-60 panel, we could not examine the response within individual cancer categories. Additional models including mouse patient-derived xenografts or other clinically relevant approaches may be needed to further investigate the physiological effects of Ayurvedic products in specific tumor types.Our analysis examining NCI60 response patterns for 75 INPs and standard reference compounds and their similarities allowed us to elucidate potential common mechanisms of action and molecular features associated with response to these INPs. We identified a number of genes and several biological pathways that were associated with sensitivity and resistance to specific INPs and/or entire INP clusters. Our findings provide a proof of principle that INPs may represent compounds of interest for cancer drug discovery and further studies should increase our understanding of their possible mechanisms of action.Additional file 1. Supplementary Figure 1. Heatmap of median logGI50 values of Indian natural products and reference compounds. Each row represents an Indian natural product or a standard reference compound and each column represents a cell line in the NCI-60 cancer cell line panel. The color key represents the logGI50 levels with negative values (blue) representing sensitivity of a cell line to the product and positive values (red) representing resistance to a product. Missing data are represented as black. The range of logGI50 values was -12.5 to -0.25 molar units.Additional file 2. Supplementary Figure 2. Hierarchical clustering of INPs and reference compounds based on their median logLC50 values across NCI60 cell lines. The tree was inferred using the UPGMA (‘average’) method and was based on Euclidean distances. The tree is presented as an unrooted radial phylogram. The scale in the top left corner is provided for the branch lengths, which were derived from Euclidean distances. Clustered products are displayed with sparse labeling, in which only a random subset of INP labels is displayed.Additional file 3. Supplementary Figure 3. Hierarchical clustering of INPs and reference compounds based on their median total growth inhibition (TGI) values across NCI60 cell lines. The tree was inferred using the UPGMA (‘average’) method and was based on Euclidean distances. The tree is presented as an unrooted radial phylogram. The scale in the top left corner is provided for the branch lengths, which were derived from Euclidean distances. Clustered products are displayed with sparse labeling, in which only a random subset of INP labels is displayed. Additional file 4. Supplementary Figure 4. Graphical overview of significant associations logGI50 of Indian natural products with gene expression. Shown are significant associations with FDR adjusted p < 0.05, which are listed in Table Additional file 5. Supplementary Figure 5. Graphical overview of significant associations of logGI50 of Indian natural product subtrees 1 and 3 with molecular pathways from Reactome, KEGG, and WikiPathways. Shown are significant associations identified by g:Profiler with FDR adjusted p < 0.05. (A) Positive associations for Subtree 1. (B) Positive associations for Subtree 3. (C) Negative associations for Subtree 3. Additional information about each association shown in the Figure is provided in Supplementary Tables Additional file 6. Supplementary Figure 6. Graphical overview of significant associations of logGI50 of Indian natural products with protein-changing SNVs in cancer-related genes, which are listed in Table p < 0.05. INPs are presented by colored circles, with colors corresponding to their subtree assignment based on clustering of their logGI50 values shown in Fig. t-statistic in the Student’s t-test. An arrow toward an INP indicates a positive value of the t-statistic, suggesting increased cell line resistance to that INP in the presence of a variant. In contrast, an arrow toward a variant indicates a negative value of the t-statistic, suggesting increased cell line sensitivity to that INP in the presence of a variant.Additional file 7. Supplementary Table 1: Positively correlated pathways in Subtree 1Additional file 8. Supplementary Table 2: Positively correlated pathways in Subtree 3Additional file 9. Supplementary Table 3: Negatively correlated pathways in Subtree 3Additional file 10. Supplementary Table 4: All queried Ayurvedic INPs from the PUBLIC COMPARE portalAdditional file 11. Supplementary Table 5: Concordance between the clustering of Indian natural products and reference compounds based on logGI50, logLC50, and TGI values


Transcriptome signature reversion (TSR) has been extensively proposed and used to discover new indications for existing drugs  for various cancer types. TSR relies on the assumption that a drug that can revert gene expression changes induced by a disease back to original, i.e. healthy, levels is likely to be therapeutically active in treating the disease. Here, we aimed to validate the concept of TSR using the PRISM repurposing data set, which is—as of writing—the largest pharmacogenomic data set. The predictive utility of the TSR approach as it has currently been used appears to be much lower than previously reported and is completely nullified after the drug gene expression signatures are adjusted for the general anti-proliferative downstream effects of drug-induced decreased cell viability. Therefore, TSR mainly relies on generic anti-proliferative drug effects rather than on targeting cancer pathways specifically upregulated in tumor types. Transcriptome signature reversion (TSR) has been extensively used with the aim to discover new indications for existing drugs  for various cancer types . TSR relet al. [50) of compounds tested in a single cell line of that tumor type.The typical TSR drug repositioning study proceeds according to the following approach: first, the genes upregulated and downregulated in the tumor tissue as compared with the adjacent ‘healthy normal’ tissue (tumor signature) are identified using differential gene expression analysis. Next, either the Connectivity Map database or its successor the Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 database is screened for gene expression signatures of drugs (drug signatures), which reverse the differentially expressed genes (DEG) identified in the first step in the opposite direction , 3. Finaet al. , which dHowever, drugs identified using TSR may be targeting the downstream proliferative ‘effect’ rather than the upstream ‘cause’ of the proliferative phenotype characterizing a specific cancer type. Indeed, it has been shown that drugs that decrease cell viability show similarity in gene expression perturbation signatures, which is linked to transcription factors regulating cell death, proliferation and division time . In otheIn this study, we aim to comprehensively validate the concept of TSR using a series of 18 different solid tumor types with at least four cell lines per tumor type and 400+ drugs for each of these tumor types based on the recent release of the PRISM repurposing data set . In addiTo answer the main research objective of validating the predictive utility of connectivity scores for prioritizing drug repurposing candidates, we combined publicly available data from the PRISM, LINCS and The Cancer Genome Atlas (TCGA) databases . Data acdepmap.org’ (release with filenames ‘secondary-screen-dose-response-curve-parameters.csv’ and ‘secondary-screen-cell-line-info.csv’). Four parameters that result from fitting the dose response curve were available, called ‘slope’, ‘auc’, ‘ec50’ and ‘ic50’, in the data set. The most natural choice would be to use the half maximal inhibitory concentration (IC50) as this was used in the paper of Chen B et al.; however, the IC50 was only estimable in 51% of experiments because of the limited concentration range used in the experiments. The half maximal effective concentration (EC50) was also not a great choice to compare drugs with each other because of the expected differences in the maximal response between drugs and its high variability (ranged from 1 × 10−7 to 4 × 10304). The slope has a wide data range, which includes negative numbers (−4891 to +18 537) and is therefore difficult to interpret. The dose–response area under the curve (AUC) therefore appeared to be the best option as it has the narrowest range (0.004174 to 4.889162) of all parameters with a relatively simple interpretation: it represents the fraction of cells left after drug exposure averaged over all the tested concentrations normalized to cells receiving no drug treatment, such that AUC values below 1 indicate sensitivity to treatment. The nonnegative nature of the distribution also makes it possible to log-transform, making it easier to include in statistical models and to display in figures.As of writing, the PRISM repurposing data set constitutes the largest data available on cancer cell line viability, significantly more than earlier publicly released high-throughput databases (i.e. GDSC1000 and CTRPv2)  . The daDrug signatures, based on data generated by the LINCS Program , were cagdc.xenahubs.net  [2-FC (Counts + 1) expression below 6 were removed, so that only the 13 386 most highly expressed genes remained  . Twenty-remained .2-FC estimates and standard errors of each tumor type, the rma function in the metafor package (version 3.0-2) was used with all default options.To create the tumor gene expression signatures, the samples of each tumor type were first normalized using the upperquartile method using the ‘calcNormFactors’ function from ‘edgeR’ package (version 3.35.1). Next the data were analyzed using the ‘voom’ function from the ‘limma’ package (version 3.49.4), with a fixed effect for the tumor versus normal contrast. To create the ‘meta-analysis mean tumor signature’ used in Because each drug in PRISM is not always tested in exactly the same set of cell lines, a linear mixed model was used to separate the effect of cell lines and drugs. This step summarizes the AUC into an mnAUC. The interpretation of the mnAUC is the same as the original AUC, but it now represents the average fraction of cells left after drug exposure of a set of cell lines. First a linear mixed model was fitted using the ‘lme4’ package (version 1.1-27.1) using the following formula:ranef is the posterior Bayes estimates for the respective drug in the mixed model. This calculation was done for all drugs and cell lines at the same time and for the subsets of experiments that used cell lines that are associated with a particular TCGA tumor type.Then, the mnAUC was calculated using the following formula:2-FC after drug exposure of each gene after (y-axis) on the logarithm of the mnAUC (x-axis). The corrected DE is taken to be the residual, i.e. the part of the variation in differential expression not explained by the mnAUC .To calculate a connectivity score, one first needs to create a tumor signature. The first step is to remove any genes that were not measured in the LINCS microarray data, leaving 952 overlapping genes. The next step would be to only include genes that pass a certain threshold. The paper of Chen et al.  only inc76 genes . To minihttps://github.com/Bin-Chen-Lab/RGES. The benefit of the newer version is that it does not produce any connectivity scores of 0 in specific situations as described in the paper of Chen et al. [The connectivity scores were calculated using both the original method using the ‘connectivityScore’ function in the ‘PharmacoGx’ package (version 2.5.2) and the newer version ‘cmap_score_new’ available from n et al. , which chttps://maayanlab.cloud/Enrichr/ in April 2022.GSEA was performed using Enrichr, an enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists . Using tAll correlations mentioned in the results used the nonparametric Spearman correlation . This nonparametric method is used to obviate the assumption that the relationship between the variables is linear and has the advantage that it is invariant to monotone transformations of the variables (i.e. taking the logarithm of a variable does not change the correlation coefficient).PRISM and LINCS share 506 drugs that fit the inclusion criteria of the differential expression of drugs after drug exposure being measured in at least five cell lines. Of these included drugs, the cell viability was tested on between 89 and 479 cancer cell lines with a median of 445 , and theP < 0.05 (The mnAUC was centered around 1 (i.e. no effect versus control in the average cancer cell line) for the majority of drugs, with a sizeable minority having an mnAUC below 1, which signifies anticancer efficacy . The assP < 0.05 , with mo2-FC = 0) from mnAUC >1, but starts to increase and decrease, respectively, when the mnAUC decreases below 1. Note that a positive correlation in this context implies that the gene is downregulated in the presence of drugs that decrease cell viability, whereas a negative correlation implies that the gene is upregulated. Drugs with an mnAUC >1 have on average far fewer DEG than those with mnAUC <1  are on average upregulated in most tumor tissues .When the log tissues . SupplemP-value: 3 × 10−7), with other strong contenders being ‘DNA replication’ , ‘Mismatch repair’ , ‘Base excision repair’  and ‘Cellular senescence’ .To find out if there is a common link between the set of genes that are both frequently upregulated in cancer and strongly positively associated with mnAUC after drug exposure . As the results in R2 obtained by adding the connectivity score are only 0.01 and 0.02%, respectively. In addition, none of the P-values obtained go below the multiple testing corrected statistical significance threshold of 0.003. It should be noted in The hypothesis that drugs can normalize gene expression changes induced by a disease such as cancer are therapeutically active in treating this disease is attractive and rational. However, our study shows that TSR as currently used in the discovery of novel drugs for the treatment of cancer mainly relies on selecting drugs that have general anti-proliferative effects rather than drugs that interact specifically with the transcriptome characteristic for the tumor type.This is best illustrated using R2 of 1.6%), this turns out to be negligible compared with simply using the average AUC of the drugs in other cell lines (median R2 of 95.5%). Furthermore, adding the connectivity score as a covariate to the average efficacy of the drugs in other cell lines only increases the median R2 by 0.01%, which is a statistically insignificant amount for 16 out of the 18 tumor types included in the connectivity score analyses. This is further evidence to support the hypothesis that the connectivity score using the uncorrected drug signatures only captures a fraction of the generic anti-proliferation potential of a drug and does not provide any increased specificity toward cancer sub(types), which would be useful to support a drug repurposing effort.Indeed, after removing the effect of drug-induced decreased cell viability from the drug signatures by conditioning on effects seen in other cell lines at the same mnAUC, the connectivity scores are no longer predictive of drug effectivity . In addiR2) of 7% for invasive breast (n = 100 drugs), 26% for liver hepatocellular carcinoma (n = 24 drugs) and 16% for colon adenocarcinoma (n = 58 drugs) [et al., we used the same method of calculating connectivity scores, similarly combined the 6- and 24-h drug signatures and in one of our four tested scenarios used the same log2-FC and a statistical significance cutoff . A difference is that in our study each drug’s effect on cell viability is based on the results of all cell lines belonging to a specific tumor type in the PRISM data set, whereas the study by Chen et al. selected a single cell line as representative for each tumor type. In addition, we tested 400+ drugs for each of the 18 tumor types, whereas Chen et al. only included 24–100 drugs. All aspects combined, we believe our study constitutes a more comprehensive effort to test the underlying TSR hypothesis by using more drug data, by varying the method of selecting the tumor signature genes and by combining the results of all cell lines belonging to particular tumor type.Previous systematic research reported much higher predictive ability using TSR and connectivity scores as compared with our estimates, namely, explained variabilities  effects contained in the drug signature as well. Many of the drugs decreasing cell viability will use different mechanisms of action and thus the expected correlation between mnAUC and gene expression of genes in upstream pathways will be relatively weak, compared with the correlation between mnAUC and genes in the downstream pathways associated with cell viability in the same direction. Therefore, the corrected drug signatures are still expected to contain upregulated and downregulated genes specific to their upstream, primary mechanism of action although the magnitude of the upregulation and downregulation might have been reduced. Despite this, it remains possible some upstream pathways may on average be affected similarly in response to drugs decreasing cell viability as when the pathway is directly targeted. While this possibility exists and may hold true for some pathways, previous research has demonstrated that many drug signatures belonging to the same drug class cluster together in statistical analyses and therefore do contain unique information . Additio The results from this paper are primarily relevant to the use of TSR for the purpose of finding new anticancer drugs. However, we suggest that our findings should serve as a more general caveat to the use of TSR outside of oncology that the relatively nonspecific method of attempting to reverse all DEG associated with a specific disease may not be optimal, and there should be an attempt to separate out the effect of different gene expression pathways and their upstream and downstream effects on the transcriptome.Despite the presented evidence supporting a refined view of TSR efforts, we believe that TSR still has potential left to be useful as a drug repurposing method within oncology. For such efforts, we have three recommendations:First, it seems necessary to use single-cell RNA-seq data instead of bulk RNA-seq data. Using bulk RNA-seq data is conceptually problematic because the gene expression of all cell types present in the sample is measured at the same time. Solid tumor samples consist of tumor cells intermixed with other cell types such as immune cells, endothelial cells and stromal cells, whereas bulk adjacent normal samples contain varying mixtures of normal cells, none of which may be of the cell type from which the first tumor cell originated. For example, clear cell renal cell carcinoma (ccRCC) tumors are believed to originate from proximal convoluted tubule of the nephron , implyinin vivo-situation lacking the interaction of the tumor cells with the tumor microenvironment.Second, separating out the effect of driver events from passenger events is required. Tumor cells have to acquire many different driver events (e.g. mutations and copy number events) in specific sequences before they become numerous enough to cause symptoms and the patient is diagnosed with a tumor . BecauseThird, the scope of TSR could be broadened to non-tumor cell types present in the tumor microenvironment. Tumors survive by manipulating their environment to serve their needs, such as suppressing immune cells and stimulating endothelial cells to grow new blood vessels. Existing drugs that target the CTLA-4 receptor on T cells (e.g. ipilimumab) and VEGF receptor on cells of the vascular endothelium (e.g. sunitinib) already prove that this can be an effective strategy. For example, the ideal contrasts in these cases would be to compare the gene expression of inactivated immune cells in the tumor to those in the blood and cells in the vascular endothelium of the tumor to those in healthy tissues outside of the tumor. It would be especially interesting to do this for patients for whom therapy failed immediately or after the development of resistance, as the interactions between the tumor cells and non-tumor cells of interest are likely facilitated through as of yet understudied pathways which may be targetable by a drug already on the market.We believe that future efforts taking these recommendations into account may help to improve the specificity of drug repurposing.Transcriptome signature reversion (TSR) is a widely used computational method to find new uses for existing drugs, for example, against specific tumor types. It assumes that drug gene expression signatures that can reverse the gene expression signature of the malignant phenotype are more likely to be therapeutic in treating that phenotype.The connectivity score (measuring gene signature reversion), while statistically significant in most cases, only explains a median of 1.6% of the variation in the sensitivity of cell lines to drugs. Additionally, it does not provide any predictive benefit over the mean sensitivity measured using other cell lines not descended from the same tumor type.Removing the impact of decreased cell viability from the drug signatures completely nullified the predictive performance. This implies that the connectivity score as currently used only quantifies the generic anti-proliferative potential of drug, and cannot be used to reposition drugs against specific tumor types.We discuss several possible improvements on the current use of TSR, which may make it more useful for drug repositioning in the future.Supplemental_information_bbac490Click here for additional data file.


Malignant pleural mesothelioma (MPM) is a disease of the pleura related to asbestos exposure. Despite the advancements in new therapeutic frontiers, it has a dismal prognosis and very limited treatment options. To find novel weapons in the care of MPM, we undertook a drug-repurposing approach that consists of evaluating existing drugs already approved for other human diseases. We screened 1170 drugs, and we observed that cephalomannine, a taxane; ouabain, a cardiac glycoside; thonzonium bromide, an antifungal surfactant; and emetine, an emetic alkaloid, had marked activity against immortalized and patient-derived primary MPM cell lines. These compounds were shown to be promising, and they will be evaluated in further studies, both in vitro and in vivo. We believe that drug repurposing is a valuable strategy to facilitate and accelerate the definition of novel treatment options for the management of MPM.The lack of effective therapies remains one of the main challenges for malignant pleural mesothelioma (MPM). In this perspective, drug repositioning could accelerate the identification of novel treatments. We screened 1170 FDA-approved drugs on a SV40-immortalized mesothelial (MeT-5A) and five MPM  cell lines. Biological assays were carried out for 41 drugs, showing the highest cytotoxicity and for whom there were a complete lack of published literature in MPM. Cytotoxicity and caspase activation were evaluated with commercially available kits and cell proliferation was assayed using MTT assay and by clonogenic activity with standard protocols. Moreover, the five most effective drugs were further evaluated on patient-derived primary MPM cell lines. The most active molecules were cephalomannine, ouabain, alexidine, thonzonium bromide, and emetine. Except for alexidine, these drugs inhibited the clonogenic ability and caspase activation in all cancer lines tested. The proliferation was inhibited also on an extended panel of cell lines, including primary MPM cells. Thus, we suggest that cephalomannine, ouabain, thonzonium bromide, and emetine could represent novel candidates to be repurposed for improving the arsenal of therapeutic weapons in the fight against MPM. Malignant pleural mesothelioma (MPM) is a rare tumour of the pleura mainly caused by a past exposure to asbestos and often diagnosed in advanced stages . As firs2 in Medium 199 , supplemented with 2 mmol/L l-glutamine (Euroclone S.p.A.), 20 ng/mL hEGF , 100 U/mL penicillin, 10% FBS (Euroclone S.p.A.), 100 μg/mL streptomycin (Euroclone S.p.A.), and 0.4 μg/mL hydrocortisone (Sigma-Aldrich). The local Ethic Committee approved the study .All the materials and methods have been published in detail on a previous manuscript . Here, w® Luminescent Cell Viability Assay . For the preliminary screening, all compounds are tested initially at a single dose of 10 μM, and only at a later stage, a selection of them will progress to the full five-dose assay. This one-dose screen methodological approach has already been used successfully before [® Luminescent Cell Viability Assay, as reported previously [The FDA-approved drug library was composed of 1170 entities  . Cell viy before ,17,18,19eviously . Three i® Life Science , whereas thonzonium bromide and pralatrexate were purchased from MedChemExpress .Individual chemicals, i.e., albendazole, alexidine, azelastine, bosutinib, carmofur, cladribine, digoxigenin, dronedarone, emetine, miconazole, pentamidine, pitavastatin, sertraline, solifenacin, sulcanozole, tadalafil, terfenadine, thioridazine, trifluorothymidine, and toremifene were purchased from Cayman Chemical Company . Additionally, 10-deacetylbaccatin, allyl-thiourea, aminoacridine, bazedoxifene, cephalomannine, cetrimonium bromide, ciclopirox, clomifene, cytarabine, fenbendazole, ouabain, oxethazaine, penfluridol, pyrithione zinc, tioconazole, thioguanine, and vinorelbine were purchased from Sigma-Aldrich. Flubendazole was purchased from LKT Laboratories, Inc. , and gemcitabine and niclosamide were purchased from AdipoGen® 3/7 Assay (Promega) was used for detecting the activation of caspases 3/7, a marker of apoptosis. The assay was carried out according to the manufacturer’s instruction and as reported in the previous study [® Omega microplate reader . Three independent experiments with three technical replicates each were carried out.Drugs showing a dose-response activity on the previous assay were further studied for proliferation with MTT or SRB assays, both described in detail in the previous manuscript . For CFAus study . Lumines2, and growth was monitored daily and recorded using a light microscope . Then, after excluding aggregates of outliers showing extra-large dimensions or unusual shapes, frankly isolated spheroids were analysed at the microscope for their size or morphology. The size was measured as area occupied on the surface of the visual field by counting (with the software ImageJ) the total number of pixels covered by spheroids. Then, the average spheroid area in each well was calculated as a ratio of the total pixels shared by the total number of spheroids  and expressed as “spheroid average size” (arbitrary unit). Alternatively, we used the grade of damage to spheroids, seen as sloughing of outer layers. The average damage in each well was measured as the ratio of the “number of damaged spheroids/total number of spheroids” and expressed as “morphological score” (arbitrary unit). Both methods of counting were carried out by two independent readers blinded for the state of spheroids. Data were reported as mean ± standard error of the mean (SEM) of two experiments, each performed in duplicate.Among our panel of cells, we firstly assessed that only MeT-5A, Mero-25, IST-Mes2, MSTO-211H, HMC7, MMP1, and MMP4 were able to form spheroids. Cells were seeded in Nunclon™ Sphera™ 96-Well microplates , allowing spheroids to aggregate for 48 h before treatments with either controls or drugs. TB and CM were used at concentrations of 0.2, 1, and 5 μM, whereas OB and EM at concentrations of 0.025, 0.05, and 0.1 μM. All treatments lasted for 72 h. Spheroids were kept at 37 °C and 5% CO50s was carried out with the software GraphPad Prism 7 . To rank drugs according to the overall activity, the −Log10 of the IC50s summed across all the cell lines was calculated.Similar to what was reported in , the valp-value of 0.05 was employed as the statistical threshold of significance.For cell proliferation, the ODs obtained from three independent experiments  were analysed with the multivariate analysis of variance (MANOVA) with interaction . Within this type of analysis, the differences between control and treatments at each dose were obtained through the Sidak multiple comparison test. CFA, caspase activation, and spheroids’ formation assays were evaluated with the one-way analysis of variance and the Dunnett’s multiple comparisons test. The statistical analyses were carried out with GraphPad Prism 7 and with Statgraphics Centurion XVI . A ® assay, and it was performed on MeT-5A  and five permanent MPM cell lines. The re-evaluation allowed us to identify 90 cytotoxic drugs active in at least four out of five MPM cell lines and to a various extent, also in the MeT-5A cell line. The results of these 90 molecules are reported in In the present study we re-evaluated the results of our recent screening of 1170 FDA-approved drugs . It was According to the published literature, 56 of the 90 drugs were never assayed in MPM models (either in vitro or in vivo). Thus, we selected 38 of these drugs and evaluated whether their cytotoxicity could be validated in a dose-dependent manner  in the MeT-5A, Mero-14, Mero-25, IST-Mes2, NCI-H28, and MSTO-211H cell lines. As a control, we also employed four of the thirty-four positive molecules previously published as being cytotoxic in MPM cells: cytarabine, gemcitabine, pralatrexate, and vinorelbine.50s are reported in 50s were added together from across all cell lines and their −Log10 were calculated. Thus, the overall activity of these compounds and their rankings are represented in The complete set of dose–response curves used for the calculation of the ICAmong the compounds cytotoxic at the lowest doses, there were several molecules known to be active on MPM, such as pralatrexate, gemcitabine, cytarabine, and vinorelbine. However, in the present work, we deepen the investigation on cephalomannine , ouabain , alexidine , thonzonium bromide , and emetine , as they showed the strongest cytotoxicity compared to the other drugs. In any case, the remaining molecules are still of interest, and they could be the objects of future studies. The dose–response curves for CM, OB, AX, TB, and EM are shown in (a) CM. CM caused decreased proliferation, statistically significant in all cell lines at 72 h after the treatment (p < 0.0001) (p < 0.0001) ((b) OB. A statistically significant decrease of proliferation was observed in all cell lines analyzed, starting from 48 h after the treatment (p < 0.0001). (c) AX. Treatment with AX at 0.1 μM did not elicit significant changes in any cell line under investigation, neither in the proliferation ((d) TB. Upon treatment of 1 μM TB, we observed an inhibition of the growth of MPM cell lines following 72 h of culturing post treatment (p < 0.0001) (p < 0.0001) ((e) EM. Treatment with EM caused a statistically significant reduction of the proliferation of Mero-14, IST-Mes2, and MSTO-211H at 72 h (p < 0.0001) (p < 0.01). In addition, EM prevented all the cell lines from forming colonies (p < 0.0001)  . However 0.0001) . Finally 0.0001) . (b) OB.reatment . OB alsoreatment . Figure feration  nor in tferation  nor in tferation . For thireatment . MeT-5A reatment . This direatment . In addi 0.0001) . Finally 0.0001) . (e) EM. 0.0001) . EM also 0.0001) .The toxicity of the selected molecules was further assayed by using the same doses on a different panel of MPM cell lines of epithelioid origin , two non-malignant cell lines , and three primary cell lines derived from MPM patients .After 72 h of treatment, a statistically significant reduction in cell viability caused by the drugs analysed was confirmed in almost all the MPM cell lines, except for CM in Mero-41 and TB in Mero-95  [Candida albicans where it can inhibit the vacuolar “V” ATPase [Psychotria ipecacuanha plant. It disturbs DNA, RNA, and protein synthesis [Strophantus gratus plant. Based on its positive ionotropic activity occurring via the inhibition of Na/K-ATPase, OB is used in the treatment of congestive heart failure and cardiac arrhythmias [The goal of the present study was the identification of novel drugs active against the malignant behavior of MPM cells with a drug-repurposing approach. Thus, 1170 FDA-approved drugs were screened on one non-MPM (MeT-5A) and five MPM  cell lines, and CM, OB, AX, TB, and EM were among the most active. The activity was further confirmed for all the molecules, except for AX, in other in vitro systems. We are aware that the preliminary screening performed at a single dose of 10 μM could poses a bias in the selection of the best active molecules. In fact, some drugs could be effective against MPM at higher doses or eliciting mechanisms not detected through the set of assays employed in this paper . Moreover, since a single-dose assay could be less informative about the toxic activity of a drug than a full-range dose assay, some promising candidates may have been missed. However, the FDA-approved drug library employed in this work has been successfully used at this concentration previously ,19, and nadensis ,23. The nadensis ,25. Its nadensis . TB is a strains , Cooperi” ATPase ,30. Here” ATPase  showed, ” ATPase ,33. EM i” ATPase  extracteynthesis ,36,37, aynthesis . OB is ahythmias ,40. Howehythmias .Drug repurposing could represent a valuable strategy, especially when alternatives are not available for the cure of unmet diseases, such as MPM. Theoretically, CM, OB, and TB should be further investigated with in vivo studies followed by clinical trials. Despite the reduced costs, the clinical research of repurposed drugs could still be challenging, as further studies need to be carried out on formulations, doses, posology, and toxicity profiles. For example, TB has been approved for topic uses, but its effects when administered through other modalities is not known. Moreover, it would be very important to add knowledge on the interactions with other chemotherapeutic agents or in the context of immuno-therapy, a type of research that is very time demanding and resource consuming. Since immuno-therapy was shown to be less effective in MPM than in other tumors , novel cIn conclusion, the present study allowed the identification of several molecules active against MPM cell lines never previously reported with such an activity. However, it should not be forgotten that there were more molecules placed lower in the rank that appeared potentially interesting and worthy of an evaluation in future experiments. Here, CM, OB, TB, and EM showed marked activity on MPM cells, warranting their evaluation in future in vitro and in vivo experiments, also to ascertain their underlying mechanisms of action. Once additional evidence on their effectiveness is proven, it could be foreseen as an application in a potential combined therapy.


NOTCH1 are drivers of T-cell type acute lymphoblastic leukemia/lymphoma. The γ-secretase inhibitor (GSI), which suppresses the function of NOTCH1, is expected to be a molecular-targeted agent. NOTCH1 is also expressed in other malignant neoplasms. We aimed to determine the function of NOTCH1 expression and the effects of GSI on adult T-cell leukemia/lymphoma (ATL) caused by long-term human T-cell leukemia virus type I (HTLV-1) infection.Activated mutations in We analyzed the expression of NOTCH1 in six ATL- and HTLV-1-infected cell lines and investigated the influence of activated NOTCH1  together with GSI on cell proliferation.NOTCH1 mutations were undetectable in six ATL- and HTLV-1-infected cell lines, regardless of abundant NOTCH1 expression. Moreover, GSI did not suppress the growth of ATL cell lines.Activated NOTCH1 found in ATL- and HTLV-1-infected cell lines was undetectable after incubation with GSI, regardless of Tax expression (HTLV-1-coded protein). Whole-exome sequencing revealed that activated NOTCH1-mutation-negative ATL cell proliferation.These findings suggested that NOTCH1 protein is constitutively activated but is likely a passenger during The online version contains supplementary material available at 10.1186/s12885-022-10003-w. NOTCH1 was discovered by analyzing the DNA flanking the breakpoints of a recurrent t  chromosomal translocation, which juxtaposes truncated NOTCH1 with T-cell receptor β, directing the overexpression of N-terminally truncated polypeptides, in < 1% T-cell acute lymphoblastic leukemia/lymphoma (T-ALL) -S-phenyl-glycine t-butyl ester  was dissolved in 10 mM dimethyl sulfoxide (DMSO) stock solution.Cell lines were cultured for 96 h with the indicated concentrations of DAPT or DMSO. Cell viability was then determined using MTT assays .We created libraries using Ion AmpliSeq™ Exome technology (Thermo Fisher Scientific Inc.) and then genotyped amplicons by whole-exome sequencing (WES) using an Ion Proton™ platform  as described by the manufacturer. Sequences were aligned against a reference genome (GRCh37/hg19) using TMAP (Thermo Fisher Scientific Inc.), and then genotyped variants were confirmed by Sanger sequencing.p < 0.05 were considered statistically significant.Data are shown as means ± standard error (SE). Statistical significance was determined using Student t-tests. Values with Expression of NOTCH1 protein is excessive in ATL and HTLV-1-infected T-cell lines.Figure HTLV-1-Tax protein did not affect NOTCH1 protein expression in ATL and HTLV-1- infected T-cell lines.Tax mRNA, S1TcTax05, and S1TcTax10 [We analyzed NOTCH1 protein expression in the Tax-negative S1T-cell line and a clone expressing 1TcTax10 . The expActivated NOTCH1 dissipation was affected by GSI.NOTCH1 rearrangement t  [NOTCH1 mutation  generated via GS-cleavage and labeled using a TM/RAM domain antibody was detected in ATL- and HTLV-1-infected T-cell lines, except for the Su9T01 cell line Fig. . Activat3;q11.2)  than in Growth of ATL and HTLV-1-infected T-cell lines was not suppressed by GSI.NOTCH1 was constitutively activated but probably acted as a passenger in the proliferation of ATL and HTLV-1-infected T-cell lines without NOTCH1 mutation . Activated NOTCH1 mutations, especially in the PEST domain and inactivated FBXW7 mutations, were undetectable in six ATL- and HTLV-1-infected cell lines , in HD-MAR and HT-1 T-ALL cell lines, both of which are dependent on the NOTCH1 signaling pathway for proliferation. GSI reduced proliferation in t -harboring HD-MAR and HT-1 cells, where NOTCH1 is truncated inside the HD under transcriptional control of the T-cell receptor alpha variable [NOTCH1 mutation, needs to be developed to provide a better view of the role of NOTCH1 in ATL pathogenesis. Another reason for the discrepancy is that we could not discount the possibility that additional mutations that occurred during culture for several years might have resulted in NOTCH1 signaling becoming dispensable for growth in vitro. Furthermore, we examined high-concentration of DAPT (up to 10 μM) for 96 h, but it may be the case that the actual effect on the proliferation of ATL/HTLV-1-infected T-cell lines only occurs after long-term incubation.Pancewicz et el. reported that 30% of ATLs have PEST domain single-substitution mutations and showed that the GSI inhibition of NOTCH1 signaling reduces tumor cell proliferation and tumor formation in mice engrafted with ATL. They concluded that NOTCH1 signaling could result in cellular proliferation in ATL . Althougvariable . The weavariable . Howeverhttps://www.ncbi.nlm.nih.gov/clinvar/). Our data suggested that NOTCH1 p.Arg879Gln could be a loss-of-function mutation, and the entity described herein provides a model cell line for topics of clinical and scientific interest.We detected NOTCH1 p.Arg879Gln in Su9T01 cells Table . This vaProgression might be less dependent on the NOTCH1 signaling pathway in ATL cells than in T-ALL.Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.