Targeted therapies have shown significant patient benefit in about 5–10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo. The prediction of growth factor induced cancer cell growth was improved significantly by combining a signaling model with machine learning. A team led by Andreas Raue at Merrimack Pharmaceuticals, attempted to better understand growth factor-dependent tumors and their potential treatment with receptor-targeting antibodies. Interestingly, prediction of tumor response improved significantly by adding prior knowledge from a mechanistic signaling model. This conceptually new approach relies solely on publicly available gene expression data and can be readily applied in drug development and development of clinical trials. In patient data, correlation between growth factor expression in the tumor microenvironment and its predicted response were identified. This consolidates the belief of an addiction of tumors to growth factors abundant in the tumor microenvironment, and might enable a more robust patient stratification in the future. We find that about half of the cell lines do not respond to any of the ligands whereas the other half of the cell lines respond to a least one ligand. We compare the observed ligand-induced proliferation with the response to treatment with antibodies targeting the ErbB receptor family members, a subfamily of four closely related receptor tyrosine kinases (RTKs): EGFR (ErbB1), HER2/c-neu (ErbB2), HER3 (ErbB3) and HER4 (ErbB4) as well as the insulin growth factor receptor (IGF-1R) and the hepatocyte growth factor receptor (Met). Not surprisingly, the antibodies targeting the respective RTK inhibit ligand-induced proliferation. The antibodies also inhibited basal proliferation in some cell lines that do not respond to exogenous ligand addition, which could be driven by autocrine signaling.17 Large collections of genetic and genomic data led to efforts to disentangle the complex mechanisms using machine-learning algorithms.21 It was previously shown that simulated patient-specific signaling responses derived from mechanistic signaling models using RNA sequencing data from patient biopsies can be robust biomarkers that are predictive of patient outcome.22 Here, we combined machine learning and mechanistic modeling to predict which cell lines proliferate in the presence of ligand. We used RNA sequencing data as inputs into a comprehensive mechanistic model capturing the ErbB, IGF-1R and Met signaling pathways. Our novel approach uses simulated signaling features and mutation status of a specific cell line as inputs into a Bagged Decision Tree, which predicts whether tumor cells proliferate in the presence of a growth factor. We achieved a substantial gain in accuracy compared to predictions based on RNA sequencing data alone by inclusion of simulated signaling features such as the area under curve of distinct heterodimers and phosphorylated S6 for in vitro models.The need has been recognized for computational approaches to deal with the complexity of signal transduction and its dysregulation in cancer to ultimately understand drug activity.Applying this approach to patient data, the prediction of ligand-dependent tumor samples based on mRNA data from The Cancer Genome Atlas (TCGA) revealed that colorectal and lung cancer are the two indications most responsive to EGF, which agrees with the approval of EGFR inhibitors in these indications. In addition, the prediction of responders in patient samples revealed a correlation between predicted tumor growth and measured ligand expression in the tumor microenvironment, which argues for a co-evolution of ligand production and the ability of the tumor cells to respond to stimulation.23 Seribantumab (MM-121), a monoclonal antibody targeting ErbB3;16 MM-131, a bispecific antibody co-targeting Met and EpCAM;24 and Istiratumab (MM-141), a bispecific antibody co-targeting IGF-1R and ErbB3.25 Fig. α = 0.05 . Per this screen approximately 45% of cell lines responded to EGF, 55% of cell lines responded to HRG, 33% of cell lines responded to HGF and 7% of cell lines responded to IGF-1. The low response rate to IGF-1 in this proliferation screen may reflect the presence of IGF-1 in the low-serum medium and the modest absolute inhibition point to the importance of IGF-1 mediated signaling for survival rather than for proliferation.26 We and others observed a generally weaker MAPK activation via IGF-1R  for response to the exogenously added ligands EGF, HRG, HGF, and IGF-1 . Bound receptors can form homo and heterodimers and subsequently undergo endocytosis. After internalization, the receptor dimers can get either dephosphorylated and recycled to the cell surface or they get degraded in the lysosomes.35 Downstream of the receptor, all homo- and heterodimers except the ErbB3-homodimer, which cannot trans-phosphorylate due to its lack of intrinsic kinase activity, can activate the MAP kinase cascade as well as the PI3K/AKT pathway. ERK and AKT phosphorylation converge in the phosphorylation of S6K1 and S6. Several known feedback mechanisms between the pathways 27 were implemented in the computational model. Mathematical details, executable code to simulate the model and instructions to replicate our findings are available in the biomodels.org.To construct a comprehensive signal transduction model that could be used to predict proliferation in response to growth factors for all 58 cell lines, we built on a previously published model of ErbB receptor signaling.Met Fig.  as well 37 Size and complexity of the computational model were chosen to reflect the available experimental data, and to facilitate efficient computation. This is particularly important during model parameter calibration, which uses parameter estimation algorithms to match the available experimental data as closely as possible (see Methods section for details). Mutations were not implemented in the computational signaling model as they appear to increase the signaling baseline but not necessarily the signaling dynamics.38 However, the mutation status for each cell line was used in the machine learning classification.The computational model is constructed with the aim to capture the signaling dynamics of key components of the signaling pathway including receptor homo- and heterodimerization. It is not intended to be a complete compendium of all the known molecular interactions.39 for the receptor phosphorylation as well as for phospho-MEK, phospho-ERK, phospho-AKT, and phospho-S6 across all seven cancer cell lines  were used. Only the two cell lines H322M and IGFROV-1 were included in the cell line proliferation screen in Fig. 40 In this case, it was possible to restrict all kinetic rate constants to the same value and to adjust only the receptor expression for individual cell lines. Due to the analytically calculated basal activation levels of all homo- and heterodimers as well as of the downstream components, which were derived from steady-state constraints,41 the receptor expression impact the signaling response throughout the model. Therefore, the individual receptor expression of each cell line enables distinct model responses upon ligand stimulation. The receptor expression was measured using quantitative flow-cytometry (qFACS) in combination with RNA sequencing data (see Methods section). The model can accurately describe the time course data of seven training cell lines, with 85.7% of the data points within two standard deviations of the model uncertainty  and compared to the experimental data. The goodness of the predictions for the validation cell lines was equivalent to the goodness of fit of the training cell lines . To this end, previously publishedFurther insights into growth factor signaling and signal processing by the cancer cells can be gained by analyzing the computational model and why it can capture the distinct signaling dynamics across cell lines. This analysis revealed the importance of different homo and heterodimers in encoding information as a function of the ligand(s) present. The largest effect of heterodimerization on signal output is seen within the ErbB family. The interplay between the receptors explains the slower, more sustained receptor activation in response to EGF in the BxPc-3 cells, which are characterized by a high ratio of EGFR to other receptors Fig. . In contIGF-1 is distinct from the other growth factors in our screen. IGF-1 displayed a much weaker ability to induce proliferation and similarly the effect of IGF-1 signaling in co-stimulation experiments is distinct to the HRG/EGF or HGF/EGF co-stimulation experiments. The time-course data for co-stimulation of IGF-1 with either EGF or HRG did not show deviations from the respective stimulation with IGF-1 alone , we trained bootstrap-aggregating (bagged) decision trees (BDTs). BDTs are highly efficient for multivariate analysis and allow for a comprehensive interpretation of the chosen features.48 The second feature set did not contain the receptor expression explicitly but only signaling features derived from the computational model based on the receptor expression and the ligand stimulation , we use our model to predict if an individual patient tumor would show a proliferation response if stimulated by the ligand of interest. The patient data set includes 2909 samples from patients with breast, colorectal, lung, and ovarian cancer. The input to our model is receptor RNA expression measured by RNA sequencing for each tumor sample. The measured RNA expressions between our in vitro cancer cell line data and the data from patient tumors were on different scales. Therefore, we normalized the expressions to their respective means. Subsequently, the expression of EGFR, Her2, ErbB3, Met, and IGF-1R were used to perform model simulations and to extract the signaling features needed to predict ligand-dependent tumors using the previously described BDT algorithm. The number of ligand-dependent tumors differed within indications and ligand . The number of predicted ligand responsive tumors was highest for HRG followed by EGF and lowest for HGF , a Met blocking antibody in a Phase 3 study in NSCLC.55 Similarly, EGFR inhibitors have not yet proven to result in clinical benefit in breast cancer,56 which is also in agreement with the predicted low EGF dependence of breast cancer. The predicted high responsiveness of breast cancer and lung cancer to HRG seems to agree with the retrospective analysis of two clinical studies with Seribantumab and the finding that HRG expression appears to be predictive of patients responding to therapy.57 Unfortunately, the precision of the predictions cannot be assessed as no outcome data to treatment with ligand blocking antibodies is available for the TCGA data set. The predicted ligand-dependence only considers the molecular makeup of individual tumors. However, a tumor that is predicted to be ligand-dependent would respond only if the respective ligands were also present in sufficient amounts in the tumor microenvironment. The local concentration of ligands, however, cannot be inferred from our analysis and it is difficult to match to the in vitro data that was used for model training. This impacts the predicted number of ligand-dependent tumors in this data set.In the previous section, we showed that a mechanistic computational model in combination with decision tree classification can predict in vitro proliferation. Next, we applied this novel approach to patient data. Using the data from the TCGA Research Network  amounts of the corresponding ligand compared to the predicted ligand independent tumors, if we compare the mean expression. This suggests that tumors that express ligands evolved to be sensitive to ligands, or vice versa. In our opinion, this is an indirect proof that the model predictions can be applied to data from patient tumors and that they could be clinically relevant.However, apart from the relative number of ligand-dependent tumors, we observed a significant correlation between ligand expression and the predicted response to ligands Fig. . The pre58 Targeted therapy and combinations thereof have become important areas of drug development with rising FDA approval rates in the last years.60 However, by studying cellular fate across multiple cell lines and indications, we and others have learned that complex interactions between receptors as well as positive and negative feedback regulation between signaling pathways can diminish drug efficacy. To obtain a deeper understanding of cell response to exogenous stimuli, phenotypic responses need to be studied in the context of multiple signaling pathways as well as mutation status. In this work, we developed a computational model describing multiple signaling pathways and show that a BDT algorithm using simulated signaling features can accurately predict ligand-dependent proliferation in vitro.The research to understand and therapeutically battle various cancer types has made significant progress over the last two decades, decreasing the overall mortality by roughly 2% per year since 2001.28 The fact that the developed computational model can accurately describe the signaling responses across multiple cell lines enables the prediction of signaling dynamics for cell lines of the proliferation screen that were not used to construct the computational signaling model.The signaling model incorporates the ErbB receptor family as well as the Met and IGF-1R receptors. Parameters of the model were estimated based on a variety of time-resolved data from seven different cell lines including a wide range of ligand concentrations with comprehensive single ligand and co-stimulations. The cell lines cover a broad range of ratios of receptor expression. The ligand concentrations used in the proliferation screen were in the range of concentrations used for the signaling experiments. While retaining a good fit to the experimental data, we could keep all kinetic parameters in the signaling model constant and just vary the receptor expression to describe the experimental signaling data for each cell line. It is important to note that this is not a direct proof that the reaction rate constants are identical between the cell lines. However, our model with all rate constants set to the same values, is in line with the receptor phosphorylation and downstream signaling data. We do not argue that the model presented here is correct in all of its aspect, but we could show that the signaling dynamics predicted by the model are useful for predicting the cellular responses to ligand stimulation and that this presents an approach that should be explored further by incorporating clinical patient response data. The computational model not only describes the data for the seven training cell lines but also predicts the signaling responses of two additional, independent validation cell lines. We showed that the goodness of fit is dependent on the absolute receptor expression and the formation of different homo- and heterodimers. The observed variability in model response was achieved by differences in internalization, degradation and recycling rates for different receptor homo- and heterodimers. Saturation of different downstream model components is sensitive to the receptor expression. The analytically solved steady states for all cell lines were important to implement the complex receptor dimerization properties in the signaling model. Thus, ligand stimulation results in a complex re-distribution of receptor homo- or heterodimerization and facilitated distinct downstream activation patterns. The calculated steady states, especially for the ErbB receptor family, were found in concordance with measured basal total and phosphorylation levels of 39 breast cancer cell lines utilized from.63 and carve out regions in feature space that best distinguish between different data classifications, here proliferation vs. stasis upon ligand stimulation. BDTs were trained on the in vitro proliferation screen across 58 cell lines. The algorithm was trained with either receptor expression and ligand stimulation conditions as Boolean columns or with signaling features extracted from the computational model. The signaling features included the integrated area under curve of all receptor complexes as well as the phosphorylation of AKT, ERK, and S6. Both feature sets lead to a better prediction of proliferation compared to control, where response was predicted based on random data. However, the signaling features allowed for a more robust and statistically improved prediction of proliferation. In addition, the computational model allows us to gain insights into the underlying processes driving ligand-dependent proliferation. In all cases homo or heterodimerization of the ErbB receptors was important. In the case of EGF, the PI3K mutation status mattered in addition. For HRG and HGF possible RAS mutations together with AKT and S6 phosphorylation were important features to predict cell proliferation. However, the importance of features in the tree-based approach is very sensitive to the utilized data, and additional measurements are needed to infer the role of MAPK and PI3K signaling in inducing growth.To predict proliferation in response to ligand stimulation, we linked simulated signaling features to the data of the cell proliferation screen using a supervised machine learning approach. Tree-based classification algorithms are widely used for machine learningSimulated signaling features are advantageous over using receptor expression directly as input features for two reasons: First, the dynamic range of receptor activation as well as of the downstream components is described quantitatively and renders the model outputs more robust to receptor expressions, which span multiple orders of magnitude. Second, the interplay between receptors and the included feedback mechanisms adds a source of information on top of the receptor expression and ligand information alone, resulting in a non-linear input transformation that improves the detection of regions governing proliferation.57To demonstrate the applicability of this novel approach to patient samples, data from 2909 patients with breast, colorectal, lung or ovarian cancer were analyzed. For these samples, model simulations were conducted to extract signaling features required for BDT prediction of ligand-dependence. Interestingly, we observed a significant correlation between the measured ligand expression and the predicted ligand-dependence for breast and colorectal cancer. This may be a consequence of evolutionary adaption of these tumor cells due to the growth advantage from ligand-mediated signaling. Therefore, the presence of ligands in the tumor micro-environment may be a favorable biomarker for RTK-directed drug treatment. This rationale together with possible mutations, which contributed substantially in the BDT training, are currently being explored in the clinic.64 with additional prior knowledge may allow us to better bridge between quantitative time-resolved data and large-scale genomic and phenotypic data. To understand ligand mixtures and pathway redundancy a greater variety of single ligands, e.g., FGF and PDGF, or stimulations with ligand mixtures might aid to more accurately determine model parameters for receptor dimerization, trafficking and downstream activation in the future. Further, or model is currently limited to MAPK and PI3K pathways. Future work, should expand on this approach by including other signaling pathways, e.g., data from the JAK-STAT signaling pathway. An expanded model may improve the accuracy of the predictions as well as additional perturbations like specific gene knockdown etc. would help to improve the signaling model.However, both the prediction of proliferation and the mechanistic computational model have limitations. For one, machine learning in feature-space regions that are not covered by many cell lines is not efficient as illustrated by IGF-1 in the proliferation screen data-set. The mechanistic model is limited in its ability to fully reproduce data in cases of either receptor overexpression  might prove to be far more prevalent than oncogene addiction given the high ligand prevalence in solid tumors and potentially as much more complex since multiple ligands are expressed in the tumor microenvironment. To successfully treat patients with ligand-dependent tumors with targeted inhibitors  or rational combinations of targeted inhibitors, a better understanding of ligand-dependence is crucial, especially given the redundancy of signaling pathways within a tumor cell and tumor heterogeneity. We argue that the mechanistic understanding of changes in receptor stoichiometry based on individual ligands or ligand mixtures result in non-obvious signaling responses that are relevant to the ultimate phenotypic response. The work presented here demonstrates that for targeted therapies to be successful in the clinic the ligand hierarchy as well as co-dependence need to be understood and most likely require the measurement of multiple ligands and respective receptor expression in tumor biopsies. New methodologies like single cell RNAseq67 Yet, significant improvement in predictions proved to be challenging across multiple approaches and data sets. With the presented approach, mechanistic knowledge can be easily combined with known datasets from RNA sequencing, copy-number alterations and mutation information to improve the prediction of patient-individual drug response and unravel the interplay between complex signaling and cellular fate.The presented novel approach of using BDTs in conjunction with simulated signaling features is the beginning of how complex mechanistic models and large data sets can be combined to understand cell-specific complexity but also heterogeneous tumors better. We demonstrated that mechanistic computational models of signaling pathways can help bridge between large scale in vitro observations and clinical hypotheses. In the future, selected in vivo studies should be used to validate rational combination regimens. Previous efforts predicting drug sensitivity based on large and diverse data sets found that gene expression data proved most valuable, together with exploiting non-linear relationships and addition of prior knowledge of biological pathways.® was obtained from Promega and reconstituted fresh for each experiment.All cell lines used in the viability screen were purchased from American Type Culture Collection. In brief, cells were cultured in RPMI 1640 medium (Life Technologies) supplemented with 10% fetal bovine serum  and 1% penicillin-streptomycin  at 37 °C and 5% CO2. Recombinant human heregulin1−β1 (NRG1b) EGF domain and HGF were obtained from PeproTech, and recombinant human IGF-1 and EGF were obtained from RD Systems. Seribantumab, MM-131, Istiratumab and MM-151 was manufactured in-house by the Merrimack Pharmaceuticals Protein Engineering Department and stored at 4 °C. CellTiter-GloTo measure cell viability in a three-dimensional spheroid culture, cells were seeded into 384-well low- binding multi-spheroid culture plates  in the relevant growth medium supplemented with 4% FBS and 1% pen/strep at a density of 1500 cells/well. To allow for spheroid formation, plates were incubated for 24 h, after which cells were treated with ligands  and/or inhibitors  in 4% FBS containing medium. Following 72 h of incubation, cell viability was determined by incubation with CellTiterGlo® reagent for 10 min and measuring well luminescence on an Envision (Perkin Elmer) plate reader.The available data consists of three different data sets, which include time-resolved concentration measurements of activated and total receptors as well as of various phosphorylated downstream targets. The largest data set comprises nine cell lines with measurements of the EGFR, HER2 and ErbB3 receptors of the ErbB family and the IGF1-receptor, together with the downstream targets ERK, AKT, S6K1 and S6. Four different ligand concentrations of EGF, HRG, and IGF-1 ranging from 0.156 to 10 nM are used and 12 measurement time points up to 240 min are taken. In addition, co-stimulations of the respective ligands are available for two of the nine cell lines. Out of the nine cell lines, six cell lines are used for model calibration, the remaining three to validate the model. Cell lines used for calibration include H322M , BxPc-3 (pancreatic cancer), A431 (epidermoid cancer), BT-20 (breast cancer), ADRr (ovarian cancer) and IGROV-1 (ovarian cancer). BT474 (breast cancer), MDA-MB-231 (breast cancer) and ACHN  are utilized to validate the model.Measurements after either EGF or BTC stimulation are available for one of the calibration cell lines, ADRr, with ligand concentrations between 0.11 and 9.26 nM, and 12 measurement time points up to 240 min. These include phosphorylation of EGFR, HER2, ErbB3, ERK, and AKT. Apart from that, measurements with HGF and EGF as well as their co-stimulation is available for ACHN, which is used for validation with respect to HRG and IGF-1, spanning the same concentrations and measurements up to 120 min. Therein, phosphorylated EGFR and Met phosphorylation as well as phospho-ERK and phospho-AKT are measured. The receptor concentrations in all experiments are measured by ELISA whereas the downstream components are measured by lysate microarray.Cells were trypsinized, washed, and stained using fluorescently labeled antibodies, see list below. Antibodies were labeled as previously described (16). Receptor numbers were determined by assessing the antibody-binding capacity of the fluorescently labeled antibody via quantitative fluorescence-activated cell sorting. Antibody-binding capacity was determined using Simply CellularQuantumBeads , per the manufacturer’s instructions. List of antibodies and target receptors used in qFACS method: Erbitux (EGFR), Trastuzumab (HER2), Anti-human HER3 Ab generated by Merrimack, A12 IgG (anti-human IGF1-R), Anti-human Met, Mouse Anti-Human EpCAM-APC .68Matching high density lysate matrices from nine cells lines  were generated for both the ELISA and lysate microarray studies. Lysate matrices were developed by treating each cell line with EGF, HRG1b1, IGF1, individually at four different doses , as well as in all two-way combinations of these three ligands at a single 2.5 nM dose of each ligand  in 96-well plates (Corning). Antibodies directed at pAKT (S473), pMEK (S217/S221), pERK (T202/Y204), p-S6K1 (T389), and pS6 (S235/S236) were obtained from Cell Signaling Technology. Cells were cultured using standard tissue culture techniques in RPMI supplemented with 10% FBS and penicillin/streptomycin. Cells were counted using a hemocytometer and seeded at 10,000 cells/well into 40x 96-well tissue culture plates (Corning). After 24–48 h, once cells had reached approximately 50% confluency, medium was aspirated and replaced with low- in medium (RPMI supplemented with 0.5% FBS and penicillin/streptomycin) (Gibco). 16–24 h later, 2x ligand solutions prepared in low-serum medium were added simultaneously to all 96 wells of each plate. Plates were returned to the incubator for the prescribed incubation times, then placed on ice to stop ligand stimulation. Medium was aspirated from all wells, cells were washed once with ice-cold PBS, and then lysed in 30 μL/well of lysis buffer. Twelve time points as well as an untreated time zero were collected for all conditions . Lysates were then collected and frozen. For ELISA lysates, M-PER buffer (ThermoFisher) was supplemented with protease and phosphatase inhibitors (Roche) and NaCl to a final concentration of 150 mM. For lysate microarrays, lysis buffer was prepared as previously described.69 Protein specific antibodies were used for capture in all cases, while an anti-phospho tyrosine antibody was used for detection in all phosphor assays  using the Aushon 2170 micro-arraying robot in 2x4 8-pin mode . Slides were washed in 100 mM Tris-HCl pH 9.0 at RT for several days, followed by 3x 5 min PBST, after which slides were spun dry. ProPlate 16-well slide modules (GraceBioLabs) were then attached. Arrays were blocked in Odyssey Blocking Buffer  at 4 °C for 1 h, after which blocking solution was replaced with 1:500 primary antibodies  mixed with 1:1000 anti-beta-actin antibody (mouse) (Sigma A1978) for 24 h at 4 °C with agitation. Slides were then washed 3x 5 min with PBST, then incubated in secondary antibodies (1:1000 anti-rabbit-800 and 1:1000 anti-mouse-680 (LiCor) in 5% BSA/PBST) at 4 °C for 5 h. Arrays were washed briefly in PBST, ProPlate modules were removed, and whole-slides were washed 3x 5 min in PBST. Slides were then spun dry at room temperature. Slides were scanned on the LiCor Odyssey scanner at 21 μm resolution and under the “highest” quality setting in both the 700 and 800 nm channels. Spot intensities were extracted using the LiCor ImageStudio software with manual spot alignment.Total and phospho ELISAs of EGFR, ErbB2, ErbB3, and IGF-1R were performed as previously described.x of the model has a biological counterpart. The time evolution x(t) of the model concentrations is obtained by integration of the corresponding system of ODEsθd. These are linked to measured concentrations of the involved constituents y(t) by an observational functionθo. Both observational and dynamic parameters are comprised in θ. To compare the model response to measured data at time points ti, the scaled log-likelihood is calculated viaχ2(θ).Mechanistic models based on ordinary differential equations (ODEs) are frequently used for the description of biochemical reaction networks. They are composed of kinetic rate equations and every component 71 Therein, an implicit BDF integration method72 with attached KLU sparse solver was chosen.73 The inner derivatives of the likelihood needed in gradient-based parameter estimation were computed via forward sensitivities supplied to the integration algorithm.74 Numerical optimization was conducted using a trust-region based, large scale nonlinear optimization algorithm implemented in the MATLAB function LSQNONLIN.75 For the mathematical modeling and visualization, the open-source and freely available d2d framework,76 based on MATLAB, was used.Since analytical solutions of non-linear ODE systems are in general not available, a numerical integration must be performed. In this work, the dynamical system and its sensitivities were integrated by the CVODES integrator of the SUNDIALS suite.77 A good correlation between mRNA and protein expression was previously shown in an independent study.78 By fitting a linear model  and receptor mRNA expression from the Cancer Cell Line Encyclopedia (CCLE) for 124 cancer cell lines.BioModels.org with the identifier MODEL1708210000. In addition, the computational model including all proteomic data, the phenotypic data from the in vitro viability screen together with the RNA sequencing data obtained from the CCLE 77 and the TCGA Research Network (http://cancergenome.nih.gov/), respectively, have been deposited within freely available modeling toolbox Data2Dynamics . It includes MATLAB code and documented script files to readily perform all analysis steps outlined in this publication. The code folder is also available in the Figshare repository (10.6084/m9.figshare.5331544). In addition, the mechanistic signaling model was also implemented in the open-source R package dMod 71 . The package contains the experimental data for all calibration cell lines and allows to simulate model trajectories.The signaling model and code for machine learning analysis from this publication have been deposited to Supplementary Figure and TextSources of cell lines


Drosophila induces cell migration, which is suppressed by the loss of JNK signaling and enhanced by the gain of JNK signaling. APLP1 activates JNK signaling through phosphorylation of JNK, which up-regulates the expression of matrix metalloproteinase MMP1 required for basement membranes degradation and promotes actin remodeling essential for cell migration. Our data thus provide the first in vivo evidence for a cell-autonomous role of APLP1 protein in migration.The amyloid precursor like protein-1 (APLP1) is a member of the amyloid precursor protein (APP) family in mammals. While many studies have been focused on the pathologic role of APP in Alzheimer's disease, the physiological functions of APLP1 have remained largely elusive. Here we report that ectopic expression of APLP1 in  Amyloid precursor-like protein 1 (APLP1) is a member of the highly conserved amyloid precursor protein family that includes amyloid precursor protein (APP) and amyloid precursor-like protein 2 (APLP2) in mammals . Membersin vivo and in vitro [APP family proteins have also been implicated in neuronal migration, yet this function has been controversial. It was originally reported that neurons in APP/APLP1/APLP2 triple knockout mice over-migrated and accumulated in the marginal zone , suggestin vitro , and dowin vitro . RNAi knin vitro , suggestin vitro . Furtherin vitro . TogetheThough APP family proteins have conserved structure and could compensate for each other to certain extent in double and triple knockout mice , APLP1 lDrosophila to human, and regulates a wide range of cellular activities including proliferation, differentiation, migration and apoptosis [Drosophila [The c-Jun N-terminal Kinase (JNK) pathway is evolutionarily conserved from poptosis . Recentlpoptosis  and Aβ iosophila , 36. HowDrosophila has been used as an animal model to study the in vivo function of APP, for the genetics of Drosophila is clear and Drosophila is easy to manipulate. In Drosophila, there is also an APP-like protein, APPL, indicating the role of APPL may be conserved from Drosophila to human. In this report, we investigate the function of APLP1 in Drosophila. We found that expression of APLP1 in Drosophila wing disc triggers cell migration, which is suppressed by the loss of JNK signaling and enhanced by the gain of JNK signaling. APLP1 activates JNK signaling through phosphorylation of JNK, which results in elevated expression of JNK target genes including puc and mmp1, and actin polymerization. Thus, this work provides the first evidence that APLP1 promotes JNK-dependent cell migration in vivo.Drosophila. Interestingly, expression of APLP1 along the anterior/posterior (A/P) compartment boundary in third-instar wing discs under the control of patched-Gal4  gene. puc encodes a JNK phosphatase that negatively regulates JNK activity [puc mutants  encoding the Drosophila JNK [DN , which was consistent with APLP1 in cell migration, indicating a universal role of APLPs in cell migration. To examine whether APLP1 induced JNK-mediated cell migration is conserved from  protein , in cellof BskDN  or Puc  triggered puc-LacZ expression  or in the salivary gland also activates JNK signaling, resembling that of Hep expression  and lung carcinoid tumor [https://www.oncomine.org/), many more cancers, including Esophageal cancer, Ovarian cancer, Sarcoma, and Kidney cancer, have a portion of samples showing significantly higher expression of APLP1 in multiple studies, suggesting APLP1 may play an important role in some subtypes of cancers during tumorigenesis. Finally, higher APLP1 expression was observed in more advanced stages in lung cancer [Consistent with our findings, APLP1 has been found to be significantly up-regulated in several types of human cancers including invasive ductal breast carcinoma (id tumor  (Figure g cancer  (Figure Drosophila media and crosses were performed at 25°C unless otherwise indicated. UAS-APLP1, UAS-APPLsd were kindly provided by Dr. Merdes [pucH246, UAS-mmp1-IR, UAS-mmp2-IR, UAS-TIMP1, UAS-RasV12, UAS-Rho1-IR, UAS-p35, UAs-P53DN were obtained from Bloomington Stock Center; sd-Gal4, UAS-BskDN, UAS-Puc, UAS-Egr, UAS-Hep [ptc-Gal4, en-Gal4, pucE69 34, UAS-LacZ [All stocks were raised on standard . Merdes ; pucH246 UAS-Hep ; ptc-GalUAS-LacZ  were prerd instar larvae were dissected for each genotype as described [For statistical analysis of cell migration, 20-30 3escribed . Discs wAntibody staining of the imaginal discs was performed as previously described . The folrd instar larvae wing discs were dissected in PBST and stained for β galactosidase activity as described [For X-gal staining, 3escribed .


Increasing evidence shows that static magnetic fields (SMFs) can affect cell proliferation but mixed results have been reported. Here we systematically examined the effects of 1 T (Tesla) SMF, which is close to the SMF intensity that patients are exposed to MRI (magnetic resonance imaging) scanners in hospitals, for its effect on 15 different cell lines, including 12 human and 3 rodent cell lines. Our results show that 1 T SMF does not have apparent impact on cell cycle or cell death. However, at higher cell density, it reduced cell numbers in six out of seven solid human cancer cell lines. We found that both cell type and cell density had evident impacts on SMF effects. Moreover, the EGFR-Akt-mTOR pathway, which varies significantly between different cell types and densities, contributes to the differential effects of SMF. In addition, SMF also increases the efficacy of Akt inhibitors on cancer cell growth inhibition. Therefore 1 T SMF affects cell proliferation in a cell type- and cell density-dependent manner, and the inhibition effect of 1 T SMF on multiple cancer cells at higher cell density may indicate its clinical potential in late stage cancer therapy. However, the seemingly inconsistent observations are mostly due to the different magnetic field parameters and multiple experimental variables. It is obvious that magnetic fields of different types (static or time-varying magnetic fields), field intensity  or frequencies  can lead to diverse and sometimes completely opposite results [Although there are numerous reports of  results –4.Besides various parameters of the magnetic fields, different biological samples in individual studies often have distinct genetic background, which makes them respond to the magnetic fields differentially. For example, Aldinucci et al. found that 4.75 T SMF significantly inhibited Jurkat leukemia cell proliferation but did not affect normal lymphocytes , but othIn comparison to Dynamic/Time-varying Magnetic Fields, static magnetic field (SMF) is more suitable to study the biological effects and their underlying mechanisms because they have less variable parameters. Electromagnetic fields from power lines, microwave ovens and cell phones are all dynamic/time-varying magnetic fields, whose effects on human bodies are still debated and causing widespread public health concerns. In contrast, SMF is characterized by steady, time-independent field strengths, and the reported biological effects of SMFs are mostly negligible or even beneficial. The core component of the MRI (magnetic resonance imaging) machines in most hospitals is a strong SMF with field intensities ranging between 0.1-3 T, in combination with pulsed radiofrequency magnetic fields. The SMF intensities in the 0.1-3 T range are currently considered to be safe to human bodies because no severe health consequences have been reported. The discomforts in patients such as dizziness are all temporary, which disappear after the MRI examination. However, mixed experimental reports from the laboratories are in the literature, which seem to be controversial. Some studies show that SMFs in this range do not affect cell growth or cell cycle , 10, whiHere in this study, we chose 1 T SMF to test its effect on 15 different cell lines side-by-side, including 12 human cell lines (7 solid cancer and 5 non-cancer cell lines) and 3 rodent cell lines. We found that 1 T SMF not only affected cell proliferation in a cell type-dependent manner, but also cell density-dependent manner. We revealed that cell growth of most human solid cancer cell lines we tested, but not non-cancer cell lines, can be inhibited by 1 T SMF at higher cell densities, in which the EGFR-Akt-mTOR pathway may play essential roles.5 cells/ml, cultured with or without 1 T SMF for 2 days and examined them side-by-side  can effectively inhibit human nasopharyngeal cancer CNE-2Z cell proliferation . However5 cells/ml for cell seeding, depending on which concentrations could reach maximum confluence at the end of experiments. We found that there was no significant cell morphology change by 1 T SMF treatment  and the mTOR pathway could be affected by SMFs , 11. SpeNext we compared the cancer vs. non-cancer cells from human lungs. We examined different densities of A549 lung cancer cells and HSAEC2-KT normal lung cells side-by-side to see whether the EGFR-Akt-mTOR pathway is differentially regulated by cell type and cell density Figure . Not sur5 cells/ml, the cell number of CHO-EGFR cells were increased by 1 T SMF , the cell number of CHO-EGFR cells are reduced by 1 T SMF . Their treatment time goes far beyond the MRI machines. However, our results showed that the cell growth for one of the seven cancer cell lines, the bladder cancer EJ1 cell, was not reduced by SMF at all. In addition, we have only tested 7 human solid cancer cell lines, whether other cancer cells in suspension, such as various types of leukemia cells, can be affected in the same pattern is still unknown. Therefore, although at higher cell density, 1 T SMF reduced the cell number in majority of cancer cell lines we tested, further investigations are strongly needed to explore the clinical potential of SMFs on cancer growth inhibition. It is very possible that only some cancer cell types can be inhibited by SMFs, which is likely relevant to their different genetic background. In addition, the tendency of SMFs to promote some cancer cell growth at lower cell density should also raise caution about the potential oncogenic effect at early cancer stages for this prolonged SMF exposure. More investigations are needed to address this issue.One limitation of our study was that we did not address the potential issue of metabolism, nutritional competition for cells at high density, which is apparently more stringent than cells at lower density. Theoretically, SMFs may affect cell metabolism, for example, utilization of carbon and nitrogen sources, oxygen consumption, respiration etc. However, we recently started to examine two types of leukemia cells in suspension and found that 1 T SMF inhibited both of them at lower cell concentration but not at higher cell concentrations. Therefore we think it is less likely that the reasons mentioned above such as oxygen consumption or nutrient competition are the major factors. In the meantime, it has been shown that moderate intensity SMFs could affect intracellular signaling pathways , 33. AltIt was known that the biological effects of magnetic fields can be influenced by the magnetic field types, strength, frequency, treatment time and other parameters, which all contribute to the mixed results of biological effects of magnetic field in the literature. Here by systematically testing 15 different cell lines at 4 different cell densities, we revealed that both cell types and densities are key factors that influence the effects of SMF. In addition, EGFR-Akt-mTOR likely contributes to the cell types and densities induced variations. The fact that most cancer cell lines can be inhibited at high density indicates the clinical potential of SMF in solid tumors or end-stage tumors.We tested 15 cell lines in total, which are all adherent cell lines. For the 12 human cell lines, we chose 7 cancer cell lines  and 5 non-cancer cell lines . The three rodent cell lines are Chinese Hamster Ovary cell line CHO and mouse embryo fibroblast cell line NIH-3T3, as well as CHO cells that overexpresses EGFR-FLAG (CHO-EGFR cells). All cells were from ATCC except for CHO-EGFR, which was constructed as previously described [5 cells/ml are equivalent to the density of 2 x 104 cells/cm2. On the second day, they were placed in regular full-sized CO2 cell incubator  that has accurate control of temperature (37°C), humidity and CO2 (5%)  . The shaThe magnet surface dimension is 5 cm x 5 cm and the diameter of the cell culture plates we used was 3.5 cm . TherefoAs mentioned above, cells were plated at different concentrations in 35 mm cell culture plates. After SMF exposure, bright field images were taken before the cells were harvested by trypsinization. An aliquot of the cells were counted by hemocytometer and the rest cells were used for flow cytometry analysis (cell death and cell cycle). Experiments were repeated for at least three independent times by two researchers. The results were gathered together for analysis.4 cells per sample. Data were analyzed using ModFit LT. Experiments were done for at least two times and representative results were shown in the figures.Cells were trypsinized and washed with PBS. Then they were fixed in 70% ice-cold ethanol overnight at 4 °C before they were washed with PBS, and incubated in PI (propidium iodide) solution (BD Pharmingen) for 30 min at room temperature in the dark. Samples were then analyzed on a BD Flow Cytometry . For each condition, we collected 1 x 106 cells/ml. Then 100 μl of them was transferred to a 5 ml culture tube and 5 μl of FITC-Annexin V + 5 μl of PI (FITC-Annexin V Apoptosis Detection Kit was from BD PharmingenTM) were added to the tube, mixed, and incubated in the dark for 15 min at room temperature. Then, 400 μl of binding buffer was added to the stained cells before they were analyzed by flow cytometry within 1 h. Approximately 1 x 104 cells were collected by flow cytometer. Experiments were done for at least two times and representative results were shown in the figures.Cells were trypsinized and washed twice with ice-cold PBS before they were resuspended in binding buffer at 10TM XRS+ System and Beijing Tanon Fine-do X6. ImageJ software was used to quantify the protein relative level shown by Western blots.Cells grown in tissue culture plates were lysed directly in plates by 200 or 100 μl of M-PER lysis buffer (Pierce) supplemented with protease and phosphatase inhibitor cocktail at 4°C for 20 min. The whole cell lysate was mixed with 2 x SDS loading buffer, boiled, and subjected to Western blotting. The PVDF membrane was blocked with 5 % NFDM (non-fat dry milk) at room temperature for around 1 h. Corresponding primary antibodies were diluted in AbDil-Tween  at 1:1000 dilution. Primary antibodies used include phospho-specific antibodies, EGFR, Akt, S6K, 4EBP1, and the HRP-linked anti-rabbit and anti-mouse IgG antibodies which were from Cell Signaling Technology. The mouse monoclonal antibodies for beta-tubulin and beta-actin were from Beijing TransGen Biotech. All primary antibodies were diluted in AbDil-Tween at 1:1000 dilution (TBS supplemented with 2% BSA and 0.1% Tween-20) and HRP conjugated secondary antibodies were diluted in TBS with 0.1 % Tween-20 and 5 % NFDM at 1:5000 dilution. Western blotting results were obtained by Bio-Rad ChemiDoc5 cells seeded at the beginning of the experiment.To ensure the reproducibility of the experiments, most experiments in this work were repeated by two independent researchers on different days. Each researcher did their experiment independently, according to the same protocol. For quantifications presented in the figures, experiments were repeated for at least three independent times (n=3). In each experiment, one plate of sham control and one plate of magnetic exposure were examined. Each plate had 1-10 x 10For quantifications in this manuscript, mean values are shown in all figures, and standard deviations are shown as error bars. All images shown in figures are representative results from multiple experiments. Comparisons between different treatments were analyzed by a two-tailed Student t test. P values are labeled in figures for where data were compared.We calculated the coefficient of drug interaction (CDI = AB/(A x B)) of SMF in combination with Akt inhibitors for their effects on cell numbers in Figure


Int. J. Mol. Sci. 2017, 18, 928). SIPS is a growth-arrested state in which the cells acquire flattened and enlarged morphology, remain viable, secrete growth-promoting factors, and can give rise to tumor-repopulating progeny. The impact of SIPS on radiosensitivity measured by short-term assays remains largely unknown. We report that in four p53 wild-type human solid tumor-derived cell lines : (i) the conventional short-term growth inhibition assay (3 days post-irradiation) generates radiosensitivity data comparable to that measured by the laborious and time-consuming colony formation assay; (ii) radiation dose-response curves obtained by multiwell plate colorimetric/fluorimetric assays are markedly skewed towards radioresistance, presumably reflecting the emergence of highly enlarged, growth-arrested and viable cells; and (iii) radiation exposure  does not trigger apoptosis or loss of viability over a period of 3 days post-irradiation. Irrespective of the cell-based assay employed, caution should be exercised to avoid misinterpreting radiosensitivity data in terms of loss of viability and, hence, cell death.In most p53 wild-type human cell types, radiosensitivity evaluated by the colony formation assay predominantly reflects stress-induced premature senescence (SIPS) and not cell death ( Activation of the p53 signaling pathway by ionizing radiation was originally proposed to result either in cell cycle checkpoint activation to facilitate DNA repair and promote survival, or in apoptotic cell death . Based SIPS is a sustained growth arrested state characterized by the acquisition of flattened and enlarged cell morphology in cells that retain viability for a long time (months) post-treatment and acquire the ability to secrete factors that can promote proliferation and invasiveness in cell culture models and tumor development in vivo ,11. In aWAF1  protein is the founding member of the CIP/KIP family of cyclin-dependent kinase (CDK) inhibitors [The p21hibitors ,17. It ihibitors ,19. Conshibitors ,6,7,20.The purpose of the present study was to determine the impact of SIPS on radiosensitivity when evaluated by short-term tests in the commonly-used p53 wild-type cancer cell lines HCT116 (colon carcinoma), MCF7 (breast carcinoma), SKNSH (neuroblastoma), and A172 . These cell lines are known to respond to moderate doses of ionizing radiation  by exhibiting sustained nuclear accumulation of p21, coupled with growth arrest through SIPS. We demonstrate that radiosensitivity as measured by multiwell plate colorimetric/fluorimetric assays is markedly skewed towards radioresistance when compared to that measured by growth inhibition (3 days post-irradiation) and colony formation (10 days post-irradiation) assays, and that the response measured by these cell-based assays reflects growth inhibition (SIPS) and not apoptosis or loss of viability.The colony formation assay  for assessment of radiosensitivity in cultured human cells was developed by Puck and Marcus over half a century ago . It has 50) were comparable when measured by colony formation and growth inhibition assays.As alluded to earlier, in most cell types, activation of the p53 pathway in response to moderate doses of ionizing radiation  results in activation of early cell cycle checkpoints, suppression of apoptosis and sustained growth arrest through SIPS ,3,4. ConInterestingly, we have recently reported that these two assays also indicate a comparable degree of radiosensitivity for solid tumor-derived cell lines that lack p21 or wild-type p53 activity . RadioseAlthough the colony formation assay is considered to be the “gold standard” for genotoxicity assessment, it has several drawbacks. Aside from being time consuming and labor intensive, preparation of good quality single-cell suspensions and evaluation of borderline macroscopic colonies are not trivial for some cell types, requiring considerable expertise. In addition, some solid tumor-derived cell lines  cannot be reliably evaluated by this assay because of their poor cloning efficiencies, and some cell lines  do not yield a good single-cell suspension by conventional approaches  .The growth inhibition assay circumvents these limitations. As pointed out previously , in addiShort-term cell metabolic activity assays are widely used to assess radiosensitivity and chemosensitivity with solid tumor-derived cell lines ,25,26,27In a typical assay, the cells are plated at “optimal” densities in the wells of a microtiter plate, exposed to a genotoxic agent, and incubated for 3 days. XTT (or MTT) is added to each well, and cultures are incubated for a few hours to allow the viable cells to covert the yellow tetrazolium salt (XTT or MTT) to its colored formazan derivative; XTT formazan is orange in color whereas MTT formazan is purple. The number of cells inoculated per well and the duration of cell incubation with XTT or MTT need to be optimized for each cell line.MTT is readily taken up by the cells, but its formazan metabolite is water insoluble, requiring a solubilization step before data evaluation. XTT, on the other hand, requires an electron coupling step to facilitate its uptake by the cells, but the XTT metabolite is water soluble.After these treatments, either with (MTT) or without (XTT) the metabolite solubilization step, the medium overlaying the control (sham-treated) cultures turns purple (MTT) or orange (XTT), as a result of conversion of the yellow tetrazolium salt to a colored metabolite by viable cells. Genotoxic treatment results in a decrease in color intensity of the medium, which is proportional to the number of viable cells/well at the time of incubation with XTT or MTT . The resThe CellTiter-Blue assay is also widely used for genotoxicity assessment. This assay is based on the ability of living cells to convert the redox dye resazurin (blue) into the resorufin product (pink). Resazurin has little intrinsic fluorescence until it is converted to its metabolite, which is highly fluorescent (579 nm excitation/584 nm emission). Thus, the CellTiter-Blue assay not only eliminates the need for electron coupling or metabolite solubilization steps required for tetrazolium-based assays, but it also enables fluorimetric evaluation of the data, which is considered to be more sensitive than absorbance measurements.The properties of MTT make it particularly useful for microscopic assessment of individual cells in the absence of solvent treatment. To this end, cultures are incubated with medium containing MTT to allow viable cells to covert MTT to its purple, water-insoluble formazan. Under these conditions, MTT metabolites appear as intercellular formazan granules as well as needle-like formazan crystals . We usedSeveral pitfalls of multiwell plate colorimetric assays have been extensively reviewed, including the impact of cell density and possible interference of test compounds with optical density measurements ,31,32,33Thus, although many suppliers refer to these colorimetric assays in terms of “Cell Viability” or “Cytotoxicity,” all such assays that are performed in a multiwell plate format lack specificity and can lead to incorrect interpretations. As illustrated in 50 values measured by growth inhibition and multiwell plate assays (both measured 3 days post-irradiation) amounted to ~3 Gy and >8 Gy, respectively.Assuming that radiation exposure would minimally alter the size of cancer cells, radiosensitivity as measured by multiwell plate assays would be expected to be comparable to that measured by growth inhibition and colony formation assays. This is not always the case, however; cancer cells undergoing SIPS following radiation exposure exhibit a markedly enlarged morphology. Sohn et al. , for exaIn most human cell types, activation of p53 signaling in response to genotoxic stress has been shown to supress (rather than promote) apoptosis ,19. SuppApoptosis in our previous work was evaluated by microscopic examination of Hoechst 33342-stained cells for morphological criteria (nuclear condensation and fragmentation). We sought to verify these observations by flow cytometric determination of Annexin V-positive  cells. The results are presented in As a positive control, we also determined the apoptotic response of selected cancer cell lines  following incubation with high concentrations of the chemotherapeutic drug cisplatin or the nuclear export inhibitor leptomcyin B. Treatment with cisplatin (60 μM) or leptomycin B (2 μM) for 3 days resulted in apoptosis in a high proportion of HCT116 cells when evaluated by Annexin V/flow cytometry  and morpv/v) fetal bovine serum as described in [Mycoplasma contamination.The four cancer cell lines used in the present study were purchased from the American Type Culture Collection . Cells were cultured as monolayers in DMEM/F12 nutrient medium supplemented with 5% , the CellTiter-Blue reagent  and the tetrazolium dyes MTT and XTT  were used as recommended by the manufacturers.60Co γ-rays was performed in a Gammacell 220 unit as described [Exposure to escribed .Radiosensitivity evaluation by growth inhibition, colony formation and 96-well plate assays was performed as described . Flow cyIn the current study, we have demonstrated that the conventional growth inhibition assay generates radiosensitivity data comparable to that obtained by the slower and more technically challenging colony formation assay for p53 wild-type cancer cell lines. On the other hand, the response measured by multiwell plate colorimetric/fluorimetric assays is markedly skewed towards radioresistance, which we assume to reflect the emergence of highly enlarged, growth-arrested and viable cells post-irradiation . In addition, we have confirmed that exposure to moderate  doses of ionizing radiation does not induce apoptosis (as judged by the Annexin V/flow cytometry approach) or loss of viability in the p53 wild-type cancer cell lines that we examined. These observations, in concert with those recently reported by us for p53 null or mutant p53-expressing cancer cell lines , give crShort-term multiwell plate assays are indispensable for high throughput studies, e.g., screening compound libraries for genotoxicity (proliferation block and/or cell death) towards the US NCI panel of cancer cell lines. However, it is becoming increasingly evident that the effect measured by such assays primarily reflects growth inhibition and not loss of viability ,19,23. O


In contrast, the commercially available line HepaRG, postulated to be a surrogate for hepatocytes and an ideal tool for mutagenicity tests, did not detect IQ and was relatively insensitive towards ROS. All other lines failed to detect two or more compounds. HCC1.2 cells have a high and unstable chromosome number and mutated p53, these features distract from its use in routine screening. HepG2 was frequently employed in earlier studies, but pronounced inter-laboratory variations were observed. HuH6 was never used in genotoxicity experiments and is highly promising, it has a stable karyotype and we demonstrated that the results of genotoxicity experiments are reproducible.Cell lines which are currently used in genotoxicity tests lack enzymes which activate/detoxify mutagens. Therefore, rodent-derived liver preparations are used which reflect their metabolism in humans only partly; as a consequence misleading results are often obtained. Previous findings suggest that certain liver cell lines express phase I/II enzymes and detect promutagens without activation; however, their use is hampered by different shortcomings. The aim of this study was the identification of a suitable cell line. The sensitivity of twelve hepatic cell lines was investigated in single cell gel electrophoresis assays. Furthermore, characteristics of these lines were studied which are relevant for their use in genotoxicity assays . Three lines  detected representatives of five classes of promutagens, namely, IQ and PhIP (HAAs), B(a)P (PAH), NDMA (nitrosamine), and AFBThe online version of this article (10.1007/s00204-017-2109-4) contains supplementary material, which is available to authorized users. One of the fundamental problems of in vitro tests is the inadequate representation of drug metabolizing enzymes in cell lines which are currently used in routine screening of chemicals. Therefore, exogenous liver-derived enzyme homogenates are prepared from rodents which have been treated with enzyme inducers Kirkland . The homIn the early 1990s, Darroudi and Natarajan  showed t2O2) was included in all experimental series as a positive control, this compound is directly active and causes damage of the DNA via formation of reactive oxygen species (ROS).The use of the different human-derived liver cells in mutagenicity tests was not based on targeted comparative screening of currently available cell lines and we hypothesized that other lines which are currently available may be equally or even more suitable for the detection of mutagens. Therefore, we performed a comprehensive study in which we compared the sensitivity of a panel of cell lines towards representatives of different classes of genotoxic carcinogens which require enzymatic activation in genotoxicity experiments. In addition to HepG2, Hep3B, HCC1.2 and HepaRG, eight lines were included which have never been used in genotoxicty assays before. The test compounds which were used are listed in Table The DNA damaging properties of the different compounds were monitored in single cell gel electrophoresis (SCGE) assays which are based on the determination of DNA damage in an electric field and are increasingly used in genetic toxicology  the morphology of the cells which provides information about their origin and similarity to primary liver cells, (2) their karyotype allowing to draw conclusions concerning their chromosomal stability, (3) measurements of the mitotic activities providing information about the duration of the repair phase which is needed for the design of tests which require cell division  and (4) the 1 , benzo(a)pyrene (B(a)P, CAS-No. 50-32-8), hydrogen peroxide  and N-nitrosodimethylamine  were purchased from Sigma-Aldrich . 2-amino-3-methyl-3H-imidazoquinolone  came from Toronto Research Chemicals  and 2-amino-1-methyl-6-phenylimidazopyridine  from Santa Cruz Biotechnology, Inc. . Anti-GADPH antibody (ab9485), anti-mutant-p53 antibody (ab32049) and anti-p53 antibody (ab1101) were purchased from Abcam plc . Anti-rabbit IgG HRP conjugate (W4018), anti-mouse IgG HRP (W4028) conjugate came from Promega Corporation .Citric acid (CAS-No. 77-92-9) and di-natriumhydrogenphosphate-dihydrate (CAS-No. 10028-24-7) for McIlvaine buffer were obtained from Merck . Quinacrine dihydrochloride (CAS-No. 69-05-6) and Thymol (CAS-No. 89-83-8) for quinacrine staining solution, aflatoxin BLow melting point agarose (LMPA) and normal melting point agarose (NMPA) were acquired from Gibco . Inorganic salts, dimethyl sulfoxide (DMSO), propidium iodide, hydrogen peroxide, Triton X-100, Trizma base, fetal bovine serum (FBS), Dulbecco’s Phosphate Buffered Saline (DPBS), Dulbecco’s modified Eagle Medium (DMEM), Eagle’s Minimum Essential Medium (EMEM), Minimal essential Medium Eagle (MEME), Roswell Park Memorial Institute Medium (RPMI-1640) were purchased from Sigma-Aldrich . William’s E medium came from Thermo Fisher Scientific . Trypsin–EDTA was ordered from Life Technologies .The origin of the cell lines is listed in Table 2. All lines except HepaRG™ were grown in TC-treated flasks and dishes  as described in previous publications  was diluted to obtain a final concentration of 0.2 mg/ml collagen. Subsequently, the growth areas of the flasks or dishes were covered with 0.1 ml/cmThe identity of the cell lines (except of HepaRG™) was verified by short tandem repeat (STR) analyses  in 5.0 ml medium. DMSO was added drop-wise , then the suspensions were distributed equally to cryovials , which were placed in a polystyrene box at − 80 °C for 24 h (to ensure slow freezing) and transferred to liquid nitrogen.® Cell Counter and Analyzer System  which was used as described by the manufacturer . Cell lines were grown in Petri dishes  in 5.0 ml medium; ca. 5 × 105 cells were seeded in each dish at the start of the experiments. After 24 h intervals, the cells were detached with trypsin–EDTA  and 50 µl of these suspensions were transferred to CASY-cups . For each experimental point, three plates were evaluated. On the basis of the results, means and standard deviations (SDs) were calculated. Nonlinear fits with exponential growth equations (least square fits) were calculated to determine the doubling times .The proliferation kinetics and size of the cells were monitored by use of a CASYCell lines were cultured and prepared according to standard cytogenetic techniques; at least 20 metaphases per cell line were karyotyped. Slides, which had been stained with quinacrine solution , were incubated in McIlvaine solution , covered with a cover slip and analyzed using Applied Spectral Imaging Case Data Manager .p53 gene in the different cell lines was analyzed using real-time quantitative PCR (RT-qPCR) according to the description of Straser et al. , all other compounds in DMSO. The final DMSO concentrations in the media did not exceed 1.0%. The exposure time in experiments with AFB1, B(a)P and NDMA was 24 h and in assays with PhIP and IQ 48 h. To terminate the treatment, the cells were washed twice with PBS (1.0 ml), trypsinized  and then resuspended in 400 µl culture medium containing FBS. Subsequently, the suspensions were transferred to Eppendorf tubes and centrifuged . Cell pellets were resuspended in cold PBS. H2O2 treatment was conducted with cells embedded on the slides by exposure to different concentrations (10–50 µM) of cold H2O2 solution in PBS for 5 min, followed by washing with cold PBS (5 min).Briefly, cells were sub-cultured in 1.0 ml medium in 24–well tissue-culture plates . After 24 h, the media were changed and the cells exposed to different concentrations of the test compounds or to the solvent controls (PBS or DMSO). H® Cell Counter and Analyzer System   Doi .The proliferation kinetics of the different cells are shown graphically in Figures S2a and b. On the basis of the experimental data, the doubling times were calculated Table . The “faThe results of the karyotyping experiments are summarized in column four of Table Western Blot and RT-qPCR analyses  also in assays with HCC1.2 while relatively strong differences of the responses were observed with HepG2. Experiments with HepaRG were not included as the reproducibility of comet experiments was addressed already in a study by Le Hegarat et al. (Induction factors (IF) represent the ratios of %DNA in tail induced in chemically treated cells and respective untreated controls. It can be seen that the values obtained in two independent experiments with different mutagens are in general similar in experiments with HuH6 and for certain model compounds P, five NDMA, four PhIP, and three IQ. Three hepatic lines  were sensitive towards representatives of all groups, HepRG to four compounds, HuH7 to two; only four lines were not responsive at all.The results of the SCGE experiments confirm our assumption that several human-derived hepatoma cell lines detect the genotoxic properties of different groups of DNA reactive carcinogens without addition of exogenous enzyme activation mix. Seven out of twelve lines were sensitive towards AFB1 and B(a)P, which were positive at concentrations between 5 and 10 µM while higher doses of IQ and NDMA (in most cases ≥ 100 mM) were required to cause significant effects. It is notable that HCC1.2 was clearly more sensitive towards these latter compounds as the other lines.The sensitivity of the different hepatic lines was in general similar. The most pronounced effects were observed in general with AFBp53 and an epithelial morphology, its chromosome number is in a narrow range and twice as high as in primary hepatocytes; the doubling time is similar to that of most other lines.The most important finding of the present study is the identification of a hepatic line (HuH6) which is apparently more suitable for the detection of genotoxins as the other liver-derived lines. This line was isolated more than 40 years ago from a hepatic tumor of a 1-year-old boy Doi . HuH6 cep53. These findings indicate that they are not suitable for routine testing of chemicals; as mentioned above, it was stressed by Fowler et al. P, PhIP, and NDMA) and their sensitivity was in most cases identical or lower as that of the other cell types . With the heterocyclic aromatic amine IQ, negative results were obtained under all experimental conditions. These observations are in agreement with previous findings of Lehegerad et al. (N-acetyl transferase (NAT) . It is well documented that ROS play a crucial role in the induction of DNA damage by a large number of chemicals including peroxides and PAHs and also by radionuclides  IARC . The latAT) IARC  which arAT) IARC . In thisAT) IARC ; data foAT) IARC  are not AT) IARC . AnotherAn ideal cell type for the detection of genotoxic carcinogens would be primary human hepatocytes. However, it is difficult to obtain these cells  and they are not available for routine testing. We found only two papers concerning comet assays with primary liver cells. The first by Monteith and Vanstone  describeTaken together, the results of the present study show that several human-derived liver cell lines detect promutagens without addition of rodent-derived liver homogenate. Furthermore, they indicate that HuH6 is the most promising line which may be useful for routine testing of chemicals. Results of experiments with HepaRG cells indicate that differentiated cells express higher levels of drug metabolizing enzymes Below is the link to the electronic supplementary material.


VEGFR1/2, expression of PDGFR α/β in particular has been documented as characteristic of metastatic disease correlating with poor prognosis. Therefore, we have been suggested that the clinically approved multi‐kinase angiogenesis inhibitor Axitinib, which specifically targets these kinases, might constitute a promising option for medulloblastoma treatment. Indeed, our results delineate anti‐neoplastic activity of Axitinib in medulloblastoma cell lines modelling the most aggressive c‐myc‐amplified Non‐WNT/Non‐SHH and SHH‐TP53‐mutated tumours. Exposure of medulloblastoma cell lines to Axitinib results in marked inhibition of proliferation and profound induction of cell death. The differential efficacy of Axitinib is in line with target expression of medulloblastoma cells identifying VEGFR 1/2, PDGFR α/β and c‐kit as potential markers for drug application. The high specificity of Axitinib and the consequential low impact on the haematopoietic and immune system render this drug ideal multi‐modal treatment approaches. In this context, we demonstrate that the clinically available PI3K inhibitor GDC‐0941 enhances the anti‐neoplastic efficacy of Axitinib against c‐myc‐amplified medulloblastoma. Our findings provide a rational to further evaluate Axitinib alone and in combination with other therapeutic agents for the treatment of most aggressive medulloblastoma subtypes.Aberrant receptor kinase signalling and tumour neovascularization are hallmarks of medulloblastoma development and are both considered valuable therapeutic targets. In addition to  Medulloblastoma is the most common malignant brain tumour of childhood. Despite current intensive therapy including a combination of surgery, chemotherapy and radiation, still one‐third of the patients succumb to this disease via vascular endothelial growth factor receptor VEGFR 1/2, platelet‐derived growth factor receptor α/β (PDGFR α/β) and c‐kit is critical for medulloblastoma development and progression Signalling Axitinib has been approved by the Food and Drug Administration for the treatment of advanced renal cell carcinoma While the anti‐angiogenic capacity of Axitinib has been extensively delineated, only few reports show that the anti‐tumour activity of Axitinib is also mediated by inhibition of its target kinases VEGFR1‐3, PDGFR α/β and c‐kit expressed by the tumour cells themselves c‐myc‐amplified Non‐WNT/Non‐SHH and SHH‐TP53‐mutated medulloblastoma variants associated with a very poor prognosis in vitro and in vivo the anti‐neoplastic potential of the phosphoinositid‐3‐kinase (PI3K) inhibitor GDC‐0941 for medulloblastoma therapy c‐myc‐amplified medulloblastoma. Thus, our data provide a rational for further assessment of Axitinib alone and in combination with other therapeutic approaches such as PI3K inhibitors for medulloblastoma treatment.Here, we report that Axitinib displays anti‐proliferative, anti‐clonogenic and pro‐apoptotic activity in cell lines modelling the most aggressive Axitinib and GDC‐0941 were obtained from LC Laboratories . The primary antibodies pSTAT3 , STAT3 (124H6), pAKT (Ser473), AKT (11E7) and GAPDH were purchased from Cell Signalling while secondary antibodies were purchased from Dianova . Carboxyfluoreszein‐succinimidyl ester (CFSE) was purchased from Invitrogen , while Hoechst 33258 was provided by Sigma‐Aldrich .l‐glutamine supplemented with 1 mM sodium pyruvate (PAA), 1% penicilline/streptomycine (Invitrogen) and 10% foetal bovine serum .The human medulloblastoma cell lines Daoy (HTB 186) and D283 Med (HTB‐185) were obtained from American Type Culture Collection (ATCC). The medulloblastoma cell line MEB‐Med‐8A was generated by Prof. T. Pietsch. The medulloblastoma cell lines Daoy, D283 Med and MEB‐Med‐8A were maintained in complete medium, namely Dulbecco's modified Eagle's medium , with 5), D283 Med (5 × 105) and Daoy (3 × 105) cells were seeded in 6‐well cell culture dishes in complete medium. After overnight cultivation, the cells were treated with the respective drug concentration for 24 and 48 hrs. Dimethylsulfoxid (DMSO) served as control. Thereafter, dead and living cells were harvested and resuspended in an equal volume of PBS. Dead cells were identified by trypan blue stain according to the supplier's instructions. Results of microscopic cell count by means of a haemocytometer were confirmed by a flow cytometric analysis. Dead cells were excluded from analysis by Hoechst33258 stain. For each sample, the total number of living cells measured within 100 sec. was determined.MEB‐Med‐8A (7 × 105) and D283 Med (5 × 105) and Daoy (3 × 105) cells were seeded in 6‐well cell culture dishes in complete medium. After overnight cultivation, the cells were treated with respective concentrations of Axitinib and GDC‐0941 for 48 hrs. Thereafter, floating and attached cells were collected, stained with Hoechst33258 and analysed by flow cytometry. Proliferation was traced by CFSE staining and normalized to the control DMSO, while dead cell was determined by Hoechst33258 staining.Medulloblastoma cells were stained with CFSE according to the supplier's instructions. MEB‐Med‐8A  and MEB‐Med‐8A (1000 cells/well) were plated in 6‐well cell culture dishes. The cells were allowed to adhere and spread properly for 12 hrs at 37°C. Thereafter, the cells were exposed to the stated concentration of Axitinib. After 48 hrs of exposure, the cells were washed with standard medium to remove any trace of the inhibitor and cultured for another week. Colony numbers and colony size were assessed by IMAGEJ. Particles smaller than 40 pixelg, and the protein concentration was determined by means of the Bradford assay. During the entire procedure, cells and lysates, respectively, were kept at 4°C. A total protein concentration of 25 μg derived from medulloblastoma cell lines was separated by SDS‐polyacrylamide gel electrophoresis and transferred to nitrocellulose membranes . The membranes were blocked for 1 hr at RT in 1× Tris‐buffered saline containing 0.1% tween‐20 (TBST) supplemented with 5% BSA. Thereafter, the membranes were incubated with the primary antibodies (1/1000) overnight at 4°C and subsequently with the respective secondary antibody  for 1 hr at room temperature. Immunoreactivity was detected by chemiluminescence and quantified by means of a ChemiDoc XRS Imaging System (Bio‐Rad).The medulloblastoma cell lines Daoy, MEB‐Med‐8A, D283 Med and D341 were exposed to different Axitinib concentrations for 1 and 48 hrs, respectively. Thereafter, the cells were harvested and subsequently lysed in RIPA buffer (Sigma‐Aldrich) containing protease and phosphatase inhibitors ). The lysates were centrifuged 10 min. at 10,000 × t‐test was applied to determine statistical significance of differences between groups. P < 0.05 (*) was considered as statistically significant. Values stated within text and figures represent mean ± standard deviation.The two‐sided Student's c‐myc‐amplified Non‐WNT/Non‐SHH medulloblastoma while the third analysed cell line, Daoy, shows markers of a subgroup of SHH tumours characterized by a mutation of tumoursuppressor protein 53 in vitro concentrations of 0.5 and 1 μM Axitinib corresponding to plasma levels observed in patients  Vandetanib We previously showed that the clinically available PI3K inhibitor GDC‐0941 exerts significant anti‐neoplastic efficacy against medulloblastoma c‐myc‐amplified Non‐WNT/Non‐SHH medulloblastoma lines MEB‐Med‐8A and D283 Med, Axitinib reduces viable cell number in a dose‐dependent manner starting at the lowest concentration of 0.5 μM, while in the SHH‐TP53‐mutated cell line Daoy, a significant decrease was only observed at 2 μM, the highest concentration applied. In both the c‐myc‐amplified Non‐WNT/Non‐SHH medulloblastoma cell lines, an additive cytoreductive effect of GDC‐0941 is noticeable when combined with 1 and 2 μM Axitinib.In the % in MEB‐Med‐8A and to 14 ± 3% in D283 Med. In MEB‐Med‐8A, cell loss was complete after combined exposure to 2 μM Axitinib and 1 μM GDC‐0941. Yet, in D283 Med, there was no further loss in viable cell number. Daoy, in contrast, is considerably more resistant to Axitinib. Viable Daoy cells are 90 ± 22 and 91 ± 19% at concentrations of at 0.5 μM and 1 μM Axitinib. Notably, cytoreductive activity is documented at 2 μM Axitinib with 30 ± 2.5% cell surviving, which is significantly lower than after exposure to 1 μM GDC‐0941 alone but without any additional effect by the drug combination.After treatment of MEB‐Med‐8A and D283 Med with 1 μM Axitinib, a decline to 39 ± 5% and 36 ± 5% residual viable cells is observed, respectively, while after exposure to 1 μM GDC‐0941, 19 ± 1.6% and 47 ± 5% cells survive. Compared to either drug alone, the combination of 1 μM Axitinib with 1 μM GDC‐0941 significantly decreases the number of viable cells to 3.6 ± 1We further dissected the anti‐neoplastic effect of Axitinib with regards to its pro‐apoptotic and anti‐proliferative capacity based on the combined CFSE‐Hoechst33258 stain. There was a substantial cell death effect upon escalation of Axitinib from 1 to 2 μM. After treatment with 2 μM Axitinib, 75 ± 4% of dead cells were detected in MEB‐Med‐8A and 36 ± 3% in Daoy. In MEB‐Med‐8A and Daoy, this exceeds the cell death observed with 1 μM GDC‐0941 resulting in 40 ± 3.5% and 9 ± 4% cell death, respectively. Compared to single drug application, the Axitinib‐GDC‐0941 combination significantly enhances cytotoxicity in MEB‐Med‐8A for all Axitinib concentrations, while in Daoy, dose escalation of Axitinib had the major cytotoxic effect. In D283 Med, increasing the Axitinib dose has no effect on cell death, which remains below 20% as is the case with 1 μM GDC‐0941 alone. However, in combination with GDC‐0941, cytotoxicity is twice as pronounced reaching 43 ± 10% and 37 ± 8% when coadministered with 1 and 2 μM Axitinib, respectively. Moreover, AnnexinV/7ADD staining reveals that Axitinib alone or in combination with GDC‐0941 exerts a pro‐apoptotic effect on all three medulloblastoma cell lines Fig. .With regards to the anti‐proliferative activity, in MEB‐Med‐8A and Daoy, the most prominent effect is achieved with 2 μM Axitinib alone compared to all other experimental conditions with 57 ± 3.5% and 43 ± 1.5% of growth inhibition, respectively. At lower Axitinib concentrations, the combination with GDC‐0941 augmented the anti‐proliferative effect in MEB‐Med‐8A and D283 Med compared to single Axitinib application at the respective concentration. However, there was no additional effect compared to GDC‐0941 alone in MEB‐Med‐8A and Daoy.To date, targeted therapies have become an integral element of cancer treatment c‐myc‐amplified Non‐WNT/Non‐SHH medulloblastoma variant While Axitinib is currently under clinical investigation in glioblastoma, this is a first report documenting profound anti‐neoplastic activity in paediatric medulloblastoma cell lines The profound loss of cell viability in all three MB lines can be attributed to marked pro‐apoptotic and anti‐proliferative activity. Axitinib arrests proliferation in G2/M phase Fig.  and intein vitro experiments proof anti‐neoplastic efficacy against different medulloblastoma cell lines. Morover, in vivo, drug accumulation or retention as a result of newly formed dysfunctional tumour vessels might lead to spatial Axitinib concentration in the brain tumour tissue that exceeds the patient plasma concentration in vivo, in addition to direct anti‐tumour effects, modulation of the tumour microenvironment contributes to MKI efficacy. Thus, a recent report on a phase II clinical study in recurrent glioblastoma concludes that Axitinib improves response and progression‐free survival In clinical pharmacokinetic studies, there is a high degree of variability in plasma levels even in healthy volunteers. However, although it is well documented that Axitinib is primarily metabolized by CYP3A4/5 and to a lesser extent by CYP1A2, CYP2C19 and UGT1A1, in two meta‐analyses, no association between plasma levels and polymorphisms of these enzymes was found. in vivo xenograft model that the oral administration of the broad‐spectrum MKI Sorafenib and Pazopanib attenuate tumour growth and prolong survival in vitro cytotoxicity of Axitinib shown here further in vivo studies evaluating Axitinib in orthotopic medulloblastoma mouse models are warranted. Pazopanib and Sorafenib also target VEGFR1‐3, PDGFR α/β and c‐kit among other kinases and affect medulloblastoma proliferation and cell survival by inhibition of STAT3 and, to a lesser extend, AKT phosphorylation In medulloblastoma, we have previously shown in an orthotopic c‐myc amplification compared to treatment with either drug alone. Combination of Axitinib with the AKT inhibitor GDC‐0941 results in near to complete abrogation of viable cells in MEB‐Med‐8A and D283 Med. Corresponding to the enhanced cytotoxicity of the Axitinib‐GDC‐0941 combination, AKT and STAT3 signalling are markedly impaired. While there is a cumulative suppressive effect of Axitinib and GDC‐0941 on STAT3 activity, as expected the specific PI3K inhibitor dominates the inhibitory effect on AKT. The significance of PI3K/AKT signalling has not only been delineated for c‐myc‐amplified medulloblastoma but also for cerebellar cells overexpressing c‐myc resulting in a similar molecular profile and PI3K dependency c‐myc‐amplified MEB‐Med‐8A and D283 Med medulloblastoma cells, in the SHH‐TP53‐mutated medulloblastoma line Daoy, at the highest dose, the anti‐neoplastic effect of Axitinib exceeds the cytotoxic activity of GDC‐0941 and combination of the two drugs has no additional effect. Of note, across the different medulloblastoma entities, not only the activity of STAT3 and AKT but also protein levels are diminished by the applied inhibitors, which is most likely because of the role of STAT3 and AKT as key regulators of protein expression With acceptable tolerability including limited haemotological side effects, Axitinib is particularly well suited for combination therapy c‐myc‐amplified Non‐WNT/Non‐SHH and SHH‐TP53‐mutated tumours. In this setting, marked anti‐proliferative property of Axitinib is complemented by profound cytotoxic capacity. We further show that Axitinib anti‐neoplastic efficacy correlates with the target expression profile on the tumour cells. As the availability of anti‐cancer agents with partially redundant target profiles such as Axitinib, Vandetanib, Sorafenib, Sunitinib and Pazopanib is steadily increasing, biomarkers supporting the choice of MKI are of increasing importance. With regards to multi‐modal treatment approaches, PI3K/AKT pathway inhibition by means of GDC‐0941 enhances the efficacy of Axitinib against c‐myc‐amplified medulloblastoma. Our data suggest that Axitinib single treatment or in combination with PI3K inhibitors such as GDC‐0941 might prove a promising strategy to clinically target the most aggressive c‐myc‐amplified and SHH‐TP53‐mutated medulloblastoma subtypes.In conclusion, our study demonstrates that Axitinib, a highly specific clinically available inhibitor of angiogenesis, displays marked cytotoxic activity against paediatric medulloblastoma cell lines modelling most aggressive The authors declare that they have no conflict of interest.Fig. S1 Expression of the cancer stem cell markers CD24, CD44 and CD133 is absent in MEB‐Med‐8A.Click here for additional data file.Fig. S2 Axitinib induces a G2/M‐phase cell cycle arrest in medulloblastoma.Click here for additional data file.Fig. S3 Determination of pro‐apoptotic effects of Axitinib in medulloblastoma cells.Click here for additional data file.Table S1 Relative expression of Axitinib target proteins in medulloblastoma cell lines Click here for additional data file.