Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel interpretability approach, which in addition to the set of modeled features considers also the genes and processes outside of this set. Our approach outperforms simpler matrix factorization models, achieving R  Drug sensitivity data from cancer models are frequently generated to provide the basis for the discovery of molecular markers to predict drug efficacy. To predict the response of a specific cell line to a specific drug, there is a need of computational models that can leverage the abundance of information about drugs and cancer cell lines.Matching the optimal drugs for individual cancer patients remains a crucial problem of precision medicine6. Despite their drawbacks11, multi-omics cell line data can provide important insights about the molecular mechanisms underlying susceptibility to distinct drugs. Arguably, the subclass of kinase inhibitor drugs is best characterized by their kinase inhibition profiles, which, apart from the intended on-targets, manifest also off-target effects. Despite their frequent use during the early phases of drug development, when inhibitory profiles of kinase inhibitors are optimized, to our knowledge such data has not been used for modelling of drug response.Strongly parallelized assay formats provide a variety of data that can be used to comprehensively describe the characteristics of both cancer cell lines and drugs14, ranging from traditional algorithms18 to models based on neural networks and deep learning24. Recently, the problem has also been addressed using generative modeling, including variational autoencoders27, as well as using the reinforcement learning framework28.Computational drug sensitivity prediction has been approached by many machine learning methodologies31. Recently, this approach has been modified by introducing non-linearity in the projection step, where the projections are computed by neural networks or autoencoders, but the corresponding hidden representations are still connected via a dot product in the linear fashion. Dot product, however, as a simple linear function, has a limited ability to capture the complex user-item interactions in the hidden space. To address this issue, deep neural networks have been proposed to replace the dot product for modeling the user-item interactions in the latent space33. Since neural networks are known as the universal approximators34, they are expected to be more suitable to learn complex relationships between the hidden representations of the users and items and the response variable.The problem of drug sensitivity prediction can be stated as a recommendation problem, where cancer cell lines and drugs are analogous to users and items, respectively. The goal is to recommend the best drug for a given cell line. One of the most popular recommender system techniques is matrix factorization (MF), where the user-item interaction matrix is decomposed into a product of two lower-dimensional rectangular matrices. The problem of so called matrix factorization with side information incorporates features of users and items in the factorization process. The simplest approach to such MF problems involves linear projection of the features to lower-dimensional hidden space, followed by computing the dot product between corresponding user and item hidden representations in order to obtain user-item interaction prediction35. Moreover, deep neural networks have a reputation of being difficult to interpret due to their non-linearity and complex structure. The majority of so called explainable artificial intelligence methods focus on finding attributions between specific neurons in the network by analyzing the underlying gradient flow39. Although useful, these methods provide rather standard utilities (e.g. feature importances), often available also for traditional machine learning models. Moreover, the insights derived from such interpretability approaches are limited by the features chosen for training the model.While the neural-network based models are more expressive, previous analyses point out that the deep learning models do not necessarily outperform simpler models when the latter are finely tuned, and that some published neural network model results are hard to reproduceWe argue that a desired recommender system for the problem of drug sensitivity prediction should satisfy several objectives. First, it should solve a multi-task learning problem, i.e. model multiple drugs and cell lines simultaneously. This allows to capture general mechanisms driving the drug-cell lines interactions. Second, it should achieve state-of-the art predictive performance, especially in the task of predicting drug sensitivities for new cell lines. This is due to the fact that in this setting, the new cell line mimics a new patient, and the recommendation problem corresponds to identifying the best therapy for that patient. Finally, the model should be interpretable. Specifically, the model should explain the rationale behind its predictions and provide biological and pharmacological insights regarding the mechanism underlying the drugs-cell lines interactions. The emphasis on model interpretability is crucial in the context of its potential clinical applications.To address these objectives, we develop a recommender system model for drug sensitivity prediction, called Drug Efficacy Estimation Recommender System (DEERS). DEERS incorporates two autoencoders to project the drug and cell line features, respectively, into lower dimensional representations, and uses a feed forward network to predict the sensitivities of the cell lines to the drugs based on their hidden representations. The proposed framework brings several advantages. First, the model solves a multi-drug and multi-cell line sensitivity learning problem and utilizes cell lines biological data and drugs inhibition profiles as side information Fig. a,b. SecoThe crucial aspect of the proposed interpretability approach is that it offers the widest possible assessment of the specific genes and biological processes that underlie the action of the drugs on the cell lines. The novelty of this approach stems from the fact that it considers also such genes and processes that were not included in the set of modeled features. Using the interpretablity approach, we demonstrate that the low-dimensional representations of the model capture the high dimensional features of drugs or cell lines, specifically the molecular patterns of cell lines and drug inhibition profiles that govern the response of distinct cell lines to drugs Fig. c. FinallDEERS was developed with two aims in mind. One, to achieve state-of-the art predictive performance in predicting the response of cancer cell lines to kinase inhibitor drugs. Second, to identify the biological mechanisms that drive this response. Below, we evaluate the performance of DEERS in comparison to other models and conduct its interpretability analysis.3 database. GDSC provides two sensitivity measurements, summarizing the dose-response curve: area under the curve (AUC) and log half maximal inhibitory concentration (IC50), defined as a drug concentration needed to reduce cell viability by 50%.The drug sensitivity measurements were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC)The predictive performance of DEERS is compared with four other methods. Two of those, Elastic net and XGBoost, are traditional, frequently used machine learning algorithms. Remaining two, referred to as Lin MF and Autoen MF, are versions of matrix factorization with side information see “ and AUC In general, IC50 as a prediction target is easier to learn than AUC. Indeed, in terms of correlation between predicted and true response values, better results are obtained by all models for IC50 than for AUC.With IC50 as the response variable, the DEERS model mostly outperforms or at least performs similarly well as the other two matrix factorization-based models with regard to all of the six performance metrics , and the CCLE dataset in terms of cell lines and drugs, along with corresponding, min–max-scaled CCLE IC50 values. The data regarding the intersection between GDSC and CCLE, as well as CCLE IC50 values were extracted using the PharmacoDB package41. The resulting dataset contained 351 common cell lines and 5 common drugs , constituting 1747 pairs in total. The cell lines and drugs were described by the same features as in the original GDSC dataset. We next used the GDSC data corresponding to the remaining 571 cell lines that are not present in the CCLE-GDSC intersection dataset and all 74 drugs to train DEERS. From those 571 cell lines of GDSC, 50 were randomly chosen to construct the validation dataset for tuning the learning and dropout rates tes see “” sectionTaken together, these results demonstrate that thanks to its deep neural network-based recommender system architecture and utilization of informative drug features, DEERS obtains state-of-the art performance in predicting cell lines sensitivity to drugs in a multitask setup. In contrast to the other well performing model, XGBoost, however, DEERS obtains highly informative reduced-dimension representations of the cell line and drug features, respectively. This aspect of the model is discussed below.As the first step of the DEERS model interpretability analysis, we computed the attributions between the input features and the hidden dimensions using Integrated Gradients see “” sectionBLK, BRSK1, BTK, CSK, DDR1, EGFR, EPHA2, FGFR2, GAK, LCK, MET, NLK, NUAK1, NUAK2, PIM2, PLK2, RIOK1 and ZAP70, as well as one-hot encoded tissue indication features corresponding to leukemia, lung NSCLC, lymphoma, myeloma, pancreas and urogenital system.The exact groups of features corresponding to hidden dimensions of the drug autoencoder and to hidden dimensions of the cell line autoencoder are listed in Tables In the next step of the interpretability analysis, we associate each hidden dimension of the cell line autoencoder with a biological process. To this end, for each hidden dimension and each gene, we correlate the values of the hidden dimension with the expression values of the gene across cell lines. For a given hidden dimension, the obtained correlations are then ranked and we apply gene set enrichment analysis (GSEA) to identify biological processes positively or negatively correlated with that dimension Fig. c. ImportWhen inspecting the heatmap Fig. , we iden39. The attributions are first computed separately for each cell line and IC50 as the response variable, and next summarized by averaging over all cell lines. Second, for each compound we display the cell lines in two chosen dimensions of the hidden space of the cell line autoencoder, and color them by their IC50 response to the compound. In this way, we identify such regions in this space that are correlated with sensitivity to the compound. Finally, we explore in detail how well one chosen hidden dimension correlates with the true response and we list the biological processes that are associated with that dimension  are inhibited by PHA-79388746. Interestingly, cell line features corresponding to the CDK family do not obtain top attribution values. Instead, the highest average attributions are associated with the expression of BTK, PIM2 and TEC genes, suggesting that their activity in the cell lines is important for PHA-793887 action  identifies a region corresponding to a good response of PHA-793887  with activity against CDK2, CDK1 and CDK4BTK, PIM2 and TEC genes), indicating some similarity between these two drugs. Hidden dimensions 3 and 4 allow to visualize regions in the cell line hidden space with distinctive responses In the final step of the interpretability analysis, we associate biological processes to all of the analyzed drugs. This analysis is based on the idea behind the bottom panels of Fig. entclass1pt{minimaFive of the drugs targeting the RTK signaling pathway Fig. a are posDrugs targeting the PI3K/MTOR signaling pathways generally share very similar profiles of association with biological processes Fig. b. All buIn general, these results can serve as a validation and explanation of the model predictions, as well as provide insights regarding the drugs mechanisms of action and drivers of the cell lines response. Considering drug-biological process associations within a certain drug category enables insights into drugs action on a more general level than putative targets or target pathway information alone.In this work, we propose a deep neural network recommender system-based approach to the problem of kinase inhibitor sensitivity prediction based on side information about drugs and cancer cell lines. The proposed model, DEERS, combines dimensionality reduction of the cell line and drug features using autoencoders and neural network-based prediction based on the obtained hidden representations. The modeled drug features are the strengths of inhibition of kinases by the drugs. The cell line features include expression and mutation calls for the same kinases in cancer cell lines, complemented by primary tissue type of origin for the cell lines. To our knowledge, this type of modeling using these types of input data has not been applied before to predict sensitivity to kinase inhibitors.Our focus on modeling kinase inhibitors is motivated by the fact that binding profiles across kinases represent exquisite data to characterize such drugs. Alternative information about drugs could be the list of specific known drug targets. In contrast to continuous and rich data about kinase inhibition, however, annotations of known targets are relatively incomplete. The quality of the kinase binding data that we used is assured by a standardized assay platform interrogating a large number of kinases. Therefore, off-target inhibition effects are most likely captured completely. An alternative could be to use information on which signaling pathways are affected by a drug since this information is often provided in drug databases. However, clearly the information about target pathways is only high-level, less detailed than using kinase binding data, and suffers from incomplete understanding about the complete set of pathways that a drug effects in different cellular contexts.Despite its advantages, the data used to describe the drugs can also be seen as a source of limitation of this study. While usage of continuous inhibition profiles provides more information regarding drugs’ action, these types of data are available only for a subset of drugs. In comparison, utilizing raw data (e.g. SMILES) would lead to more labelled samples for training, which could improve the predictive performance. However, using continuous inhibition profiles sheds a light on drugs’ mechanisms of action w.r.t. their response on cell lines and provide more interpretable drugs’ representations. The ablation study regarding inhibition profiles and putative targets shows that drugs’ interactions across a large enough panel of kinases can provide more informative profile of drugs, even in the absence of their putative targets. This provides valuable insight on which properties of the drugs are useful to model in these kind of prediction tasks.The DEERs model aims at two goals: 1) high predictive performance and 2) outstanding model interpretability. Our analysis constitutes a thorough comparative assessment of model performance, evaluating both traditional and variants of matrix factorization-based methods. Out of the two traditional models, XGBoost achieves better results than Elastic net, indicating that accounting for non-linear interactions among features is crucial for prediction performance. DEERS outperforms the other two matrix factorization-based approaches, Lin MF (basic matrix factorization model) and Autoen MF . We observe little difference in performance between the Lin MF model and Autoen MF  were used to train and assess the performance of the presented models. Drug sensitivity of a cell line is the prediction target of our modeling approach.55. The features set of these drugs consisted of binding strength across a panel of 294 protein kinases  and binary mutation calls 21 features) were extracted for all cell lines. Furthermore, the dummy-encoded tissue type was added, producing additional 18 binary features, yielding the final set of 241 biological features for 922 cell lines  activation function, and the 10-dimensional hidden representation layer. The subsequent feed-forward network consists of a 20-dimensional input layer, followed by two hidden layers of length 512 and 256 with the ReLU activation. The regularization of the system is incorporated via the dropout with 0.5 probability, applied in the first, 512-dimensional hidden layer of the feed-forward network.DEERS is a deep neural network-based recommender system. It consists of three major parts: drug autoencoder, cell line autoencoder and the subsequent feed-forward neural network. Fig. b58,59. Ti and cell line j feature vector representations along with the corresponding response value, Consider a training data point consisting of original drug mentclass2pt{minimmentclass2pt{minimJ is the cost function, MSE denotes mean squared error, d is a weight of the dependence penalty, m, nth entry of matrix DEERS has three outputs and three main optimization goals: minimizing the differences between d control the contribution of the particular optimization task in the general optimization goal of the system. Setting all of these weights to zero would result in a network without decoding tasks and no dependence restrictions on the hidden dimensions of the drugs and cell lines.Intuitively, the cost function weights We compare the proposed model to four other methods; two of which are based on traditional machine learning algorithms, while the other two are forms of matrix factorization.60 and XGBoost61. The former is a linear model and the latter is a more complex, nonlinear model.In order to evaluate the traditional methods in a multi-task setting, where the data for all drugs and all cell lines are modeled at once, the traditional methods are used to predict drug response for the union of drugs and cell lines features. To this end, for every data point (30.The compared matrix factorization models aim at solving a similar matrix-factorization type of problem Fig. a and can62.The second of the compared matrix factorization-based models is an non-linear extension of the basic model, where the dimensionality reduction is performed via one-layered autoencoders and data reconstruction is also taken into consideration Fig. b. SimilaIn order to assess the performance of the considered models on the unseen cell lines, we construct the validation and test sets by first randomly selecting two sets of 100 unique cell lines each. We then extract the data points containing selected cell lines, producing the validation and test sets with Before the training, the input cell line data were preprocessed by standard scaling of the continuous gene expression data so that every feature has zero mean and unit standard deviation, while binary coding variants and dummy encoded tissue types were unmodified. For the input drug data, all features were standardized in the same way as the gene expression. Since the GDSC AUC values are in the range of , they were not scaled, while the log IC50 values were linearly preprocessed with min-max scaler to the  range. Notably, all values necessary to perform each of the applied preprocessing schemes were calculated only on the training set and applied to the validation and test sets.d, regularization type and learning rate. We establish the DEERS architecture as consisting of two-layer autoencoders, with 10-dimensional hidden representations (Fig. We use the training and validation sets in order to find the optimal set of hyperparameters, consisting of: network architecture, cost function weights mentclass2pt{minimmentclass2pt{minimAfter all parameters are found, we use them to train the model using the union of training and validation sets, and apply the resulting model to the test set in order to assess the performance. We repeat this procedure ten times with different cell lines in training, validation and test sets in order to improve the robustness of the results.We adopt the similar methodology for the compared models, where we first tune the hyperparameters using training and validation sets, and then apply the final retrained model to the test set, using the same data splits for training, validation and testing for all models. For the compared models, we perform this experimental procedure five times. In addition, we incorporate a simple data augmentation scheme, where we add a random gaussian noise with zero mean to the cell lines gene expression data and the corresponding AUC or IC50 values. The standard deviations of cell lines and response noise were 0.6 and 0.15, respectively. The augmentation was performed iteratively in every batch during training, tripling the original batch size. This data augmentation scheme was added for the two models involving autoencoders, i.e. both the Autoen MF and the DEERS model.This analysis aims at an explanation of the model predictions from the biological standpoint. In order to incorporate all available data for model interpretation, we first re-train the model with all available 922 cell lines and 74 drugs, without excluding any cell lines, and using IC50 as a drug response metric.The interpretation of the hidden dimensions concerns assigning a biological meaning to the individual dimensions of the hidden space. To this end, we first pass the input drugs and cell lines input representations into their corresponding, already trained autoencoders, producing a 10-dimensional representation for each 294-dimensional input data vector corresponding to a drug and a 10-dimensional representation for each 241-dimensional input data vector corresponding to a cell line, respectively.39, by computing the attributions between input features and the ten neurons constituting the hidden representation layers. This is performed separately for the drug and the cell line autoencoders, and the attributions are averaged across the drugs and cell lines, respectively. As a result, we obtain drugs and cell lines feature-representation attribution matrices of size To compute the association of each input feature with each hidden dimension, we utilize the Integrated Gradients methodj corresponds to a single cell line hidden representation, and every column c represents the values of a given hidden variable across all cell lines. Next, we examine the full genome-wide gene expression data of the full set 17,419 genes extracted from GDSC. In this way, this analysis goes beyond the restricted set of the modeled input 241 cell line features. Using this data, we construct a j corresponds to a single cell line gene expression profile, and every column g represents the expression values of a given gene across the examined cell lines. We then compute a gth column of cth column of In this interpretability analysis, we exploit the fact that the cell line autoencoder in DEERS is trained to reconstruct the data and to find low-dimensional representations that reflect the true properties of the analyzed cell lines. The produced hidden representations of the cell lines are organized into a mentclass2pt{minim63 against gene sets that are involved in specific biological processes as defined by the Biological Process GO Terms (Fig. 65. We then extract the top 15 enriched terms with the smallest FDR value for every hidden dimension, which indicates the general biological mechanisms are most related to that dimension. Finally, we eliminate the redundant gene ontology terms using the Revigo tool66, assigning the set of biological mechanisms to every dimension of the cell lines hidden space.Given such correlation matrix mentclass2pt{minimSupplementary Figures.Supplementary Table S1.Supplementary Table S2.


E. coli harboring this biosynthetic gene cluster cause DNA damage and tumorigenesis in cell lines and pre-clinical models, yet fundamental knowledge regarding colibactin function is lacking. To accurately assess the role of pks+ E. coli in cancer etiology, the biological mechanisms governing production and delivery of colibactin by these bacteria must be elucidated. In this review, we will focus on recent advances in our understanding of colibactin’s structural mode-of-action and mutagenic potential with consideration for how this activity may be regulated by physiologic conditions within the intestine. Colibactin is a secondary metabolite encoded by the  Escherichia coli (E. coli) are a group of Gram-negative facultative anaerobic bacteria isolated from multiple body sites of almost every individual, paradoxically implicated in both intestinal homeostasis and various pathologies depending on species and subspecies classification . Thi. Thipks ug motif . Precoliug motif  21]. Co. Copks gug motif  . Pks. Pkspks+AX sites , suggestein Ku80 , a pathwein Ku80 . Alternause DSBs  expectedpks+ infection in vitro, as separation of mammalian cells and bacteria by cell-impermeable membranes attenuates colibactin’s cytotoxic effects  tha thapks oks genes . Transcrnditions . These pene clbA , which iene clbA . The regnditions . As expenhibited . Additiohimurium . Thus, ppks+ E. coli or the host diet similarly alter transcription of clb genes. Spermidine, a polyamine produced during bacterial metabolism or scavenged from the environment is necessary for the production of colibactin [clbA transcription and pks+ genotoxicity [pks transcription can be attenuated by inhibitory factors , suggesh anemia  or otherE. coli prevalence. For example, chemically-induced inflammation following dextran sodium sulfate (DSS) treatment reduces populations of anaerobic Bacteroidetes by approximately 70% while increasing levels of various aerobic species by approximately 25% [E. coli strain doubled in DSS treated mice, and colonization in E. coli-naïve mice corresponded with a decrease in mucosal Bacteroidales colonization [pks+ E. coli, accounting for as much as 13% (IBD) or 26% (CRC) of all E. coli strains isolated from these patients [pks+ strains within the colonic mucosa. This hypothesis is borne out by studies in murine models of CAC utilizing azoxymethane treated −/−Il10 mice, showing that the presence of pks+ E. coli promotes DNA damage and neoplastic transformation under inflammatory conditions [−/− mice with no inflammatory response [pks+ E. coli colonization during inflammation by inhibiting nitrate reductase activity abrogates pks-associated tumorigenesis [E. coli populations in colitis patients renders this an unlikely preventative treatment option to limit tumor progression in IBD patient.Inflammatory bowel disease (IBD) creates a pro-neoplastic environment within the colonic epithelium  that protely 25% . In thisnization . Severalpatients ,42,43. Tresponse . In simiigenesis . Howeverpks expression in inflamed tissue may be attenuated as a byproduct of IBD treatments which dampen inflammatory cascades or directly inhibit clb gene transcription. In murine CAC models gavaged with pks+ E. coli, inflammation promotes transcription of multiple clb genes involved in colibactin biosynthesis during tumor initiation [Min/+Apc) and spontaneous (−/− ApcMin/+Il10) CAC models by modulating microbiota composition and transcription, while co-housing anti-TNF treated with control mice prevented these microbial change and inhibited the anti-tumor effects of TNF neutralization [pks+ E. coli colonization level, pointing to a change in microbial activity [in vitro [+ E. colipks, and the evidence suggests mesalamine directly inhibits colibactin production and ICL formation in vitro [pks+ E. coli  to avoid perturbations from host defenses or the intestinal environment . Biofilmal cells . In two in-situ hybridization and 16S rRNA gene sequencing to investigate the composition of mucosal biofilms in CRC patients, frequently identifying Proteobacteria in mucosal biofilms from both tumor and normal adjacent tissue, with a high prevalence of pks+ E. coli (68%) in proximal CRC biofilms [pks+ E. coli seems likely given their ubiquitous distribution throughout biofilms isolated from CRC patients. Co-infection with the biofilm initiating strain enterotoxigenic Bacteroides fragilis (ETBF) and pks+ E. coli in specific pathogen free (SPF) mice demonstrate that biofilm association promotes mucosal invasion, expansion of pks+ E. coli populations, and a concomitant increase in γH2AX histological staining and tumor burden relative to monoassociation [pks+ E. coli and host epithelial cells by degrading the protective mucus layer through an undetermined mechanism  or colibactin (E. coli) diminished tumor burdens, suggesting the existence of a cooperative network between these toxins [Several studies have used a combination of biofilms ,59. Biofbiofilms , suggestociation . ETBF mammentary ), minimie toxins . Howeverpks+ E. coli association with microbial biofilms enhances colibactin’s carcinogenic activity, possibly by increasing proximity to host epithelial cells and enhancing total abundance of these bacteria within the mucosa. In these scenarios, normally innocuous genotoxins secreted by the bacteria may occur in elevated doses near replicating host cells resulting in carcinogenic activity and CRC initiation or progression.Altogether, these findings suggest that pks-induced DNA damage, cells may undergo apoptosis, transformation, or senescence. Adoption of a senescent state induces a senescence-associated secretory phenotype (SASP) characterized by increased secretion of various growth factors that promote proliferation of nearby cells , that has a long history of use as a beneficial probiotic [pks biosynthetic gene cluster [pks peptidase ClbP is actually essential for the antibacterial activity of EcN, contributing to the maturation of siderophores that enable this probiotic to outcompete pathogenic bacteria [clbA mutation [Overrepresentation of robiotic  despite  cluster . Counterbacteria . Whetherbacteria , others mutation . Thus, mPks+ bacteria may have relevance to cancer outside of the intestine. These bacteria frequently colonize extraintestinal sites and colibactin mutational signatures have been reported in a subset of patients with head and neck, urinary tract, neuroendocrine tumors, and ovarian cancer [+ E. colipks causes DNA damage within the regenerative compartment of the bladder after transurethral infection in mice, suggesting a potential role for these microbes in bladder cancer [pks+ E. coli in association with DNA damage in biopsies from prostate cancer patients [pks+ Klebsiella pneumoniae in a subset of hospital patients with liver abscess [pks+ E. coli may be influenced by many factors altering colibactin production and localization within the intestinal tract. Colibactin’s instability suggests it may only exhibit oncogenic activity while in close association with host epithelial cells, and limits mechanistic studies aimed at studying the effects of the toxin without influence from other aspects of bacterial infection or unrelated virulence factors. Because all colibactin-related studies utilize pks or clb mutants  ,6,13 Pkss  inhabiting the intestine is unclear at best. It is intriguing to note that certain secondary metabolites generated from biosynthetic gene clusters are implicated in bacteria anti-phage defense response [The most evident question is related to colibactin’s function within the microbiota. What biological advantage is gained by response , therebyresponse . There ipks-derived mutational signatures are acquired during childhood [E. coli in the human microbiome, especially at early age [pks+ E. coli, the need for childhood screening is nonexistent. If this moment ever arises, the challenge would be to design precision microbiome interventions since antibiotic approach would likely do more harm than good in this young population. Engineered probiotics [pks+ microbes or produce metabolites which directly interrupt colibactin activity. Such small molecule inhibitors may come from a self-resistance component of the pks island itself, the cyclopropane hydrolase ClbS, which disrupts active cyclopropane sites in colibactin [pks+ isolates from individuals against phage libraries may allow for the creation of personalized phage cocktails with high activity for patient-specific strains. Such an approach has been used in pre-clinical model of CRC driven by pks+ E. coli [Bdellovibrio bacteriovorus could represent another means to selectively deplete pks+ E.coli from a complex community [pks/colibactin’s discovery to molecular structure characterization and potential in vivo function. Yet, much remains to be addressed regarding this fascinating molecule and the years to come promise to be exciting for this field of research. Evidence suggests that hildhood ,31 and tarly age , raise tobiotics  may be dlibactin ,84. Phaglibactin , and scr E. coli . In addiommunity . In conc


WEE1 expression in TNBC clinical samples as well as higher expression of biomarkers of sensitivity to WEE1 inhibitor.The WEE1 G2 checkpoint kinase acts as a negative cell cycle regulator for entry into mitosis (G2‐to‐M transition). This comment extends a recent Advanced Science paper by reporting higher WEE1‐dependency of triple negative breast cancer (TNBC) cell lines, pejorative prognostic value of  Lamballe et al. identify the combination treatment with BCL‐XL and WEE1 inhibitors as a promising therapeutic approach in triple‐negative breast cancer (TNBC). TNBC is the most aggressive molecular subtype of breast cancer, but patients with TNBC have less benefited from recognized molecular targets than patients with other subtypes. In the adjuvant setting, the only systemic treatment currently approved remains chemotherapy. New systemic therapies are urgently needed. The Lamballe's study represents a promising new avenue for treatments targeting the cell cycle in TNBC. CDK4/6 inhibitors, which prevent phosphorylation of the RB tumor suppressor, thereby invoking cancer cell cycle arrest in G1, were recently approved for treatment of endocrine receptor‐positive (ER+) breast cancers. But TNBC has been considered a poor candidate because of frequent loss of RB expression or high cyclin E expression, both of which being expected to confer resistance to CDK4/6 inhibitors. Moreover, many TNBC cell lines showed resistance to CDK4/6 inhibition in vitro and in vivo. Recent works have pointed out that targeting other mitotic checkpoints in TNBC might help overcoming treatment resistance or synergizing drug effect. The WEE1 G2 Checkpoint Kinase acts as a negative regulator of entry into mitosis (G2 to M transition) by protecting the nucleus from activated cyclin B1‐complexed CDK1, and is thought to exert protumorigenic functions by securing a tolerable level of genomic instability, an intrinsic feature of cancer cells. In order to reinforce and extend the Lamballe's results, we performed in silico analyses of WEE1 in large datasets of pre‐clinical and clinical breast cancer samples.In their recent paper, Analysis was based on the CERES dependency score, a lower score indicating a higher likelihood that the gene is essential in a given cell line. In the subset of breast cancer cell lines, the CERES dependency scores for WEE1 were below the median of all pan‐essential genes (score ←1) in all cell lines, but no significant difference was observed between the TNBC versus non‐TNBC cell lines (data not shown). Such high‐level impact of WEE1 knock‐out reflects a very strong pan‐cancer cell WEE1‐dependency. We then investigated the consequence of WEE1 chemical inhibition, which is less drastic than the WEE1 knock‐out, in breast cancer cell lines according to the TN/non‐TN subtypes. For this, we used the primary PRISM Repurposing dataset containing the results of pooled‐cell line chemical‐perturbation viability screens for 4518 compounds tested against 578 cell lines. Consistently with the Lamballe's results, we observed an increased sensitivity to the MK‐1775 WEE1 inhibitor in the TNBC cell lines  compared to the non‐TNBC cell lines   screening datasets Marcotte et al.: the Broad Institute Project Achilles, the DRIVE Novartis Project, and the breast cell line dataset.WEE1 mRNA expression in 8636 primary breast cancers, including 1847 TNBC, gathered from 36 public gene expression data sets. In TNBC, high WEE1 expression (defined as expression above median expression level in the whole data set) was associated with high pathological grade, pT2 size, and basal‐like 1 and mesenchymal Lehmann subtypes . In multivariate analysis, WEE1 expression remained associated with MFS ; P = 2.92E‐02, Wald test), suggesting independent prognostic value . Finally, the TNBC “WEE1‐high” samples displayed, as compared to the “WEE1‐low” samples, higher CCNE1 expression  and more frequent chromosomal instability , two markers recently associated with higher sensitivity to MK‐1775 inhibitor in breast cancer models.Given the differential sensitivity of TNBC versus non‐TNBC cell lines to WEE1 modulation, we assessed the prognostic value of WEE1 expression in a large series of TNBC clinical samples. Altogether, these results nicely complement the Lamballe's results and further support the development of WEE1‐targeting therapies in TNBC.Thus, our data not only confirm the increased sensitivity of TNBC cell lines to the MK‐1775 WEE1 inhibitor on a larger panel of cell lines, but also show the higher WEE1‐dependency of TNBC cell lines and the independent pejorative prognostic value of The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Both HPV-positive and HPV-negative cervical cancers are associated with aberrant metabolism, although the oncogenic drivers remain elusive. Here we show the assessment of the metabolomic profiles of four distinct cervical cell lines, a normal and three cancer cell lines, one HPV-negative (C33A) and two HPV-positive , employing an ultra performance liquid chromatography and a high resolution mass spectrometry. Out of the total 462 metabolites, 248 to 326 exhibited statistically significant differences, while Random Forests analysis identified unique molecules for each cell line. The two HPV+ cell lines exhibited features of Warburg metabolism, consistent with the role of the HPV E6 protein. SiHa and HeLa cells displayed purine salvage pathway activity, while C33A cells revealed synthesis of cytidine, via a novel mechanism. These data document a highly dynamic HPV-specific rewiring of metabolic pathways occurring in cervical cancer. Therefore, this approach can eventually provide novel mechanistic insights into cervical carcinogenesis. The particular metabolic deviations, reflect specific responses and a subsequent adaptation of the tumour cell to the particular acquired genetic and epigenetic aberrations generated both within the tumour cell populations and in the cellular microenvironment of the tumour. This metabolic response is tightly linked with cell proliferation and nutrient availability, stress signalling and regulation of energy homeostasis, coupled with external signalling events and the modification of particular metabolic pathways, such as carbohydrate metabolism and biosynthetic processes1. The resulting modified cancer metabolism provides the tumour cell with the flexibility and ability to acquire-under severe conditions, such as hypoxia, the appropriate and/or alternative nutrients necessary for its survival, the building of new biomass2, and the maintenance of the biochemical\homeostasis3. This network of aberrant metabolic reactions is closely integrated with major oncogenic pathways, mutational patterns and epigenetic regulators. These oncogene-driven alterations, affecting the activation of key transcription factors, lead to the deregulation of energy sensor pathways and cell cycle control, and result in autocrine and paracrine mechanisms, without compromising cell survival4.Systematic studies on several tumour types, have revealed a pattern of metabolic rewiring occurring during carcinogenesis, representing one of the hallmarks of cancer5. This particular reprogramming is induced either directly or indirectly by the viruses via modulation of upstream signalling pathways5. Human papilloma virus (HPV) represents one of the most potent oncogenic viruses, and is responsible for all cases of cervical cancer and for the majority of anal, vaginal, oral cavity and pharyngeal carcinomas. Specifically, the high-risk HPV types 16 and 18, exhibit a unique ability to induce cell proliferation in the basal and parabasal cell layers. By upregulating the expression of multiple viral gene products, especially E6 and E7 oncoproteins, these high-risk HPV types, can inhibit p53 and RB tumour suppressors, respectively, which can lead to the disruption of critical cellular regulatory pathways controlling cell cycle entry, differentiation, apoptosis, cell polarity6, and to the establishment of an aberrant cellular metabolism5.A particular oncogenic mechanism leading to carcinogenesis and also to metabolic reprogramming, is the viral oncogenesis mediated by several human oncogenic viruses, which induce carcinogenesis in a multi-step process, by targeting crucial cellular pathways and key proteins, such as control of cell cycle progression, proliferation and evasion from cell death and eventually establishing a metabolic reprogramming pattern7, however, our current understanding of the relative contribution of these oncogenic drivers in the development and progression of cervical cancer remains incomplete6.It is of interest, that both HPV-positive and HPV-negative cervical cancers are associated with disruption of these same pathways9, and three cervical cancer cell lines, one HPV-negative (C33A), and two HPV-positive (SiHa HPV16+ and HeLa HPV18+). Specifically, HeLa cells have been derived from a patient with cervical adenocarcinoma and contain human papilloma virus 18 (HPV-18) sequences, while p53 expression has been reported to be low with normal levels of pRB7. The cells exhibit epithelial morphology and grow adherently, reproducing an entire generation about every 24 h. SiHa cells were established from a Japanese patient with squamous cell cervical carcinoma grade II, contain an integrated human papillomavirus type 16 genome , and can form poorly differentiated epidermoid carcinoma (grade III) in nude mice7. The C-33 A cell line is one of a series of lines (ATCC CRL-1594 and ATCC CRL-1595) derived by N. Auersperg from cervical cancer biopsies7. The line exhibits an adherent epithelial morphology, expressing high levels of p53, while pRB is present but abnormal in size. The cells are negative for human papillomavirus DNA and RNA. Lastly, HCK1T cells were recently established and immortalized from normal human cervical keratinocytes, by the introduction of the human catalytic subunit of telomerase reverse transcriptase (hTERT), and represent a reliable normal human cervical keratinocyte model, and subsequently a suitable in vitro model system for HPV-mediated multistep carcinogenesis9.Therefore, the goal of the current study was to systematically characterize for the first time, the metabolomic profiles of four informative cervical cell lines in order to identify specific metabolic derangements driving carcinogenesis, which can be attributed to the presence or absence of the initial HPV infection. To this end, we characterized the metabolomic profiles of four distinct and complementary informative cervical cell lines, i.e. a normal cervical keratinocyte (HCK1T) cell lineEmploying an ultra performance liquid chromatography and high resolution-accurate mass spectrometry approach on these complementary cell lines, offers a reliable comparison of the metabolomics effects of the two most common high-risk HPV types versus the HPV-negative and normal cervical cells, and can eventually lead to the discovery of novel mechanisms of viral carcinogenesis.Principal component analysis (PCA) was employed to determine whether the four cell lines can be segregated based on differences in their overall metabolic signature. Results revealed very strong segregation of each cell line, forming four tightly grouped clusters  from the six-pairs comparisons of cell lines, as well as those approaching significance (0.05 < P < 0.10), is shown in Extended Data Table Following normalization to Bradford protein concentration, log transformation and imputation of any missing values with the minimum observed value for each compound, the Welch’s two-sample Transformed cells are characterized by cytoadaptation to optimize substrate availability for deregulated growth and biomass generation. Within this context, the four cell lines exhibited differential utilization of carbohydrates. Specifically, HPV-positive HeLa and SiHa cells, showed preferred accretion of glucose and glycolytic intermediates, such as glucose-6-phosphate, fructose-6-phosphate, and lactate  in virtually all cases of cancer6. Both HPV-positive and HPV-negative cervical cancers are associated with aberrant proliferation; however, the contribution of the individual oncogenic drivers remains elusive. In this study, we characterized the metabolomic profiles of four distinct informative cervical cell lines, in order to identify specific metabolic derangements –in the presence or absence of HPV, driving carcinogenesis. To this end, a normal cervical (HCK1T) cell line and three cervical cancer cell lines, one HPV-negative (C33A), and two HPV-positive (SiHa HPV16+ and HeLa HPV18+) were used. This study represents a rational extension of our recent studies on cervical cancer at the transcriptomic11 and the proteomic level12, utilizing either cervical cancer tissue specimens or the secretome of the four cell lines, respectively. To our knowledge, the present study represents the first report focusing on the metabolomics signatures of cervical cancer cell lines, both HPV-positive and HPV-negative, as well as normal.Metabolic reprograming represents one of the hallmarks of carcinogenesisP ≤ 0.05) among the four cell lines, while Random Forests analysis identified molecules distinguishing the four cell lines, indicating that the individual cell lines exhibited highly robust and distinct global biochemical profiles.The dataset documented a total of 462 biochemicals, of which 248 to 326 exhibited statistically significant differences . This may serve to fuel 5-phosphoribosyl diphosphate (PRPP) production, an important intermediate for purine synthesis, particularly in SiHa cells.Analysis of the carbohydrate metabolism revealed that both HPV-positive cells exhibited the Warburg effect. This is consistent with a recent report showing that HPV E6 oncoprotein promotes Warburg metabolism by stabilizing the expression of the glycolytic mediator HIF-1α15, possibly mediated via metabolic reprogramming observed in cancer cells, as exemplified by the well-described cisplatin resistance16. The mechanism by which this resistance is mediated, has been found to arise by the overexpression and high enzymatic activity of particular enzymes of the pathway, such as glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase and transketolase15. Eventually, silencing or downregulation of their activity by putative inhibitors via novel drug delivery systems targeting selectively cancer cells, could potentially restore cisplatin sensitivity17. However, many aspects of the cisplatin resistance mechanisms are still elusive and warrant further studies. Additionally, the altered expression of selected miRNAs in resistant cell lines compared to their parental HeLa cell line, suggests that drug resistance is also associated with distinct miRNAs, which can constitute alternative targets for the treatment of cervical cancer18. It is conceivable that in our study, the documented rewiring of the PPP in the two HPV-positive cell lines might reflect the putative aberrant expression of the above enzymatic and miRNA activities, without necessarily leading to drug resistance. To precisely address this question in the future, a thorough analysis of the enzymes involved in the PPP along with the candidate miRNAs is required, followed by studies of the potential effects of the corresponding specific PPP inhibitors.It is important to emphasize the fact that PPP plays a significant role in maintaining a high cell proliferation rate, and thus represents the main cellular metabolic activity for the development of the so-called tumor drug resistanceFurthermore, the PPP pathway plays also a crucial role in the metabolic coordination of glycolysis, via production of nicotinamide adenine dinucleotide phosphate (NADPH), which is not only required for biosynthesis of lipids, but is also a crucial antioxidant for neutralization of reactive oxygen species (ROS) produced during rapid proliferation of cancer cells to help maintain redox homeostasis. Due to a lack of associated changes in reporters for TCA cycle and glycolysis in the HPV-negative cells, the reduced levels of the PPP intermediates in these cells, may represent alternate utilization of carbohydrate oxidation for glycerol production, perhaps to provide backbone for glycerolipid synthesis.19.Regarding the analysis of nucleic acid metabolism, our study revealed a unique pattern for each cell line. Specifically, the HPV-positive cells displayed high levels of purine derivatives, perhaps to facilitate nucleic acid production and support growth. Instead, normal HCK1T cells exhibited preference for adenosine metabolism, while HPV-negative C33A cells uniquely exhibited high concentrations of cytidine and cytidine 5′-monophosphate (5′-CMP), through a novel mechanism which remains to be determined. However, since the documented levels of cytidine 5′-phosphocholine –which is required for lipid biosynthesis by cytidine, do not change in proportion to cytidine, further work is required to elucidate the role of cytidine in C33A growth. The low levels of β-alanine in HCK1T cells, can be explained by the recent data establishing a link between the relative abundance of β-alanine along with pantothenic acid and glycerophosphoglycerol, to the glycolytic activity of cancer cell lines, as measured by lactate productionN-methyltransferase) results in induction of mitochondrial activity and ATP synthesis and may play a key role of sustained SiHa growth20. While NAD/NADH is central to energy homeostasis, it is also a required cofactor for the sirtuin family of protein deacetylases. Thus, elevation of these metabolites in HPV-positive cells, could not only drive increased energy demands, but also support a host of proteome and genome modifications to maintain high growth rates.SiHa cells displayed characteristically high levels of NAD, NADH and methylated 1-methylnicotinamide, in the context of nicotinamide metabolism. Elevation of these metabolites could not only drive increased energy demands, but also support a host of proteome and genome modifications to maintain high growth rates. Further, recent studies provide compelling evidence that elevations of the enzyme responsible for methylation of nicotinamide to 1-methylnicotinamide . Alternatively, dipeptides also may play a role in the management of oxidative and glycation stress and HCK1T cells may co-opt these biochemicals to help neutralize oxidative stress.Finally, our study documented for the first time the presence of striking increases in dipeptide levels in normal HCK1T cells. The uptake of small peptides by the Slc15A family of oligo/dipeptide transporters, provides an effective and energy-saving intracellular source of amino acids. This may represent a unique pathway deployed by HCK1T cells to provide alternate sources of components to increase biomass and fuel growth. Indeed, this has been observed in transformed hematopoietic stem cells (HSC), where increases in dipeptide levels are thought to be required to sustain stem cell maintenance24, where the prominent and significant pathways found to be deregulated between the cervical cancer and the normal cell line, were those of the metabolism of proteins, metabolism of nucleotides, unwinding of DNA, glycolysis, gluconeogenesis apoptosis and oxidative stress-induced senescence. These pathways actually reflect the malignant phenotype of the cancer cells with high metabolic requirements, increased cell turnover and damage control mechanisms. Normal HCK1T cells were unique in many ways, including relative lack of glutathione, adapting instead to utilize dipeptide metabolism to provide sources of organic acids to maintain biomass and help neutralize growth-driven oxidative stress.In conclusion, our study documented for the first time the effects of the adaptation of HPV-positive and HPV-negative transformed cells by rewiring metabolic pathways to fuel unrestrained growth and exhibiting differential deregulation of several cellular processes, resulting in distinct HPV-specific metabolic profiles, and including molecules in the carbohydrate, nucleotide, lipid, and glutathione pathways. Interestingly, the present findings are consistent and fully support our recent high resolution proteomic data of these informative cell lines26. Therefore, these data provide the impetus to further elucidate both unique and common aspects of perturbed growth profiles of human cervical cancer cells.Collectively, these results reflect highly dynamic differences among the cellular groups, consistent with the recently established notion that cancer heterogeneity is not compatible with one unique cancer cell metabolic map2, as previously described by us12. ΗCK1T cells were a kind gift of Dr Tohru Kiyono9, and were cultured as proposed9 in Defined Keratinocyte Serum-Free Medium (Gibco-Invitrogen), supplemented with 5 ng/ml EGF  and 50 μg/ml of BPE . For harvesting, following the removal of the growth medium, ~ 3 × 106 of attached cells were washed once with PBS, and the bottom of the flask was lightly scraped using a cell scraper in 1 ml solution of 80% ice-cold methanol:20% water, and the detached cells were suspended in PBS. Cell handling was performed on ice. The samples were further centrifuged at 1200 rpm, the supernatant was removed and the cellular pellets (~ 100 μl in size) from six biological replicates for each line, were flash frozen and stored at –80 °C until further use. Extraction of samples were prepared using the automated MicroLab STAR system from Hamilton Company . Recovery standards were added prior to the first step in the extraction process for quality control (QC) purposes. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min  followed by centrifugation. The resulting extract was divided into five fractions: two for analysis by two separate reverse phase (RP)/ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by hydrophilic interaction liquid chromatography (HILIC)/UPLC-MS/MS with negative ion mode ESI, and one sample was reserved for backup27. Samples were placed briefly on a TurboVap  to remove the organic solvent. The sample extracts were stored overnight under liquid nitrogen before preparation for analysis.SiHa, HeLa and C33A cells were purchased from ATCC and cultured in DMEM, supplemented with 10% FBS, 1% P/S  at 37 °C and 5% CO28. Overall process variability was determined by calculating the median RSD for all endogenous metabolites  present in 100% of the pooled matrix samples. Experimental samples were randomized across the platform run with QC samples spaced evenly among the injection.Several types of controls were analyzed in concert with the experimental samples: a pooled matrix sample generated by taking a small volume of each experimental sample; extracted water samples served as process blanks; and a cocktail of QC standards that were carefully chosen not to interfere with the measurement of endogenous compounds were spiked into every analyzed sample, allowed instrument performance monitoring and aided chromatographic alignment. Instrument variability was determined by calculating the median relative standard deviation (RSD) for the standards that were added to each sample prior to injection into the mass spectrometers27. The sample extract was dried and then reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds. Specifically, the extract was gradient eluted from a C18 column  using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analyzed using acidic positive ion conditions; however, it was chromatographically optimized for more hydrophobic compounds. In this method, the extract was gradient eluted from the same aforementioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5 mM ammonium bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column  using a gradient consisting of water and acetonitrile with 10 mM ammonium formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slightly between methods but covered 70–1000 m/z. Raw data files were archived and extracted as described below.All methods utilized a Waters  ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific  Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution29, to account for differences in metabolite levels due to differences in the amount of material present in each sample. Collectively, all interpreted data were normalized based on protein content, while all statistics performed were on natural log transformed data, given the well-documented right skew of metabolomics data. Finally, the Welch’s two-sample t-test was used to identify biochemicals that differed significantly between the four cell lines.Peaks were quantified using area-under-the-curve. Values were normalized in terms of raw area counts. For a single day run, this was equivalent to the raw data. Each biochemical in OrigScale was rescaled to set the median equal to 1. Then, missing values were imputed with the minimum. For studies spanning multiple days, a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound was corrected in run-day blocks by registering the medians to equal one (1.00) and normalizing each data point proportionately (termed the “block correction”). For studies that did not require more than one day of analysis, no normalization was necessary, other than for purposes of data visualization. In certain instances, biochemical data have been normalized to an additional factor, such as total protein, determined by Bradford assay as previously described by us30. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities, utilizing a maintained library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and chromatographic data  on all molecules present in the library31. The biochemical identifications were based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library ± 10 ppm, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores were based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum.Raw data was extracted, peak-identified and QC processed using Metabolon’s hardware and software32. The hardware and software foundations for these informatics components were the LAN backbone, and a database server running Oracle 10.2.0.1 Enterprise Edition.The informatics system consisted of four major components, the Laboratory Information Management System (LIMS), the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization tools for use by data analystst-test was used to identify biochemicals that differed significantly between experimental groups. For statistical significance testing, p-values < 0.05, were considered to be significant. An estimate of the false discovery rate  was also calculated to take into account the multiple comparisons that normally occur in metabolomic-based studies, whose p-value (P < 0.05) was considered as a cut-off for significance33. Random Forests analysis34 was used to bin individual samples in groups based on their metabolite similarities and differences and to identify metabolites that differentiated among the cell lines. Both principal component analysis (PCA) and hierarchical clustering were used as unsupervised methods for grouping the data, as previously described by us35.The Welch’s two-sample Extended Data legends.Extended Data Figure 1.Extended Data Figure 2.Extended Data Figure 3.Extended Data Table 1.Extended Data Table 2.


How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes. Cancer cells adapt their metabolism to promote fast cellular proliferation and long-term maintenance ,2,3,4, tGenome-scale metabolic models (GEMs), which are at the core of some bottom-up systems biology approaches, can be used to predict cell physiology  under different conditions and improve our understanding of cell metabolism ,23. SincHowever, genetics is not the only determinant of metabolic phenotype. Cancer cell metabolism, similar to any other type of cell, is also influenced by metabolic constraints imposed by environmental and tissue-specific contexts ,45,46,47However, including concentrations of nutrients into GEMs in order to study their impact on cancer cell metabolism is not yet widely implemented. The existing methods for including nutrients concentrations into GEMs, which are based on the correlation of exchange fluxes with relative estimates of consumption and/or secretion rates, were applied to study the metabolic adaptation of Escherichia coli in complex nutritional systems ,52.In the present study, we simultaneously constrain GEM and integrate linearly both: cell-intrinsic factors (gene expression level) and cell-extrinsic factors (nutrient availability). We applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates genes expression and nutrient availability data into the fluxes through a scaling factor. The maximum allowable flux value of reactions associated with genes was constrained as a function of RAS  calculatWe designed GENSI as a method to integrate relative gene-expression and nutrient availability data into the human genome-wide metabolic reconstruction. Our purpose was reducing the solution space of optimal fluxes to provide results that can predict cell physiologies based on both cell-intrinsic and cell-extrinsic factors. Cancer cell metabolism, which aroused our interest, is also influenced by metabolic constraints imposed by environmental contexts. One of the important environmental determinants of cancer cell metabolism is diet, which can affect the availability of nutrients within tumours.The GENSI workflow along with an illustration of the method performed on a GEM model is presented in The first step of the GENSI framework consists of pre-processing of the GEM model and includes: (1) preparing the model and includes blocking of the uptake fluxes of various metabolites that are not present in NA, conversion of the gene identifiers that they are compatible with symbols used in RNA-seq data; (2) conversion of the gene-expression levels into RAS score ; and (3)In the next step GENSI simultaneously translates RAS and MUR into the fluxes using a scaling factor of the RAS that is adjusted until experimentally observed features appear. As the direct correlation of gene expression data with maximal flux is not possible to apply in human metabolic map RECON3D , due to The GENSI framework was applied to integrate transcriptomic data from two hepatocellular carcinoma cell lines, Huh7 and PLC, and NA data. In our study, NAs were limited by the composition of the medium. We cultured both cell lines in six different conditions with various concentrations of glucose and glutamine. We call the NA data from the medium with 25 mM glucose and 4 mM glutamine concentration “NA1”, 25 mM glucose and 0 mM glutamine “NA2”, 5 mM glucose and 4 mM glutamine “NA3”, 5 mM glucose and 0 mM glutamine “NA4”, 0 mM glucose and 4 mM glutamine “NA5” and 0 mM glucose and 4 mM glutamine “NA6” . Based on the observed experimental differences in growth rate between the media and cell lines we found a scaling factor that translated RAS and MUR data into the fluxes that matched the observed growth rates.We obtained different GEM-RAS-MUR models see  constraiFor our two cell lines, the RNA-seq dataset published by Ma et al.  reports We were confronted with a well-known issue with RNA-seq data integration in metabolic models, i.e., zero expression levels, including zeros in mRNAs encoding enzymes catalyzing essential reactions or combinations of enzymes that are essential. Of the 2232 metabolic genes, 262 and 310 had TPM scores equal to zero in Huh7 and PLC, respectively. Such zeros could be due to true absence or correspond to technical zeros where the gene had in fact been transcribed but was somehow not measured. Technical reasons may include inefficient cDNA synthesis due to tertiary structure formation, amplification bias, or low sequencing depth. Additionally, zeros may occur due to transcription bursting between somehow synchronized individual cells  or too sHuh7 and PLC being both hepatoma cell lines with a different history of oncogenesis, we further expected them to differ mostly in the expression of oncogenes and perhaps other genes involved in signal transduction or management of the genome, but not much in genes encoding metabolic enzymes. With respect to the metabolic genes, we expected both cell lines to be transcriptionally addicted to the same metabolic Warburg rewiring at the level of transcription. With this expectation, a plot of the expression levels of Huh7 versus the expression levels of the same genes in PLC should show all genes close to the diagonal line. Although for the majority of genes the correlation fell close to the diagonal, there were quite a few genes for which the expression levels differed between the two cell lines A.We first looked at the genes that did correlate: In the RNA-seq dataset (prior to correcting for zeros as discussed above), 186 metabolic genes (∼8% of the 2232 genes) did not come with any transcript in either cell line. In total, 215 metabolic genes (∼10%) exhibited non-zero TPM values below the genome-wide median (∼1 TPM), again in both cell lines. Together, these constituted a common set of 401 metabolic genes that were expressed at a low level. On the high-abundance side, the two cell lines shared 1422 genes (∼63%) that were more highly expressed than the median gene. A 623-genes subset of these 1422 (∼28%) was commonly expressed above the genome-wide mean ( 39 TPM). These genes behaved in line with our expectation of metabolic similarity between these cell lines. They came however with a possibly important exception of some 425 genes that were off-diagonal in In accordance with the above, the expression ratio between the two cell lines was still mostly distributed narrowly around 1. The corresponding probability distribution was largely log-normal, but not quite: it had long ‘tails’ on either side, suggesting that a disproportionate number of metabolic genes were much more expressed in Huh7 than in PLC and vice versa C.There are at least two reasons why the different expression of metabolic genes between two cell lines might not affect the activities of the corresponding biochemical reactions. First, the differences in expression level between the two cell lines could be in enzyme subunits that are abundant as compared to other subunits that are equally expressed. Second, the two cell lines may express different proteins (isoenzymes) that catalyze the same reaction. Recon3D dealt with this issue qualitatively through its gene-reaction coupling rules. We used a quantitative version of these rules to assign a Reaction Activity Score. The RAS for a metabolic reaction reflects the expression levels of isoenzymes and components of multi-component complexes that may catalyze that reaction . We then asked whether the metabolic differences we found between the two cell lines would disappear when correcting for these isoenzyme and enzyme subunit issues by assigning reaction activities. In In metabolic networks like RECON3D, all metabolites with defined exchange reactions can be taken up and secreted, i.e., compounds enter and exit the extracellular environment via ‘exchange’ reactions. The GEM is not able to import compounds unless an exchange reaction from the external environment to the inside of the cell is present. To predict the effects of nutritional differences in terms of all components in the medium including the various concentrations of glucose and glutamine, between our six different medium conditions , on the global metabolic behavior of the two cell lines we first converted nutrient availability data into maximum uptake rates (MURs) that describe the rate of maximum possible uptake over time for substances present in the medium. We defined the MUR for the exchange reaction i, is a factor independent of the reaction identity, and i. When reaction i is reversible its forward flux is bounded by i is irreversible the flux bound in the impossible direction remains zero and the flux bound in the possible direction is set to In the GENSI framework, we proposed a variant of the approach published by Graudenzia et al. . We set It is a priori unclear how large the factor should be. There are two possible sources of limitation for the model: the MUR  and the RAS (consequent to transcriptomics). We here wish to examine the case where the enzyme expression levels begin to impose limitations on the model output. We perform FBA analysis  startingWe analyzed RAS with the focus on metabolic differences between the cell lines by computing the steady-state flux pattern for the maximal biomass synthesis flux for each medium and for each cell line across a range of values for the factor 0−4,1.6] . For eitWhen decreasing the factor alpha to below 0.5 the first effects of the transcriptomics start to be noticeable, i.e., growth predictions for the two cell lines start to diverge in some medium conditions. When decreasing the factor The fact that MUR 1-4 converges to similar biomass synthesis flux optima for low levels may reflect a shared limiting reaction downstream of  the exchange reaction the flux bound of which keeps monotonically decreasing with decreasing  compare B,C.We observe that nontrivial predictions for limitations imposed by medium composition and gene expression can be computed by GENSI method, such as that (i) both in the absence and in the presence of glutamine the growth rate should be independent of glucose concentrations between 5.6 and 25 mM, yet decrease appreciably in the absence of glucose, (ii) the specific growth rate of Huh7 cells is lower than that of PLC cells, (iii) in the absence of glucose, the cells should be able to grow on glutamine, but (iv) growth rate on glutamine alone should be much lower than on glucose alone.FBA is oblivious of metabolic regulation other than that it philosophizes about what flux should be optimal for the cell in view of some objective. The transcriptome and extracellular-concentrations informed flux bounds that we here implemented, merely define ranges of the fluxes rather than that they precisely predict the fluxes. Moreover, fluxes through intracellular biochemical reactions are also determined by metabolic regulation , i.e., bWe hereby can only predict the ranges of fluxes that are consistent with transcriptome and extracellular nutrient concentrations and this is done here by flux variability analysis (FVA)  to test In Glutamine uptake is only essential for both PLC and Huh7 in medium 5 and for PLC only in medium 3. In medium 3 for PLC it is then required at a very low amount to achieve optimal growth (as indicated by the upper end of the bar) whereas in medium 5 in both cell lines the maximal uptake bound has to be hit to achieve maximal growth  both cell lines should be capable of consuming the 5.6 or 25 mM glucose offered to them, (ii) they are not addicted to a To verify whether GEM-RAS-MUR models obtained via GENSI give reliable predictions, we measured the concentration of the crucial components in the medium during culturing. We observed a decrease in the concentration of glucose with time such that some 5 mM was consumed by the PLC cells B. In theWe additionally measured the production of lactate and pyruvate by cells using the same media conditions as above. In the case of the PLC cell line, the production of lactate was the same during the first 24 h for NA1-NA4, the lactate concentration reaching some 5 mM at the end of this time period C,D. AftePyruvate production was quite small for PLC cells, from 0.15 mM (NA1 and NA6) to 0.4 mM (NA4) during the first 24 h. Pyruvate production was higher for media without glutamine . For PLC cells, the lactate/pyruvate ratio (L/P) showed a positive relationship with the glucose consumption, being highest for NA1, and lowest for NA5. For Huh7 cells, the time-series for pyruvate F look siConsistent with the hypothesis that both cell lines exhibited a Warburg effect, they produced an amount of lactate proportionate to the amount of glucose that was present initially. Apparent differences merely derived from the glucose running out after some 30 h in the experiment starting at only 5.6 mM of glucose. Only part of the glucose was transformed to lactate, around ∼43% for low glucose media and perhaps a little as ∼33% in glucose-rich media. Thus, lactate production corresponded to only part of what might have been expected for full glucose conversion to lactate. This deficit in lactate secretion can be explained by the utilization of glucose for oxidative phosphorylation and as a source of carbon for the new biomass. Consistently, we did not observe any difference between NA1 and NA2 for both cell lines nor did we observe such differences between NA3 and NA4 for PLC; for Huh7, small deviations were noticed. For the two cell lines we examined, this proves the independence of lactate production from glutamine access.In this work, we follow recently exploring interest on the impact of the diet on cancer cell metabolism and progression. We explore the idea that the utilization of nutrients by cancer cells is determined not only by cancer genetics but also by the metabolic environment.To investigate whether cancer progression may be mediated through changes in the access to nutrients, genetics and the availability of nutrients have to be employed simultaneously. Genome-scale models that integrate all known metabolic reactions occurring in an organism into a single map give us such an opportunity. The gene-reaction coupling rule and exchange reactions allow for the integration of gene expression and nutrient availability data, respectively. Here, we aim to provide an FBA-based framework, named GENSI, for studying the influence of the nutrient availability on the rate of proliferation and metabolic phenotype based on transcriptomic and NA data. Our method could be applied to the study of cell metabolism of every cancer type and therefore could lead to a better understanding of how diet impacts cancer cell metabolism and identifying how different cancer types respond to different nutrients composition. GENSI translates the relative importance of gene expression and nutrient availability into the fluxes and generates high-quality GEM-based specific models (GEM-RAS-MUR) that can be used to predict metabolic changes upon nutrition shift by flux balance analysis.Further, we applied the proposed method to study the influence of the glucose and glutamine availability on their consumption, some metabolic signatures, and the growth rate of the hepatocellular carcinoma cell lines PLC and six for Huh7. We, therefore, selected cell types that do exhibit the Warburg effect clearly. We used transcriptomic data from the cell population in its entirely and the models are a representation thereof. Single-cell transcriptomic studies suggest that cancer cell populations are heterogeneous, but single-cell metabolomics does not yet enable us to examine the consequences of metabolism. Thus, we assumed in the model that the cell population was homogeneous.Our findings offer support for the predictive potential of genome-scale metabolic maps together with transcriptomic data sets and nutrient availability data. The predictions address the carbon and energy metabolism of cancer cells. Because much of this metabolism is essential for cell survival, the potential may translate to new drug targets in a long-neglected area of drug discovery. Indeed, we have shown that dependencies on nutrient availability and gene expression can be computed and that the results are then relevant enough to be compared with experimental work.Notwithstanding these successes, our methodology comes with a number of issues. One of these relates to the translation of the expression level information to flux. In contrast to several methods developed to extract context-specific models ,32,34 thdatasets . We did In silico experiments on GEM-RAS-MUR models showed that the specific growth rate of Huh7 cells is lower than that of PLC cells, which was also observed during experiments on these cell lines. Given the positive correlation between the in silico biomass flux and the experimentally determined growth rate, we speculated that the in silico results pertaining to the range of uptake and secretion fluxes of various metabolites might also correlate with experimental results. Modeling showed that in the absence and in the presence of glutamine the growth rate should be independent of glucose concentrations between 5.6 and 25 mM. Furthermore, experimentally the growth rate reached the same level for four nutrient availability (NA1–NA4) that contained 5.6 and 25 mM of glucose for both HCC cell lines. Moreover, both approaches, computational and experimental, showed that in the absence of glucose, the cells should be able to grow on glutamine but with a much lower growth rate than on glucose alone.According to the flux variability analysis predictions, both cell lines should have a higher potential; they should be capable of consuming the 5.6 or 25 mM glucose offered to them. Yet, our observations indicated that PLC cells confronted with the 25 mM did not consume more than the around 5 mM during the first 24 h. They did not make use of that full potential. We conclude that there is a maximum amount of glucose per unit time that the cells ‘wish to’ handle at glucose levels above a few mM, because other issues than energy and carbon may limit the cells’ growth. We use the term ‘wish to’ to indicate that this may be an issue of metabolic regulation: the gene expression would allow for higher glucose uptake fluxes.The cells that had consumed in the first 24 h some 5 mM of the 5.6 mM glucose they had been incubated with, continued to grow for the next 24 h at virtually the same growth rate; they must have done this at a much-reduced glucose consumption rate. Since they also stopped producing lactate, we suspect that the small amount (approximately 0.5 mM) of glucose left provided the cells with sufficient ATP to drive their continued growth. The cells may have reverted from lactate production to respiration with its more than 15-fold higher ATP yield. This highlights that there may be a limitation to these cells’ addiction to lactate production: these cancer cells can shift to glucose oxidation. We observed a slightly different effect in the case of the Huh7 cell line: the consumption of glucose was almost two times higher in rich glucose medium (25 mM) than low on (5 mM) during the first 24 h. In silico analyses predicted and experiments confirmed that both cell lines, at glucose concentrations around 5 mM, made use of all available glucose to grow maximally. They also showed that upon glucose depletion and if asked to grow at the maximal growth rate, the cells should shift to glucose respiration .Significant progress has been made toward developing the tool to study how diet and nutrition affect cancer progression. We obtained GEM-RAS-MUR models for the hepatocarcinoma cell lines and used them to predict cell growth and metabolite consumption/production. Our methodology could be applied to different types of cancer to investigate how cells respond to the diet and how to mediate these responses. Potential future applications of the GEM-RAS-MUR model would be to predict cell addictions and metabolic requirements in order to modulate them for dietary aid in cancer treatment. Moreover, GENSI methodology is not limited to cancer research, it could be applied to study the metabolism of any healthy and/or sick cells to study how diet and nutrition affect metabolic phenotype, proliferation, and functions of the cell.GEM Model. The most comprehensive genome-scale model of human metabolism RECON3D [ RECON3D  that incRNA-seq data. RNA sequencing data for both cell lines was obtained through NCBI’s GEO database. Specifically, the RNA-seq dataset from [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE86602, accessed on 7 November 2018. The RNA-seq dataset pertains to cells grown in DMEM containing high glucose , http://merav.wi.mit.edu/, accessed on 7 November 2018) for both the Huh7 and PLC/PRF/5 cell lines. There was data available from two experiments for Huh7 and one for PLC/PRF/5. All MERAV microarray datasets were renormalized together [set from  was acceg 5% CO2 . The RNAg 5% CO2  (http://together . For theNA data. The formulation of the media used in cell culturing was used as Nutrient availability data. The standard Dulbecco’s Modified Eagle Medium (DMEM) with different concentration of glucose and glutamine was used . IndeedBiochemically, triglycerides are synthesized starting from glycerol-3-phosphate and various lipid tails esterified to CoA. Each of these lipid tails can assume any of the three positions in the triglyceride molecule. We observed that without adding triglyceride uptake (or a ditto source) to the metabolic map, the first intermediate in the pathway  could not be net-produced , using the mygene module in Python , in order to match them to genes represented in the transcriptomics datasets. Out of the 2248 genes in Recon3D, we were unable to match 103 to the dataset on the basis of their gene symbol alone. We then additionally searched the dataset for known gene aliases and this led us to identifying an additional 87 transcripts. In total, 16 genes of Recon3D  on inward exchange flux is zero  the objective function Z.In all simulations in this work, we apply the computational technique of FBA  and FVA Z), V is the vector of fluxes through all reactions, Whereas FVA:https://github.com/ThierryMondeel/HCC_flux_balance_analysis (accessed on 24 March 2021).All Python and MATLAB code, Jupyter Notebooks and raw data files are available as part of a Github repository Cell lines For our study we chose two HCC cell lines, i.e., the hepatitis infection–negative Huh7 and the hepatitis infection–positive PLC/PRF/5. Huh7 is a well differentiated hepatocyte-derived cellular carcinoma cell line originating in a liver tumor in a 57-year-old Japanese male. Huh7 is an immortal cell line of epithelial-like, tumorigenic cells. Its cells adhere to the surface of flasks or plates and typically grow as 2D monolayers. Furthermore, known as the Alexander hepatoma cell line, PLC/PRF/5 is a human hepatoma originally taken from the liver of a patient with primary liver cell carcinoma who was persistently infected with hepatitis B virus. Furthermore, these cells adhere to the surface of flasks or plates and grow as 2D monolayers [nolayers ,68. HumaCell culture The Huh7 and PLC/PRF/5 cell lines were cultured in Dulbecco’s modified Eagle’s media  at various initial concentrations of glucose and glutamine  solution of trypsin . The total number of cells in the consequent supernatant was determined by hemocytometer counting (viable plus non-viable). Mean growth rate was determined by counting six non-overlapping sets of sixteen corner squares selected at random, and these four times at each time point.Metabolic assays The concentrations of glucose, glutamine and lactate in samples from the cells’ supernatant were determined by High Performance Liquid Chromatography (HPLC) based on calibration curves made with standard solutions. The samples were taken from supernatant at the end of experiment, filtered using 0.22