Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines. During preclinical drug development, the ability of cancer cell lines to faithfully model human disease is important for identifying potential therapeutic strategies. Here, using transcriptomic datasets of over 1000 cell lines, the authors evaluate how representative each line is of its cancer type and present their cell line selection tool. Cell line experiments, which often serve as the first step in translational research for clinics, are extensively used in verifying potential drug candidates and newly identified targets. In the past decades, more than ten thousand cell lines were established for various studies with the same goal: to simulate the actual disease mechanism. Due to metabolic alterations in cell line models5 and reduced biological availability in humans, drug candidates may still have a high chance of failing in the in vivo studies and clinical trials, even though they are successfully tested in vitro based on more than one cell line. Therefore, whether and to what extent each cell line model can reflect a disease phenotype, especially after establishing immortalized and stabilized cell lines from primary cells and culturing them in vitro, remains a critical question in life science.With the sharp increase in the application of (multi-)omics technology and computational modeling of biological systems, novel therapeutic targets and drug candidates for human diseases have been identified through in silico analysis7 and in the cell line section of the Human Protein Atlas (HPA) project8. The transcriptomics data generated by these studies enabled the comparison of global gene expression between the cell lines and the corresponding tumor based on the transcriptomics data (RNA-seq) in The Cancer Genome Atlas (TCGA) project. Despite a few studies that have attempted to map cancer cell lines to the corresponding diseases based on the molecular signatures11, which facilitates the selection of appropriate cell lines for cancer research, these studies did not further extend their comparison of cell line gene expression to normal tissues and individual cell types—a critical step which will help us to understand the characteristics and representative of cell lines to their origin12.Tremendous efforts have been made to reveal the molecular characteristics and genetic specificity of cell lines, including the transcriptomics data  of human cell lines in the Cancer Cell Line Encyclopedia (CCLE)https://v22.proteinatlas.org/humanproteome/cell+line) in an open access format.In this study, we first analyze the transcriptomics data of more than 1000 human cell lines and compare the global gene expression landscape of the human cell lines with the gene expression data from human tissues, tumors, and single-cell types. Second, we systematically evaluate if and which cell lines are representative of specific cancer types. Next, we analyze the cell line characteristics at pathway and cytokine levels for providing a deeper understanding of the cell phenotype as well as generating a reference state for a better design of in vitro experiments. We finally provide all the information in the cell line section of the HPA portal  of these cell lines using the same pipeline for RNA-sequencing data preprocessing. We then applied principal component analysis to extract 398 PCs preserving 80% of the total variance in the full dataset, followed by Uniform Manifold Approximation and Projection (UMAP)As shown in Fig. n = 1019) or HPA (n = 69) cell lines and independently categorized the genes based on their specificity of expression in the HPA and CCLE datasets 15, and found that 1,614 genes were overlapped between two different datasets. Based on gene set overrepresentation analysis (GSOA)17, these genes were strongly associated with DNA replication and nuclear division, suggesting these genes are indispensable for cell cycle progression and cell proliferation , and applied gene expression specificity classification to the TCGA dataset at the cohort level, which enabled the comparison of the gene expression specificity between CLDs and the TCGA cohorts.Focusing on cancer research, here we integrated 45 HPA cancer cell lines with 973 CCLE cancer cell lines (33 pairs of common cell lines) to obtain a total of 985 cancer cell lines to study cancer-specific cell line characteristics. Specifically, twelve cancer cell lines and one cancer type  were only available in the HPA dataset but not in the CCLE dataset. By averaging the gene expression nTPM in cell lines within each cancer type (defined by the primary disease of a cancer cell line), 985 cancer cell lines were aggregated into 27 groups based on cancer types. Hereafter, we termed CLD (cell line disease) to represent cell lines grouped by the same cancer type. Subsequently, protein-coding genes were categorized into five different groups according to the expression specificity in CLDs Table , includiBased on the criteria in Table At the cancer type level, CLDs were hierarchically clustered based on the averaged expression of cell lines from the same cancer type, resulting in three distinct clusters of CLDs Fig. . In line20 for comparison. The tissue-enriched and single-cell type-enriched genes were derived from 56 tissues and 51 cell types from 13 human tissues (both are non-disease), respectively, with similar gene expression specificity strategies applied. As shown in Fig. To evaluate if cell lines are representative of the corresponding tumors, we compared the CLD-enriched genes with the corresponding TCGA cohort-enriched genes, meanwhile including tissue-enriched and single-cell type-enriched genes defined by the HPAApart from the overrepresentation analysis of the enriched genes, additionally, we deployed a correlation-based analysis to evaluate the consistency between the genome-wide transcriptomic expression of cell line cancer types and the one from the corresponding TCGA cancer cohorts. In brief, gene expression of the TCGA samples from the same cohort was averaged, and the correlations between CLDs and TCGA cohorts were calculated by Spearman’s ρ. As shown in Fig. P-value < 1E-30 for the most significant ones in Supplementary Fig. We then compared primary and metastatic cell lines to investigate the changes in gene expression induced by metastasis. In terms of primary cell lines, the high concordance between CLDs and tissues/cancers/single-cell types is maintained, with similar significant levels of overrepresentation 9. We found that 65 of our selections were also included in the TCGA-110-CL panel, with a p-value of 1.55E-44 based on hypergeometric testing  gene—one of the TCGA-LIHC signature genes, which is also a hallmark gene of liver functions21, was highly expressed in the top-ranked liver cancer cell lines  was reported as “undifferentiated” in the first 12 cell lines, but 9 of the last 12 cell lines were reported as “undifferentiated”. The imbalance between highly- and lowly-ranked cell lines regarding their differentiated state possibly explains why some cell lines are lowly prioritized. Taken together, these results demonstrated the validity of the use of the approaches for cell line prioritization.For some cancer types, e.g., liver cancer (LIHC), the commonly used cell lines such as Hep-G2 and Huh-7 for in vitro study were selected as the best candidates out of the total 24 liver cancer cell lines. We then specifically inspected the expression of the TCGA-LIHC signature (867 genes elevated in TCGA-LIHC cohorts compared to the others) in the 24 liver cancer cell lines, and found that the expression of the TCGA-LIHC signature was generally higher in the top-ranked cell lines than in the lowly ranked cell lines Fig. . For insnes Fig. , distingnes Fig. , suggestnes Fig. . We obsep-value < 0.05; Supplementary Fig. In addition, for each TCGA cohort, we also applied our cell line prioritization algorithm to evaluate cell line fidelity at the pathologic stage and molecular subtype levels . Based on the mean squared error (MSE) of the pathway and cytokine activity between each pair of the cell lines from this study and the Genentech datasets, the 461 common cell lines showed significantly lower MSE than all the pairs of the non-common cell lines at both pathway and cytokine levels  of the prioritized cell lines between pathway/cytokine analysis and transcriptome-wide analysis  formed a distant cluster that can be easily separated from the other cell lines 32, and this can be observed in our analysis as demonstrated by the high level of androgen signaling in VCaP  the global similarity between cell line and TCGA cohort (correlation-based); and (2) the preservation of disease signature in cancer cell lines (GSEA-based). Especially the adoption of the GSEA, which showed discriminative in estimating fundamental tumor biological processes in cell lines  omics profilingnes Fig. , has notAs a showcase, we explored the details of the selection procedure in liver cancer cell lines and elucidated the reasons why the commonly used cell lines Huh-7 and Hep-G2 outperform the others for liver cancer studies. Results showed non-ignorable differences between 24 liver cancer cell lines, in terms of the correlation (similarity) to the corresponding TCGA cohort LIHC, and the preservation of the elevated genes in the TCGA-LIHC cohort. This suggested that cell lines, even derived from the same primary diseases, are rather heterogeneous with diverse phenotypes. The major differences between cell lines could be induced by the donors, the processes during the establishment of the cell line models, and the culture procedure, however highlighting the importance of choosing the best cell line models for cell experiments.40, providing direct evidence of the expression and translation of the biomarkers for the disease phenotype. Integrating cell line proteomics and metabolomics41 will undoubtedly benefit the comprehensive depiction of cell line characteristics. Nevertheless, since our approaches for pathway and cytokine inference were based on biodata mining from publicly available transcriptomics, these results are important resources for cell line comparison that could not be derived from other omics platforms.In some cases, the selection of the most appropriate cell line may not completely follow the computational prioritization given in this study. For example, for drug-target selection, researchers may want to know if a specific gene, cytokine, or pathway is highly expressed in a cell line. To help interpret cell line characteristics, we calculated the pathway and cytokine activity scores. As demonstrated in several prostate and breast cancer cell lines for their AR-/ER-specificity and different levels of pro-/anti-inflammatory cytokines, these results can preciously depict the phenotypic features of the cell lines. As a complement, recent studies have enabled large-scale proteomic profiling in human cancer cell lines13, the TMM normalized gene expression can minimize the batch effects caused by sampling and different technology platforms, especially when a small number of genes are very highly expressed, facilitating the sample-wise comparison between gene expression in cell lines and tissues, organs, single-cell types, and cancer biopsies. In addition, key features of the cell lines have been demonstrated by the pathway and cytokine analysis and will be presented aside to provide a deep understanding of cell line phenotype and to aid the better design of preclinical experiments. Furthermore, we provided RRID for the cell lines in the HPA database, linking them to the information page of the Cellosaurus database42. This will help users to bypass the selection of mislabeled or transdifferentiated cell lines due to historical reasons35. By incorporating more than 1,000 transcriptional profiles of human cell lines in the HPA database, the newly launched cell line section facilitates the exploration and comparison of the expression of human protein-coding genes across cell lines and HPA tissues19, tumors43, single-cell types20, and blood immune cells13.For all the cell lines analyzed in this study, we processed the gene expression normalization using the same pipeline applied to the other HPA resources, and present the normalized expression nTPM of these cell lines in the HPA database. Based on our previous analysis39, metabolomics41 and drug response datasets46 to comprehensively evaluate and expand the main findings in this study.We here report that cell lines derived from metastatic cancers from the same tissue of origin are more similar to each other as compared to the cell lines metastasized to the same site from different origins. This suggests that although thought to be largely dedifferentiated, tumor cells keep some key features of their original cell types. This reinforces the idea that the treatment strategy for metastatic cancer should be designed based on the primary site and emphasizes the importance of the identification of cancers of the unknown primary site. Future works would focus on multi-omics analysis of cell lines using genomics and recently released proteomics47, cell line models still face inherent limitations. For example, in most cases, a maximum of two cell lines can be co-cultured together, limiting the study of cellular interactions between different cell types as a whole system, the latter of which plays an important role in the tumor microenvironment and progression. Furthermore, as extensively evaluated by the recent single-cell sequencing studies, tumor cell type composition is rather heterogeneous, which also complicates the design of the in vitro preclinical experiments. Advanced cancer models such as patient-derived xenografts and organoids48 may overcome this pitfall. Nevertheless, our study demonstrated that cell lines, as a single cell type, preserve the most discriminative and informative signatures of the corresponding cancer types, thus could be properly used as a starting point for cancer research with an appropriate selection of cell lines, the latter of which could much rely on the newly launched cell line section of the Human Protein Atlas—a comprehensive resource for the characterization and exploration of human cell lines.Despite advanced cell culture techniques such as 3D culture which enable a better simulation of the true in vivo environment6 were included in this study. CCLE cell lines were annotated based on the publicly available DepMap 2022Q2 annotation file  (Supplementary Data https://www.cellosaurus.org/)42. Based on the DepMap annotation, cell lines were categorized by primary disease , primary or metastasis, lineage, and sample collection site. This assigned the 1,019 cell lines into 26 cancer types and one non-cancerous group consists of mostly fibroblast cell lines. Two cell lines without disease information were denoted as “Unknown”.A total of 1,019 cell lines from CCLEA total of 69 cell lines from HPA were included in this study. HPA cell lines were annotated by unique internal ID, RRID, cell line name, tissue, and origin. Additional publicly available information (including sex) obtained from the DepMap 2022Q2 annotation file was added to extend the HPA cell line annotation (Supplementary Data 6 were downloaded from the GEO using SRA Toolkit (v2.11.3) and were subsequently converted into raw fastq files. The RNA-seq data processing followed the same pipeline as the HPA project. Transcript expression levels were quantified by mapping sequences to the human reference genome GRCh38.p13 cDNA using Kallisto (v0.46.2)49. Based on the Ensembl version 103 annotation, the transcript abundances were aggregated into gene level as transcripts per million (TPM) by tximport (v1.22.0)50 without the inclusion of non-protein-coding transcripts. This resulted in a total of 20,090 protein-coding genes included for further analysis, and their expression was converted into pTPM  by scaling the sum of the TPM to 1 million per sample. The pTPM expression was normalized by trimmed means of M (TMM)51 using the tmm function provided in the R package NOISeq (v2.38.0)52 with a median column as the reference, with the parameters doWeighting = T and logratioTrim = 0.3, and the resulting expression was denoted as nTPM.The SRA files of the 1,019 cell lines from the CCLE 2019 data13 were processed using the same pipeline.The 69 human cell lines from the HPA database53. Tumor purity of the TCGA tumors was estimated based on the ESTIMATE18 algorithm. TCGA samples with a tumor purity score <0.7 were considered highly infiltrated by immune and stromal cells and were thus removed from the analysis, resulting in 6,082 qualified samples from 26 TCGA cohorts for downstream analysis  of primary tumors and primary blood-derived cancers from 27 TCGA cohorts were retrieved using the R package TCGAbiolinks (v2.27.2)n = 1076 cell lines for 26 disease labels including “non-cancerous”). The top 5000 most variable genes were selected and scaled before classification. The analysis was performed by the scikit-learn package  in Python (v3.9.7).Based on the nTPM expression of HPA + CCLE cell lines, logistic regression (solver = “lbfgs”) and random forests (number of estimators = 100) were used to test the ability of the expression data in correctly classifying cell line disease types. The test was performed under stratified 5-fold cross-validation with 100 repeats, hence, only a disease type that has no less than five cell lines was included in this analysis . The agglomeration method was set as “complete-linkage” for hierarchical clustering. The same hierarchical clustering was applied on all analyzed 1,019 CCLE and 69 HPA cell lines.19 and single-cell type20 based on the HPA version 21, as well as TCGA cohorts analyzed in this manuscript. Details can be found accordingly in our previously published papers as well as in the HPA website resources (https://www.proteinatlas.org/about/download).The same approach for gene specificity categorization was applied to the gene expression data of the HPA tissueP-values corrected by the Benjamini-Hochberg procedure. In addition, functional analysis of gene sets was performed by gene set overrepresentation analysis (GSOA) based on the Gene Ontology biological process terms by the R package clusterProfiler (v4.2.2)17.The CLD-enriched genes were compared with the tissue-enriched, TCGA cohort-enriched, and single-cell type-enriched genes by hypergeometric testing for significance, with the adjusted The 985 cancer cell lines from both HPA and CCLE were analyzed for their representability of the corresponding TCGA cohorts  between the cancer cell lines and their corresponding TCGA cohorts for the first. For this, for each gene in a TCGA cohort, the nTPM values were averaged per cohort. Then, for each TCGA cohort, Spearman’s ρ was calculated based on the TCGA averaged nTPM values and the nTPM values of the disease-matched cell lines based on the common 20,056 protein-coding genes.2 transformation of the fold change. Finally, for each cell line, gene log2 fold changes were sorted from high to low, followed by the GSEA of the TCGA cohort elevated genes against the sorted gene list. It is expected that cell lines showing high concordance to the matched TCGA cohort should present high log2 fold changes of the TCGA cohort elevated genes relative to the disease baseline expression. The results were represented as the normalized enrichment score (NES), with a positive value showing high consistency between a cell line and a disease-matched TCGA cohort. The significance levels of the enrichment were presented as adjusted P-values corrected from the raw P-values based on the Benjamini-Hochberg procedure. The GSEA was performed by the R package fgsea (v1.20.0)54.For the second approach, we calculated the enrichment of the TCGA cohort elevated gene  in cell lines by gene set enrichment analysis (GSEA). The concept is that genes that have an elevated expression in a TCGA cohort can be considered as the cohort signature and their high expression should be reflected by cell line models. To test this, similar to the approach where we calculated gene specificity, for the 27 CLDs, gene expression was averaged per disease, resulting in the mean expression for each of the 27 CLDs. Then, the average expression per disease was further averaged as the disease baseline expression. After that, for every cell line, we calculated the fold change of every gene relative to the disease baseline expression, followed by the logFor both approaches, cell lines were ranked based on Spearman’s (ρ) and NES from high to low, respectively. Then, the two ranking lists were combined, and cell lines were reordered according to their average rank  pathologic stages and molecular subtypes of the analyzed TCGA samples were retrieved using the R package TCGAbiolinks. Specifically, molecular subtypes were retrieved using the package function PanCancerAtlas_subtypes, and the column “Subtype_Selected” was selected as the final categorization. For each TCGA cohort, gene expression nTPM was averaged per stage, respectively. Then, Spearman’s correlation was calculated between stages and cohort-matched cell lines. Considering the limited number of pathologic stages per cohort, here we selected genes having fourfold higher expression in one stage than any other stage within a TCGA cohort as the stage signature, and calculated the enrichment of the stage signature in the cohort-matched cell lines using the same approach to the GSEA of TCGA disease signature in all cancer cell lines. Finally, correlation-based and GSEA-based ranking lists were combined to obtain the final ranking list for cell line prioritization for the disease stage  for pathway analysis. Then, the read counts were normalized by DESeq2 (v1.34.0)55 with respect to the size factor of each cell line and were further transformed by variance stabilizing transformation into log2 space. To calculate the relative pathway’s activities across all cell lines, the normalized values were centered by subtracting the mean value per gene. Then, the R package decoupleR (v2.0.1)56 was used to calculate the relative pathway’s activities based on the top 100 signature genes per pathway obtained from the R package progeny (v1.16.0) as suggested by the original publications57. By default, the decoupleR was executed using the top performer methods benchmarked 56, and the results were integrated to obtain a consensus score presented as z-score to represent the pathway activity. Here, a consensus z-score above 1 or below −1 was considered significant, resulting in 4,352 (29%) significant pathways among the total 14,770 (14*1055) calculated pathways  of the total 45,365 (43*1055) cytokine activities were significant 26 (n = 610 cell lines) as well as the TCGA dataset analyzed in this study. For this, the expression of the Genentech and TCGA datasets was independently processed and analyzed for the pathway and cytokine activities in the same way as for the HPA and CCLE cell lines. After averaging the gene expression nTPM of the TCGA dataset per cohort, the pathway and cytokine activities were inferred for TCGA at the cohort level. The mean squared error was calculated based on the pathway and cytokine activities, respectively, between the HPA + CCLE cell lines and the Genentech cell lines as well as the matched TCGA cohorts. Further, similar to the transcriptomics-based cell line prioritization, HPA and CCLE cell lines were prioritized for the corresponding TCGA cohorts based on the combined ranking list of pathway and cytokine similarity represented by mean squared error   no less than 0.8 for the number of selected PCs. After PCA, the Uniform Manifold Approximation and Projection (UMAP)14 analysis was performed by the R package uwot (v0.1.11) to compress the PCs into two dimensions for visualization.Dimensionality reduction was performed on the nTPM expression data, the PROGENy pathway activity, and the CytoSig cytokine activity for sample visualization. First, we applied principal component analysis (PCA) using the R package pcaMethods (v1.86.0)Wilcoxon rank-sum test  was used to evaluate the statistical difference between groups. Statistical tests were performed in R (v4.1.2).Further information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting SummaryDescription of Additional Supplementary FilesPeer Review FileSource Data


Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, without the consideration that response outcomes may primarily attribute to a few finer-level ‘subcomponents’, such as privileged substructures of the drug or gene signatures of the cancer cell, thus producing predictions that are hard to explain. Herein, we present SubCDR, a subcomponent-guided deep learning method for interpretable CDR prediction, to recognize the most relevant subcomponents driving response outcomes. Technically, SubCDR is built upon a line of deep neural networks that enables a set of functional subcomponents to be extracted from each drug and cell line profile, and breaks the CDR prediction down to identifying pairwise interactions between subcomponents. Such a subcomponent interaction form can offer a traceable path to explicitly indicate which subcomponents contribute more to the response outcome. We verify the superiority of SubCDR over state-of-the-art CDR prediction methods through extensive computational experiments on the GDSC dataset. Crucially, we found many predicted cases that demonstrate the strength of SubCDR in finding the key subcomponents driving responses and exploiting these subcomponents to discover new therapeutic drugs. These results suggest that SubCDR will be highly useful for biomedical researchers, particularly in anti-cancer drug design. The ability to predict the response of a cancer patient to a therapeutic drug is a major goal in modern oncology. Computational methods have led to significant progress in predicting cancer drug response (CDR), but they ignore that response outcomes may be highly associated with specific ‘subcomponents’, such as privileged substructures of drugs or gene signatures of cancer cells. To understand the effect of subcomponents in CDR prediction, we present a subcomponents-guided deep learning method, named SubCDR. SubCDR is based on multiple deep neural networks capable of extracting functional subcomponents from the drug SMILES and cell line transcriptome, and decomposing the response prediction between the drug and cell line into identifying pairwise interactions between their respective subcomponents. With this interaction form, we can obtain a traceable path to explicitly identify subcomponents contributing significantly to the response outcome. We validate our method on the GDSC dataset and demonstrate improvements over the state-of-the-art CDR prediction methods. Furthermore, we show the strength of SubCDR in interpreting the CDRs and discovering new drugs with sensitive responses from the view of subcomponent interaction. Precise identification of drug response in cancers holds great promise for developing personalized therapy regimens to increase survival and reduce patients’ expenses. Since testing multiple drugs for a cancer patient is infeasible for practical and financial reasons , there iRecent years have witnessed numerous computational methods proposed to predict CDRs, which intend to relate inputs  to desired outputs . The early network-based methods constructed bio-networks with cell lines, drugs, and their known associations, and then converted the problem to a link prediction task, undertaken on the random walk  or inforIn the context of CDR prediction, the response outcome or bio-activity is not only signalling pathway-related but may also be highly associated with the finer-level ‘subcomponents’, such as privileged substructures of drug molecules and gene signatures of cancer cells –23. For To address the above challenge, we propose a subcomponent-guided deep learning method for accurate and interpretable CDR prediction, named SubCDR. Instead of modeling at the entire drug (or cell line)-level, we first devise a knowledge-driven decomposition module to extract subcomponents with biological functions. For the drug (or cell line), subcomponents are depicted as a set of substructures (or gene subsets) decomposed by the molecular structure (or transcriptome profile). Afterwards, we imitate the CDR process between each drug and cell line as pairwise interactions between their respective subcomponents, and adopt a scoring function to measure the intensity of pairwise interactions, which results in an interaction map. Finally, a powerful graph convolutional network (GCN) is deployed on the interaction map to learn the representation used for decoding into the response outcome. Inspired by recommendation systems with side information , we also50  values corresponds to a drug-cell line combination. IC50 /J indicates side information about the i/j-th drug/cell line. Further, we feed the I and J into a fully-connected layer fa to output their latent vectors as the final side information   to decomi, j), we design a commonly used decoder from the learned representation (hij) and side information .The hyperparameters of SubCDR are recommended as follows. In the extraction of drug subcomponents, we set the radius and fixed length of ECFPs to 2 and 512, respectively; the maximum length To comprehensively evaluate the performance of SubCDR, we randomly selected 90% instances from the dataset to compile the cross-validation set and used the remaining 10% instances as the independent test set. For the cross-validation set, we performed the following three practical scenarios:Warm start. The 5-fold cross-validation (5-CV) was implemented by randomly dividing all instances into 5 equal parts, and the training and test sets share common drugs and cell lines.Cold start for cell line. The 5-CV was implemented by randomly splitting instances on the cell lines to guarantee that the test set only includes unseen cell lines in the training set.Cold start for drug. Similar to the scenario above, the 5-CV was conducted on the drugs, where the test set only contains the drugs absent in the training set.2). To further confirm the classification accuracy of models, we binarized the log-transformed IC50 value in accordance with a threshold used in previous studies [50 value below -2.0 (-2.0 corresponds to IC50 of approximately 0.135 μM) were classified as positive instances; otherwise, they were classified as negative instances. The ratio of positive and negative instances is around 1:10. We then expanded the above SubCDR framework into the classification task by adding a Sigmoid activation at the last layer and taking binary cross entropy (BCE) as the loss function. The classification performances are evaluated by two metrics: the area under curve (AUC) and the area under the precision-recall curve (AUPR).Additionally, we trained the model on the whole cross-validation set, and then made predictions over the independent test set for a more objective evaluation . Three common metrics are used for measuring the statistical correlation between observed values and predicted response values under a regression task, encompassing Root Mean Squared Error (RMSE), Pearson’s Correlation Coefficient (PCC), and the Coefficient of determination  in the dataset is far less than the number of cell line types (800), so the models are more difficult to get enough knowledge for migrating in unseen drugs. From the overall results of cold starts, our model can be applicable to the prediction of unseen drugs/cell lines.The performances of SubCDR and baseline methods are shown in 2 scores. For the classification tasks we conducted on the independent test, the performances for all methods are shown in From the regression performances on the independent testing , SubCDR 2, AUC, and AUPR, as shown in In addition to the global performance of the methods, we focused on the predicted result of instances divided into different cancer and target pathway types. Each drug was annotated with a target pathway type in accordance with the GDSC database , and eacIn the prediction phase, SubCDR generated an interaction map scoring interaction intensities between subcomponents for an input drug-cell line instance, and subcomponents (or their interactions) that contribute significantly to the response outcome will return higher scores. Here, we visualized the interaction maps as heatmaps to highlight the subcomponent interactions with higher scores. By comparing and analyzing the heatmaps for many instances, we observed partial cases that can prove the strength of SubCDR in highlighting the critical subcomponents that influence the response outcomes and interpreting the prediction in a biologically meaningful order:In https://www.cancer.gov/about-cancer/treatment/drugs/lung). Otherwise, a few drugs with diverse structures produced similar response outcomes on the same cell line. For example, the pan-BRPF bromodomain inhibitor OF-1 and the mTOR kinase inhibitor OSI-027 differ significantly in molecular structure, but they responded similarly in the esophageal carcinoma cell line EC-GI-10, and similar situations also occurred in the responses of drugs AGI-6780 and AZ6102 with breast invasive carcinoma cell line T47D  significantly reduced the sensitivity to chronic myelogenous leukemia cell lines such as EM-2, KU812, and LAMA-84, and the carbonyl group also received the highest scores in their subcomponent maps . That maine T47D . ExplainAs the interpretability of SubCDR underlines crucial subcomponents, here we investigate whether these subcomponents can guide the discovery of new drugs with sensitive responses. Depending on the findings in 50 values  for these two drugs as the candidates that may produce sensitive responses  [To further verify the ability of SubCDR to discover novel sensitive CDRs, we paid attention to the unmeasured drug-cell line combinations mentioned in Section 2.1. We trained the SubCDR using all the measured instances in the dataset and then predicted the unmeasured combinations. Here, two clinically approved drugs, Gemcitabine and Bortezomib, were taken for verification. We selected the top 10 cell lines with the lowest predicted log-transformed ICesponses , and the -6.502)  and can  -6.502) . Many st -6.502) . Bortezo -6.502) . In Liew -6.502) , the pro -6.502) . The aboSubCDR provides an interpretable CDR prediction framework that integrates a line of modules for handling subcomponent extraction, subcomponent interaction and side information acquisition. To investigate the necessity of each module in our model architecture, we conducted several comparisons between SubCDR with its variants:SubCDR without side information (w/o SI). We removed the side information learned from the known CDR matrix.SubCDR without GRU and CNN layer (w/o GC). We removed the GRU/CNN layer used to extract the hidden features of the drug/cell line subcomponents, and directly used ECFPs and gene expression data to calculate the interaction map.SubCDR without interaction network (w/o IG). We removed the interaction network and GCN layer, and then output the learned embedding by applying a flattening layer to the interaction map.SubCDR without GCN layer (w/o GCN). We replaced the GCN layer with a gated GNN  capable SubCDR without subcomponent parts (w/o SP). We removed the extraction and interaction modules related to drug and cell line subcomponents, and assembled the rest into a simplified prediction model with ECFP as drug input and gene expression data as cell line input.2 dropped from 0.9535 to 1.2437, 0.9428 to 0.8813, and 0.8701 to 0.8166, respectively, implying the usefulness of this potential knowledge. The result of the variant (w/o GC) showed that using the GRU/CNN layer to extract the latent features of drug/cell line subcomponents contributes to the prediction. As expected, the performance of the variant (w/o IG) signified that the prediction is boosted using the interaction network to capture the associations among subcomponent interactions. From the variant (w/o GCN) result, we observed that the GCN layer outperforms the gated GNN on CDR prediction. After removing the subcomponent parts (w/o SP), the performance of SubCDR was not improved but slightly degraded, showing that such a subcomponent design does augment interpretability as well as facilitates prediction. Overall, SubCDR with the above five modules together delivered superior predictive performance, and removing any modules will compromise its predictive power.The comparisons were operated on the independent testing, and the results are shown in Predicting cancer drug response (CDR) is a major goal in cancer research for computer-aided anti-cancer drug discovery and precision medicine. In this work, we developed a subcomponent-guided deep learning model for interpretable CDR prediction, named SubCDR, which offers a novel view of understanding the biological significance behind predictions, in terms of subcomponent interactions between drugs and cell lines. The predictive ability of SubCDR has been validated on the GDSC dataset and compared with state-of-the-art methods under various evaluation settings. Extensive case studies have exhibited the capacity of SubCDR in interpreting predictions and finding the relevant subcomponents driving response outcomes.Despite our efforts, there is still room for improvement. The performance of SubCDR falls short of expectations when predicting CDRs for unseen drugs and cell lines. In such cold start issues, there is no prior knowledge about unseen drugs/cell lines, and SubCDR can only infer responses based on the similarity of feature spaces between seen and unseen drugs/cell lines. Inspired by the knowledge graph (KG) on the biomedicine field , incorpoS1 Text(a) Classification of CGC genes in different cancer cell lines. (b) Implementation of the baseline methods.(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig50) values are all greater than -2.AUC (up) and AUPR (down) scores of all methods across the different cancer types (defined in the TCGA study) of cell lines and target pathway types of drugs. Of note, AUC/AUPR scores cannot be calculated for some target pathway group data, because they do not include instances of positive (sensitive) labels, i.e., ln(IC(TIF)Click here for additional data file.S4 Fig(a) Cases of drug Gefitinib’s response to cell line NCI-H1650 and NCI-H1568. (b) Cases of drug Dactolisib’s response to cell line KARPAS-45 and P30-OHK. (c) Cases of drug Cytarabine and Gemcitabine responding to cell lines HCC78, NCI-H1792, and NCI-H1993, respectively.(TIF)Click here for additional data file.S1 Table(a) Annotation information for drugs. (b) Annotation information for cell lines.(DOCX)Click here for additional data file.


Proteolysis targeting chimeras (PROTACs) are a familyof heterobifunctionalmolecules that are now realizing their promise as a therapeutic strategyfor targeted protein degradation. However, one limitation of existingdesigns is the lack of cell-selective targeting of the protein degradingpayload. This manuscript reports a cell-targeted approach to degradereceptor-interacting serine/threonine-protein kinase 2 (RIPK2) inHER2+ cell lines. An antibody-PROTAC conjugate is prepared containinga protease-cleavable linkage between the antibody and the correspondingdegrader. Potent RIPK2 degradation is observed in HER2+ cell lines,whereas an equivalent anti-IL4 antibody-PROTAC conjugate shows nodegradation at therapeutically relevant concentrations. No RIPK2 degradationwas observed in HER2– cell lines for both bioconjugates. Thiswork demonstrates the potential for the cell-selective delivery ofPROTAC scaffolds by engaging with signature extracellular proteinsexpressed on the surface of particular cell types. This uniqueMoA results in a longer-lasting pharmacological effect relative toconventional noncovalent inhibition,6 enablinglower dosages for their application in vivo.(10Proteolysis targeting chimeras (PROTACs) are heterobifunctionalmolecules that selectively degrade a protein of interest (POI). in vivo.−9 A furt11 which is reflected in their suboptimal pharmacokineticproperties.13 Incorporating a cell-targetingmodule into PROTAC designs has the potential to deliver the PROTACcargo to the desirable cell type(s) and subsequently minimize off-targettoxicity  combine the ability to selectively delivera molecular payload, such as a PROTAC, to specific cell types, therebybypassing the need for extensive optimization of the cell uptake propertiesto the PROTAC scaffold , drug accumulationinto a target cell type, and the diversity of POI can be targetedby Ab conjugation is still in its infancy. Herein, we expand the scopeof Ab-PROTAC conjugates by demonstrating the cell-selective and targeteddegradation of serine- and threonine-protein kinase 2 (RIPK2) in HER2+cell lines (An emerging platform for the cell-selective deliveryof PROTACsis their conjugation to an antibody (Ab).scaffold 1B. Whilell lines 1C.1 linker.27 A second antibody, anti-IL-4pascolizumab, was also selected as a negative control.28 A terminal alkyne was incorporated onto thesecond nitrogen of the diBrPD to act as a flexible handle for potentialdownstream functionalization.Our design approach involvedcovalently linking a RIPK2 PROTACto each Ab scaffold via a disulfide rebridging reagent .1 was attached to the VC-PAB S6 via a carbonate linkage,which was then linked to the diBrPDby an amide bond (Scheme S3). Conjugationto the anti-HER2 mAb, trastuzumab, was achieved by reduction of theinterchain disulfides by TCEP followed by addition of the diBrPD rebridgingreagent S10 to form conjugate ADC-2, whichidentified a drug-to-Ab ratio (DAR) of 4.0  of 4.0 2. These 2 andADC-3 was assessed in a SKOV3 HER2+ ovarian cancer cellline. The anti-HER2ADC-2 showed similar levels of RIPK2 degradation comparedto the parent PROTAC, whereas the anti-IL-4 ADC-3 showedno degradation at 10 nM. An unexpected observation was RIPK2 degradationusing ADC-3 at concentrations above 100 nM . As the SKOV3 cells do nothave membrane-bound IL-4, we rationalized that the observed degradationmight be due to nonspecific uptake mechanisms, such as macropinocytosis.30RIPK2 degradation using ADC-2 compared to that of PROTAC 1 alone . Wesurmise that this is due to the uptake and release of the PROTAC fromthe conjugate slowing down the initial rate of degradation. To confirmthat RIPK2 degradation occurred via the ubiquitin–proteasomepathway, SKOV3 cells were treated with PROTAC 1, ADC-2, or ADC-3 in the presence of 10 μM MG132,a known proteasome inhibitor.31 No degradationwas observed for all compounds, confirming that degradation occursvia a proteasome-dependent pathway . Intact MS analysisof ADC-2 after 275 days of storage in pH 7.4 PBS at 4°C also revealed no degradation of the conjugate .Shortening of the incubation time (6 h) resultedin less RIPK2degradation by ADC- 1 alone S2. Wesu 1 alone S2. Wesu2 and ADC-3 was then tested in a HEK293HER2– cell line using a HiBiTassay. Both ADC-2 and ADC-3 exhibited RIPK2degradation at higher concentrations, with a more prominent effectas the concentration exceeded 100 nM. This agrees with the fact thatthe similar degradation observed in SKOV3 cells is likely due to nonspecificuptake. Most importantly, no degradation was observed at 10 nM forboth conjugates compared to 50% degradation when PROTAC 1 was used (Cell-selective targeting ofADC-was used 4A. Againwas used 4B.In summary, we have demonstrated the cell-selectivedegradationof RIPK2 in HER2+ SKOV3 cells using an Ab-PROTAC conjugate. This approachcomplements ADC developments and provides a design strategy to usePROTACs, which have suboptimal physicochemical properties or wherecell-selective delivery of the PROTAC payload is required.


Knowledge on the molecular and clinical characteristics of head and neck squamous cell carcinoma (HNSCC) is vast. However, an effective therapy that increases the life expectancy of these patients, with a 5-year overall survival of 50%, is still unknown. Here we evaluated the combined effect of the pro-apoptotic protein rhTRAIL with the replication-competent wild-type HSV-1 virus in head and neck cancer cell lines. We observed a difference in the modulation profile of proteins related to apoptotic pathways in the studied cell lines. The HCB289 exhibited caspase-9 activation in the presence of the HSV-1 virus, while the UD-SCC-2 exhibited caspase-8 activation in the presence of rhTRAIL. Both cell lines exhibited PARP activation by combining rhTRAIL and HSV-1 virus treatment. Flow cytometry analysis exhibited greater induction of late apoptosis for the HCB289 and UD-SCC-2 after the combination treatment of the HSV-1 and rhTRAIL. However, the UD-SCC-2 also presented induction of late apoptosis by the presence of rhTRAIL in monotherapy. These data suggest an enhancement of the effect of the combination treatment of the rhTRAIL and the HSV-1 on reducing viability and induction of cell death. In 90% of cases, head and neck squamous cell carcinoma (HNSCC) appears to be the most common morphology of the other histological types1. Worldwide, HNSCC has an incidence of 650,000 and a mortality rate of 330,000 new cases per year, with the highest incidence and mortality of cancer of the lips and oral cavity2. Men have a higher frequency of cases than women, with a ratio ranging from 2:1 to 4:13.Head and neck cancer is a group of tumors affecting the upper aerodigestive epithelium, mainly involving the lips, larynx, pharynx, paranasal sinuses, nasal cavity, and oral cavity5. Among them, oncolytic gene therapy (based on replicating competent viruses) has shown promising results in HNSCC. Most HNSCC tumors do not develop distant metastasis and the local/regional nature of HNSCC tumors facilitates the application of intratumoral injections8. Such characteristic favor local treatment and avoid systemic dissemination of the therapeutic vector. In addition, herpes simplex virus type 1 (HSV-1)-derived oncolytic viruses have already been evaluated in clinical trials and Talimogene laherparepvec (T-VEC), a genetically modified virus from HSV-1, has already been approved by the Food and Drug Administration (FDA) for the treatment of melanoma10. The T-VEC was analyzed for HNSCC in one study of phase Ib in combination of pembrolizumab, however the combination has an efficiency similar to pembrolizumab alone compare to other historical HNSCC studies and the phase III did not proceed11.Because 20–30% of patients do not respond to standard treatments due to local recurrence, alternative medicines for the treatment of HNSCC have been developed, some of which have previously passed animal testing and are now in the clinical trial phase16. Apo2L/TRAIL triggers apoptosis by binding to functional death receptors (DR4 and DR5)18, but, regardless of the fact that these receptors are highly expressed in HNSCC lines, many tumor cell lines end up showing resistance to treatment with TRAIL monotherapy19.Improvement of oncolytic virotherapy could be achieved by cell death induction, combining its use with pro-apoptotic recombinant proteins. TNF-related apoptosis-inducing ligand (Apo2L/TRAIL protein) has been widely explored in due to its specificity in inducing apoptosis in tumor cells without affecting healthy cells23. Studies on TRAIL ligand agonists have shown that, although they can induce cell death in sensitive tumors, they need to be combined with other therapies to increase their efficacy and utility in primary tumors16.Several TRAIL receptor agonists are being evaluated in clinical trials, such as the rhTRAIL ligand and monoclonal antibodies Thus, combining both therapeutic approaches, oncolytic virotherapy and TRAIL-mediated apoptosis, may be promising in for HNSCC treatment. In this work, we evaluated the combined treatment of rhTRAIL protein and replication-competent wild-type HSV-1 virus in HNSCC cell lines.24.Five HNSCC  and an immortalized human keratinocytes (HaCaT) cell lines were treated with rhTRAIL ligand and/or wt-HSV-1 virus for 48 and 72 h . On the other hand, we did not observe a significant change in the caspase-3 cleavage under different treatment conditions  or in combination with HSV-1 at MOI 0.2 (286.84%) or 1.0 (324.09%). Besides, PARP cleavage significantly increases at HSV-1 presence at MOI 1.0 (3.19 times higher) and in combination with rhTRAIL on both concentrations (3.65 and 3.77 times higher). Increased Caspase-3 cleavage was reported at rhTRAIL monotherapy (3.38 times higher) and in the combination of HSV-1 at MOI 1.0 (2.45 times higher) compared to negative control. No differences were detected for caspase-9 cleavage under different treatment conditions  which demonstrated that the treatment was responsible for decreased viable cells detection as the cells entered the cell death process, mainly for rhTRAIL and HSV-1 presence for HCB289  and rhTRAIL alone (41.8% for 48 h) and in combination with the virus for UD-SCC-2 (42.3% at MOI 1.0 for 48 h posttreatment)  by combining rhTRAIL and HSV-1 at MOI 1.0 for both HCB289 (50.9% for 24 h) and UD-SCC-2 (47.7% for 48 h) cell lines  expression was examined to investigate the possible mechanism responsible for modulating the apoptotic pathway triggered by the virus and the rhTRAIL ligand. After 24 and 48 h of the treatment with rhTRAIL, HSV-1 or both, no differences are observed in DR-5 receptor expression on HCB28 and UD-SCC-2 cell lines Figs. , 8. Thes26. The soluble form of TRAIL is well tolerated by non-cancer cells; and traces can be found in the healthy adult plasma (~ 100 pg/mL)28. Moreover, its cognate functional cell death receptors, DR4 and DR5, are ubiquitous expressed in several tumor cell types17. Several clinical studies are based on recombinant human TRAIL protein (rhTRAIL) and agonist antibodies to TRAIL receptors. However, none of these molecules alone or combined with other therapeutic agents have acquired the efficacy expected in clinical trials25.This study explores an advanced head and neck cancer treatment using wild-type HSV-1 infection in combination with the rhTRAIL protein. TRAIL ligand can be naturally released as a soluble form by proteolysis, mostly promoting apoptosis in tumor cells32. Griffith and colleagues showed, using a TRAIL-expressing adenovirus, that the rapid expression of TRAIL protein in tumor cells triggered apoptosis via caspase-8 activation29. A similar response was observed in our study, where rhTRAIL in monotherapy or combination with HSV-1 virus induced apoptosis by activating caspase-8 cleavage in the UD-SCC-2 cell line.Currently, many therapies use gene therapy to target the TRAIL gene and combinatorial use of this ligand with various other molecules, such as chemotherapies30. Likewise, we showed by flow cytometry analysis that the combination of HSV-1 virus and rhTRAIL treatments induced apoptosis in the HCB289 and UD-SSC-2 cell lines . Besides, TRAIL-treated UD-SCC-2 cell line also showed induction of late apoptosis/necrosis as monotherapy. These results demonstrate the potential of combining therapeutic approaches to attack tumor cells.Jeong and Yoo treated 14 tumor cell lines and a colorectal cancer murine model with a TRAIL/ANGPT1-co-expressing oncolytic vaccinia virus, showing that the dual expression of these molecules was effective in inducing apoptosis and inhibiting tumor growth in the in vivo assays, followed by caspase 3 and CD8 expression in tumors31. Authors showed that the armed oncolytic virus triggered apoptosis through caspase-3 and caspase-8 cleavage in the SW480 cell line, mainly when combined with radiotherapy31. In our work, we showed apoptosis involvement by PARP cleavage when HCB289 and UD-SCC-2 cell lines were treated with the combination of rhTRAIL and HSV-1 virus.In the same way, Gao and colleagues demonstrated a synergistic effect when treating colorectal cancer cell lines with a TRAIL-expressing oncolytic adenovirus in combination with radiotherapy32. Although in our study, we did not evaluate the expression of RIP1/RIP3, we observed cell- and treatment-specific cell death response profiles. Thus, HSV-1 virus induced caspase-9 cleavage in the HCB289 cell line, while caspase-8 cleavage was observed when rhTRAIL was inoculated, either alone or in combination with the virus.Moreover, as shown by Hu and co-workers, when treating lung tumor cell lines with a poxvirus-derived oncolytic virus expressing TRAIL, cells undergone apoptosis, while the parental poxvirus non-expressing TRAIL triggered RIP3-dependent necrosis24. They observed by western blotting that cleavage of PARP was not detected by either TRAIL or oHSV as monotherapy in glioblastoma cells, indicating that oHSV-mediated cell death is primarily not dependent on caspase/PARP activation24. In contrast, oHSV-TRAIL showed cleavage of caspase-8, -9, and PARP and induction of apoptosis in the cell lines resistant to TRAIL or oHSV alone24. The authors inferred that oncolytic virus-based gene therapies might be feasible for different tumor types with different molecular profiles24. Our study also did not detect PARP cleavage by TRAIL monotherapy, but it was possible to detect PARP cleavage for the HCB289 cell line in the presence of HSV-1 virus at both concentrations (MOI 0.2 and MOI 1.0). This highlights that the HCB289 cell line resistant to TRAIL monotherapy could be induced apoptosis by the presence of the virus.In another study, Kaoru and colleagues tested in glioblastoma (GBM) and stem cell-derived (GSC) cell lines the biological effect of an HSV-1-based oncolytic virus (oHSV), TRAIL, and a recombinant virus with TRAIL expression (oHSV-TRAIL)15, we showed that the HaCaT cell line also showed a reduction in cell viability. Although often used as a normal control, it is worth mentioning that HaCaT is an aneuploid immortalized keratinocyte cell line, thus a limitation of our work. Regardless of this fact, the main objective of this proof-of-concept work was to evaluate the combined effect of rhTRAIL and HSV-1 oncolysis against HNSCC. In this regard, authors understand that the use of a wild-type HSV-1 virus could never be used in a clinical trial setup, and we point this out as a limitation of the study. To this end, additional experiments must be conducted to further establish proper attenuation of the therapeutic approach.Although rhTRAIL was described as able to induce apoptosis mainly on tumor cellsTo our knowledge, this is the first study combining HSV-1 virus and TRAIL as therapy for HNSCCs, showing that the combination potentiates the viability reduction through cell death induction. Our results open new therapeutic approaches for HNSCC patients, which should be explored in further studies.2. Cell lines authentication was performed by the Department of Molecular Diagnostics, Barretos Cancer Hospital. Genotyping confirmed the identity of all cell lines, as previously described by Oliveira et al33. Moreover, all cell lines were tested for mycoplasma through the MycoAlert™ Mycoplasma Detection Kit , following the manufacturer’s instructions.Vero cells  were used to produce the wild-type HSV-1 virus. Four HNSCC-derived cell lines  were used in the experiments. The immortalized human keratinocytes cell line, HaCat (gently donated from Rui M. Reis) was used as a non-tumoral control. All cell lines were maintained in DMEM medium supplemented with 10% fetal bovine serum  and 1% penicillin/streptomycin , in a humidified incubator at 37 °C with 5% CO34 . Viral amplification was performed at MOI 0.01 in Vero cells in M199 medium supplemented with 1% FBS  and 1% penicillin/streptomycin , in a humidified incubator at 37 °C with 5% CO2. Viral particles were collected 72 h post-infection. Intracellular viral particles were extracted by three cycles of freeze and thaw, followed by three cycles of sonication. Cellular debris were eliminated by centrifugation at 600 × g for 20 min (4 °C), and supernatants were collected and stored at –80 °C until required.The virus used in this work was an HSV-1 virus (strain 17), cloned as a bacterial artificial chromosome35. A serial dilution was perfomed to determinate the number of plaque-forming unit per mL (pfu / mL) by duplicate into 12-well. We maintained the plate under shaker  for one hour and thirty minutes with 400 uL volume of infection per well and after the infection time, the volume per well was completed to 800 µL, with 200 µL of M199 medium and 200 µL of CMC 1% . The plate was in a humidified incubator at 37 °C with 5% CO2. After 72 h, the wells were stained with 1 mL crystal violet (0.5%) for 5 min and the lysis plates counted. The virus titer was determined by concentration in pfu/mL of our HSV-1 virus stock was calculated by considering the mean of the duplicate for the number of plaques counted from the given dilution with the following Eq.  at 100 ng/mL, HSV-1 MOI 0.2 and/or HSV-1 MOI 1. PBS was used as negative control. MTS assay was performed 48 and 72 h post-treatments. Absorbance was measured in a microplate reader  at a wavelength of 490 nm.Cell viability assay was performed using the Cell Titer 96 Aqueous One Solution Cell Proliferation Assay , following the manufacturer´s recommendations. Briefly, HNSCC cells were seeded into 96-well plates at 5 × 103VO4, 10 mM of NaF, 10 mM de sodium pyrophosphate, 1% NP-M de EDTA e 1 mM de PMSF). Cell debris were eliminated by centrifugation at 14,000 × g for 15 min (4 °C) and proteins assayed by SDS-PAGE (acrylamide/bis-acrylamide) electrophoresis. Nitrocellulose membrane – transferred proteins  were blocked for 1 h in 5% low-fat milk + TBS-T ] and incubated with the primary antibodies anti-PARP , anti-DR-5 , anti-caspase-9 , anti-caspase-8 , anti-caspase-3 , anti-α-tubulin  were incubated overnight at 4 °C. After incubation, membranes were incubated with the secondary antibodies anti-rabbit IgG HPR  or anti-mouse IgG HPR (1:5000); Cell Signaling Technology; #7074, Danvers, MA, USA) as required. Detection was performed using Amersham ECL Prime Western Blotting Detection Reagent  or SuperSignal West Femto  on the ImageQuant LAS 4000 mini .Cells were scrapped and resuspended in lysis buffer (50 mM of Tris (pH 7.6–8. 0), 150 mM NaCl, 5 mM of EDTA, 1 mM of Na5 per well. After treatment, trypsinized cells were resuspended in 500 µL of Binding Buffer 1X and subsequently stained with annexin V-PE and 7AAD. Stained cells were evaluated with the BD Accuri C6 flow cytometer  and analyzed using the FlowJo software .Flow cytometry analysis was done using PE Annexin V apoptosis detection kit l , following manufacturer’s instructions. Briefly, cells were seeded into 12-well plates at 2.5 × 10p value was set at < 0.05 .Statistical analysis was done using GraphPad Prism software (version 8.4.3 for Windows). Cell viability was analyzed by non-normal distribution Kruskal–Wallis test. Western blotting and apoptosis assay were analyzed by one-way ANOVA. All data are shown as the mean ± standard deviation (SD), in duplicate and triplicate assays. Statistical significance for Supplementary Information 1.Supplementary Information 2.


We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC However, information describing drug-target interactions, especially at the protein level, remains underutilized due to the unique nature of associated data acquisition methods. For example, the recent DREAM challenge  and downstream cellular phenotypes, while further suggesting potential opportunities for leveraging computational models in inhibitor therapy design.In this work, we describe a framework to create an integrated kinome inhibition state data set by combining kinobeads and KINOMEscan data, and then leverage the entirety of this data into predictive models. This combined set contains single-dose inhibitor profiling data for a total of ∼800 kinases and kinase interacting proteins, spanning almost 1,000 kinase inhibitors that target a diverse section of the overall kinome space. When leveraged within a machine learning framework, and supplemented with baseline gene expression data, we are able to predict the sensitivity of ∼450 cancer cell lines in the DepMap screening dataset, with a reasonable Rhttps://www.biorxiv.org/content/10.1101/2022.12.06.519165v1).Portions of this text were previously published as part of a preprint  to create the set of cancer cell line sensitivities and their gene expression characteristics:The following were downloaded from the DepMap portal ( 1.DepMap secondary repurposing screen (“secondary-screen-dose–response-curve-parameters.csv”) 2.CCLE gene expression set (“CCLE_expression.csv”)The high-throughput screening data gathered in PDAC patient-derived cell lines was gathered from The scripts implementing these descriptions are all available through github.Klaeger et al. Kinobead Kinase Inhibition Profiles: As previously described  vs control condition (without inhibitor). These were divided by 100 to convert the scale to 0–1 to match the Kinobeads relative intensity data.Creating the Combined Kinome Inhibition Profiling Set: We took the kinobeads dataset and the KINOMEscan dataset and concatenated them into one large set containing inhibitor-kinase interaction states for ∼800 total kinases and kinase interactors. We left out assays that included recombinantly mutated kinases but left those with naturally occurring post-translational modifications. The vast majority (99.95%) of the inhibitor-kinase pairs represented was unique for either assay type, but for the 0.05% inhibitor-kinase pairs, we took the mean value of the measurements across the two assay types. Additionally, any missing values were imputed with the default “no interaction” value of 1. In the end we were left with kinome inhibition states for ∼1,000 kinase inhibitors.Dataset of Cancer Cell Line Sensitivity to Drugs from DepMap: The DepMap repurposing dataset contains cell viability measurements across multiple doses, but since our dataset of kinome states is restricted to single-dose measurements, we extracted single summary statistics of cell line sensitivity to kinase inhibitors: Dose–response area under the curve (AUC) and half-maximal inhibitory concentration (IC50). We extracted these by reading in the “secondary-screen-dose–response-curve-parameters” dataset into R, which contains curve parameters for a log–logisitic curve fit to the cell viability dose response curve and filtered it down to cell line name, IC50, AUC and other associated metadata.Matching of Kinase Inhibitors between Profiling Dataset and Cell-Line Sensitivity Dataset: The compound names from each dataset were read into R, and the package Webchem  regression using the glmnet engine , random  1.LASSO  a.Penalty (1E−10–0.9) 2.Random Forest  a.Trees (100–2000) 3.XGBoost  a.Trees (100–1000) b.Tree Depth (4–30)After final model selection, we fit the model on the entire dataset and then made predictions on inhibitor-cell line pairs not found in the original DepMap screening data.BT-474, HCC1806, SUM-159 and SKBR-3 cells were assayed as described previously . Briefly50 values from the 9-point cell viability curves.The functions “ComputeAUC” and “ComputeIC50” from the R package dr4pl  was usedKinase inhibitors have been profiled using a number of assays, but for this study we have focused on a specific subset of kinase inhibitors that have been assayed using the kinobead/MS-based method  or the Ki.e., it is a representation of the uniqueness of its kinome inhibition state. Inhibitors that have similar effects on the kinome will have similar coordinates, while disparate inhibitors will have coordinates that are far apart. Using this, we can examine the diversity of kinome space targeting in our dataset, based on the origin of kinome profiling data. Our analysis shows that integrating KINOMEscan data for 800 inhibitors vastly increases the kinome space targeted  and measured Pearson’s correlation of inhibition states for the same compound between both assays. We found a reasonable degree of correlation (Pearson’s R ∼0.5) across all data. In addition, we additionally found a much higher degree of agreement between data types (90%) when both assays were cut off at 80% inhibition (a “strong” hit in either assay) . As a reTo connect these kinase inhibitors and their induced inhibition states with their corresponding phenotypes in cancer cell lines, we make use of the DepMap repurposing screen, which uses the PRISM assay  to run h50). These properties are highly correlated with each other, having a Pearson’s correlation coefficient ∼0.9 , and their Pearson’s R correlation was calculated. We additionally calculated Spearman’s correlation coefficients, and ensured all correlated features were significant at the 0.5 level. The full table of feature correlations with significance values is attached in To identify which features were most correlated with drug sensitivity, we examined bivariate associations of each of the ∼21,000 features  were compared individually to the outcome of cell line sensitivities , and we further annotated each kinase with whether they are considered by current NIH guidelines as understudied (“Dark”) or well-characterized (“Light”)  . Interes“Light”) , but 40%2 of ∼0.69 for predicting IC50).In addition to the baseline gene expression data, all the cell lines in the DepMap database have three other profiling data types available: copy number variation, gene essentiality from CRISPR/KO, and baseline proteomics. To see if inclusion of these data into models would improve predictions, we integrated these with the modeling dataset of kinome inhibition states and gene expression and used identical modeling strategies described above to select correlated features, build, and evaluate LASSO, random forest, and XGBoost models . We founAfter fitting the model on 70,000 cell line-drug combinations, predictions were made on 1.2 million unseen (not previously seen by the model) drug-cell line combinations . ApproxiWe then further validated the model by predicting inhibitor effects from collected RNAseq data in tumor (two samples) and stroma (one sample) derived cell lines from PDAC patients . Importa50) and then built machine learning models that integrate these kinome states with cell line baseline gene expression values to predict cell line responses to kinase inhibitors. Finally, we predicted cell line sensitivity to previously untested kinase inhibitors in breast cancer and patient-derived PDAC cell lines and validated them experimentally.Kinase inhibitors are one of the fastest growing classes of targeted cancer therapies, but only a small fraction of the druggable kinome has been explored to date . We havePrediction of therapy response for cancer cell lines has been demonstrated through various methods, mostly utilizing chemical structure information, baseline gene expression and gene mutation status. Drug-target interaction data is relatively under-utilized for phenotype prediction, but offers opportunities for biological hypothesis building, especially for compounds with uncharacterized mechanisms of action. Kinome profiling data provides an exciting opportunity to use a wide array of functionally relevant drug-target interactions, and is almost unique (except for GPCR inhibitors and HDAC inhibitors) in terms of ability to assay potential off target interactions. While we have previously shown that kinome profiling data generated from the kinobeads assay is informative for cell viability prediction, KINOMEscan data is more readily accessible, publicly available for many uncharacterized compounds, and easier to generate. In addition, integration of this data vastly broadens the kinome space capable of being targeted and increases the number of inhibitors that can be virtually screened as potential therapeutics.It is important to note that this model linking kinome inhibition states to cell line response is generalizable to any human cancer sample, provided the sample has baseline transcriptomic data available. We have shown in this work and previously that models can reasonably extrapolate to kinase inhibitors that have not been tested before in well-characterized cell lines. Significantly, in this work we have extended the scope of the model by using novel RNAseq data from patient-derived PDAC cell lines, and testing kinase inhibitors against both tumor and stroma cell lines and achieving reasonable prediction accuracy.Using tree-based models like gradient boosting lends us the ability to explain to some degree which features most influenced cell viability. Shapley importance values generated from the best-performing model show that the inhibition states of kinases had overwhelmingly more predictive power compared to baseline gene expression values, with FLT3 as the most important feature. FLT3 mutations are observed in 30% of acute myeloid leukemia (AML) patients, and various FLT3 inhibitors are commonly prescribed for treatment . HoweverThere are still many limitations to the results reported in this study. The creation of a combined kinome profiling dataset involves gluing together results from different assay types. Although the assays produce the same output (ratio of kinase in treatment sample to kinase in control), there may be numerous methodological artifacts that add noise to the data. We have attempted to address this by analyzing model performance on each assay type individually, and we can see that responses to inhibitors with data originating from KINOMEscan are noisier and more difficult to predict than inhibitors with data from kinobeads. This discrepancy is potentially due to the imbalance in training data availability for the KINOMEscan inhibitors, with only 15 inhibitors having annotated cell line sensitivity data available. In the future, as more cell line sensitivity testing is performed for compounds in the KINOMEscan dataset, model performance for this assay may improve. Additionally, model performance also decreases when shifting from using gene expression data obtained from well-characterized cancer cell lines to using in-house gene expression of novel patient-derived cell lines. This is potentially due to the innate and significant heterogeneity that exists in such samples and because the models have been trained on baseline transcriptomics set of the given ∼450 cell lines.Additionally, there are important limitations to our initial analysis of correlations to cell line responses. While we observe higher correlations overall for kinome inhibition states, these are complicated by the sample imbalance in our abundance of drug-specific inhibition states (∼200 per cell line) to gene expression values (one per cell line). This imbalance, characterized by the ratio of drug-specific kinome inhibition states to cell-line specific baseline gene expressions, is not only inherent to our study design but also reflective of the clinical scenario we aim to model. In a real-world setting, a patient is likely to have a single point of genomic profiling, most plausibly from a pre-treatment biopsy. Subsequent acquisition of multiple “cell-induced” genomic signatures in response to a variety of treatments is not only challenging but also often impractical in a clinical setting. This underscores the importance and value of drug-specific descriptors, like the kinome inhibition states in our study, which can be obtained from high-throughput assays such as kinobeads and kinomescan. Consequently, while the imbalance does complicate interpretation, it also reinforces the relevance and applicability of our approach. We caution that the observed higher correlation for kinome inhibition states may be partly due to this sample ratio imbalance. Nonetheless, our primary measure of the relative importance of the data types is through the described multivariate machine learning analyses. We believe that these methods, which leverage the full complexity of the data, provide a more robust and comprehensive assessment of the information contained in each data type.It should be possible to extend these models to incorporate multiple kinase inhibitors in combination. This is significant, given the frequency of resistance to targeted cancer monotherapies  and the While targeted therapies such as kinase inhibitors have had significant clinical impact, much work remains to better understand how modulation of the kinome leads to both desirable and undesirable phenotypic effects. The results presented here provide an example of one approach where knowledge of the inhibition state of the kinome can be linked to downstream phenotypes through predictive models, greatly enhancing our ability to predict the effects of existing inhibitor treatments as well as facilitating the design of novel targeted therapies in the future.10.7717/peerj.16342/supp-1Figure S1Click here for additional data file.10.7717/peerj.16342/supp-2Figure S2Click here for additional data file.10.7717/peerj.16342/supp-3Table S1Click here for additional data file.10.7717/peerj.16342/supp-4Table S2Click here for additional data file.


Innovative therapeutic strategies for esophageal squamous cell carcinoma (ESCC) are urgently required due to the limited effectiveness of standard chemotherapies. C-Terminal Binding Protein 1 (CtBP1) has been implicated in various cancers, including ESCC. However, the precise expression patterns and functional roles of CtBP1 in ESCC remain inadequately characterized. In this study, we aimed to investigate CtBP1 expression and its role in the resistance of ESCC to paclitaxel, an effective chemotherapeutic agent. Western blotting and immunofluorescence were applied to assess CtBP1 expression in the TE-1 and KYSE-50 cell lines. We observed the marked expression of CtBP1, which was associated with enhanced proliferation, invasion, and metastasis in these cell lines. Further, we successfully generated paclitaxel resistant ESCC cell lines and conducted cell viability assays. We employed the CRISPR/Cas9 genome editing system to disable the CtBP1 gene in ESCC cell lines. Through the analysis of the drug dose–response curve, we assessed the sensitivity of these cell lines in different treatment groups. Remarkably, CtBP1-disabled cell lines displayed not only improved sensitivity but also a remarkable inhibition of proliferation, invasion, and metastasis. This demonstrates that CtBP1 may promote ESCC cell malignancy and confer paclitaxel resistance. In summary, our study opens a promising avenue for targeted therapies, revealing the potential of CtBP1 inhibition to enhance the effectiveness of paclitaxel treatment for the personalized management of ESCC.  Esophageal cancer (EC) ranks the eighth most diagnosed cancer and the sixth most prevalent cause of cancer death worldwide . The twoSurgery remains to be one of the most effective therapeutic approaches for EC, with perioperative chemotherapy or chemotherapy–radiation administered to locally advanced EC patients. However, not all the patients are eligible for resection ,9,10,11.C-Terminal Binding Protein 1 (CtBP1) is a transcriptional co-repressor that represses numerous cellular processes such as apoptosis . By reprIn this paper, we developed a treatment strategy that combines Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) genome editing with conventional chemotherapy. We first utilized CRISPR/Cas9 to knockout (KO) CtBP1 in chemoresistant ESCC cells, preventing it from producing a functional protein. This led to the increased sensitivity of the ESCC cells to the anticancer drug paclitaxel. We also examined the impact of CtBP1-KO on cell viability, chemosensitivity, and metastatic potential. This strategy may offer a promising avenue for developing new therapies to combat chemoresistance in ESCC.Chemoresistance is a significant challenge in the treatment of many cancers, including ESCC. To investigate chemoresistance, we established PTX-resistant cell lines (KYSE-50/PTX and TE-1/PTX) through the gradual exposure of parental cells (KYSE-50 and TE-1) to increasing doses of paclitaxel. These PTX-resistant cells served as the foundation for generating different experimental groups, including scramble gRNA as negative control (Ctrl) Clones, the Paclitaxel alone group (PTX), the KO Clones alone (KO), and the KO Clones with Combination Paclitaxel Treatment (KO + PTX). These distinct groups were employed in three metastatic potential assays: a cell migration assay, a cell invasion assay, and a wound healing assay. The objective of these assays was to unveil the influence of CtBP1 on the metastatic potential of PTX-resistant ESCC cells.We assessed the expression levels of CtBP1 in human ESCC cells, specifically, the KYSE-50 and TE-1 cell lines. Our Western blotting results A–D and iTo uncover the functional consequences of CtBP1 expression in ESCC, we employed a CRISPR-mediated knockout strategy. By utilizing two CtBP1-specific single guide RNAs (sgRNAs) along with two scramble sequences as controls (Ctrl), we successfully disabled CtBP1 expression in PTX-resistant cell lines. Western blot analysis A–D confip = 0.0019), respectively. Similarly, the parental KYSE-50 displayed an IC50 of 4.940 nM, while the KYSE-50/PTX exhibited an IC50 of 38.38 nM (p = 0.0013)  values for the parental TE-1 cell line and TE-1/PTX were 6.812 and 50.83 nM ( 0.0013) A,B. p = 0.0033) and (KYSE-50/PTX) 7.743 nM vs. Ctrl 36.08 nM (p = 0.0086)  were 9.901 nM vs. Ctrl 49.44 nM ( 0.0086) A–D. The We further evaluated the role of CtBP1 in cell proliferation and colony formation by conducting a series of experiments on genetically engineered CtBP1-deficient ESCC cell lines. First, we examined the effect of the CRISPR/Cas9-mediated knockout of CtBP1 on cell growth and chemosensitivity to paclitaxel. We conducted a proliferation assay using the CCK8 reagent to measure the cell population after treatment and found that only the KO + PTX group showed significant inhibition of proliferation compared to the normal control (NC), suggesting that CtBP1 plays a critical role in promoting cell growth in ESCC cells A,B. FurtOur results are consistent with previous studies demonstrating that CtBP1 promotes cell proliferation and inhibits apoptosis in cancer cells . FurtherCtBP1, a well-established gene expression regulator, has been implicated in the metastatic behavior of cancer cells. To investigate the impact of CtBP1 expression on ESCC cell metastatic behavior, we conducted a series of assays encompassing migration, invasion, and wound healing dynamics. Migration and invasion capabilities were evaluated through Boyden chamber assays, while wound healing was evaluated by the collective cell migration assays.Given that the IC50 of parental cell lines is below 10 nm, we opted for a standardized dose to treat all groups and observe the effects. All treatment groups were subjected to 10 nM paclitaxel treatment. These cells were divided into five experimental groups: normal control (NC), scramble sgRNA (Ctrl), PTX treatment alone group, KO alone, and the synergistic KO combined with paclitaxel treatment (KO + PTX). Notably, our results show that the synergistic combination of paclitaxel and CtBP1 KO reversed resistance and significantly inhibited cell migration and invasion in parental sensitive and PTX-resistant cells . AdditioThese findings provide valuable insights into the biological behavior of ESCC cells and underscore the potential significance of targeting CtBP1 as a therapeutic strategy for this malignancy.Paclitaxel resistance and tumor recurrence are common in patients with locally advanced esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant chemotherapy. To improve prognosis, a better understanding of the molecular mechanisms of paclitaxel resistance in ESCC is necessary. In this study, we demonstrated that ESCC cells expressed significantly higher levels of CtBP1 and were associated with tumorigenesis and poor response to paclitaxel treatment. Using a CRISPR-directed gene editing technique, we successfully disabled CtBP1 alleles in paclitaxel-resistant ESCC cells, and observed that CtBP1 KO suppressed oncogenic expression, inhibited proliferation, migration and invasion in ESCC cells, and sensitized them to paclitaxel. Paclitaxel is a microtubule-targeting agent that inhibits cell proliferation, stabilizes cellular microtubules, and induces apoptosis . RecentlIn our previous research, we conducted a comprehensive investigation of biomarkers for predicting lymphatic metastatic recurrence and prognosis in ESCC ,27. Our Based on our previous works, the present study focused on a novel gene CtBP1, which is associated with “cancer hallmarks” and has been observed to promote tumorigenesis when overexpressed . It is aTo investigate the mechanisms of drug resistance in cancer cells, it is crucial to develop drug-resistant cancer cell lines. This is typically achieved by gradually exposing cancer cells to increasing concentrations of specific drugs over an extended period . PreviouTo comprehensively investigate the role of the CtBP1 gene in chemoresistance in ESCC, we generated two PTX-resistant cell lines (KYSE-50/PTX and TE-1/PTX) by gradually exposing them to escalating concentrations of paclitaxel. The resultant cell lines exhibited significant resistance to paclitaxel treatment, providing a valuable model for studying chemoresistance in ESCC. To deepen our understanding of the role of CtBP1 in paclitaxel resistance in ESCC, we employed an advanced and highly precise technique known as CRISPR-Cas9 genome editing. Using this technique, we were able to specifically target and KO the CtBP1 gene in ESCC cells, creating paclitaxel-resistant cell models that lacked the expression of CtBP1. This allowed us to study the effects of CtBP1 KO on the molecular pathways involved in paclitaxel resistance, as well as to investigate potential therapeutic targets that may be impacted by the loss of CtBP1 expression. By employing cutting-edge technologies such as CRISPR-Cas9 genome editing, we aim to make significant strides towards understanding the mechanisms of cancer drug resistance and developing effective treatments for patients with ESCC. Upon disabling the CtBP1 gene in these resistant cell lines, we observed enhanced sensitivity to paclitaxel in the CtBP1 KO group . We alsoOur findings suggest that CtBP1 expression plays a crucial role in the development of chemoresistance and tumorigenesis in ESCC. Targeting CtBP1 expression using CRISPR-Cas9-mediated gene editing holds great promise as a novel therapeutic strategy for inhibiting the growth and progression of ESCC tumors and sensitizing cells to paclitaxel. Additionally, the limited expression of CtBP1 in most adult tissues and its ability to reactivate developmental programs when markedly expressed make it an attractive target for cancer therapy. Moreover, further research directed at expanding our current understanding of transcriptional corepressors in oncogenesis could significantly impact the development of novel therapeutic approaches. While our study primarily focuses on investigating CtBP1’s role in acquired paclitaxel resistance, we recognize the intriguing potential for mechanistic connections with microtubule dynamics. It is conceivable that CtBP1’s functions might intersect with these processes. For instance, CtBP1 has been implicated in epithelial–mesenchymal transition (EMT), a phenomenon associated with cytoskeletal rearrangements and changes in cellular motility . FurtherOur study provides valuable insights into the potential of targeting CtBP1 expression as a promising therapeutic approach for ESCC. Furthermore, highlighting the importance of investigating potential links between CtBP1 and microtubule dynamics offers avenues for exploring novel resistance mechanisms and gaining a deeper understanding of CtBP1’s implications in cancer biology.® and dimethyl sulfoxide (DMSO) were purchased from Sigma-Aldrich . The VEC-TASHIELD® Antifade Mounting Medium with DAPI was obtained from Vector Laboratories . The Transwell™ chambers and Corning® BioCoat™ Matrigel® Invasion Chamber were both acquired from Corning .The chemicals and reagents used in this study were obtained from various commercial suppliers. Paclitaxel (PTX) was procured from Med Chem Express , while Giemsa dye was obtained from Biosharp Biotechnology Co., Ltd. . Polybrene2. Paclitaxel-resistant cell lines KYSE-50 and TE-1 were generated from the parental cell lines by gradually increasing the concentration of paclitaxel  in accordance with established protocols  × 100  × 100 , where ACell migration assays were conducted using Transwell™ chambers with a polycarbonate filter . The upper chamber was seeded with parental controls (NC) and PTX-resistant cells, comprising all other experimental groups. Specifically, 200 μL of serum-free medium was added to the upper chamber, while the lower chamber received 750 μL of RPMI 1640 medium supplemented with 10% FBS, serving as the chemoattractant. Following a 48-h period, the medium was withdrawn, and the chambers underwent two PBS-1X washes. Migrated cells were then fixed with 4% paraformaldehyde at room temperature for 15 min, followed by three washes with PBS-1X. The cells were then permeabilized with methanol for 15 min, washed twice with PBS-1X, stained with Giemsa  for 25 min, and washed twice with PBS-1X. To count invading cells, ten randomly selected fields of view were captured for each chamber using a digital camera attached to an inverted microscope . The experiments were performed in triplicate.® BioCoat™ Matrigel® Invasion Chambers  with 8.0 µm polyester (PET) membranes. NC and PTX-resistant cells were seeded into the upper chambers with 200 μL of serum-free media. The lower chambers were filled with 750 μL RPMI 1640 medium supplemented with 10% FBS, which served as a chemoattractant. After 48 h of incubation at 37 °C with the 5% CO2, the medium was removed, and the chambers were washed twice with PBS-1X. Non-invading cells were removed from the upper surface of the membrane by scrubbing with a cotton-tipped swab, while invading cells were fixed with 4% paraformaldehyde for 15 min at room temperature. After washing three times with PBS-1X, the cells were permeabilized with methanol for 15 min, washed twice with PBS-1X, and stained with Giemsa  for 25 min. The chambers were washed twice with PBS-1X again, and randomly selected fields of view (ten per chamber) were imaged using a digital camera mounted on an inverted microscope . Invading cells were then counted in each field. The experiments were performed in triplicate.The invasion assay was performed using Corningp < 0.05 was considered statistically significant.Statistical analysis was performed using GraphPad Prism 9 . The data are presented as mean SD. The two-tailed unpaired Student’s test was used for the comparison of n = 2 groups.