During the development of cell lines for therapeutic protein production, a vector harboring a product transgene is integrated into the genome. To ensure production stability and consistent product quality, single-cell cloning is then performed. Since cells derived from the same parental clone have the same transgene integration locus, the identity of the integration site can also be used to verify the clonality of a production cell line. In this study, we present a high-throughput pipeline for clonality verification through integration site analysis. Sequence capture of genomic fragments that contain both vector and host cell genome sequences was used followed by next-generation sequencing to sequence the relevant vector-genome junctions. A Python algorithm was then developed for integration site identification and validated using a cell line with known integration sites. Using this system, we identified the integration sites of the host vector for 31 clonal cell lines from five independent vector integration events while using one set of probes against common features of the host vector for transgene integration. Cell lines from the same lineage had common integration sites, and they were distinct from unrelated cell lines. The integration sites obtained for each clone as part of the analysis may also be used for clone selection, as the sites can have a profound effect on the transgene’s transcript level and the stability of the resulting cell line. This method thus provides a rapid system for integration site identification and clonality verification. After transfection, the resulting cells are heterogeneous and have a wide range of productivity and other properties. Single cell cloning is performed, and high producing cell lines are isolated as candidates to be the production cell line. To ensure the consistency and quality of the products produced by the cells over time, regulatory agencies require the demonstration of clonality, i.e. that cells originated from a single transfected ancestor cell.2Chinese hamster ovary (CHO) cells are one of the most commonly used cell lines used to produce therapeutic proteins.3 and even stability of the region.4After transfection and entry into the nucleus, the vector integrates into the genome of the host cell randomly. One cell may have one or more integration events depending on the vector used and its dose. The chance of two integration events occurring on the same site, either on both alleles of the same chromosome or in the genome of two different cells, is extremely low. A characteristic of clonally derived cells is thus that all cells within the population should have the same integration site(s) of the vector on the genome. Identifying the integration site of the product gene and demonstrating that two sublines of cells have the same integration site can thus be taken as evidence that the two originated from the same ancestor. Furthermore, the genome context of the integration site can be explored to reveal information on epigenetic accessibility, transcriptional activity,5 splinkerette-PCR,6 linear amplification mediated PCR (LAM-PCR),7 and targeted locus amplification (TLA)8 have been developed to identify integration sites of transgenes. These methods can be applied to demonstrate clonality. LAM-PCR for integration site identification has been used to analyze and track clonal lineages of blood cells following lentiviral gene therapy,10 while TLA has been used in CHO cells for identification of clones with the same integration sites after pool selection and single cell cloning.11Several PCR-based assays such as inverse PCR,In this study, we established a high throughput method that can be used to support monoclonality in different production cell lines generated using random integration of plasmids. By designing one set of probes for the common features of the host vector used to introduce the gene of interest, selective capture was utilized to simultaneously sequence vector-containing DNA from dozens of clonal cell lines from multiple independent vector integration events with different product genes. This method also allowed for a relatively simple bioinformatic analysis to identify these integration sites through the use of a Python algorithm. Cell lines were separable based on their integration sites, and we were able to demonstrate the clonality of the original cell populations. This method thus provides a rapid and cost-efficient tool for clonality verification and integration site identification in product producing CHO cells.13 The organization of the tricistronic vector used for introducing the transgene is illustrated in The monoclonal antibody producing cell line, SH-87, has been described previously.Cell lines used for clonality analysis were all derived from the same CHO-K1 host cell line using the vector shown in For integration site analysis of cell line SH-87, probes (120bp in length) were focused on the ends of the linearized vector . The firFor clonality analysis of platform cell lines, probes (120bp in length) were designed to capture the regions of the linearized vector that are common in all transfected cell lines at 5x coverage with tiling . SequencAn overview of the experimental methods is shown in For all other cell lines, gDNA was extracted using a Qiagen Blood and Cell Culture DNA Max Kit . 100ng of gDNA from each cell line was sheared using a Covaris E220 ultrasonicator  to obtain fragments with an average length of 250bp. Further library preparation was performed using an Agilent SureSelect HS Reagent Kit . Libraries for all cell lines were pooled prior to sequence capture following the Agilent SureSelect system instructions. Captured DNA was eluted, amplified, and sequenced on one lane of Illumina MiSeq  using 100bp paired end reads for a total of 25 million reads.14 (version 0.33). Reads were then mapped to the CriGri-PICR release of the Chinese hamster genome15 using BWA-MEM16 (BWA release 0.7.17). The host vector sequence was added to the genome sequence as an additional scaffold. Duplicate reads were removed using the MarkDuplicates command in Picard tools 2.18.16. The resulting SAM (Sequence Alignment/Map) file, which describes how reads are aligned to the genome, was used for further analysis. Samtools17 (version 1.9) was used to create BAM files for visualization in IGV18 (version 2.4.19).Fastq files from sequencing were trimmed to remove adapter sequences using Trimmomatichttps://doi.org/10.13020/9wgm-mj51). Sequence capture data from cell line SH-87 was used for validation of the method, as integration sites from this cell line had been previously determined using whole genome sequencing and confirmed by PCR/Sanger sequencing.13An analysis pipeline was written in Python 3.6.3 to identify integration sites from mapped sequencing data  of the SAM file and the SA:Z tag added by BWA-MEM for chimeric alignments to identify reads which contain a split alignment between the vector and genome . These aNext, the CIGAR (Concise Idiosyncratic Gapped Alignment Report) tag for each alignment was used to find the exact vector-genome junction position. The CIGAR string is a compact method to report how bases within a read align to the reference genome, specifying which bases match, are deleted/inserted, or are clipped  in the case of split reads. Column 4 of the SAM file format reports the leftmost  position that is aligned for the read alignment. If the CIGAR string begins with an alignment match (M), the length of the match is added to the position to obtain the vector-genome junction . Otherwi13 All six junctions were found by the algorithm  were used in this study. Cell lines characterized from lineages A, B, and E consisted of a clonal parental cell line and a set of subclones. Unrelated cell lines C-1 and D-1 were added to the analysis to increase the diversity of cell lines examined. Cell line A-1 was run in duplicate (labeled A-1 and A-1D) for a total of 32 samples. gDNA from these samples was sheared, and fragments containing probed vector regions were captured and sequenced.5  for each sample ranged from 0. 46 - 5.6 x 105 . The per5 . This coThe library size varied with cell lineage, with the cell lines from lineage E having on average significantly more unique reads after sequencing than those from lineage A or B , had an additional vector-genome junction on the same scaffold as the integration site, either 1kbp  would suggest possible non-clonality.The method presented here allows for rapid verification of clonality or common lineage between different cell lines. Use of sequence capture increases the relevant information extracted from sequencing, and the bioinformatic analysis is rapid for processing the low number of reads required from each sample. Additionally, through the use of capture probes targeting common regions of the host vector, future clones can easily be added to the analysis without the need for new probe design. In general, a longer sequencing read length and an increased library size for each sample may have improved this dataset and would be helpful for detecting rarer integration events. The library size may be increased either through reducing the number of samples pooled before capture, or by increasing the input DNA from each sample to reduce PCR duplicates from library preparation. Despite some of these issues in sequencing, the pipeline was robust in its ability to distinguish cell lines of different lineages based on their integration sites. This method is thus a valuable, accessible tool to address clonality verification.Supplementary Information


A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub ( Tumor tissues are mixtures of different cell types. Identification and quantification of constitutional cell types within tumor tissues are important tasks in cancer research. The problem can be readily solved using regression-based methods if reference signals are available. But in most clinical applications, only partial references are available, which significantly reduces the deconvolution accuracy of the existing regression-based methods. In this paper, we propose a partial-reference based deconvolution model, PREDE, integrating the non-negative matrix factorization framework with an iterative optimization strategy. We conducted comprehensive evaluations for PREDE using both simulation and real data analyses, demonstrating better performance of our method than other existing methods. PLOS Computational Biology Methods paper.This is a Tumor tissues are heterogeneous and consist of different cell types including tumor cells (or sub-clones) and various microenvironmental cell types such as infiltrating immune cells and stromal cells –3. The iWith the rapid development of high-throughput sequencing technology, a large number of genome, epigenome, transcriptome and proteome data of tumor samples have been profiled. Such biomedical big data provide a possibility to study tumor heterogeneity from the molecular perspective by using computational methods. Although the recent emerging single-cell sequencing technology strives to tackle these problems by measuring expression profiles of thousands to millions of cells simultaneously, it is yet not feasible to be conducted for large cohort studies due to, for example, expensive cost and extensive dropout events . TherefoIn recent years, many computational methods have been proposed for bulk data deconvolution –10. At pHowever, in real clinical practice, only a fraction of cell types is known while the rest are unknown so the deconvolution problem should be subject to partial reference. A straightforward way to deal with this problem is to use current available information of known cell types as the reference to deconvolve the whole mixture signals, or assume all unknown proportion to be from one cell type . Howeverpartial-reference based deconvolution (PREDE) model based on the non-negative matrix factorization (NMF) framework using an iterative optimization strategy to address the above challenges. Using the expression profiles of the available cell types as input, PREDE could simultaneously estimate both the proportions of all cell types and the expression profiles of unknown cell types. We performed comprehensive evaluations for the proposed deconvolution method in comparison with other existing methods using both simulated data and real dataset of tumor samples. The results demonstrated that PREDE could effectively deconvolve mixture tumor samples from partial reference signals and could reveal novel insights into tumor heterogeneity and clinical prognosis.In this paper, we proposed a Y = WH+ϵ, where Y represents expression matrix of heterogeneous samples, W is basis matrix representing the quantitative expression profiles of constitutional cell types and H is the proportion matrix. If quantitative profiles of cell types (i.e. W) are known, it is so-called reference-based deconvolution. Alternatively, if both basis matrix W and proportion matrix H are unknown, it is so-called reference-free deconvolution.As the previous methods did , we assuW as W1, and the unknown portion as W2, i.e., W = . Given expression matrix Y for all tumor samples and basis matrix W1, PREDE infers the basis matrix W2 for unknown cell types and overall proportion matrix H. The main workflow of PREDE is briefly illustrated in W1 fixed in each iteration , PREDE is actually the typical reference-based deconvolution method. On the other hand, if expression profile of any cell type is unavailable , PREDE then becomes the typical reference-free method.Our PREDE method can be viewed as a generalization of previous reference-based and reference-free deconvolution algorithms. If known reference W. Gene expression matrix Y for mixture samples is then obtained by multiplying W with a randomly proportion matrix H generated from the Dirichlet distribution, followed by an additional error matrix with Gaussian distribution. Y and available reference matrix W1 were used as inputs of PREDE and Akaike information criterion (AICc) was employed to determine the optimal number of cell types.We conducted a series of simulations to comprehensively evaluate the performance of PREDE, by considering three factors in the simulations: noise ratio, expression similarity between cell lines and proportion of rare cell types. To this end, we downloaded gene expression profiles of 91 lung cancer cell lines from the CCLE dataset and selected some of them as reference c in determining the number of cell types from mixture samples. Following the above simulation procedure, we randomly selected 3, 6 and 10 lung cancer cell lines to generate 100 mixture samples respectively, and assumed only a fraction of cell lines to be known. c scores when W consists of 3, 6 and 10 cell types, but only 1, 4 and 8 of them are supposed to be known. As expected, AICc decreased first and then gradually increased with the increase of numbers of cell types, and correctly achieved the minimum values at K = 3, 6 and 10 respectively. We further investigated the accuracy of AICc in determining the total number of cell types when different numbers of known cell types were used for the input. When the total number of cell types was 6, AICc successfully reaches the minimum value at K = 6, regardless of the numbers of known cell types (K1 = 1), the predicted number of cell types is slightly underestimated  between true and predicted proportions of available cell types for all four methods at different levels of noise . PREDE obtained the lowest biases and relatively stable results at all levels of noise, compared to qprog, dcq and CIBERSORT  , dcq  is also capable of inferring gene expression profiles of unknown cell types. We also evaluated the performance of our method when one unknown cell type is highly similar to known cell types. To this end, we constructed two simulation datasets by selecting different sets of lung cancer cell lines from the CCLE dataset. The first is ‘low similarity set’, which consists of 6 cell lines (4 known and 2 unknown) with relatively low Pearson correlation coefficient (PCC) (0.75~0.8) between each pair of cell lines. The second is ‘high similarity set’, which also consists of 4 known and 2 unknown cell types but one unknown cell line is highly correlated with a known one (with PCC about 0.95). For both datasets, PREDE and RF were used to infer proportions of unknown cell lines, where prediction accuracies were measured by MAE and Pearson correlations between predicted and true proportions. We found that for both criteria, PREDE showed relatively lower biases and higher Pearson correlations in recovering the proportions of unknown cell type compared with RF .K = 5 (K = 6). Then we sought to evaluate the performance of the PREDE and RF in inferring proportion and expression profile of the unknown rare population when K was given. PREDE showed a consistently lower proportion bias and higher profile correlation than RF when proportions of the rare cell type changed from 0.07 to 0.10 , AICc achieved its minimal value at K = 5 . This in to 0.10 . Similar methods .c curves . If the number of known cell types is less than K-1, they will treat the remaining cell types (>1) as a merged single cell type. Thus, both DeMixT and ISOpure work when only one tissue in the above rat tissue mixture data is unknown , but only PREDE works even when two tissues are unknown .We compared our method with two state-of-the-art methods for the same problem, i.e., DeMixT  and ISOpWhen the profile of one tissue was assumed to be unknown, PREDE outperformed DeMixT and ISOpure in proportion estimation in the case that liver or lung profile was unknown  and in pSince DeMixT and ISOpure are designed specifically for tumor tissue deconvolution, in addition to rat-tissue mixture data, we further evaluated DeMixT, ISOpure and our method based on a synthetic dataset used in n = 20) downloaded from the GEO database (GSE65136) where the corresponding flow cytometry measurement of proportions are available [W), and 3, 5 and 7 of them are selected as the known reference W1 to test PREDE as well as four other immune cell deconvolution tools, i.e., RF, CIBERSORT [We further evaluated our method on a gene expression dataset of PBMC samples  between the predicted and the true proportions were shown in Another superiority of PREDE is the ability to infer proportions of unknown cell types . When onc values. As expected, different subtypes of tumor samples showed distinct immune cell infiltrating patterns . In addition, our method (as well as other deconvolution methods) fails to separate cell types that evolve on a continuum. It is our future work to integrate time-course data or to incorporate single-cell expression profiles as pseudo-time reference for more reliable deconvolution. Third, our method (as well as qprog and RF) assumes the error to be independently and identically Gaussian distributed across different genes, which may not hold for other types of biological data such as DNA methylation or RNA-seq counts data. So further attention should be paid on developing new methods free of such error assumption for partial reference-based deconvolution.Despite its merit, our study still suffers from the following limitations. First, our method needs the number of cell components as input, which can be correctly inferred by minimizing the Akaike information criterion in the simulation study. However, for tumor mixture tissues, the problem was far more complicated because every two cells in tumor tissue can be different. Cells in a tumor tissue can be classified into different numbers of groups at different levels of similarity thresholds. In other words, all cells in a tumor tissue form a hierarchical structure where one can get any number of clusters depending on ‘similarity’ between cells within each group. Therefore, we encourage the users to try different https://portals.broadinstitute.org/ccle) to generate mixed samples with different mixing proportions. Second, a benchmark dataset for cancer immunology study is generated by mixing 3 breast cancer cell lines from CCLE, 3 types of immune cells  from GEO with accession number GSE22886 [We simulated three synthetic datasets to comprehensively evaluate our method. First, gene expression profiles of 91 lung cancer cell lines are downloaded from the Cancer Cell Line Encyclopedia database .In addition, we employed gene expression data of peripheral blood mononuclear cells (PBMCs) of 20 samples (GSE65136) as well as the corresponding flow cytometry measurements  to benchhttps://gdc.nci.nih.gov). Expression profiles of seven immune cells  are available from the Human Primary Cell Atlas (HPCA) database [Furthermore, for real data application, we downloaded level 3 gene expression data of all Breast invasive carcinoma (BRCA), Skin cutaneous melanoma (SKCM) and Bladder urothelial carcinoma (BLCA) samples from GDC data portal , a standardized measure of dispersion of a probability distribution, has been commonly used in feature selection for various high-throughput data ,44. In tn×m matrix Y as the expression profiles of n genes in m tumor samples, we assume that these tumor samples are mixtures of K cell types with different mixing proportions. Denote the basis matrix W as expression profiles for these n genes in K cell types, and the proportion matrix H as proportions of the K cell types in m samples. The observed data Y is assumed to be a linear combination of cell type-specific expression profiles, i.e., Y = WH+ϵ, where ϵ is an n×m error matrix. We aim to solve W and/or H from Y. If the basis matrix W is known, the problem is the typical reference-based deconvolution, which can be readily solved by the constrained linear regression . If botlgorithm ,W as W1, and unknown portion as W2, i.e., W = . Given expression matrix Y for all tumor samples and known reference matrix W1, we aim to infer the overall proportion matrix H and unknown basis matrix W2. Thus the above partial-reference based deconvolution problem can be formulized toW2, H1 and H2; (b) column sum of H1 and H2 is less than 1.However, in real clinical practice, only a fraction of the cell types in the tumor samples might be known. We denote the known portion of basis matrix We term the above problem  as an iW2;Start with a random initialization of W1 and estimate hij≤1 and Fix wij≥0;Estimate Repeat steps (ii) and (iii) until convergence or a specific number of times.c). The basic formula of AICc is [N is the sample size, p is the number of model parameters, and SSR is the sum of squared residuals between true and estimated gene expression profiles for all mixture samples. In the NMF framework (include PREDE as well), the samples size should be counted in the level of genes, i.e., N = n×m, and p = K(n+m)−nK1, where n, m, K, K1 are numbers of mixture samples, features, all cell types and known cell types, respectively. Compared with the original AIC, the AICc imposes a higher penalty when sample size is small, and approximates AIC when samples size increases. We calculated AICc for a reasonable range of potential cell type numbers  and the predicted optimal number of cell types were determined by the minimum AICc value.We used Akaike information criterion (AIC)  to deter AICc is :AICc=NlW) and the estimation accuracy of cellular proportion matrix (H), which were assessed by Pearson correlation coefficient and the mean absolute error (MAE) between true and predicted cell type proportions.We evaluated the performance of PREDE from the following two aspects: the estimation accuracy of basis expression matrix  at different levels of noise and (B) at different proportions of unknown cell types.(TIF)Click here for additional data file.S2 FigAccuracies of (A) proportion estimation and (B) profile estimation of our method based on ‘low similarity set’ and ‘high similarity set’.(TIF)Click here for additional data file.S3 FigK when proportion of rare cell types increases from 0.01 to 0.15.AICc values at different numbers of (TIF)Click here for additional data file.S4 FigAccuracies of (A) proportion and (B) profile estimations by PREDE and RF when proportion of rare cell type increase from 0.07 to 0.10.(TIF)Click here for additional data file.S5 FigProportion estimations for rare cell type by four methods at (A) different proportions of the rare cell type and (B) noise ratios.(TIF)Click here for additional data file.S6 Figc values at different numbers of cell types when cancer cells or immune cells are unavailable, respectively.Blue line or red line show AIC(TIF)Click here for additional data file.S7 FigEight cell lines including three breast cancer cell lines, three immune cell lines and two normal cell lines were mixed together with proportions 60%, 20% and 20% respectively. (A-B) Accuracies of profile and proportion estimations when cancer cell lines are unknown; (C-D) Accuracies of profile and proportion estimations when immune cell lines are unknown.(TIF)Click here for additional data file.S8 FigEight cell lines including 3 breast cancer cell lines, 3 immune cell lines, and 2 normal cell lines were mixed to simulate 100 tumor samples. Mean expression profiles of cancer cell lines, normal cell lines and immune cell lines were respectively treated as unknown cell components to validate all three methods using the rest cell lines as input. Estimations of (A) cellular proportion and (B) expression profile for the unknown cellular component by the three methods.(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.


Gemcitabine is a drug of choice in the treatment of human pancreatic cancer. Chemo-resistance to this drug is common and has been attributed to a variety of distinct mechanisms, involving > 100 genes. A recently developed small-molecule G-quadruplex ligand, the trisubstituted naphthalene diimide compound CM03, has previously been shown to have equivalent potency to gemcitabine in the pancreatic cancer cell line MIA PaCa-2. We report here on cell lines of increased resistance to gemcitabine that have been generated from this line, with the most resistant having 1,000-fold reduced sensitivity to gemcitabine. These resistant lines retain nM sensitivity to CM03. The molecular basis for the retention of potency by this G-quadruplex ligand has been examined using whole transcriptome data analysis with RNA-seq. This has revealed that the pattern of pathways down regulated by CM03 in the parental MIA PaCa-2 cell line is largely unaffected in the gemcitabine-resistant line. The analysis has also shown that the expression patterns of numerous genes involved in gemcitabine sensitivity are down regulated in the resistant line upon CM03 treatment. These results are supportive of the concept that G-quadruplex small molecules such as CM03 have potential for clinical use in the treatment of gemcitabine-resistant human pancreatic cancer. Pancreatic ductal adenocarcinoma (PDAC: ca 85% of cases), is the most common form, and is also one of the most intractable of cancers to treatment. It has a bleak prognosis that has barely changed in over 20 years, with < 5% of patients surviving for five years7.The standard chemotherapy for PDAC has been the nucleoside analogue gemcitabine  in their promoter regions31, and which have been identified as playing roles in PDAC36. By contrast, gemcitabine affects, in large part, a quite distinct set of genes, which are often not quadruplex-containing. It is also notable that CM03 shows in vivo anti-tumour activity27 in the KPC genetic mouse model, in which gemcitabine does not produce significant responses37. In the light of this evidence that CM03 and gemcitabine have distinct and orthogonal targets of action, we hypothesised that CM03 would show significant anti-proliferative activity in PDAC cell lines in which gemcitabine resistance has been generated. We report here the results of a study that addresses this concept. We have used the same approach as before27, examining data from RNA-seq analyses in order to be able to compare results with those from our previous study. We also discuss, in the light of the results presented here, the role that CM03 and related compounds could play in the clinic for the treatment of drug resistant PDAC.We have recently reported that several small-molecule naphthalene diimide derivativesM03 Fig. b having Gemcitabine-resistant (GemR) pancreatic cancer MIA PaCa-2 cell lines were generated for three different gemcitabine concentrations , as described in the Methods section. The parental MIA PaCa-2 cells were designated as gemcitabine sensitive cells. The GemR cell lines were maintained at the three gemcitabine concentrations at which resistance was established. Prior to any experiment, gemcitabine was withdrawn from the culture medium for seven days. The resistant line at the highest gemcitabine concentration (3.0 µM), is the primary focus of the present study, and was designated as GemMIA-R3.38 and as modified for use with quadruplex-binding small-molecule compounds39. The dose–response curves for CM03 are closely similar in both parental MIA PaCa-2 and GemMIA-R3 cells . The CM03 GI50 values remained comparable in all three GemMIA resistant cell lines to that in the parental cell line. This indicates that the mechanism of gemcitabine resistance does not influence the effects of CM03 on cell viability and by implication, on its mechanism of action. We have also examined the ability of the quadruplex ligand CX-546140 to reduce growth in these two cell lines, in order to ascertain whether this structurally very dissimilar compound retains activity in both cell lines. We find that it has reduced cell-growth inhibitory activity compared to CM03 but similarly retains its activity in the GemMIA-R3 line -based method with a treatment period of 96 h, as described previouslyne Table , whereasne Table .Figure 227 in the former would remain largely unaltered in GemMIA-R3 cells. In order to validate this hypothesis, a series of RNA-seq experiments were undertaken in order to profile transcriptome changes in the parental MIA PaCa-2 and GemMIA-R3 cell lines after 6 h and 24 h treatment with CM03.Since CM03 has equivalent potency in the parental MIA PaCa-2 and derived GemMIA-R3 cell lines Table , it was 2FC < − 0.5, FDR < 0.1) and about 1,640 genes upregulated  in gem-resistant cells relative to the gem-sensitive parental line  increased in both cell lines with duration of CM03 treatment although the numbers themselves varied . The definitions of these limits are given in the figure legend Fig. . Overall27. Cells were treated with 400 nM CM03 for 6 h and 24 h, i.e. using the same conditions as in the RNA-seq experiments. In general, the expression pattern for this panel of genes is strikingly similar for both parental and GemMIA-R3 cell lines, although the level of down-regulated expression is consistently greater in the GemMIA-R3 line, with most changes having **P < 0.01, using Student’s t-test. Notably, several genes highlighted in Fig. MAPK11, TP73 and BCL-2 had expression changes which are not statistically significant or *P < 0.05 in parental cells yet were more significantly down-regulated in GemMIA-R3 cells. It is notable that the sole gene that is not down regulated in either cell line, KRAS, has the lowest number of PQs , providing further evidence of the validity of the GemMIA-R3 line as authentically gemcitabine-resistant. Some genes that promote gemcitabine resistance were slightly up-regulated  and others were highly up-regulated . The effect of CM03 has been to down-regulate the expression of a number of these genes such as SHH, GLI1, RRM2 and to a greater extent the MAPK11, MAPK12 and AKT1 genes. These genes are highly enriched in PQs and are consequently also important players in CM03 action in the parental line.Many genes have been implicated in gemcitabine resistance in MIA PaCa-2 cells, and in clinical gemcitabine resistance. The differential expression of a number of these genes has been examined here Table . The dat10 is highly down-regulated (log2FC = − 2.99) in GemMIA-R3 cells, in accordance with the high level of resistance in this line. In addition, expression of the cytidine deaminase gene CDA , further increases chemo-resistance to gemcitabine  in gemcitabine metabolism, which metabolizes and activates gemcitabinene Table . CM03 ha14, as well as GLI transcription factors, by upregulating SOX2 signalling23. Two key genes in the Hh pathway, sonic hedgehog ligand (SHH) and transcription factor GLI1 are highly up-regulated in GemMIA-R3 cells, with log2FC values of 2.5 and 3.6, respectively. These two genes, which have high PQ representation, are slightly downregulated by CM03 but not statistically significantly. The MAPK11, MAPK12 and AKT1 genes are not upregulated in the GemMIA-R3 cell line, whereas their expression is highly down-regulated upon CM03 treatment (Table The hedgehog (Hh) signalling pathway has also been found to play a role in promoting gemcitabine resistanceST6GAL1 gene (Table 40 and it notable that CM03 treatment results in an increase in the number of DNA damage foci27. The ST6GAL1 gene is highly over-expressed (log2FC = 2.84) in the GemMIA-R3 cell line, presumably contributing to its high level of gemcitabine resistance. CM03 treatment results in significant down-regulation of ST6GAL1 (log2FC = − 1.16) in GemMIA-R3 cells compare to its small effect in parental cells (log2FC = − 0.11). This large difference may be because that the ST6GAL1 gene is more accessible in gem-resistant cells than in sensitive cells. CM03-mediated downregulation of ST6GAL1 should reduce or reverse the effect of overexpressed ST6GAL1 on increasing chemo-resistance.Analogous behaviour has been found for the ne Table , which e24. Expression of the FOXM1 gene did not significantly alter in the GemMIA-R3 cell line compared to the parental line, but CM03 treatment results in some downregulation (log2FC = − 0.52).The Forkhead box protein M1 (FOXM1) is an oncogenic transcription factor and its elevated expression is associated with gemcitabine resistance in patients with pancreatic cancer20. In accord with this, FASN gene expression level is slightly increased in the GemMIA-R3 cell line. The FASN gene contains 40 PQs .Elevated expression of fatty acid synthase (FASN) has been correlated with reduced response to gemcitabine and inhibition of FASN results in synergistic effects on gemcitabine treatmentDCK, CDA, GLI1 and ST6GAL1.A cell line (GemMIA-R3) has been derived from the parental MIA PaCa-2 PDAC cell line, with ca 1,000- fold resistance to the clinically used drug gemcitabine, using a repeated passage approach. Its validity as a resistant line is supported by the changes in expression for several genes associated with gemcitabine resistance, notably 50 for cell growth inhibition) in parental MIA PaCa-2 cells. Its potency is fully retained in the GemMIA-R3 line, as well as in an equivalent gemcitabine resistant line derived from the PANC-1 PDAC line, indicating that potency is not restricted to a single PDAC resistant line, and may be a more general phenomenon. The structurally unrelated G-quadruplex compound CX-5461 is also equipotent in parental and resistant lines, suggesting that the mechanism of action of this G-quadruplex compound also involves distinct genes and pathways to gemcitabine and its resistance.The G-quadruplex small-molecule CM03 compound has ca 10 nM potency , as well as some that are in common with those involved in CM03-induced down-regulation of proliferation and resistance (such as GLI1), is down-regulated by CM03 treatment. This suggests that CM03 treatment of gemcitabine-resistant cells could result in a reduction in gemcitabine resistance. This concept has yet to be evaluated.Several other quadruplex ligands have been previously reported as having activity in chemo-resistant cell lines27 and was used as the > 95% pure hydrochloride/formate salt. This has a pH of 6.95 in H2O. Gemcitabine, CM03 salts and CX-5461 were dissolved in PBS and stocks of 1 mM were prepared and kept frozen and away from light prior to use. Drugs were filtered through 0.22 µm pore-size filter units before addition to appropriate cell culture media.Gemcitabine as the hydrochloride salt was purchased from Sigma-Aldrich (cat no. G6423). CX-5461 was purchased from Adooq Bioscience (cat no. A11065). Compound CM03 was synthesised in-house2 in humidified incubators and routinely passaged.MIA PaCa-2 and PANC-1 cell lines were purchased from ATCC (cat no. CRL-1420 and CRL-1469) and maintained in Dulbecco's Modified Eagle's Medium (DMEM) with 4,500 mg/L glucose and 2 mM L-glutamine  supplemented with 10% foetal bovine serum , 0.1 mg/ml streptomycin and 100 U/ml penicillin  and only for MIA PaCa-2 cells: 2.5% horse serum . Cells were maintained at 37 °C and 5% CO39. 50% growth inhibition  were determined by taking the mean absorbance at 540 nm for each drug concentration expressed as a percentage of the absorbance of untreated control wells. All experiments were performed in triplicate and the mean ± SD values were determined from at least three independent experiments.Cellular growth inhibition was measured using the sulforhodamine B (SRB) assay in 96 well plates as described previouslyGemcitabine-resistant cell lines were generated from the parental MIA PaCa-2 pancreatic cancer line by incrementally increasing the gemcitabine concentration in the culture medium over extended periods of time. A cycle of concentration increase and selection was repeated until reaching a particular target concentration . Each increment of gemcitabine concentration took around a week for cells to gain resistance to and resume cell proliferation. The whole process took several months to achieve the most resistant cell line for the highest gemcitabine concentration (3 µM). At each point, the cells were amplified, cryopreserved and maintained continuously in medium containing gemcitabine at the concentration of interest. The gemcitabine selection pressure was withdrawn for 7 days before a particular gemcitabine-resistant cell line was used in any experiment, in order to avoid any interference from gemcitabine.6 and 24 h = 1.0 × 106 cells/well) and incubated overnight. Then, cells were treated with 0.40 µM CM03 or vehicle (PBS) for 6 h and 24 h. This is the lowest concentration of CM03 that can achieve minimal cell death (10%) and was chosen for these short-term exposures, in order to investigate its direct gene targets and not collateral gene expression changes. It was the same as previously used27, maintaining consistency with the previous study in which it had been shown to cause substantial gene expression changes even after 6 h treatment. Parental cells were also included without CM03 treatment for GemMIA-R3 vs parental gene expression comparison. Total RNA was extracted from 2–4 × 106 cells using the RNeasy mini kit  and on-column DNase1 digestion  as per the manufacturer’s instructions. RNA quality (RIN > 7.0) was checked with an Agilent 2,100 Bioanalyser RNA 6,000 Nano Chip and RNA concentration was quantified using a Qubit fluorometer (ThermoFisher) and Qubit RNA HS Assay Kit . RNA-seq libraries were then generated using the NEBNext mRNA Ultra II with IDT xGen UMI adapters kit for Illumina as per the manufacturer’s instructions and sequenced using an Illumina NextSeq 500 instrument (undertaken at the UCL Genomics Facility).GemMIA-R3 cells were seeded in 100 mm plates (6 h = 2.5 × 10https://www.ncbi.nlm.nih.gov/geo/). RNA-seq data (GEO accession GSE105083) from our previous study with CM03 and the parental cell line MIA PaCa-2 was used for comparison purposes during the analyses27. Illumina run data were demultiplexed and converted to fastq files using Illumina’s bcl2fastq Conversion Software (v2.19). Then, fastq files were pre-processed to remove adapter contamination and poor quality sequences using the program Trimmomatic (v0.36)48 before being mapped to a recent human genome build (UCSC hg38: https://genome.ucsc.edu/) using the RNA-seq alignment tool STAR . Duplication levels were estimated using JE-Suite49, a Unique Molecule Identifier program to filter out duplicates and reads that are the result of PCR amplification were marked. Next, reads per transcript were counted using the program FeatureCounts50 (v1.4.6p5) before normalisation, modelling and differential expression analysis using the SARTools (v1.3.2) package51.The raw and processed sequencing data has been deposited in the GEO public functional genomics data repository with GEO accession no GSE148200 (≥3N1−7G≥3N1−7G≥3N1−7G≥3), was used as previously reported27, in gene promoters (defined for this purpose as being up to 2 kilobases upstream of the transcription start site (TSS) and 100 bases downstream) and in exons and introns. These numbers of putative quadruplexes (PQs) were taken from the earlier study27, computed as reported previously53.For the numbers of putative G4 sequences in an individual gene (PQs in Table 2 fold changes (Log2FC) and false discovery rate (FDR) for drug treatment versus untreated, with the following assignment: Down Strong = genes with Log2FC ≤ − 1 and FDR < 0.05; Down = genes with Log2FC < − 0.5 and FDR < 0.1; Strong Up = genes with Log2FC ≥ 1 and FDR < 0.05; Up = genes with Log2FC > 0.5 and FDR < 0.1. The signalling pathway enrichment analysis was done using the DAVID functional annotation tool (https://david.ncifcrf.gov/)54 on the Up and Down gene lists. The Pathview maps tool (https://pathview.uncc.edu/)55 was used to visualise top affected signalling pathways containing coloured DEGs from Up and Down gene lists to indicate their level of expression.Differentially expressed genes (DEGs) were split into different subsets according to their logACTB, GAPDH, and TUBB, and the fold change was determined using 2−ΔΔCt.Parental MIA PaCa-2 and GemMIA-R3 cell lines were seeded and treated exactly same as in RNA-seq experiment. After extracting total RNA, the RNA concentrations were measured with a NanoDrop 2000/2000c spectrophotometer (ThermoFisher). The cDNA libraries were then prepared from a determined amount of RNA using a SuperScript III First-Strand Synthesis System  as per the manufacturer’s instructions. RT-qPCR was performed using Power SYBR Green Master Mix  with a 50 ng template and 150 nM primers in an AriaMx Realtime PCR System (Agilent). Primers were purchased from Sigma (KiCqStart SYBR Green Primers) and Eurofins Genomics. At least three independent experiments were carried out in triplicate. The data was used to determine the Ct values, which were normalized to the geometric mean of three housekeeping genes Supplementary information


Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent malignancies and a major cause of cancer related death worldwide, especially in China. Cell lines are widely used disease models for basic medical research, however, well characterized ESCC cell models from China were seldom reported. Misidentifying and cross-contaminations of cell lines also hamper the way of producing solid and reproductive data.CSEC216 was originated from a 45-year-old male ESCC patient from Chaoshan littoral, China. Specimens were minced into fragments and seeded in T-25 flask for primary culture. Immunoflourescence staining was performed for identifying the origination and proliferation activity. In vitro migration and invasion abilities was tested by transwell assay. DNA Short Tandem Repeats profiling was implemented for cell authorization. Karyotype was investigated by spectrum karyotyping. Whole genome sequencing was utilized to investigate genomic alterations. Background information and genomic mutation data of published ESCC cell lines were obtained from online databases.CSEC216 was an uncontaminated cell line, exhibited epithelial cell features with polygonal morphology and adherent growth as monolayer. Immuno staining demonstrated its epithelial origination and high proliferation rate. The Population Doubling time was 29.7 h. The karyotype demonstrated tumor cell patterns with aneuploidy and complex chromosomal aberrations. Mutation signatures, genes with SNA or CNA of CSEC216 and published ESCC cell lines were similar with the mutation spectrum of original ESCC tumors.ESCC cell line CSEC216 from high incidence region in China was established with no cross-contamination. Biological features were studied. Genomic mutation features of CSEC216 and 28 ESCC cell lines were characterized which provided thorough cytogenetic background that facilitated future usage. Esophageal cancer (EC) is the eleventh most commonly diagnosed malignancy, and the sixth leading cause of cancer mortality worldwide, an estimated number of 483,000 new cases and 439,000 deaths occurred in 2015 worldwide [Sustainable and uncontaminated cancer cell lines provide available resources for researchers to utilize. Human cell lines are now widely used in laboratories worldwide. The first continuous cell line Hela, was established in 1952 (7). In 1981, Nelson-Rees et al. reported that many cell lines had been switched or cross-contaminated with Hela cells. To date, a significant proportion of cell based research is misleading and misrepresentative, because the cell lines are of different origin to that claimed or the cross-contamination between cell lines . TherefoThere are abundant esophageal squamous cell carcinoma (ESCC) cell lines around the world. However, few of them were derived from China. Here we report a newly established and authorized ESCC cell line designated CSEC216, derived from a 45-year old male patient from the Chaoshan littoral. This cell line would serve as potent in vitro model for the exploration of the pathogenesis of ESCC. The current study also reported the genomic similarity of 28 ESCC cell lines and original ESCC tumors.CSEC216 was established from an endoscopic biopsy sample derived from a 45-year-old male patient who underwent esophageal endoscopy in the Cancer Hospital of Shantou University Medical College. The tumor located at middle thoracic esophagus, 23–30 cm from the incisor with constrictive lumen and elevated mucosa observed in the esophageal tract . The specimen was then washed 3 times in PBS and minced with a sterile scalpel into small fragments of 1 mm1 were used for growth curve plotting.The CSEC216 cell line had been subcultured for 50 passages, during which the total culturing time and population doubling time(PDT)http://www.doubling-time.com/compute.php 18/10/2015).For population doubling time calculation, 10,000 cells at passage 28 in 300 μl culture medium were inoculated into each well of a 24-well plate and culture medium was replaced every 3 days. Three wells of cells were harvested by trypsinization each time with 24 h intervals, the cell number of each well was determined using a Neubauer hemacytometer, then the cell numbers were averaged. Cell growth during log phase was used to calculate doubling time . To obtain cells at metaphase, cells at 70% confluence at passage 20 were treated with 0.03 μg/ml colcemid for 7 h then harvested by trypsinization. After neutralization by mixing the cell suspension with equal amount of culture medium and centrifugation at 1000 rpm, the supernatant was discarded. Cells were resuspended in 2 ml 0.8% sodium citrate and kept at 37 °C for 15 min. 250 μl fresh made fix solution (methanol:acetic acid = 3:1) was added to the cell suspension at 37 °C for 5 min to prefix the cells. Cells were collected by centrifugation and washed 3 times with fix solution. After the final wash, supernatant was discarded. Cells were resuspended in 500 μl fix solution and 15 μl cell suspension was dropped onto an adhesive slide, air dried and the hybridization area was marked with a diamond pen. The slide was stored at room temperature and kept from light for 7 days. SKY was performed using a SKY probe kit SkyPaint™ mixture , according to the manufacturer’s instruction). The karyotype was analysed by SkyView™ Imaging System  equipped with a Zeiss Axioplan-2 fluorescence microscope.A total amount of 0.5 μg DNA per sample was used for the DNA library preparations. Sequencing library was generated using Truseq Nano DNA HT Sample Prep Kit (Illumina USA) following manufacturer’s recommendations and index codes were added to each sample. Briefly, genomic DNA sample was fragmented by sonication to a size of 350 bp. Then DNA fragments were end-polished, A-tailed, and ligated with the full-length adapter for Illumina sequencing, followed by further PCR amplification. After PCR products were purified (AMPure XP system), libraries were analyzed for size distribution by Agilent 2100 Bioanalyzer and quantified by real-time PCR (3 nM).The clustering of the index-coded samples was performed on a cBot Cluster Generation System using HiSeq X PE Cluster Kit V2.5 (Illumina) according to the manufacturer’s instructions. After cluster generation, the DNA libraries were sequenced on Illumina HiSeq platform and 150 bp paired-end reads were generated.Raw data (short reads) in FASTQ format (.fq) were processed though in–house perl scripts. In this step, cleans data (clean reads) were obtained by removing reads containing adapter, reads containing poly-N and low quality reads from raw data. At the same time, Q20, Q30, error rate and GC content of clean data were calculated. All the further analysis was based on the high quality clean data.Mapping: Burrows-Wheeler Aligner (BWA2) software is utilized to map the paired-end clean reads to the reference genome (UCSC hg19). The original mapping result in BAM format can be obtained. Picard, GATK and Samtools are then used to do duplicate removal, local realignment, and base quality recalibration etc.http://annovar.openbioinformatics.org/en/latest/user-guide/download/) were regarded as germline mutations. Somatic SNA data were utilized for mutational signature analysis by R package MutationalPatterns to decypher the probable mechanism underlying the mutational process. COSMIC cancer mutation signatures were used as reference signatures [Single Nucleotide Alterations (SNA) GATK HaplotypeCalleris used to do variant calling and identify SNA, insertions and deletions (INDELS). In this step, raw callsets with false positive variant calls are obtained. Then variant Filtration in GATK is performed to make the raw callsets suitable for meaningful analysis. High quality mutations were retained for germline mutations filtration. Mutations identified in gnomad database  Control-FREEC was utilized to do somatic CNA detection. GISTIC algorithm was used to infer recurrently amplified or deleted genomic regions. G-scores were calculated for genomic and gene-coding regions on the basis of the frequency and amplitude of amplification or deletion affecting each gene. A significant CNA region was defined as having amplification or deletion with a G-score > 0.1, corresponding to a p-value threshold of 0.05 from the permutation-derived null distribution.IF staining against Cytokeratin , E-Cadherin ,Vimentin , and Ki-67  was performed to confirm the origination and proliferation activity of CSEC216 cells. Cells were harvested when reached 80% confluence. Then 10,000 cells were inoculated onto a coverslip in a 24-well plate and incubated for 24 h. Cells were fixed with 4% paraformaldehyde at 4 °C for 15 min, permeabilized with 0.5% Triton X-100 at room temperature for 30 min and washed with PBS after each treatment. Primary antibody was applied to the slide and incubated in humidified box at 4 °C overnight. Secondary antibody  was applied after PBS washing and incubated in a humidified chamber at 37 °C for 30 min. The slide was washed with PBS after incubation and mounted by anti-fade DAPI . Photos were taken by Zeiss Axio CSan Z1.https://www.atcc.org/STR_Database.aspx 20/11/2015) and DSMZ (https://www.dsmz.de/fp/cgi-bin/str.html 20/11/2015). Matching criteria for human cell line authentication was set as 0.8 recommended by the website.Genomic DNA of cells at passage 5 was extracted using DNA Mini Kit (QIAGEN) under manufacture’s protocol. DNA samples were sent to Genetica DNA Laboratories USA for Short Tandem Repeats(STR) profiling test. Fifteen STR loci and AMEL were tested. STR electropherogram and signed STR profiling report were shown in supplementary data. Eight STR loci and AMEL were used for online matching in the STR databases of ATCC .https://web.expasy.org/cellosaurus/) [ESCC cell lines were searched on Cellosaurus (saurus/) . KeywordCSEC216 cells showed polygonal epithelial cell morphology and adherent growth as monolayer. None of these cell lines was cultured during the CSEC216 establishment. Both results showed that CSEC216 was a unique cell line that was not misidentified and contaminated with other registered cell lines. Full report and electropherogram were shown in Additional file SKY analysis yielded representative karyotype photograph of CSEC216 cells at passage 20, which was demonstrated in Fig. Frequently observed chromosomal numerical abnormalities were presented by chromosome loose of Y,8,11,19 and chromosome gain of 1,3,5,17,20. Structural aberrations were complicated as deletion, translocation, isochromosome and derivation chromosome were frequently observed. Derivation chromosome of 2, 3, 13 composed by the most sophisticated translocations was observed, i.e. der (2) t  ttt、der(3)tttt、der (13) tttt. Chromosomal break points at p1 or q1 was common in chromosome 1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 16, 17, 18, 21, X.The genomic DNA of CSEC216 were sequenced with 30X average depth. WGS yielded 3,290,751 SNAs, 572,345 INDELs and 1860 CNA regions. After removal of germline mutations by comparing with gnomad database file (hg19_gnomad211_genome),75527 SNAs and 61866 INDELs were obtained. The genome-wide mutation rate of SNA and INDEL were 2.52 and 2.06 mutations per megabase respectively. The density of SNAs and INDELs per megabase along the chromosomes were plotted in Fig. https://cancer.sanger.ac.uk/cosmic/signatures_v2), which featured by transcriptional strand bias for T > C substitution at ApTpX context, and has been identified in all kinds of cancer types with yet unknown aetiology , of which most genes were chosen from our previous work [The etiological implication of the mutation signature extracted from all somatic SNAs of CSEC216 was vague. Since 98.8% of somatic SNAs of CSEC216 were located in intron and intergenic region, the mutational context of those SNAs could over rule the mutations that literally contributed to relative etiology. To investigate the impact of genomic mutations to those genes which were implicated to play a role in the carcinogenesis of ESCC, mutations at exon, up/down stream were extracted from SNA, INDEL and CNA data. Resulted in 910 SNAs and 1310 INDELs which affected 999 genes and 14072 genes in 1166 CNA regions. A list of 44 genes related with ESCC were selected from literatures and COSMIC database . CNA and SNA data of cell line were shown in Additional file After removing problematic cell lines, a total of 28 SNA and 24 CNA mutation data were downloaded from CCLE database  in 32.1% (9 of 28)/21.4% 6 of 28)/17.9% (5 of 28) cell lines respectively. Extreme cases were found in OE21 and TE15 where Signature C take 93.7% and 91.3% proportion among the 3 signatures , CSMD3 (39.3%), NOTCH1 35.7%), ZFHX4 (32.143%), NFE2L2 (28.6%) (Fig. %, ZFHX4 Esophageal carcinoma is one of the most common and lethal malignancies in China and worldwide , 21. ProShort tandem repeat profiling is the standard method for authenticating cell lines by using at least eight STR loci and the application of match criteria , 24. By Aneuploidy and chromosome aberrations in human solid tumors are hallmarks of gene deregulation and genome instability , 26. TheMutation process during carcinogenesis leave traces on the genome, which could be discriminated by different features, i.e. mutation type, transcriptional strand bias and sequence context. Mutation signatures are combined features of cataloged mutation types which are footprint from variety mutagenesis processes . The de CNA is one of major mutation in human cells. Normal human cells are diploid, but in tumor cells, there are copy number amplified or deleted regions in the genome . DeletioWe also investigated mutation status of ESCC related genes in ESCC cell lines. Genes that participate in cell cycle regulation, cell differentiation, oxidative damage, etc. were found to have SNA or CNA mutations in ESCC cell line. Large scale genomic data analysis had been pulled out by the TCGA . Their rIn summary, a novel and well characterized human ESCC cell line CSEC216 has been established. CSEC216 was derived from a 45-year old male ESCC patient from Chaoshan littoral, China. CSEC216 cells exhibit epithelial cell features and possess high proliferative activity, relatively low in vitro migration and invasion ability. CSEC216 exhibited hypotriploidy karyotype with complicated chromosomal aberrations. One somatic stop gain mutation and two somatic missense mutations were found in TP53 and NFE2L2 at exonic region respectively. MYC, CCND1, SOX2, PIK3CA, TP53, et al. were found gene copy number gained, while LRP1B was found copy number lost among selected ESCC related genes. Genomic background study of CSEC216 together with 28 ESCC cell lines demonstrated that those cellular disease models, to some extent, at least genetically, might be representable of ESCC. The current study could provide useful ESCC cell line information which would facilitate researchers for choosing appropriate cell model for their study.Additional file 1: Table S1. ESCC cell line information from Cellosaurus and literatures.Additional file 2: Table S2. Background information of CCLE ESCC cell lines.Additional file 3: Figure S1. Report and electropherogram of STR profiling. A, STR profiling report of CSEC216 at P5. For what’s worth mentioning, CSEC216 was originally designated as ECST109, to distinguish from widely used ESCC cell line EC109, the name was changed as CSEC216. B, Electropherogram.Additional file 4: Table S3. SNA and INDEL data of CSEC216.Additional file 5: Table S4. CNA data of CSEC216.Additional file 6: Table S5. List of 44 genes related with ESCC were selected from literatures and COSMIC database.Additional file 7: Table S6. CNA data of 24 ESCC cell lines.Additional file 8: Table S7. SNA data of 28 ESCC cell lines.Additional file 9: Figure S2. SNA spectrum of ESCC cell lines. The spectrum of 96 mutation context of 28 ESCC cell lines.


Klotho was initially introduced as an antiaging molecule. Klotho deficiency significantly reduces lifespan, and its overexpression extends it and protects against various pathological phenotypes, especially renal disease. It was shown to regulate phosphate and calcium metabolism, protect against oxidative stress, downregulate apoptosis, and have antiinflammatory and antifibrotic properties. The course of diabetes mellitus and diabetic nephropathy resembles premature cellular senescence and causes the activation of various proinflammatory and profibrotic processes. Klotho was shown to exert many beneficial effects in these disorders. The expression of Klotho protein is downregulated in early stages of inflammation and diabetic nephropathy by proinflammatory factors. Therefore, its therapeutic effects are diminished in this disorder. Significantly lower urine levels of Klotho may serve as an early biomarker of renal involvement in diabetes mellitus. Recombinant Klotho administration and Klotho overexpression may have immunotherapeutic potential for the treatment of both diabetes and diabetic nephropathy. Therefore, the current manuscript aims to characterize immunopathologies occurring in diabetes and diabetic nephropathy, and tries to match them with antiinflammatory actions of Klotho. It also gives reasons for Klotho to be used in diagnostics and immunotherapy of these disorders. Diabetes mellitus (DM) is a serious, chronic disease that is characterized by high blood glucose levels, resulting in aberrant insulin production and lower sensitivity of acceptor cells of this hormone. The prevalence of DM is increasing worldwide, and its complications are one of the leading causes of mortality from noncommunicable diseases ,2. Due tProper kidney function is essential for maintaining body homeostasis through selective plasma filtration. Blood filtration occurs in the cortex of the kidney, in the glomerulus of the nephron, which apart from a glomerulus also consists of the proximal tubule, Henle’s loop, and distal tubule. Blood flows to glomeruli and is filtered by a three-layer structure : pores between endothelial cells of blood capillaries, the glomerular basement membrane , and spaces between neighboring podocytes . A podocyte cell consists of a cell body, major processes, and foot processes that branch out from major processes and is tightly laid on blood capillaries to form slit diaphragms. The structure of the glomerulus is also supported by mesangial cells.During the course of diabetes and diabetic kidney disease, many metabolic and hemodynamic stresses occur, among others hypoxia and hyperfiltration ,4. The dα-Klotho was discovered in 1997 and simply named Klotho because no other Klotho proteins were identified at that time. It was shown to be an antiaging molecule. Mice that exhibited Klotho deficiency presented a premature aging phenotype, whereas Klotho overexpression extended their lifespan by up to 30% and protected them against many pathological phenotypes, especially renal disease ,14.Klotho is expressed mostly in the kidneys and choroid plexus in the brain. Its expression was also detected in the pituitary, parathyroid glands, the heart, and reproductive organs, with low levels in other tissues . In the KL gene. The extracellular domain of the Klotho protein can be cleaved and separated from the cell membrane by the metalloproteinases ADAM-10 and ADAM-17  and β-secretase 1 (BACE1). This proteolytic cleavage by ADAM-10 and ADAM-17 can be stimulated inter alia by insulin. The extracellular domain of Klotho contains two homologous repeat sequences (KL1 and KL2), which are separated from each other by unknown proteases. Both the whole released extracellular domain and separated KL1 and KL2 are referred to as soluble Klotho particles [KL gene that possesses a STOP codon was thought to result in the production of a shorter, secretory form of the protein [Two or three forms of α-Klotho protein exist. Membrane-bound Klotho is a 130 kDa single-pass transmembrane protein that is encoded by a  protein . However protein .Both transmembrane Klotho and soluble Klotho serve as coreceptor proteins for fibroblast growth factor (FGF) 23 (binding to FGF receptors 1–4), which promotes phosphaturia to control phosphate metabolism . Klotho The disruption of phosphate and calcium metabolism, oxidative stress, inflammation, the fibrotic process, and an increase in the ratio of β-cell and podocyte loss through apoptosis can result in pathologies that resemble premature aging. Klotho protects cells against accelerated aging and damage during the course of DM and diabetic nephropathy ,18. More+ and CD4+ T cells and CD20+ B cells), and the expression of proinflammatory cytokines, especially interleukin 1β (IL-1β) and TNF-α. High levels of proinflammatory and profibrotic IL-6 are produced in initial stages of T1DM. Moreover, IL-6-inducible autoimmunity-related gene (HIP/PAP) has been shown to be expressed in the pancreas in patients with T1DM, providing further evidence that IL-6 participates in the autoimmune process in type 1 DM [Type 1 DM is triggered by unknown environmental factors and develops in individuals with a polygenetic predisposition through the destruction of insulin-producing pancreatic β-cells. The damage to these cells is caused by impairments in the immune response and local inflammation , includiype 1 DM .Type 2 DM develops during the course of a prolonged high-glucose diet and consists of impairments in pancreatic insulin secretion and a lower cellular response to insulin stimulation. These metabolic changes are accompanied by a low-grade inflammatory process in adipose tissue, the liver, and pancreatic islets, in which IL-1β plays a major role . The proThis multiorgan inflammatory reaction, accompanied by higher levels of its marker C-reactive protein (CRP), causes pathological changes in the microvascular circulation in the kidneys, macrophage infiltration of this organ, and the local production of a wide range of proinflammatory cytokines and mediators that are detectable beginning in early stages of diabetic nephropathy in serum and peripheral blood cells in affected individuals.Inflammatory macrophage accumulation in the interstitium of the kidney correlates with glomerulosclerosis, tubular atrophy, interstitial fibrosis, albuminuria, and the loss of function of this organ. Albuminuria leads to an increase in the uptake of proteins in proximal tubules of the nephron, thereby increasing the tubular protein load, which acts as a trigger of the even greater activation of signaling cascades that cause inflammation and fibrosis in the tubule-interstitial region . M1 macrOther receptors also contribute to an accelerated immune response, albuminuria, and podocyte damage in diabetic nephropathy. These are intracellular nucleotide-binding oligomerization domain (NOD)-like receptors  that initiate NLRP3 inflammasome formation and the expression of proinflammatory molecules by immune cells , also called chemokine-CC motif ligand (CCL2)) and intracellular adhesion molecule 1 (ICAM-1) ,29. The Further immune response facilitation in diabetic nephropathy is caused by the activation of cytokine, chemokine, and angiotensin receptors that influence signaling through Janus kinase-signal transducer and activator of transcription (JAK-STAT) proteins, the mRNA levels of which correlate with the progression of diabetic nephropathy. Urine levels of MCP-1 increase in DM patients with micro- and macroalbuminuria. Moreover, MCP-1 levels correlate with the urine albumin/creatinine ratio and a decrease in renal function. In diabetic patients with macroalbuminuria, MCP-1 levels are a predictor of the progression of nephropathy. The course of the disease may also be monitored by measuring the level of other inflammation-related molecules, such as soluble TNF receptor-2 (sTNFR-2), which is associated with a decrease in the GFR. A ligand of sTNFR-2, TNF-α, and some of the other aforementioned molecules  are responsible for interstitial kidney tissue destruction through the generation of fibrotic lesions . Additio+/−), a spontaneous and persistent increase in blood pressure was observed, which was accelerated by a high-salt diet. High salt intake in Klotho+/− mice significantly increased the expression of MCP-1 in the kidneys and caused the infiltration of this organ with macrophages and T lymphocytes [+ lymphocytes (T-helper cells) in healthy elderly individuals and individuals with rheumatoid arthritis, a chronic autoimmune disorder that causes joint inflammation. In T-helper cells in these patients, Klotho was downregulated at the mRNA, protein, and enzyme activity (β-glucuronidase) levels. The role of Klotho in these cells would be to contribute to an antiinflammatory process, present in young and healthy subjects but lowered together with senescence and in inflammatory disorders, such as rheumatoid arthritis, diabetes, and diabetic nephropathy. This hypothesis has been supported by the fact that a reduction of Klotho expression and activity in T-helper cells in elderly individuals and rheumatoid arthritis patients occurred concomitantly with the downregulation of costimulatory CD28 molecules in these cells, which depend on higher levels of proinflammatory TNF-α [The downregulation of renal Klotho mRNA and protein expression leads to an increase in inflammation in the kidneys in diabetic mice . Additiophocytes . Prolongphocytes . A decrephocytes ,22, was ry TNF-α . As mentry TNF-α ,40.Klotho also stimulates the production of antiinflammatory factors. In human monocytes that were stimulated with lipopolysaccharide (LPS) to induce an immunosenescent-like phenotype, Klotho production, induced by calcitriol and transfection of cells with Klotho gene-possessing plasmid, upregulated the secretion of IL-10 . IL-10 i536, which is a NF-κB p65 subunit. The phosphorylation of RelA at Ser536, which was induced by TNF-α, caused NF-κB activation and the expression of several proinflammatory cytokines that are relevant to diabetes development, including TNF-α, which were found to be higher in the renal cortex in db/db diabetic mice and correlated with the duration of diabetes [536 and its further recruitment on IL-6, IL-8, MCP-1, and RANTES (chemokine (C-C motif) ligand 5 (CCL5)) promoters, thereby inhibiting the production of these proinflammatory molecules and protecting kidney tissue against inflammation-associated damage , demonstrated the usefulness of immunotherapy for DM and diabetic nephropathy by lowering the degree of renal inflammation. These drugs reduced development of the inflammatory reaction in the kidneys and protected against podocyte loss, glomerulosclerosis, and albuminuria. Another strategy to diminish renal inflammation and injury in an animal model of diabetic nephropathy was to inhibit NLRP3-inflammasome activation. A superoxide dismutase mimetic (MitoTempo) was used in this study to reduce oxidative stress and shown to downregulate NLRP3-inflammasome activation and renal injury. NLRP3-inflammasome activation upregulates expression of the proinflammatory cytokines IL-1β and TNF-α . Based oAbelmoschus manihot (L.) medic), traditionally used against CKD, was proved to act in part via the Klotho/TGF-β1/p38MAPK signaling pathway and to significantly lower the weight of the rats with DN, reduce their blood glucose and urine protein level, and the degrees of renal tubule damage, apoptosis, glomerulopathy, and renal fibrosis [Targeting other signaling pathways that are inhibited by Klotho has shown promising results with regard to improving health in patients with DM and diabetic nephropathy. Strategies of interfering with MyD88 signaling complex formation were tested in a mouse model of type 1 DM. Small molecules, such as L6H21, were shown to suppress the development of T1DM and kidney injury in these rodents by affecting MyD88 signaling and the expression of many proinflammatory cytokines  in kidney tubular cells. Furthermore, the abrogation of p38 MAPK signaling activation by GS-4997 in the kidneys in diabetic mice halted the progression of diabetic nephropathy and reduced the macrophage infiltration of these organs and the expression of genes that encode MCP-1 and TNF-α. Therefore, GS-4997 has been proposed as a therapeutic agent for the treatment of diabetic nephropathy in patients with T2DM. Similarly, the lower expression of MCP-1 and ICAM-1, which are inhibited by Klotho, reduced the accumulation of macrophages in the kidneys in animals with diabetic nephropathy and decreased tubulointerstitial injury after PKC-β inhibition by ruboxistaurin [fibrosis . A presefibrosis .Furthermore, a few studies have evaluated the therapeutic effects of recombinant Klotho and the stimulation of Klotho overexpression (e.g., by calcitriol, peroxisome proliferator-activated receptor γ , substanTherefore, preclinical data on Klotho have shown its potential usefulness as an early biomarker for DN development and its progression, and a possible, safe and effective agent in ameliorating diabetic kidney function ,60,62,63


However, KGF molecular and experimental information can sometimes be sparse and partially overlapping, suggesting the need for a specific annotation database of KGFs, conveniently condensing all the molecular details that can support targeted drug development pipelines and diagnostic approaches. Here, we describe KuNG FU (KiNase Gene FUsion), a manually curated database collecting detailed annotations on KGFs that were identified and experimentally validated in human cancer cell lines from multiple sources, exclusively focusing on in-frame KGF events retaining an intact kinase domain, representing potentially active driver kinase targets. To our knowledge, KuNG FU represents to date the largest freely accessible homogeneous and curated database of kinase gene fusions in cell line models.Inhibition of kinase gene fusions (KGFs) has proven successful in cancer treatment and continues to represent an attractive research area, due to kinase druggability and clinical validation. Indeed, literature and public databases report a remarkable number of KGFs as potential drug targets, often identified by  The occurrence of complex chromosomal rearrangements, such as inversions or translocations, can result in novel chimeric fusion genes, potentially representing driver events in cancer development3. Fusion genes were the focus of recent systematic analyses of Next-Generation Sequencing (NGS) transcriptomic datasets: Picco and colleagues reported the prediction of 7,430 unique gene fusions in more than 1,000 human cancer cell lines, across 42 different tissue types4; Gao and colleagues identified over 25,000 fusion transcripts, revealing 1,275 KGFs involving an intact catalytic domain in almost 10,000 clinical tumor samples, representing 33 cancer types in The Cancer Genome Atlas (TCGA)6. In this analysis, an overall low gene expression level was described for tumor suppressor gene fusions. At the same time, high gene expression was observed for fusions involving kinases and oncogenes, with the latter representing appealing druggable targets by approved or newly designed anticancer compounds6.Genomic instability is one of the hallmarks of cancer7, the majority of which are Tyrosine Kinase inhibitors. The first example of a successful KGF targeted therapy is imatinib , specifically active on a BCR-ABL1 kinase fusion in chronic myeloid leukemia patients8. Later on, more targeted drugs have been developed against well-recognized driver kinases activated in cancer due to KGFs, such as those involving ALK, RET, ROS1, NTRK or FGFR39. Remarkably, two drugs targeting KGF-activated NTRK kinases, namely entrectinib , also approved for the treatment of ROS1-positive patients with metastatic non-small cell lung cancer (NSCLC)11, and larotrectinib , got accelerated FDA approval as the first tissue-agnostic drugs for the treatment of tumors testing positive for NTRK kinase fusions, regardless of the cancer type, shedding further light on the importance of KGFs as cancer targets.As of today, there are about 50 kinase inhibitor drugs approved by FDA in Oncology14 and cancer cell lines can represent widely accessible tools for the investigation and the functional characterization of therapeutically actionable KGF targets, paralleling the genetic alterations present in primary tumor samples19. Extensive KGF molecular annotation would therefore greatly support the drug development pipeline and aid the parallel advancement of companion diagnostic tools for the assessment of patient treatment eligibility, such as multiplex amplicon approaches20 or Anchored Multiplex PCR (AMP)21.Precision medicine strongly relies on research models22; among these, the major sources of predicted KGFs in human cancer cell lines are the work by Klijn et al.13 and the LiGeA DB22. Klijn and colleagues have collected over 2,200 gene fusions resulting from the bioinformatics analysis of RNA-seq data through multiple fusion calling algorithms; however, experimental assessment of the oncogenic KGF functional role is not provided and the specific accession IDs of the transcripts involved in the fusion events are not specified. Gene fusions are reported as plain text lists, preventing interactive queries. The LiGeA database is a searchable and interactive resource of gene fusions in human cancer cell lines, containing ~1700 in silico predicted fusions identified by reanalyzing RNA-sequencing experiments performed by the Cancer Cell Line Encyclopedia (CCLE23), using four different gene fusion detection algorithms22. Though more practical, this database still suffers from the lack of experimental validation information and from incomplete sequence annotation, such as the exact breakpoints or the transcript IDs of the genes involved in the fusion events; moreover, no coverage is provided for KGFs identified in cell lines not included in the CCLE dataset. Indeed, a considerable number of human cancer cell lines with characterized and functionally validated KGFs are described in sparse literature, but are not included in any collective repository available for this kind of information.Public databases collecting newly NGS-identified gene fusions are increasingly available, as listed by Gioiosa and colleagueshttp://www.kungfudb.org/).To overcome these limitations, we have implemented the KuNG FU (KiNase Gene FUsion) database, a manually curated repository of KGFs in cancer cell lines, collecting comprehensive and detailed annotations of in-frame KGF events retaining an intact kinase domain sequence and for which experimental evidence has been reported. The above selection criteria represent fundamental KGF prerequisites for potential druggability investigations, making KuNG FU the largest homogeneous and curated database of KGFs in cell line models available to date , for the extraction of KGF information obtained from the data mining of over a million scientific abstracts, dated starting from 2013 and integrated with public datasets and previous literature, as summarized in the schema reported in Fig. 25.KuNG FU data access is equipped with a search engine that supports free-text searches, as well as filtering based on keywords, through an intuitive web interface, allowing researchers to query among a broad panel of human cancer cell lines and providing graphical summaries of the database content Fig. , Output.The KuNG FU query output  group is observed, in particular for Receptor Tyrosine Kinases (RTKs). The remaining gene fusion kinases reported in KuNG FU belong to Tyrosine Kinase-Like (TKL) and Atypical groups Fig. . Moreove35. Almost all the fusion transcripts reported in KuNG FU are specific for a single cancer type, however three fusions  were found across a variety of different cancer types.ABL1 is the most frequently rearranged kinase found in cancer cell lines, fused with different gene partners in 46 distinct cell lines Fig. , with th36. Among the 29 identified fusion partners, BCR is the most frequently observed in cancer cell lines  and RT-PCR methods43, or for multiplex amplicon and Anchored Multiplex PCR (AMP) NGS approaches44. These recently developed diagnostic strategies have some advantages, requiring low input, potentially increasing sensitivity associated to extensive amplification, short turnaround time and reducing the complexity of data analysis31.There are currently thousands of clinical trials testing the efficacy of novel kinase inhibitor drugs, and new molecules are approved every yearImportantly, KuNG FU is freely accessible to the scientific community and is open to regular updates by literature checking for new validated KGFs on a yearly basis; additionally, it offers the unique feature for users to submit their validation experiments on KGFs, thus contributing to extending and maintaining the database up to date and making the tool a comprehensive useful resource for cancer investigators and drug research.Abstracts published in PubMed between January 2013 and December 2019 were initially filtered using Python scripts for the presence of the keyword “cell line” appearing either in the Title or Abstract. The selected abstracts were then queried for the presence of the following keywords: “fusion”, “inversion”, “translocation”, “rearrangement” and “kinase” appearing either in the Title, Abstract or among the MeSH terms. A negative dictionary of terms was also created containing false positive terms to be excluded, such as “nuclear translocation”, “bacterial translocation”, “cell-cell fusion”, “membrane fusion”, etc.In-depth revision of the processed literature was applied for extensive manual curation of the identified KGFs. Reviewed information was integrated into the KuNG FU database schema.Yu et al.45, Cellosaurus database46, HUGO Gene Nomenclature Committee (HGNC) database47, Tumor Fusion Gene Data Portal27, WiKinome48, National Center for Biotechnology Information (NCBI)49 and UniProt50 websites through Python scripts. Three major fields describe the cell lines reported in KuNG FU: (1) ‘cell line name’, a unique name identifying the cell line; (2) ‘synonym/s’, cell line name synonyms (3) ‘primary tissue’, indicating the tissue from which the cancer cell was derived. Cell line names and synonyms were disambiguated by creating a list of unique identifiers extracted from Yu et al.45 and integrated with cell line synonyms from Cellosaurus46. Additional cell line description fields were included, reporting gender, ethnicity, patient age at diagnosis. Cross-contaminated cell lines, as per the register of misidentified cell lines curated by the International Cell Line Authentication Committee (ICLAC)51, were excluded from KuNG FU. In the ‘Fusion’ sub-section, HGNC47 approved gene symbols and RefSeq.49 transcript identifiers were used, with HGNC47 chromosomal coordinates of kinases and gene partners; 5′ and 3′ Gene Junction fields indicate the specific exon/intron involved in each breakpoint, and the ‘Chromosomal rearrangement’ field indicates the event generating the aberrant transcript . ‘Validation methods’ is a manually curated field indicating the technical methodologies used for experimental KGF evidence found in the supporting literature along with the PubMed unique Identifier (PMID) for the respective reference(s). Additional wet-lab techniques used for experimental validation of the KGF sequence features were retrieved from 62 scientific papers published before January 2013 and integrated in KuNG FU, referenced with the corresponding PMID. TCGA5 identifiers of the samples harboring the same KGF found in KuNG FU cancer cell lines were also extracted and provided with a link to the TCGA Data Fusion Portal27. In the ‘Kinase’ section, for each kinase, NCBI49 Entrez Gene ID and UniProt50 protein accessions were provided, together with the ‘Kinase Group name’ and the ‘KD boundaries’ fields (KD: kinase domain).All the fields listed in Table 49 reported in HGNC47 that refers to a canonical UniProt50 sequence, chosen by criteria described in UniProt/Swiss-Prot website available at https://www.uniprot.org/help/canonical_and_isoforms. The conservation of the entire kinase domain within the KGFs and the observation of the reading frame in the resulting chimeric protein were calculated by means of the AGFusion tool26.The nucleotide sequence at the breakpoint, the amino acid breakpoint position in the kinase and the length of the resulting chimeric protein were also automatically calculated and reported in KuNG FU. When the transcript ID of kinase and fusion partner could not be retrieved from literature, fusion gene breakpoints were predicted based on the transcript RefSeq ID26 (Fig. Collected data were stored in three MySQL relational tables: “cell_line_table”, storing meta information relative to cell lines; “abstract_table”, collecting the information extracted from the abstracts; “fusion_table”, reporting fields related to the KGF. MySQL database has been provided with a user-friendly graphical interface developed using AngularJS, a Javascript framework, and powered by JQuery and Bootstrap. Statistics are visualized through interactive graphs, implemented in PHP and Javascript. The fusion data stored in the database can be exported as txt and sql files, and a graphical representation of the gene fusion construct with respective protein domains is also provided through the AGFusion plot tool26 Fig. .An application form allows users to submit experimentally validated kinase fusions not previously included in the database. Upon revision, data will be added to KuNG FU with the appropriate reference, if available, together with submitters’ acknowledgments.