MicroRNAs (miRs) regulate gene expression through translation inhibition of target mRNAs. One of the most promising approaches for cancer therapy is through mimicking or antagonizing the action of miRs. In this report, we analyzed the miRnome profile of several human breast cancer cell lines to determine the influence of estrogen receptor (ER) silencing previously shown to result in epithelial to mesenchymal transition (EMT) and enhanced tumor invasion.de novo ER-) and ER-silenced (acquired ER-) pII and IM-26 or ER-expressing (YS1.2) siRNA transfected derivatives of MCF7 cells was deep sequenced on Illumina NextSeq500. Respective miRnomes were compared with edgeR package in R and Venny2.1 and target prediction performed with miRTarBase. Mimics and inhibitors of selected differentially expressed miRs associated with EMT mediators  were transfected into pII cells and mRNA targets, as well as E-cadherin and keratin 19  were measured using taqman PCR.MicroRNA extracted from MDA-MB-231  and targets (using miR antagonists) for general cancer therapies aimed at regulating either individual or multiple mRNAs, but also to re-sensitize endocrine resistant breast cancers by turning them back into a type that will be susceptible to endocrine agents. •Around 50–60% of microRNAs were significantly differentially expressed between ER- and ER + breast cancer cell lines.•Transfection of miR-200c-3p mimic into ER -ve cells induced MET and reduced cell motility.•Transfecting of miR-449a inhibitor into ER -ve cells reduced cell invasion but did not induce EMT. All cell lines were maintained as monolayers at 37 °C in an incubator gassed with an atmosphere of 5% CO2 at 95% humidity and cultivated in Advanced Dulbecco's Minimum Essential Medium (DMEM) supplemented with 5% fetal bovine serum (FBS), 600 μg/ml l-glutamine, 100 U/ml penicillin, 100 μg/ml streptomycin and 6 ml/500 ml 100 x non-essential amino acids .YS1.2 was derived from a transfection with ER shRNA plasmid that 2.2Cultures of all tested cell lines were grown to approximately 80% confluency in 75 cm flasks, detached by trypsinisation, centrifuged for 5 min at 600 g, washed with 1 ml PBS and centrifuged again for 5 min at 600 g. Cell pellets . miRNA expression data were normalized based on the total read count per sample. Read count for each miRNA was divided by the total read count in that sample and multiplied by the median total read count across all samples. After normalization, data were log2-transformed. Sample-sample clustering  was performed on pairwise Pearson correlation coefficients based on the miRNA level read counts or the normalized miRNA expression data. Differential gene expression analysis was performed separately by pairwise comparisons. For each analysis, only miRNAs with a read count >4 in at least three samples were retained for further analysis. Differential gene expression analysis was performed on the raw read count data with the edgeR package in R which is a software for examining differential expression of replicated count data from Bioconductor . Volcanode novo resistant cell line MDA-MB-231 and acquired resistance cell lines pII and IM26 were compared and predicted using the miRTarBase and analyzed as mentioned before.Venn diagrams were constructed using the Venny2.1 software (Juan Carlos Oliveros n.d. “Venny2.1) in order to compare between the ER-cell lines MDA-MB-231, pII and IM26, and with the ER + cell line YS1.2. The average of three triplicates was taken except for when two out of three had no expression and the third value was less than 3 then this miRNA was not considered, otherwise even if the mean was less than 4 the miRNA was considered for the Venn diagram. Members that were exclusively expressed in YS1.2, MDA-MB-231, all ER-cell lines or all ER-acquired resistant cell lines were considered only if significantly differentially expressed in all comparisons. Targets of miRNAs and members in the miRNA families that were differentially expressed between ER- and ER + cells were predicted using miRTarBase , and onl2.3For measurements of gene expression, total RNA was extracted using the RNeasy kit from Qiagen, (USA). Purification was carried out according to the manufacturers' instructions. Quantity and quality were measured using NanoDrop 1000 spectrophotometer (Thermo Scientific) and by 2100 Bioanalyzer microfluidic gel electrophoresis system . The purified RNA samples were stored in RNAse-free distilled water at −80 °C. cDNA was synthesized from 1 μg of total RNA using High-Capacity cDNA Reverse Transcription Kit in the presence of RNase inhibitor 2000U  following the manufacturers’ instructions. The PCR amplification was carried out on a thermal cycler  with the following parameters: 25 °C for 10 min, 37 °C for 120 min, 85 °C for 5 min.2.4t values were used to calculate ratios of target to control gene using the excel spreadsheet developed by Pfaffl [Levels of Dicer, CTNND2 and ZEB1 mRNAs were measured using corresponding TaqMan Gene Expression Assays  according to the manufacturer's instructions. The PCR cycling was carried out in a 7600 Fast real time instrument (ABI) under the following conditions: 50 °C (2 min) hold, 95 °C (10 min) hold, then 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Target gene expression was normalized to endogenous controls (actin and GAPDH). Cy Pfaffl .2.5pII cells were seeded at density of 1.5 × 10 [2.62. On the following day (48 h transfection), cells were harvested, counted, and diluted to 1 × 106 cells per ml in serum-free medium. After that, 100 μl of cells were added to the top chamber of the Cultrex dish. The lower chamber was loaded with 500 μl of DMEM supplemented with 30% FBS (used as a chemoattractant). Cells were incubated at 37 °C, 5% CO2 and allowed to invade to the bottom chamber. After 24 h, the top and the bottom chambers were aspirated and washed with 1 x cell wash buffer. Calcein-AM/cell dissociation solution complex was added to the bottom chamber and left for 1 h at 37 °C, 5% CO2. Cells internalize Calcein-AM and intracellular esterases cleave the acetomethylester (AM) moiety generating fluorescent free calcein. Invading cells were determined by recording the fluorescence emission using a microplate reader with a filter set of excitation/emission 485/535 nm.Cell invasion was assessed by Cultrex® 24 Well BME cell invasion assay purchased from Trevigen (Cat no. 3455-024-K) according to the manufacturer's instructions. For this, the invasion chamber was coated with 100 μl of 1 x basement membrane extract (BME) solution and incubated overnight at 37 °C. After 24 h transfection, cells were serum starved overnight at 37 °C and 5% CO2.7In addition to the statistical procedures already mentioned in above sections, Student's two tailed unpaired t-test or one-way ANOVA test followed by Bonferroni post hoc test were used to compare means of individual groups: p < 0.05 was considered statistically significant.33.1acquired (pII and IM26) and the de novo resistant ER-cell line (MDA-MB-231) showed that they differed in only 12–14% of expressed miRNAs.Approximately 20% of the 2588 miRNAs listed in the human miRBase sequences repository available at the time of this study were expressed in individual breast cancer cell lines  A. Fig. . the YS1.2 cell line all volcano plots show similar trends and indicate a high number of significantly differentially expressed miRNAs with wide fold changes. Interestingly, miRNAs do not show the very large differences that are often seen when comparing mRNA levels [de novo resistant cell line with the acquired resistance cell lines show differences in expression of much less magnitude. Volcano plots comparing the two acquired resistance ER-cell lines show differences of even less magnitude.In A levels . Volcanotop 50 most significantly differentially expressed miRNAs for each comparison. There is substantial similarity in the miRNAs differentially expressed in different comparisons; 38 miRNAs are shared between the three heat maps comparing YS1.2 with IM-26, and pII and MDA-MB-231 respectively. Acquired resistance ER-cell lines pII and IM-26 were also compared to the de novo resistant MDA-MB-231 cell line. In this case, only 17 differentially expressed miRNAs are shared between the two heat maps. When comparing pII and IM-26 to each other only 4 miRNAs are shared with the two other heat maps that compare de novo with acquired resistance lines  which indicate that the differentially expressed miRNAs reflect individual differences between the two cell lines.Heat maps with hierarchical clustering were constructed to show the 3.2acquired resistance ER-cell lines pII and IM-26, and the de novo resistant ER-cell line MDA-MB-231 .3.4For functional analysis targets were either predicted from miRDB database or were searched for as experimentally validated targets in miRTarBase database as mentioned in methods. This is an integrated web server for identifying miRNA-target interactions.Gene-sets associated with EMT, metastasis, motility, invasion, estrogen receptor pathway and cell adhesion were selected from the Molecular Signature Database and predicted targets for each miRNA were retrieved from miRDB 5.0. Fisher's Exact test was performed to assess the over-representation of each miRNA's target genes in each of the pre-selected gene-sets explained in methods. Only 7 results were found for all tests which are summarized in the network maps per biological process . For bio4de novo resistant MDA-MB-231 and two MCF7 derived lines in which ER had been silenced with shRNA , and also a transfected MCF7 line that had failed to down-regulate ER . Some miRNAs were subsequently selected for further studies on pII cells. We found that approximately 20% of the 2588 known miRNA sequences in the human miRnome was found to be expressed in our breast cancer cell lines. This is a common finding that miRNAs are generally down-regulated in cancer compared to normal tissues [acquired with the de novo resistant ER-cell lines  is an adhesion protein; a member of the p-120ctn superfamily . EpithelIn regard to miR-449a, pII cells transfected with miR-449a inhibitor showed no significant change in epithelial or mesenchymal markers. miR-449a was found to target Fos and Met in hepatocellular carcinoma and Flot2 in gastric cancer which was found to reduce features of EMT in these cells . The expThus, in summary, in this study we have shown that miR-449a could be considered as a tumor suppressive miRNA in ER + tumors, and that miR-200c, which targets ZEB1 and ZEB2 (major regulators of EMT) can reverse EMT in our cell line pII an ER-acquired resistance cell line. miR-29a-3p needs to be further investigated. Taken together, the net action of miRNAs determines the cell phenotype. Apparently, most of the miRNAs work as tumour suppressers rather than oncogenes since in both ER- and ER + cell lines they inhibit cell proliferation, invasion, and metastasis. This would be consistent with their lower expression in cancer cells. It seems that the reduction of the total miRNA in breast cancer is a mechanism to increase cancer ability to metastasize and invade other tissues. It would be useful to compare miRnome profiling with proteomics to determine the optimal combination of miRNAs that could be introduced or inhibited for targeted therapy. From our analysis we can conclude that EMT can be regulated by miRNAs by targeting mRNAs that are important in EMT regulation, and to re-sensitize endocrine resistant breast cancers by turning them back into a type that will be susceptible to endocrine agents . The miRConceived and designed the experiments: MAK, YAL. Performed the experiments: AA, MAK. Analyzed the data: YL, MAK, AA. Contributed reagents/materials/analysis tools: YL, MAK. Wrote the paper: MAK, YAL, AA.The authors confirm that there is no conflict of interest.


Additionally, we programmed a series of analyses, including genomic profiling, enrichment analysis, immune infiltration analysis, and therapy-related analysis to identify the mechanism of the SLC24A3 in the process of cancer in CESC. Meanwhile, qRT-PCR was used to validate that the expression of SLC24A3 mRNA in Hela and SiHa cell lines was significantly lower than in PANC-1 and HUCEC cell lines. Our finding elucidated that the SLC24A3, a sodium-calcium regulator of cells, is an indispensable factor which can significantly influence the prognosis of patients with CESC and could provide novel clinical evidence to serve as a potential biological indicator for future diagnosis and pain therapy.The aim of this study was to explore cervical carcinoma and screen a suitable gene as the biomarker used for prognosis evaluation as well as pain therapy. Low expression levels of solute carrier family 24 member 3 (SLC24A3) was involved in the appearance and development of numerous malignancies. Nevertheless, the prognostic value of SLC24A3 expression with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) patients remains uncertain. During the present study, SLC24A3 expression in CESC was retrieved from TCGA, GEO, and MSigDB databases. Based on TCGA and GEO profiles, we performed survival and difference analyses about SLC24A3 both in two GEO (GSE44001 and GSE63514) and TCGA-CESC cohorts (all  Cervical carcinoma is the principal cause of mortality in female, specifically in some developing countries, and they are the second most universal cancer type in gynecology –5. AccorCancer pain, simultaneously, could be the most common and unbearable symptoms in cancer patients, which has been shown to be associated with depression, anxiety, and reduced quality of life in cancer survivors of varied diagnoses . Currenthttp://cancergenome.nih.gov/abouttcga/policies/publicationguidelines) was applied to download gene expression quantification data and clinical information of female with CESC. Nevertheless, because the TCGA database lacks normal tissue data matched with cervical cancer, the total number of samples is only three. Hence, the Genotype-Tissue Expression (GTEx) Portal (http://xena.ucsc.edu/) was applied to obtain the expression values of the normal cervix tissue. The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo) was applied to download gene expression and clinical data of patients in GSE44001 and GSE63514 cohorts. The Molecular Signatures Database (MSigDB) (http://software.broadinstitute.org/gsea/msigdb/index.jsp) was used to extract pain-related genes. Since the data were downloaded from public databases, there is no need to get approval from ethics committee.The Cancer Genome Atlas (TCGA) database  > 1 as the cut-off criteria. We calculated the Spearman coefficients of the DEGs and key pain candidate genes, while defining DEGs with p value < 0.05 as key pain candidate genes.RNA-seq data information of 306 tumor cases and 13 normal cases was obtained from the TCGA and GTEx official website. Differential expression genes (DEGs) were screened via “edgeR” R package by comparing SLC24A3 expression of the tumor and normal cases. We chose the p values were adjusted via the false discovery rate (FDR) approach. If the mean methylation difference was >0.2 with an FDR of 5%, DMCs were reported.306 tumor and 13 normal cases were chosen to perform differential methylation analysis. Probes containing single-nucleotide polymorphism (SNPs), probes with over 10% missing values, and probes in chromosome X were excluded from the analysis. And the “imputeTS” R package was applied to impute the missing values of the selected central pattern generators (CPGs). Differentially methylated CpGs (DMCs) were determined via the Wilcoxon rank-sum test, and the N = 64) with 1000 permutations.The xCell algorithm, an R package (version 1.1.0), was applied to quantify the abundance of tumor stromal cells with CESC patients, which evaluates the integrated levels of 64 stromal cell types. xCell, a gene signature-based approach, combines the advantages of gene set enrichment with deconvolution methods, which well fits with RNA-seq and microarray data. And the gene expression data were prepared according to the xCell instructions, run R package, and undertaken using the xCell signature (https://www.cancerrxgene.org/) was used to download transcription data for almost 1000 cancer cell lines, drug response measurements as area under the curve (AUC) for antitumor drugs in cancer cell lines, and targets/pathways of drugs. The Spearman correlation analysis was performed to calculate the association between drug sensitivity and risk score. Then, we regarded |Rs| > 0.2 and applied Benjamini and Hochberg to adjust FDR and regarded an FDR < 0.05 as significant correlation. “pRRophetic” R package [50) for comparing the drug sensitivity.The Genomics of Drug Sensitivity in Cancer (GDSC)  database package  was usedThe “mafools” R package  was applHela and SiHa are human cervical cancer cell lines which were used as the test groups. Among them, Hela was a cell line of cervical adenocarcinoma and SiHa was a cell line of cervical squamous cell carcinoma. We utilized PANC-1, a cell line of pancreatic cancer, as a positive control for SLC24A3 expression. Meanwhile, we utilized HUCEC, a cell line of normal cervical, as a negative control for SLC24A3 expression. All of them were obtained from the American Type Culture Collection . We used DMEM contained with 10% FBS  to incubate Hela and SiHa cells. Then, we used RPMI 1640 supplementing 10% FBS  to incubate PANC-1 and HUCEC cells. The cells are cultured in an incubator at 37°C with 5% CO2.We used TRIzol reagent  to collect and lyse cells. Then, M-MLV reverse transcriptase  was utilized to obtain cDNA. Utilizing a LightCycler 480 II RT-PCR System , qRT-PCR was carried out as follows: 95°C for half a minute and then 40 cycles of 95°C for 5 seconds and 60°C for half a minute. In our study, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene was utilized as an internal control; sequences of primers amplified in this study were contained in Table t-test via SPSS 26.0.In this study, R programming language was the major tool for processing data. The DEGs between CESC samples and healthy controls were acquired via the “EdgeR” R package, whereas |logFC| > 2 was screened as differentially expressed pain-related genes. The box plot and the forest plot were conducted by the “ggpubr” and “forestplot” R packages, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were done via the “enrichplot” R package. Survival curves and survival analysis were conducted by the “survminer” as well as “survival” R packages. “Clusterprofiler” R package was applied for quantification of gene set activity on the gene set enrichment analysis (GSEA). The version of R bioconductor utilized in this study was R 4.1.2. The qualitative data were analyzed by p < 0.05) were screened to perform the further GSEA analysis.The flow chart of this study is presented in MSigDB , methylation status, and SLC24A3-expressed levels as inputs; our analysis proved that methylation status and the expression of SLC24A3 were significantly associated with the survival outcomes , activated dendritic cell (p = 1.72e − 02), CD56 bright natural killer cell (p = 5.62e − 03), central memory CD4 T cell (p = 4.5e − 05), central memory CD8 T cell (p = 1.12e − 06), effector memory CD4 T cell (p = 2.16e − 05), effector memory CD8 T cell (p = 1.33e − 02), gamma delta T cell (p = 4.73e − 02), immature B cell (p = 4.66e − 03), immature dendritic cell (p = 5.23e − 05), macrophage (p = 1.03e − 03), mast cell (p = 2.92e − 02), MDSC (p = 2.05e − 03), memory B cell (p = 3.37e − 02), natural killer cell (p = 2.88e − 04), natural killer T cell (p = 2.41e − 03), neutrophil (p = 1.33e − 02), plasmacytoid dendritic cell (p = 5.08e − 04), regulatory T cell (p = 1.27e − 03), T follicular helper cell (p = 4.25e − 06), type 1 T helper cell (p = 6.77e − 05), type 2 T helper cell (p = 4.58e − 02), a total of 22 immune cells, was enriched in SL24A3 high-expressed group . That consisted with our bioinformatics analysis and further validated that the SLC24A3 was lower expressed among patients with CESC.qRT-PCR revealed that the levels of SLC24A3 mRNA were obviously downregulated in Hela and SiHa cell lines compared with PANC-1 and HUCEC cell lines . Then, we retrieved pain-related gene from MSigDB database. The GSEA analysis revealed that only one gene was enriched in the pain pathway. Thus, we successfully screened out the gene SLC24A3. This study manifested that SLC24A3 had a considerable difference of expression in tumor and normal tissues of CESC, and we also proved that low-expressed SLC24A3 has well survival rates in patients with CESC. Intriguingly, we found that SLC24A3 has significantly positive correlation with a classical pain gene—SLC6A4. Previous studies have indicated that the human 5-HT transporter (5-HTT) gene (SLC6A4) features some polymorphisms in its promoter region (5-HTTLPR) that impact the 5-HTT expression , 29. IncTo verify the genetic alterations in SLC24A3 in CESC, we drew a panorama gene mutation showing the location and number of somatic mutations in SLC24A3. Copy number variations (CNV) are a usual form of genetic instability, which could directly alter the expression level of particular genes, which plays essential roles in susceptibility to disease , 32. In To further understand the functional role of the SLC24A3, GO, KEGG, and GSEA were performed, and the results indicated that SLC24A3 was greatly enriched in biological processes such as DNA repair, mitochondrion organization, NCRNA metabolic process, and reactome cell cycle mitotic.Immune infiltration of tumors is intimately related to clinical consequences in CESC. Tumor-infiltrating immune cells (TIIC) form an ecosystem in the tumor microenvironment to modulate cancer development and have exhibited underlying prognostic value . Tumor cDrug sensitivity analysis was performed to validate whether risk score could predict patients' sensitivity to chemotherapy. The results of drug analysis indicated that the therapeutic effect of mitomycin C, bleomycin, and gemcitabine could be improved when the SLC24A3 was low expressed. TMB and IPS were applied to evaluate the potential response of immune checkpoint blockade (ICB) therapies. The immunotherapy results revealed that the higher the TMB and IPS, the better the immunotherapy effect. In summary, these findings may be beneficial to clinical decision-making.The results of qRT-PCR demonstrated that the SLC24A3 mRNA was lower expressed in Hela and SiHa cell lines compared with PANC-1 (a positive control) and HUCEC (a negative control) cell lines. It was noteworthy that SLC24A3 mRNA was significantly higher expressed in PANC-1 and serves as human pancreatic cancer cell lines, compared with HUCEC cell lines. This suggested that SLC24A3 was differentially expressed in different cancer type compared with normal control tissues. As far as we know, however, no previous research has investigated whether SLC24A3 is involved in occurrence and development of pancreatic cancer. Hence, we were unable to understand how SLC24A3 exerted its function in pancreatic cancer during occurrence and development. Nonetheless, that did not influence our conclusion that the experimental results of qRT-PCR consisted with the above bioinformatics analysis obviously.κB pathway [Further, in the existing studies, scientists have revealed that Nckx3 (gene SLC24A3), one of the six isoforms of Na+/K+/Ca2+ exchanger family, is able to operate either in forward or in reverse mode relying on both ion gradients and membrane potential –42. Besi pathway . At the MSigDB databases. Then, SLC24A3 was obtained, enriched in the pain signaling pathway by GSEA. Subsequently, genomic profiling, enrichment analysis, immune infiltration, and chemotherapy and immunotherapy analysis were performed. All those gene bioinformatics suggested that SLC24A3 could be a possible molecular targeting mechanism for the prevention and pain treatment of CESC, which needs to be tested in further clinical trials.In conclusion, firstly, we retrieved five pain-related genes from TCGA, GEO, and Admittedly, some limitations indeed exist in this study. First, gene expression profile utilized in present study was from different platforms; this discrepancy may produce bias in the analysis process. Moreover, experiments to illustrate the underlying function and mechanism of SLC24A3 in the therapy of cervical cancer pain were not performed yet. Last but not least, we need to conduct in vitro model and experimental studies to validate the assumption we proposed according to the functions of the SLC24A3, along with the predicted drugs. Therefore, the function of SLC24A3 of CESC is encouraging enough to warrant advanced exploration.


SS18::SSX1 fusion gene, and recapitulates the molecular features of human synovial sarcoma tumours as shown by proteomic profiling. Comparative analysis of drug response profiles with two other established synovial sarcoma cell lines (SYO-1 and HS-SY-II) finds that ICR-SS-1 harbours intrinsic resistance to doxorubicin and is sensitive to targeted inhibition of several oncogenic pathways including the PI3K-mTOR pathway. Collectively, our studies show that the ICR-SS-1 cell line model may be a valuable preclinical tool for studying the biology of anthracycline-resistant synovial sarcoma and identifying new salvage therapies following failure of doxorubicin.Synovial sarcoma is a rare translocation-driven cancer with poor survival outcomes, particularly in the advanced setting. Previous synovial sarcoma preclinical studies have relied on a small panel of cell lines which suffer from the limitation of genomic and phenotypic drift as a result of being grown in culture for decades. Patient-derived xenografts (PDX) are a valuable tool for preclinical research as they retain many histopathological features of their originating human tumour; however, this approach is expensive, slow, and resource intensive, which hinders their utility in large-scale functional genomic and drug screens. To address some of these limitations, in this study, we have established and characterised a novel synovial sarcoma cell line, ICR-SS-1, which is derived from a PDX model and is amenable to high-throughput drug screens. We show that ICR-SS-1 grows readily in culture, retains the pathognomonic  Synovial sarcoma is a rare mesenchymal tumour type that accounts for 8–10% of all soft tissue sarcomas . It typiIn order to identify new therapeutic strategies and gain a better understanding of the biology of this disease, it is necessary to develop preclinical models of synovial sarcoma. Given its rarity, there are very few synovial sarcoma cell line models available in public repositories, with only five identified in the most recent census of sarcoma cell line models . EstabliCIC::DUX4 sarcoma [To bridge this gap, patient-derived xenografts (PDX), where tumours obtained from patients are serially passaged in mice, have emerged as a valuable tool for preclinical research as they retain much of the molecular and histopathological features of the original human tumour ,19. Seve sarcoma ,28,29,30In this study, we established a novel synovial sarcoma cell line, ICR-SS-1, which was derived from a PDX model from The Jackson Laboratory biorepository. We have further undertaken a comparative analysis of the drug response profiles of ICR-SS-1 versus two established commercially available synovial sarcoma cell lines (SYO-1 and HS-SY-II). To our knowledge, this is the first study to report a matched PDX-derived cell line for synovial sarcoma.http://tumor.informatics.jax.org/mtbwi/pdxDetails.do?modelID=J000104314, accessed on 17 June 2022). This PDX was derived from a tumour obtained from a 21-year-old male diagnosed with a grade 3 metastatic synovial sarcoma. This PDX model has been shown by The Jackson Laboratory to harbour the SS18::SSX1 fusion gene. PDX tumours were serially passaged in NOD scid gamma (NSG) mice and tumour volume calculated by ½ × length × width2 (ICR-SS-1 was established from a publicly available PDX model (J000104314) deposited in The Jackson Laboratory biorepository , 1× penicillin/streptomycin, 10 ng/mL EGF , 10 µg/mL hydrocortisone , 0.5 mg/mL collagenase (Sigma Aldrich), 0.1 mg/mL hyaluronidase (Sigma Aldrich), 100 units/mL DNase I (Sigma Aldrich), 10 µM Y-27632 , and 5% FBS (Gibco). Red blood cells were lysed using RBC lysis buffer  and remaining cells were incubated with 0.05% trypsin-EDTA (Gibco) at 37 °C. After trypsinisation, cells were treated with 1 mg/mL DNase I (Sigma Aldrich) at 37 °C, before passing through a 70 µm strainer. Mouse cell depletion beads  were used to remove contaminating murine cells.2. Medium was replenished twice weekly.Dissociated and mouse cell depleted PDX tumour cells were cultured in DMEM/Ham’s F12 1:1 with 15 mM HEPES, 1× penicillin/streptomycin, 2.4 mM L-glutamine, 5 µM Y-27632 (LC Laboratories), 5 µg/mL insulin (Sigma Aldrich), 400 ng/mL hydrocortisone (Sigma Aldrich), 10 ng/mL EGF (Peprotech), 250 ng/mL amphotericin B (Thermo Fisher Scientific), 9.62 ng/mL cholera toxin (Sigma Aldrich), and 10% FBS (Gibco). Following successful continuous growth for >10 passages in culture, the cell line was designated ICR-SS-1. HS-SY-II , SYO-1 , and NIH-3T3  cells were cultured in DMEM supplemented with 1× penicillin/streptomycin and 10% FBS (Gibco). SK-UT-1 cells  were cultured in MEM supplemented with 1× penicillin/streptomycin and 10% FBS (Gibco). Cells were grown at 37 °C with 5% COg and grown at 37 °C with 5% CO2 for 3 days before imaging.For spheroid formation, ICR-SS-1 cells (1000/well) were seeded into 96-well, round-bottom, ultra-low attachment plates (Corning). Plates were spun at 1000× ICR-SS-1 cells (1000/well) were seeded into 96-well, flat-bottom, black-walled plates . Plates were fixed in 10% neutral-buffered formalin solution (Sigma Aldrich) at respective timepoints and stained with 5 µg/mL Hoechst 33342 . Plates were scanned and cells were counted using a Celigo imaging cytometer. Cell counts were normalised to day 1 and an exponential growth equation was fitted to the data using GraphPad Prism  in order to determine doubling time.SS18 forward primer and one of SSX1, SSX2, or SSX4 reverse primers. ACTB was used as a loading control. Primer sequences are provided in RNA was extracted from J000104314 tissue and ICR-SS-1 cells using RNeasy mini and QIAshredder kits , following the manufacturer’s instructions, and used to generate cDNA via a Superscript III kit (Invitrogen). The HS-SY-II cell line was used as a positive control. PCR mixtures were made containing the common SS18::SSX or ACTB amplification are presented in Thermocycler conditions used for PTGER2 forward primer and a common reverse PTGER2 primer. Primer sequences are provided in DNA was extracted from ICR-SS-1, NIH-3T3, and SK-UT-1 cells using a DNeasy blood and tissue kit (Qiagen), following the manufacturer’s instructions. NIH-3T3 was used as a mouse positive control and SK-UT-1 was used as a human positive control. PCR mixtures were made containing either a human- or mouse-specific PTGER2 amplification are presented in Thermocycler conditions used for human or mouse Genomic DNA was harvested from J000104314 PDX tissue and ICR-SS-1 cells using a DNeasy blood and tissue kit (Qiagen), following the manufacturer’s instructions. DNA was quantified using a Qubit dsDNA HS assay kit, and STR profiles were then analysed via Eurofins cell line authentication service.ICR-SS-1, HS-SY-II, and SYO-1 cells were seeded in T25 culture flasks and incubated for 72 h in order to reach 80% confluence. Cells were then lysed in 8 M urea and 0.1 M ammonium bicarbonate (ABC), and protein concentration was measured by bicinchoninic acid (BCA) assay. For each cell line, 40 ug of total protein was reduced with 10 mM dithiothreitol at 56 °C for 40 min and alkylated with 55 mM iodoacetamide at 25 °C for 30 min in the dark. After dilution to final concentration of 2 M urea and 0.1 M ABC, each sample was digested with 0.4 g of trypsin (Thermo Scientific) at 37 °C overnight. The resulting digest was acidified to pH < 4 by trifluoroacetic acid (TFA), desalted on Pierce C18 Spin Columns (Thermo Scientific) according to the manufacturer’s protocol, and dried in a SpeedVac.Dried samples were resuspended in mobile phase A , spiked with iRT peptides , and 2 ug of total peptides were loaded onto a 2 cm × 0.1 mm trap column self-packed with ReproSil Pur C18AQ  beads. Sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry data were acquired using the same instrument parameters as previously described in Milighetti et al. . The acqhttps://biit.cs.ut.ee/gprofiler/gost, accessed on 17 June 2022) and the following setup: full list of identified proteins used as a background; Benjamini–Hochberg FDR method with 0.1 FDR threshold; GOBP and Hallmark gene set databases downloaded from MSigDB  [Proteomic profiles of the three synovial sarcoma cell lines and FFPE tissue samples were analysed by significance analysis of microarrays (SAM) using the samR package  in R. Fone 2022) ,39.t-test in Perseus. For this analysis, the log2 transformed, quantile normalised dataset was used. Technical replicates for individual cell lines were kept as separate samples and proteins with no valid values across the three cell lines were removed. A permutation-based FDR threshold of 0.05 and artificial within-group variance of 0.1 were applied in Perseus to identify significant differentially regulated proteins [Proteomic profiles of ICR-SS-1 and the other two synovial sarcoma cell lines were compared by a two-tailed proteins . Lists oICR-SS-1 (2000/well), HS-SY-II (3000/well), and SYO-1 (2000/well) cells were seeded in clear 96-well, flat-bottom plates . Plates were incubated for 24 h before replacing media with a panel of small molecule inhibitors at a concentration of 500 nM for all drugs except NVP-AUY922, which was at a concentration of 50 nM  analysis at 16 loci . There wSS18::SSX fusion gene. To evaluate if this fusion was present and retained in the ICR-SS-1 cell line, we undertook PCR analysis of SS18 and the three different possible fusion partners . Using the HS-SY-II cell line, which has the SS18::SSX1 fusion gene as a positive control [SS18::SSX1 fusion gene  gene as previously described [PTGER2  comprising four histological subtypes including synovial sarcoma [We have previously undertaken proteomic profiling by SWATH-mass spectrometry of tumour specimens from a cohort of soft tissue sarcoma patients , while the commercial SS cell lines showed elevated levels of proteins which are E2F targets   compared to the other cell lines  ( < 0.01) A. Pazopa < 0.01) B. Furthe < 0.01) C,D. The  < 0.01) C. An eva < 0.01) D, suggesSS18::SSX1 fusion gene and faithfully recapitulates the molecular features of human synovial sarcoma tumours as shown by mass spectrometry analysis.Synovial sarcoma is a rare cancer type with poor outcomes in the advanced setting despite multidisciplinary clinical management. There is a lack of effective systemic therapies for these patients and therefore an urgent need to develop new approaches to tackle this disease. Key to the identification of novel agents is the availability of well-characterised preclinical cell line models. To date, only five synovial sarcoma cell lines are available in public biorepositories , all of SS18::SSX fusion gene), it is well-established that in synovial sarcoma patients there is a wide heterogeneity in observed clinical responses to systemic therapies. For instance, only a subset of advanced synovial sarcoma patients benefits from treatment with chemotherapeutic agents such as doxorubicin and trabectedin [Despite being driven by a single translocation (the bectedin ,45,46. Tbectedin . This suBy subjecting ICR-SS-1 to a targeted small molecule inhibitor panel and comparing the drug responses to the two other synovial sarcoma cell lines, we identified agents from three different drug target classes which are effective in all three cell lines. Of these, both SYO-1 and HS-SY-II have previously been shown to be sensitive to PLK-1 inhibition  while BEIn summary, we have developed and characterised a matched PDX-derived cell line for synovial sarcoma which will add to the arsenal of preclinical tools available for the study of this rare cancer. In particular, the ICR-SS-1 cell line model may be valuable for studies focused on the biology of anthracycline-resistant sarcoma and identifying novel ways to tackle this clinically challenging problem.


Using a consistent experimental protocol, we found a large heterogeneity in the relative biological effectiveness (RBE) values of both proton and carbon-ion beams in various sarcomas and normal-tissue-derived cell lines. Our data suggest that proton beam therapy may be more beneficial for some types of tumors. In carbon-ion therapy, for some types of tumors, large heterogeneity in RBE should prompt consideration of dose reduction or an increased dose per fraction. In particular, a higher RBE value in normal tissues requires caution. Specific dose evaluations for tumor and normal tissues are needed for both proton and carbon-ion therapies.This study investigated variations in the relative biological effectiveness (RBE) values among various sarcoma and normal-tissue-derived cell lines  in proton beam and carbon-ion irradiations. We used a consistent protocol that specified the timing of irradiation after plating cells and detailed the colony formation assay. We examined the cell type dependence of RBE for proton beam and carbon-ion irradiations using four human sarcoma cell lines  and three normal cell lines . The cells were irradiated with gamma rays, proton beams at the center of the spread-out Bragg peak, or carbon-ion beams at 54.4 keV/μm linear energy transfer. In all sarcoma and normal cell lines, the average RBE values in proton beam and carbon-ion irradiations were 1.08 ± 0.11 and 2.08 ± 0.36, which were consistent with the values of 1.1 and 2.13 used in current treatment planning systems, respectively. Up to 34% difference in the RBE of the proton beam was observed between MG63 and HT1080. Similarly, a 32% difference in the RBE of the carbon-ion beam was observed between SW872 and the other sarcoma cell lines. In proton beam irradiation, normal cell lines had less variation in RBE values (within 10%), whereas in carbon-ion irradiation, RBE values differed by up to 48% between hTERT-HME1 and NuLi-1. Our results suggest that specific dose evaluations for tumor and normal tissues are necessary for treatment planning in both proton and carbon-ion therapies. Radiation oncology departments worldwide increasingly utilize particle beam therapy, including proton and carbon-ion beams ,2. PartiIn particle therapy, dose calculation is performed to estimate the dose distribution in a patient’s body. For prescribing a dose, a spread-out Bragg peak (SOBP) is created . ParticlIn carbon-ion therapy, the therapeutic dose is calculated by multiplying the physical dose by the RBE value based on biological and clinical effectiveness . HoweverClinical applications that adopt the RBE model, such as the mixed beam  and micrPrevious meta-analyses have established that the RBE value in tumor cell lines is not greater than that in normal-tissue-derived cell lines . However, for the same LET, there are less obvious and distinguishable differences in radiosensitivity between tumor and normal cells . To the Although there is an existing open access database of α- and β-values in LQMs for various heavy ions as well as photon beams , which p10 doses  for the MG63, HT1080, SW872, and SW1353 cell lines were 3.7, 6.8, 4.0, and 3.4 Gy, respectively   . With prectively . The RBEectively A; this dectively . The RBEectively B.10 and RBE values from different institutes, despite using the same cell lines. We conducted all our experiments using a consistent protocol at a single laboratory to directly compare the RBE-value dependency of tumor and normal tissue among gamma ray, proton beam, and carbon-ion irradiations.We found a large heterogeneity among RBE values for different sarcoma and normal cell lines with different kinds of irradiation. The Particle Radiation Data Ensemble database version 3.2 developed by GSI provides substantially differing DRecent studies have reported that, in proton beam irradiation, LET is higher at the distal end of the SOBP than that at the center of SOBP, which leads to a higher RBE value at distal points ,28,29. MAccording to a report by Kagawa et al., the RBE values in the HSG cell line irradiated with carbon ions were 1.66, 1.76, and 2.27 at LETs of 39.6 keV/µm, 46.6 keV/µm, and 69.6 keV/µm, respectively . The RBEReported RBE values for normal tissues have been determined using animal models . In the With proton irradiation, the combined mean RBE value of the four sarcoma and three normal cell lines was close to 1.1; this indicates that the RBE of 1.1 utilized in current treatment planning systems is appropriate for calculating tumor and normal tissue doses . HoweverThe RBE value in current carbon-ion treatment planning is based on the data of colony formation assay using HSG cells under aerobic conditions using the Heavy Ion Medical Accelerator in Chiba (HIMAC); this is because the RBE value of HSG cells was found to be close to that of other cancer cell lines ,35. EspeCurrently, a prescribed dose, determined on the basis of clinical experience, is used to compensate for uncertainties in the absolute RBE value ; this maThis study has some limitations. The RBE value, especially in tumor tissues, depends on several factors, including the speed of tumor growth and microenvironmental conditions, such as hypoxia. Although our cell survival experiments may have represented the intrinsic radiosensitivity of proliferating cells, the irradiated volume and the interactions in its substructure  can immensely impact normal tissue. To resolve these issues, it will be necessary to conduct three-dimensional cell culture or in vivo experiments.We obtained HDF   from CELL APPLICATIONS, INC. . We purchased MG63 (human osteosarcoma), HT1080 (human fibrosarcoma), SW872 (human liposarcoma), SW1353 (human chondrosarcoma), hTERT-HME1  , and NuLi-1   from ATCC .2. All sarcoma cell lines, hTERT-HME1, and NuLi-1 cells were cultured in Dulbecco’s Modified Eagle Medium (D-MEM)  containing 10% fetal bovine serum (Thermo Fisher Scientific) and 1% penicillin–streptomycin–glutamine mixed solution  under the same conditions . The hTERT-HME1 and NuLi-1 cells were immortalized by introducing the catalytic subunit of human telomerase (hTERT) or E6/E7 and hTERT, respectively.HDF cells were maintained in basal medium with a growth supplement  at 37 °C in a humidified atmosphere containing 5% CO137Cs gamma ray (0.662 MeV) irradiations were conducted using Gammacell® 40 Exactor , a gamma ray irradiator, at Osaka University Graduate School of Medicine. Proton beam irradiation was conducted at the Proton Beam Therapy Center of Hokkaido University. The cells were irradiated at the center of a 6 cm SOBP. For carbon-ion irradiation, the cells were irradiated at an LET of 54.4 keV/µm using a 250 MeV/nucleon carbon-ion beam at HIBMC. Dosimetry with a parallel plate ion chamber was conducted on the proton and carbon-ion beams before every irradiation. Gamma ray irradiation dosimetry was conducted using a Fricke dosimeter following the manufacturer’s instructions and confirmed using Gafchromic EBT3 films  by our group’s physicist.2 plastic slide flasks  6 h before irradiation as described in a previous report [2)”, where D describes radiation dose (Gy). The parameters α and β are constants describing the liner component and quadratic part of the curve, respectively. A minimum of three independent experiments for each irradiation were performed.s report . Immedias report . The LQM10 of each cell line among gamma ray, proton beam, and carbon-ion irradiations and differences in RBE values among cell lines after radiation were analyzed using Tukey–Kramer honestly significant difference test with a significance level of <0.05, using the scikit-posthocs module run on Python (version 3.6.10) [A two-tailed heteroscedastic t-test was performed to compare the RBE value of each cell line between proton beam and carbon-ion irradiations and to compare the SF between gamma ray and proton beams with a significance level of <0.05. Differences in α, β, and D 3.6.10) .Our results showed a large RBE heterogeneity in various sarcoma and normal cell lines receiving carbon-ion and proton beams based on a consistent experimental protocol at a single laboratory. With proton irradiation, the mean RBE value of the four sarcoma and three normal cell lines was close to 1.1. This indicates that the RBE value of 1.1 utilized in current treatment planning systems is appropriate for calculating the doses for tumors and normal tissues. Proton therapy may be more beneficial for some types of tumors; however, caution must be exercised when considering normal tissues, which have higher RBE dependency. With carbon-ion irradiation, RBE values were heterogeneous in both sarcoma and normal cell lines. We observed wide-ranging RBE in carbon-ion irradiation, suggesting that it is important to evaluate tumor- and normal-tissue-specific doses. In this context, for some types of tumors, large heterogeneity in RBE values should bring into consideration dose reduction or an increased dose per fraction. However, one should be cautious with regard to the dose to normal tissues owing to their high RBE dependency. For tissue-specific dose evaluations in a treatment planning system, it is necessary to estimate the RBE values of relevant cell lines by acquiring their LET-dependent survival fractions.


Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching.CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion.Selected CancerOmicsNet predictions obtained for “unseen” data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03.CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.The online version contains supplementary material available at 10.1186/s12885-022-10293-0. Cancer initiation and progression involve a sequence of gene-environment interaction events changing the gene expression and ultimately leading to the disruption of homeostasis . The phoNonetheless, the clinical efficacy of kinase-specific inhibitors is confounded by numerous factors. The success of a cancer treatment strongly depends on the underlying genetic features of the tumor, the microenvironment, the possibility of the development of drug resistance, and pharmacogenomics . NumerouThe Connectivity Map (CMap)  is frequ50) of dexamethasone, thus it induced the glucocorticoid sensitivity as expected [This technique has been shown to be effective in finding new treatments for Alzheimer’s disease (AD) and glucocorticoid-resistant acute lymphoblastic leukemia (ALL) . Two genexpected . Furtherexpected .A key limitation of current drug connectivity-mapping approaches is the subjective selection of disease signatures. To address this issue, Dr. Insight implements a new statistical model utilizing the genome-wide screening of concordantly expressed genes (CEGs) . Rather Co-expressed GSEA can also be combined with the pathway analysis in order to infer the drug mode of action in a disease context. An example is Cogena, a pathway-guided disease and drug repositioning approach that identifies drugs acting mechanistically within the framework of coordinated changes in disease transcriptomes . Cogena The compromised drug efficacy leading to the lack of tumor response to pharmacotherapy presents notable challenges in clinical oncology. Addressing this problem requires an ability to integrate vast datasets, learn intricate relations among numerous factors, and utilize existing knowledge, surpassing the analysis of gene expression alone. Deep learning is the latest technology in the field of artificial intelligence capable of performing such complex tasks by employing sophisticated nonlinear transformations to extract patterns from high-dimensional data. Not surprisingly, deep learning has already begun to significantly impact biological and biomedical research . For insMany recent strategies for precision oncology employ deep neural network (DNN)-based frameworks . For exaTo take advantage of cancer-related data having a non-Euclidean structure, we recently developed CancerOmicsNet, a graph-based deep learning system with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors , 61. In 50 and GRmax quantifying the proliferation by the value of growth rate inhibition (GR) measured in time course and endpoint assays. GR50 is the concentration of a drug at which GR is 0.5, whereas GRmax is the maximum measured GR value. Based on the sign of GRmax, 2124 effective (negative GRmax) and 1425 ineffective (positive GRmax) therapies are identified. Differential gene expression profiles for the disease state were obtained from the CCLE [The original dataset of 3549 cell line-drug combinations involving 359 cell lines and 29 drugs was previously compiled from six LINCS-Dose-Response datasets, Broad-HMS LINCS Joint Project, LINCS MCF10A Common Project, HMS LINCS Seeding Density Project, MEP-HMS LINCS Joint Project, Genentech Cell Line Screening Initiative, and Cancer Therapeutics Response Portal . These dthe CCLE . This dathe CCLE .m, 120 nm, 370 nm, 1.11 μm, 3.33 μm, and 10 μm. Mapping these data to LINCS-3549 resulted in 87 combinations of 11 cell lines and 24 drugs, referred to as the LINCS-87 dataset, which was employed to conduct the comparative benchmarks of CancerOmicsNet and the GS-based method. The LINCS-87 dataset comprises 40 effective (negative GRmax) and 47 ineffective (positive GRmax) therapies.Next, we obtained drug-perturbed gene expression profiles from the CMap  for 107,From the Team-SKI collection of 49,348 small molecules tested against 411 protein kinases, we selected 2497 molecules absent from the LINCS growth rate inhibition dataset, thus not included in the LINCS-3549 dataset used to train CancerOmicsNet. Applying Lipinski’s rule of five  identifi2) [A is the gene signature of cancer against healthy cells and B is the gene signature of the same cell type before and after drug treatment. Each gene signature comprises 11,113 genes that are present in differential gene expression profiles for the disease state from the CCLE and drug-perturbed gene expression profiles from the CMap. Disease gene expression values are converted to level-5 moderated Z-scores [B are the same) to 2 (signatures A and B are exactly opposite). For each treatment, six COS values are calculated for all drug concentrations and the one having the longest distance is selected as a metric to predict the anticancer drug response.Comparing gene signatures for drug-treated and disease cell lines is a traditional method to find potentially effective therapeutics. The GS-based method employed in this study is similar to the LINCS L1000 characteristic direction signature search engine (L1000CDS2) . This teZ-scores , 67. COSCancerOmicsNet is an AI-based system to predict a response of tumor cells to pharmacotherapy . It empl2 in a water jacketed tissue culture incubator. Pan 04.03 cells , derived from a primary tumor removed from the head-of-the-pancreas of a 70-year-old white male with pancreatic adenocarcinoma, were maintained in RPMI-1640 , human recombinant insulin (20 units/mL), and 15% fetal bovine serum. HCC70 cells , isolated from a primary ductal carcinoma from a 49-year-old black female, were maintained in RPMI-1640  and supplemented with 10% fetal bovine serum. DU 145 cells , derived from a 69-year-old white male with prostate cancer, were maintained in Eagle’s minimum essential medium (EMEM)  and supplemented with 10% fetal bovine serum.All cell lines were maintained at 37 °C and 5% CO5 cells were seeded into one well of a 6 well plate . After an overnight incubation, 200 μL of lentivirus particles consisting of ~ 3 × 105 transducing units were applied to the cells and returned to the incubator overnight. The following day, the media was replaced and cells were allowed to expand for 3 days. Next, cells were selected for transduction with the addition of 1 μg/mL puromycin. Nuclear fluorescence was visualized on an inverted fluorescent microscope maintained and under continual selection for further analysis.Incucyte nuclight red lentivirus reagent  was purchased and used to transduce Pan 04.03, DU 145, and HCC70 cell lines at a multiplicity of infection (MOI) or 1. Briefly, 3 × 10m in DMSO.JNJ-7706621 , PP1 , AZD6482 , XMD8–93 , GW2580 , and PI-103  were purchased from suppliers and resuspended to a stock concentration of 10 mm, 3.162 nm, 10 nm, 31.62 nm, 100 nm, 316.2 nm, 1 μm, 3.162 μm, and 10 μm. Cells were then imaged for 72 hours with the IncuCyte S3 system at 400 ms acquisition time in the red channel and the 10× objective. Adherent cell-by-cell analysis was conducted to quantify the number of red nuclei in each well over the 72-hour observation period. The entire experiment was repeated after a week; we refer to the first series of measurements as experiment A and the second series as experiment B.Cells were seeded at a density of 5000 cells/well in 384 well plates  in duplicate wells containing 20 μL media and incubated overnight. The following day, 20 μL of a 2× dilution series was applied to the cells to produce the final concentrations of 1 nt during the 72-hour observation period, a normalized cell count, Nnorm, is calculated as:N(t) is the number of red nuclei and N(t0) is the initial number of red nuclei recorded at the outset of measurements. This way, the normalized initial number of cells across all experiments is always 100. In addition to the normalized cell count, a relative cell count for the drug-treated group with respect to the control, Nrel, is calculated as:Nnorm is the normalized number of red nuclei for the group treated with a drug d at concentration c, and Nnorm is the normalized number of red nuclei for the same cell line in DMSO measured at the same time t.For any sample, including drug-treated and control groups, at any time GR value for a drug d at concentration c and time t is calculated as:N is the cell count for the group treated with drug d at concentration c and time t ± Δt while N is the cell count for the control group at time t ± Δt. Δt is chosen as 6 hours according to the original work [max (max stands for the maximum efficiency).Following the original paper describing the growth rate formalism , a GR vanal work . For eacCancerOmicsNet utilizes an integrated graph representation of multiple heterogeneous data, including biological networks, pharmacogenomics, kinase inhibitor profiling, and gene-disease associations . The floThe GNN model contains a series of customized graph convolution blocks Fig. F to gene2 search engine prioritizing small molecule signatures for their predicted ability to either reverse or mimic gene expression in a disease state [We first compare the performance of CancerOmicsNet to that of a traditional approach employing the gene signature analysis. As a GS-based method, we implemented an algorithm similar to the L1000CDSse state . This meTable 50) of 1.6 μm [max of − 0.07, which is indicative of a cytotoxic response. The COS distance is 1.08, therefore the GS-based method correctly predicted the sensitivity of MCF7 to dasatinib. The second example is a selective JAK1 and JAK2 inhibitor ruxolitinib [max value of − 0.25. Ruxolitinib is in phase 2 of a clinical trial against squamous cell skin cancer [In order to better illustrate the concept of the GS-based prediction of drug efficacy, we discuss two representative examples selected from the benchmarking dataset. The first example is an ATP-competitive protein tyrosine kinase inhibitor dasatinib  impedingf 1.6 μm . Dasatinolitinib  and the n cancer . The GS-Z-score (modZ) values computed for gene expression in cancer cell lines and those obtained for the drug treatment. Since the GS-based approach predicts effective treatments when drugs can potentially reverse the gene expression state of cancer cells, one would expect to find most genes in quadrants II and IV in Fig. Figure In contrast to the GS-based approach, AI-based CancerOmicsNet correctly predicted both treatments, MCF7 with dasatinib and A375 with ruxolitinib, to be effective with probabilities of 0.99 and 0.65, respectively. AI models are specifically designed to learn complex patters from the input data in order to make accurate predictions. To better understand the performance of machine learning in detecting effective treatments, high-dimensional graph embeddings from the output layer of CancerOmicsNet can be visualized in a two-dimensional space with t-distributed stochastic neighbor embedding (t-SNE), a nonlinear dimensionality reduction technique . Figure 50 values of 2 ± 0.7 nm (VEGFR1), 3 ± 0.5 nm (VEGFR2), and 6 ± 4 nm (VEGFR3) [50 of 84 ± 33 nm, mast/stem cell growth factor receptor Kit (c-KIT) at an IC50 of 8 ± 2 nm, and tyrosine-protein kinase receptor Ret (c-RET) at an IC50 of 59 ± 4 nm [We discuss the performance of CancerOmicsNet in several cases of “unseen” data, viz. treatments absent from the LINCS-3549 growth rate inhibition dataset that was originally used to train the machine learning model. Each novel prediction is supported by the evidence found in the biomedical literature. The structures of drugs selected for the literature-based validation are presented in Fig. (VEGFR3) . Althoug9 ± 4 nm . This dr9 ± 4 nm . Encoura9 ± 4 nm , Calu-3 Motesanib also has antitumor activity against breast cancer . Its pri50 of 73 nm, PDGFRβ with an IC50 of 215 nm, VEGFR1 with an IC50 of 7 nm, VEGFR2 with an IC50 of 15 nm, and VEGFR3 with an IC50 of 2 nm [m. According to CancerOmicsNet, the probability of inhibition of the 786-O cell line growth by pazopanib is 0.76. Pazopanib was also tested alone and in combination with topotecan against anaplastic thyroid cancer (cell line 8305C) [50 of 25 ± 3.2 μm. According to the Cellosaurus, 8305C  and 8505C  cell lines are closely related anaplastic thyroid cancers and CancerOmicsNet estimated that pazopanib inhibits the growth of 8505C with a high probability of 0.93.Pazopanib GW786034, Fig. , Fig. 4B of 2 nm . It exhi of 2 nm . For inse 8305C) , one of 50 of 2 to 3 nm [50 of 1 nm [50 of 100 nm [p-value of < 0.01 [Lestaurtinib  with an IC50 of 254 nm, and HeLa  with an IC50 of 284 nm [50 values of 286 ± 72 nm (HeLa), 189 ± 42 nm (HCT116), 410 ± 75 nm , 112 ± 12 nm , 416 ± 54 nm (A-375), 514 ± 63 nm , 263 ± 113 nm , and 413 ± 4 nm  [m, 500 nm, 1 μm, 2.5 μm, and 5 μm concentrations [50 of 0.5–1 μm, whereas 2.5 μm completely prevented the SCF-induced proliferation of M-07e cells.Selected predictions by CancerOmicsNet for the “unseen” data were subjected to experimental validation by live-cell time course inhibition assay. Eight drugs, whose structures are presented in Fig. f 284 nm . Anothersarcoma) . PP1 is sarcoma)  that hastrations . PP1 inhm concentration, compared to the control group consisting of vehicle (DMSO)-treated cells , brain (SK-N-AS) and non-small cell lung (NCI-H1299 and NCI-H522) cancer cell lines [m, the relative cell counts are 0.76 and 0.83, respectively. Further, GRmax values calculated for AZD6482 and XMD8–92 reported in Table The next two compounds are AZD6482  was highly resistant to GW2580 [m, U-87 MG at 600 nm, and U-138 MG at 1.0 μm [m. Further, GRmax values reported in Table The last two drugs are GW2580 Fig. H, a seleof 30 nm , and PI-nm/15 nm . Althougo GW2580 . Tested t 1.0 μm . CancerOmax values collected from these two experiments. In order to help evaluate the consistency between different experiments, each plot is divided into quadrants, labeled I-IV in Fig. max indices calculated from the data collected in each series of experiments. Encouragingly, most data points are in quadrant III (colored green in Fig. max values in both experiments, meaning that these compounds systematically inhibited the proliferation of cancer cells. A few points in quadrant I in Fig. max values observed in both experiments against the HCC70 cell line. Lastly, data points in quadrants II and IV in Fig. max values collected against the HCC70 cell line in experiments A and B. Overall, both validation experiments yielded consistent results positively validating CancerOmicsNet predictions.The experimental validation of CancerOmicsNet predictions was conducted in two series of growth rate inhibition assays, referred to as experiments A and B, carried out at a one-week interval. Figure max value is negative. Figure m. Encouragingly, 71.3% cases of CancerOmicsNet predictions tested experimentally show negative GRmax values with a mean ± standard deviation effective concentration of 1.8 ± 3.3 μm. Therefore, most drugs predicted by CancerOmicsNet as effective against target cancer cell lines not only exhibit the desired anticancer activity, but also tend to be effective at lower concentrations compared to those publicly available in a large database of anticancer agents tested against various tumors.Finally, we conducted a statistical analysis of effective drug concentrations measured in validation experiments in comparison to those obtained from the LINCS-3549 growth rate inhibition dataset. Here, a drug concentration is considered effective when the corresponding GRCancerOmicsNet is a recently developed system utilizing the AI technology to guide precision oncology. Benchmarked against a large dataset of anticancer therapies comprising multitargeted kinase inhibitors and a wide variety of tumor types, it was previously demonstrated to outperform many other approaches, including deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching . In thisFor instance, the expression of landmark genes carefully selected by the LINCS consortium may not necessarily reflect the mechanism of action of therapeutic candidates . We alsoIn general, methods employing the AI technology predict cellular responses to pharmacotherapy with a better accuracy. In our benchmarking calculations, using CancerOmicsNet yields more robust discrimination between effective and ineffective anticancer treatments compared to the GS-based approach. This improved performance of machine learning can be attributed to the utilization of multiple biomedical data, including PPI networks, gene expression patterns, gene-disease associations, and kinase inhibitor profiling. In addition, employing deep learning enables CancerOmicsNet to automatically extract meaningful features in order to effectively learn complex patterns present in these data. Interestingly, since deep learning models are not explicitly instructed which patterns to look for in the data, they may pick up on associations among multiple variables that are not easily perceptible. We expect that integrating more biological data will further increase the accuracy of anticancer treatment prediction.max values in most concentrations. In particular, pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 were the most potent against the pancreatic cancer cell line Panc 04.03. Validation experiments repeated after 1 week yielded consistent results. It is also noteworthy that, on average, anticancer drugs predicted by CancerOmicsNet were found effective in lower concentrations than active molecules in the LINCS database. We note that antiproliferative properties can be predicted for any compound that has been profiled against a panel of human kinases with respect to its inhibitory potency and selectivity.The promise of CancerOmicsNet in precision oncology is comprehensively investigated by evaluating its ability to generalize to “unseen” data comprising 288 kinase inhibitors with no growth rate inhibition values in LINCS. None of these molecules has been used to train the deep learning model, hence the term “unseen” data. The effect of these drugs on the growth of several cell lines predicted by CancerOmicsNet was first validated against the biomedical literature. Encouragingly, those inhibitors assigned high probabilities to reduce the proliferation of certain cell lines have been reported to exhibit the predicted anticancer activities in independent studies. Finally, six compounds were validated experimentally in live-cell time course assays against breast, pancreatic, and prostate cancer cell lines. The tested molecules exhibited dose-dependent antiproliferative activities with negative GR50, GRmax, and IC50. These measures often depend on the experimental setup with respect to drug concentrations and the duration of measurements [Similar to other methods to predict therapeutic effects, CancerOmicsNet has several limitations. One obvious complication is the selection of drug efficacy measure, such as GRurements . For insurements .Future directions in the development of CancerOmicsNet include the integration of other large-scale cancer data, such as the single nucleotide polymorphism and the mutation information, which would facilitate a more personalized selection of anticancer therapies based on the tumor genetic makeup. Moreover, utilizing data related to the molecular mechanisms of metastatic cells governing their mobility and plasticity would allow for the prediction of other therapeutic effects, such as the inhibition of cancer cell viability, migration, and invasion ability. We also plan to conduct a pharmacophore analysis of molecules exhibiting antiproliferative activities and expand the repertoire of therapeutics outside the range of kinase inhibitors. Including other drug classes will make it possible to extend CancerOmicsNet to predict effective combinations of molecules having different mechanisms of action. Synergistic effects are generally highly beneficial allowing the use of lower doses of the combination constituents often leading to significantly reduced adverse reactions . OverallAdditional file 1: Supplementary Video 1. Live-cell time course recording of Panc 04.03 cell line treated with JNJ-7706621 at 10 μm.Additional file 2: Supplementary Video 2. Live-cell time course recording of Panc 04.03 cell line treated with PP1 at 10 μm.Additional file 3: Supplementary Video 3. Live-cell time course recording of Panc 04.03 cell line treated with DMSO as the control.Additional file 4: Supplementary Video 4. Live-cell time course recording of DU 145 cell line treated with AZD6482 at 10 μm.Additional file 5: Supplementary Video 5. Live-cell time course recording of DU 145 cell line treated with XMD8–92 at 10 μm.Additional file 6: Supplementary Video 6. Live-cell time course recording of DU 145 cell line treated with DMSO as the control.Additional file 7: Supplementary Video 7. Live-cell time course recording of HCC70 cell line treated with GW2580 at 10 μm.Additional file 8: Supplementary Video 8. Live-cell time course recording of HCC70 cell line treated with PI-103 at 10 μm.Additional file 9: Supplementary Video 9. Live-cell time course recording of HCC70 cell line treated with DMSO as the control.


TERT promoter methylation is enriched in cancers lacking TERT genetic alterations (wild-type cancers), but its functional impact on TERT transcription remains elusive. We developed a long-read bisulfite-sequencing platform to characterize the TERT promoter methylation profile at a single-molecule level. In wild-type cancer cell lines, both epialleles were hypermethylated symmetrically on the TERT distal promoter. In the core and proximal promoter, by contrast, the transcribed epialleles were significantly more hypomethylated than the silent epialleles. Decitabine-therapy reduced the core and proximal  promoter methylation and reactivated the silent allele. We showed that TERT allele-specific expression is amenable to in vitro epigenetic manipulation in wild-type cancers.TERT promoter methylation, located several hundred base pairs upstream of the transcriptional start site, is cancer specific and correlates with increased TERT mRNA expression and poorer patient outcome. Promoter methylation, however, is not mutually exclusive to TERT activating genetic alterations, as predicted for functionally redundant mechanisms. To annotate the altered patterns of TERT promoter methylation and their relationship with gene expression, we applied a Pacific Biosciences-based, long-read, bisulfite-sequencing technology and compared the differences in the methylation marks between wild-type and mutant cancers in an allele-specific manner. Results: We cataloged TERT genetic alterations , allele-specific promoter methylation patterns, and allele-specific expression levels in a cohort of 54 cancer cell lines. In heterozygous mutant cell lines, the mutant alleles were significantly less methylated than their silent, mutation-free alleles (p < 0.05). In wild-type cell lines, by contrast, both epialleles were equally methylated to high levels at the TERT distal promoter, but differentially methylated in the proximal regions. ChIP analysis showed that epialleles with the hypomethylated proximal and core promoter were enriched in the active histone mark H3K4me2/3, whereas epialleles that were methylated in those regions were enriched in the repressive histone mark H3K27me3. Decitabine therapy induced biallelic expression in the wild-type cancer cells, whereas the mutant cell lines were unaffected. Conclusions: Long-read bisulfite sequencing analysis revealed differences in the methylation profiles and responses to demethylating agents between TERT wild-type and genetically altered cancer cell lines. The causal relation between TERT promoter methylation and gene expression remains to be established.Background:  Telomerase reverse transcriptase (TERT) encodes the catalytic subunit of telomerase, the enzyme required by most proliferative cells to counteract telomere shortening during cell divisions [TERT promoter is commonly genetically altered and/or methylated in cancer [TERT genetic alterations include heterozygous cis-activating TERT promoter point mutations—the most frequent non-coding mutation in cancer—[TERT structural variations  [TERT genetic alteration only express the genetically altered allele [ivisions . Aggressn cancer . TERT gen cancer—,4,5,6 ancations) ,7,8. Thecations) ,6,7,8,9,d allele ,11,12.TERT genetic alterations  [TERT promoter DNA hypermethylation [TERT promoter hypermethylation occurs at CpG dinucleotides located several hundred base pairs (bp) upstream of the transcriptional start site (TSS) [TERT promoter hypermethylation is cancer specific and significantly correlates with increased TERT expression and poorer patient outcome [TERT promoter hypermethylation is enriched in wild-type cancers, it is not mutually exclusive to TERT activating genetic alterations, as predicted for functionally redundant mechanisms [Many other cancers express TERT mRNA by less clearly defined means that do not involve cancers) ,15,16,17hylation ,19. TERTte (TSS) . This pa outcome ,20,21. Achanisms . In factchanisms ,24.TERT express either one or both alleles, on a case-by-case basis [Cancer cell lines with the wild-type se basis . Howeverse basis . AdditioTERT genetic alterations , allele-specific promoter methylation patterns, and allele-specific expression levels in 70 metastatic melanomas and 54 cancer cell lines. We used a long-read sequencing technique to annotate methylation patterns in consecutive CpG sites at the single molecule level. In a subset of the cell lines, we also examined how decitabine treatment, aimed at removing DNA methylation marks, affected TERT promoter methylation and gene expression per allele. Our results revealed previously unappreciated differences in the epigenetic modification and gene expression between wild-type and genetically altered cancer cell lines.In this study, we cataloged n = 50, including 13 nodular; 15 superficial spreading; 1 lentigo malignant melanoma; 21 not otherwise specified), acral (n = 11), mucosal (n = 6), and unspecified (n = 3).The samples consisted of formalin-fixed paraffin-embedded (FFPE) metastatic melanomas  from 70 The 54 cancer cell lines used in this study consisted of 11 wild-type , 2 with structural rearrangement , and 41 mutant  cell lines . WM-39, TERT promoter Sanger sequencing was performed as described previously [Genomic DNA (gDNA) was isolated from FFPE tissue samples or cell lines with Maxwell 16 LEV DNA kit  or DNeasy Blood and Tissue Kit . eviously .TERT rearrangements in clinical samples and in some cell lines  CH17-75N21- and CH17-410B01-templated probes for TERT. The Cy3-labeled TelG probe  was used for telomeric DNA FISH as described previously [ll lines  were deteviously .TERT alterations and expression  and cDNA was prepared with Vilo SuperScript . TERT mRNA expression levels were measured by qRT-PCR, as previously described . Spitz tpression .TERT promoter methylation status in FFPE clinical specimens was determined by gDNA bisulfite-treatment before PCR amplification, followed by high throughput amplicon sequencing. TERT promoter methylation status from 425 to 610 bp upstream of the TSS  was analyzed from FFPE gDNA as described previously [eviously .TERT promoter  was amplified from ~500 ng of bisulfite-treated cell line gDNA  using barcoded forward (5′-GGGTTAGGGTTTTTTA-3′) and reverse primers (5′-CCRCCTAAAAACCTACAAAAAAAAATAAC-3′). A total of 600 µL of pooled sample PCR amplicons were cleaned with 700 µL of PB beads  and eluted in 30 µL of 10 mM Tris-HCl, pH 8.5, as per the manufacturer’s instructions. Data was collected on a Pacbio Sequel instrument (University of Arizona Genomics Institute). Smrttools  were used for preprocessing the Pacbio reads. Specifically, the reads were demultiplexed by lima and circular consensus (CCS) reads were generated using the ccs tool with parameters of “—maxLength 2000—minPasses 3—minPredictedAccuracy 0.95”. The CCS reads were aligned against the human reference genome (hg19) to call methylation status using Bismark [The  Bismark .TERT promoter  were analyzed by in-house workflows. One urothelial cancer (TCCSUP) and 3 melanoma  mutant cell lines that were completely unmethylated throughout the amplicon were excluded from this analysis because the mechanism of promoter methylation is likely not involved in transcription in these cases  and 2 mutant TERT cancer cell lines (UMUC3 and A375) according to described protocols [TERT promoter was determined by Sanger sequencing and the TERT promoter mutation digital droplet PCR  assays using recommended protocols .H3K4me2/3 and H3K27me3 ChIP were performed on 3 wild-type rotocols ,24 with TERT promoter methylation patterns associated with active or silent histone marks in these cell lines, ChIP DNA was bisulfite converted using an EZ DNA Methylation-Gold Kit (Zymo Research) and PCR amplified for 45 cycles at 95 °C for 15 s, 63 °C for 20 s, and 68 °C for 40 s using primers spanning the region from 1,295,224 to 1,295,546 of the TERT promoter with 5′-GGAAAGGAAGGGGAGGGGTTGGGAG-3′ and 5′-CCCCTCCCTCRAATTACCCCACAACCTAAAC-3′. Amplicons were purified and used for high-throughput sequencing (PACBIO) or Sanger sequencing.To determine TERT allele-specific expression in cell lines was measured in rs2736098 or rs2853690  heterozygotes using ddPCR TaqMan SNP assays compatible with cDNA of spliced mRNA. Heterozygous sample gDNA allele ratios were quantified by ddPCR [allele1 or 2 gDNA ratio = /allele1 or 2 cDNA ratio = /normalized allele1 or 2 cDNA ratio = /allele-specific expression1 or 2 = /by ddPCR . Allele-Samples with mono-allelic expression had ≥9:1 allele-specific expression ratio ,30,31 asn = 4), mutant (n = 5), and TERT rearranged (n = 2) cell lines with methylated promoters and heterozygous for an assayable exonic SNP. Different drug concentrations and treatment lengths were tested to find the condition of half maximal inhibitory cell line growth. Cell line allele-specific expression measurements were then made using drug concentrations 10-fold lower and 10-fold higher than this condition. LN18, WM-51, and VMCUB3 cell lines were treated with either 0.3 or 30 µM decitabine, the NCI-H2122 cell line was treated with either 0.1 or 3 µM decitabine, the NCI-H358 cell line was treated with either 0.01 or 30 µM decitabine, UMUC3 and WM-46 cell lines were treated with either 0.03 or 3 µM decitabine, WM-4002 and 253J cell lines were treated with either 0.01 or 1 µM decitabine, and the WM-88 cell line was treated with 0.3 µM decitabine for 3–7 days  on allele-specific expression and allele-specific methylation were measured in wild-type  and the cancer-associated  promoter methylation are mutually exclusive in clinical cancer specimens. To answer this question, we selected formalin-fixed paraffin-embedded (FFPE) samples of malignant melanoma, a cancer type enriched in both promoter mutations and methylation [TERT distal promoter methylation levels in each specimen by high-throughput targeted bisulfite amplicon sequencing. Finally, we compared the relative methylation levels between wild-type and genetically altered categories.We first sought to determine if hylation ,22. We sTERT alterations: promoter point mutation, distal promoter methylation, or rearrangement , 1,295,250 C>T (n = 25), 1,295,242-43 CC>TT (n = 5), and 1,295,161 A>C (n = 1). A total of 6 of 70 melanomas (9%) had TERT rearrangement as assessed by FISH , harbored at least one of the three cancer-associated angement . One melntenance C. In tot by FISH , and 14  by FISH A. TERT wTERT distal promoters, we next asked whether mutation and methylation tended to occur on the same promoter molecule  or on separate molecules . Because FFPE samples have lower tumor purity, due to admixture of normal cells, and yield more fragmented DNA than cancer cell lines, we focused on cancer cell lines to answer this question. We performed single-molecule high-throughput bisulfite amplicon sequencing in 36 heterozygous and 5 homozygous TERT promoter mutant cell lines, derived from melanoma and other cancers . We could differentiate active and silent allele-derived methylation reads  for each cell line’s TERT promoter because we obtained a single long contiguous bisulfite converted amplicon DNA (p < 0.0001). In agreement with the previous reports [p < 0.05, one side Wilcox rank sum test). Our Chromatin Immunoprecipitation (ChIP) data were also consistent with the previous observations [Given that some metastatic melanomas harbored both mutated and methylated icon DNA . This amicon DNA A. The pr reports ,24,33,34TERT promoter methylation and mutation tend to mark separate homologous alleles in heterozygous TERT mutant cell lines [TERT distal promoter; 50% (n = 17) of the mutant cell lines had no significant difference in the methylation level at the distal promoter between the active  and silent  alleles . These results indicate that TERT distal promoter methylation may occur on the transcribed mutant alleles, and that only the core and proximal methylation opposes transcription  and identified two distinct methylation patterns  for eight wild-type cell lines , whereas those with increased core and proximal methylation were enriched in the H3K27me3 histone mark   . Thus, is report . Both prell line B. Like w profile .TERT distal promoter would perturb TERT expression, we treated four mono-allelically expressing wild-type cell lines and seven genetically altered cancer cell lines for comparison (five with point mutations and two with rearrangement). We measured transcription and methylation changes of each allele upon treatment. Decitabine treatment reactivated silent allele transcription in all wild-type cell lines  were methylated to similarly high levels in the TERT distal promoter. Finally, decitabine treatment reactivated silent alleles in wild-type cell lines but not genetically altered cell lines.The long-read methylation sequencing of cancer cell lines revealed previously unknown differences in TERT promoter methylation and expression studies. First, because TERT is often regulated at the allele level [TERT alleles are transcribed [TERT promoter point mutations and TERT structural rearrangements to identify TERT genetically altered samples; sometimes promoter mutations are taken into account while rearrangements are overlooked. Finally, we measured decitabine’s effect on TERT allele-specific expression levels instead of total TERT mRNA expression levels [Our cancer cell line analysis was different in several ways from many previous le level ,29, and nscribed ,25, we lnscribed ,37. Thisnscribed ,24,33. Tn levels ,50,51. TTERT allele-specific expression and promoter methylation in cancer has focused more on TERT mutant samples [TERT distal promoter. These findings suggested that methylation of the TERT distal promoter may be functionally compatible, or even functionally important, especially for TERT wild-type cancers. We therefore investigated whether methylation disruption perturbed TERT expression in wild-type cell lines. We found that decitabine treatment could reactivate the silent alleles in wild-type cell lines but not in genetically altered ones. In tandem with the transcriptional reactivation of the silent alleles, the methylation levels of the TERT core and proximal promoter dropped, whereas those of the distal promoter remained unchanged.Much of the previous interest in  samples ,24. Alle samples . Our sin samples . This su samples . We hencTERT promoters of all cancer cell lines, regardless of the mutational status, were hypomethylated in the core and proximal promoter. This hypomethylated area is probably synonymous with the TERT nucleosome-free region around the upstream of the TSS [TERT core and proximal promoter regions. Methylation of the nucleosome-free region is incompatible with transcription [Like promoters of all actively transcribed alleles, the transcribed  the TSS ,53,54,55 the TSS , but it  the TSS ,57,58. Dcription ,53,59,60cription .TERT distal promoter methylation is a bona fide activating mechanism equivalent to the mechanistically established genetic alterations, because decitabine treatment did not significantly change the distal promoter methylation in these experiments. It is also conceivable that decitabine’s impact on global demethylation could indirectly affect the expression of TERT. Further studies are needed to examine the transcriptional effects of methylation in wild-type cancers by specifically targeting different regions of the TERT promoter.Despite these new observations, it is still unclear whether wild-type TERT promoter alterations are ubiquitous in metastatic melanoma. Almost every metastatic melanoma in our cohort harbored one of three cancer-associated TERT aberrations . Although TERT promoter methylation was more prevalent in melanomas that contained wild-type TERT promoter, our data showed that methylation and mutation or rearrangement were not mutually exclusive at the sample level. However, targeted long-read bisulfite sequencing was not feasible with these FFPE clinical specimens to gather more comprehensive data, because their DNA is more fragmented than intact cell line DNA. (2) A potential complication of the current long-read approach could be the formation of PCR chimeras during the amplification stage prior to sequencing, which could result in chimeric reads and false allele group prediction [Our methods have some inherent limitations: (1) Analysis of clinical samples was useful in demonstrating that ediction . (3) TheTERT promoter methylation profiles of TERT wild-type and mutant cancer cells had remained unclear, particularly how they vary in the core, proximal, and frequently methylated distal promoter regions, despite having been substantially studied for more than two decades (TERT distal promoter. The TERT proximal and core promoter methylation, however, was different in each case; these regions were less hypomethylated in the silent alleles than the transcribed counterparts. Additionally, the wild-type and genetically altered cell lines responded differently to the demethylating agent decitabine. The nascent allele was reactivated by demethylating agent treatment in wild-type cell lines but not in genetically altered cell lines. These findings suggest that TERT allele-specific expression in vitro may be epigenetically regulated in wild-type cancer cell lines, in contrast to the genetically altered cells. Finally, the long-read based methylation profiling methods used for comparing allelic methylation patterns and allele-specific expression may be applicable to other genes where promoter methylation correlates with expression. This approach allows future fine mapping of the exact allelic specific regulatory elements in the promoter regions by integrating other epigenetic modifications including histone marks, chromatin opening, as well as transcription factor binding assays.The heterogeneity of allele-specific  decades A. The si


The ethyl acetate fraction showed the strongest toxicity against A498 (IC50 = 0.162 µg mL−1) and 786-O (IC50 = 0.271 µg mL−1) cells. With similar toxicity against 786-O, P1 (IC50 = 3.8 µg mL−1) and P3 (IC50 = 3.1 µg mL−1) exhibited greater effect when combined (IC50 = 2.5 µg mL−1). Results support the potential of propolis and its constituents as promising coadjuvants in renal cell carcinoma treatment.Renal cell carcinoma is the most lethal cancer of the urological system due to late diagnosis and treatment resistance. Propolis, a beehive product, is a valuable natural source of compounds with bioactivities and may be a beneficial addition to current anticancer treatments. A Portuguese propolis sample, its fractions ( According to the GLOBOCAN 2020 database, despite not being one of the most prevalent cancers, renal cell carcinoma (RCC) is the most aggressive and lethal cancer of the urological system. By 2040, its incidence and mortality are expected to increase by approximately 54 and 68%, respectively . MetastaThe demand for natural products has been increasing due to their importance in human health and their potential as new drug leads . PropoliThe cytotoxic effect of propolis has been extensively studied against diverse types of cancer cell lines, including breast ,11, prosn-hexane fraction showed no effect on the viability of any of the cell lines at the tested concentrations. On the other hand, the G18.EE-water fraction exhibited the strongest effect on cell viability, with the lowest IC50 values against all cell lines (50 > 30 µg mL−1) and Caki-2 cells. The latter was the least sensitive cancer cell line. Considering all the results  but with higher selectivity indexes.The cytotoxic activity of G18.EE and its fractions was evaluated on three different RCC cell lines-786-O, Caki-2 and A498 . The G18ll lines . However results , the G1850 values obtained ranged from 4.4 to 41 µg mL−1, CaCo2 cells being the least susceptible to both extracts (IC50 > 50 µg mL−1) . From this subfraction (FG), eight FG-derived subfractions were obtained (FG1–FG8). FG8 was selected for additional fractionation using silica column with the same solvent system , resulting in five FG8-derived subfractions (FG8-1–FG8-5). Based on the TLC profiles, three FG-derived subfractions  displayed a single spot each on TLC plate, suggesting the presence of a single compound and thus being selected for the following experiments. Solvents used in the columns for extraction and the respective yields were: FG1 , FG5  and FG8-1   was subjected to VLC, eluting successively with  F1–F22;  were obt F1–F22; . The sub 4.3 mg] .P1, P2, and P3 were analyzed by UPLC-DAD-ESI/MSn following a previously described method [−1 and 2 µL were used for injection. The Ultimate 3000 apparatus was equipped with an Ultimate 3000 Diode Array Detector  and coupled to an ion trap mass spectrometer LTQ XL  equipped with an ESI source. Analysis was run on a Hypersil Gold  C18 column  at 30 °C. The mobile phase was composed of (A) water/formic acid  and (B) acetonitrile/formic acid . The solvent gradient started with 5–40% B (0–15 min), followed by 40–100% B (15–19 min), and returning to the initial conditions. The flow rate was 200 μL min–1 and UV–Vis spectral data for all peaks were accumulated in the range 200–500 nm. The nitrogen (>99% purity) gas pressure used for mass spectrometry analysis was 520 kPa (75 psi). The mass spectrometer was operated in negative-ion mode with ESI needle voltage set at 5.00 kV and an ESI capillary temperature of 275 °C. The full scan covered the mass range from m/z 100 to 2000. CID–MS/MS and MSn experiments were simultaneously acquired for precursor ions using helium as the collision gas with collision energy of 25–35 arbitrary units. Control and data acquisition were carried out with the Thermo Xcalibur Qual Browser data system . Compounds in the subfractions (P1–P3) were identified based on a literature comparison of the ESI-MS/MS data [Subfractions d method . The driP1 were recorded in a Bruker Advance II 400 spectrometer, at 400 MHz (1H) and 100 MHz (13C), at 20 °C (see δ) were recorded in parts per million (ppm) relative to the residual solvent signal (DMSO-d6).NMR spectra of 0 °C see . The cheP1). Yellow amorphous solid. 1H NMR : δ = 3.77 , 3.85 , 6.61 , 6.86 , 7.10 , 8.03 , 10.72 , 13.02 . 13C NMR : δ = 55.5, 59.9, 94.3, 103.0, 104.1, 114.6 (2C), 122.8, 128.3 (2C), 131.4, 152.4, 152.7, 157.4, 162.3, 163.3, 182.1 [5,7-dihydroxy-6-methoxy-2-(4-methoxyphenyl)-4H-chromen-4-one . The Caki-2 cell line was cultured in McCOY’s high glucose, L-glutamine, bacto-peptone, and phenol red without sodium pyruvate and HEPES . The A498 was cultured in MEM with 20 mM HEPES without NaHCO3 and L-glutamine . In addition, 1% of an antibiotic preparation  and 10% heating activated fetal bovine serum  were used as supplements for the culture media. Cultures were grown at 37 °C and 5% CO2 in a humidified incubator.Four human renal cell lines-HK2  and 786-O, Caki-2 and A498 (RCC cells) were obtained from the American Type Culture Collection (ATCC). The HK2 and 786-O cell lines were cultured in RPMI 2.0 g/ L NaHCO®) and then were detached from the flask with trypsin  at 37 °C. Medium was then added to the flask for trypsin inactivation. Cells were collected into a 15 mL Falcon tube and centrifuged for 5 min at 900 rpm, at 4 °C. Fresh medium was used to resuspend the cells, and 10 µL of the cell suspension were collected for subsequent addition of 20 µL of trypan blue  for cell counting (Neubauer chamber).To prepare cells for the cytotoxicity assay, flasks with sub-confluent cells were washed with phosphate-buffer 1× , each of the four fractions , each of the three subfractions  and two combinations of the subfractions with equal parts of each subfraction . Controls were performed with 0.25% of DMSO (vehicle) for IC50 determination (concentration that inhibits cell viability in 50%). Following this incubation period, the adherent cells were fixed by adding 50 µL of cold trichloroacetic acid 10%  and incubating for 60 min, at 4 °C. The plates were then washed with deionized water, dried overnight, and 50 µL of 0.1% SRB solution (Sigma–Aldrich) in 1% acetic acid (PanReac) were added to each plate well. After 30 min incubation at room temperature, unbound SRB was removed by washing with 1% acetic acid and air-dried overnight. Bound SRB was solubilized with 100 µL of 10 mM Tris base  and the absorbance was measured at 490 nm in a microplate reader . IC50 values were determined by transforming the absorbance values into a sigmoidal dose-response curve and extracting the concentration of the extracts/fractions needed to inhibit the viability of the kidney cells by 50%, using the GraphPad Prism Software Version 6.0. The selectivity index (SI) was calculated based on the IC50 values obtained for the samples against the cell lines, using the following formula: Propolis cytotoxicity was assessed by the sulforhodamine B (SRB) assay as previously described , which eCytotoxicity is higher for the cancer cell line than for the non-neoplastic HK2 cell line when SI values > 1.50 determination using the GraphPad Prism 5 software for logarithmic transformation after applying a sigmoidal dose-response non-linear regression. For multiple comparisons one-way ANOVA supplemented with post-hoc Tukey test was performed. Different letters mean statistical differences (p < 0.05) between mean values.Experiments were performed in triplicate and repeated at least three times for IC50 values, and importantly, very high selectivity indexes, when compared to the data available in the literature. This suggests that Portuguese propolis from Gerês, as well as its chemical constituents, could be used as alternatives or adjuvants to conventional anticancer treatments. They could be also used to help tackle drug-resistance and toxic effects by reducing the dose of the therapeutic drugs commonly used in RCC treatment.Cancer is one of the leading causes of death worldwide and is expected to cause more than 16 million deaths in 2040 [