Cell line authentication is important in the biomedical field to ensure that researchers are not working with misidentified cells. Short tandem repeat is the gold standard method, but has its own limitations, including being expensive and time-consuming. Deep neural networks achieve great success in the analysis of cellular images in a cost-effective way. However, because of the lack of centralized available datasets, whether or not cell line authentication can be replaced or supported by cell image classification is still a question. Moreover, the relationship between the incubation times and cellular images has not been explored in previous studies. In this study, we automated the process of the cell line authentication by using deep learning analysis of brightfield cell line images. We proposed a novel multi-task framework to identify cell lines from cell images and predict the duration of how long cell lines have been incubated simultaneously. Using thirty cell lines’ data from the AstraZeneca Cell Bank, we demonstrated that our proposed method can accurately identify cell lines from brightfield images with a 99.8% accuracy and predicts the incubation durations for cell images with the coefficient of determination score of 0.927. Considering that new cell lines are continually added to the AstraZeneca Cell Bank, we integrated the transfer learning technique with the proposed system to deal with data from new cell lines not included in the pre-trained model. Our method achieved excellent performance with a precision of 97.7% and recall of 95.8% in the detection of 14 new cell lines. These results demonstrated that our proposed framework can effectively identify cell lines using brightfield images.  Many isolated cell lines were subsequently found to be contaminated by faster growing cell cultures, such as HeLa cells, due to poor cell culture practice leading to the misidentification of cell lines1. The use of a misidentified cell line can lead to false conclusions and irreproducible experiments, consequently leading to a waste of time, money and resource3. It is estimated that industry wide 10–20% of preclinical effort was wasted due to misidentified cell lines, estimated to cost the industry 28 billion USD per year4.Over the last 50–60 years, cell lines have become a staple of biological research, resulting in rapid developments in the fields of cell and molecular biology, however, this increased use of cell lines has brought to light the issue of cell line authentication5. In spite of the prevalence of STR profiling, there are limitations. Microsatellite instability and loss of heterozygosity, especially in cancer cell lines, can make validation and authentication challenging using STR profiling6. In one study involving hematopoietic cancer cell lines it was found that the effect of long term culture, subcloning, and selection led to genetic drift, thereby significantly altering the DNA fingerprinting and over time some cell lines may also go through genetic and transcriptional evolution (GTE)8. All of these variances can make it harder to discriminate between cell lines using STR profiling8. In addition, due to time and cost restrictions, standard practice is to test the cells once they have been fully expanded and frozen, however this process results in wasted time and effort if the sample then fails STR profiling. It was therefore desirable to develop a new authentication approach to complement STR testing. Ideally, the new method should be easy to use, quick, and cost effective, and would enable early identification of the cell line that could be built into standard laboratory practice as well as overcoming the limitations of STR profiling10.Though there are many methods for cell line authentication, short tandem repeat (STR) profiling, also sometimes referred to as DNA fingerprinting, has been the most widely used and is recommended as the standard by the American Type Culture Collection (ATCC) Standards Development Organization Workgroup ASN-000211. They used a SVM classifier to learn the extracted feature vectors and got 70.57% accuracy on the 6-class classification of ANA Hep-2 cells. Murphy et al. utilized Zernike moment, object finding and edge detection to describe subcelluar location patterns of Hela cells which are labeled with antibodies against 10 endoplasmic reticulum protein separately12. Their proposed method achieved 83% accuracy on the classification task. Abdullah et al. collected 350 white blood cell (WBC) images and applied cell segmentation to extract geometric properties such as cell shape, nucleus size13. They compared 6 conventional ML classifiers on the classification of WBC images and reported Multinomial Logistic Regression algorithm outperformed other methods. However, the conventional machine learning methods rely on the pre-determined feature engineering like extracting morphology features from segmented cells. And such feature engineering methods should be adjusted according to the specific data14.With the advancement of machine learning (ML) approaches, automated cell image classification is a possible solution to allow fast analysis at low cost, as well as potentially identifying changes in cell morphology that could be indicative of undesirable qualities such as genetic drift or cellular senescence. Ponomarev et al. extracted 17 dimensional features  from stained ANA Hep-2 cell images15. By using the features of actin cytoskeleton structures, they proposed a convolutional neural network (CNN) based on VGG-16 to classify the microscopy images into three categories and reported the performance of CNN outperforms their biological experts in the classification task. Akogo et al. used MobileNet to perform image classification in their 5 cell line dataset  with 96.67% accuracy16. Most recently, Mzurikwao et al. trained two CNNs to classify 4 cancer cell lines  and their isogenic cell lines using brightfield images10. Compared with STR profiling, deep learning methods improve the cost and efficiency as these methods only need cellular images to train CNNs and the trained models are used to directly predict the identities for held-out test data. However, because of the lack of centralized available datasets and the intrinsic difficulties in analyzing multi-batch cellular images from different cell lines over time, the previous studies only conducted their experiments on small-scale cell line datasets of no more than ten cell lines. Whether or not CNN can identify tens even hundreds of cell lines is an open question. The relationship between the incubation times and cellular images has not been analyzed in previous studies. In addition, new cell lines are continually added to the AstraZeneca Cell Bank therefore, how to deal with data from new cell lines not included in the model requires consideration.Deep learning approaches brings up the possibility of extracting the discriminative features from data without the need of the pre-determined feature engineering. Oei et al. collected 522 fluorescence cell images from 3 cell lines  using confocal immunofluorescence microscopyDataset: We have established two cellular datasets. i) The first dataset consists of 47,671 brightfield images of 30 cell lines, which is 23 GB of data. The dataset was curated from a set of experiments where 30 cell lines were cultured and each cell culture flask was imaged at 2–3 h intervals using the IncuCyte. To our knowledge, it is one of the largest such collections of data in the literature for cell line authentication. ii) The second dataset includes 860 cell images from 14 new cell lines not included in the 30 above. The 14 cell lines were incubated by using the same conditions as the previous 30 cell lines and we used this dataset to validate whether or not our proposed method can identify new cell lines without training model from scratch.Cell image recognition framework: We proposed a novel multi-task cell image recognition framework to (i) identify and authenticate cell lines and (ii) predict the duration of how long cell lines have been incubated simultaneously. The cell line classification network (CLCNet) learns image-level features from the cell images and outputs the predicted probabilities of the cell line labels for each input image. The extracted convolutional features of CLCNet are integrated with the cell line regression network (CLRNet) to predict the incubated time points for bright-field images.Identify new cell lines: We integrated a transfer learning approach with the proposed framework to identify images from new cell lines which are not included in the pre-trained model (currently we have 30 cell lines). This is an important aspect, as we are unlikely to be able to acquire very large numbers of images for every cell line that we wish to authenticate.Validation: We conducted comprehensive validation experiments to justify the significance of our proposed framework on the established datasets. The prediction performance of CLCNet reached the accuracy of 99.8% and the f1-score of 99.7% in the classification of the 30 cell lines. CLRNet achieved the coefficient of determination score (R2-score) of 0.927 in the prediction of the incubated times for cell images. In the detection of 14 new cell lines, our proposed method obtained the precision of 97.7% and the recall of 95.8%. These results demonstrated that our proposed framework can effectively authenticate the identities of cell lines from brightfield images.Therefore, in this study, we aim to answer these questions by developing an automated cell line authentication method by using deep learning-based analysis of routine brightfield cell line images. Our main contributions reported in this paper are:2 flask (Corning) at a density of 0.5–2 × 106 cells per flask. The flasks were then added to the Incucyte S3  system and brightfield images collected from 21 different locations across the flask every 2–3 h over a time period between 3 and 7 days. (2) The second dataset includes images from 14 cell lines, as listed in Table In this study, we have collected sample images from the registered cell lines in AstraZeneca Cell Bank where the selection criteria for cell types is “commonly used cell lines” based on the frequency of cell lines has been requested and the readily availability of corresponding images. We have established two cell line datasets. 1) The first dataset consists of 30 different cell lines, as listed in Table  The firsOur proposed framework is shown in Fig. 17. (3) Grayscale: The scaled cell images are gray but have three channels. The grayscale method is applied to convert the images into one channel. (4) Data Normalization: Data normalization aims to restrict the image pixels within a specific range and ensure each pixel has similar data distribution10. Each cell image is normalized by subtracting the image mean  and dividing by image standard deviation, to improve the convergence speed while training the neural networks.Before the model training of CLCNet, we carried out the following pre-processing procedures: (1) Unify image format: The collected images’ formats include JPEG and TIFF. To ensure the consistency, we have unified all images’ formats to JPG before further processing for this study. (2) Image scaling: The original image resolution for the established dataset is 1408 18 as the backbone of CLCNet for classification. The model structure used in our system is shown in Fig. 19 and a ReLU activation function. Five depthwise separable convolutional (DSC) blocks are stacked to further learn spatial and hierarchical features from the inputs. Each DSC block includes two or three DSC layers and one maxpooling layer. DSC layer consists of a depthwise convolution  and a pointwise convolution . The weights of the new FC layer are initialized. During the model training, only the weights of the FC layer will be updated with new data and the weights of other layers are fixed. The training set of the 30 cell lines is combined with the training set of the 14 cell lines. The underlying concept is that having been trained on 30 cell lines, the model will have learned a good representation of brightfield images for discriminating between different cell lines, and as such good classification performance can be obtained for new cell lines by only retraining the final classification layer. The combined test set is used to validate whether or not the updated model can keep good classification performance on the 30 cell lines and identify the new 14 cell lines.The authors declare this study don does not require ethical approval.23 and Scikit-learn 0.24.024 packages. The hyperparameters for CLCNet and CLRNet were set as follows: The SGD optimizer with the momentum = 0.9 and weight_decay = 5e−4 is adopted. The initial learning rate is 0.001 and it decays by factor = 0.1 every 25 epochs. The batch size and maximum training epochs are 20 and 100 separately. The two cell datasets were split into fivefolds separately then fivefold cross-validation (CV)25 was applied to evaluate the model performance. The CV iterated five times that fourfolds are used for training and validation, and the remaining fold for testing in each iteration. We deployed all the experiments using a single 32 GB Nvidia V100 GPU.We implemented all models and benchmark experiments using Python 3.7 with Pytorch 1.9.0In order to evaluate our proposed system comprehensively, we replaced the backbone of CLCNet with three SOTA CNNs: MobileNet, VGG19, ResNet50. To seek a fair comparison, the data selected for training, validation and testing are constant during the classification and regression tasks. The results of the classification and regression on the 30 cell lines are shown in Table To further view how the deep network distinguished the 30 cell lines, we used t-SNE embedding to reduce the dimension of the convolutional features of CLCNet (e.g. 2048 dimensions) and visualized the processed features in 2d space. The t-SNE plot of the 30 cell lines is shown in Fig. The confusion matrix of the transfer learning technique for identifying 14 new cell lines is shown in Fig. We demonstrated the advantages of the transfer learning method compared with the way of training model from scratch in Fig. 16. In our experiments, the Xception model outperformed the other architectures, which can be attributed to the DSC blocks which decouple spatial correlations from cross-channel correlations and allows more efficient training. It should be noted that MobileNet also includes several DSC layers in its structure26, however MobileNet is lightweight and targets the deployment on a mobile platform ahead of outright performance. Hence, we picked up the Xception as the backbone of CLCNet for classification. We used the t-SNE plots . The classification accuracy for the 14 cell lines reached 96.5% and the performance of identifying the 30 cell lines was not influenced (99.8% accuracy) by re-training. Although the transfer learning technique needs to re-train the part of CLCNet, its training speed was 480 times faster than training the model from scratch. These results suggested that the transfer learning method is possible to solve the issue.27 or open set recognition28 can automatically decide data is seen or unseen without extra model training. These methods can potentially be integrated with the transfer learning method to solve this issue. Besides, flagging or analyzing the growth processes of stem cells will be an application direction of our proposed regression model. There is also the question of how much training data is necessary: How many cell lines are required for the base training (is 30 cell lines enough)? How many cell lines can this be expanded to via transfer learning (200 cell lines or more) whilst maintaining a satisfactory level of performance? Finally, we have demonstrated a proof of concept for rapid cell line authentication. When this system is deployed into the laboratory, the development of guidelines for interpreting the confidence value outputted by CLCNet, and the incubation time predicted by CLRNet, how to assess performance and how to integrate with current laboratory practice are required.In this paper we have established a proof of concept for how an image-based AI method can be used for cell line authentication, including the use of transfer learning to extend classification to cell lines for which we have only a small number of example images. In doing so we have identified a number of areas for future research. Firstly, the ability to identify and quantify contamination of cell lines with small amounts of another cell line would be a useful functionality. Secondly, having to retrain the model when adding new cell lines might be a drawback as greater numbers of cell lines are built up. Some new techniques like conformal predictionIn this study, we have attempted to automatically authenticate cell lines by using deep neural networks on brightfield images. We proposed a novel multi-task cell image recognition framework to authenticate cell lines and predict the duration of incubation. We established a large dataset consisting of 47,671 brightfield images of 30 cell lines. The classification and regression performance on the dataset were excellent. We also integrated transfer learning with our proposed system to identify new cell lines and obtained 96.7% accuracy. These results demonstrated that our proposed framework can effectively authenticate the identities of cell lines on brightfield images.Supplementary Figures.Supplementary Table S1.


The aim of this systematic review is to provide an overview of the state of the art on in vitro models for ovarian cancer studies, with focus on patient derived studies, which provides a personalised approach to treatment of patients with ovarian cancer.There is an unmet biomedical need for ex vivo tumour models that would predict drug responses and in turn help determine treatment regimens and potentially predict resistance before clinical studies. Research has shown that three dimensional models of ovarian cancer (OvCa) are more realistic than two dimensional in vitro systems as they are able to capture patient in vivo conditions in more accurate manner. The vast majority of studies aiming to recapitulate the ovarian tumour morphology, behaviors, and study chemotherapy responses have been using ovarian cancer cell lines. However, despite the advantages of utilising cancer cell lines to set up a platform, they are not as informative as systems applying patient derived cells, as cell lines are not able to recapitulate differences between each individual patient characteristics. In this review we discussed the most recent advances in the creation of 3D ovarian cancer models that have used patient derived material, the challenges to overcome and future applications. Worldwide there were nearly 300,000 cases of OvCa diagnosed in 2018 with the 5-year survival between 30–50% despite the advances in diagnostics and treatment. In the UK alone, there were 7300 new cases reported in 2017 with 4200 deaths in 2018, and the numbers continue to steadily increase [The current standard for treatment of EOC is cytoreductive surgery combined with platinum-based chemotherapy . In patiThere have been attempts to create patient-derived OvCa models since the first report by Griffon et al., in 1995 . In the In order to create a model of OvCa that would represent the in vivo environment and reflect the processes happening in the body it is important to understand the general morphology and physiology of OvCa tumours and the way it spreads.It is well established that ovarian tumours consist of mixture of epithelial, stromal, immune, and endothelial cells which form the complex tumour microenvironment ,43,44,45The primary microenvironment for the ovarian carcinoma cell is the mesothelium ,47. An iFibronectin as well as integrins play an important role in spheroid growth and the abundant presence of several isoforms of fibronectin in malignant ascites suggests the importance of the microenvironment in OvCa metastasis . Furuya The unique anatomical location of the ovary renders OvCa cells the ability to easily metastasise in comparison to other cancers. Once the cancer cells have detached as single cells or clusters from the primary tumour, it is thought that they metastasise through a passive mechanism, carried by the physiological movement of peritoneal fluid to the peritoneum and omentum.It is not entirely clear whether single cells detach and then aggregate to form spheroids, or if the cells detach as cell clumps that stay together while floating in ascites . The othIt is also necessary to consider when creating a tumour model that in order for the tumour to grow over a certain size (>1–2 mm) a process of neovascularisation has to occur . HoweverIt seems evident from the up-to-date scientific reports that there are certain conditions to be met for an ideal OvCa tumour model to be achieved, and these key requirements are summarised in Animal models, three-dimensional (3D) models and two-dimensional (2D) cell culture are currently the most widely used methods for creating various tissue models including tumour tissues. 2D cell cultures (in T-flasks or microplates) have dominated the in vitro landscape for the last decade ,61. ReseIn order to replicate genuine tumour morphology and microenvironment animal models and 3D models have been created. Currently, animal models are considered to be gold standard in pre-clinical studies. They are more realistic than 2D in vitro systems as they are able to capture patient in vivo conditions in a more accurate manner. Currently, both small and large animal models are available for OvCa. They are mostly murine models (mouse and rat), but also include hens and SCID pigs ,77,78,79Three-dimensional models offer the potential for ex vivo research of cell to cell interactions thus making the study of the nature of tumours easier, especially where extracellular matrix cover and stromal cells are added ,88,89,903D models of OvCa have become highly interesting for investigation as according to recent oncogenesis theories the progression of OvCa involves detachment of cancer cells from the in situ carcinoma into cell aggregates/spheroids, and further attachment to mesothelial-lined surfaces.  For instMost OvCa in vitro remodelling approaches in 3D involve, like in other diseases ,97,98,99Spheroid systems are one of the oldest and most widely used 3D cell culture set up in the field of tumour model development, drug discovery, therapeutic assessments etc. They are cell aggregates/clusters, which are formed as a result of cell-cell adhesion without attachment to culture vessel surfaces. In vitro 3D approaches have been established till date for spheroid formation like, forced floating method, hanging drop method, encapsulation-based method, 3D bioprinting and agitation method to name a few ,101,102.Similar to other cancer models, spheroids are also one of the most widely used system for in vitro models of OvCa and its metastasis. Ishiguro et al., have done a comprehensive overview of the representative methods for spheroid cultures of cancer cells, which include organotypic multicellular spheroids, multicellular tumour spheroids, tumour-derived organoids and tumour-derived spheroids . For exaHowever even with extensive research of OvCa using spheroid models, they have certain inherent disadvantages. Due to their spatial characteristics, high diffusion gradient in terms of nutrients and oxygen is formed within them. This in turn results in the formation of necrotic cores at the center and decreasing cellular proliferation over time like it has been shown for other types of cancer models ,104,105.Although relatively easy to use, the disadvantages associated with spheroids models have resulted in the development of 3D cancer models with more structural complexity and stability using natural and artificial biomaterials in the form of hydrogels and polymeric scaffolds. Hydrogels are cross-linked polymeric networks consisting of a high amount of water and are able to simulate the native tissues in terms of architectural and spatial characteristics, biocompatibility and also allows nutrient and oxygen diffusion. Hydrogels can be formed of natural molecules like collagen, Matrigel and other extra cellular matrix (ECM) proteins or of synthetic materials like poly–ethylene glycol (PEG), agarose, alginate etc. Similar to other cancers, OvCa 3D models using hydrogels have been established by various groups ,107,108.To overcome the limitations of the above methods there have been a number of developments in tissue engineering to construct polymeric scaffolds-based tumour models. Those include reports describing scaffolding systems for OvCa ,81,117, Graphic summary and comparison of different types of platforms used to replicate and research OvCa models can be found in the The vast majority of studies aimed to research OvCa have used cancer cell lines with very few utilising patient derived samples. Despite the advantages of using cancer cell lines to set up a platform/system, i.e., availability, consistency/reproducibility, cancer cell lines are not as informative as patient derived cells, as they would not able to recapitulate differences between each individual patient characteristics, including differences in drug sensitivities due to changes in gene expression following culture and passage . FurtherThe focus of this review is to explore the existing knowledge base and evidence with regards patient derived samples of OvCa cells for fabrication of a 3D model to study the properties in vitro.This review subscribes to the new PRISMA  guidelines .The eligibility criteria were studies published in English language and there were no other limitations to the search. Thorough literature search was performed using electronic databases of MEDLINE, EMBASE, Library, Information Science & Technology Abstracts, British Library Document Supply Centre Inside Serials & Conference Proceedings, ScienceDirect, Oxford Handbooks Online, Academic Search Index, Supplemental Index, Complementary Index, Directory of Open Access Journals, British Library EthOS, Digital Access to Scholarship at Harvard (DASH), University Press Scholarship Online, BioOne Complete, Center for Research Libraries, Research Starters, Oxford Medicine Online, Cochrane Database of Systematic Reviews, Oxford Bibliographies, Gale OneFile: Health and Medicine, Kent, Surrey and Sussex NHS Libraries, Springer Protocols, McGraw-Hill Medical, VleBooks, Kortext eBook Catalogue, BMJ Best Practice, ClinicalTrials.gov, Emerald InsightNCI  , Directory of Open Access Books by two reviewers to identify relevant studies. Bibliographies of the relevant literature were also screened for any additional studies that were missed through the electronic search. Additional searching of the grey literature has also been conducted.The exact syntax of search terms included ‘scaffold’, ‘three-dimensional’, ‘3D’ and ‘spheroid’ each of which were combined with term ‘ovarian cancer’. These terms were used as historically there has been interchangeability between the terms of “3D models” and “scaffolds”. All search terms were expanded, and all sub-categories were included. Thesaurus search was also used to identify additional terms. All duplicates were removed. The search was independently run by two of the authors. Databases last accessed 5 October 2022. The search protocol available on demand.Initial screening of article headings was performed identifying the potential studies that could be used in current review. The selected studies were further screened using the abstracts and the irrelevant ones were excluded. The full text of these potentially eligible studies was retrieved and assessed for eligibility. Any discrepancies over the eligibility of particular studies were resolved through discussion with two other reviewers.The type of cells used for the 3D model were exclusively patient derived OvCa cells or newly established cell lines derived directly from primary OvCa cells.Only multicellular tumour spheroid and tumour-derived spheroid models were reviewed in the current study.The main focus of the research was to build a three-dimensional model of OvCa cells regardless of method used to accomplish it.The studies included in the current review meet the following criteria:Studies that used established OvCa cell lines or other types of cancer cells; organotypic multicellular spheroid and tumour derived organoid models and studies that used 3D modelling in their research but did not mention methods of construction were not included in the current review.Data was extracted from the selected papers using pre-designed data collection forms. Attempts were made to contact authors where data was missing from the papers.Following the electronic database search 3839 articles were identified and 2844 articles remained after removal of duplicates. Title screening resulted in rejection of 2435 papers as they did not meet the inclusion criteria. Out of the remaining 409 articles 326 were excluded based on abstract review.Full manuscripts of all the remaining papers were reviewed. Following the screening of the bibliographies of the full manuscripts two additional papers were also reviewed.established OvCa cell lines were used for creating tumour spheroids opposed to patient derived samples;not OvCa cells used;animal models studied;only conference abstract was available which did not contain enough information for detailed analysis;review article;3D model construction was described in the previous study of the same group, sensitivities were evaluated in the article;in vivo formed spheroids were the focus of the research;topic was found irrelevant.The exclusion of the papers after abstract and full manuscript review was based on the following criteria: Twenty-five full papers and eleven conference abstracts were included in the review.The vast majority of the studies on OvCa models use in their research well-established cell lines, which in itself becomes a significant limitation as we discussed above. In this review we would like to focus on the papers that have used patient-derived samples to create OvCa models.The relevant reports on 3D models for OvCa have been overviewed in To our knowledge the first study to utilise human tissue for construction of 3D ovarian tumour model was reported by Griffon et al., who were able to demonstrate spheroid growth in situ in eight cell suspensions out of eighteen, harvested from eight solid tumours, nine ascitic fluids and one pleural effusion. They noted that effusions were more productive in spheroid formation, than solid tumours and that samples obtained from mucinous ovarian adenocarcinomas did not form spheroids at all. Other studies have been able to establish new cell lines derived directly from fresh tumour specimens to further study the spheroid formation . Again, It becomes evident from the analysis of It is also evident from the literature that spheroid models are the most widely used systems for 3D in vitro models of OvCa, although few groups have assessed complex systems like hydrogels. Amongst the spheroid models, forced floating technique using commercially available specialised spheroid forming ultra-low attachment dishes is the most common method ,17,20,22Grun et al., established spheroids in Rotary Cell Culture System and highlighted that the used method had allowed spheroids to grow for longer periods and reach significantly greater ‘tumour’ volumes than when using hanging droplet method. Although in their study authors were able to reach a maximum diameter of 4 mm, extensive areas of necrosis were present . LoessneThe biggest size of spheroids was demonstrated by Grun et al., who used the Rotary Cell Culture System to culture them , whereasTHBS1, PECAM1 genes and others [One of the key findings of Kryczek et al., was that deletion of ALDH+ cells or CD133+ cells dramatically reduced the quantity and size of spheres formed, whereas their simultaneous deletion drastically reduced sphere formation. Furthermore, they were able to show that the expression of ALDH+ and CD133+ gradually reduced following prolonged in vitro cycles . Other sd others ; Grun etd others . Anotherd others .A lot of the studies aimed to look at the cellular characteristics of the established spheroids. For example, it was shown by Puiffe et al., that compact spheroids were able to form by the cell line culture in the presence of patient derived acellular fraction of ascites suggesting the importance and subtleties of ascites in modulating the tumour microenvironment. They also studied the effects of ascites on invasion, proliferation and gene expression in the developed spheroids . The samAll of the above studies confirm the benefits of 3D models in the investigation of the tumour characteristics, microenvironment and its ‘behavior’ and as it was highlighted by Maru et al., the choice of 3D system should be dependent on the end goal/objectives . A key tThe similarity to in vivo conditions of the patient derived 3D culture has a major advantage of helping in treatment studies for cancer patients. The first study to utilise this was by Griffon et al., who assessed the radiosensitivity (0–8 Gy) of primary OvCa cells from various locations using tumour spheroid model. They observed extensive variation between 3D spheroids from different patients in terms of their response to radiotherapy, highlighting the need for personalised treatment protocol for patients in a clinical setting . SimilarThese reports have shown the most recent advances in personalised approach to treatment prediction in OvCa patients, however it is still evident from this analysis that further research is required of patient derived studies in 3D due to their obvious advantages for investigation of tumour characteristics and more importantly better and personalised drug screening.The vast majority of the 3D in vitro models developed for OvCa studies are spheroid type models and hydrogel types of models. Despite advantages of spheroids , such as simplicity of fabrication, achievement of heterogeneity in phenotype and gene expression and altered cell metabolism they do have several limitations. Specifically, there is an exceedingly high variability of their aggregate forming densities, which is also supported in the most recent research papers ,30, theyFinally, as it is evident from the review there are currently no studies that have been able to create OvCa polymeric scaffold 3D model utilising patient-derived cells, which would significantly improve patient care by predicting the efficacy of potential chemotherapy treatment and be a further step forward into investigating the microenvironment and biology of EOC.There is a clear need for the development of an accurate, robust, 3D system which will enable the culture and drug screening of patient derived ovarian tumours. Such a system will allow screening of drugs as well as genetic analysis of the cancer of a specific individual, therefore, optimising/tailoring the treatment towards that individual. Furthermore, such an in vitro 3D system which would account for the tumour microenvironment heterogeneity, would help elucidate developmental and evolutionary aspects of the disease. Finally, for the development of such system with a tangible clinical outcome a systematic rigorous experimentation with patient derived tumours (and not with cell lines) is essential.While this review covers studies of 3D OvCa models utilising patient derived samples, we have limited ourselves to multicellular tumour spheroid and tumour-derived spheroid models; and the review focused mainly on the models of EOC.


A/X and LewisB/Y antigens. Moreover, the GSL expression correlated with relevant glycosyltransferases that are involved in their biosynthesis as well as with transcription factors (TFs) implicated in colon differentiation. Additionally, correlations between certain glycosyltransferases and TFs at mRNA expression level were found, such as FUT3, which correlated with CDX1, ETS2, HNF1A, HNF4A, MECOM, and MYB. These TFs are upregulated in colon-like cell lines pointing to their potential role in regulating fucosylation during colon differentiation. In conclusion, our study reveals novel layers of potential GSL glycans regulation relevant for future research in colon differentiation and CRC.Colorectal cancer (CRC)–associated changes of protein glycosylation have been widely studied. In contrast, the expression of glycosphingolipid (GSL) patterns in CRC has, hitherto, remained largely unexplored. Even though GSLs are major carriers of cell surface carbohydrates, they are understudied due to their complexity and analytical challenges. In this study, we provide an in-depth analysis of GSL glycans of 22 CRC cell lines using porous graphitized carbon nano–liquid chromatography coupled with electrospray ionization–mass spectrometry. Our data revealed that the GSL expression varies among different cell line classifications, with undifferentiated cell lines showing high expression of blood group A, B, and H antigens while for colon-like cell lines the most prominent GSL glycans contained -Lewis  •Undifferentiated cell lines showed high expression of blood group A, B, and H antigens.•A/X and LewisB/Y antigens.Colon-like cell lines are high in GSLs carrying -Lewis•A/X and LewisB/Y antigens associated with expression of FUT3 and CDX1.-Lewis•I-branching was elevated in undifferentiated cells. A/X and LewisB/Y antigens for colon-like cell lines while undifferentiated cell lines showed higher abundances of glycans with blood group A, B, and H antigens. Combining glycomics with transcriptomic data of relevant genes, significant correlations of Lewis structures with glycosyltransferase FUT3 and transcription factors were revealed, including CDX1.We performed an in-depth analysis of the GSL glycan profiles of 22 CRC cell lines revealing high expression of -Lewis Colorectal cancer (CRC) was the fourth most commonly diagnosed cancer 6.1%) and the second leading cause of cancer death 9.2%) worldwide in 2018  high-mannose types, and decreased bisected N-glycans. For the O-glycans, a higher expression of core 1 and core 2 O-glycans were found in CRC, an increase of Tn-antigen, T-antigen, as well as a decrease of core 3 and core 4 O-glycans. Moreover, sialyl-LewisA has been found to be highly expressed in the colon and rectum during neoplastic transformation as well as cancer progression. This antigen is known to be involved in the adhesion of cancer cells to E-selectin present on endothelial cells, playing an important role in the metastasis of the colon cancer cells   and GM2 , contribute to the tumor-associated angiogenesis (Glycosphingolipids (GSLs) play essential roles in various biological processes including cellular development, differentiation, and cell fate decisions . Taking ramide]) . Anotherramide]) . Misonou tissues . Ganglio tissues . In addiogenesis , 13, 14.ogenesis , 16.O-glycome of 26 CRC cell lines revealed a high abundance of I-branched and LewisA/X antigens for colon-like cell lines while undifferentiated cell lines were dominated by truncated α2,6-core sialylated glycans in the absence of Lewis structures  nano–liquid chromatography (LC) electrospray ionization tandem mass spectrometry (MS) in negative ionization mode. Associations between glycosylation features and gene expression levels of glycosyltransferases (GTs) and transcription factors (TFs) were explored, shedding light onto the potential regulation of glycosylation pathways in colon differentiation.4, HCl, ammonium bicarbonate, and Dowex cation-exchange resin (50W-X8) were obtained from Sigma–Aldrich. KOH and 100% glacial acetic acid were from Honeywell Fluka. C18 Ziptips were purchased from Millipore and TopTip  from Glygen Corporation. Reverse phase (RP) tC18 solid-phase extraction (SPE) cartridges (50 mg) were purchased from Waters, and SPE bulk sorbent Carbograph was from BGB Analytik USA LLC. 2-Propanol was obtained from Biosolve Chimie. Acetonitrile (MeCN) of LC-MS grade was from Biosolve. Endoglycoceramidase I (EGCase I), α1-3,4 fucosidase, α1-2,4,6 fucosidase O, α2-3 neuraminidase S, purified bovine serum albumin, 10 × GlycoBuffer 1, and 1 × EGCase I reaction buffer were purchased from New England Biolabs. Dulbecco’s modified Eagle’s medium (DMEM) and Hepes-buffered RPMI1640 medium were purchased from Gibco (Thermo Fisher Scientific). Fetal bovine serum (FBS) was from Bodinco BV. Penicillin/streptomycin from Invitrogen, 0.5% trypsin-EDTA solution 10× was ordered from Santa Cruz Biotechnology, and T75 cell culture flasks were obtained from Greiner-Bio One B.V. Ultrapure water generated by an ELGA PURELAB system was used for all solvent preparations and washing steps.TFA and chloroform were obtained from Merck. Methanol (MeOH) of ultra LC-MS grade was purchased from Actuall Chemical. NaBH2 in humidified air at 37 °C. A trypsin/EDTA solution was used to detach the cells when approximately 80% confluence was reached. The medium was used to terminate the enzymatic activity. The harvested cells were counted with a TC20 automated cell counter. The cells pellets were obtained by centrifuging at 1500g for 5 min after being washed twice with 1 × PBS. The prepared cell pellets were stored at −20 °C. All CRC cell lines have been tested by means of the Promega PowerPlex Fusion System 5C autosomal STR kit. More details about cell culturing, cell origin, and the characteristics of the cell lines are provided in The human CRC cell lines were provided by the Department of Surgery and the Department of Cell and Chemical Biology of Leiden University Medical Center as well as the Department of Molecular Cell Biology and Immunology of Amsterdam UMC. Cells were cultured in RPMI1640 medium, except for cell lines LS174T, SW1398, and SW948, which were cultured with DMEM. All media were supplemented with 10% FBS and 1% antibiotics [streptomycin (5 mg/ml) and penicillin (5000 IU/ml)]. Cultures were performed in a cell incubator with 5% CO2O, followed by the addition of 500 μl of chloroform and 350 μl of MeOH; after each addition the plate was shaken on a horizontal shaker for 10 min, followed by sonication for 10 min. Afterward, the sample was shaken for 4 h at room temperature (RT), followed by centrifugation for 15 min at 3000g at 20 °C. A 400 μl mixture of chloroform/MeOH  and 400 μl of MeOH/H2O  were added sequentially to the sample after collection of the upper phase. The sample mixture was shaken overnight at 1000 rpm at RT followed by the collection of the upper phase. Another two extractions were performed after adding 400 μl of MeOH/H2O  to the sample, followed by a centrifugation step (15 min at 3000g at 20 °C). The collected upper phase was combined into a single glass vial and dried under a vacuum. The dried sample was dissolved in 100 μl of MeOH and incubated for 30 min on a shaker, followed by the addition of 100 μl of H2O to the suspended sample. The sample was loaded onto the tC18 RP SPE cartridge, which was preconditioned with the sequential addition of 1 ml chloroform/MeOH , 1 ml MeOH, and 2 ml MeOH/H2O . The loaded cartridge was washed using 2 ml of MeOH/H2O , and elution was performed by sequentially adding 2 ml of MeOH and 2 ml of chloroform/MeOH . The combined eluate was dried under vacuum.A detailed procedure for the extraction and purification of GSLs from cells was described in a previous study . In brie2O, 4 μl EGCase I reaction buffer, and 2 μl EGCase I enzyme. The reaction system (42 μl) was incubated at 37 °C for 36 h. The purification of released glycans was performed on tC18 RP SPE cartridge preconditioned with 2 ml of MeOH and 2 ml of H2O. The loaded cartridge was washed twice with 200 μl of H2O. A sample mixture that contained the flowthrough and the solution after washing was collected and was lyophilized under a vacuum.The GSL glycan head groups were enzymatically released from extracted GSLs on the basis of the manufacturer’s instruction. In brief, the purified GSLs were suspended with 36 μl of H4 in 50 mM KOH, followed by incubation at 50 °C for 3 h. The reaction was quenched using 4 μl of glacial acetic acid. The mixture was loaded onto the RP C18 Ziptips containing 50W-X8 resin, which was preconditioned with 3 × 60 μl of 1 M HCl, 3 × 60 μl of MeOH, and 3 × 60 μl of H2O. The loaded cartridge was washed twice with 40 μl of H2O. The flowthrough and washing were collected and dried under a vacuum. To remove residual borate, 150 μl of MeOH was added twice during the lyophilization process.The purified GSL glycans were dissolved in 40 μl of freshly prepared 1 M NaBH2O with 0.1% TFA (v/v) with shaking at RT. The empty TopTip containing 50 μl of PGC slurry was preconditioned sequentially with 60 μl of MeOH, 3 × 60 μl of 80% MeCN with 0.1% TFA (v/v), and 3 × 60 μl of H2O with 0.1% TFA (v/v), followed by sample loading. The loaded column was washed with 2 × 60 μl of H2O with 0.1% TFA (v/v). The sample was eluted with 2 × 40 μl of 60% MeCN with 0.1% TFA (v/v); after pooling the two eluates, the sample was dried under vacuum.PGC cleanup was performed as described in a previous study . Briefly2O. A fraction of the sample (2 μl) was loaded onto a Hypercarb PGC trap column  and separated on a Hypercarb PGC nano-column  within a Dionex Ultimate 3000 nanoLC system and analyzed with an amaZon speed ion trap MS . Solvent A consisted out of 10 mM ammonium bicarbonate and solvent B out of 60% MeCN in 10 mM ammonium bicarbonate. The separation was performed over a linear nanoLC gradient of solvent B ranging from 1% up to 65% in 80 min with flow rate of 0.5 μl/min. The column was washed with 95% solvent B for 10 min. The GSL glycans were analyzed with capillary voltage of 1000 V, 280 °C of drying gas at 5 l/min, and the nebulizer was set at 3 psi. To enhance sensitivity, 2-propanol enriched nitrogen gas was used for enhancing sensitivity  (https://cran.r-project.org/bin/windows/base/old/4.0.5/) using packages “tidyverse,” “Rcpm,” “pcaMethods,” “stringi,” “readxl,” “ggplot2,” “ggrepel,” “reshape2,” "ggpubr," “tidyHeatmap,” “corrplot,” “ComplexUpset,” and “data.table,”Structural assignments were based on accurate mass, elution properties during the separation on a PGC-nanoLC column, the manual inspection of MS/MS spectra according to previously identified diagnostic ions in negative mode for GSLs, exoglycosidase digestion, as well as known biosynthetic pathways of GSL glycans , 25 http 10 May) , 27. Brui.e., analyses of independent cell cultures). More information about the technical replicates of each sample is given in We performed an in-depth analysis of GSL glycans in 22 CRC cell lines by PGC-nanoLC-electrospray ionization-MS/MS in negative ionization mode. Full technical replicates were performed for all CRC cell lines. In addition, for the cell lines Colo320 and HCT15, biological replicates were performed LewisA/X antigens. The undifferentiated cell lines exhibited 20 unique GSL glycans, with glycosylation features such as blood groups A, B, and H as well as I-branched glycans. Only three GSL glycans were found only in the unassigned cell lines, all belonging to the (neo)lacto-series with B-LewisB/Y, blood group B, and I-branched antigens.Gangliosides were expressed in all CRC cell lines with various abundances, ranging from 0.2% in colon-like cell line SW1463 up to 100% in the neuroendocrine carcinoma-derived cell line Colo320 (unassigned cell line) . GM3 wasα1,2-, and α1,3/4-fucosylation; total-, α2,3-, and α2,6-sialylation; LewisA/X, and (A) LewisB/Y; and blood group A, B, H, as well as I-branching. The analyzed 22 CRC cell lines were classified into two groups based on colon cell differentiation: colon-like cell lines (7) and undifferentiated cell lines (10), and the remaining cell lines were unassigned (5) as there was no conclusive data available that would have allowed their assignment  mainly grouped in the top left of the score plot and showed a higher expression of glycans with LewisA/X and (A-)LewisB/Y antigens, as well as fucosylation . In all colon-like cell lines sialyl-LewisA/X antigens were detected at high abundance. Specifically, mucin-secreting cell lines LS180 and LS174T were characterized by the very high expression of LewisA/X and α1,3/4-fucosylation. LewisB/Y antigens showed highest expression in colon-like cell line SW948 while A-LewisB/Y antigens were found to have the highest expression in colon-like cell line SW1463  and HCT15 (55%), respectively . Only twN-glycolylneuraminic acid (Neu5Gc) were found in most of the undifferentiated cell lines with various abundances ranging from 0.2% (SW837) up to 5% (RKO)  . It is w5% (RKO)  and thei5% (RKO) .A) was driven by a high expression of (a- and b-series) gangliosides, gangliosides with GalNAc, and sialylation . Gangliosides and specifically a-series gangliosides were highly expressed in the unassigned cell lines HCT8, SW1398, C10, and Colo320 as well as undifferentiated cell lines Caco-2, DLD-1, HCT116, and one colon-like cell line (WiDr). A high abundance of b-series gangliosides was detected in undifferentiated cell lines RKO and SW837 as well as for the unassigned cell lines Colo320 and C10. Gangliosides with GalNAc were highly expressed in undifferentiated cell lines SW837, Caco-2, and HCT116 as well as unassigned cell lines SW1398 and Colo320. The high expression of α2,3-sialylation, overall sialylation, and gangliosides was observed in undifferentiated cell lines Caco-2, DLD-1, and HCT116, colon-like cell line WiDr, as well as unassigned cell lines HCT8, SW1398, C10, and Colo320. On the other hand, globo-series glycans were highly abundant in undifferentiated cell lines HCT15, SW480, Caco-2, HCT116, and colon-like cell line SW1116 as well as the unassigned cell line HCT8. The findings and relative quantification of GSL glycans in CRC cell lines are summarized in The clustering of CRC cell lines C10, Colo320, WiDr, DLD-1, and HCT116 A was driST3GAL5 and the expression of gangliosides , specifically the a-series gangliosides . The gene B4GALNT1, which encodes the corresponding GT responsible for adding GalNAc to Gal in ganglioside biosynthesis, was positively correlated with gangliosides containing a GalNAc . Moreover, ST8SIA1, whose corresponding GT is responsible for the biosynthesis of b-series gangliosides, positively associated with ganglioside expression . In addition, the expression of (neo)lacto-series glycans showed positive correlation with B3GNT5 , which encodes the key enzyme for the biosynthesis of (neo)lacto-series glycans. No significant correlation was found between globo-series glycans and A4GALT, which encodes the GT that catalyzed the initial step of their biosynthesis.To get further insights into the glycosylation phenotypes, a correlation analysis was performed between the gene expression of GTs and expression of glycosylation features presented in CRC cell lines. A dataset containing mRNA expression of relevant GTs involved in the biosynthesis of GSL glycans was obtained from a previous study . The corα2,3-sialylation showed positive correlation with ST3GAL6, which encodes the enzyme responsible for adding the α2,3-linked sialic acid. Additionally, ST6GAL1 encoding the GT, which adds a sialic acid in α2,6-linkage, showed a trend toward a positive correlation with α2,6-sialylation. FUT3 expression had significant positive correlations with fucosylation, α3,4-fucosylation, LewisA/X, sialyl-LewisA/X, LewisB/Y, and A LewisB/Y. FUT2 showed significant positive correlation with LewisB/Y antigen expression. Moreover, positive correlations were found between B3GNT5 and α3,4-fucosylation, fucosylation, LewisA/X, and LewisB/Y. GCNT2 encoding a GT responsible for synthesis of I-branched glycans showed a trend toward a positive correlation with I-branched glycans , although it did not meet the significance threshold. All significant r-values are indicated in p-values see Further correlations were found between several terminal glycosylation motifs and responsible transferases. For example, CDX1, ETS2, HNF1A, HNF4A, MECOM, and MYB showed higher expression in colon-like cell lines in comparison to undifferentiated cell lines, which had higher expression of MLLT10, MSX1, SIX4, ZNF286A, and ZNF286B lacto-series was found with ETS2 and MYB. Meanwhile, SIX4 showed high expression in undifferentiated cell lines and revealed a positive correlation with blood group H that was highly expressed in undifferentiated cell lines , which was also highly expressed in the undifferentiated cell line LOVO dimeric LewisA/X. Similarly, the sialyl-LewisA/X (GSL glycome) had positive correlations with the additional O-glycan features such as LewisA/X, sulfo-LewisA/X, and dimeric LewisA/X. All p- and r-values can be found in We recently performed an in-depth ll lines  which al-glycome  includedIn this study, the GSL head groups of 22 CRC cell lines were investigated using a PGC-nanoLC-MS/MS platform, showing a highly diverse GSL glycome. The correlations of specific glycosylation features were investigated with corresponding GTs involved in the biosynthesis of GSL glycans as well as with certain TFs. In addition, this study revealed the associations between TFs and GTs related to the GSL biosynthetic pathway.A/X, LewisB/Y antigens, and α1,3/4-fucosylation . Th. ThO-gly-glycans . Similar-glycans . The Cac glycans . Interes-glycome  (20). WhC tumors .α2,3-sialylation is suggested to play an essential role in metastasis with regards to EMT sialylation was found for the undifferentiated cell lines DLD-1 and HCT116 as well as colon-like cell line WiDr. The aberrant expression of sialylation may result from the dysregulation and/or altered activity of sialyltransferases. Compared to α2,3-sialylation, the α2,6-sialylation is critical for the malignant and metastatic progression, for which ST6GAL1 plays an important role  in colon-like cell lines were significantly correlated with expression of FUT3 and presented positive correlations with TFs that are upregulated in colon-like cells including CDX1  on N-glycans Lewising CDX1 . CDX1 asing CDX1 , 41. An -glycans , 41. Meascovered  and play of FUT3 , which and HNF4A . Furthercolonies . Taking Our work revealed a strikingly diverse expression of GSL glycans in 22 CRC cell lines and provides an insight into the alteration of glycosylation during the development of CRC. The specific GSL glycome signatures were found for each CRC cell line classification. Additionally, correlations were found between GSL glycans and their corresponding GTs, which are known to be involved in the GSL biosynthesis providing valuable information on the regulation of GSL glycan expression in CRC cell lines. Besides, we hypothesize that certain TFs might play a role in the expression of fucosylated GSL glycans through the regulation of FUT3 on the basis of the discovered correlations between GSL glycans and selected GT and TF genes as well as between GTs and certain TFs. However, mechanistic studies are needed to confirm the role of specific TFs such as CDX1 in the regulation of GSL glycans expression. In conclusion, our in-depth analysis of GSL glycans profiling in CRC cell lines provides an informative resource for future research on biomarkers for CRC and paves the way for further studies on the regulation of GSL glycans in CRC.via the following link: https://glycopost.glycosmos.org/preview/164822151461d35202311d4 with pin code: 5211.The raw mass spectrometric data files supporting the findings of this study are available in GlycoPOST in mzXML format, with the identifier GPST000239, accessible This article contains The authors declare no competing interests.


PARP inhibitors  are presently in clinical trials for advanced prostate cancer (PC). Resistance mechanisms are not fully understood in PC compared to ovarian and breast cancers. Our study aimed to identify new molecular mechanisms that affect acquired olaparib-resistance. We developed new resistant PC cell line models derived from original PC cell lines. We identified that DNA repair, autophagy, and the Rho-associated coiled-coil containing protein kinase 2 (ROCK2) could be potential targets to reverse the acquired olaparib-resistance.Background: Poly (ADP-ribose) polymerase inhibitors (PARPi) were initially deployed to target breast and ovarian tumors with mutations in DNA damage response genes. Recently, PARPi have been shown to be beneficial in the treatment of prostate cancer (PC) patients having exhausted conventional therapeutics. Despite demonstrating promising response rates, all patients treated with PARPi eventually develop resistance. However, PARPi resistance in PC is not well understood, and further studies are required to understand PARPi resistance in PC to propose strategies to circumvent resistance. Methods: Starting from well-established olaparib-sensitive PC cell lines , we derived olaparib-resistant (OR) PC cell lines and performed a microarray analysis. Results: The olaparib IC50 values of OR cell lines increased significantly as compared to the parental cell lines. Gene expression analyses revealed that different pathways, including DNA repair, cell cycle regulation and autophagy, were affected by acquired resistance. A total of 195 and 87 genes were significantly upregulated and downregulated, respectively, in all three OR cell lines compared to their parental counterparts. Among these genes, we selected BRCC3, ROCK2 and ATG2B for validation. We showed that ROCK2 expression, basal autophagy and homologous recombination (HR) efficiency were increased in all OR cell lines. Conclusions: Our study provides a new in vitro model to study PARPi resistance in PC and suggests new possible targets to reverse resistance and prolong the benefits of PARPi treatment. Advances in next-generation hormonal therapy (NHT), such as enzalutamide and abiraterone, or chemotherapy with docetaxel, have helped to improve overall survival in prostate cancer (PC). However, resistance to these therapies is common and associated with an increased risk of aggressive metastatic disease for which effective treatments are limited. Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) are a novel class of anticancer therapeutics that have become part of standard treatment for breast and ovarian cancers carrying BRCA mutations and represent a therapeutic option for treatment-refractory PC. PARPi treatment induces synthetic lethality in tumors carrying mutations in DNA damage response (DDR) genes such as BRCA1, BRCA2 or ATM, which are found to be somatically mutated in approximately 30% of metastatic PC patients . The PROPARPi target PARP1/2 enzymes, which are essential for DNA repair mechanisms, especially base excision repair (BER). The inhibition of BER results in the transformation of single-strand break to double-strand break, which is often not resolved in homologous recombination (HR)-deficient tumors leading to cell death. Innate resistance to PARPi is mainly associated with the absence of mutations that impair HR ,7. OtherHere, we developed three olaparib-resistant (OR) PC cell lines by exposing LNCaP, C4-2B and DU145 PC cells to increasing concentrations of olaparib over a period of six months. PC-OR cell lines harbored a higher resistant profile compared to the wild type (WT) PC cells without affecting their doubling time or morphology. Microarray experiments were performed and allowed a transcriptome analysis between olaparib-sensitive and -resistance cell lines. In addition to identifying pathways and genes potentially implicated in acquired resistance in PC, this study also provides useful models for future studies on acquired olaparib-resistance in PC.TM Live-Cell Imaging System .Human PC cell lines, LNCaP (RRID:CVCL_0395) and DU145 (RRID:CVCL_0105), were purchased from the American Type Culture Collection . The LNCaP C4-2B, denoted C4-2B in the study, (RRID:CVCL_4784) cell line was kindly gifted by Dr. Martin Gleave . All cell lines were verified by short tandem repeat (STR). Cell lines were cultured in RPMI 1640 medium  supplemented with 10% fetal bovine serum , 0.5 µg/mL amphotericin B , and 50 µg/mL gentamicin . All cells were grown in 5% CO2 at 37 °C. OR cells derived from LNCaP, C4-2B and DU145 were obtained by culturing parental cell lines in increasing concentrations (0.5 to 70 µM) of olaparib  for 6 months. Doubling time and cell morphology were determined using the IncuCytev/v) methanol and 0.5% (m/v) blue methylene . Colonies were counted under a stereomicroscope and reported as a percentage of the control. IC50 values were determined using Graph Pad Prism 9 software . Each experiment was performed in duplicate and repeated three times.LNCaP WT/-OR were seeded at 1000 cells/well while C4-2B WT/-OR and DU145 WT/-OR were seeded at 500 cells/well in 6-well plates. The next day medium was removed and replaced with RPMI complete medium containing olaparib (from 0.00125 to 40 µM). After 7 days of treatment, cells were fixed with methanol and stained with a solution of 50% . Data were analyzed using the Transcriptome Analysis Console (Affymetrix®). To determine enriched pathways, GSEA analysis was performed using the GSEA 4.2.1 software . The Gene Ontology Biological Process (GOBP) was used as the gene set database, and signal-to-noise ratio was selected for ranking gene metric parameters. Bubble charts were created using the R software and ggplot2 extension [RNA from WT and OR cell lines  was extracted as described previously . Gene exxtension . Volcanoxtension . Downregxtension .®, Life Technologies Inc., Carslbad, CA, USA, 4472908). Sequence primers for target genes, ATG2B, BRCC3, ROCK2 and Actin are available in ß-actin gene expression.Total RNA was extracted from PC WT and OR cell lines using the RNeasy kit . A total of 1 µg of RNA was used for the reverse transcription using the QuantiTect Reverse Transcription Kit . A total of 1 µL of diluted (1:10) reverse-transcribed products was used for Q-PCR using sequence specific primers (400 nM) and the SYBR Select Master Mix  and transferred in Trans-Blot Turbo Mini 0.2 μm nitrocellulose membranes . Membranes were blocked with 5% milk in PBS-Tween for 1 h and incubated overnight at 4 °C with primary antibody in BSA 1%. Membranes were next incubated with secondary antibody for 1 h, and signal was detected using the ChemiDoc MP Imaging System . ß-Actin was used as a loading protein control and each experiment was repeated three times. Image J [Proteins were extracted from cell lines using mammalian protein extraction reagent  containing a protease and phosphatase inhibitor cocktail . Protein concentration was determined by Bradford assay . Twenty micrograms of protein were separated in precast 4–15% gradient Tris-glycine SDS-polyacrylamide gels (Mini-PROTEANß-Actin (AC14) , PARP1 , PAR/pADR , goat anti-mouse , goat anti-rabbit  and rabbit anti-goat .® anti-fade reagent with DAPI  was used for mounted coverslips onto slides.Autophagic flux was measured as described previously . BrieflyHR and NHEJ activities were measured using the reporter plasmids pcDNA-GFP HR and pCDNA-GFP NHEJ, a gift from Dr. Jean-Yves Masson , as described previously . Brieflyt-test, which was justified appropriately for every experimental design. The data were normally distributed, and the variance between groups that were compared was similar. A p value of less than 0.05 was considered statistically significant. FDR q-value was automatically calculated during the GSEA analysis.Statistical analyses were performed using Graph Pad Prism 9  using the two-tail Student p = 0.05), 28.9-fold change for C4-2B-OR (p = 0.024), and 3.78-fold change for DU145-OR (p = 0.0080)  . Doublin 0.0080) c,d. Sinc 0.0080) . OR cellFDR) q-value = 0.0009), RNA splicing  and DNA repair , all of which had a normalized enrichment score (NES) above 2.0 (IFT172/80) were upregulated in OR cell lines , “DNA repair” and “recombinational repair”  were associated with a higher NES. “Non-homologous end-joining” (NHEJ) and “single strand DNA break repair” pathways such as base or nucleotide excision repair (BER/NER) were not affected by the acquisition of olaparib resistance. GSEA analysis also revealed that BRCA1/BRCA2-Containing Complex Subunit 3 (BRCC3) and Werner syndrome helicase (WRN) were upregulated in all three OR cell lines compared to WT , with an NES = 1.86. This pathway includes genes encoding for protein that increase levels of autophagy such as beclin-1 (BECN1), ATG2B, or components of the mTOR complex. Among these genes, G protein subunit alpha i3 (GNAI3) and TSC complex subunit 1 (TSC1) were upregulated in OR PC cell lines  ≥ 1.3 and a fold-change (log2) ≤ −0.6 and ≥ +0.6 (ROCK2) or Cyclin-dependent kinase 12 (CDK12), and genes regulating RNA processing, such as mago homolog exon junction complex subunit (MAGOH) or SR-related CTD associated factor 11 (SCAF1) were the most represented among the 195 upregulated genes   = 2.65). Other genes, such as GNAI3 or TSC1, found in the GSEA gene set of “positive regulation of macroautophagy” were not significantly affected. Genes involved in DNA repair mechanisms, such as RAD54B  = 2.54), were also significantly upregulated in OR cell lines. We classified genes for the “DNA repair” pathway obtained from our GSEA analysis based on the order of enrichment  = 1.87), WRN  = 2.09), USP45  = 1.52) and the xeroderma pigmentosum complementation group A  = 1.32) were significantly upregulated. No pathways seemed significantly enriched in the 87 downregulated genes. Genes that were significantly downregulated included GRAM domain containing 4  = 3.82) followed by H2A clustered histone 17  = 3.03) and Keratin associated protein 21–1  = 2.66). Overall, DNA repair mechanisms, autophagy but also apoptosis, cell cycle and mRNA processing may be involved in the acquisition of olaparib resistance in PC.To further analyze the genes that were associated with olaparib resistance, the list of differentially expressed genes of the three OR cell lines was compared and revealed that all three shared 222 genes that were upregulated and 178 genes that were downregulated a. These d ≥ +0.6 b. A totad ≥ +0.6 . The topd ≥ +0.6 . Fold ened genes . ATG2B frichment c. Not alATG2B, BRCC3 and ROCK2, respectively) [p = 0.04), unlike protein expression, where no significant variation was observed (p = 0.05) and DU145-OR  for RNA and only in DU145-OR  for protein. Interestingly, RNA and protein expression of ROCK2 were upregulated in all PC OR lines .We next decided to select three genes from our previous analyses depending on the literature and their role in autophagy, DNA repair and cell cycle regulation (ctively) ,22,23. Aobserved . BRCC3 wTM Autophagy Tandem Sensor construct containing the LC3-II protein tagged with red and green fluorescence protein (RFP/GFP) to measure the basal level of autophagy in our cell lines (p = 0.033) and C4-2B-OR  compared to respective PC WT cell lines . NHEJ was also increased but in DU145-OR only . Therefore, PC-OR cell lines had a higher basal level of autophagy and HR efficiency compared to their WT counterparts.To determine functional changes associated with PC OR, we measured autophagy and HR/NHEJ efficiency in PC-WT/-OR cell lines. We used the Premoll lines a,b. Thisll lines b. No sigll lines c. All thMechanisms of PARPi antitumor activity and resistance are well described for breast and ovarian cancers. These inhibitors are also promising therapeutic agents to prolong life and ameliorate the response to commonly used therapies for PC patients. However, PARPi resistance is also a potential and expected risk that is not well understood in PC. In this study, we developed olaparib-resistant PC cell models derived from PC cell lines LNCaP, C4-2B and DU145 to identify genes and mechanisms involved in acquired resistance. Our analysis showed that several pathways, such as DNA repair or autophagy, may regulate olaparib resistance.BRCC3, WRN, USP45 and XPA, were also found in our volcano plot analyses. BRCC3 and WRN are known to play a role in HR, USP45 and XPA in NER. Interestingly, these genes are linked to resistance to radio- and chemotherapies for cancer. Studies have shown that their expression is increased after treatment, inducing an increase in DNA repair and resulting in resistance to these treatments [Our GSEA overview analysis showed that “double strand break repair” and “recombinational repair” were significantly enriched with an NES over 2.0. Genes from these pathways, such as eatments ,26,27. TMAGOH, SCAF11 and PLRG1) have been shown to play a role in tumorigenesis and cancer aggressiveness in glioma, liver, and gastric cancers [BRCA1 gene [Interestingly, some upregulated genes found in RNA processing  [Autophagy is a complex pathway, which can be pro- or anti-tumorigenic depending on the cancer type . It is nncreased . We did ncreased a,b. Someon (ALR) ,45. Thison (ALR) . Based oROCK2 plays an essential role in the G1/S phase transition, and studies have shown that its depletion or inhibition leads to both a cell cycle arrest and a senescent phenotype associated with a modification of the composition of the senescence-associated secretory phenotype (SASP) [GRAMD4 and sirtuin-3 (SIRT3), with decreased gene expression suggesting a possible negative regulation of apoptosis in the PC OR cell lines.Many genes upregulated in the OR cell lines are important in cell cycle regulation d. Among e (SASP) ,47. Olape (SASP) ,49. Incre (SASP) . Interese (SASP) . It is ae (SASP) . We idenOur study is limited to the measurement of variations in gene expression between PC WT and PC OR cell lines and gives a first overview of pathways and genes possibly implicated in acquired resistance. Further experiments on ROCK2, either addressing the effects of depletion or overexpression, would need to be conducted to conclude if this protein is important for the resistance seen in PC OR cell lines. Moreover, confirmatory experiments involving the inhibition of autophagy or HR are also necessary to determine their effect on the acquired resistance to olaparib. As in vitro experiments have limitations, it will be important to verify the status of specific pathways and genes directly in tissues from patients resistant to olaparib to understand their clinical relevance.ROCK2 gene, pathways such as autophagy or HR may present an opportunity to reverse acquired resistance and reinforce the effect of PARPi in prostate cancer.To summarize, our study provides a set of PC cell lines with varying degrees of olaparib resistance to study acquired PARPi resistance in PC. Our microarray analysis showed that three major pathways, DNA repair, particularly double-strand DNA breaks, autophagy and senescence, were potentially upregulated in OR cell lines. We also showed by functional assays alterations in DNA repair and autophagy in OR cell lines. Targeting the


CDH1 and EPCAM in A2780cis compared to the cisplatin-sensitive parental cells. These changes were concomitant with gene expression down-regulation. DNA hypomethylation associated with transcription up-regulation of the mesenchymal marker TWIST2 was observed in the resistant cells. Azacytidine treatment confirmed DNA methylation role in regulating gene expression of CDH1, EPCAM and TWIST2 genes. A2780cis cell line undergoes EMT like changes, and EMT genes are regulated by DNA methylation. To that end, a better understanding of the molecular alterations that correlate with chemoresistance may lead to therapeutic benefits such as chemosensitivity restoration.Ovarian cancer is one of the lethal gynecologic cancers. Chemoresistance is an essential reason for treatment failure and high mortality. Emerging evidence connects epithelial-mesenchymal transition (EMT) like changes and acquisition of chemoresistance in cancers. Including EMT, DNA methylation influences cellular processes. Here, EMT-like changes were investigated in cisplatin-resistant A2780 ovarian cancer cells (A2780cis), wherein role of DNA methylation in some EMT genes regulations was studied. Cell viability assay was carried out to test the sensitivity of A2780, and A2780cis human cancer cell lines to cisplatin. Differential mRNA expression of EMT markers using qPCR was conducted to investigate EMT like changes. CpG methylation role in gene expression regulation was investigated by 5-azacytidine (5-aza) treatment. DNA methylation changes in EMT genes were identified using Methylscreen assay between A2780 and A2780cis cells. In order to evaluate if DNA methylation changes are causally underlying EMT, treatment with 5-aza followed by Cisplatin was done on A2780cis cells. Accordingly, morphological changes were studied under the microscope, whereas EMT marker’s gene expression changes were investigated using qPCR. In this respect, A2780cis cell line has maintained its cisplatin tolerance ability and exhibits phenotypic changes congruent with EMT. Methylscreen assay and qPCR study have revealed DNA hypermethylation in promoters of epithelial adhesion molecules  In 2021, 2% of all new cancer cases was estimated to be ovarian cancers2. Patients with ovarian cancer have a poor prognosis and high mortality rate due to its deceptive onset and lack of early diagnosis3. The low survival rate of ovarian cancer is due to metastasis and acquisition of chemoresistance4. Cisplatin (cis-diamminedichloroplatinum [II]) is a platinum compound widely used in solid tumors treatment many (including ovarian cancer)6. Cisplatin induces DNA lesions and activates several signaling pathways such as DNA repair mechanisms7. When DNA damage fails to be repaired, cells undergo apoptosis8. Cisplatin resistance is the main obstacle that limits the treatment effectiveness9. The mechanisms that underlie the platinum drug resistance are multifactorial. Several cellular processes are responsible for the resistant phenotype, such as drug influx or efflux alterations, DNA repair, cell cycle, and apoptosis11.Ovarian cancer is one of the deadliest gynecologic cancers17. EMT is a cellular procedure where epithelial cells lose their cell–cell adhesion and cell polarity and gain a metastatic capability18. The molecular signatures of EMT are the loss of epithelial cell markers CDH1 and the up-regulation of mesenchymal markers, which comprise EMT transcription factors SNAIL and TWIST20. Increased findings implicate EMT in promoting aggressiveness in ovarian cancer. Reports observed activation of EMT during the progression of ovarian cancer23. Expression signatures of enriched EMT genes have been observed and used to specify poor prognosis groups of ovarian cancer24. EMT induced an invasive phenotype in ovarian cancer cells through downregulation of CDH1 and up-regulation of CDH2 in Hey and OVCA433 cells25. EMT’s transcription factor SNAI1 induced EMT in ovarian cancer cells (SKOV3 cells) and enhanced the invasiveness26. Furthermore, EMT like changes have been reported in various chemoresistant cancers such as gastric cancer27, non-small cell lung cancer29, nasopharyngeal carcinoma cells30, breast cancer cells32, and ovarian cancer cells17. Many reports showed that ovarian cancer cells undergoing chronic exposure to cisplatin or paclitaxel generate cells that exhibit an EMT phenotype and molecular feature33. Additionally, multiple epigenetic events, such as DNA methylation, histone modifications, and non-coding RNA could contribute to the acquisition of chemoresistance36.Recent evidence suggests that epithelial-mesenchymal transition (EMT) processes might play a role in ovarian cancer progression and chemoresistance development37. Commonly, gene promoter hypermethylation is associated with reduced expression, while hypomethylation results in increased gene expression39. DNA methylation changes at CpG islands associated with transcriptional silencing have been described in cisplatin-resistant cancer cell lines42. For example, DNA methylation of several genes, including  was linked to ovarian cancer initiation and chemotherapy resistance44. A study in ovarian and breast cancer cell lines with doxorubicin tolerance described methylation changes in genes contributing to chemoresistance and identified hyper-methylation of CDH1, BRCA1, SULF2 and DNAJC15 genes, in addition to hypo-methylation of APC, ABCB1 and HIC1 genes35. Based on these considerations, the need to study the linkage between EMT and DNA methylation regulation has become a key to understanding the chemoresistant phenotype.DNA methylation is the most well-known epigenetic mechanism that occurs at CpG islands influencing cellular processes by regulating gene expressionThis study aims to determine if there are any significant differences in the expression of EMT gene regulators between two commercially-available ovarian cancer cell lines (A2780 and A2780cis), which differ in the degree of chemoresistance. This work also focuses on CpG methylation changes in these EMT genes.2, a circularity of 0.81 and an aspect ratio of 1.19. However, A2780cis cells were larger and more elongated with a cell area of 11,600 μm2 with a circularity of 0.43 and an aspect ratio of 4.26 , MRP1 (MDR-related protein 2) and MRP2 (MDR-related protein 2) using qRT-PCR might help understanding if there is a correlation between cisplatin tolerance and expression of ABC transporter genes in A2780cis cell line.ABC transporter genes are responsible for chemotherapy cellular response. Therefore, assessing the mRNA expression level of MDR1 transcript level (5.8 fold) in A2780cis cell line compared to the parental cell line. On the other hand, MRP1 and MRP2 expression did not significantly change in A2780 and A2780cis cell lines.Figure CDH1, and EPCAM, was significantly reduced by 0.02 and 0.013 fold, respectively in the A2780cis cells compared with A2780 cells. The expression of mesenchymal markers, SNAIL, and TWIST2, were higher by 4.9 and 20.3 fold, respectively in the A2780cis cells compared with the parental cell line. Still, no significant expression changes in CDH2 nor VIM genes could be observed . However, no significant changes were observed in CDH2 nor in VIM expression in MDA-MB-231 in comparison with MCF7 cells  in T98G, the more resistant cell line, whereas SNAIL and TWIST2  were up-regulated  determines assay sensitivity and it was represented by ΔCt between the Rsd and R0 reactions and it ranged from 3.2 to 13.6.CDH1 assay revealed a 13.47% hypermethylation in the densely methylated portion after the acquisition of cisplatin resistance. The methylation of the region from (− 306 to − 82 bp), which contains  restriction sites, increased from 24.76 to 39.11% in A2780 and A2780cis, respectively  region is unmethylated in A2780 cell line and no significant differences in methylation between A2780cis and its parental cell line  that contains  restriction sites was 3.37% hypermethylated, 88.98% intermediate methylated and 7.65% unmethylated. In A2780cis, In A2780cis, the fraction of unmethylated DNA was 21.76%  was implicated as a core mechanism mediating drug resistance53. EMT is a biological mechanism characterized by loss of cell adhesion, as well as loss of cell polarity and gain motility18. EMT results in changes in cell morphology associated with alterations in epithelial and mesenchymal markers expression54. EMT like phenotype has been reported in chemoresistant variant cell lines generated upon multiple rounds of chemotherapy treatment such as; cisplatin resistant ovarian cancer cells 15, A2780/PTX, NOS-PR, TAOV-PR, and SKOV-PR paclitaxel resistant ovarian cancer cells17. Similarly, chemotherapeutic resistance promotes EMT like changes in other cancer cell lines, including non-small cell lung cancer29, gastric cancer27 nasopharyngeal carcinoma cells30, and breast cancer cells32. Partial EMT has been proposed as an important mechanism in cancer metastasis and chemoresistance57. Cells that have partial EMT phenotype display properties of both epithelial and mesenchymal cells, indicating that EMT is being induced but not completed58. Galle et al. discussed the role of partial EMT in resistance acquisition, they showed that 3 ovarian cell lines  underwent partial EMT when they acquired chemoresistance42. In this present study, it was proven that the resistant variant A2780cis cell line underwent partial EMT. This was confirmed by observing a morphological change from small circular shaped to elongated shaped cells and changes in molecular markers of EMT; significant reduction in CDH1 and EPCAM and upregulation of the transcription factors, SNAIL and TWIST2. Additionally, no significant expression changes were observed in the studied mesenchymal markers such CDH2, VIM, ZEB1, ZEB2, SLUG, TWIST1 that should be upregulated in completed EMT.In this study, it was demonstrated that the A2780cis cell line still has the tolerance capacity to cisplatin and its resistance is accompanied by an increase in the expression of 64. From this observation, The EMT like changes detected in A2780cis were compared with changes in breast and glioma cell line models known as resistant cancer cells47. MDA-MB-231 and T98G cancer cells were used as reference cell lines models to figure if the acquired changes observed in A2780cis are common in other cancer cells, resistant to therapy. The same molecular changes consistent with morphological alteration in cisplatin resistant MDA-MB-231 cells were detected compared with the sensitive MCF7 cells. The same transcripts changes in CDH1, EPCAM, SNAIL and TWIST2 genes were also detected in chemoresistant T98G cell line in comparison with the less cisplatin tolerance cells U-87-MG. This indicates the importance of CDH1, EPCAM, SNAIL and TWIST2 genes in EMT mechanism associated with cisplatin resistance. Similarly, no significant expression changes were observed in the studied mesenchymal markers in the resistant cancer cells such CDH2, VIM, ZEB1, ZEB2, SLUG, TWIST1 that which are upregulated in completed EMT. These data indicate that these cell lines have a partial EMT state.Multiple studies have shown that naïve cancer cell lines and clinical tumor samples can be divided according to their mesenchymal/epithelial phenotype, this sorting could determine the sensitivity to chemotherapy in various cancers, including ovarian, breast, and lung cancersCDH1 is a Ca2+ dependent adhesion molecule that binds by its extracellular domain to CDH1 on the adjacent cell creating a bridge between the cell’s cytoskeletons65. Many studies revealed that CDH1 down-regulation may be associated with cancer cells resistant to chemotherapy that can be attributed to the EMT mechanism activation66. Acquisition of paclitaxel chemoresistant induces EMT phenotypic changes and CDH1 down-regulation in NOS-PR and A2780/PTX ovarian cancer cell line17. EPCAM is an epithelial cell surface transmembrane glycoprotein that mediates homophilic cell–cell adhesion68. In ovarian cancer cells, EPCAM upregulation is connected to a more favorable prognosis and more effective platinum-based therapy69. Galle et al., found EPCAM expression is down-regulated in addition to CDH1 in resistant variant cancer cells due to EMT process activation42. EMT transcription factors such as SNAIL are considered as direct repressors of CDH1 as they bind to E-boxes existing on the CDH1 promoter72. Hojo et al., observed that ovarian cancer cell lines OVCAR8 and COV318 with high Snail/CDH1 showed more motile and cisplatin resistant phenotypes than OVSAHO and Kuramochi cell lines that have low SNAIL/CDH159. TWIST2 is considered as a direct repressor of CDH1, it can bind directly on E-boxes existing on the CDH1 promoter to suppress its expression and it can repress CDH1 expression indirectly through activation of other signaling pathways73. Studies show that TWIST2 expression is a prognostic indicator for overall survival and disease-free survival and its overexpression correlates with poor prognosis and is associated with CDH1 down-regulation giving mesenchymal cell phenotype on ovarian cancer tumors75. Wang et al., demonstrate that TWIST2 plays a critical role in the cisplatin resistance of ovarian cancer. They found that TWIST2 expression was up-regulated in resistant variant C13K ovarian cancer cell line compared to the cisplatin sensitive ovarian cancer cell line OV200876.77. Upon initiation of EMT, DNA methylation of the genome selectively undergoes CpG site methylation changes, which regulate transcription of EMT-related genes79. In the current study, the investigation of methylated CpG islands role in the modulation of gene expression of EMT regulated genes was done in A2780 cancer cell lines. DNA methyltransferase inhibitor 5-aza induces gene expression of CDH1, EPCAM, SNAIL, and TWIST2, which indicates that DNA methylation may regulate these genes. Chang et al., used gene expression profiling after cancer cells treatment with 5-azadeoxycytidine, they identified genes that were dysregulated in cisplatin resistant cancer cells and reactivated by the DNA methyltransferase inhibitor80. Here, it found that epithelial gene promoters CDH1 and EPCAM became significantly more methylated in A2780cis compared to the parental cell line. These promoter methylation changes correlate with significant gene expression down-regulation. Boettcher et al., profiled DNA methylation of 800 selected CpG islands and identified hypermethylation in CDH1 CpG islands in breast and ovarian doxorubicin resistance cancer cells35. EPCAM overexpression has been linked to promoter hypomethylation EPCAM-negative cells treated with a DNA methyltransferase inhibitor prompted EPCAM expression in various cancers types including ovarian cancer83. A recent study reported consistent methylation changes across multiple cancer cell lines that differed in chemoresistace. Specifically, hypermethylation of epithelial marker genes such as CDH1 and EPCAM promoters and hypomethylation of mesenchymal marker genes such as SNAIL in resistant versus parental cell lines42. Analysis of SNAIL promoter region predicted a CpG island surrounding the TSS, the DNA methylation status in the genomic region (− 688 to − 165 bp) was examined, no methylation was observed in CpGs located in this region of SNAIL promoter in A2780 and its resistant variant cells, although there were changes in SNIAL expression between the two cell lines and after azacytidine treatment. Literature has described changes in the histone modifications regulating SNAIL gene expression84. A single study described changes in the methylation of CpG island located in the first intron after 1000 pb from TSS in EMT models of cancer cells85. The differential methylation in SNAIL gene could be possibly identified in the intron region in A2780 cell line. CpGs island of mesenchymal transcription factor TWIST2 promoter showed DNA hypomethylation in A2780cis compared to the parental cells, this hypomethylation coincides with gene expression up regulation due to EMT activation. TWIST2 methylation changes were observed in various cancers such as colorectal cancers, prostate cancer, and chronic lymphocytic leukemia, this epigenetic event might be the underlying mechanism for TWIST2 transcriptional regulating87.DNA methylation is one of the best described epigenetic mechanisms that regulate gene expression. Aberrant DNA methylation is observed in CpG dinucleotides clustered around the TSS of genes, called CpG islands, leading to gene expression dysregulationCDH1, SNAIL) which indicates that the treatment of demethylating agent under the pressure of cisplatin cause a partial reversing of EMT with cell shape changes. This causality of methylation changes, EMT and cell shape changes caused by acquired chemoresistance to cancer therapy were noted in Galle et al. study by exposing treatment-resistant cell lines during cell divisions to the demethylating agent 5-aza-2′-deoxycytidine, and observing the reversing of EMT42.To test whether DNA methylation changes are causally underlying EMT induced by acquired chemoresistance, A2780cis cells were exposed to noncytotoxic doses of the demethylating agent 5-aza under the pressure of cisplatin. The changes in cell shape of A2780cis cells towards epithelial shape was observed. This treatment caused a reversal in the expression pattern of two EMT markers . Morphological phenotypes of cell lines were assessed when the cell density was up to 70% confluence using Eclipse TS100 inverted light microscope . The Shape descriptors of the cells were assessed using Fiji image analysis software88, three parameters were investigated: cell area for cell size, circularity which is defined as 4*(area/perimeter2) and aspect ratio which is defined as the ratio of the major to the minor axis of a fitted ellipse as a measure of cell shape. Particles that have perfect circular shapes have circularity and aspect ratios equal to 1. Values of aspect ratios that are higher than 1 are associated with elongation and lower circularity values are associated with cellular bumps.Human ovarian cancer cell lines A2780  and A2780cis (the resistant variant), human breast cancer cell lines MCF7 and MDA-MB-231, and human glioblastoma cancer cell lines U-87 MG and T98G were purchased from European Collection of Authenticated Cell Cultures (England). A2780 and A2780cis cell lines were grown in complete RPMI-1640 medium containing 10% FBS, 2 mM glutamine, 0.1 mg/ml each of penicillin and streptomycin. To maintain A2780cis resistance to cisplatin, 1 μM of cisplatin  was added to the media every 3 passages. MCF7 and MDA-MB-231 cell lines were grown in EMEM containing 10% FBS, 2 mM glutamine, 1% non-essential amino acids (NEAA), 0.1 mg/ml each of penicillin and streptomycin. U-87 MG and T98G cell lines were cultured in EMEM, 2 mM Glutamine, 10% FBS and 1% Sodium Pyruvate (NaP). All cell cultures were kept in a 5% (v/v) CO4 cells/well) for MTT assay and allowed to attach O/N. Different concentrations of cisplatin were used to treat cells for 24 h. MTT solution  was added to each well and incubated for 4 h. at 37 °C. Then absorbance values were measured at 550 nm using Multiskan Ascent absorbance plate reader . Cell viability was determined as follows:Cells were plated into 96-well-plates , and ABC transporters (ATP-binding cassette transporters) genes; , quantitative real-time PCR was performed using Maxima™ SYBR™ Green/ROX 2 × qPCR Master Mix  for 40 amplification cycles using StepOne™ Real-Time PCR System . Relative transcript fold changes were calculated using the ΔΔCt method with GAPDH as a reference gene. All reactions were run in triplicate. Primers sequences are detailed in Tables Total RNA was extracted from A2780, A2780cis, MCF7, MDA-MB-231, U-87 MG, T98G cell lines, from A2780 treated with 5-azacytidine (5-aza), and from A2780cis treated with cisplatin and 5-aza using the RNeasy Mini kit . First-strand cDNA synthesis was carried out from 3 μg total RNAs were reverse transcribed to cDNAs using M-MLV reverse transcriptase  for 2 h. at 37 °C. To calculate the relative expression of EMT regulating genes; . The culture medium was removed every 24 h. and replaced with a fresh medium containing 0.1 μM 5-aza. Treated and mock treated cells were collected after 5 treatment days and total RNAs were extracted as described above.Genomic DNAs from A2780 and A2780cis cells were extracted using the QIAamp DNA Mini kit  according to the manufacturer’s instructions. Isolated DNAs were quantified using NanoVue Plus .89. Genomic DNA of A2780 and A2780cis cells were divided into four parts and treated with different digestions: (1) Rs: two methylation-sensitive enzymes MSRE (HhaI + AciI) or (HpaII + AciI) depending on the frequency of their restriction sites within the studied fragments, which are cutting only unmethylated DNA, (2) Rd: one methylation-dependent restriction enzyme McrBC (MDRE), which is cutting only methylated DNA or (3) Rsd: both MSRE and MDRE enzymes , and (4) R0: neither MSRE nor MDRE (mock control). illustrations of the studied section of CDH1, EPCAM, SNAIL, and TWIST2 genes as shown in Supplementary  from European Bioinformatics Institute (EMBL-EBI) to identify the CpG sites associated with the proximal promoter and transcription start site (TSS) for four genes. Sets of locus-specific PCR primers were designed to amplify gDNA at proximal CpG located within 1000 bp (±) of the transcription start site for each gene. Primers sequences, genomic loci, numbers of CpGs nucleotides and number of restriction sites contained in amplified amplicons are listed in Table Quantitative PCR-based methylation analysis (Methylscreen assay) was performed to analyze DNA methylation of genes that have differential expression between A2780 and the resistant variant cells and that expression increased after 5-aza treatment. Methylscreen assay is based on combined restriction digestion of DNA with methylation sensitive and methylation dependent restriction enzymes, MSRE and MDRE respectivelyementary . Each 50The Ct values from R0, Rs, Rd and Rsd, reactions were used to calculate the initial amount of DNA in each digest before PCR as follows:The DNA methylation (%) was calculated as follows:90.Hypermethylated DNA fraction (HM) = Rs/(R0 − Rsd) × 100; unmethylated DNA fraction (UM) = Rd/(R0 − Rsd) × 100; intermediately methylated DNA fraction (IM) = 1-HM-UM. If or ΔCt(Rd − R0) or ΔCt(Rs − R0) < 1.0, The DNA methylation (%) was calculate as following: HM = 1-UM, UM = 1-HMCDH1, EPCAM, SNAIL1, TWIST2 were studied by qPCR.In order to find out if DNA methylation changes are causally underlain EMT, A2780cis cells were cultured and treated with 0.2 μM 5-Azacytidine . The culture medium was removed every 24 h and replaced with a fresh medium containing 0.2 μM of 5-aza for 5 days, subsequently, cells were treated with 10 μg/ml cisplatin. Treated and mock treated cells were photographed and collected for RNA extraction and cDNA synthesis as described above. The relative expression changes of EMT regulating genes P ≤ 0.05, ** = P ≤ 0.01, *** = P ≤ 0.001.GraphPad Prism Version 7.0  was used to generate graphical figures and to perform statistical analysis. In MTT assay, non liner regression was used in the statistical study. For MethylScreen assay and qPCR study one-tailed student t test was used in the statistical study. Data are expressed as the mean ± SD. Statistical significance was defined as * = Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.