Doxorubicin (DOX) is a potent chemotherapeutic drug used as the first line in breast cancer treatment; however, cardiotoxicity is the main drawback of the therapy. Preclinical studies evidenced that the association of simvastatin (SIM) with DOX leads to a better prognosis with reduced side effects and deaths. In this work, a novel pH-sensitive liposomal formulation capable of co-encapsulating DOX and SIM at different molar ratios was investigated for its potential in breast tumor treatment. Studies on physicochemical characterization of the liposomal formulations were carried out. The cytotoxic effects of DOX, SIM, and their combinations at different molar ratios , free or co-encapsulated into pH-sensitive liposomes, were evaluated against three human breast cancer cell lines . Experimental protocols included cell viability, combination index, nuclear morphological changes, and migration capacity. The formulations showed a mean diameter of less than 200 nm, with a polydispersity index lower than 0.3. The encapsulation content was ~100% and ~70% for DOX and SIM, respectively. A more pronounced inhibitory effect on breast cancer cell lines was observed at a DOX:SIM molar ratio of 2:1 in both free and encapsulated drugs. Furthermore, the 2:1 ratio showed synergistic combination rates for all concentrations of cell inhibition analyzed . The results demonstrated the promising potential of the co-encapsulated liposome for breast tumor treatment. Breast cancer was the most common cancer in the world in 2021, and more than 2.2 million cases were diagnosed . The difference between the experimental groups was tested using a one-way analysis of variance (ANOVA), followed by the Tukey test. In vitro studies of nuclear morphology were evaluated two-way, followed by the Bonferroni test. Differences were considered significant when the In this study, we proposed that SIM and DOX be co-encapsulated into pH-sensitive liposomes, aiming to increase antitumor efficacy. Herein, we reported the development and physicochemical characterization of SpHL-D-S, as well as their effects against different human breast cancer cells .The physicochemical characteristics of the liposomal formulations are summarized in Regarding encapsulation efficiency, values of almost 100% and above 60% were obtained for DOX and SIM, respectively. For SIM, data evidenced that an increase in concentration did not result in a proportional drug-encapsulated increase, suggesting a possible saturation of the bilayer. Encapsulation efficiency at the theoretical concentration of 2 mg/mL was around 16% lower compared to 1 mg/mL. On the other hand, no significant difference was observed after DOX encapsulation for any of the formulations, demonstrating that SIM co-encapsulation did not alter the ability of the liposomal system to carry DOX. The molar ratio values calculated for formulations containing both drugs were equal to 1:1, 1:1.7, and 1:1.2 when the liposomes were prepared with 1 mg/mL of each drug, 1 mg/mL of DOX and 2 mg/mL of SIM, and 2 mg/mL of both drugs, respectively.Based on these results, we defined the initial SIM concentration at 1 mg/mL to prepare all formulations containing the co-encapsulated drugs at molar ratios (DOX:SIM) 1:1, 1:2, and 2:1, respectively. These liposomes were further used for in vitro assays. The same physicochemical investigations were carried out  summarized in All three cell lines tested  were sensitive to treatment with DOX and SIM. However, DOX showed higher cytotoxicity than SIM, as can be observed by the values of half-maximum inhibitory concentration . Encapsulation of DOX into pH-sensitive liposomes did not significantly alter its cytotoxicity against MDA-MB-231 compared to the free drug. Analysis of SpHL-D-S at molar ratios of 1:2 and 2:1 demonstrated more pronounced cytotoxic effects  than SpHL-D.50 values lower than those obtained for MDA-MB-231. The combination of drugs did not increase the cytotoxic activity compared to DOX, either in the free or the co-encapsulated form for the SK-BR-3 cell line.The SK-BR-3 cell line was the most sensitive to treatment with DOX and SIM, as demonstrated by ICFor the MCF-7 strain, lower sensitivity was detected compared to others, especially after treatments with liposomal formulations. In this cell line, there was no significant difference between the free DOX and the DOX:SIM combination in the free form for any of the molar ratios evaluated. However, co-encapsulation of DOX and SIM at 1:1 and 1:2, respectively, significantly reduced cytotoxic activity compared to SpHL-D. It is noteworthy that the control group (SpHL) had no effects on cell viability and was similar to untreated cells, indicating no significant toxicity of the formulation excipients  , irregular (I), small and regular (SR), and large and regular (LR). The change in nuclear morphology can occur in processes associated with cell death. These modifications include the nuclear condensation and fragmentation observed in apoptosis, the nuclear size increase observed in senescence, and increases in nuclear irregularity under chemical or physical stresses .The NMA data obtained after different treatments are shown in p < 0.001) in LR nuclei compared to free-form DOX at the same ratio. The increase in the levels of LR nuclei demonstrated an increase in senescence induction.After DOX treatment, either in the free form or SpHL-D, MDA-MB-231 data analysis showed N nuclei ranging from 55 to 65%, SR equal to 10%, and LR varying from 25 to 35%. The values obtained after SIM treatment in both forms were 85%, 3%, and 12% for N, SR, and LR, respectively. These findings are in agreement with viability cell studies and reinforce the lower cytotoxic activity of SIM for this cell line. A significant reduction in LR nuclei was also verified, followed by increased N nuclei, after treatment with free DOX:SIM 1:1 or 1:2 compared to free DOX. In contrast, a significant increase in LR nuclei was detected after SpHL-D-S 1:2 and SpHL-D-S 2:1 treatments. It was also observed that the encapsulation of DOX:SIM 1:1 and 1:2 resulted in a significant increase  was used. As shown in The DOX and SIM release profile from SpHL-D-S (2:1) was evaluated using a dialysis method at two different pH levels (7.4 and 5.0). Before the release study, various release conditions were tested to determine the sink conditions for both active substances (data not presented), and the HEPES buffer plus Tween 80 , despite the size having been increased by SIM presence . This faAs regards encapsulation efficiency, higher values were observed for SIM (>60%) and DOX . The high encapsulation obtained by the DOX active loading is often explained by the formation of insoluble DOX-sulfate crystals inside the aqueous core of liposomes . These cIt is well-known that the antitumor efficacy of pH-sensitive liposomes depends on their ability to release the drug into the tumor region. In an acidic environment, these formulations, composed of DOPE (a fusogenic lipid), can fuse or destabilize, releasing the encapsulated content ,37. HereAs breast cancer shows multiple subtypes with histopathological and biological differences that can lead to different treatment responses, we have chosen to evaluate the behavior of the formulations in two luminal subtype cell lines, namely MCF-7 and SK-BR-3, and one basal subtype cell line, MDA-MB-231. The first one is positive for estrogen receptors (ER) and progesterone receptors (PR) and negative for human epidermal growth factor receptor 2 (HER2). The second is ER-/PR-/HER2+, and the last is triple-negative breast cancer (ER–/PR–/HER2) .The cytotoxicity assessment showed more pronounced sensitivity for SK-BR-3, followed by MDA-MB-231 and MCF-7, especially after DOX: SIM 2:1 treatment in free or co-encapsulated form. In addition, SpHL-D-S at the molar ratio of 2:1 presented a synergistic effect for all lines tested. Considering that the potential cytotoxic activity of SIM is related to its ability to modulate some effects on reactive oxygen species (ROS), deregulate caspase cascades, and inhibit 3-hydroxy-3methyl-glutaryl-coenzyme A reductase (overexpressed in cancer cells) ,42,43,44In this study, we developed a novel formulation of pH-sensitive liposomes containing DOX and SIM. Our results show that this system is pH-responsive and stable in the lyophilized form for at least 90 days. The viability, CI, and NMA data pointed out that the 2:1 molar ratio can act synergistically, improving the inhibitory results of proliferation and induction of death of breast cancer cells. Furthermore, the migration results reinforce the notion that the combination of DOX and SIM significantly improves the inhibition of cell proliferation. However, further studies are needed to understand the molecular mechanisms involved.In summary, the present study identified a new strategy for a potential combination therapy. SpHL-D-L 2:1 showed suitable physicochemical properties, release behaviors, and cytotoxicity responses to be considered a promising alternative for further in vivo breast cancer therapy.


Here we report the first whole genome characterization of eight canine OS cell lines, including single nucleotide variants, copy number variants and other structural variants. Many alterations previously characterized in primary canine OS tissue were observed in these cell lines, including TP53 mutations, MYC copy number gains, loss of CDKN2A, PTEN, DLG2, MAGI2, and RB1 and structural variants involving SETD2, DLG2 and DMD. These data provide a new framework for understanding how best to incorporate in vitro findings generated using these cell lines into the design of future clinical studies involving dogs with spontaneous OS.The characterization of immortalized canine osteosarcoma (OS) cell lines used for research has historically been based on phenotypic features such as cellular morphology and expression of bone specific markers. With the increasing use of these cell lines to investigate novel therapeutic approaches prior to  This is particularly relevant in preclinical evaluation of targeted therapeutics, which rely on knowledge of the spectrum of genetic alterations in cancer cells. As such, cell line whole genome and exome sequencing  are increasingly evaluated contemporaneously with primary tumor samples . Si. SiDLG2  tissues .TP53, MYC, CDKN2A/B and DLG2, were preserved, some mutations previously identified at a high incidence were absent in the cell lines evaluated. In particular, no SNVs were identified in SETD2 in the eight OS cell lines evaluated. However, mutations in histone 3 lysine 36 (H3K36) specific lysine demethylase genes  were present in all cell lines  cell lines) and NEDD4L (7/8 (88%)) )  Tables, Established cell lines have long been used to study tumor biology and response to targeted therapies. More recently, single gene evaluation and WES have been used to chart the mutational landscape of canine cancer cell lines, providing a crucial resource for prospective studies , 53–70. SETD2 in the cell lines used in this study. However, SETD2 was deleted in two cell lines, and mutations in H3K36 lysine demethylases were present, suggesting that mechanisms driving H3K36 dysregulation are a fundamental feature of canine OS. Concordance with SNV/INDEL calls between the same cell lines included in our analysis and the WES analysis by Das, et al. was moderate, and the discrepancies noted were likely due to several factors. Different sequencing and variant calling methods are known to have low concordance. In addition, the use of distinct variant filtering thresholds and different germline databases likely resulted in the removal of divergent sets of mutations. Additionally, the selective pressures of in vitro culture and ongoing genomic instability typically drive the development of significant genetic heterogeneity between different strains of the same cell line [While many of the known simple somatic mutations associated with canine OS were conserved among the cell lines evaluated in this study, some mutations typically found in primary OS tissues were absent. One striking feature was the lack of simple somatic mutations in ell line , 72.An increased mutational burden was identified in OS cell lines compared to OS tissues. In part, this likely represents a type I error due to lack of a matched germline sample. This is especially relevant in the HMPOS cell line, which was determined to originate from a village dog based on our breed calling algorithm and has a variety of single nucleotide polymorphisms not cataloged in our germline variant resource files. As most genetic variants are rare , and vilet al. study for the same cell lines. Nevertheless, due to the above challenges and lack of orthogonal validation of our variant calls, we recommend that researchers validate variants of interest with low allelic fraction prior to additional downstream analysis.Incorporation of a matched germline control is commonly used to minimize false positive mutation calling in WES and WGS datasets. We developed a stringent filtering pipeline for both simple somatic and structural variants to reduce the occurrence of false positives due to the lack of a matched germline sample  Figs. ThOverall, our data demonstrate that the chaotic genomic landscape of canine OS cell lines is concordant with that observed in primary canine OS tumor tissue, defined by high structural complexity and few recurrent point mutations. It is not surprising that some of the common SNVs and SVs found in OS tumor tissue were not identified in this small subset of cell lines, likely due to evolution of the cell lines over time in culture. Perhaps most notably, conservation of mutations in pathways with redundant functional relevance underscores the probable biologic importance of these aberrations in OS. This study highlights important features of each of these cell lines, creating a roadmap for researchers pursuing hypothesis driven precision medicine research.Last, we have detailed the use of specific tools and modified scripts in this manuscript to facilitate implementation of this pipeline in other canine WES/WGS datasets in which matched germline reference samples are not available. Additionally, as minor changes in versioning and run parameters for computational tools can markedly alter outputs, we have made available our methodologies to facilitate future use of this approach in other canine sequencing datasets.Canine OS cell lines are largely representative of the genomic landscape of primary canine OS tissues. Evaluation of the genomic landscape, including structural variation, is important to accurately identify pathway dysregulation in complex cancers when using cell lines in research.S1 FigDetailed SNV calling pipeline.(TIF)Click here for additional data file.S2 FigDetailed SV calling pipeline.(TIF)Click here for additional data file.S3 FigRainfall plots for each cell line, with associated density plots demonstrating distance between mutations on a log10 scale.(PDF)Click here for additional data file.S4 FigMutation signatures and signature composition of each cell line.(PDF)Click here for additional data file.S5 FigDenoised copy number segmentation plots and alternate-allele fraction ratios for each cell line. Copy ratio segments are highlighted alternating between blue and orange, while the denoised median is represented by the black lines.(PDF)Click here for additional data file.S1 TableSequencing metrics for each cell line.(XLSX)Click here for additional data file.S2 TableResults of STR profiling of canine OS cell lines.(XLSX)Click here for additional data file.S3 TableBreed calling consensus and sex determination from each cell line.(XLSX)Click here for additional data file.S4 TableSingle nucleotide variants passing filters called in each cell line.(XLSX)Click here for additional data file.S5 TableMutation burden was determined by dividing the total number of mutations in each cell line and dividing by the combined size of the chromosomes. Total calculated mutation burden (mutations/Mb) is provided for each cell line, as well as the size of each chromosome.(XLSX)Click here for additional data file.S6 TableComparison of calls from this dataset to five published OS datasets.(XLSX)Click here for additional data file.S7 TableStructural variants called by Manta in each cell line.(XLSX)Click here for additional data file.S8 TableCopy number variants and overlapping genes associated with each region called in each cell line.(XLSX)Click here for additional data file.S9 TableDetailed summary of curated list of structural variants.(XLSX)Click here for additional data file.


Cutaneous T-cell lymphoma (CTCL) is a rare form of cancer with local as well as systemic manifestations. Concomitant bacterial infections increase morbidity and mortality rates due to impaired skin barrier and immune deficiency. In the current study, we demonstrated that the in vitro anti-lymphoma potential of erufosine is diminished by TWIST1 expression and micellar curcumin substantially increases its antineoplastic activity. Pharmacokinetic analysis showed that the micellar curcumin (MCRM) used in our study was characterized by low zeta potential, slow release of curcumin, and fast cell membrane penetration. The combination ratio 1:4 [erufosine:MCRM] achieved strong synergism by inhibiting cell proliferation and clonogenicity. The combined antiproliferative effects were calculated using the symbolic mathematical software MAPLE 15. The synergistic combination strongly decreased the expression of TWIST1 and protein kinase B/Akt as proven by western blotting. Significant reductions in NF-κB activation, induction of apoptosis, and altered glutathione levels were demonstrated by corresponding assays. In addition, the synergistic combination enhanced the anti-staphylococcal activity and prevented biofilm formation, as shown by crystal violet staining. Taken together, the above results show that the development of nanotechnological treatment modalities for CTCL, based on rational drug combinations exhibiting parallel antineoplastic and antibacterial effects, may prove efficacious. Staphylococcus aureus, and were intimately associated with the disease stage  significantly inhibited the biofilm formation of MRSA. The effect of the combination of concentrations 80 µM MCRM and 20 µM ERF is equal to that of ERF after a single application, demonstrating full biofilm eradication . In loweIn the present study, we compared and evaluated in detail for the first time, to our knowledge, the in vitro efficacy of the alkylphosphocholine ERF in combination with curcumin incorporated in micellar formulation with a prolonged release on T-cell lymphoma cell lines with and without expression of the oncogene TWIST1. The transcription factor TWIST1 is not detected in normal peripheral blood mononuclear cells. Any increase in its expression levels is abnormal and has been identified as a marker of metastasis and poor prognosis in patients with MF and SS . FurtherStaphylococcus aureus  were proven to be synergistic for HuT-78 cells and additive for MJ cells A in diffUndoubtedly, an important outcome was the complete suppression of the TWIST1 expression after the application of the synergistic combination [20 μM ERF:80 μM MCRM] on HuT-78 cells A compareThe inhibition of NF-κB and PKB/Akt logically led to the induction of apoptosis in the treated cell lines. As visible in Last but not least, the synergistic combination fully inhibited the formation of MRSA biofilm, which can bring additional benefits for patients with concomitant staphylococcal infections as this will further help reduce inflammation and limit the appearance of infected skin lesions. ERF alone is also a potent inhibitor of the MRSA biofilm, as published before , and the473 and/or Thr308, and deactivation of the p65 subunit of NF-κB. Taken together, our experimental findings indicate that it is feasible to develop and investigate new micellar CRM formulations in rational combinations with the alkylphosphocholine ERF for future local and/or intravenous CTCL treatment perspectives. The low toxic profile of the two substances allows their combination to be used in a broader pharmacological regimen, with the combined antineoplastic, anti-inflammatory, and antibacterial activity assisting in overcoming primary and secondary drug resistance of CTCL-derived cells.The anti-lymphoma effect of ERF in CTCL cell lines was dependent on the TWIST1 expression. The combination between ERF and MCRM in a ratio of 1:4 achieves a synergistic or additive effect depending on the cell line, which was due to inhibition of TWIST1, dephosphorylation of PKB/AKT at Ser


Ewing sarcoma (EWS) is an aggressive bone and soft tissue tumor with high susceptibility to metastasize. The underlying molecular mechanisms leading to EWS metastases remain poorly understood. Epigenetic changes have been implicated in EWS tumor growth and progression. Linking epigenetics and metastases may provide insight into novel molecular targets in EWS and improve its treatment. Here, we evaluated the effects of a selective G9a histone methyltransferase inhibitor (BIX01294) on EWS metastatic process. Our results showed that overexpression of G9a in tumors from EWS patients correlates with poor prognosis. Moreover, we observe a significantly higher expression of G9a in metastatic EWS tumor as compared to either primary or recurrent tumor. Using functional assays, we demonstrate that pharmacological G9a inhibition using BIX01294 disrupts several metastatic steps in vitro, such as migration, invasion, adhesion, colony formation and vasculogenic mimicry. Moreover, BIX01294 reduces tumor growth and metastases in two spontaneous metastases mouse models. We further identified the sialidase NEU1 as a direct target and effector of G9a in the metastatic process in EWS. NEU1 overexpression impairs migration, invasion and clonogenic capacity of EWS cell lines. Overall, G9a inhibition impairs metastases in vitro and in vivo through the overexpression of NEU1. G9a has strong potential as a prognostic marker and may be a promising therapeutic target for EWS patients. Ewing sarcoma (EWS) is an uncommon, aggressive and poorly differentiated tumor, most often occurring in children and young adults . MultimoMetastatic disease remains a huge challenge for researchers due to the high complexity of the tumor cell mechanisms controlling this process . AlthougEHMT2 (euchromatic histone lysine N-methyltransferase 2) and catalyzes the dimethylation of histone H3 lysine 9 (H3K9me2), which is associated with transcriptional repression [The histone methyltransferase G9a is encoded by pression . G9a plapression , 12. Prepression , lung capression , malignapression , breast pression  and colopression . This ovpression , 19. On pression . HoweverIn the present study, we revealed a remarkable role of G9a in the metastatic process in EWS. We first characterized and evaluated the expression of G9a in EWS tumors related to the clinicopathological features of EWS. We then investigated its role in the metastatic process in EWS cell lines and in a spontaneous metastases mouse model. We identified NEU1 (neuraminidase 1) a novel G9a target as well as one of the direct effectors of G9a activity in the metastatic process. Together, these findings indicate that G9a plays a relevant role in EWS metastatic behavior and is a potential therapeutic target for patients with metastatic EWS, the subgroup of patients carrying the worst prognosis.EHMT2 mRNA expression using the R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl). The overall survival and the disease-free survival rates of EWS patients were significantly worse in the group of patients with high levels of EHMT2 expression, in two independent series (Savola and Dirksen)  and disease-free survival (p = 0.0325)  Fig. , B. We nEHMT2 expression in primary tumors, recurrent tumors and metastases, in a public database (Savola) as well as in our EWS patient series. Our data showed that metastatic tumor samples had a significantly higher expression of EHMT2/G9a than primary or recurrent tumor samples, at both the mRNA (p = 0.0008) and the protein (p = 0.0005) levels , , Fig. . CollectRecently, we have suggested that the G9a histone methyltransferase could be a target for EWS therapy by using the specific and selective inhibitor BIX01294. Selected from the epigenetic drug library screening, BIX01294 was identified as one of the most effective inhibitors to repress the proliferation of EWS cell lines in vitro . HoweverWe first investigated how BIX01294 affects the growth of EWS cells lines. For this, we analyzed the proliferation IC50 values in twelve established EWS cell lines, seven cell lines of other tumor types and one non-tumor stem cell line (hMSC). Our results revealed that BIX01294 inhibited the proliferation of all EWS cell lines to a similar degree except for the CADO-ES cell line, which showed a higher resistance Fig. . StrikinWe then evaluated the effects of BIX01294 on G9a activity by the assessment of histone 3 dimethylation at lysine 9 (H3K9me2), as a specific target, at intermediate (IC50) and high (IC90) concentrations of BIX01294 at 24 and 48 h  significantly increased after treatment with an IC90 dose  Fig. . To testTo study the effects of G9a inhibition on cellular adhesion capacity, and taking into consideration that the interactions of cancer cells with the endothelium determines the metastatic spread , we analTo study whether BIX01294 treatment could also affect the final steps of the metastatic process, we performed a clonogenic assay to assess the ability of EWS cells to form colonies after exposure to intermediate or high doses of BIX01294. The percentage of colony formation was significantly reduced in a dose-dependent manner upon IC50 or IC90 doses of BIX01294, by 20% or 60% in RM82, and by 40% or 60% in CADO-ES cells, respectively, as compared to untreated cells Fig. G, H. ThuCD44, receptor tyrosine kinase (EphA2) and tissue factor pathway inhibitor 1 (TFP1) were upregulated in all cell lines when they formed VM  decreased significantly in the control cells (HUVEC and MUM 2B)  Fig. . Howeverine Fig. . ConsequAn analogous assay was performed under in situ treatment, by seeding all cell lines in a collagen matrix with medium containing BIX01294. We observed a higher inhibition of VM biomarker genes after BIX01294 pre-treatment than after in situ treatment in the HUVEC, MUM 2B and RM82 cell lines   by using two different anti-EHMT2 siRNA constructs. Indeed, we observed an increase of NEU1 expression after EHMT2/G9A inhibition at both the mRNA and protein levels in CADO-ES and RM82 cell lines  to the regulatory region of NEU1 promoter  by chromatin immunoprecipitation (ChIP). We confirmed that G9a was binding to NEU1 promoter in CADO-ES and RM82 cell lines  after IC50 or IC90 treatment with BIX01294 for 24 h in the CADO-ES or RM82 cell line (Supplementary Table T1) Fig. ; Fig. 5BTo investigate whether directly overexpressing NEU1 would impair the metastatic capacity and replicate the effects of G9a inhibition, we used the same experimental approaches as described above for BIX01294 functional studies in EWS cell lines see Fig. . We demoFinally, to determine whether the cell phenotype induced by G9a inhibition through treatment with BIX01294 was mediated by re-expression of NEU1, we raised rescue assays. NEU1 protein levels were depleted using esiRNA, and was compared to a negative control (EGFP) and the wild type cell lines. There were not differences between WT and the negative control . To confirm the drug-mediated effect, Ki67 nuclear expression was evaluated in tumor samples. The results revealed a significant decrease in Ki67 positive cells after treatment in BALB/c mice in accordance with the observed growth inhibitory effects Fig. . AdditioFollowing tumor resection, we monitored the animals for the appearance of lung metastases. At the end of the experiment, no macrometastasis, detected by luciferase activity or single-eye lung ex vivo evaluation, were observed in treated or untreated mice. Therefore, we evaluated the micrometastatic foci in the lungs (metastases detected by ex vivo and H&E), using lung sections stained with H&E , no toxicity was present in both models of experimental mice with BIX01294 treatment , and a negative correlation between NEU1 expression with G9a (p = 0.0242) and the number of metastatic foci (p = 0.0396). These in vivo results are in concordance with those obtained in vitro, and both revealed a role of G9a in the metastatic progression of EWS.Given the links between G9a and NEU1 expression and the metastatic process, we performed a multivariate analysis and a principal component analysis. G9a expression and the number of metastases had a positive relationship, and both of these variables had a negative relation with NEU1 expression Fig. . To confEWS is an extremely aggressive tumor with a clear tendency for local recurrence and distant metastases, often leading to a fatal outcome . The molNovel treatments currently use epigenetic drugs alone or in combination to improve the survival rate in different types of cancers , 37. TheEHMT2/G9a expression significantly higher than that of primary and recurrent tumor samples. These results are supported by many studies that have previously described G9a to be overexpressed in various tumors [We now report that high expression of G9a (both at the mRNA and the protein levels) in EWS defines a subset of patients with a worse prognosis. Moreover, we also show that metastatic samples had an s tumors –17, suggs tumors . We obses tumors ). In this tumors . Moreoves tumors , 41, ands tumors .The effectiveness of G9a inhibition was not restricted to proliferation, as we also demonstrated that the in vitro metastatic processes were impaired in EWS cell lines. We performed functional assays with BIX01294 treatment to evaluate the biological significance of G9a in the different steps of the metastatic process in vitro. EWS cell migration and invasion was impaired after G9a inhibition. As freely-circulating tumor cells must adhere to the endothelium, we studied the adhesion of tumor cells lines using different extracellular matrix components; notably, our results showed that G9a inhibition reduced adhesion of EWS cell lines. BIX01294 also impaired colony formation, which is essential to promote cell growth in a secondary location. Therefore, our results demonstrated the relevance and role of G9a in the metastatic process, in agreement with the results obtained in EWS patients. Accordingly, in other tumors, G9a has been shown to promote cancer invasion, migration, adhesion and metastases , 25, andCD44, TFPI-1 and EphA2 [In addition to the various steps evaluated in the metastatic process, EWS cell lines can form VM. This mechanism has been described as an enhancer of metastatic progression in cancer . Blood lnd EphA2 . Our expnd EphA2 . These dNEU1 as one of the targets involved. We confirmed that G9a inhibition (by either BIX01294 or siRNA anti-EHTM2) induced NEU1 overexpression in vitro. To date, however, the role of NEU1 in metastases is contradictory. NEU1 overexpression has been described to reduce metastases in vivo and to suppress migration, invasion and adhesion in vitro, yet its silencing results in the opposite effects in colon cancer [NEU1 siRNA knockdown inhibits cell invasion [NEU1 gene expression is direct in EWS cell lines, through dimethylation of Histone 3 in its promoter by G9a binding/activity. Therefore, we propose that NEU1 is one of the molecular direct targets of G9a that controls the malignant properties of EWS cells. This novel role of G9a/NEU1 represents a metastatic molecular mechanism that could be explored in other tumors. However, in light of other studies, it would be necessary to understand the specific role of NEU1 in each cellular context and its relationship with epigenetic disorders.Exploring the molecular mechanisms promoted by G9a in the metastatic process in EWS, we propose n cancer . On the n cancer . In pancn cancer ; in ovarinvasion . Our resTo evaluate our results in vivo, we used a metastatic EWS spontaneous metastases mouse model  in two mBIX01294 not only reduced tumor growth but also impaired the metastatic capacity of EWS cell lines in vivo. The percentage of mice with metastatic foci in lungs was statistically reduced in both mice models, regardless of the aggressiveness of the tumor growth. In agreement with our results, lung metastases were drastically reduced in SCID mice after injecting the tumor cells carrying the G9a knockdown into the lateral tail veins . Along tIn summary, our studies implicate G9a as having a central role in the metastatic process in EWS that involves the sialidase NEU1. Our findings contribute to understand the metastatic process and provide a new treatment option for inhibition of metastatic phenotypes in EWS patients. G9a pharmacological inhibition could be used as an adjunctive treatment to standard treatment for EWS patients, which is based fundamentally on cytotoxic action. Thus, this approach would combine a reduction of local tumor growth with an inhibition of the metastatic process.112 formalin‐fixed paraffin‐embedded (FFPE) EWS tumor samples, 44 of which corresponding to primary tumors without treatment, were obtained from the Department of Pathology at the Hospital Universitario Virgen del Rocío  and HUVR-IBiS Biobank. Approval of the Ethics Committee of our institution was obtained, and written informed consent was obtained before including samples and data in the HUVR-IBiS Biobank. Patients and samples characteristics are summarized in Supplementary Table Proteins were extracted from cell lines in RIPA buffer  NP40, 50 mM Tris-HCl pH 8.0, 0.1% (v/v) SDS, 1 mM EDTA, and 0.5% (w/v) deoxycholate), supplemented with 10 mM NaF and 2 mM NaOv protease inhibitors. Immunoblotting was performed using the primary antibodies listed in Supplementary Table Expression levels of selected genes were analyzed by RT-qPCR. RNA was isolated from cell lines using the miRVana miRNA Isolation Kit . The quantity and quality of the total RNA was determined with the Nanodrop ND-2000 Spectrophotometer (Thermo Scientific). Prior reverse transcription was performed using TaqMan Reverse Transcription Kit  in GeneAmp PCR 9700 thermocycler. All RT-qPCR measurements were obtained in a 7900HT Fast Real Time PCR System with ExpressionSuite Software v1.1 (Applied Biosystems). TaqMan probes are listed in Supplementary Table 5, 5 × 105, 1 × 104, or 15 × 103 cells per well in 24-well dishes coated by Basement Membrane Matrix . After 24 h, pre-treated cells were added and cultured in complete medium, and non-treated cells, in complete medium with BIX01294. Cell cultures on 24-well dishes without matrix were used as negative controls, and cultures of HUVEC and MUM 2B were used as positive controls. Tube formation were visualized and photographed under the Olympus BX61 microscope.EWS cell lines (RM82 and CADO-ES), MUM 2B and HUVEC were plated at 2 × 10EHMT2 transcript were used: #4392420_ID:s21469 for siRNA EHTM2_A and #4392420_ID:s21470 for siRNA EHTM2_B. Select Negative Control #1 siRNA  was included as a control for off-target effects. EWS cell lines were transfected using Lipofectamine® RNAiMAX Transfection Reagent  following the manufacturer’s instructions.Two pre-designed siRNA against the NEU1 cDNA of Human NEU1 Gene cDNA clone plasmid  was amplified using the follow primers: Fw −3′CAGGATCCGCCACCGATATGACTGGGGAGCGACC-3′ and Rv 5′-CTACGGAATTCGGCCGCACTAGTGATTCAGA-3′ . The amplicon was cloned into retroviral plasmid pBABE-puro  for EWS cell line transfection. Retroviral EWS cell line infection was performed using the protocol described previously [The eviously . At 24 hMann–Whitney U-test for two groups, and one-way analysis of variance test for more than two groups followed by Tukey’s multiple comparisons post-test, were used to evaluate differences between control and treatment conditions. The overall survival and disease-free survival time were analyzed using the Kaplan–Meier estimator and the Wilcoxon test. Biplots associated with principal component analysis was used to determine the relationship between different variables and Multivariate Spearman’s test was used to determine the correlations between different variables. Normal distribution of data was assessed before application of parametric tests. The variance was similar between groups that were being statistically compared. The data met the assumptions of the tests.p-values of ≤0.05 were considered statistically significant. Analyses were performed using the Prism 6.01 software (GraphPad) and multivariate analyses were performed using the JMP 10 statistical software . The average of at least three biological replicates performed in three technical replicates with SD is presented in all experiments. No data were excluded from the analyses.For all analyses, For cell experiments study, samples were no randomized and sample size was determined to be adequate based on the magnitude and consistency of measurable differences between groups, usually the number is three or more. For xenograft mice experiment, no statistical methods were used to predetermine sample size, which was determined based on previous experimental observations.Additional methods are described in Supplementary Material and Methods.Supplemental MaterialSupplementary Table1Supplementary Material & Methods


For patients with a variety of severe diseases, including primarily hematopoietic malignancies, immunodeficiency syndromes, and genetic disorders, allogeneic hematopoietic stem cell transplantation (aHSCT) represents a potentially curative therapeutic approach. In this context, despite significant progress in the optimization of aHSCT, the development of graft-versus-host disease (GVHD) remains a challenge to long-term transplant success after aHSCT. It is associated with significant morbidity and mortality and is the major cause of non-relapse mortality.GVHD occurs when donor T cells are primed by recipient antigens subsequently eliciting an inflammatory response against the host. Clinically, two types of GVHD are distinguishable: an acute form (aGVHD), and a chronic form (cGVHD). In brief, the main characteristics: aGVHD occurs in 30-50% of aHSCTs and is a multi-organ disorder resulting from inflammatory cytokines and donor T cells which primarily damage skin, liver, gastrointestinal tract, and eye. cGVHD, with a prevalence of 30-70% of aHSCTs, is induced by T and B cells resulting in a heterogeneous immunological complication affecting virtually every organ.Traditionally, broad immune-suppressive drugs (with considerable toxicities) including calcineurin inhibitors (CNI) (cyclosporin or tacrolimus), together with methotrexate or mycophenolate mofetil (MMF), and mTOR inhibitors (Sirolimus/Rapamycin) are used as GVHD prophylaxis. But despite first success reports, significant GVHD still occurs with these drugs. Other prophylaxis strategies like pre-transplant anti-thymocyte globulin (ATG) are effective in reducing severe GVHD but have no survival benefits and steroids have serious side effects.One of the most critical challenges in aHSCT is the development of less toxic and more targeted therapies that maintain the graft-versus-leukemia/tumor (GVL/T) effect but suppress GVHD while facilitating enhanced immune reconstitution relative to existing strategies. Recently, several prophylaxis strategies for GVHD have been developed and others are currently in development, including, for example in the case of haploidentical HSCT, post-transplant cyclophosphamide (PTCy), which seem to be very promising.In the frame of this specific Research Topic, we aimed to collect recent developments of innovative methods for both prevention and treatment of GVHD, without impairment of GVL. In 8 original research articles and 4 reviews, this edition provides a deep insight into the role of the microbiome and metabolism as well as recent advances of small molecule and cell therapy development.Mohamed et al., summarize metabolic pathways contributing to GVHD and discuss metabolic targets for acute and chronic GVHD in immune and non-immune cell as well as the immunomodulatory function of microbial metabolites. Furthermore, they examine the metabolic effects of co-inhibitory pathway blockade (PD-1) and cellular therapies (Tregs/MSCs/Bregs) in aHSCT. The mini review by Karl et al., provides an overview of metabolic T cell alterations in GVHD and illustrates the impact of conventional GVHD therapy on T cell metabolism.We are glad, that experts in the field highlight the recent progress in the broad field of immune cell metabolism with two comprehensive reviews. Ghimire et al., investigates the role of G-protein coupled receptors (GPR43 and GPR109A) which engage microbial derived metabolites, like short chain fatty acids, in the mitigation of GVHD in intestinal biopsies from patients after allo-HSCT. A second study by Heidrich et al., describes an association of dental biofilm microbiota dysbiosis with the risk of aGVHD.Recent studies have shown the association of microbiome dysbiosis and aGVHD. Here, primary research by Campe and Ullrich. Moreover, Agbogan et al., explore the immunomodulatory effect of adoptively transferred CpG-activated B cell progenitors to alleviate GVHD symptoms. In addition, Scheurer et al., describe an in vitro generated sub-population of CD11b+CD11c+ myeloid-derived suppressor cells (MDSCs) as potent immune modulators leading to the prevention of GVHD without negatively affecting tumor cytotoxicity. Another innovative and attractive strategy using CRISPR/Cas9 has been described by Majumder et al., for genetical engineering of naïve T cells pre transplant as a method for GVHD prevention in a major murine mismatch model.In the context of cell therapy development, the biological relevance of T helper cell lineage defining transcription factors as potential targets for GVHD therapy has been delineated in a review article by Braun and Zeiser thoroughly review the role of kinase inhibition as novel treatment strategies for acute and chronic GvHD after allo-HCT. Thangavelu et al., evaluate the efficacy of a novel agonist of the retinoic X receptor (RXR), IRX4204, to treat cGVHD in two complementary murine models with bronchiolitis obliterans or sclerodermatous manifestations. Primary research by Matos et al., analyzes a possible association of anti-thymocyte globulin (ATG) treatment and serum levels of 25-hydroxyvitamin D3 and 1,25-dihydroxyvitamin D3 in 4 HSCT cohorts with different vitamin D3 supplementation. Lastly, Hadjis et al., characterize post-transplant cyclophosphamide as superior in ameliorating pre-clinical GVHD compared to five other optimally dosed chemotherapeutics  that vary in mechanisms of action and drug resistance.In addition, the recent therapeutic advances in the area of drug development, e.g. small molecules and antibodies, are also addressed. Finally, this Research Topic makes us again aware of how complex the regulation of GVDH is and in which fragile balance between GVHD and GVL patients after aHSCT find themselves. We are aware that this issue can only compile a first selection of innovative findings and treatment strategies that are currently being developed for the prevention and treatment of GVHD.GVHD biology and treatment remains a field that is always influenced by current research developments and new advances can be expected in a short time. Therefore, we will continue to monitor the field and provide updates to the authorship of Frontiers in Immunology.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.EU is a member of Phialogics and receives research support from BMS and Gilead. AB is a scientific cofounder of Aamuthera Biotech GmBH and Dualyx NV.The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Cancer cell lines have been widely used for decades to study biological processes driving cancer development, and to identify biomarkers of response to therapeutic agents. Advances in genomic sequencing have made possible large-scale genomic characterizations of collections of cancer cell lines and primary tumors, such as the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA). These studies allow for the first time a comprehensive evaluation of the comparability of cancer cell lines and primary tumors on the genomic and proteomic level. Here we employ bulk mRNA and micro-RNA sequencing data from thousands of samples in CCLE and TCGA, and proteomic data from partner studies in the MD Anderson Cell Line Project (MCLP) and The Cancer Proteome Atlas (TCPA), to characterize the extent to which cancer cell lines recapitulate tumors. We identify dysregulation of a long non-coding RNA and microRNA regulatory network in cancer cell lines, associated with differential expression between cell lines and primary tumors in four key cancer driver pathways: KRAS signaling, NFKB signaling, IL2/STAT5 signaling and TP53 signaling. Our results emphasize the necessity for careful interpretation of cancer cell line experiments, particularly with respect to therapeutic treatments targeting these important cancer pathways. Using a support vector machine learning approach and multi-omics data, dysregulation of key cancer driver pathways is revealed in cancer cell lines compared to primary tumors. Cancer cell line models grow quickly, are comparatively cost-effective, and are readily available. Their integration into preclinical research has led to remarkable advances in cancer characterization and treatment2. As mutation detection has become more accurate, multiple studies have reported that head and neck cancer cell lines tend to harbor more mutations than their tumors of origin4. A recent study showed that colorectal cancer cell lines recapitulate colorectal tumor subtypes, but that cell lines have more mutations than tumors5. In general, the current consensus concerning cancer cell lines as primary tumor models is that cell lines share many of the original tumor characteristics, but can harbor genetic changes of poorly characterized significance; and that some cancer cell lines may not even be molecularly appropriate or representative models for their tumor of origin6.However, additional genomic characterization of cancer cell lines has indicated that the transition from in vivo to in vitro may introduce key genomic alterations. One of the first groups to compare tumor and cell line used microarray gene expression profiles to identify breast cancer cell lines that seemed to be genetically inappropriate models for breast carcinoma6. It is essential that preclinical researchers know which biological pathways behave similarly in vivo and in vitro, and even more importantly, which pathways demonstrate altered activity as a result of alterations in environmental signals and stressors in the in vitro growth setting. If a key pathway behaves differently in cell lines as compared to primary tumors, preclinical testing of a drug targeting that pathway will not accurately predict patient tumor response.Nevertheless, cancer cell lines continue to be widely used in cancer research and therapeutic discovery. As the focus on molecularly targeted therapeutics grows, so does the need to thoroughly characterize how cancer cell lines diverge phenotypically from tumors due to their in vitro growth environmentIn order to characterize the specific pathway alterations that occur between primary tumors and tumor-derived cell lines, we analyze three types of high-throughput molecular data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE). We perform transcriptomic analysis of bulk RNA sequencing of TCGA and CCLE samples and identify a set of differentially expressed genes. We integrate micro-RNA sequencing from the same projects and identify an interaction network of micro-RNA (miRNA), long non-coding (lncRNA) and protein-coding genes that is aberrantly expressed in cell lines compared to tumors. This network implicates four key cancer driver pathways that are often the subject of preclinical drug evaluation in cell lines, but whose activity in cell lines is not representative of original tumors. We use proteomic quantification data from the same studies to demonstrate that the aberrant cancer driver pathway expression observed in cell lines extends to the proteomic level. We also demonstrate similar findings in separate datasets of single-cell RNA sequencing from tumors and cancer cell cultures.7  which are the most useful and important for classifying a new tumor or cell line based on a trained model8. Feature identification using SVM allowed us to identify the set of genes that best differentiate between tumor and cell line regardless of whether each gene is more highly expressed in the tumor group or the cell line group. The SVM approach has been shown to eliminate noisy results in a high-dimensional gene expression comparison, by drawing out the greatest sources of variation across all samples10. In order to ensure robust results, we repeated the classification on fifty different random 80/20 test/training splits of the data. After sorting the genes by their SVM-assigned feature importance coefficients, we merged the top 10% of genes from all fifty classifications, resulting in 1854 genes that were in the top 10% of most important genes for each classification (Supplementary Data We then used a support vector machine (SVM) linear classifier within the Python 11. We found 27 gene sets with significant enrichment in the SVM-derived differentially expressed protein-coding genes  on the 1799 protein-coding genes using the Hallmark cancer pathway set from the Molecular Signatures Database (mSigDB v7.0)12. miRNet identified 227 miRNA with known interactions to the 54 lncRNA. In turn, these 227 miRNA had 580 known gene targets among the 1799 differentially expressed coding genes . GSEA of the 580 coding genes revealed 24 Hallmark gene sets with significant enrichment . Notably, our results . This pathway did not show differential expression between CCLE and TCGA . We also verified that this signal was not a disease-specific artifact by repeating the SVM after subsetting the data to the disease with the largest number of samples in TCGA (BRCA) and the smallest number of samples (DLBC). The same four cancer driver pathways were identified in both analyses  and lowest (PRAD) ESTIMATE scores . The four cancer driver pathways were identified as differentially expressed in both analyses  represents the protein counterpart of the STAT5 gene. The antibody NFKBP65_pS536 binds to phosphorylated p65, one of the two protein subunits of NFKB. Phosphorylation of p65 is one of several molecular mechanisms known to activate the NFKB pathway20.We next examined whether the downregulation of these pathways extended beyond gene expression into protein activity. Proteomics quantification of many CCLE and TCGA samples was performed using Reverse Phase Protein Array (RPPA) in the MD Anderson Cell Lines Project (MCLP) and The Cancer Proteome Atlas (TCPA)ata Fig. . PIK3R1 We noted significantly lower protein expression of PIK3R1, Cyclin D1, and STAT5 in the cell-line data, consistent with our gene expression results in the corresponding pathways. This carries important implications for the applicability of preclinical drug tests against these targets in cancer cell lines. Interestingly, the phosphorylation level of p65 is higher in cell lines than tumors, opposite the gene expression of the NFKB signaling pathway. This suggests that p65 phosphorylation may be playing a different role in cell lines, and underscores the importance of examining multiple types of data to elucidate complex molecular interactions.KRAS and TP53 genes22, we investigated whether there is a correlation between diseases with heavy mutation burden in these genes and diseases with higher correlation between tumor and cell line, with the assumption that cell lines derived from mutated tumors maintain those mutations. We found that there is no correlation between mutational burden and tumor-cell-line correlation; in fact, in the case of the TP53 pathway, there is a slight inverse correlation between the two factors . To further investigate the effect of KRAS and TP53 mutation status on the differentially expressed cancer driver pathways, we subset the TCGA and CCLE datasets to samples carrying a non-silent KRAS or TP53 mutation and repeated the SVM analysis (see “Methods”). In both the KRAS-mutant and TP53-mutant analysis, all four cancer driver pathways were again identified as differentially expressed in tumor compared to cell line. These results indicate that dysregulation of these pathways occurs in cancer cell lines regardless of the mutational status of the primary tumor, and is unrelated to the activating DNA mutations.Because activation of the KRAS and TP53 pathways is associated with mutations in the 25. miRNA directly affect gene expression by binding mRNA and targeting them for degradation28. In this method of expression control, lncRNA regulate miRNA, while miRNA regulate gene expression . With the exception of LINC00707, 10 of the 11 lncRNA are significantly underexpressed in CCLE  are more highly expressed in cell lines than tumors. Cytoscape was used to visualize the lncRNA-miRNA-coding gene network colored by gene type or by LFC  data from CCLE and TCGA. We used the ComBat method to correct for experimental batch effects mir-497, mir-195, mir-148a, and mir-152 directly inhibit genes in the KRAS/RAF/MEK/ERK pathway32. The TP53 pathway is repressed by mir-339, and the TP53-associated gene TP53INP1 is regulated by mir-9234. mir-519d directly represses STAT3, a key gene in the IL2/STAT5 signaling pathway35. The NFKB pathway is activated by mir-301a, which has lower expression in cell lines compared to tumors, in keeping with lower NFKB activity in cell lines36.In keeping with the lncRNA “sponge” regulatory model, the lncRNA are underexpressed in cancer cell lines, which could be involved with the observed overexpression of a majority of the miRNA whose expression is known to be kept in check by these lncRNA. The aberrant overexpression of inhibitory miRNA could explain the observed underexpression of key genes in four important cancer driver pathways in cancer cell lines. Consistent with this idea, we noted several miRNA with higher expression in cell lines that are known to play roles in regulation of the four cancer driver pathways. H19, a lncRNA strongly associated with the cancer stem cell state37, but show increased expression of mir-1 and mir-206, which promote cellular differentiation by blocking anti-differentiation signaling targets39. In addition, CCLE samples show reduced expression of CERS6-AS1, IQCH-AS1, and LINC00240, lncRNA implicated in mediating tight junctions or extracellular matrix interactions, which are features of epithelial and endothelial cells43. At the same time, CCLE samples have comparatively high expression of mir-9, which directly represses E-cadherin, a well-known epithelial marker44. E-cadherin repression is known to induce the epithelial-mesenchymal-transition, a process which plays a role in cancer progression from an epithelial state to a motile and invasive metastatic state45. CCLE samples display reduced expression of E-cadherin/CDH1, and higher expression of mesenchymal markers, including N-cadherin/CDH2, MUC1, and claudins CLDN1, CLDN2, CLDN346 .Because lncRNA and miRNA are known for cell-type-specific expression, we hypothesized that the observed dysregulation of lncRNA-miRNA expression networks is caused by biological selection for a subset of cancer cells which are more likely to survive the cell-line derivation process and thrive in a cell culture setting. Consistent with this hypothesis, both stem cell and epithelial cell-specific lncRNA and miRNA display reduced expression in cancer cell lines. Specifically, CCLE samples have reduced expression of The observed reduced epithelial and stem cell expression in cancer cell lines suggests that cancer cell culture conditions select for the subset of cancer cells with a mesenchymal, invasive and metastatic phenotype. Overall, these results indicate that selection against specific cancer cell types in tumor-derived cell lines may cause global downregulation of key cell-type-specific lncRNA, potentially allowing overexpression of a variety of miRNA, many of which play important roles in regulating cancer signaling pathways. However, more work is needed to fully investigate this hypothesis.7, we examined whether these cell lines show more representative expression of the 4 cancer driver pathways. We repeated the SVM after subsetting the CCLE dataset to the TCGA-110-CL and the TCGA dataset to the tumor types in the TCGA-110-CL  data from hepatocellular carcinoma (HCC) cell lines and patient samples. Within the scRNAseq patient tumor data, we differentiated the malignant cells from the normal cell infiltrate , very similar to the bulk RNA sequencing analysis . We again observe enrichment of the same four cancer driver pathways, with the exception of the KRAS signaling pathway in the first analysis.50. Two-dimensional cancer cell cultures have enabled researchers to discover how cancers arise, characterize cancer cell types and growth patterns, and identify effective pharmaceuticals through drug screens1. Today, personalized tumor-derived 2D and 3D cultures are increasingly in use for the identification of precision therapies for individual patients53. The rise of precision medicine and small-molecule inhibitor development brings an urgent need for molecular characterization of the cell culture models used widely for therapeutic development. Large-scale genomic efforts such as The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) have enabled comprehensive comparison of cancer cell cultures and primary tumors.The ability to model and manipulate cancer cells has empowered therapeutic discovery since the derivation of the first cancer cell lineHere, we provide a comparative multi-omic analysis of cancer cell lines and primary tumors by leveraging several types of genomic data from large public compendia. Our gene expression analysis reveals reduced expression of key cancer driver pathways, including KRAS signaling, TP53 pathway, IL2/STAT5 signaling, and NFKB signaling in cancer cell lines. These results are recapitulated in a comparative analysis of protein levels for key proteins from each pathway. Our analysis indicates the need for caution when interpreting in vitro preclinical testing results of inhibitors targeting these pathways.54. Subsequent optimization of these compounds led to success in clinical trials, but initial results in cell lines did not predict in vivo results. Targeting both wild-type and mutant KRAS in cancer has been notably unsuccessful; in particular, several high-throughput screens of KRAS-mutant cancer cell lines identified compounds which subsequently only partially reduced tumor volumes in xenograft models56. In contrast, targeting the cholesterol biosynthesis pathway, which is not differentially expressed in cancer cell lines by our analysis, has been promising both in vitro and in vivo through the usage of the statin family of drugs58. In addition, targeted inhibition of cyclin-dependent kinases (CDKs) by agents such as PD-0332991/palbociclib has been promising in phase I and II clinical trials, and this drug was originally identified in cancer cell lines60. CDKs are active during cell cycle entry from G0 and during the G2M checkpoint pathway, which were not differentially expressed in cancer cell lines by our analysis. We note that apart from the four cancer driver pathways identified as dysregulated in cell lines, preclinical testing in cell lines can in many cases reliably predict in vivo responses.In fact, the consequences of preclinical testing in cell lines against these pathways have already been felt. For example, during preclinical testing several MDM2/TP53 small-molecule inhibitors displayed potent cancer cell-line inhibitory activity, but could only achieve partial tumor regression in xenograft modelsTo identify potential causes of dysregulation of cancer driver signaling in cell lines, we analyzed the expression of lncRNA and miRNA implicated in regulation of these pathways. In cell lines, we found reduced expression of a set of lncRNA predicted to regulate a downstream network of regulatory miRNA, which are in turn overexpressed. Several of these miRNA are directly involved in specific inhibition of these cancer driver pathways, linking their overexpression to the observed reduced expression of cancer driver pathways.61. It is well known that the presence of serum in culture media encourages cellular differentiation, and cancer cell lines grown in serum-free conditions contain larger populations of cancer stem cells63. This hypothesis is supported by the markedly lower expression of stem cell-specific lncRNA in cell culture, and higher expression of pro-differentiation miRNA. Because cancer stem cell populations are known for their chemoresistance, and even small populations are thought to be capable of tumor recurrence66, it is essential that preclinical models accurately model the response of cancer stem cells to potential therapeutics.We speculate that our results may indicate a partial loss of cancer stem cells in cancer cell culture due to the presence of serum in culture media. All CCLE cell lines were cultured in RPMI or DMEM media with 10% fetal bovine serum7. Our study provides additional information by identifying the set of genes which accounts for the highest variation between human tumors and cell culture models, and by identifying specific, clinically important cancer driver pathways which are poorly recapitulated in cancer cell culture models. Additionally, our finding of global cancer driver pathway dysregulation in cell lines may help to explain biologically the previous study’s finding of low correlation between some tumors and cell lines. We investigated the TCGA-110-CL cancer cell-line panel identified in the previous study, which has a higher overall cell-line-tumor gene expression correlation. The same four cancer driver pathways were identified by an SVM comparing these cell lines to disease-matched primary tumors, and in particular, the KRAS pathway had the lowest overall cell-line-tumor correlation. This indicates that even this subset of cancer cell lines does not fully recapitulate the cancer driver signaling of primary tumors, and studies on these pathways must be interpreted with caution.A recent study by Yu et al. also included a pan-cancer analysis of TCGA and CCLE data, finding that tumor type is not a large factor in overall tumor-cell-line correlation, but that some cell lines are poor models of their primary tumor type and tumor purity plays a large part in tumor-cell-line correlations7, we sought to assess the impact of tumor purity on our cancer driver pathway dysregulation findings by leveraging single-cell RNA sequencing data from HCC patients and cell lines. The TP53 and NFKB pathways remained significantly enriched in HCC tumor cells compared to cell lines when normal infiltrating cells were removed from the tumor-cell population, while the KRAS and IL6/STAT3 pathways were only significantly enriched with a less stringent statistical cutoff. These results indicate that the KRAS and IL2/STAT5 findings from the TCGA-CCLE gene expression comparison may be partially due to lower tumor purity. Moreover, this analysis indicates that normal cell infiltrate contributes a molecular signature to bulk gene expression data and highlights the utility of single-cell data. Finally, we found that the HCC cell lines most closely resemble the most invasive and metastatic HCC subtype, unlike the HCC tumor cells which resemble all three HCC subtypes, consistent with our hypothesis that cancer cell culture selects for the most invasive malignant cells. We saw similar results in an analysis of matched melanoma brain metastasis cell lines and tumors. A caveat of this analysis is that it focuses on only two types  of solid tumor; a broader investigation of single-cell RNA sequencing data in this context from multiple tumor types and cell lines will be informative when those data become available.In light of this finding that tumor purity impacts the correlation between tumor and cell lineTaken together, our results underscore the need for caution when interpreting preclinical cancer testing results in multiple model types, and point to specific signaling networks which can serve as litmus tests for the accuracy of past and future cancer laboratory models. We suggest several potential solutions to improve the efficacy of tumor-derived cell lines. Cancer cell culture in serum-free conditions may improve the maintenance of tumor stem cell populations and reverse the dysregulation of important regulatory gene networks. Specific efforts to model the immune microenvironment in cancer-derived organoids may improve cancer driver pathway expression related to the tumor microenvironment. A potential solution may be genetic manipulation of tumor-derived models with an emphasis on preserving or rescuing the intrinsic cancer driver pathway expression which is most at risk for dysregulation. Overall, this study provides much-needed genomics-based considerations for future preclinical cancer model development and result interpretation.n samples = 10,535) and CCLE (n samples = 933) were downloaded from the UCSC Xena browser. These data were processed uniformly through the TOIL UCSC RNA sequencing data processing pipeline  to remove technical batch effects67. Both datasets were normalized by log2(TPM + 1) and duplicate genes were averaged. Genes not expressed in 80% of samples were removed, and 20% of the lowest varying remaining genes were removed, leaving 46865 remaining genes. Both datasets were subset to the 19 overlapping cancer types for subsequent analysis . All heatmaps use a random subset of samples from each dataset with equal numbers from each disease. The random subset method is used because the CCLE data holds approximately 1/10 the number of samples as the TCGA data, making it impossible to visually detect gene expression differences in heatmaps containing all samples from both datasets.Gene expression transcripts per million (TPM) matrices from TCGA (TCGA somatic mutation data were downloaded from UCSC Xena (xena.ucsc.edu). CCLE somatic mutation data were downloaded from the Broad Institute CCLE Database .68. CCLE Nanostring probe miRNA quantification data were downloaded from the Broad Institute CCLE database: https://portals.broadinstitute.org/ccle/data69. For dataset comparability, miRNA naming formats were harmonized, and duplicates were averaged. Because different miRNA sequencing methods were used in each dataset, ComBat was used to batch-correct the data29. Pre- and post-batch effect correction data were then log2(count + 1) normalized for downstream visualization and analysis. Supplementary Fig. 5 shows pre- and post-batch effect correction expression distributions of several housekeeping genes to validate successful correction71.TCGA micro-RNA (miRNA) Illumina sequencing read counts data were downloaded from the Genomic Data Commons Data Portalhttps://tcpaportal.org/tcpa/download.html and http://tcpaportal.org/mclp/#/download). Both datasets were subset to the 16 overlapping cancer types for subsequent analysis .Level 4 Reverse Phase Protein Array (RPPA) data for the TCGA and CCLE samples were downloaded from the The Cancer Proteome Atlas (TCPA) portal . HCC tumor 10x Genomics single-cell RNA sequencing datasets from seven samples from two patients were downloaded from the Gene Expression Omnibus (accession GSE112271). All single-cell gene expression data analysis was performed using scanpy (v1.6.7) in Python (v3.6.8). The datasets were combined, and cells expressing >5000 genes and/or >30 mitochondrial genes were excluded, leaving 52,630 cells. Leiden clustering was performed using the top 40 Principal Components, cell types assigned based on cell-type markers  containing 10% fetal bovine serum (FBS) and 1% (v/v) antibiotic/antimitotic. Early passage cells (<P3) were grown to confluency and then harvested for single-cell RNA sequencing analysis as described above.Hepatocellular carcinoma (HCC) circulating tumor cell and cell line 10x Genomics single-cell RNA sequencing datasets were downloaded from the Gene Expression Omnibus (accession GSE103867). The cell-line cells were selected based on the expression of In Python (v3.6.8), the sklearn module (v0.21.2) was used with linear kernel to train a support vector machine (SVM) on 50 random 80/20 splits of the merged TCGA-CCLE gene expression dataset, in which 50 different training sets (and corresponding test sets) were resampled from the same data. The top 10% of genes from each training run based on the magnitude of their feature weights coefficients were merged in a non-duplicate manner, to account for variation in prediction results based on the training and corresponding test set, resulting in 1858 genes. Gene set enrichment from this analysis revealed 26/100 enriched pathways were immune-related, so a non-redundant immune gene list was created by merging all genes from the 26 enriched immune pathways (Supplementary Data P value < 0.05. Significantly enriched gene sets were identified using gene set enrichment analysis11 with FDR q-value < 0.05. For bulk RNA-seq, heatmaps are generated using seaborn clustermap method in Python with z-scores calculated per gene. For single-cell RNA-seq, heatmaps are generated using scanpy heatmap method in Python with values scaled between 0 and 1. Figures were produced using pandas v0.25.3, matplotlib v3.0.3, and seaborn v0.9.0 in Python v3.6.8. Numerical data used to generate figures are available here: https://drive.google.com/drive/u/1/folders/16pqRgDIxS0Wj_H9rIwyjB11a0IE6TAXH All data were used for each figure, except when otherwise indicated.Expression comparisons of individual genes between datasets were performed using a two-sided Mann-Whitney significance test with significance defined as Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types and chemotherapeutic drug resistance is a stumbling block in improving the overall survival of PDAC patients. The nature of specific drug resistant subpopulation within pancreatic ductal adenocarcinoma is believed to be partly attributed to epithelial-mesenchymal transition (EMT) and cell stemness. Various PDAC cell lines show various degrees of resistance to chemotherapeutic agents including gemcitabine (GEM) and 5-fluorouracil (5-FU). In-depth understanding of drug resistance mechanisms and profile heterogeneities could lead to the development of novel and precise therapeutic strategies for addressing the chemo-resistant dilemma in PDAC patients.Cytotoxicity assays were performed by CCK8 in ten common PDAC cell lines including AsPC-1, BxPC-3, CAPAN-1, CFPAC, HPAFII, MIA PaCa-2, PANC-1, Patu-8988, SW1990 and T3M4. RNA-seq data of the ten cell lines were downloaded from Cancer Cell Line Encyclopedia (CCLE) database and subsequently analyzed for differentially expressed genes (DEGs). Based on first-line chemotherapy regimens of PDAC, DEGs between resistant and sensitive cell lines were validated by qRT-PCR. Enriched pathways of differentially expressed genes between the resistant and sensitive cell lines were acquired by Metascape database.We found that the top two toxic drugs for PDAC cell lines were paclitaxel (PTX) and GEM. Among the ten PDAC cell lines, SW1990 was the most resistant PDAC cell line with the highest IC50 levels for three drugs, while MIA PaCa-2 and BxPC-3 were the most sensitive PDAC cell lines. Differential expression analysis revealed the highest number of DEGs associated with cisplatin (CIS) sensitivity up to 642 genes, of which 181 genes were upregulated and 461 genes were downregulated in CIS-resistant cell lines. The least number of DEGs are associated with GEM sensitivity, of which 37 genes were highly expressed in GEM-resistant PDAC cell lines and 25 genes were lowly expressed. Enrichment analysis of the DEGs revealed that pathways associated with drug resistance were mainly extracellular matrix and cell–cell junction related pathways.PDAC cell lines showed diverse sensitivities to commonly used chemotherapeutic agents, which was caused by differential gene expression between the resistant and sensitive cell lines. The heterogeneity and its associated genes were enriched in extracellular matrix and cell–cell junction related pathways. Our study first portrayed the sensitivity profile to chemotherapeutic drugs of PDAC, which would benefit the chemoresistance mechanism study by reemphasizing the vital role of extracellular matrix and cell–cell junction related pathways and helping the selection of suitable PDAC cell lines.The online version contains supplementary material available at 10.1186/s12935-022-02785-7. PDAC is currently the fourth leading cause of cancer-related death in the USA, with an increasing incidence and poor outcome and constitutes one of the most lethal of the common malignancies with a poor five-year survival rate below 11% .Chemotherapeutic regimens of PDAC are often palliative rather than curative, which means insensitivity and toxicity restrict the effectiveness of these drugs thus resulting in a very marginal improvement in the survival rate of PDAC patients with advanced disease . The maiHere, we characterized the effects of five conventional PDAC chemotherapeutic drugs on ten human PDAC cell lines to determine the subgroups between chemotherapeutic sensitivity and resistance, and constructed the profile of chemotherapeutic sensitivity of PDAC cell lines. Then DEGs and their associated pathways between drug-resistant and sensitive cell lines were analyzed and enriched with the RNA-seq data from public databases. In addition, PDAC cells co-culture with pancreatic stellate cells (PSCs) were performed to verify the role extracellular pathways on drug resistance. Our results revealed abundance of DEGs associated with multidrug resistance not reported before providing hope for deliberately dealing with chemoresistance of PDAC.Ten common PDAC cell lines were used in this article, including AsAC-1, BxPC-3, CAPAN-1, CFPAC, HPAFII, MIA PaCa-2, PANC-1, Patu-8988, SW1990 and T3M4. T3M4 were generously provided by Dr. Chengcheng Wang  and the others were all purchased from American Type Culture Collection (ATCC). All the cell lines were validated by detecting their specific short tandem repeats . AsPC-1 and SW1990 were cultured in RPMI-1640 modified medium (Hyclone). BxPC-3 was cultured in RPMI (Corning). CAPAN-1 and CFPAC were cultured in Iscove's Modified Dubecco's Medium . HPAF-II, MIA PaCa-2, PANC-1, Patu-8988 and T3M4 were cultured in Dulbecco's Modified Eagle Medium (DMEM)/High Glucose (Hyclone). The culture medium of CAPAN-1 was added with 20% fetal bovine serum (FBS) and the culture medium of the other cell lines were added with 10% FBS.All five chemotherapeutic drugs, GEM , 5-FU , PTX , IRI  and CIS , were obtained from Peking Union Medical College Hospital.Gene-specific siRNAs and nonsense control were provided by Tsingke . PDAC cells were transfected with siRNA using Lipofectamine 3000 . After the knockdown efficiency was confirmed by quantitative RT-PCR (qRT-PCR) and western blot, the cells were used for subsequent experiment. The sequences of siRNAs used were listed in Additional file 5/mL) were seeded in the upper berth of the co-culture chamber and PDAC cells (1*105/mL) were seeded in the bottom of co-culture chamber. Co-culture for 48 h and RNA were extracted from PDAC cells.For indirect co-culture, PSC cells . Subsequently, place the plates in incubator under 37℃ for 2 h and determine its light absorption value at 450 nm and 630 nm using an enzyme-linked immunosorbent detector . The difference between absorbance values at 450 nm and 630 nm indirectly reflects the number of living cells.Cell viability values under the drug concentration gradients were conducted non-linear fitting . The 50% inhibitory concentration (IC50) values were defined as the concentration of drug that inhibited cell growth by 50% relative to the untreated control. IC50 values were calculated using GraphPad Prism 9.0 software .http://portals.broadinstitute.org/ccle). The DEGs between drug resistant and sensitive PDAC cell lines were obtained by using edgeR (bioconductor.org/packages/release/bioc/) in R 3.6.0. Those genes with a 丨log2(fold change)丨 ≥ 2 and adjusted P value < 0.05 in the default Benjamini–Hochberg false discover rate (FDR) method were considered to have statistical significance, which was shown via volcano plot. We then used Metascape (https://metascape.org/) based on Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to comprehensively analyze the biological functions, cellular components, molecular functions and pathways of the DEGs between resistant and sensitive PDAC cell lines. Venn diagrams were obtained by using jvenn (http://jvenn.toulouse.inra.fr/).The set of sequence-based mRNA expression data (RNA-seq data) of the ten PDAC cell lines were downloaded from CCLE database  according to the manufacturer’s instructions. The sequences of primers used in qRT-PCR were listed in Additional file Total RNA was extracted from PDAC cell lines using TRIzol  and processed for reverse transcription and quantitative PCR using a Reverse Transcription System  and a One Step SYBR ® PrimeScriptWhole cell lysates were obtained with RIPA lysis buffer  containing 1% protease and phosphatase inhibitors  on ice. The cell lysates were centrifuged at 12, 000 rpm for 15 min at 4 °C to remove undissolved impurities and collect the supernatants. The protein concentration was quantified using a BCA protein assay kit . Then, proteins were separated by 10% SDS-PAGE and transferred to 0.22 μm polyvinylidene fluoride (PVDF) membranes. The non-specific binding sites on the membrane were blocked with 5% milk for 1 h. After blocking, the membrane was first incubated with the primary antibody of UCP2  overnight at 4 °C and then with the secondary antibody at room temperature for 1 h. Finally, super-sensitive ECL assay kit  was used to show the immune response.The RNA expression levels were analyzed by applying the unpaired parametric t-tests. Statistical analyses were performed using GraphPad Prism 9.0 . A p value < 0.05 was considered statistically significant.We conducted cytotoxicity assay by treating ten common PDAC cell lines, namely AsPC-1, BxPC-3, CAPAN-1, CFPAC, HPAF-II, MIA PaCa-2, PANC-1, Patu-8988, SW1990 and T3M4, with five first-line chemotherapeutic drugs of PDAC, namely GEM, 5-FU, PTX, IRI and CIS. Inhibition curves based on cell inhibition ratio were shown classified by cell lines  guideline. Current clinical chemotherapy for PDAC is mostly based on 5-FU and GEM , 13. HenSeveral multidrug resistance associated genes were confirmed by qRT-PCR. We stimulated MIA PaCa-2 and BxPC-3 PDAC cell lines with 5-FU and GEM, respectively, and observed a time-dependent increase in the expression of drug resistance associated genes, including TMEM178B, ANPEP, DNALI1, TFPI2, UCP2, GATA5, VSTM1, FAM196B, DZIP1, DNER and RGS5  in the sensitive cell line MIA PaCa-2 and the drug-resistant cell line PANC-1 and found that heterogeneity between PDAC cell lines still persisted when response to clinically common combinations of chemotherapeutic agents  Up-regulated DEGs in resistant PDAC cell lines of each drug. (B) Down-regulated DEGs in resistant PDAC cell lines of each drug. Figure S3. Knocking down UCP2 results in reduced drug-resistant ability of PANC-1. (A) qRT-PCR to verify the efficiency of UCP2 siRNAs. (B) Western blot to verify the efficiency of UCP2 siRNAs. (C) Cell viability curve of PANC-1 after knocking down UCP2. Figure S4. Synergetic effects of 5-FU+IRI+CIS on sensitive cell line MIA PaCa-2 and resistant cell line PANC-1.Additional file 2: Table S1. Intersection of DEGs between resistant and sensitive PC cell lines.Additional file 3: Table S2. Intersection of DEGs pathway enrichment for each first-line PC chemotherapeutic drugs.Additional file 4: Table S3. The sequences of siRNA used in this article.Additional file 5: Table S4. The sequences of primers used in this article.