Using a consistent experimental protocol, we found a large heterogeneity in the relative biological effectiveness (RBE) values of both proton and carbon-ion beams in various sarcomas and normal-tissue-derived cell lines. Our data suggest that proton beam therapy may be more beneficial for some types of tumors. In carbon-ion therapy, for some types of tumors, large heterogeneity in RBE should prompt consideration of dose reduction or an increased dose per fraction. In particular, a higher RBE value in normal tissues requires caution. Specific dose evaluations for tumor and normal tissues are needed for both proton and carbon-ion therapies.This study investigated variations in the relative biological effectiveness (RBE) values among various sarcoma and normal-tissue-derived cell lines  in proton beam and carbon-ion irradiations. We used a consistent protocol that specified the timing of irradiation after plating cells and detailed the colony formation assay. We examined the cell type dependence of RBE for proton beam and carbon-ion irradiations using four human sarcoma cell lines  and three normal cell lines . The cells were irradiated with gamma rays, proton beams at the center of the spread-out Bragg peak, or carbon-ion beams at 54.4 keV/μm linear energy transfer. In all sarcoma and normal cell lines, the average RBE values in proton beam and carbon-ion irradiations were 1.08 ± 0.11 and 2.08 ± 0.36, which were consistent with the values of 1.1 and 2.13 used in current treatment planning systems, respectively. Up to 34% difference in the RBE of the proton beam was observed between MG63 and HT1080. Similarly, a 32% difference in the RBE of the carbon-ion beam was observed between SW872 and the other sarcoma cell lines. In proton beam irradiation, normal cell lines had less variation in RBE values (within 10%), whereas in carbon-ion irradiation, RBE values differed by up to 48% between hTERT-HME1 and NuLi-1. Our results suggest that specific dose evaluations for tumor and normal tissues are necessary for treatment planning in both proton and carbon-ion therapies. Radiation oncology departments worldwide increasingly utilize particle beam therapy, including proton and carbon-ion beams ,2. PartiIn particle therapy, dose calculation is performed to estimate the dose distribution in a patient’s body. For prescribing a dose, a spread-out Bragg peak (SOBP) is created . ParticlIn carbon-ion therapy, the therapeutic dose is calculated by multiplying the physical dose by the RBE value based on biological and clinical effectiveness . HoweverClinical applications that adopt the RBE model, such as the mixed beam  and micrPrevious meta-analyses have established that the RBE value in tumor cell lines is not greater than that in normal-tissue-derived cell lines . However, for the same LET, there are less obvious and distinguishable differences in radiosensitivity between tumor and normal cells . To the Although there is an existing open access database of α- and β-values in LQMs for various heavy ions as well as photon beams , which p10 doses  for the MG63, HT1080, SW872, and SW1353 cell lines were 3.7, 6.8, 4.0, and 3.4 Gy, respectively   . With prectively . The RBEectively A; this dectively . The RBEectively B.10 and RBE values from different institutes, despite using the same cell lines. We conducted all our experiments using a consistent protocol at a single laboratory to directly compare the RBE-value dependency of tumor and normal tissue among gamma ray, proton beam, and carbon-ion irradiations.We found a large heterogeneity among RBE values for different sarcoma and normal cell lines with different kinds of irradiation. The Particle Radiation Data Ensemble database version 3.2 developed by GSI provides substantially differing DRecent studies have reported that, in proton beam irradiation, LET is higher at the distal end of the SOBP than that at the center of SOBP, which leads to a higher RBE value at distal points ,28,29. MAccording to a report by Kagawa et al., the RBE values in the HSG cell line irradiated with carbon ions were 1.66, 1.76, and 2.27 at LETs of 39.6 keV/µm, 46.6 keV/µm, and 69.6 keV/µm, respectively . The RBEReported RBE values for normal tissues have been determined using animal models . In the With proton irradiation, the combined mean RBE value of the four sarcoma and three normal cell lines was close to 1.1; this indicates that the RBE of 1.1 utilized in current treatment planning systems is appropriate for calculating tumor and normal tissue doses . HoweverThe RBE value in current carbon-ion treatment planning is based on the data of colony formation assay using HSG cells under aerobic conditions using the Heavy Ion Medical Accelerator in Chiba (HIMAC); this is because the RBE value of HSG cells was found to be close to that of other cancer cell lines ,35. EspeCurrently, a prescribed dose, determined on the basis of clinical experience, is used to compensate for uncertainties in the absolute RBE value ; this maThis study has some limitations. The RBE value, especially in tumor tissues, depends on several factors, including the speed of tumor growth and microenvironmental conditions, such as hypoxia. Although our cell survival experiments may have represented the intrinsic radiosensitivity of proliferating cells, the irradiated volume and the interactions in its substructure  can immensely impact normal tissue. To resolve these issues, it will be necessary to conduct three-dimensional cell culture or in vivo experiments.We obtained HDF   from CELL APPLICATIONS, INC. . We purchased MG63 (human osteosarcoma), HT1080 (human fibrosarcoma), SW872 (human liposarcoma), SW1353 (human chondrosarcoma), hTERT-HME1  , and NuLi-1   from ATCC .2. All sarcoma cell lines, hTERT-HME1, and NuLi-1 cells were cultured in Dulbecco’s Modified Eagle Medium (D-MEM)  containing 10% fetal bovine serum (Thermo Fisher Scientific) and 1% penicillin–streptomycin–glutamine mixed solution  under the same conditions . The hTERT-HME1 and NuLi-1 cells were immortalized by introducing the catalytic subunit of human telomerase (hTERT) or E6/E7 and hTERT, respectively.HDF cells were maintained in basal medium with a growth supplement  at 37 °C in a humidified atmosphere containing 5% CO137Cs gamma ray (0.662 MeV) irradiations were conducted using Gammacell® 40 Exactor , a gamma ray irradiator, at Osaka University Graduate School of Medicine. Proton beam irradiation was conducted at the Proton Beam Therapy Center of Hokkaido University. The cells were irradiated at the center of a 6 cm SOBP. For carbon-ion irradiation, the cells were irradiated at an LET of 54.4 keV/µm using a 250 MeV/nucleon carbon-ion beam at HIBMC. Dosimetry with a parallel plate ion chamber was conducted on the proton and carbon-ion beams before every irradiation. Gamma ray irradiation dosimetry was conducted using a Fricke dosimeter following the manufacturer’s instructions and confirmed using Gafchromic EBT3 films  by our group’s physicist.2 plastic slide flasks  6 h before irradiation as described in a previous report [2)”, where D describes radiation dose (Gy). The parameters α and β are constants describing the liner component and quadratic part of the curve, respectively. A minimum of three independent experiments for each irradiation were performed.s report . Immedias report . The LQM10 of each cell line among gamma ray, proton beam, and carbon-ion irradiations and differences in RBE values among cell lines after radiation were analyzed using Tukey–Kramer honestly significant difference test with a significance level of <0.05, using the scikit-posthocs module run on Python (version 3.6.10) [A two-tailed heteroscedastic t-test was performed to compare the RBE value of each cell line between proton beam and carbon-ion irradiations and to compare the SF between gamma ray and proton beams with a significance level of <0.05. Differences in α, β, and D 3.6.10) .Our results showed a large RBE heterogeneity in various sarcoma and normal cell lines receiving carbon-ion and proton beams based on a consistent experimental protocol at a single laboratory. With proton irradiation, the mean RBE value of the four sarcoma and three normal cell lines was close to 1.1. This indicates that the RBE value of 1.1 utilized in current treatment planning systems is appropriate for calculating the doses for tumors and normal tissues. Proton therapy may be more beneficial for some types of tumors; however, caution must be exercised when considering normal tissues, which have higher RBE dependency. With carbon-ion irradiation, RBE values were heterogeneous in both sarcoma and normal cell lines. We observed wide-ranging RBE in carbon-ion irradiation, suggesting that it is important to evaluate tumor- and normal-tissue-specific doses. In this context, for some types of tumors, large heterogeneity in RBE values should bring into consideration dose reduction or an increased dose per fraction. However, one should be cautious with regard to the dose to normal tissues owing to their high RBE dependency. For tissue-specific dose evaluations in a treatment planning system, it is necessary to estimate the RBE values of relevant cell lines by acquiring their LET-dependent survival fractions.


Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching.CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion.Selected CancerOmicsNet predictions obtained for “unseen” data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03.CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.The online version contains supplementary material available at 10.1186/s12885-022-10293-0. Cancer initiation and progression involve a sequence of gene-environment interaction events changing the gene expression and ultimately leading to the disruption of homeostasis . The phoNonetheless, the clinical efficacy of kinase-specific inhibitors is confounded by numerous factors. The success of a cancer treatment strongly depends on the underlying genetic features of the tumor, the microenvironment, the possibility of the development of drug resistance, and pharmacogenomics . NumerouThe Connectivity Map (CMap)  is frequ50) of dexamethasone, thus it induced the glucocorticoid sensitivity as expected [This technique has been shown to be effective in finding new treatments for Alzheimer’s disease (AD) and glucocorticoid-resistant acute lymphoblastic leukemia (ALL) . Two genexpected . Furtherexpected .A key limitation of current drug connectivity-mapping approaches is the subjective selection of disease signatures. To address this issue, Dr. Insight implements a new statistical model utilizing the genome-wide screening of concordantly expressed genes (CEGs) . Rather Co-expressed GSEA can also be combined with the pathway analysis in order to infer the drug mode of action in a disease context. An example is Cogena, a pathway-guided disease and drug repositioning approach that identifies drugs acting mechanistically within the framework of coordinated changes in disease transcriptomes . Cogena The compromised drug efficacy leading to the lack of tumor response to pharmacotherapy presents notable challenges in clinical oncology. Addressing this problem requires an ability to integrate vast datasets, learn intricate relations among numerous factors, and utilize existing knowledge, surpassing the analysis of gene expression alone. Deep learning is the latest technology in the field of artificial intelligence capable of performing such complex tasks by employing sophisticated nonlinear transformations to extract patterns from high-dimensional data. Not surprisingly, deep learning has already begun to significantly impact biological and biomedical research . For insMany recent strategies for precision oncology employ deep neural network (DNN)-based frameworks . For exaTo take advantage of cancer-related data having a non-Euclidean structure, we recently developed CancerOmicsNet, a graph-based deep learning system with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors , 61. In 50 and GRmax quantifying the proliferation by the value of growth rate inhibition (GR) measured in time course and endpoint assays. GR50 is the concentration of a drug at which GR is 0.5, whereas GRmax is the maximum measured GR value. Based on the sign of GRmax, 2124 effective (negative GRmax) and 1425 ineffective (positive GRmax) therapies are identified. Differential gene expression profiles for the disease state were obtained from the CCLE [The original dataset of 3549 cell line-drug combinations involving 359 cell lines and 29 drugs was previously compiled from six LINCS-Dose-Response datasets, Broad-HMS LINCS Joint Project, LINCS MCF10A Common Project, HMS LINCS Seeding Density Project, MEP-HMS LINCS Joint Project, Genentech Cell Line Screening Initiative, and Cancer Therapeutics Response Portal . These dthe CCLE . This dathe CCLE .m, 120 nm, 370 nm, 1.11 μm, 3.33 μm, and 10 μm. Mapping these data to LINCS-3549 resulted in 87 combinations of 11 cell lines and 24 drugs, referred to as the LINCS-87 dataset, which was employed to conduct the comparative benchmarks of CancerOmicsNet and the GS-based method. The LINCS-87 dataset comprises 40 effective (negative GRmax) and 47 ineffective (positive GRmax) therapies.Next, we obtained drug-perturbed gene expression profiles from the CMap  for 107,From the Team-SKI collection of 49,348 small molecules tested against 411 protein kinases, we selected 2497 molecules absent from the LINCS growth rate inhibition dataset, thus not included in the LINCS-3549 dataset used to train CancerOmicsNet. Applying Lipinski’s rule of five  identifi2) [A is the gene signature of cancer against healthy cells and B is the gene signature of the same cell type before and after drug treatment. Each gene signature comprises 11,113 genes that are present in differential gene expression profiles for the disease state from the CCLE and drug-perturbed gene expression profiles from the CMap. Disease gene expression values are converted to level-5 moderated Z-scores [B are the same) to 2 (signatures A and B are exactly opposite). For each treatment, six COS values are calculated for all drug concentrations and the one having the longest distance is selected as a metric to predict the anticancer drug response.Comparing gene signatures for drug-treated and disease cell lines is a traditional method to find potentially effective therapeutics. The GS-based method employed in this study is similar to the LINCS L1000 characteristic direction signature search engine (L1000CDS2) . This teZ-scores , 67. COSCancerOmicsNet is an AI-based system to predict a response of tumor cells to pharmacotherapy . It empl2 in a water jacketed tissue culture incubator. Pan 04.03 cells , derived from a primary tumor removed from the head-of-the-pancreas of a 70-year-old white male with pancreatic adenocarcinoma, were maintained in RPMI-1640 , human recombinant insulin (20 units/mL), and 15% fetal bovine serum. HCC70 cells , isolated from a primary ductal carcinoma from a 49-year-old black female, were maintained in RPMI-1640  and supplemented with 10% fetal bovine serum. DU 145 cells , derived from a 69-year-old white male with prostate cancer, were maintained in Eagle’s minimum essential medium (EMEM)  and supplemented with 10% fetal bovine serum.All cell lines were maintained at 37 °C and 5% CO5 cells were seeded into one well of a 6 well plate . After an overnight incubation, 200 μL of lentivirus particles consisting of ~ 3 × 105 transducing units were applied to the cells and returned to the incubator overnight. The following day, the media was replaced and cells were allowed to expand for 3 days. Next, cells were selected for transduction with the addition of 1 μg/mL puromycin. Nuclear fluorescence was visualized on an inverted fluorescent microscope maintained and under continual selection for further analysis.Incucyte nuclight red lentivirus reagent  was purchased and used to transduce Pan 04.03, DU 145, and HCC70 cell lines at a multiplicity of infection (MOI) or 1. Briefly, 3 × 10m in DMSO.JNJ-7706621 , PP1 , AZD6482 , XMD8–93 , GW2580 , and PI-103  were purchased from suppliers and resuspended to a stock concentration of 10 mm, 3.162 nm, 10 nm, 31.62 nm, 100 nm, 316.2 nm, 1 μm, 3.162 μm, and 10 μm. Cells were then imaged for 72 hours with the IncuCyte S3 system at 400 ms acquisition time in the red channel and the 10× objective. Adherent cell-by-cell analysis was conducted to quantify the number of red nuclei in each well over the 72-hour observation period. The entire experiment was repeated after a week; we refer to the first series of measurements as experiment A and the second series as experiment B.Cells were seeded at a density of 5000 cells/well in 384 well plates  in duplicate wells containing 20 μL media and incubated overnight. The following day, 20 μL of a 2× dilution series was applied to the cells to produce the final concentrations of 1 nt during the 72-hour observation period, a normalized cell count, Nnorm, is calculated as:N(t) is the number of red nuclei and N(t0) is the initial number of red nuclei recorded at the outset of measurements. This way, the normalized initial number of cells across all experiments is always 100. In addition to the normalized cell count, a relative cell count for the drug-treated group with respect to the control, Nrel, is calculated as:Nnorm is the normalized number of red nuclei for the group treated with a drug d at concentration c, and Nnorm is the normalized number of red nuclei for the same cell line in DMSO measured at the same time t.For any sample, including drug-treated and control groups, at any time GR value for a drug d at concentration c and time t is calculated as:N is the cell count for the group treated with drug d at concentration c and time t ± Δt while N is the cell count for the control group at time t ± Δt. Δt is chosen as 6 hours according to the original work [max (max stands for the maximum efficiency).Following the original paper describing the growth rate formalism , a GR vanal work . For eacCancerOmicsNet utilizes an integrated graph representation of multiple heterogeneous data, including biological networks, pharmacogenomics, kinase inhibitor profiling, and gene-disease associations . The floThe GNN model contains a series of customized graph convolution blocks Fig. F to gene2 search engine prioritizing small molecule signatures for their predicted ability to either reverse or mimic gene expression in a disease state [We first compare the performance of CancerOmicsNet to that of a traditional approach employing the gene signature analysis. As a GS-based method, we implemented an algorithm similar to the L1000CDSse state . This meTable 50) of 1.6 μm [max of − 0.07, which is indicative of a cytotoxic response. The COS distance is 1.08, therefore the GS-based method correctly predicted the sensitivity of MCF7 to dasatinib. The second example is a selective JAK1 and JAK2 inhibitor ruxolitinib [max value of − 0.25. Ruxolitinib is in phase 2 of a clinical trial against squamous cell skin cancer [In order to better illustrate the concept of the GS-based prediction of drug efficacy, we discuss two representative examples selected from the benchmarking dataset. The first example is an ATP-competitive protein tyrosine kinase inhibitor dasatinib  impedingf 1.6 μm . Dasatinolitinib  and the n cancer . The GS-Z-score (modZ) values computed for gene expression in cancer cell lines and those obtained for the drug treatment. Since the GS-based approach predicts effective treatments when drugs can potentially reverse the gene expression state of cancer cells, one would expect to find most genes in quadrants II and IV in Fig. Figure In contrast to the GS-based approach, AI-based CancerOmicsNet correctly predicted both treatments, MCF7 with dasatinib and A375 with ruxolitinib, to be effective with probabilities of 0.99 and 0.65, respectively. AI models are specifically designed to learn complex patters from the input data in order to make accurate predictions. To better understand the performance of machine learning in detecting effective treatments, high-dimensional graph embeddings from the output layer of CancerOmicsNet can be visualized in a two-dimensional space with t-distributed stochastic neighbor embedding (t-SNE), a nonlinear dimensionality reduction technique . Figure 50 values of 2 ± 0.7 nm (VEGFR1), 3 ± 0.5 nm (VEGFR2), and 6 ± 4 nm (VEGFR3) [50 of 84 ± 33 nm, mast/stem cell growth factor receptor Kit (c-KIT) at an IC50 of 8 ± 2 nm, and tyrosine-protein kinase receptor Ret (c-RET) at an IC50 of 59 ± 4 nm [We discuss the performance of CancerOmicsNet in several cases of “unseen” data, viz. treatments absent from the LINCS-3549 growth rate inhibition dataset that was originally used to train the machine learning model. Each novel prediction is supported by the evidence found in the biomedical literature. The structures of drugs selected for the literature-based validation are presented in Fig. (VEGFR3) . Althoug9 ± 4 nm . This dr9 ± 4 nm . Encoura9 ± 4 nm , Calu-3 Motesanib also has antitumor activity against breast cancer . Its pri50 of 73 nm, PDGFRβ with an IC50 of 215 nm, VEGFR1 with an IC50 of 7 nm, VEGFR2 with an IC50 of 15 nm, and VEGFR3 with an IC50 of 2 nm [m. According to CancerOmicsNet, the probability of inhibition of the 786-O cell line growth by pazopanib is 0.76. Pazopanib was also tested alone and in combination with topotecan against anaplastic thyroid cancer (cell line 8305C) [50 of 25 ± 3.2 μm. According to the Cellosaurus, 8305C  and 8505C  cell lines are closely related anaplastic thyroid cancers and CancerOmicsNet estimated that pazopanib inhibits the growth of 8505C with a high probability of 0.93.Pazopanib GW786034, Fig. , Fig. 4B of 2 nm . It exhi of 2 nm . For inse 8305C) , one of 50 of 2 to 3 nm [50 of 1 nm [50 of 100 nm [p-value of < 0.01 [Lestaurtinib  with an IC50 of 254 nm, and HeLa  with an IC50 of 284 nm [50 values of 286 ± 72 nm (HeLa), 189 ± 42 nm (HCT116), 410 ± 75 nm , 112 ± 12 nm , 416 ± 54 nm (A-375), 514 ± 63 nm , 263 ± 113 nm , and 413 ± 4 nm  [m, 500 nm, 1 μm, 2.5 μm, and 5 μm concentrations [50 of 0.5–1 μm, whereas 2.5 μm completely prevented the SCF-induced proliferation of M-07e cells.Selected predictions by CancerOmicsNet for the “unseen” data were subjected to experimental validation by live-cell time course inhibition assay. Eight drugs, whose structures are presented in Fig. f 284 nm . Anothersarcoma) . PP1 is sarcoma)  that hastrations . PP1 inhm concentration, compared to the control group consisting of vehicle (DMSO)-treated cells , brain (SK-N-AS) and non-small cell lung (NCI-H1299 and NCI-H522) cancer cell lines [m, the relative cell counts are 0.76 and 0.83, respectively. Further, GRmax values calculated for AZD6482 and XMD8–92 reported in Table The next two compounds are AZD6482  was highly resistant to GW2580 [m, U-87 MG at 600 nm, and U-138 MG at 1.0 μm [m. Further, GRmax values reported in Table The last two drugs are GW2580 Fig. H, a seleof 30 nm , and PI-nm/15 nm . Althougo GW2580 . Tested t 1.0 μm . CancerOmax values collected from these two experiments. In order to help evaluate the consistency between different experiments, each plot is divided into quadrants, labeled I-IV in Fig. max indices calculated from the data collected in each series of experiments. Encouragingly, most data points are in quadrant III (colored green in Fig. max values in both experiments, meaning that these compounds systematically inhibited the proliferation of cancer cells. A few points in quadrant I in Fig. max values observed in both experiments against the HCC70 cell line. Lastly, data points in quadrants II and IV in Fig. max values collected against the HCC70 cell line in experiments A and B. Overall, both validation experiments yielded consistent results positively validating CancerOmicsNet predictions.The experimental validation of CancerOmicsNet predictions was conducted in two series of growth rate inhibition assays, referred to as experiments A and B, carried out at a one-week interval. Figure max value is negative. Figure m. Encouragingly, 71.3% cases of CancerOmicsNet predictions tested experimentally show negative GRmax values with a mean ± standard deviation effective concentration of 1.8 ± 3.3 μm. Therefore, most drugs predicted by CancerOmicsNet as effective against target cancer cell lines not only exhibit the desired anticancer activity, but also tend to be effective at lower concentrations compared to those publicly available in a large database of anticancer agents tested against various tumors.Finally, we conducted a statistical analysis of effective drug concentrations measured in validation experiments in comparison to those obtained from the LINCS-3549 growth rate inhibition dataset. Here, a drug concentration is considered effective when the corresponding GRCancerOmicsNet is a recently developed system utilizing the AI technology to guide precision oncology. Benchmarked against a large dataset of anticancer therapies comprising multitargeted kinase inhibitors and a wide variety of tumor types, it was previously demonstrated to outperform many other approaches, including deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching . In thisFor instance, the expression of landmark genes carefully selected by the LINCS consortium may not necessarily reflect the mechanism of action of therapeutic candidates . We alsoIn general, methods employing the AI technology predict cellular responses to pharmacotherapy with a better accuracy. In our benchmarking calculations, using CancerOmicsNet yields more robust discrimination between effective and ineffective anticancer treatments compared to the GS-based approach. This improved performance of machine learning can be attributed to the utilization of multiple biomedical data, including PPI networks, gene expression patterns, gene-disease associations, and kinase inhibitor profiling. In addition, employing deep learning enables CancerOmicsNet to automatically extract meaningful features in order to effectively learn complex patterns present in these data. Interestingly, since deep learning models are not explicitly instructed which patterns to look for in the data, they may pick up on associations among multiple variables that are not easily perceptible. We expect that integrating more biological data will further increase the accuracy of anticancer treatment prediction.max values in most concentrations. In particular, pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 were the most potent against the pancreatic cancer cell line Panc 04.03. Validation experiments repeated after 1 week yielded consistent results. It is also noteworthy that, on average, anticancer drugs predicted by CancerOmicsNet were found effective in lower concentrations than active molecules in the LINCS database. We note that antiproliferative properties can be predicted for any compound that has been profiled against a panel of human kinases with respect to its inhibitory potency and selectivity.The promise of CancerOmicsNet in precision oncology is comprehensively investigated by evaluating its ability to generalize to “unseen” data comprising 288 kinase inhibitors with no growth rate inhibition values in LINCS. None of these molecules has been used to train the deep learning model, hence the term “unseen” data. The effect of these drugs on the growth of several cell lines predicted by CancerOmicsNet was first validated against the biomedical literature. Encouragingly, those inhibitors assigned high probabilities to reduce the proliferation of certain cell lines have been reported to exhibit the predicted anticancer activities in independent studies. Finally, six compounds were validated experimentally in live-cell time course assays against breast, pancreatic, and prostate cancer cell lines. The tested molecules exhibited dose-dependent antiproliferative activities with negative GR50, GRmax, and IC50. These measures often depend on the experimental setup with respect to drug concentrations and the duration of measurements [Similar to other methods to predict therapeutic effects, CancerOmicsNet has several limitations. One obvious complication is the selection of drug efficacy measure, such as GRurements . For insurements .Future directions in the development of CancerOmicsNet include the integration of other large-scale cancer data, such as the single nucleotide polymorphism and the mutation information, which would facilitate a more personalized selection of anticancer therapies based on the tumor genetic makeup. Moreover, utilizing data related to the molecular mechanisms of metastatic cells governing their mobility and plasticity would allow for the prediction of other therapeutic effects, such as the inhibition of cancer cell viability, migration, and invasion ability. We also plan to conduct a pharmacophore analysis of molecules exhibiting antiproliferative activities and expand the repertoire of therapeutics outside the range of kinase inhibitors. Including other drug classes will make it possible to extend CancerOmicsNet to predict effective combinations of molecules having different mechanisms of action. Synergistic effects are generally highly beneficial allowing the use of lower doses of the combination constituents often leading to significantly reduced adverse reactions . OverallAdditional file 1: Supplementary Video 1. Live-cell time course recording of Panc 04.03 cell line treated with JNJ-7706621 at 10 μm.Additional file 2: Supplementary Video 2. Live-cell time course recording of Panc 04.03 cell line treated with PP1 at 10 μm.Additional file 3: Supplementary Video 3. Live-cell time course recording of Panc 04.03 cell line treated with DMSO as the control.Additional file 4: Supplementary Video 4. Live-cell time course recording of DU 145 cell line treated with AZD6482 at 10 μm.Additional file 5: Supplementary Video 5. Live-cell time course recording of DU 145 cell line treated with XMD8–92 at 10 μm.Additional file 6: Supplementary Video 6. Live-cell time course recording of DU 145 cell line treated with DMSO as the control.Additional file 7: Supplementary Video 7. Live-cell time course recording of HCC70 cell line treated with GW2580 at 10 μm.Additional file 8: Supplementary Video 8. Live-cell time course recording of HCC70 cell line treated with PI-103 at 10 μm.Additional file 9: Supplementary Video 9. Live-cell time course recording of HCC70 cell line treated with DMSO as the control.


TERT promoter methylation is enriched in cancers lacking TERT genetic alterations (wild-type cancers), but its functional impact on TERT transcription remains elusive. We developed a long-read bisulfite-sequencing platform to characterize the TERT promoter methylation profile at a single-molecule level. In wild-type cancer cell lines, both epialleles were hypermethylated symmetrically on the TERT distal promoter. In the core and proximal promoter, by contrast, the transcribed epialleles were significantly more hypomethylated than the silent epialleles. Decitabine-therapy reduced the core and proximal  promoter methylation and reactivated the silent allele. We showed that TERT allele-specific expression is amenable to in vitro epigenetic manipulation in wild-type cancers.TERT promoter methylation, located several hundred base pairs upstream of the transcriptional start site, is cancer specific and correlates with increased TERT mRNA expression and poorer patient outcome. Promoter methylation, however, is not mutually exclusive to TERT activating genetic alterations, as predicted for functionally redundant mechanisms. To annotate the altered patterns of TERT promoter methylation and their relationship with gene expression, we applied a Pacific Biosciences-based, long-read, bisulfite-sequencing technology and compared the differences in the methylation marks between wild-type and mutant cancers in an allele-specific manner. Results: We cataloged TERT genetic alterations , allele-specific promoter methylation patterns, and allele-specific expression levels in a cohort of 54 cancer cell lines. In heterozygous mutant cell lines, the mutant alleles were significantly less methylated than their silent, mutation-free alleles (p < 0.05). In wild-type cell lines, by contrast, both epialleles were equally methylated to high levels at the TERT distal promoter, but differentially methylated in the proximal regions. ChIP analysis showed that epialleles with the hypomethylated proximal and core promoter were enriched in the active histone mark H3K4me2/3, whereas epialleles that were methylated in those regions were enriched in the repressive histone mark H3K27me3. Decitabine therapy induced biallelic expression in the wild-type cancer cells, whereas the mutant cell lines were unaffected. Conclusions: Long-read bisulfite sequencing analysis revealed differences in the methylation profiles and responses to demethylating agents between TERT wild-type and genetically altered cancer cell lines. The causal relation between TERT promoter methylation and gene expression remains to be established.Background:  Telomerase reverse transcriptase (TERT) encodes the catalytic subunit of telomerase, the enzyme required by most proliferative cells to counteract telomere shortening during cell divisions [TERT promoter is commonly genetically altered and/or methylated in cancer [TERT genetic alterations include heterozygous cis-activating TERT promoter point mutations—the most frequent non-coding mutation in cancer—[TERT structural variations  [TERT genetic alteration only express the genetically altered allele [ivisions . Aggressn cancer . TERT gen cancer—,4,5,6 ancations) ,7,8. Thecations) ,6,7,8,9,d allele ,11,12.TERT genetic alterations  [TERT promoter DNA hypermethylation [TERT promoter hypermethylation occurs at CpG dinucleotides located several hundred base pairs (bp) upstream of the transcriptional start site (TSS) [TERT promoter hypermethylation is cancer specific and significantly correlates with increased TERT expression and poorer patient outcome [TERT promoter hypermethylation is enriched in wild-type cancers, it is not mutually exclusive to TERT activating genetic alterations, as predicted for functionally redundant mechanisms [Many other cancers express TERT mRNA by less clearly defined means that do not involve cancers) ,15,16,17hylation ,19. TERTte (TSS) . This pa outcome ,20,21. Achanisms . In factchanisms ,24.TERT express either one or both alleles, on a case-by-case basis [Cancer cell lines with the wild-type se basis . Howeverse basis . AdditioTERT genetic alterations , allele-specific promoter methylation patterns, and allele-specific expression levels in 70 metastatic melanomas and 54 cancer cell lines. We used a long-read sequencing technique to annotate methylation patterns in consecutive CpG sites at the single molecule level. In a subset of the cell lines, we also examined how decitabine treatment, aimed at removing DNA methylation marks, affected TERT promoter methylation and gene expression per allele. Our results revealed previously unappreciated differences in the epigenetic modification and gene expression between wild-type and genetically altered cancer cell lines.In this study, we cataloged n = 50, including 13 nodular; 15 superficial spreading; 1 lentigo malignant melanoma; 21 not otherwise specified), acral (n = 11), mucosal (n = 6), and unspecified (n = 3).The samples consisted of formalin-fixed paraffin-embedded (FFPE) metastatic melanomas  from 70 The 54 cancer cell lines used in this study consisted of 11 wild-type , 2 with structural rearrangement , and 41 mutant  cell lines . WM-39, TERT promoter Sanger sequencing was performed as described previously [Genomic DNA (gDNA) was isolated from FFPE tissue samples or cell lines with Maxwell 16 LEV DNA kit  or DNeasy Blood and Tissue Kit . eviously .TERT rearrangements in clinical samples and in some cell lines  CH17-75N21- and CH17-410B01-templated probes for TERT. The Cy3-labeled TelG probe  was used for telomeric DNA FISH as described previously [ll lines  were deteviously .TERT alterations and expression  and cDNA was prepared with Vilo SuperScript . TERT mRNA expression levels were measured by qRT-PCR, as previously described . Spitz tpression .TERT promoter methylation status in FFPE clinical specimens was determined by gDNA bisulfite-treatment before PCR amplification, followed by high throughput amplicon sequencing. TERT promoter methylation status from 425 to 610 bp upstream of the TSS  was analyzed from FFPE gDNA as described previously [eviously .TERT promoter  was amplified from ~500 ng of bisulfite-treated cell line gDNA  using barcoded forward (5′-GGGTTAGGGTTTTTTA-3′) and reverse primers (5′-CCRCCTAAAAACCTACAAAAAAAAATAAC-3′). A total of 600 µL of pooled sample PCR amplicons were cleaned with 700 µL of PB beads  and eluted in 30 µL of 10 mM Tris-HCl, pH 8.5, as per the manufacturer’s instructions. Data was collected on a Pacbio Sequel instrument (University of Arizona Genomics Institute). Smrttools  were used for preprocessing the Pacbio reads. Specifically, the reads were demultiplexed by lima and circular consensus (CCS) reads were generated using the ccs tool with parameters of “—maxLength 2000—minPasses 3—minPredictedAccuracy 0.95”. The CCS reads were aligned against the human reference genome (hg19) to call methylation status using Bismark [The  Bismark .TERT promoter  were analyzed by in-house workflows. One urothelial cancer (TCCSUP) and 3 melanoma  mutant cell lines that were completely unmethylated throughout the amplicon were excluded from this analysis because the mechanism of promoter methylation is likely not involved in transcription in these cases  and 2 mutant TERT cancer cell lines (UMUC3 and A375) according to described protocols [TERT promoter was determined by Sanger sequencing and the TERT promoter mutation digital droplet PCR  assays using recommended protocols .H3K4me2/3 and H3K27me3 ChIP were performed on 3 wild-type rotocols ,24 with TERT promoter methylation patterns associated with active or silent histone marks in these cell lines, ChIP DNA was bisulfite converted using an EZ DNA Methylation-Gold Kit (Zymo Research) and PCR amplified for 45 cycles at 95 °C for 15 s, 63 °C for 20 s, and 68 °C for 40 s using primers spanning the region from 1,295,224 to 1,295,546 of the TERT promoter with 5′-GGAAAGGAAGGGGAGGGGTTGGGAG-3′ and 5′-CCCCTCCCTCRAATTACCCCACAACCTAAAC-3′. Amplicons were purified and used for high-throughput sequencing (PACBIO) or Sanger sequencing.To determine TERT allele-specific expression in cell lines was measured in rs2736098 or rs2853690  heterozygotes using ddPCR TaqMan SNP assays compatible with cDNA of spliced mRNA. Heterozygous sample gDNA allele ratios were quantified by ddPCR [allele1 or 2 gDNA ratio = /allele1 or 2 cDNA ratio = /normalized allele1 or 2 cDNA ratio = /allele-specific expression1 or 2 = /by ddPCR . Allele-Samples with mono-allelic expression had ≥9:1 allele-specific expression ratio ,30,31 asn = 4), mutant (n = 5), and TERT rearranged (n = 2) cell lines with methylated promoters and heterozygous for an assayable exonic SNP. Different drug concentrations and treatment lengths were tested to find the condition of half maximal inhibitory cell line growth. Cell line allele-specific expression measurements were then made using drug concentrations 10-fold lower and 10-fold higher than this condition. LN18, WM-51, and VMCUB3 cell lines were treated with either 0.3 or 30 µM decitabine, the NCI-H2122 cell line was treated with either 0.1 or 3 µM decitabine, the NCI-H358 cell line was treated with either 0.01 or 30 µM decitabine, UMUC3 and WM-46 cell lines were treated with either 0.03 or 3 µM decitabine, WM-4002 and 253J cell lines were treated with either 0.01 or 1 µM decitabine, and the WM-88 cell line was treated with 0.3 µM decitabine for 3–7 days  on allele-specific expression and allele-specific methylation were measured in wild-type  and the cancer-associated  promoter methylation are mutually exclusive in clinical cancer specimens. To answer this question, we selected formalin-fixed paraffin-embedded (FFPE) samples of malignant melanoma, a cancer type enriched in both promoter mutations and methylation [TERT distal promoter methylation levels in each specimen by high-throughput targeted bisulfite amplicon sequencing. Finally, we compared the relative methylation levels between wild-type and genetically altered categories.We first sought to determine if hylation ,22. We sTERT alterations: promoter point mutation, distal promoter methylation, or rearrangement , 1,295,250 C>T (n = 25), 1,295,242-43 CC>TT (n = 5), and 1,295,161 A>C (n = 1). A total of 6 of 70 melanomas (9%) had TERT rearrangement as assessed by FISH , harbored at least one of the three cancer-associated angement . One melntenance C. In tot by FISH , and 14  by FISH A. TERT wTERT distal promoters, we next asked whether mutation and methylation tended to occur on the same promoter molecule  or on separate molecules . Because FFPE samples have lower tumor purity, due to admixture of normal cells, and yield more fragmented DNA than cancer cell lines, we focused on cancer cell lines to answer this question. We performed single-molecule high-throughput bisulfite amplicon sequencing in 36 heterozygous and 5 homozygous TERT promoter mutant cell lines, derived from melanoma and other cancers . We could differentiate active and silent allele-derived methylation reads  for each cell line’s TERT promoter because we obtained a single long contiguous bisulfite converted amplicon DNA (p < 0.0001). In agreement with the previous reports [p < 0.05, one side Wilcox rank sum test). Our Chromatin Immunoprecipitation (ChIP) data were also consistent with the previous observations [Given that some metastatic melanomas harbored both mutated and methylated icon DNA . This amicon DNA A. The pr reports ,24,33,34TERT promoter methylation and mutation tend to mark separate homologous alleles in heterozygous TERT mutant cell lines [TERT distal promoter; 50% (n = 17) of the mutant cell lines had no significant difference in the methylation level at the distal promoter between the active  and silent  alleles . These results indicate that TERT distal promoter methylation may occur on the transcribed mutant alleles, and that only the core and proximal methylation opposes transcription  and identified two distinct methylation patterns  for eight wild-type cell lines , whereas those with increased core and proximal methylation were enriched in the H3K27me3 histone mark   . Thus, is report . Both prell line B. Like w profile .TERT distal promoter would perturb TERT expression, we treated four mono-allelically expressing wild-type cell lines and seven genetically altered cancer cell lines for comparison (five with point mutations and two with rearrangement). We measured transcription and methylation changes of each allele upon treatment. Decitabine treatment reactivated silent allele transcription in all wild-type cell lines  were methylated to similarly high levels in the TERT distal promoter. Finally, decitabine treatment reactivated silent alleles in wild-type cell lines but not genetically altered cell lines.The long-read methylation sequencing of cancer cell lines revealed previously unknown differences in TERT promoter methylation and expression studies. First, because TERT is often regulated at the allele level [TERT alleles are transcribed [TERT promoter point mutations and TERT structural rearrangements to identify TERT genetically altered samples; sometimes promoter mutations are taken into account while rearrangements are overlooked. Finally, we measured decitabine’s effect on TERT allele-specific expression levels instead of total TERT mRNA expression levels [Our cancer cell line analysis was different in several ways from many previous le level ,29, and nscribed ,25, we lnscribed ,37. Thisnscribed ,24,33. Tn levels ,50,51. TTERT allele-specific expression and promoter methylation in cancer has focused more on TERT mutant samples [TERT distal promoter. These findings suggested that methylation of the TERT distal promoter may be functionally compatible, or even functionally important, especially for TERT wild-type cancers. We therefore investigated whether methylation disruption perturbed TERT expression in wild-type cell lines. We found that decitabine treatment could reactivate the silent alleles in wild-type cell lines but not in genetically altered ones. In tandem with the transcriptional reactivation of the silent alleles, the methylation levels of the TERT core and proximal promoter dropped, whereas those of the distal promoter remained unchanged.Much of the previous interest in  samples ,24. Alle samples . Our sin samples . This su samples . We hencTERT promoters of all cancer cell lines, regardless of the mutational status, were hypomethylated in the core and proximal promoter. This hypomethylated area is probably synonymous with the TERT nucleosome-free region around the upstream of the TSS [TERT core and proximal promoter regions. Methylation of the nucleosome-free region is incompatible with transcription [Like promoters of all actively transcribed alleles, the transcribed  the TSS ,53,54,55 the TSS , but it  the TSS ,57,58. Dcription ,53,59,60cription .TERT distal promoter methylation is a bona fide activating mechanism equivalent to the mechanistically established genetic alterations, because decitabine treatment did not significantly change the distal promoter methylation in these experiments. It is also conceivable that decitabine’s impact on global demethylation could indirectly affect the expression of TERT. Further studies are needed to examine the transcriptional effects of methylation in wild-type cancers by specifically targeting different regions of the TERT promoter.Despite these new observations, it is still unclear whether wild-type TERT promoter alterations are ubiquitous in metastatic melanoma. Almost every metastatic melanoma in our cohort harbored one of three cancer-associated TERT aberrations . Although TERT promoter methylation was more prevalent in melanomas that contained wild-type TERT promoter, our data showed that methylation and mutation or rearrangement were not mutually exclusive at the sample level. However, targeted long-read bisulfite sequencing was not feasible with these FFPE clinical specimens to gather more comprehensive data, because their DNA is more fragmented than intact cell line DNA. (2) A potential complication of the current long-read approach could be the formation of PCR chimeras during the amplification stage prior to sequencing, which could result in chimeric reads and false allele group prediction [Our methods have some inherent limitations: (1) Analysis of clinical samples was useful in demonstrating that ediction . (3) TheTERT promoter methylation profiles of TERT wild-type and mutant cancer cells had remained unclear, particularly how they vary in the core, proximal, and frequently methylated distal promoter regions, despite having been substantially studied for more than two decades (TERT distal promoter. The TERT proximal and core promoter methylation, however, was different in each case; these regions were less hypomethylated in the silent alleles than the transcribed counterparts. Additionally, the wild-type and genetically altered cell lines responded differently to the demethylating agent decitabine. The nascent allele was reactivated by demethylating agent treatment in wild-type cell lines but not in genetically altered cell lines. These findings suggest that TERT allele-specific expression in vitro may be epigenetically regulated in wild-type cancer cell lines, in contrast to the genetically altered cells. Finally, the long-read based methylation profiling methods used for comparing allelic methylation patterns and allele-specific expression may be applicable to other genes where promoter methylation correlates with expression. This approach allows future fine mapping of the exact allelic specific regulatory elements in the promoter regions by integrating other epigenetic modifications including histone marks, chromatin opening, as well as transcription factor binding assays.The heterogeneity of allele-specific  decades A. The si


The ethyl acetate fraction showed the strongest toxicity against A498 (IC50 = 0.162 µg mL−1) and 786-O (IC50 = 0.271 µg mL−1) cells. With similar toxicity against 786-O, P1 (IC50 = 3.8 µg mL−1) and P3 (IC50 = 3.1 µg mL−1) exhibited greater effect when combined (IC50 = 2.5 µg mL−1). Results support the potential of propolis and its constituents as promising coadjuvants in renal cell carcinoma treatment.Renal cell carcinoma is the most lethal cancer of the urological system due to late diagnosis and treatment resistance. Propolis, a beehive product, is a valuable natural source of compounds with bioactivities and may be a beneficial addition to current anticancer treatments. A Portuguese propolis sample, its fractions ( According to the GLOBOCAN 2020 database, despite not being one of the most prevalent cancers, renal cell carcinoma (RCC) is the most aggressive and lethal cancer of the urological system. By 2040, its incidence and mortality are expected to increase by approximately 54 and 68%, respectively . MetastaThe demand for natural products has been increasing due to their importance in human health and their potential as new drug leads . PropoliThe cytotoxic effect of propolis has been extensively studied against diverse types of cancer cell lines, including breast ,11, prosn-hexane fraction showed no effect on the viability of any of the cell lines at the tested concentrations. On the other hand, the G18.EE-water fraction exhibited the strongest effect on cell viability, with the lowest IC50 values against all cell lines (50 > 30 µg mL−1) and Caki-2 cells. The latter was the least sensitive cancer cell line. Considering all the results  but with higher selectivity indexes.The cytotoxic activity of G18.EE and its fractions was evaluated on three different RCC cell lines-786-O, Caki-2 and A498 . The G18ll lines . However results , the G1850 values obtained ranged from 4.4 to 41 µg mL−1, CaCo2 cells being the least susceptible to both extracts (IC50 > 50 µg mL−1) . From this subfraction (FG), eight FG-derived subfractions were obtained (FG1–FG8). FG8 was selected for additional fractionation using silica column with the same solvent system , resulting in five FG8-derived subfractions (FG8-1–FG8-5). Based on the TLC profiles, three FG-derived subfractions  displayed a single spot each on TLC plate, suggesting the presence of a single compound and thus being selected for the following experiments. Solvents used in the columns for extraction and the respective yields were: FG1 , FG5  and FG8-1   was subjected to VLC, eluting successively with  F1–F22;  were obt F1–F22; . The sub 4.3 mg] .P1, P2, and P3 were analyzed by UPLC-DAD-ESI/MSn following a previously described method [−1 and 2 µL were used for injection. The Ultimate 3000 apparatus was equipped with an Ultimate 3000 Diode Array Detector  and coupled to an ion trap mass spectrometer LTQ XL  equipped with an ESI source. Analysis was run on a Hypersil Gold  C18 column  at 30 °C. The mobile phase was composed of (A) water/formic acid  and (B) acetonitrile/formic acid . The solvent gradient started with 5–40% B (0–15 min), followed by 40–100% B (15–19 min), and returning to the initial conditions. The flow rate was 200 μL min–1 and UV–Vis spectral data for all peaks were accumulated in the range 200–500 nm. The nitrogen (>99% purity) gas pressure used for mass spectrometry analysis was 520 kPa (75 psi). The mass spectrometer was operated in negative-ion mode with ESI needle voltage set at 5.00 kV and an ESI capillary temperature of 275 °C. The full scan covered the mass range from m/z 100 to 2000. CID–MS/MS and MSn experiments were simultaneously acquired for precursor ions using helium as the collision gas with collision energy of 25–35 arbitrary units. Control and data acquisition were carried out with the Thermo Xcalibur Qual Browser data system . Compounds in the subfractions (P1–P3) were identified based on a literature comparison of the ESI-MS/MS data [Subfractions d method . The driP1 were recorded in a Bruker Advance II 400 spectrometer, at 400 MHz (1H) and 100 MHz (13C), at 20 °C (see δ) were recorded in parts per million (ppm) relative to the residual solvent signal (DMSO-d6).NMR spectra of 0 °C see . The cheP1). Yellow amorphous solid. 1H NMR : δ = 3.77 , 3.85 , 6.61 , 6.86 , 7.10 , 8.03 , 10.72 , 13.02 . 13C NMR : δ = 55.5, 59.9, 94.3, 103.0, 104.1, 114.6 (2C), 122.8, 128.3 (2C), 131.4, 152.4, 152.7, 157.4, 162.3, 163.3, 182.1 [5,7-dihydroxy-6-methoxy-2-(4-methoxyphenyl)-4H-chromen-4-one . The Caki-2 cell line was cultured in McCOY’s high glucose, L-glutamine, bacto-peptone, and phenol red without sodium pyruvate and HEPES . The A498 was cultured in MEM with 20 mM HEPES without NaHCO3 and L-glutamine . In addition, 1% of an antibiotic preparation  and 10% heating activated fetal bovine serum  were used as supplements for the culture media. Cultures were grown at 37 °C and 5% CO2 in a humidified incubator.Four human renal cell lines-HK2  and 786-O, Caki-2 and A498 (RCC cells) were obtained from the American Type Culture Collection (ATCC). The HK2 and 786-O cell lines were cultured in RPMI 2.0 g/ L NaHCO®) and then were detached from the flask with trypsin  at 37 °C. Medium was then added to the flask for trypsin inactivation. Cells were collected into a 15 mL Falcon tube and centrifuged for 5 min at 900 rpm, at 4 °C. Fresh medium was used to resuspend the cells, and 10 µL of the cell suspension were collected for subsequent addition of 20 µL of trypan blue  for cell counting (Neubauer chamber).To prepare cells for the cytotoxicity assay, flasks with sub-confluent cells were washed with phosphate-buffer 1× , each of the four fractions , each of the three subfractions  and two combinations of the subfractions with equal parts of each subfraction . Controls were performed with 0.25% of DMSO (vehicle) for IC50 determination (concentration that inhibits cell viability in 50%). Following this incubation period, the adherent cells were fixed by adding 50 µL of cold trichloroacetic acid 10%  and incubating for 60 min, at 4 °C. The plates were then washed with deionized water, dried overnight, and 50 µL of 0.1% SRB solution (Sigma–Aldrich) in 1% acetic acid (PanReac) were added to each plate well. After 30 min incubation at room temperature, unbound SRB was removed by washing with 1% acetic acid and air-dried overnight. Bound SRB was solubilized with 100 µL of 10 mM Tris base  and the absorbance was measured at 490 nm in a microplate reader . IC50 values were determined by transforming the absorbance values into a sigmoidal dose-response curve and extracting the concentration of the extracts/fractions needed to inhibit the viability of the kidney cells by 50%, using the GraphPad Prism Software Version 6.0. The selectivity index (SI) was calculated based on the IC50 values obtained for the samples against the cell lines, using the following formula: Propolis cytotoxicity was assessed by the sulforhodamine B (SRB) assay as previously described , which eCytotoxicity is higher for the cancer cell line than for the non-neoplastic HK2 cell line when SI values > 1.50 determination using the GraphPad Prism 5 software for logarithmic transformation after applying a sigmoidal dose-response non-linear regression. For multiple comparisons one-way ANOVA supplemented with post-hoc Tukey test was performed. Different letters mean statistical differences (p < 0.05) between mean values.Experiments were performed in triplicate and repeated at least three times for IC50 values, and importantly, very high selectivity indexes, when compared to the data available in the literature. This suggests that Portuguese propolis from Gerês, as well as its chemical constituents, could be used as alternatives or adjuvants to conventional anticancer treatments. They could be also used to help tackle drug-resistance and toxic effects by reducing the dose of the therapeutic drugs commonly used in RCC treatment.Cancer is one of the leading causes of death worldwide and is expected to cause more than 16 million deaths in 2040 [


TP53. BRCA2 was nonsense-mutated (p.Q780*) and CREBBP was missense-mutated (p.P279R) in LuPanc-1. CDKN2A was missense-mutated (p.H83Y) in LuPanc-2. Notably, only LuPanc-2 harbored a partial or complete deletion of DPC4. LuPanc-1 cells exhibited high basal and transforming growth factor (TGF)-β1-induced migratory activity in real-time cell migration assays, while LuPanc-2 was refractory. Both LuPanc-1 and LuPanc-2 cells responded to treatment with TGF-β1 with the activation of SMAD2; however, only LuPanc-1 cells were able to induce TGF-β1 target genes, which is consistent with the absence of DPC4 in LuPanc-2 cells. Both cell lines were able to form spheres in a semi-solid medium and in cell viability assays, LuPanc-1 cells were more sensitive than LuPanc-2 cells to treatment with gemcitabine and FOLFIRINOX. In summary, both patient-derived cell lines show distinct molecular phenotypes reflecting their individual tumor biology, with a unique clinical annotation of the respective patients. These preclinical ex vivo models can be further explored for potential new treatment strategies and might help in developing personalized (targeted) therapy regimens.The prognosis of pancreatic ductal adenocarcinoma (PDAC) is exceedingly poor. Although surgical resection is the only curative treatment option, multimodal treatment is of the utmost importance, as only about 20% of tumors are primarily resectable at the time of diagnosis. The choice of chemotherapeutic treatment regimens involving gemcitabine and FOLFIRINOX is currently solely based on the patient’s performance status, but, ideally, it should be based on the tumors’ individual biology. We established two novel patient-derived primary cell lines from surgical PDAC specimens. LuPanc-1 and LuPanc-2 were derived from a pT3, pN1, G2 and a pT3, pN2, G3 tumor, respectively, and the clinical follow-up was fully annotated. STR-genotyping revealed a unique profile for both cell lines. The population doubling time of LuPanc-2 was substantially longer than that of LuPanc-1 (84 vs. 44 h). Both cell lines exhibited a typical epithelial morphology and expressed moderate levels of CK7 and E-cadherin. LuPanc-1, but not LuPanc-2, co-expressed E-cadherin and vimentin at the single-cell level, suggesting a mixed epithelial-mesenchymal differentiation. LuPanc-1 had a missense mutation (p.R282W) and LuPanc-2 had a frameshift deletion (p.P89X) in  Pancreatic cancer is currently the fourth most common cause of cancer-related deaths and is predicted to be the second leading cause of cancer-related mortality by the year 2030 . The majSurgical resection is the only curative treatment option for pancreatic cancer. However, only 10–20% of patients present with resectable tumors at the time of diagnosis, 30–40% present with a borderline resectable tumor, and 50–60% present with metastatic disease [The adjuvant treatment of patients with resectable PDAC by chemotherapy results in better overall survival. Modified combination therapy consisting of 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin (mFOLFIRINOX) resulted in a significantly better median overall survival of 54.4 months compared to that of gemcitabine alone (35.0 months) . HoweverCDKN2A (encoding the cyclin-dependent kinase inhibitor p16INK4) and DPC4, encoding SMAD4, a central mediator of transforming growth factor (TGF)-β signaling, has been implicated in modifying the response to gemcitabine or 5-FU [Overall, the 5-year survival rate of PDAC patients remains as low as 5–8% . The sel or 5-FU ,15,16. H or 5-FU . Taken tPreclinical models of PDAC are essential for drug development and the identification of predictive biomarkers. Primary, patient-derived cell lines are an indispensable tool among ex vivo models. Primary cell lines can provide valuable information on the phenotype and genotype of individual tumors, allow for functional assays of the individual tumor biology, and be used for the development of personalized treatment strategies by enabling individualized therapeutic drug testing ex vivo. However, the efficient ex vivo expansion of human carcinomas in tissue culture, especially that of PDAC, remains technically challenging. The Cancer Cell Line Encyclopedia (CCLE) from the Broad Institute comprises 60 pancreatic cell lines. Nevertheless, the full spectrum of the complex and diverse tumor biology of PDAC is not reflected sufficiently by these cell lines, and the clinical annotation and oncological outcome of the respective patient are mostly missing.Here, we describe two newly established patient-derived cell lines from surgical PDAC specimens named LuPanc-1 and LuPanc-2. Both cell lines were comprehensively characterized and tested with respect to the standard therapy regimens gemcitabine and FOLFIRINOX.TM with 4.5 g/L glucose (Gibco) supplemented with 20% (v/v) fetal calf serum (FCS) and 100 μg/mL streptomycin/penicillin. The cells were cultured at 37 °C in a humidified incubator with 5% CO2. The cells began to form colonies after about 7 d. Subsequently, the medium was replaced every 3–4 d. The number of cocultured fibroblasts was periodically reduced by differential trypsinization. After reaching stable growth of the cultured tumor cells, fibroblasts were depleted by an immunomagnetic assay based on fibroblast-specific antigens . Single-cell-derived clonal cultures were established from primary cultures by limited dilution, as previously described for the permanent PDAC cell line PANC-1 [® 21 System , according to the manufacturer’s instructions.The tissue was collected directly after the surgical resection of the tumor after approval by the University of Lübeck’s local ethics committee (# 16-281). The tissue was processed within 1 h after surgical resection and cut into 3–5 mm pieces using a scalpel. Pieces were dissociated using collagenase I  for 120 min at 37 °C. The cells were plated on tissue culture dishes in DMEM GlutaMAXe PANC-1 . The celGrowth curves for both cell lines were established by counting cells in three independent experiments every 24 h for 7 and 10 d for LuPanc-1 and LuPanc-2, respectively, after seeding 80,000 cells per well in a six-well plate. The final cell count was carried out after harvesting the cells using Accumax (PAN Biotech) in an improved Neubauer hemocytometer. The total cell number was determined by averaging the cell count in eight 1 mm squares. The cell numbers were normalized to the cell number initially counted 24 h after seeding.2. Extinction was quantified with a plate photometer, according to the manufacturer’s instructions. The excitation wavelength was set to 545 nm, and the emission wavelength was set to 600 nm. Each experiment was performed in triplicate and repeated three times.To measure the sensitivity to chemotherapy, 6000 cells of LuPanc-1 and 8000 cells of LuPanc-2 growing in a log phase were seeded per well in 96-well plates, and chemotherapeutics were added 24 h after cell seeding. For gemcitabine treatment, gemcitabine  was added to a final concentration in a range of 2.5–320 nmol/L for LuPanc-1 and 10–12,800 nmol/L for LuPanc-2. For FOLFIRINOX treatment, 5-FU, irinotecan, leucovorin, and oxaliplatin were combined, and the final concentration ranged as follows: 5-FU (Medac) 0.005–426 µmol/L, irinotecan (Medac) 0.07 nmol/L–5.78 µmol/L, oxaliplatin (GRY) 0.062 nmol/L–4.96 µmol/L, and leucovorin (Sigma Aldrich) 0.078 nmol/L–6.19 µmol/L. After 72 h, cell metabolism was measured by a Resazurin reduction assay. Resazurin (STEMCELL) was diluted in DPBS to a concentration of 10 mg/mL. This stock solution was further diluted in the medium, 20 µL was added per well to a final concentration of 40 µg/mL, and the cells were incubated for 2 h at 37 °C in a humidified incubator with 5% CO® Culture Slides; Corning, NY, USA and Sarstedt, Nümbrecht, Germany). The cells were permeabilized with 0.1% Triton-X and sequentially treated with goat serum, primary monoclonal mouse anti-human CK 7 antibody , primary monoclonal rabbit anti-human E-cadherin antibody , primary monoclonal mouse anti-human vimentin antibody , anti-Mouse Alexa Fluor® 594 secondary antibody , and anti-Rabbit Alexa Fluor® 488 secondary antibody . Appropriate negative controls were performed. Coverslips were mounted with Fluoromount-G medium including DAPI . The cells were visualized using a BZ-9000 (BIOREVO) fluorescent microscope  with a 20× PlanFluor EL NA 0.45 Ph1 objective lens under 0.285 s of exposure time for CK7 and 0.2 s of exposure time for E-cadherin and vimentin.The cells were cultivated for 72 h, with subsequent fixation with 4% paraformaldehyde in chamber slides -based assays were employed. For the Soft Agar-based assay, a total of 1000 viable single-cells were resuspended in 0.25% agar  and added on top of the precoated 48-well plate with 1.3% agar containing DMEM with 20% FBS. For the sphere formation assay in BME, 1000 cells were resuspended in 50 µL DMEM with 20% FBS, mixed with 150 µL Cultrex , and then plated on 48-well plates. During spheroid cultivation, the medium was replaced every 3 d. Sphere formation in both assays was monitored for 14 d. All experiments were performed in two independent biological replicates with three technical replicates each.® technology , as outlined in detail in previous publications [v/v) of collagen I and collagen IV (30 μL) to facilitate the adherence of the cells and thus enhance the duration of the signal recording. Each well was loaded with 60,000 (LuPanc-1) or 80,000 (LuPanc-2) cells in standard growth medium (see above). Cells were allowed to settle in the laminar flow hood for 30 min at RT, after which the assay was started and run for 24 or 48 h. Some assays were performed in the absence or presence of recombinant human (rh) TGF-β1  or the TGF-β type I receptor/ALK5 serine/threonine kinase inhibitor SB431542 . Data acquisition  and analysis were performed with the RTCA software .The migratory/invasive activities of LuPanc-1 and LuPanc-2 were determined with xCELLigenceications ,20. The DNA was extracted from both cell lines using the AllPrep DNA/RNA/Protein Mini Kit , according to the manufacturer’s instructions. Targets were enriched using the Agilent SureSelectXT kit, and libraries were prepared using the NEB Next Ultra DNA Library Prep Kit. Sequencing was performed on a NovaSeq 6000 using the v1.5 chemistry.fastp [Picard Tools (v2.18.4), and postprocessing was performed following GATK’s [Variant Effect Predictor , and annotations were converted into MAF format using vcf2maf (V1.6.21) (http://doi.org/10.5281/ZENODO.593251 (accessed on 24 April 2022); coverage for each variant was extracted directly from the vcf INFO field. The top 20 frequently mutated genes (FLAGS) [Sequencing data (paired-end fastq files) were trimmed  using fastp  (v0.23.0fastp  (v0.7.15g GATK’s . Single-mosdepth (v0.3.2) [Copy number aberrations were detected using Control-FREEC (v11.4) , and seq(v0.3.2) , resultiFor the Sanger sequencing of the KRAS locus, the following primers were used (5′–3′): forward: CCTTATGTGTGACATGTTCT; reverse: ATGGTCCTGCACCAGTAATA (amplicon size: 218 bp). Sanger sequencing was carried out as described previously .DPC4, SMAD2, SERPINE1, SNAI2, SOX2, TBP and GAPDH are given in The total RNA was isolated from LuPanc-1 and LuPanc-2 cells with an RNAeasy kit . A total of 2.5 μg of RNA of each sample was reverse transcribed  with 200 U of M-MLV Reverse Transcriptase and 2.5 μM random hexamers. Target gene expression was quantified by qPCR on an I-Cycler  with Maxima SYBR Green Mastermix  and normalized to the expression of either TATA box-binding protein (TBP) or GAPDH. The PCR primer sequences for We established two patient-derived primary pancreatic cancer cell lines from PDACs (LuPanc-1 and LuPanc-2). LuPanc-1 cells were derived from a PDAC of a 64-year-old male patient who presented to our surgical department with a locally advanced tumor of the pancreatic tail with infiltration of the stomach. The patient underwent distal pancreatectomy, splenectomy, and simultaneous partial resection of the stomach. The histopathological assessment of the resected tumor revealed a moderately differentiated ductal adenocarcinoma of the pancreas . The patient received adjuvant chemotherapy with a reduced dose of FOLFIRINOX due to intolerance. The patient developed a local recurrence and peritoneal carcinomatosis 9 months after the surgical resection of the primary tumor and subsequently received palliative chemotherapy with gemcitabine/nab-paclitaxel.LuPanc-2 cells were derived from a locally advanced PDAC of a 63-year-old female patient. The patient presented with a tumor located in the pancreatic corpus with local infiltration of the portal vein and celiac trunk. The patient underwent an extended multiorgan pancreatic corpus and tail resection (Appleby procedure) at our department. The histopathological assessment of the resected tumor revealed a poorly differentiated ductal adenocarcinoma of the pancreas . The patient received adjuvant chemotherapy with FOLFIRINOX but developed a retrogastric local recurrence 8 months after the resection of the primary tumor. The patient subsequently received palliative radiotherapy.For both cell lines, cells were isolated immediately after the surgical resection of the tumor by mechanical and enzymatic dissociation of a representative specimen. After 7 days of cultivation, first, adherent colonies of epithelial tumor cells surrounded by fibroblasts appeared, as shown exemplarily for LuPanc-1 in Both established cell lines were authenticated by STR genotyping for 21 loci . No idenFirst, we determined the general growth behavior of both cell lines. The population doubling times of LuPanc-1 and LuPanc-2 cells were estimated to be 44 h and 84 h, respectively, as measured by manual cell counting, indicating that LuPanc-2 cells grew substantially slower than LuPanc-1 cells A. To furThe aggressiveness of pancreatic cancer cells is associated with the acquisition of mesenchymal characteristics in comparison to those cells that are well-differentiated and epithelial-like . Thus, wTP53 had a missense mutation in LuPanc-1 (p.R282W) and a frameshift deletion (p.P89X) in LuPanc-2 (BRCA2 was nonsense-mutated (p.Q780*) in LuPanc-1. CDKN2A (encoding p16INK4) had a missense mutation (p.H83Y) in LuPanc-2 and CREBBP had a missense mutation (p.P279R) in LuPanc-1. We further specifically analyzed KRAS, DPC4, RNF43, ARID1A, TGFBR2, GNAS, RREB1, and PBRM1, which have been described to be recurrently mutated in PDAC by the Cancer Genome Atlas Research Network [KRAS, BRCA2, CDKN2A, and DPC4 as well as gains of RNF43, GNAS, and PBRM1 in both cell lines. For ARID1A, we detected a loss in LuPanc-1 but a gain in LuPanc-2. Notably, a complete loss of DPC4  and a loss of RREB1 were found in LuPanc-2 but not LuPanc-1 cells, while, conversely, LuPanc-1 but not LuPanc-2 cells showed a loss of TP53 and a gain of CREBBP. KRAS codons 12 and 13 in the WES data, we specifically analyzed these loci by subsequent Sanger sequencing and detected a codon 12 mutation  in both cell lines.We profiled the genomic landscape of both cell lines by whole-exome sequencing (WES). After the removal of commonly mutated genes without any effect, 135 and 76 mutations were detected in LuPanc-1 and LuPanc-2, respectively. Most of the mutations were missense mutations in both cell lines, followed by nonsense mutations . MutatioLuPanc-2 A. BRCA2  Network , but nonThe mutational signatures and, in particular, the presence of mutant KRAS suggested that both cell lines possess a transformed phenotype. To scrutinize this functionally, we performed sphere formation assays, exploiting the ability of cancer cells to grow and generate visible colonies (spheres) from single-cells when cultured in a semi-solid medium. As expected, both cell lines generated large numbers of spheres in soft agar and Matrigel, with LuPanc-2 cells forming substantially more spheres than LuPanc-1 cells .DPC4 (encoding SMAD4) and TGFBR2 (encoding the type II receptor), have been described to be recurrently mutated in PDAC and to be associated with increased or decreased resistance to gemcitabine [SERPINE1 , SNAI2 (encoding SNAIL2/SLUG), or SOX2, respectively , which represents a non-invasive and label-free approach for the continuous  monitoring of cell migration and invasion on a cell culture level . For LuPHaving shown above that LuPanc-1 cells have retained sensitivity to rhTGF-β1 with respect to the activation of Smad proteins and invasion-associated target genes, we sought to know whether rhTGF-β1 would also impact the cells’ migratory behavior in vitro. To this end, this growth factor increased the migratory activity over that of control cells . The IC50 for gemcitabine was 13.6 nM (IC90 of 50.5 nM) for LuPanc-1 and 357.1 nM for LuPanc-2 (IC90 of 2812 nM)  was 573.8 nM (IC90 of 5659 nM) for LuPanc-1 (50 (or IC90) for FOLFIRINOX since we were unable to obtain a normal dose–response curve.As described above, gemcitabine and FOLFIRINOX are essential chemotherapeutics used in clinical routine practice for PDAC treatment. Accordingly, the sensitivity of both cell lines was tested in Resazurin-based viability assays for gemcitabine monotherapy and FOLFIRINOX by determining the respective half-maximal inhibition concentration (IC2812 nM) . The IC5PDAC is a highly malignant carcinoma with an exceedingly poor prognosis. The overall 5-year survival rate of PDAC patients is as low as 5–8% . SurgicaPreclinical studies using patient-derived cell lines may be useful in predicting the response of a particular patient to gemcitabine/FOLFIRINOX or for the development of alternative and novel treatment strategies. Several studies have described specific molecular subtypes of PDAC based on transcriptional profiles ,11,12. TIn this study, we established two novel PDAC-derived cell lines named LuPanc-1 and LuPanc-2 from the surgical specimens of PDAC patients. Both cell lines presented with a transformed phenotype, as evidenced by their mutational spectra and their ability to form spheres in semi-solid media, also suggesting that the cells have stem cell properties. Clinical follow-up was annotated for both patients, and both corresponding cell lines were characterized comprehensively at the molecular level and functionally with respect to invasive behavior and the response to chemotherapeutic drugs.Primary, patient-derived cell cultures are a highly relevant tool because they reflect the in vivo state and physiology better compared to immortalized secondary cell lines. Both cell lines established in this study were taken into the primary culture directly from surgical specimens. They can be maintained in the culture without changes in cell dynamics and a loss of viability, obviating the need for cell immortalization ,44. MostFinally, one of the main drawbacks in the design of personalized treatment strategies is the marked inter-tumor heterogeneity in PDAC. As mentioned above, although the CCLE database already contains 60 pancreatic cancer cell lines, these include histological entities other than PDAC, and those within the PDAC fraction are likely to not represent the full spectrum of PDAC histomorphological subtypes. Moreover, a substantial number of those cell lines have been derived from metastases rather than the primary tumor, which means that these do not necessarily respond in the same way to chemotherapeutic drugs as the cancer cells from the patient’s primary tumor. With respect to histomorphological differentiation, LuPanc-1 cells present with a mixed phenotype co-expressing both epithelial and mesenchymal markers at a single-cell level, as demonstrated for E-cadherin and vimentin by the immunofluoresecence and immunoblot analysis of single-cell-derived clonal cultures. This phenotype has so far only been described for the PANC-1 cell line and has been shown to reflect the presence of subpopulations of cells with different EMT phenotypes . This mi50 357.1 nM) compared to LuPanc-1 (IC50 13.6 nM). LuPanc-1 showed a similar response to gemcitabine compared to other commonly used cell lines such as CAPAN-1 (11.5 nM), BxPC-3 (18 nM), MIA PaCa-2 (36–40 nM), and PANC-1 (50 nM) [We also determined the response of both cell lines to gemcitabine and FOLFIRINOX, which are the relevant treatment regimens in current clinical care. LuPanc-2 was substantially more resistant to gemcitabine (IC 50 nM) ,50,51,52 nM ,50,5 (50 nM) . SurprisBRCA2 nonsense mutation might offer a rationale for an alternative treatment strategy based on PARP inhibitors such as olaparib.Both patients who gave rise to our cell lines received additive therapy with FOLFIRINOX after surgery but developed a local recurrence in a short postoperative interval of 9 and 8 months, respectively. The patient from whom LuPanc-1 was derived received gemcitabine treatment for the local recurrence, which is in line with a good response of the derived cell line in vitro. However, the detected The epithelial-mesenchymal phenotyping of both cell lines showed that while the phenotype of LuPanc-2 cells was tentatively epithelial based on the presence of CK7 and E-cadherin, along with the absence of vimentin protein expression, LuPanc-1 cells combined both epithelial and mesenchymal traits, revealing a mixed E/M phenotype. Cells that co-express markers of both subtypes have been postulated to possess a higher stem cell potential and cellular plasticity. Tumor cells with this mixed phenotype, which is also referred to as partial or hybrid EMT, have been shown to be more metastatic , which iDPC4 in LuPanc-2 cells by CNV analysis, which was subsequently confirmed by SMAD4-specific qPCR. DPC4 has been described to be recurrently mutated in PDAC, and its genomic deletion was significantly correlated positively with disease progression [DPC4 was associated with an altered sensitivity to treatment with gemcitabine [DPC4 status and the gemcitabine response in this panel of PDAC cell lines, among DPC4 isogenic cell lines, DPC4-defective cells were about twofold less sensitive to gemcitabine than wildtype cells. A correlation analysis for IC50 values and TGF-β pathway activities in these isogenic cells confirmed a negative correlation with TGF-β pathway activities [50 in LuPanc-2 cells. However, it should be mentioned that, in another study, both SMAD4-deleted PDAC cell lines and patient-derived organoids exhibited a higher sensitivity toward gemcitabine (and other cell cycle-targeting agents) due to the upregulation of cell cycle-related genes [INK4 inactivation were three- to fourfold less sensitive to gemcitabine [CDKN2A, we speculate that the combined alterations in CDKN2A and DPC4 are causal for the high gemcitabine IC50 of LuPanc-2 cells. Another interesting issue is whether the inability of LuPanc-2 cells to exhibit basal migratory activity in vitro is a direct consequence of their unresponsiveness to TGF-β1 or the SMAD4 loss, i.e., due to the failure of autocrine TGF-β to stimulate cell motility.Of particular interest was the finding of the deletion of gression  and negagression . In addicitabine ,15,57. Acitabine . Despitetivities , matchined genes , suggestcitabine . Since LCell lines and, in particular, primary patient-derived cell lines are an essential tool for preclinical drug stratification and can help in developing personalized treatment regimens ,60. Howe


The inhibition of poly(ADP-ribose) polymerases (PARPs) and ataxia telangiectasia and Rad3-related (ATR) would be an alternative approach for cancer treatments. The aim of this study is to investigate the synergy of the different combinations of PARP inhibitors  and ATR inhibitor AZD6738. A drug combinational synergy screen that combines olaparib, talazoparib, or veliparib with AZD6738 was performed to identify the synergistic interaction, and the combination index was calculated to verify synergy. TK6 isogenic cell lines with defects in different DNA repair genes were used as a model. Cell cycle analysis, micronucleus induction, and focus formation assays of serine-139 phosphorylation of the histone variant H2AX demonstrated that AZD6738 diminished G2/M checkpoint activation induced by PARP inhibitors and allowed DNA damage-containing cells to continue dividing, leading to greater increases in micronuclei as well as double-strand DNA breaks in mitotic cells. We also found that AZD6738 was likely to potentiate cytotoxicity of PARP inhibitors in homologous recombination repair deficiency cell lines. AZD6738 sensitized more genotypes of DNA repair-deficient cell lines to talazoparib than to olaparib and veliparib, respectively. The combinational approach of PARP and ATR inhibition to enhance response to PARP inhibitors could expand the utility of PARP inhibitors to cancer patients without BRCA1/2 mutations. Genomic instability is one of cancer hallmarks . Cancer It has been hypothesized that DNA damage response (DDR) pathways mediated by ataxia-telangiectasia mutated (ATM) and ATR are responsible for cell survival in response to PARP inhibitors . There iIn this study, we investigated effects of ATR inhibitor AZD6738 in potentiating the cytotoxicity of three PARP inhibitors using different DNA repair-deficient TK6 isogenic cell lines as a model. We show that combining PARP inhibitors with AZD6738 contributes to an increase in replication-associated DNA damage and micronucleus induction. We also reveal that a synergistic effect is likely to be observed in homologous recombination repair deficiency cell lines. AZD6738 specifically synergizes some PARP inhibitors in particular DNA repair-deficient cell lines, providing rationale for scrutinizing synergistic interactions between PARP and ATR inhibitors in immortalized or cancer cell lines that have defects in another HRR-associated gene.in vitro experiments required mock treatment at specific concentrations of DMSO if the final concentration of DMSO was higher than 0.1%.Three PARP inhibitors and one ATR inhibitor were purchased from the MedChemExpress, LLC: olaparib (cat. no. 763113-22-0), talazoparib (cat. no. 1207456-00-5), veliparib (cat. no. 912444-00-9), and AZD6738 (cat. no. 1352226-88-0). All drugs were reconstituted and stored in dimethyl sulfoxide . Some WT), tumor suppressor p53-binding protein 1 knockout (−/−53BP1), RAD54 knockout (−/−RAD54), ATM knockout (−/−ATM), and breast cancer type 1 susceptibility protein conditional knockout (AID/AIDBRCA1) human cell lines [2. The AID/AIDBRCA1 cell line was engineered by tagging the miniauxin-induced degron (mAID) into the C-terminal end of the BRCA1 gene [BRCA1 gene expression for in vitro experiments, AID/AIDBRCA1 cells were cultured in the medium containing 250 μM 3-indoleacetic acid  for 2 h before exposing to drugs and for entire experiments.Human lymphoblastoid TK6-derived wild-type  alone, 0.45 μM AZD6738 alone, or combinations of each PARP inhibitor and AZD6738 for 20 h. Cells were fixed with 4% paraformaldehyde for 10 min at room temperature and permeabilized with 0.3% Triton-X  for 10 min at room temperature. DNA content was stained by propidium iodide  and assessed by flow cytometry on a Navios flow cytometer and Kaluza analysis 2.1 software . At least 10,000 events were analyzed per sample.Asynchronized cells were treated with indicated PARP inhibitor  alone, 0.45 μM AZD6738 alone, or combinations of each PARP inhibitor and AZD6738 were used to treat asynchronized cells for 20 h before harvesting. Cells were harvested onto glass slides using a Shandon Cytospin® 4 cytocentrifuge . A cell staining protocol for micronuclei and serine-139 phosphorylation of the histone variant H2AX (gH2AX) focus formation assays has been described previously [PARP inhibitor  alone, AZD6738 alone, or combinations of each PARP inhibitor and AZD6738 for 72 h. For the combination of PARP inhibitor and AZD6738, the drug combinations were serially plated with constant ratio concentration in 96-well plates. Cell viability was determined by CellTiter-Glo® Luminescent Cell Viability Assay . The combination index (CI) values at IC50 were calculated using CompuSyn version 1.0 .Asynchronized cells were seeded into 96-well plates at a final concentration of 30,000 cell/ml in 200 p values were calculated using Student's t-test. For gH2AX focus formation assay, medians were shown, and p values were calculated using the Mann-Whitney test. For CI calculation, averages and standard errors were displayed.Statistical analysis was performed using GraphPad Prism software version 8 . All results except gH2AX focus formation assay and CI calculation were expressed as averages and standard deviations, and WT and the other DNA repair-deficient cell lines. The increased G2/M population was obviously observed in −/−ATM and AID/AIDBRCA1 cell lines treated with PARP inhibitor alone. These data suggest that olaparib, talazoparib, and veliparib induce G2/M arrest. In contrast to PARP inhibition, the treatment of AZD6738 alone decreased the G2/M population in every indicated genotype compared to control treatments. Moreover, combining olaparib, talazoparib, or veliparib with AZD6738 significantly decreased the G2/M population in every indicated genotype compared to PARP inhibitor treatment alone , −/−ATM (CI = 0.605 ± 0.05), and AID/AIDBRCA1 cell lines (CI = 0.63 ± 0.06). For the combination of olaparib and AZD6738, the synergistic interaction was observed in both −/−ATM (CI = 0.59 ± 0.01), and AID/AIDBRCA1 cell lines (CI = 0.57 ± 0.12), while combining veliparib with AZD6738 showed the synergy in only the AID/AIDBRCA1 cell line (CI = 0.55 ± 0.10)  . These rBRCA1/2 genes. In addition to BRCA1/2, several somatic mutations in genes associated with the HRR pathway, such as ATM, BAP1, and PALB2 genes, have been recognized [BRCA1/2. However, the impacts of DNA repair dysfunction on response to the combination of PARP and ATR inhibitors are not fully understood.Nowadays, several PARP inhibitors have been approved for cancer patients. However, most cancer patients who are prescribed the PARP inhibitor have to have germline mutations in cognized . Both recognized . In addicognized . Previoucognized , 21. TheWT cell lines when combining each individual PARP inhibitor with AZD6738. It indicates that AZD6738 tends to potentiate PARP inhibitors in HRR-deficient cells. A significantly increased number of micronuclei at basal levels in RAD54-, ATM-, and BRCA1-deficient cell lines, which show the synergistic interaction between some types of PARP inhibitors and AZD6738, are also observed. However, in a 53BP1-deficient cell line, a number of micronuclei at basal levels are significantly elevated (Supplementary Table This study showed that AZD6738 abolishes the G2/M checkpoint activated by PARP inhibitors, leading to the increases in both micronuclei and DNA damage that is consistent with a previous study . Though WT, 53BP1-, and RAD54-deficient cell lines. Moreover, the combination of talazoparib and AZD6738 shows the synergistic interaction in RAD54-, ATM-, and BRCA1-deficient cell lines except WT and 53BP1-deficient cell lines. However, veliparib in combination with AZD6738 is exclusively synergistic in a BRCA1-deficient cell line. This finding suggests that cancers with DNA repair deficiency due to the mutations in HRR pathway-associated genes apart from BRCA1/2 could synergistically respond to the specific combinations of PARP and ATR inhibitors, and the combination strategy of PARP and DDR inhibitors could probably be expanded to treat other DNA repair-deficient cancers besides BRCA1/2-deficient cancers. Nevertheless, the reasons why some combinations of PARP inhibitors and AZD6738 show the synergism in specific HRR-deficient cell lines are unclear. For example, talazoparib, which is 100-fold more potent than olaparib, while veliparib is the weakest one [This study showed novel opportunities for PARP inhibitors by combining with DDR inhibitors in DNA repair-deficient cell lines. Mechanistically, the synergistic interaction of AZD6738 combined with PARP inhibitors is similar to a previous study . Althougkest one , shows t50 values of AZD6738 among different genotypes, even though an increased number of micronuclei were noticed in DNA repair-deficient cell lines, especially in a BRCA1-deficient cell line. According to the reported IC50 values of AZD6738 from this study (Supplementary Table This study also showed that there is no considerable difference in ICBRCA1/2. Moreover, PARP inhibitor dosages, especially talazoparib whose side effects are nearly similar to conventional chemotherapeutic drugs [For a clinical implication, the synergism demonstrated in this study supports the rationale to further investigate the combination of PARP and ATR inhibitors in HRR-deficient cells beyond BRCA1/2 that may give advantages for cancer patients without germline mutations in ic drugs , could bIn conclusion, this work emphasizes the value of investigating the possible indications of the combination of PARP and ATR inhibitors for cancer treatments to enhance the efficacy of PARP inhibitors and expand their use to other groups of cancer patients. This work demonstrates the evidence that supports the development of cancer therapy through the combination strategy of PARP and ATR inhibitors, and assesses HRR deficiency as predictive biomarkers to identify individuals tending to respond to PARP and ATR inhibitor combination treatments. The use of DDR inhibitors to synergistically potentiate PARP inhibitors could be an alternative option for cancer patients in the future.