Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines . Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line  and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704 Cancer is a major cause of death in developed countries and increasingly also in developing countries. Based on the GLOBOCAN 2018 estimates of cancer incidence and mortality by the International Agency for Research on Cancer, an estimated 18.1 million cancer cases (17.0 million excluding nonmelanoma skin cancer) were newly diagnosed in 2018, and 9.6 million individuals (9.5 million excluding nonmelanoma skin cancer) died from cancer in 2018. The cancer incidence increases with age. Hence, the number of cancer cases is anticipated to rise further as a consequence of a rising lifespan. Thus, research is needed to improve cancer therapies.2. However, many cell lines are misidentified, i.e. they have been swapped or contaminated with other cell lines, and as a result, researchers may work with cells that are not what they think they are. This has been a problem since the work with cancer cell lines started and continues to be a problem today. Short Tandem Repeat (STR) analysis has been available as a reliable method to identify the genetic origin of a cancer cell line for a while3. Short Tandem repeats are accordion-like stretches of DNA containing core repeat units of between two and seven nucleotides in length that are tandemly repeated from approximately a half dozen to several dozen times4. By comparing this DNA, scientists have successfully managed to determine samples coming from the same genetic origin, and have been widely applied in forensic studies. Although STR test have been available for a while, 15–20% of the currently used cell lines have been estimated to be misidentified5. For example, in a study based on the analysis of 482 different human tumor cell lines, up to 96 cell lines were misidentified6. Moreover, another study6 found that STR profiling alone is insufficient to exclude inter-species cross-contamination of human cell lines, and the study argued for the need of additional testing and authentication methods. Hence, additional authentication methods that can be easily applied in the laboratory are highly desirable.Cancer cell lines are cancer cells that have been isolated from human or animal cancers and that can be grown continuously as cell cultures in a laboratory. They are important and probably the most commonly used model system for both studying cancer biology and the discovery of novel anti-cancer drugs11. We have established the Resistant Cancer Cell Line (RCCL) collection, the worldwide largest collection of drug-adapted cancer cell lines, currently consisting of > 1,500 models of acquired resistance12. Automated image recognition approaches may be an additional way to authenticate cells that may complement STR analysis and enable the differentiation between isogenic cell lines13.Additionally, methods are needed that reliably discriminate between isogenic cell lines, such as clonal sublines and drug-adapted cancer cell lines, since this is not achievable using STR analysis. Drug-adapted cancer cell lines are used as models of acquired drug resistance and have been used to identify many relevant drug resistance mechanisms14 used decision tree algorithms to perform classification of cancer cells into benign and malignant with a precision of 97.7%. In another study, Smita et al.15, found that SVM performed best out of a range of conventional machine learning models at classifying cancer images. The use of conventional machine learning to perform classification can be complex, relying on manual feature extraction (feature engineering) from images, which can be challenging13. The performance of most conventional machine learning methods are highly dependent on feature engineering16. Therefore, some researchers have turned their attention to deep learning approaches, which can extract and organize the discriminative features from data without the need of manual feature engineering. Deep learning approaches have achieved remarkable performance in tasks such as speech recognition17, natural language processing18, and image classification19. Deep learning has begun to be used for image recognition in cancer, for example, Angel et al.20 and Jinhua et al.21 both used convolutional Neural Networks (CNN) for detecting the presence of invasive tumour cells in breast cancer tissue samples.Conventional machine learning models such as Support Vector Machines (SVM), Artificial Neural networks (ANN), Naïve Bayes and Decision Trees, have been used to perform cancer cell classification based on microscopy images. These models have been able to detect similarities/differences between cancer cell images, which can be missed by the human eye. Nusaibah et al.22. When learning on a limited amount of data, data augmentation and transfer learning are two methods that can be used to improve the performance of deep learning models. Data augmentation is a technique which is used to artificially increase the diversity and size of the training sample. This is done by performing operations like shift, zoom, flip and rotation on the available training data. Zeshan at.al23 reported how data augmentation contributes to the performance of deep learning models in classification of medical images. Authors further analysed which data augmentation techniques retain the properties of the original image as it contributes to the model performance. Another study has surveyed the significance of different data augmentation techniques in fields which is difficult to get enough training data sets24.However, the performance of deep learning largely depends on the amount of training data available. Training a deep learning model is computationally expensive and time-consuming25, VGG26, MobileNet27, and InceptionResNet V228. Most of the existing pre-trained models for image classification are trained on the Large-Scale Visual Recognition Challenge (ILSVRC) ImageNet dataset29, a general object recognition dataset which includes 1000 classes30. Transfer learning technique was used to classify lung cancer types in a study by Nicolas et al.31 with a high level of accuracy (0.97 AUC- Area under the Receiver Operating Characteristic Curve). Using Inception V3, a pre-trained model on ImageNet, it was also possible to predict the ten most mutated genes in lung adenocarcinoma (LUAD)32, using the same model31. Sung-Jin et al.33 used VGG19, another pre-trained model on ImageNet, to perform classification in molecular diagnostics, a collection of techniques used to analyse biological markers in the genome and proteome. Mark et al.22 explored several deep learning architectures to detect glaucomatous optic neuropathy in fundus photography with Transfer learning, outperforming all other methods. J. Huang et al.34 proposed InceptionResNet V2 and MobileNet as a feature extractor together with Faster Regions Convolutional Neural network (R-CNN) as a meta-architecture for the image identification and detection. InceptionResNet V2 has provided a good accuracy at the cost of more computational time, whereas MobileNet’s accuracy is comparable to the VGG model but has 1/30 of the computational cost. Google’s Inception V3, a deep learning model pre-trained on ImageNet, was used to perform the classification of histopathological images of breast cancer in a study by Jongwon et al.35.Transfer learning involves the use of pre-trained models designed to perform a particular task in one context, their top layers are removed and new layers attached, to be trained to perform a new task, in a different context. Since only the last few layers of the model will be retrained, computation costs and training time are reduced and only a smaller amount of data is needed. There are several pre-trained models available, such as GoogLeNet36 trained a CNN to classify between normal and breast cancer cells using actin-labelled fluorescence microscopy images based on the knowledge that the actin cytoskeletons differ between normal tissue and tumour tissue, while others have used inverted phase microscopy images to classify breast cancer cell lines from normal breast cell lines37. Most recently, Meng et al.38 used a CNN trained on cell images of single cells from ultra-high-throughput microscopy  to classify four different cell types with > 99% accuracy. However, they used two epithelial breast cancer cell lines , one acute myeloid leukaemia cell line (THP-1), and peripheral blood mononuclear cells (PBMCs).In the laboratory environment, microscopic images are the most digitised form of images which can be produced from different samples under investigation. Deep learning and its techniques have been playing an important role in computerised analysis of these microscopic images. For example, Oei et.al28. Inception incorporates each convolution in parallel and concatenates them at the end as proposed by C. Szegedy et al.27. The key benefit of the architecture is that, it increases the number of units at each stage without increasing the computational complexity much28. Each inception unit has several non-linear convolution modules with various resolution which makes it more applicable to tasks that need to process data with multiple resolutions like medical images33. As Inception networks tends to be very deep, and residual connections adopted from ResNet perform good in training deep networks, the filter concatenation stage of the Inception networks can be replaced by residual connections28 and give rise to InceptionResNet which takes advantage of both Inception and Residual architectures. Another model, MobileNet, deploys Depthwise Separable Convolution which applies a single filter to each input channel instead of standard convolution29. In MobileNet, a standard convolution is factorised into Depthwise convolution and a 1 × 1 convolution called a pointwise convolution. Depthwise convolution applies a single filter to each input channel. The pointwise convolution then applies a 1 × 1 convolution to combine the outputs of the Depthwise convolution. The MobileNet reduces the computational costs by avoiding standard convolution which filters and combines inputs into a new set of outputs in one step29.In this study, we investigate the use of two deep learning models, MobileNet and InceptionResNetV2, in the authentication of cancer cell lines through a classification process by using the microscopic images of the cancer cell lines. Both models are based on convolutional neural networks (CNN), which have the capabilities of finding their own related features from the training data without the need of feature engineering. InceptionResNet combines the two major concepts of Inception and ResNet models as used by C. Szegedy et al.38, whose method depends on expensive machinery that is only available in very few specialised laboratories, our approach is based on phase-contrast images from well plates or cell culture flasks that can be obtained with every standard inverted cell culture microscope and will, therefore, be available to every standard cell culture lab. Our authentication method for drug-adapted sublines based on deep learning will complement STR and help to ensure that researchers know what cell lines they are working with.The cell line data we used contain a set of four parental cancer cell lines and their sublines adapted to anti-cancer drugs. This dataset enables us to consider the ability to distinguish between cancer cell lines of different genetic origins and also those of the same genetic origin. We further perform separate classifications of the four parental cell lines and their respective drug-adapted sublines in a separate classification task as part of the authentication process; this is a task which cannot be performed by STR authentication. In contrast to Meng et al.34. For model training, random initialisation of the model weights (training from scratch) and transfer learning methods were used and compared. In our analysis, for transfer learning, we used weights of the models pre-trained on ImageNet, a large-scale dataset used for Large-Scale Visual Recognition Challenge (ILSVRC), which contains 1.2 million images of general objects29. Several strategies were tested for data pre-processing and training in order to find the optimum strategy and the optimum model configuration for our problem  collection7. The cisplatin-resistant ovarian cancer sublines had been adapted to 1 µg/mL (COLO-704rCDDP1000) or 2 µg/ml (EFO-27rCDDP2000 and EFO-21rCDDP2000) cisplatin. The oxaliplatin-resistant UKF-NB-3 subline (UKF-NB-3rOXALI4000) was adapted to 4 µg/mL oxaliplatin39. Table The dataset consisted of microscopy images of parental cancer cell lines and their sublines, which had been adapted to grow in the presence of anti-cancer drugs. The set of cancer cell lines consisted of three ovarian cancer cell lines  and their sublines adapted to the anti-cancer drug cisplatin, as well as the neuroblastoma cell line UKF-NB-3 and its subline adapted to the anti-cancer drug oxaliplatin. The ovarian cancer cell lines were obtained from DSMZ . The neuroblastoma cell line was established from a bone metastasis of a stage IV neuroblastoma patientThis dataset was collected at the School of Bioscience, University of Kent, Canterbury Campus, UK. All the images are in RGB, JPEG (Joint Photographic Experts Group) format with a resolution of 2560 × 1922 pixels.As most of the available pre-trained models for transfer learning are trained on non-medical images, we proposed a multi-stage transfer learning through an inter-mediate training step using breast cancer cell images, to make sure our selected model had the knowledge of medical images before fine-tuning it for our cancer cell lines cell line authentication task. A multi-stage transfer learning is a technique of transfer learning in which a model can undergo several transfer learning before fine-tuned to its target task. Mostly, the intermediate transfer learning stage involves training a model with a data set from the domain similar to domain of the target task. For this multi-stage transfer learning, we used publicly available breast cancer cells data set. The data had been produced by the Laboratory of Vision, Robotics and Imaging (VRI) at the Federal University of Parana, and collected from 82 patients and comprised of 7909 microscopy images of breast tumour tissue. It is divided into two categories with 5429 malignant samples and 2480 benign samples, both with 700 × 460 pixels resolution and 3 channel RGB.40 and different data augmentation methods were performed41. Data normalisation is a technique of restricting features of the data to within a specific range42. In machine learning, the technique ensures each parameter (pixels in our case) has similar data distribution. Data normalisation was performed to remove variance in brightness and to enhance the contrast of the images. This was done by subtracting each image from its mean pixel-intensity value to make convergence faster while training the network. In the case of RGB images, the mean was calculated channel wise. All the processing conducted in this paper was done only on the cancer cell line dataset which is our target task. Our models were trained with both grayscale and RGB formats for comparison; the conversion to grayscale, data normalisation and augmentation were done online during the training process. For image splitting, the dataset was separated into two main categories, parental cancer cell lines and their drug-adapted sublines. The four parental cancer cell lines, their four sublines of drug-adapted and the combination of the two which form a group of eight cell lines were treated as three separate authentication problems. tenfold cross validation was performed on all three tasks. Each fold had 10% of the data set selected randomly and without repetition. As we had a relatively small data set, data augmentation approaches were used to artificially increase the sample size. As different data augmentation techniques have been proved to contribute different performance on the model24, two data augmentation approaches were employed and compared, which are nearest width shift and Constant width shift. Examples of augmented images are shown in the supplementary information, in Fig. Prior to model training, pre-processing steps like image splitting, cross validation, conversion to grayscale, data normalisation43. The data preparation procedures, training and model selection procedures are illustrated in insets A and B of Fig. A pilot classification was conducted to determine the optimal model and training strategy for the cell lines authentication task. In this process, three authentication tasks were performed by using the two selected models with different method combinations. The three tasks were (a) the authentication of parental cancer cell lines, (b) the authentication of drug treated cancer cell lines and (c) the authentication of the combined dataset of parental and drug treated cancer cell lines . In the experiments performed in the pilot classification, the images were resized by squashing the image to 299 × 299 pixels as a predefined input shape for InceptionResNetV2 and 224 × 224 pixels for MobileNet. As the predefined input shape of our best performing model was 299 × 299 pixels, several tests to transform the input image into the required input shape from our sample images were performed and compared. Govindaiah et al.Centre crop the image without resizing.Resize the image to 1280 × 961 pixels which is half of the resolution of the original image and then centre crop the image.Resize the image to 640 × 480 pixels which is a quarter of the resolution of the original image and then centre crop the image.Resize the image to 426 × 320 pixels which is 1/6 of the resolution of the original image and then centre crop the image.To maintain the aspect ratio, the following methods were applied and then compared with image squashing and reported.45. Hence, determining the number of layers to be added on a pretrained model for transfer learning becomes an important aspect. In fact, the added layers play the crucial role in the learning of specific features for the target problem by the model. When using transfer learning, it is essential to control the depth at which the amount of ‘knowledge’ transfer between the source and target problem is optimal for the target problem. In deep neural networks, most of the parameters are in the fully connected layers46. Furthermore, different popular models which has won ImageNet competition has used different number of fully connected layers, AlexNet has 3 fully connected layers, GoogLeNet and ResNet which both contributed to the development of our best performing model as they use Inception and ResNet techniques, respectively, both have one fully connected layer47. We performed an experiment to find the optimal number of fully connected layers to be added and the number of neurons in those layers for our problem.In deep learning models, layers closer to the input learn general features of the input data while deeper layers closer to the output learn more specific features for the target problem48. The idea is contradicted in49 where it is stated that models trained with large batch size have high chance to generalize more poorly than those trained with small batch sizes. Studies51 suggested a batch size of no more than 64 samples. In addition, a large batch size has high computational cost when training a deep learning model; previous studies53, recommended large batch size when training with large datasets, but this choice comes with optimization difficulties. Since there is no clear recommendation of the batch size to use in general, different batch sizes were tested to find the optimum batch size value for our problem.The batch size, which is the number of samples processed before the model is updated during training, has an influence on the model performance in terms of both accuracy and training time. It is reported that greater the batch size the higher the accuracy of the model54. The used models in our case were trained with ImageNet, a non-medical image dataset. To make sense of transfer learning, a multi stage transfer learning was considered by training the model through medical images, before fine tuning it on our targeted task. Some investigations into medical images have adopted multi-stage transfer learning by fine-tuning their models through other publicly available medical data sets, before fine tuning the model to their target problem. Ravi et al.45 observed an increment of 4 percent in F1-score when performing multi-stage transfer learning by using publicly available breast cancer data set to fine tune a deep learning model as an intermediate transfer learning stage, before fine tuning the model for their target task of classification of breast cancer. As there are common high-level features in cancer cells such as shape and size55, we used the breast cancer cells data set as an intermediate transfer learning stage, since there is no public available data set to our specific problem (cell lines authentication), to the best of our knowledge.A common practice in deep learning is that training and testing data should come from the same domain. In some scenarios, such as training a deep learning model with medical images, that practice becomes more challenging as it is hard to obtain sufficient medical images. Transfer learning could provide a solution by training the models with easily obtained data from a different problem and fine tune the model with the smaller dataset for the target problem. Transfer learning makes sense when low-level features from the problem you are transferring from could be helpful and are relevant to the problem you are transferring toUnlike in the pilot classification task and in the fine-tuning of the optimal model were the training, validation and test folds were fixed, at this stage, nine folds were used for training and validation and one for testing for all the authentications conducted by using the optimal fine-tuned model using the optimal training strategy. The process was repeated 10 times for each fold to be used for training, validation and testing, the mean results with their standard deviation of all 10 folds are reported in this paper. In addition, we investigated the effect of sample size on cancer cell line authentication and the results are reported in this paper. The predictions of any trained deep learning model are subjected to a degree of uncertainty; a trained model can perform well in identifying some classes and perform poorly on other classes. It is necessary to understand how a trained model performs in identifying individual classes trained on instead of just an overall performance, as some classes are hard to authenticate compared to others. We studied by investigating the confidence of our optimal model in authentication of each cancer cell line separately and the findings reported. All the experiments conducted using our optimal model are as explained bellow;To complement the current authentication methods used in biology research (i.e. STR), which is not able to differentiate between parental cancer cell lines and their drug-adapted sublines, eight-class authentication of the mixed four (4) parental and the four (4) drug treated cancer cell lines was conducted. To apply STR profiling for authentication, standardized protocol and a data-driven, quality-controlled and publicly searchable database will be required which is a complex and time-consuming process. To complement STR with a cheaper and quicker computerised method of authenticating cancer cell lines, we further performed authentication of the four (4) parental cancer cell lines and the authentication of the four (4) drug-resistant sublines separately using our optimal fine-tuned model. Furthermore, we perform a two-class task to authenticate the parental cell lines and their drug-adapted sublines in one approach.57. To study the effect of sample size, we studied our selected optimal model performance at different training sample sizes. The number of images in the test fold were kept constant while the number of images in the training folds were reduced in steps of 20%.Deep learning can easily overfit when trained on a small sample size, hence it is important to study the effect of sample size when training deep learning models to avoid overfitting58. This might be due to different reasons like the quality of data of that particular class or the confusion of the model due to similarities existing between the classes with low confidence. This rises the importance of studying the confidence of a trained model in prediction. I Cortés-Ciriano et.al59, studied about model confidence in assessing prediction uncertainties of their trained deep learning model for drug discovery application. To study the model’s confidence, some studies advise not to use prediction probabilities as the confidence of a deep learning model60. Researchers in61 and62 instead, used the probabilities of classes generated by the trained CNN models as the confidence score to reject noises and keep the predictions with high class probabilities in a face detection task. Also, in an object detection network by Redmon et.al58, probabilities of the bounding boxes containing objects were used as the confidence score of the model. We therefore studied the prediction probabilities of our model in predicting a small set of randomly selected samples from our test set by extracting their probabilities of the assigned class. The predicted probabilities were extracted from the softmax layer of the trained model. Post processing of the softmax layer has proved to be useful in delivering calibrated class probabilities in classification problems64. Both probabilities in correct and wrong predictions were studied and reported in this paper.Despite the high accuracy attained by a trained CNN model, it can have difficulties in predicting some of the classesk-fold cross validation (k = 10 in our case) because when comparing model’s performance using simple train/test split, different results may be obtained each time when different data samples are selected in the train/test sets49. It is more computationally expensive to train a model with RGB than with grayscale images. This is because RGB images carries more information which can be essential for a model to learn. Hence the two ways training with grayscale and with RGB images were tested and results compared. Both models were trained with normalised images. For data augmentation several hyper-parameters were selected. The hyper-parameters selected for augmentation reflected that the cells can be anywhere in an image independently of how the cell culture vessel is placed under the microscope. The selected method and the optimal training strategy were determined during the pilot classification task, which were later fine-tuned. InceptionResNet V2 was found to be the optimal model for our problem over MobileNet through the pilot classification task, using RGB images, nearest width shift data augmentation with transfer learning techniques (Method combination V). In fine-tuning of the optimal model, we also found that, distorting the aspect ratio by resizing the input image direct to the required input shape does not have an effect on the model performance in our problem. Multi-stage transfer learning had little impact on our problem, this might be due the kind of data we used to fine tune our model in a multi-stage process, breast cancer cells dataset, being different from our target task (cancer cell lines) data set as the breast cancer cells images were produced from the stained biopsies which are highly associated with colour changes while our cancer cell lines data set were produced from cell culture grown in the lab. Our optimal model was trained to perform different authentication tasks to automate the standard existing authentication method, the STR. Further authentication tasks were conducted using the optimal model which cannot be done by the standard authentication method, the STR, and achieved remarkable results. The following subsections presents our results.We adopted a Pilot classification was conducted on three main authentication tasks, which are the authentication of the four classes of parental cancer cell lines, four classes of their drug-adapted sublines and an eight-class combination of the previous two. This was done by fixing the training, validation and test folds through all the three tasks to determine the optimal model with the optimal method combination.40. As the RGB images contain more information, this makes them computational expensive to train compared to grayscale images. Both data augmentation techniques, Nearest width shift and Constant width shift, had a positive impact by significantly increasing the F1-score in both models, with nearest width shift performing better than constant width shift. With reference to training strategies selections in Table Figures The performance of the models (F1-scores), with the optimal strategy (method combination V) observed in our pilot classification on the authentication of an eight-class authentication task, is shown in Table Based on results from the three authentication tasks, InceptionResNet V2 was selected as the optimum performing model compared to MobileNet. Also, a combination of RGB images, data augmentation and transfer learning (training strategy V) was observed to be the optimal configuration.As described in the “45 in which the intermediate stage dataset was of the same domain as the target task. Our target task, in fact, includes images from ovarian and neuroblastoma cancer cells, although they are all digital images of cancer cells. The small increment of F1-score in our case means there might be some little common features among cancer cells from different types of cancer.A modest increment of 1 percent was observed on the F1-score when a multi-stage transfer learning was conducted compared to the single stage transfer learning. This may mean that, in multi-stage transfer learning, our optimal model was able to learn extra cancer related features from the breast cancer cells data set. This increment of F1-score produced by multi stage transfer learning might have been small because we used images of breast cancer in the intermediate stage which is little different from our target task, Unlike a study by Ravi et.alThis section presents results of the optimal model, following hyper-parameter tuning, which are based on tenfold cross validation. With our fine-tuned optimal model and training strategy of 0.96 F1-score on authentication of four classes of parental cancer cell lines and an average of 0.91 F1-score on the four classes of drug-adapted sublines. An average of 0.91 F1-score was obtained on the eight-class authentication task. We also obtained an average of 0.95 F1-score in two-classes for authentication between the parental and its drug treated subline of the same cell line. These results are presented and further explained in the following subsections.Using the fine-tuned optimal model and training strategies, our model performed better on authentication of parental cancer cell lines than on their drug-adapted sublines Table  as two sOn the combined data set classification problem (eight-class task), which is a more complex authentication task as it combines both four classes of parental and drug-treated cancer cell lines, an average of 0.91 F1-score is obtained with a standard deviation of 0.03, as shown in Table 65, which might be a reason why our model was able to differentiate between the parental and the drug-treated cancer cells.Two class authentications were also performed to see how the model performs in classifying between the parental cell lines and their drug-adapted sublines Table . AlthougThe promising results of our method means that it may be used for authentication of cancer cell lines in conjunction with STR or for tasks where STR cannot be utilised.Table rCDDP2000 (mean F1-score 0.76) and EFO-27rCDDP2000 (mean F1-score 0.71), with the highest standard deviations out of all other cell lines. Further analysis on this case is presented later in the paper to find out the reason of this case.When training a machine learning model, the model can learn to classify some classes well and fail on other classes of the same problem. This might be due to the quality of the data in a particular class, imbalances between the datasets or a lack of diversity among the training samples in a particular class that prevents the model from capturing the patterns. It is important, therefore, to see how the trained models perform in classifying each class separately. The model performance in authenticating each class was studied and reported in Table It is important to understand how confident a trained model is in predicting the unseen dataset. Low confidence when making a correct prediction or high confidence when making an incorrect prediction may indicate the low performance of a model while high confidence in correct predictions and low confidence in wrong confidence may indicate the high performance of a model.rCDDP2000 and EFO-21rCDDP2000, respectively. We conducted a further investigation on the EFO-21 and EFO-27, both parental and drug treated cancer cell lines to find out the reasons of the low model performance on the two cell lines, as reported in “The probabilistic confidence of the model when making correct predictions was compared to when incorrect predictions were made Table . This waDue to the low performance in authentication of the drug-adapted sublines of EFO-21 and EFO-27 in the per-class performance of the optimal model Table  and alsoThe results show that our optimal model performed well in the authentication of the parental cancer cell lines, but was less reliable in the authentication of the cisplatin-resistant sublines Table . These r7. By using deep learning, we have demonstrated that, it is possible to authenticate cancer cell lines, including parental cancer cell lines and their drug-adapted sublines, based on image recognition. Our data also suggest that resistance formation to a certain drug may be associated with specific morphological changes. An improved understanding of such processes may enable the further development of image-based strategies to gain mechanistic insights. Furthermore, our results suggest that, apart from the drug-adapted cancer cell lines developing the same resistance mechanism, cancer cell lines treated with the same drug develop much similar resistance mechanisms compared to those treated with different drugs, as it was demonstrated in the studying of the trained model confidence. Hence, our results are not only promising with regard to the development of novel cell line authentication approaches but also provide initial evidence that image-based methodologies can be developed as tools for the performance of functional and mechanistic studies. This is a proof of concept that, image-based methods with deep learning can be used to assist the current existing state of the art authentication methods like the STR.The common issue of misidentification of cancer cell lines requires new ways for performing cell line authentication in a laboratory environment. Current authentication methods are expensive, time-consuming and cannot differentiate between cell lines of the same genetic origin, like parental cancer cell lines and their drug-adapted sublines. Resistance formation is associated with morphological changes that make drug-adapted cell lines distinguishable from the parental cell line. Hence, approaches using computer-aided digital image analysis can be used to develop effective authentication approaches that can be easily included into the daily laboratory routine and which may complement, to assist other authentication methods. Such methods may also enable the discrimination between cell lines of the same genetic origin, a task for which established methods are currently lacking. Such approaches have the potential to improve the reliability of research results due to the reduction of the use of misidentified cell lines45 because they used a dataset from their domain in the intermediate stage, compared to a 1% increase in F1-score in our case, this is because we were limited with the unavailability of publicly available cancer cell lines to be used in the intermediate stage. This means researchers should look for datasets from the domain of their target task when in need of applying multistage transfer learning. Finally, studying of a model confidence might be used as a way for detecting the prediction uncertainties of a trained model as it has been demonstrated in this paper.To this end, this thesis has successfully demonstrated the application of CNN in the authentication of cancer cell lines. Based on the pilot classification task result, techniques like transfer learning and data augmentation has significantly improved the model’s performance. Furthermore, a multi-stage transfer learning has shown a significant increase of 4% in F1-score in another study by Ravi et al.Supplementary Information.


This approach highlighted a subgroup of genes, which value as promising candidate therapeutic targets was further confirmed by protein expression analyses. The in vitro activity of 15 inhibitor drugs against either these genes or their pathways was then analyzed, in order to identify the most active ones in terms of inherent activity and ability to overcome cisplatin resistance. NSC130813  and triptolide, both targeting NER factors, proved to be the two most active agents, without evidence of cross-resistance with cisplatin. Combined in vitro treatments showed that NSC130813 and triptolide, when administered together with cisplatin, were able to improve its efficacy in both drug-sensitive and resistant osteosarcoma cells. This evidence may indicate an interesting therapeutic future option for treatment of osteosarcoma patients who present reduced responsiveness to cisplatin, even if possible effects of additive collateral toxicities must be carefully considered. Moreover, our study also showed that targeting protein kinases belonging to the mitogen-activated protein kinase (MAPK) or fibroblast growth factor receptor (FGFR) pathways might indicate new promising therapeutic perspectives in osteosarcoma, demanding for additional investigation.Treatment of high-grade osteosarcoma, the most common malignant tumor of bone, is largely based on administration of cisplatin and other DNA damaging drugs. Altered DNA repair mechanisms may thus significantly impact on either response or resistance to chemotherapy. In this study, by using a panel of human osteosarcoma cell lines, either sensitive or resistant to cisplatin, we assessed the value as candidate therapeutic targets of DNA repair-related factors belonging to the nucleotide excision repair (NER) or base excision repair (BER) pathways, as well as of a group of 18 kinases, which expression was higher in cisplatin-resistant variants compared to their parental cell lines and may be indirectly involved in DNA repair. The causal involvement of these factors in cisplatin resistance of human osteosarcoma cells was validated through gene silencing approaches and  Osteosarcoma (OS) is the most common malignant tumor of bone, which accounts for about 5% of childhood and adolescence neoplasms. High-grade OS is usually treated with neoadjuvant chemotherapy protocols based on cisplatin (CDDP), doxorubicin, methotrexate, and ifosfamide. However, despite this aggressive approach, 35–45% of patients still recur and experience an unfavorable outcome –5.Three out of the four conventional drugs, which are most commonly used in first-line chemotherapy for high-grade OS, induce DNA damages either directly (CDDP and ifosfamide) or indirectly (doxorubicin). Therefore, resistance mechanisms related to DNA damage response can significantly impact on OS chemotherapy unresponsiveness. Among these drugs, CDDP is the agent which has most extensively been studied in relation to DNA repair. A consistent body of evidence is showing that the onset of clinical unresponsiveness to CDDP usually creates further therapeutic complications, because patients can also become cross-resistant to the other DNA damaging chemotherapeutic drugs used in first- or rescue treatment protocols , 6.ERCC excision repair 1 (ERCC1) negatively impacts on the clinical responsiveness to CDDP-based treatments and on patients' outcome  , 7, 8. W outcome . HoweverIn addition to NER, other DNA repair pathways, first of all the base excision repair (BER), have been indicated or proved to be implicated in CDDP resistance of several human tumors –12, but Cellular response to CDDP-induced DNA damage is also mediated by downstream effects on cell cycle and mitosis regulation , 11. TheBased on our previously (unpublished) gene expression analyses, we observed that CDDP-resistant human OS cell lines showed increased expression of several kinases in comparison with their corresponding parental cells. Among these kinases, 18 can be targeted by inhibitor drugs of which some have already entered clinical trials or have shown promising preclinical activities in human cancers different from OS.In this study, we first confirmed the expression level of these 18 kinases in human OS CDDP-resistant variants in comparison with their parental cell lines.Moreover, the role of genes belonging to NER or BER pathways and of the aforementioned 18 kinases for CDDP resistance in human OS cells was estimated, in order to indicate new candidate markers, which may be considered to overcome resistance to CDDP in OS patients.in vitro efficacy of drugs targeting the most significantly emerged genes or pathways has been assessed.Finally, the in vitro studies were performed on the U-2OS and Saos-2 human OS cell lines and a panel of variants resistant to CDDP .The The U-2OS and Saos-2 cell lines were purchased from the American Type Culture Collection . Variants resistant to CDDP were established by exposing the drug-sensitive U-2OS and Saos-2 parental cell lines to stepwise increasing concentrations of CDDP and characterized as previously described .DNA fingerprint analyses of 17 polymorphic short tandem repeat sequences were performed for all cell lines, confirming their identity.2 atmosphere. Drug resistant variants were continuously cultured in presence of CDDP at the concentration used for their selection.All cell lines were cultured in Iscove's modified Dulbecco's medium (IMDM), supplemented with penicillin (20 U/ml)/streptomycin (20 U/ml)  and 10% heat-inactivated fetal bovine serum , and maintained at 37°C in a humidified 5% COAnalyses focused on genes belonging to the NER and BER pathways, which are known to play key roles for CDDP resistance in several human cancers, and on the 18 druggable protein kinases selected on the basis of our previous observations, which indicated their increased expression in U-2OS- and/or Saos-2-derived CDDP-resistant variants in comparison with their parental cells . ExpressIn a first set of experiments, each gene was silenced by transfecting cells with three different siRNAs specific for different regions of the same gene  for 24 h, whereas controls were cultured in presence of scrambled siRNAs. Transfection was performed by using each siRNA at a final concentration of 5 nM and 0.3 or 1.25 μl lipofectamin RNAiMAX  per well in a 96-well or 24-well plate according to the manufacturer's protocol. After 24 h, medium was changed and cells were maintained in siRNA-free medium for additional 48 h. The extent of gene silencing was estimated at 72 h for each siRNA by qRT-PCR on the ViiA 7 instrument (Thermo Fisher Scientific) in order to identify the siRNA with the strongest effect on mRNA down-regulation. Gene expression analysis was performed using the TaqMan® Gene Expression Cells-to-CT™ Kit  and appropriate TaqMan® Gene Expression Assays (Applied Biosystems) listed in in vitro sensitivity to CDDP was estimated on the basis of drug dosage response curves, assessed by using the 3--2,5-dephenyltetrazolium bromide (MTT) assay kit . For all cell lines, the IC50 value (CDDP concentration inducing 50% growth inhibition) was determined inside each experimental condition. To quantify the extent of the increased CDDP sensitivity after gene knock-down, ratios between the IC50 values of cells incubated with scrambled siRNAs and those of silenced cells were calculated.The siRNAs producing the highest mRNA down-regulation were then selected to verify whether the inhibition of a specific gene expression was related to a corresponding increase in CDDP sensitivity. For this second set of experiments, 48 h after seeding and transfection, the cells were incubated with different dosages of CDDP for additional 48 h. Controls were incubated with scrambled siRNAs. The Cells were cultured in petri dishes until confluence, harvested by scraping and lysed in RIPA buffer supplemented with protease and phosphatase inhibitor cocktails (Thermo Fisher Scientific) and Benzonase . Protein concentrations were determined by the Bradford Protein Assay . Equal amounts of cell lysates (80 μg) were separated by SDS-PAGE on 4–20% gradient gels (Thermo Fisher Scientific) and then transferred onto nitrocellulose membranes . Then, membranes were blocked in 5% BSA in 1 X TBS containing 0.1% Tween-20  and incubated in primary antibodies  overnighProtein loading was assessed by coomassie R-250 staining (Bio-Rad). Fold changes in protein expression level were determined by densitometric analysis of western blots and autoradiographs using the publicly available ImageJ software .in vitro efficacy, are listed in the The drugs targeting the prioritized genes/pathways, which have been tested for their In vitro drug efficacy was assessed in terms of in vitro growth inhibition activity estimated with the MTT assay (as described above) on the two parental cell lines (U-2OS and Saos-2) and their CDDP-resistant variants with, respectively, the lowest and the highest resistance level . For three drugs, X80, quercetin and SSR128129E, the CellTiter-FluorTM Cell Viability assay  was used according to the manufacturer's protocol. For each cell line, the drug IC50 value was determined, in order to highlight the possible presence of cross-resistance due to the mechanisms present in CDDP-resistant variants.in vitro association of each prioritized drug with CDDP was determined after 96 h of combined treatment with the IC50 dosage of each drug. In drug sequence experiments, cell lines were sequentially exposed for 48 h to their corresponding IC50 dosage of CDDP and then to the IC50 dosage of each prioritized drug for additional 48 h. These combinations were then repeated with the opposite sequence. The type of interaction in terms of synergism, antagonism or additivity, was defined on the basis of the combination index (CI) of each two-drugs combination, which was calculated with the equation of Chou-Talalay by using the CalcuSyn software . By following the CalcuSyn software indications, the drug–drug interaction was classified as synergistic (SYN) when CI was lower than 0.90, additive (ADD) when CI ranged between 0.90 and 1.10, or antagonistic (ANT) when CI was higher than 1.10.The two most active drugs which emerged from these analyses were prioritized for further evaluations. The efficacy of the CDK3, FLT4, MAP2K3, MAP2K5, MAPK1, MAPK3, PIK3C3.Expression levels of genes listed in RNA interference was used to determine the causal involvement in CDDP resistance of the genes listed in in vitro CDDP sensitivity. Cell lines silenced with the siRNAs listed in the ERCC1, ERCC2/XPD, ERCC3/XPB, ERCC4/XPF, and XPA increased CDDP sensitivity in the U-2OS/CDDP300 and U-2OS/CDDP1 μg resistant variants. Silencing of ERCC1, ERCC2/XPD, and ERCC4/XPF increased CDDP sensitivity also in U-2OS parental cellsSilencing of FGFR1 silencing increased CDDP sensitivity in the U-2OS/CDDP1 μg and in parental cellsMAP2K3, MAPK3, and PIK3CB was associated with an increase of CDDP sensitivity in the U-2OS/CDDP4 μg resistant variant.Silencing of Additional reversal activity of CDDP resistance, with IC50 ratios close to the 2.0 cut-off value, was observed for the knock-down of these and other genes , even ifERCC1, ERCC2/XPD, ERCC3/XPB, ERCC4/XPF, XPA, MAP2K3, MAPK3, and PIK3CB. FGFR1 was also selected based on its ability to reverse CDDP resistance also if its expression was found to be moderately higher than in parental cells. All these genes showed evidence of increased expression in CDDP resistant variants and their knock-down proved to be associated with an enhancement of CDDP sensitivity.By coupling the results derived from the assessment of gene expression level in association with CDDP resistance  and the To further validate these genes as candidate therapeutic targets to overcome CDDP-resistance, their expression at protein level was assessed by western blot in both U-2OS and Saos-2 parental cell lines and all their CDDP-resistant variants. All proteins encoded by these prioritized genes proved to be expressed in all cell lines. In the U-2OS series , there wWhen considered together, these results further supported the indication of all these genes as candidate drug targets.in vitro activity of drugs listed in The As shown in In U-2OS variants , higher By considering together the findings obtained in both U-2OS and Saos-2 cell line series, the most active DNA repair-targeting agents, without evidence of cross-resistance, proved to be NSC130813  and triptolide (targeting ERCC3/XPB).The most active kinase-targeting drugs, without evidence of cross-resistance with CDDP, were ulixertinib , hypotemycin (targeting the MAP2K pathway), PD173074 and FIIN-2 (both targeting FGFR1).In order to verify whether treatment with each inhibitor drug was able to increase sensitivity to CDDP, the same group of cell lines were incubated with increasing CDDP concentrations in the absence (control) or presence of the IC20 dosage of each inhibitor drug. A ratio ≥ 2.0 (meaning a decrease of at least 2-fold of CDDP-IC50 in presence of the inhibitor drug) was considered as indication of a drug-induced CDDP sensitization.NSC130813  and triptolide proved to be the two drugs with the most relevant activity, being able to increase CDDP sensitivity for more than 2-fold in all the U-2OS and Saos-2 cell lines .For all these reasons, NSC130813  and triptolide were prioritized for evaluation in combination experiments with CDDP.NSC130813  and triptolide were tested in combination with CDDP, in order to verify whether these treatments lead to positive interactions. As shown in Sequential drug exposure experiments  mainly rMany chemotherapeutic drugs, including several agents used in first-line and rescue chemotherapy protocols for OS exert their activity by directly or indirectly damaging DNA. Consequently, the ability of tumor cells to repair drug-induced DNA damages significantly impacts upon efficacy of these compounds , 10, 11.Our study focused on a group of DNA repair genes and kinases, which we found to be upregulated in our panel of CDDP-resistant human OS cell lines, in order to verify whether they could be considered as new candidate therapeutic targets. In particular, analyses focused on genes belonging to the NER and BER pathways and on 18 druggable protein kinases, which resulted to be overexpressed in association with the development of CDDP resistance.in vitro CDDP sensitivity, confirming its involvement in reduced sensitivity to this drug. The genes that emerged to be most strictly related to CDDP unresponsiveness, were prioritized as candidate targets for the second phase of the study, in which their increased protein expression was confirmed in CDDP-resistant cells, justifying the subsequent in vitro studies of drugs interacting with these markers or their pathways.After a screening to identify the most effective siRNA to knock-down each prioritized gene, drug-sensitive and resistant cell lines were silenced and treated with CDDP, in order to identify those genes which down-regulation produced a corresponding increase of the The impact of these genes for DNA repair activity in our experimental models was further confirmed by functional analyses, in which the cells' capability to repair CDDP-induced DNA damages was assessed after silencing of each prioritized gene by the COMET assay . This evin vitro activity in CDDP-resistant variants compared to parental cell lines, suggesting the presence of cross-resistance mechanisms. The possible reasons for this apparent cross-resistance were not further explored because they were beyond the aims of this study. However, it can be hypothesized that cross-resistance might be due either to differential expression of transporters that recognize these drugs as substrate and efflux them out of the cells or to detoxification processes that are more active in CDDP resistant cells and inactivate these agents. Other reasons may be the activation of alternative or redundant pathways, which replace the function of the targeted pathway in CDDP resistant cells, which consequently become less sensitive to these drugs.Drugs targeting the prioritized targets or pathways were selected on the basis of their reported promising activity in other experimental models and/or their use in clinical trials for human tumors. Among the 15 evaluated agents, TGX221, AZD6482, FR180204, AZD4547, GDC0994 and, at a lower extent, quercetin showed a reduced NSC130813  and triptolide (targeting ERCC3/XPB) proved to be the two agents with the most relevant activity on both CDDP-sensitive and -resistant cell lines. Moreover, these two drugs did not show evidence of cross-resistance with CDDP and proved to reverse CDDP resistance in all drug-sensitive and -resistant cell lines. For these reasons, they were further tested in combined treatments  with CDDP, in order to verify whether these combinations may lead to positive interactions.When considered together, results obtained by the combined treatments indicated that NSC130813  and triptolide have to be administered together with CDDP, in order to improve its efficacy in both drug-sensitive and resistant OS cells. If transferred to a clinical setting, this association has to be considered regarding possible effects of additive collateral toxicities.NSC130813 , also known as [4-[(6-chloro-2-methoxy-9-acridinyl)amino]-2-[(4-methyl-1-piperazinyl)methyl]], is a compound which was shown to act synergistically with CDDP and mitomycin C by interfering DNA repair through the disruption of the interaction between ERCC1 and ERCC4/XPF . TargetiTriptolide is a diterpene triepoxide isolated from a traditional Chinese medicinal plant with anti-inflammatory, immunosuppressive, contraceptive and antitumor activities . In the It has been demonstrated that triptolide covalently binds to human ERCC3/XPB, inhibiting its DNA repair-related activity , 24, 25.On the basis of this body of evidence, we have explored whether in OS cells triptolide-mediated inhibition of NER may improve CDDP activity. Our findings indicated that inhibiting DNA repair through the simultaneous administration of CDDP and triptolide may be a new interesting treatment avenue to overcome CDDP resistance in OS.ClinicalTrials.gov Identifier: NCT03117920), acute myeloid leukemia , and different advanced solid tumors , but results of these regimens are presently not available. Moreover, other triptolide derivatives and analogs have been used in clinical studies aimed to test their efficacy and safety  and PD173074 (targeting FGFR1) showed some promising activity, without evidence of cross-resistance with CDDP. Although these drugs were not further analyzed in combined treatments with CDDP, the obtained findings suggest that targeting protein kinases that influence DNA repair activities may indicate new promising therapeutic perspectives in OS, demanding for additional investigation. This perspective is particularly interesting because there are many protein kinase inhibitors in various stages of clinical development worldwide and the majority of them are used for cancer treatment  models may further support their clinical use. All these activities are presently planned and will focus on the drugs screened and highlighted by this study.Our results can also be the basis for further In high-grade OS, when patients fail to respond to first-line treatment and relapse, therapeutic options and drugs effective for rescue chemotherapy protocols are scarce, also because resistance mechanisms developed against first-line chemotherapeutic drugs can also be responsible for reduced responsiveness to the agents used in the subsequent regimens.Inhibition of DNA repair can be considered as a promising treatment strategy to enhance the efficacy of currently available DNA damaging drugs.There are several genes and proteins involved in modulating the cellular response to DNA damage, each one may serve as target to enhance the efficacy of conventional therapeutic modalities. The current efforts in the development and deployment of several classes of DNA repair targeting compounds justify the hope to achieve new tailored treatment approaches through the use of these inhibitor drugs, which may ultimately drive toward innovative regimens aimed to improve patient outcomes.The evidence emerged within this study about the possibility of successfully combining CDDP with drugs targeting DNA repair factors or protein kinases involved in these processes may indeed indicate new therapeutic options for specific OS patient cohorts, who have reduced cure probabilities.Datasets generated and analyzed in this study will be made available by the authors to qualified researchers, upon justified request.in vitro drug testing. ET, MF, SV, and AF-R performed the gene silencing screening. ET and MF carried out the DNA damage evaluations. ET, MF, CH, and SL took part in the molecular biology and western blot studies. ET, MF, CH, MP, and MS performed the statistical analyses. All authors have made a substantial, direct, and intellectual contribution to the work.ET, MF, CH, and MS designed the study. MS coordinated the study and drafted the manuscript, which was revised by all authors, who approved it for publication. ET, MF, AF-R, MP, and FM carried out the cell culture-based experiments and the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. CH is Associate Editor of the section Pharmacogenetics and Pharmacogenomics of Frontiers in Pharmacology.


CPT2, LDHA, MCM2, and SKP2, in both BrCa and PCa cell lines. Hence, [C16Pyr][Amp] was shown to be a promising anticancer therapeutic agent for BrCa and PCa cell lines.Breast (BrCa) and prostate (PCa) cancers are the most common malignancies in women and men, respectively. The available therapeutic options for these tumors are still not curative and have severe side effects. Therefore, there is an urgent need for more effective antineoplastic agents. Herein, BrCa, PCa, and benign cell lines were treated with two ionic liquids and two quinoxalines and functional experiments were performed—namely cell viability, apoptosis, cytotoxicity, and colony formation assays. At the molecular level, an array of gene expressions encompassing several molecular pathways were used to explore the impact of treatment on gene expression. Although both quinoxalines and the ionic liquid [C2OHMIM][Amp] did not show any effect on the BrCa and PCa cell lines, [C16Pyr][Amp] significantly decreased cell viability and colony formation ability, while it increased the apoptosis levels of all cell lines. Importantly, [C16Pyr][Amp] was found to be more selective for cancer cells and less toxic than cisplatin. At the molecular level, this ionic liquid was also associated with reduced expression levels of  Cancer is one of the leading causes of disease-related death worldwide. In particular, breast (BrCa) and prostate (PCa) cancer are the first and second most common cancers in women and men, respectively . ConcernIonic liquids (ILs) and quinoxalines have emerged has potential anticancer drugs ,13. ILs Quinoxalines are chemical compounds whose structures are similar to quinolone antibiotics. Quinoxaline derivates are synthetic heterocyclic compounds formed by the fusion of two aromatic rings, i.e., benzene and pyrazine, with a nitrogen atom replacing carbons in the naphthalene ring . AlthougConsidering the promising applications of both ILs and quinoxalines in biomedical research and the pharmaceutical industry, we aimed to investigate the effect of these synthetic compounds—in particular, two ILs, one with imidazolium ([C2OHMIM][Amp]) and the other with pyridinium ([C16Pyr][Amp]) cations, as well as quinoxaline-1,4-dioxide and 2-methylquinoxaline-1,4-dioxide—in BrCa and PCa cell lines with a malignant phenotype. These four compounds were chosen as they have shown promising results in previous publications ,22.Ten immortalized epithelial cell lines from the breast and prostate were used to assess the impact of four compounds—namely two quinoxalines  and two ILs based on ampicillin . The BrCa cell lines used were HTB22 (MCF-7), HTB133 (T-47D), HCC1937, and MDA-MB-231, while 22Rv1, LNCaP, Du145, and PC-3 were the PCa cell lines chosen. The remaining two cell lines were benign and used as controls: MCF-10A (breast) and RWPE-1 (prostate).50) of each cell line and the dose–response curves were evaluated with 3--2,5-diphenyltetrazolium-bromide (MTT) assay. The IC50 of the compounds was determined by exposing each cell line to different concentrations of the compounds . Considering these results, phenotypic assays were only performed with the IL [C16Pyr][Amp], at both the IC50 and at the double IC50 concentrations for each cell line.For each compound, the half-maximal inhibitory concentration  in both BrCa and PCa cell lines  than in tumor cells, indicating that cisplatin is non-specific for tumor cells and induces toxicity to normal cells [Amp] presented the highest SI value for MDA-MB-231 (SI = 11.00). This value is 2.5-fold the SI value found for cisplatin, which is a promising result since MDA-MB-231 is an aggressive cell line derived from a metastatic site of pleural effusion. Similarly, although to a lower extent, the SI values of [C16Pyr][Amp] for HTB22 and HCC1937 were also remarkable. Compared to cisplatin, the SI value of [C16Pyr][Amp] was 8-fold higher in HTB22. Concerning the PCa cell lines, [C16Pyr][Amp] exhibited the greatest SI value in LNCaP (SI = 7.16), which is approximately 11-fold the value obtained for cisplatin. Importantly, in PC-3, this compound also presented an interesting SI (SI = 2.63), especially because of the metastatic and castration-resistant nature of this cell line. Hence, [C16Pyr][Amp] might be a potential compound for clinical purposes, with the highest selectivity for tumor cells without significantly impairing normal cell lines when compared to cisplatin.Knowing the relevance of this chemotherapeutic agent in the clinic, we used this drug as a control. In our study, [C16Pyr][Amp] presented lower ICll lines . Moreoveal cells ,39,40. IOverall, a time- and dose-dependent reduction in the viability of BrCa and PCa cells was observed upon [C16Pyr][Amp] treatment . The decThese results were paralleled by those obtained in the analysis of the resistance to apoptosis. Indeed, [C16Pyr][Amp] significantly increased apoptotic levels in a dose-dependent manner in all cancer and benign cell lines . The HTBRegarding the cytotoxic effect, [C16Pyr][Amp] promoted a slight lactate dehydrogenase (LDH) increase in the medium (data not shown) comparative to the control condition, which is indicative of necrosis and cytotoxicity due to a damaged plasma membrane . Herein,The in vitro colony formation assay is based on the ability of a single malignant cell to grow and divide, thereby forming colonies, contrarily to healthy cells . All celCPT2, LDHA, MCM2, and SKP2 . Concerning the MCM2 gene, a higher difference was depicted in HTB133  and MDA-MB-231 , although 22Rv1 also presented a statistically significant decrease. Both the LDHA and SKP2 genes also depicted significantly decreased expression in the BrCa cell lines, SKP2 being also significantly downregulated in both PCa cell lines, although to a lower extent. Curiously, the hormone-dependent cell lines (HTB133 and 22Rv1) depicted a more impressive decreased expression of these genes compared to the respective BrCa hormone-independent and PCa castration-resistant (MDA-MB-231 and Du145) cell lines.Considering the antitumor activity of [C16Pyr][Amp] exhibited in the functional assays, the main cellular pathways possibly affected by [C16Pyr][Amp] treatment were assessed through the identification of altered gene expression that could corroborate the anticancer effects shown. For this purpose, the cell lines that best responded to treatment with [C16Pyr][Amp], identified in the previous trials and representative of the wide range of breast and prostate tumors, were chosen. Specifically, two hormone-dependent (HTB133 and 22Rv1) and two hormone-independent BrCa and castration-resistant PCa (MDA-MB-231 and Du145) cells were treated with the highest [C16Pyr][Amp] concentration and further tested via transcript analysis. A commercial custom array panel of cancer research was used to evaluate the expression of several genes involved in cell cycle, apoptosis, DNA repair, cellular metabolism, and mTOR or MAPK/ERK pathways. From the 96 genes analyzed in the array, four were downregulated in the four tested cell lines upon [C16Pyr][Amp] treatment: and SKP2 . The CTPDespite technological and social development, cancer remains an important cause of morbidity and mortality. The global increment in cancer incidence and mortality rates, among several other complex reasons, is a consequence of demographic factors  and socioeconomic development, which alter the prevalence and distribution of cancer risk factors . BrCa an50 determination, demonstrated that this compound has potent antiproliferative activity against five different human cancer cell lines, but with no exploration of the functional and genetic alterations of the cells treated. In that study, [C16Pyr][Amp] proved to be active in doses between 0.005 and 132.700 µM [Amp][Amp] in BrCa and PCa cell lines. These lower concentrations are possibly related to the hydrophobic nature of the [C16Pyr] cation conferred by the alkyl side chain [Amp] and [C16Pyr][Amp], in BrCa and PCa cell lines. These compounds were previously reported to display a growth inhibitory effect in HTB133 and PC-3 cancer cells . AlthougMIM][Amp], in thistabolism  and, conde chain . ILs arede chain . This inde chain . Indeed,de chain ,54. [Amp] formulation in inhibiting or attenuating the aggressive features of cancer cells. Overall, this compound was effective in attenuating the malignant phenotype of cancer cells by reducing cell viability and colony formation ability while inducing apoptosis. Moreover, the SI of [C16Pyr][Amp] revealed selectivity toward the majority of the tested tumor cells, mainly for MDA-MB-231 (SI = 11.00) and LNCaP (SI = 7.16), contrarily to the cisplatin SI values. This is a rather promising result, mainly for MDA-MB-231, since it is an aggressive cell line derived from a metastasis of pleural effusion. Moreover, [C16Pyr][Amp] presented a 2–11-fold augmentation of the SI values compared to cisplatin, a drug commonly used in the clinic, thus suggesting the higher selectivity of [C16Pyr][Amp] than cisplatin. In general, cancer is mainly treated with long-term intensive chemotherapy sessions to shrink the tumor so that it can effectively be removed by surgery, if necessary. However, chemotherapy is a highly debilitating therapeutic approach for patients, with severe side effects due to the lack of selectivity toward tumor cells. Despite its cytotoxicity, cisplatin is a widely known chemotherapeutic agent frequently used in the clinic, as well as docetaxel, doxorubicin, cyclophosphamide, and paclitaxel, already reported in BrCa and PCa cell lines . In ordeThe MTT assay is among the most commonly used methods to evaluate the cytotoxicity of a chemical through the analysis of the capacity of mitochondrial enzymes to reduce the tetrazolium dye MTT to formazan crystals ,56. HoweKaushik et al. reported that ammonium and imidazolium ILs also inhibit the colony formation capability of brain cancer cells in a concentration-dependent manner . Herein,CPT2, LDHA, MCM2, and SKP2 genes upon treatment with [C16Pyr][Amp]. These genes are mainly involved in cellular metabolism and cell division and their decreased expression corroborates the exacerbated effects observed on cell viability and apoptosis after [C16Pyr][Amp] exposure. CPT2 encodes a mitochondrial enzyme involved in lipid metabolism, regulating fatty acid oxidation (FAO) in normal cells [Amp], a gene expression panel of 96 genes was analyzed and revealed downregulation of the al cells . In cancal cells . The stral cells . Indeed,al cells . [Amp] exposure paralleled the reduced cell proliferation found in in vitro assays. Indeed, MCM2 has been proposed to be an alternative proliferation marker to ki67 in the BrCa model [Amp] treatment suggests a role of this IL in DNA replication and cell cycle rate reduction in both BrCa and PCa cell lines.[C16Pyr][Amp] treatment of the BrCa and PCa cell lines also significantly downregulated nwinding ,78. MCM2nwinding ,80. Morenwinding  and prednwinding . Herein,Ca model . Both MC samples , showing kinases ,86. SKP2 kinases ,88,89,90 kinases . Accordiechanism . Hence, ctivated . In the Our study demonstrated that [C16Pyr][Amp] has potential as an anticancer therapeutic agent in BrCa and PCa cell lines with disparate phenotypes. The tumorigenic features of these cell lines were impaired upon [C16Pyr][Amp] treatment, leading to a decreased number of malignant viable cells, accompanied by increased cell death, mainly by apoptosis. The aggressiveness of malignant cells was also quantitatively diminished, herein reflected by a reduction in colony formation capacity. At the molecular level, downregulation of the genes involved in two well-known hallmarks of cancer, namely cellular energetics and sustaining the proliferative signaling in the context of DNA replication and cell cycle regulation, might contribute to the malignant behavior attenuation of cell lines upon [C16Pyr][Amp] treatment in phenotypic assays. The selectivity and lower toxicity of [C16Pyr][Amp] compared to cisplatin render it a promising therapeutic agent that might be administered alone or in combination with the currently used conventional therapies. In vivo studies are needed to further evaluate the antitumor effect of C16, as well as to identify targetable proteins that may serve as surrogate markers of therapy response in BrCa and PCa patients.v/v) was used to culture the PC-3 cell line; Keratinocyte Serum-Free Medium (SFM) was used to culture the RWPE-1 cell line (Gibco). Specifically, MCF-10A was cultured with DMEM/F12  supplemented with 5% horse serum (Gibco), 20 ng/mL of Epidermal Growth Factor (EGF) , 0.5 mg/mL of hydrocortisone , 100 ng/mL of cholera toxin (Sigma-Aldrich), 10 µg/mL of insulin (Sigma-Aldrich), and 1% penicillin–streptomycin (Gibco). All cells were maintained at 37 °C in a humidified atmosphere containing 5% CO2 and routinely tested for Mycoplasma spp. contamination .Ten American Type Culture Collection (ATCC)  immortalized epithelial cell lines available at the Instituto Português de Oncologia do Porto laboratory were cultured in the recommended medium supplemented with 10% fetal bovine serum (FBS)  and a 1% antibiotic–antimycotic (Anti–Anti (100×), Gibco, Waltham, MA, USA). The cancer cell lines chosen present distinct features, covering a large spectrum of breast and prostate tumors, while the benign cell lines were used as controls. Briefly, minimum essential media (MEM) was used to culture the HTB22 and Du145 cell lines; Roswell Park Memorial Institute (RPMI)-1640 was used to culture the HTB133, HCC1937, 22Rv1, and LNCaP cell lines; Dulbecco’s Modified Eagle Medium (DMEM) was used to culture the MDA-MB-231 cell line; RPMI-1640/Ham’s F-12 Nutrient Mixture  and two ILs ([C16Pyr][Amp] (C16) and [C2OHMIM][Amp]) based on ampicillin. The quinoxaline derivates were purified by reduced pressure sublimation, and thermal stability was verified by differential scanning calorimetry (DSC), as previously described ,94. The 50 value and the dose–response curves of each compound were determined with an MTT assay. Cells were seeded onto 96-well flat-bottomed culture plates at 2 × 103 (MCF-10A and Du145), 7 × 103 (HTB22), 2.5 × 103 , 4 × 103 (22Rv1), 5 × 103 (LNCaP), and 1.6 × 103 (PC-3) cells per well. The IC50 value was determined using nonlinear regression (curve fit) with all logarithmic absorbance values. For control purposes, cell lines were also exposed to the compounds’ vehicles, i.e., purified water and DMSO for the quinoxalines and ILs, respectively. To assess the toxicity of the compounds, cisplatin was used as a control. All cells were treated 24 h after being cultured (day 0) and were evaluated with 72 h of exposure to the compounds (day 3). The IC50 values of cisplatin were calculated for all cell lines after 72 h with concentrations of 50 nM, 500 nM, 5 µM, 50 µM, and 100 µM, considering the IC50 values previously described in the literature [Amp] and cisplatin was carried out using Wilcoxon matched pair tests and Pearson’s correlations. For RNA expression in the RT2 profiler PCR array, p-values were calculated based on a Student’s t-test of the replicate 2(−ΔΔCT) values for each gene in the control and treatment groups. Analysis was performed with GraphPad Prism 7, and statistical significance was set at p < 0.05.One-way analysis of variance (ANOVA) with the post-hoc Dunnett’s multiple comparison test was used to compare the results obtained in each parameter for the different compounds concentrations and the control/vehicle, when appropriate. Comparison of IC


Canonical Wnt/ β-catenin pathway is one mechanism being activated in platinum-resistant epithelial ovarian cancer (EOC). Detecting potential targets for Wnt pathway modulation as a putative future therapeutic approach was the aim of this study.Biological effects of different Wnt modulators  on the EOC cell lines A2780 and its platinum-resistant clone A2780cis were investigated via multiple functional tests. Immunohistochemistry (IHC) was carried out to compare the expression levels of Wnt marker proteins  in patient specimens and to correlate them with lifetime data.We could show that activated Wnt signaling of the platinum-resistant EOC cell line A2780cis can be reversed by Wnt manipulators through SB216763 or XAV939. All Wnt manipulators tested consecutively decreased cell proliferation and cell viability. Apoptosis of A2780 and A2780cis was enhanced by triptolide in a dose-dependent manner, whereas cell migration was inhibited by SB216763 and triptolide. IHC analyses elucidated significantly different expression patterns for Wnt markers in the serous subtype. Herein, higher plasmatic snail/ slug expression is associated with improved progression-free (PFS) and overall survival (OS).According to the described effects on EOC biology, all three Wnt manipulators seem to have the potential to augment the impact of a platinum-based chemotherapy in EOC. This is promising as a dominance of this pathway was confirmed in serous histology.The online version of this article (10.1007/s00432-020-03317-4) contains supplementary material, which is available to authorized users. Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic malignancies and the seventh most common cancer in women worldwide  were cultured in Roswell Park Memorial Institute 1640 medium with GlutaMAX  supplemented with 10% fetal bovine serum  at 37 °C in the presence of 5% COXAV939 is a small molecule inhibitor of Wnt signaling pathway. It acts through binding and inhibiting tankyrase's catalytic poly ADP ribose polymerase (PARP) domain, which usually destabilizes axin as part of the destruction complex  and following this, in contrast to XAV939, an activator of the Wnt/ β-catenin pathway  were seeded in 96-well plates overnight and afterwards incubated with different concentrations of SB216763  , XAV939  (Sigma-Aldrich) and triptolide  (Sigma-Aldrich).5 × 10S-phase-dependent synthesis of DNA during the cell cycle and, therefore, cellular proliferation was analyzed with thymidine analog 5-bromo-2′-deoxyuridine (BrdU) enzyme-linked immunosorbent assay (ELISA) after 72 h. Cell viability was determined using a 3--2,5-diphenyltetrazolium bromide (MTT) (Sigma-Aldrich) colorimetric assay after 48 h and 72 h. The procedures of both techniques were already reported by our colleagues , cells treated with 2‰ (for incubation with SB216763) and 5‰ (for incubation with XAV939) dimethyl sulfoxide (DMSO) were carried out. For the evaluation the control without DMSO was set 100%.The M30 CytoDeath apoptosis assay is used for determination of early apoptosis. A specific epitope of cytokeratin 18, which is presented after cleavage by caspases during apoptosis, is detected. OC cells were grown on microscope slides to subconfluency, incubated for 48 h in the presence of triptolide at different concentrations , fixed and stored at − 20 °C. After thawing, washing and incubation with M30 CytoDEATH antibody   according to the manufacturer's protocol. Following induced cell death, mouse monoclonal antibodies bind against cytoplasmic histone-associated DNA-fragments (mono- and oligonucleosomes). In brief, 5 × 104 cells/well were grown overnight in 96-well culture plates and then incubated with 6.25 nM, 12.5 nM, 25 nM and 50 nM of triptolide for 24 h. Details on the further procedure can be found in Geiger et al.  for 5 min and methanol for 5 min, followed by freezing at − 20 °C. Manufactured slides were treated with anti-β-catenin IgG  . Significant differences to controls were determined by one-way ANOVA followed by Dunnett's multiple comparisons test or two-way ANOVA followed by Sidak's multiple comparisons test. A probability of n = 109], endometrioid [n = 21], clear cell [n = 11], mucinous [n = 12]) who underwent radical cytoreductive surgery in our department between 1990 and 2002. Histopathological diagnoses were established by a specialized gynecologic pathologist with staging and grading according to TNM and FIGO (WHO) classification. 75.2% of patients presented with advanced disease (FIGO IIB-IV), while only 24.8% were diagnosed in early disease (FIGO I-IIA). Except for patients in stage FIGO IA with low-grade histology, all patients received adjuvant platinum-based chemotherapy. Lifetime data  from EOC patients were taken from our patient charts, the Munich Cancer Registry and aftercare calendars. Median age at primary diagnosis was 59.0 years with a 95% confidence interval (CI) of 57.0–61.0 years. 28 relapses and 101 deaths were documented. A summary of patient characteristics can be found in Table Specimens represent a cohort of 153 patients with EOC  using a microtissue arrayer . Every tumor sample was used for three biopsies, resulting in 459 TMAs in total. Afterwards, sections of 5 µm were prepared and transferred to microscope slides. To determine whether there was enough representative tumor tissue left, a haematoxylin and eosin stain was done.IHC was performed using a combination of pressure cooker heating and the ZytoChem-Plus HRP Polymer-Kit with DAB as chromogenic substrate according to a previous publication by our lab . Furthermore, expression-dependent differences in overall survival (OS) (median ± SEM) and progression-free survival (PFS) (median ± SEM) were tested by chi-square statistic of the Log-Rank test (Mantel-Cox) in Kaplan–Meier curves with SPSS Statistics 25 . P-values < 0.05 were considered to be statistically significant for further analyses.Mean values of the three representative IHC scores of every probe were calculated for further analysis. GraphPad Prism 8 was used for the comparison of immunoquantitation between histological subtypes, FIGO stages and platinum-response (< 6 months to primary surgery)/ -sensitive (≥ 6 month to primary surgery) with Mann–Whitney-p < 0.05 or p < 0.001) shift from ß-catenin staining from the nucleus to the membrane was noted following SB216763 (100 µM) and XAV939 (50 µM) treatment in comparison to controls for both cell lines A2780 and A2780cis. The biggest impact presented XAV939 treatment in A2780cis, where the cell proportion with nuclear ß-catenin localization decreased from 99.1% (control with 5‰ DMSO) to 25.2% (p < 0.001)  impairment of proliferation in A2780 (17.1%) and A2780cis (8.9%). While XAV939 (50 µM) showed no influence in both cell lines, SB216763 (100 µM) also significantly (p < 0.05) inhibited proliferation in A2780cis (84.5%) , we compared the highest concentrations of each inhibitor against its control. Controls for triptolide without DMSO were set 100.0%. After 72 h, triptolide (50 nM) displayed a significant (p < 0.01 or p < 0.001) at the selected concentrations of 100 µM SB216763, 50 µM XAV939 and 50 nM triptolide . But in contrast to cell proliferation assay, metabolic activity decreased for all three inhibitors in platinum-sensitive  and -resistant cells  in MTT assay. Except for SB216763 and XAV939 in A2780cis, these findings were highly significant (p < 0.001) in a dose-dependent manner up to 95.4% for A2780 and to 53.1% for A2780cis, respectively. Controls with 0‰ DMSO showed 100.0% M30 CytoDEATH negative cells  increased significantly (p < 0.01 or p < 0.001) dose-dependent rise of absorbance up to 8.9-times in A2780 and 2.7-times in A2780cis at 50 nM   in endometrioid , clear cell  and mucinous  type compared to serous  histology. Moreover, in comparison to serous subtype, expression of plasmatic snail/ slug (8.8 ± 0.2) and membranous E-cadherin (7.6 ± 0.3) was significantly lower in endometrioid  and clear cell  histology. Representative stainings are displayed in Fig. To understand the role of Wnt pathway in the clinical context, immunoquantitative comparisons of Wnt signaling markers among four histological subtypes  were performed. IHC score revealed multiple significant expression differences especially of serous histology compared to other subtypes Table . Membranp < 0.01]), ß-catenin (plasma) (8.8 ± 0.4 to 6.7 ± 0.3 [p < 0.001]) and E-cadherin (plasma) (7.7 ± 0.5 to 6.8 ± 0.3 [p < 0.05]) were noted for FIGO stage I-II compared to FIGO stage III-IV  (9.2 ± 0.4 to 7.9 ± 0.2 [No significant expression differences were identified for Wnt markers in platinum-resistant compared to platinum-sensitive patients (data not shown).p = 0.001]). A similar trend was noted for OS, although this difference did not reach statistical significance (50.4 ± 13.2 to 27.6 ± 3.6 months [p = 0.058])  expression with a cut-off IHC score of seven or beneath  is a key mechanism in cell migration and Wnt signaling is one of EMT's major pathways  after SB216763 or XAV939 treatment in both cell lines. In parallel to the functional tests, both drugs led to an immunohistochemical shift of β-catenin from the nucleus  to the membrane , which finally contrasts the general role of SB216763 as an activator of the pathway Supp. 1 BrdU cell proliferation and MTT cell viability assay for different Wnt signaling manipulators in different concentrations in A2780 and A2780cis after 72h: (a) In the BrdU assay only triptolide displayed a dose-dependent decrease of cell proliferation in A2780 and A2780cis, whereas SB216763 and XAV939 showed no dose-dependency . (b) In the MTT assay, according to the findings in the BrdU assay, only triptolide displayed a dose-dependent decrease of cell metabolic activity in A2780 and A2780cis, whereas SB216763 and XAV939 showed no dose-dependency Below is the link to the electronic supplementary material.


Uveal melanoma is a rare intraocular malignancy. Half of the patients diagnosed will develop metastases within 10 to 30 years, most commonly in the liver. Although there has been a significant development in the treatment of melanoma, no effective treatment to prevent or treat metastases of uveal melanoma is available. Oncolytic viruses are now being studied for various types of cancers and show promising results. Preclinical results show cytolytic activity of enteric cytopathic human orphan virus type 7 (ECHO-7) strain Rigvir in human melanoma, rhabdomyosarcoma, gastric adenocarcinoma, lung carcinoma and pancreas adenocarcinoma cell lines. The aim of this study was to test the possible cytolytic activity in human uveal melanoma cell lines.The results suggest cytolytic activity of oncolytic ECHO-7 virus strain Rigvir in MP41, 92-1 and Mel-202 cell lines. While uveal melanoma is a rare disease it is one of the most commonly diagnosed intraocular malignancies, which comprises about 5% of all melanoma cases reported . Most ofBRAF mutations [GNAQ and GNA11 [BAP1 gene shows inactivating mutations in 85% of aggressive tumours and is considered to be a marker of metastatic disease [Cutaneous and uveal melanoma both arise from melanocytes, however, genetically they are different. In cutaneous melanoma most of the mutations concern the mitogen-activated protein kinase pathway (MAPK). This pathway is responsible for cell growth, differentiation and survival. The mutations altering this pathway most commonly are found in the serine/threonine-protein kinase B-Raf (BRAF) kinase; 40–50% of cutaneous melanoma have nd GNA11 . These gnd GNA11 . The tum disease .Due to the distinctive genetic features of uveal and cutaneous melanoma, specific model systems for each should be used in preclinical studies and active substance testing. Until recently, there was a lack of cell models for uveal melanoma research because most of the available cell lines were from cutaneous melanoma. However, recently a couple of uveal melanoma cell lines have been developed . They inCytolytic effect of the present ECHO-7 virus strain has been observed in human melanoma, rhabdomyosarcoma, gastric adenocarcinoma, lung carcinoma and pancreas adenocarcinoma cell lines . CurrentThe oncolytic potential of Rigvir has been observed in several cancer cell lines. Therefore, the experiments were extended to examine the effect on cell viability of oncolytic ECHO-7 virus strain on uveal melanoma cell lines. The following seven cell lines were tested: Mel–202, MP41, MP38, MP65, MP46, MM28 and 92-1.Only 3 of the seven uveal melanoma cell lines could be successfully propagated in the laboratory, Mel-202, MP41 and 92-1, and used in the analysis. The results indicate that Rigvir had cytolytic effect in Mel-202, MP41 and 92-1 uveal melanoma cell lines.2 growth conditions.Cell lines MP38 , MP65 (ATCC CRL-3299), MP41 (ATCC CRL-3297), MP46 (ATCC CRL-3298), MM28 (ATCC CRL-3295) were obtained from ATCC, and 92-1  and Mel-202 (cat.no. CLS3527) were obtained from Sigma-Aldrich. All cell lines are of human origin. The cell lines were cultured in Roswell Park Memorial Institute Medium (RPMI) + 20% foetal bovine serum (FBS) + 1% penicillin/streptomycin under 37 °C, 5% CO6 TCID50/ml) was obtained from the manufacturer.ECHO-7 virus strain  was treated with gamma irradiation; the measured absorbed dose in the samples was 41.6 -43.3 kGy.2 in a 24 well plate. After 24 h cultivation (to ensure complete cell attachment to plate surface) the medium was renewed.10,000 cells for culture 92-1 and MP41, 30,000 cells for Mel-202 were seeded with culture medium (RPMI + 20% FBS + 1% penicillin/streptomycin) per 1 cmTwo concentrations of ECHO-7 virus Rigvir strain were used: The number of viral particles added was multiplicity of infection (MOI) 7 and 70 (1% and 10% (v/v), respectively). Also, two concentrations of inactivated ECHO-7 virus strain were added: MOI of 7 and MOI of 70 as negative control. An equal volume of medium (without virus) was added to the control cells. Each test group was run in triplicate (n = 3).Cell proliferation was monitored for 96 h with a live cell imaging system . Phase contrast microscopy images were taken after 0, 3, 6, 12, 24, 48, 72 and 96 h. The live cell imaging system was set to recognize differences in cell population and morphology of different cell growth phases.The inhibitory effect of ECHO-7 virus Rigvir strain was calculated at the end of culturing time (96 h), using the formula: Inhibition (%) = 100-(100xA/B), where A stands for cell number with Rigvir at the end of culturing time, and B is control  the cell number at the end of the culturing time. GraphPad Prism 8 was used for two-way ANOVA statistical analysis. The difference was taken as statistically significantly different when P < 0.05.The results show that MP41, Mel-202 and 92-1 cell line cells detached from the cultivation surface when ECHO-7 virus Rigvir strain was added to the medium. Control samples with PBS and inactivated Rigvir continued to proliferate. This observation indicates a cytolytic/cytotoxic effect of Rigvir in these three cell lines  was 86.4% and by MOI of 70 ECHO-7 viral particles it was 85.9% Fig. a.92-1 cells treated with MOI of 70 Rigvir particles also showed earlier cytotoxic response than for the MOI of 7 treatment group. The maximal inhibition of cell growth by MOI of 7 Rigvir compared to control (PBS) was 92.4, and by MOI of 70 it was 95.5%  was 99.3% and by MOI of 70 it was 99.4%  and Gα11 (encoded by GNA11) having 90% homologous amino acid sequences [The present results suggest that of ECHO-7 virus strain Rigvir has cytolytic activity in 92-1, MP41 and Mel-202 cell lines. These cell lines originate from primary uveal melanoma tumours, possess a loss of heterozygosity of chromosome 3, and show BAP1 protein expression. The MP41 cell line has a mutation in GNA11 (c.626 A > A/T), the 92-1 cell line has mutations in GNAQ (c.626 A > T) and EIF1AX (c.17 G/A). The Mel-202 cell line has mutations in GNAQ (c. 629 G > A) and SF3B1 (c.1793 C > T) . These mequences .The results suggest a cytolytic effect of Rigvir in the MP41, Mel-202 and 92-1 human uveal melanoma cell lines. Currently there is no treatment to prevent or treat metastasis in the uveal melanoma patients and it is crucial that new treatment for this type of cancer are being sought.The results show that Rigvir decreases uveal melanoma cell proliferation under in vitro conditions; further experiments are necessary to elucidate if Rigvir oncolytic virus would also have clinical effect in uveal melanoma patients.This study is limited by the three uveal cell lines that were propagated in the laboratory. In order to use a live cell imaging system and capture the cytolytic effect, a certain cell layer density should be reached. Four of the cell lines have long cell doubling times (up to 96 h) that limited their use in the real time imaging system. Further studies should involve 3D cell models; however, a majority of the commercially available cell lines unfortunately do not form even cell monolayers.