Diffuse large B‐cell lymphoma (DLBCL) is the most common subtype of non‐Hodgkin lymphoma. Histone deacetylase inhibitors (HDACis) have been widely applied in multiple tumours, but the expected efficacy was not observed in DLBCL. Therefore, this study is aimed to explore superior HDACis and optimise a relative combinational therapeutic strategy.The antitumour effects of the drug were evaluated by Cell Counting Kit‐8 (CCK‐8) assay and apoptosis analysis. Single‐cell RNA sequencing (scRNA‐Seq) was used to analyse the intratumoural heterogeneity of DLBCL cells. Whole‐exome sequencing and RNA sequencing were performed to analyse the genetic and transcriptional features. Western blotting, qRT–PCR, protein array, immunohistochemistry, and chromatin immunoprecipitation assays were applied to explore the involved pathways. The antitumour effects of the compounds were assessed using subcutaneous xenograft tumour models.LAQ824 was screened and confirmed to kill DLBCL cells effectively. Using scRNA‐Seq, we characterised the heterogeneity of DLBCL cells under different drug pressures, and c‐Fos was identified as a critical factor in the survival of residual tumour cells. Moreover, we demonstrated that combinatorial treatment with LAQ824 and a c‐Fos inhibitor more potently inhibited tumour cells both in vitro and in vivo.Altogether, we found an HDACi, LAQ824, with high efficacy in DLBCL and provided a promising HDACi‐based combination therapy strategy. LAQ824, a superior histone deacetylase inhibitor (HDACi), effectively kills diffuse large B‐cell lymphoma (DLBCL) cells, especially when combined with a c‐Fos inhibitor.Single‐cell analysis in DLBCL cells treated with a high dose of LAQ824 identified c‐Fos as a critical factor in the survival of residual tumour cells.The acetylation of H3K9 and phosphorylation of H3S10 contribute to increasing c‐Fos under the pressure of LAQ824. The human DLBCL cell lines SUDHL‐4 and SUDHL‐10 were purchased from FuHeng Cell Center . The human DLBCL cell lines SUDHL‐6, SUDHL‐8, and DB were purchased from Shanghai Zhong Qiao Xin Zhou Biotechnology Co., Ltd. Human DLBCL cell lines CTB1, KIS1, HBL‐1, MEDB1, BJAB, RI‐1, U2932, and FARAGE were purchased from Nanjing Daona Biological Technology Co., Ltd. . U2932, HBL‐1, FARAGE, CTB‐1, KIS1, BJAB, RI‐1, SU‐DHL‐4, SU‐DHL‐10, U2940, SU‐DHL‐6, and DB cells were grown in RPMI‐1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS), L‐glutamine, and penicillin/streptomycin. The WILL1 and SU‐DHL‐8 cell lines were grown in RPMI‐1640 medium supplemented with 20% FBS, L‐glutamine, and penicillin/streptomycin, and the OCI‐LY3, OCI‐LY7, and MEDB1 cell lines were maintained in IMDM (Gibco) complete medium.2.2An inhibitor of c‐Fos, difluorobenzocurcumin (CDF), was purchased from BOC Science . T‐5224 (c‐Fos/AP‐1 inhibitor) was purchased from MedChemExpress. The HDACis dacinostat (LAQ824), vorinostat (SAHA), entinostat (MS‐275), belinostat (PXD101), tucidinostat (chidamide), and abexinostat (PCI‐24781) were purchased from Selleck. Antibodies against phospho‐Chk2 (Thr68), phospho‐ATM (Ser1981), c‐Fos, phospho‐histone H2AX (Ser139), cleaved PARP, cleaved caspase‐3, and cleaved caspase‐9 were purchased from Cell Signaling Technology . Antibodies against PARP, Caspase‐3, Caspase‐9, BCL‐2, CHK2, and ATM were purchased from Proteintech. Acetyl‐histone H3 antibodies were purchased from Millipore. Anti‐H3K9ac antibody was obtained from ABclonal. Anti‐pH 3Ser10 was obtained from Thermo Fisher Scientific.2.33, the mice were randomly divided into three groups and treated with vehicle, LAQ824 (75 mg/kg), or a combination of LAQ824 and CDF (10 mg/kg). LAQ824 was injected intravenously, once every 5 days, for four consecutive treatments. CDF was injected intraperitoneally daily for 24 days. Tumours and body weights were measured twice a week. Tumour volumes were monitored with callipers and calculated as follows: volume (mm3) = (length × width2)/2. At the moment of death, tumour tissues were collected for further analyses.Male nonobese diabetic, severe combined immunodeficiency (NOD SCID) mice (8–12 weeks of age) were selected to inoculate U2932 cells  subcutaneously through the armpits of the forelimbs. U2932 cells were resuspended in phosphate‐buffered saline (PBS) and then mixed with Matrigel in a volume ratio of 1:1. When the tumour reached a size of 100∼300 mm2.418F‐FLT PET is a sensitive imaging method for displaying high‐grade lymphoma. It has high sensitivity for detecting lymphoma and can effectively detect the proliferation of lymphoma in vivo. As a positron tracer reflecting cell proliferation, 18F‐FLT can be applied to positron emission tomography (PET) imaging to observe the proliferation of tumours in the body at the molecular level noninvasively and quantitatively, which improves the specificity of tumour diagnosis and provides a method to detect tumour response to treatment. In this study, we treated the constructed DLBCL model mice under different conditions, injected 18F‐FLT intravenously before and on the third day after administration, and performed a static PET scan of the whole body 1 hour later. The radioactive uptake value of each tissue was obtained in partment model (PMOD).2.5All drugs were dissolved in dimethyl sulfoxide (DMSO) and stored at −20°C before application. For in vivo injection, LAQ824 was diluted in saline solution with 4% DMSO and 40% PEG, whereas CDF was diluted in RPMI‐1640 medium with 10% FBS.2.6Histologic sections from relapsed, refractory, and initially diagnosed DLBCL samples were collected at the Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Science, and stained with c‐Fos antibody. Immunohistochemical (IHC) staining of histological sections from mouse formalin fixed paraffin‐embedded (FFPE) tissues was performed for c‐Fos, CHK2, Ki67, and p‐H3S10. Antibodies used in immunohistochemistry were purchased from Abcam .2.7P values were calculated by Student's t test and were considered statistically significant when P values were < .05. Spearman's rank correlation coefficient was applied to analyse correlative studies. Predicted IC50 data for LAQ824 in different DLBCL cell lines were accessed through the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/). The IC50 values (Sanger GDSC1) data and c‐Fos expression (Expression Public 21Q1) data in different cell lines were acquired via the DepMap portal . Combination indexes (CIs) were analysed by the Chou Talalay algorithm method.All of the experiments were conducted in triplicate. GraphPad Prism software version 8 was used to statistically analyse the data. 33.1www.cancerRxgene.org) database. Dacinostat (LAQ824), a HDACi with low IC50 values in most DLBCL cell lines, was selected   Figure . As an e) Figure . To furt6 Figure . Meanwhis Figure . We alsos Figure .3.6Previous studies have found that the expression of c‐Fos is closely related to lysine 9 acetylation (H3K9ac) and serine 10 phosphorylation of histone H3 (p‐H3S10).3.7To explore whether other HDACis can induce the upregulation of c‐Fos, five HDACi  that have been applied clinically or undergoing clinical trials were selected. The expression of c‐Fos was significantly increased by treatment with all five HDACis Figure . In addi3.8We further assessed the activity of LAQ824 and the c‐Fos inhibitor CDF in a mouse xenograft model using 18F‐FLT PET and tumour volume analysis. LAQ824 delayed tumour growth, and its effect was dramatically enhanced when combined with CDF Figure . IHC res4Considering the beneficial effect of HDACi in DLBCL, we examined the underlying reasons and explored a superior HDACi.The heterogeneity and clonal evolution of haematological tumours have created obstacles and challenges in clinical diagnosis and treatment. The emergence and maturity of scRNA‐Seq technology provide unprecedented resolution for analysing the pathophysiology of human diseases and offer clues for optimal individualised management of patients. A recent study identified the biological heterogeneity of DLBCL cells at the single‐cell level, which provided opportunities for therapeutic targeting.Furthermore, our data elucidated the function of LAQ824 and its enhanced curative effect in combination with c‐Fos inhibitors. These results demonstrate the mechanistic basis for therapeutic targeting of DLBCL and preclinical evidence. In addition, c‐Fos levels in R/R DLBCL patients were higher than those in newly diagnosed patients. Therefore, c‐Fos could be used as a prognostic biomarker. However, this result needs to be verified in a larger cohort of patients.5In this project, we screened HDACi, LAQ824, which can effectively kill DLBCL cells and explored its molecular mechanisms. The core regulator c‐Fos was elucidated for the first time to facilitate the survival of residual tumour cells and thus contribute to refractory or recurrent DLBCL. Monitoring the dynamic expression of c‐Fos might provide an early warning for intervention in patients who have received HDACi treatment. Our research offers a new perspective for understanding the unsatisfactory effect of HDACis and represents a promising HDACi‐based combination therapy strategy for DLBCL.The authors declare that they have no conflict of interest.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Figure S4Click here for additional data file.Figure S5Click here for additional data file.Supporting InformationClick here for additional data file.


PHH and McA, but not HepG2 or Huh7 cells, accumulated more lipid with 11 mM glucose plus fatty acids , but only PHH increased lipid accumulation in response to fructose (p < 0.001). Considerable variation was observed between PHH cells from different individuals. Lipid accumulation in PHH was increased by insulin (p = 0.003) with inter-individual variability. Similarly, insulin increased lipid accumulation in both HepG2 and McA cells, with a bigger response in McA in the presence of fatty acids (p < 0.001 for fatty acid × insulin). McA were more insulin sensitive than either HepG2 or Huh7 cells in terms of AKT phosphorylation (p < 0.001 insulin × cell type interaction). Hence, glucose and fructose can contribute to the accumulation of lipid in PHH with considerable inter-individual variation, but hepatoma cell lines are not good models of PHH.Non-alcoholic fatty liver disease (NAFLD) begins with lipid accumulation within hepatocytes, but the relative contributions of different macronutrients is still unclear. We investigated the impact of fatty acids, glucose and fructose on lipid accumulation in primary human hepatocytes (PHH) and three different cell lines: HepG2 (human hepatoblastoma–derived cell line), Huh7 (human hepatocellular carcinoma cell line) and McA-RH7777 . Cells were treated for 48 h with fatty acids (0 or 200 μM), glucose (5 mM or 11 mM) and fructose . Lipid accumulation was measured via Nile Red staining. All cell types accumulated lipid in response to fatty acids ( Non-alcoholic fatty liver disease (NAFLD) is estimated to affect 25% of the world’s adult population . It is dEstablishing an appropriate cellular model in which to directly determine the impact of nutrients on hepatic lipid accumulation is challenging. Due to the inherent risk of performing biopsies, it is difficult to access human liver for isolation of ex vivo primary hepatocytes. Liver biopsy is normally only performed on patients already suffering from liver disease , and pharmacological and nutritional treatment prior to surgery may impact on cell viability and functionality. Furthermore, genotypic and phenotypic differences between individuals may impact on the existing level of steatosis within the isolated cells. While primary animal hepatocytes have frequently been used to study hepatic lipid metabolism, the translatability of such findings to humans can be questioned, as major differences in lipid and lipoprotein metabolism between species have been reported . Human hIn the present study, we investigated the impact of different concentrations of fatty acids, glucose and fructose on the accumulation of lipid in primary human hepatocytes (PHH), HepG2, Huh7 and McA cells. We also explore the role of insulin sensitivity on the responsiveness of the different cell types to these treatments.Chemicals were obtained from Sigma-Aldrich, Poole, UK unless otherwise stated.Patients gave full consent and were anonymised by the Nottingham Health Sciences Biobank (study number ACP 94). This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human patients were approved by the Nottingham University Hospital Research Ethics Committee (reference 04/Q2403/70). Written informed consent was obtained from all patients.v/v) foetal bovine serum (FBS) and hepatocyte supplements  and sodium bicarbonate to pH 7.4). After 15–16 h, PHH were then incubated for 4 h in FBS-free medium (above media without the FBS) before adding treatment media (see below). In total cells from 6 different livers were used in the study and allocated to experiment 1 and 2 (described below) depending on number of cells available.Human liver tissue was obtained from patients undergoing liver resection for secondary liver cancer. All liver sections used were histologically normal and showed no sign of steatosis. The fresh liver segment was cannulated and subjected to a collagenase-based perfusion method to isolate viable hepatocyte cells, based on an original method developed to isolate pig hepatocytes . These w2.HepG2 (human hepatoblastoma–derived cell line) and McA (rat hepatocellular carcinoma cell line) were obtained from American Tissue Culture Collection and Huh7 (human hepatoblastoma–derived cell line) from JCRB Cell Bank. Cells were maintained individually in T75 flasks in high glucose (25 mM) Dulbecco’s Modified Eagle Medium containing 10% FBS, 100 U/mL penicillin, 100 μg/mL streptomycin and 2 mM L-glutamine. Cells were passaged every 3–7 days. Incubators were set at 37 °C and 5% CO4 (HepG2), 1.5 × 104 (Huh7), 2 × 104 (McA) or 4.5 × 104 (PHH) cells per well. These densities were determined to ensure a confluent monolayer by the end of the experiment. Cells were plated in Williams Medium E with hepatocyte supplements (see above) for all cell types for consistency. After approximately 18 h in FBS containing medium, the cell lines were washed with PBS before adding FBS-free treatment media.Cells were seeded onto collagen coated 96-well plates at densities of 1.25 × 10In experiment 1, PHH and hepatoma cell lines were treated with different combinations of fatty acids, glucose and fructose in the presence of 10 nM insulin. For PHH, cells from each liver were plated on 2 separate plates and each treatment was replicated in 5 wells/plate . The experiment was performed on cells from 5 different livers .5 cells per well. After approximately 18 h, the insulin and FBS were removed for 5 h, then half the wells (n = 3 per cell line) were stimulated with 100 nM insulin for 15 min as previously described [As before, HepG2, Huh7 and McA cells were seeded into collagen coated 12-well plates in the same plating medium but all at a density of 3 × 10p < 0.05. Data for the ratio of phosphor-AKT/AKT was analysed by 2-way ANOVA (cell type × insulin).Statistical analyses were carried out using Genstat 21st Edition. Data for PHH was analysed by three-way ANOVA (glucose × fructose × fatty acid or insulin × glucose × fatty acid) with blocking for plate and liver. Data for cell lines was analysed by three-way ANOVA with blocking for plate. Two-way ANOVA (cell type × insulin), with blocking for gel, was used for AKT phosphorylation experiments. The cell lines and primary cells could not be combined into one analysis due to the problem of technical versus biological replication and determination of fluorescence at different gains. Power calculations indicated that to detect a treatment difference of 0.3, at a significance level of 0.050, with a power of 0.800, using a two-sided test, required a replication of 22. Technical replication was n = 30 for each cell line and n = 50 for all PHH combined (experiment 1) and n = 30 for experiment 2. It was not possible to increase biological replication for PHH due to the availability of liver samples. Significance was attributed at In total, PHH were isolated from 6 different individuals of both sexes and of varying age . Histolop = 0.004 for both), with lipid accumulation being higher in cells treated with fatty acids in the presence of 11 mM compared with 5 mM glucose. Only PHH showed a response to fructose, with intracellular lipid increasing with increasing concentrations independent of the concentration of glucose or addition of fatty acids. However, it should be noted that there were large variations in response of PHH from different individuals . McA showed the greatest response of the cell types studied and again there was variation between PHH from different livers, with two of the three studied showing a significant effect  with the McA cells showing the biggest increase in the ratio with insulin treatment, indicating that McA cells were considerably more insulin sensitive than the other two cell lines.To further explore the insulin sensitivity of the three hepatoma cell lines, the phosphorylation of AKT in response to insulin was explored by Western Blotting. While it is well established that NAFLD is associated with insulin resistance there is also evidence that specific components of the diet can contribute to its development. Animal  and humaWhile it was not possible to quantitatively compare the amount of lipid in PHH and hepatoma cell lines (due to different gain settings), the captured images clearly show that the hepatoma cells accumulated much less lipid in control, fatty acid–free media compared to PHH. Furthermore, PHH from different individuals clearly showed marked differences in the amount of lipid accumulatedin the absence of added fatty acids. While histological examination of donor liver samples suggested that none of the samples used were clinically steatotic, both genetic and phenotypic factors could influence intracellular lipid content.All four cell types accumulated lipid consistently and reproducibly after 48 h treatment with 200 μM fatty acids. This ability of fatty acids to induce lipid accumulation in hepatoma cell lines and primary hepatocytes is well characterised ,16,17,27In contrast to the relatively consistent effects of fatty acids on the different cell types, the effects of glucose and fructose were very different. PHH and McA both showed fatty acid × glucose interactions, with an increase in lipid accumulation between 5 mM and 11 mM glucose only seen when fatty acids were present. However, it is important to note that this effect was inconsistent across PHH from the 5 livers, again suggesting genetic or phenotypic factors have the potential to impact on this response. By contrast, no effect of glucose was seen on lipid accumulation in HepG2 or Huh7 cells. Previous studies have shown HepG2 cells accumulate intracellular lipid with increased glucose treatment, but these have used supra-physiological glucose concentrations in excess of 20 mM. For example, Green et al.  and Hao The results of the current study showed that while fructose increased lipid accumulation in PHH  it had no effect in any of the three hepatoma cell lines. Other studies ,36 have Another possible reason for the difference in response of cell types to glucose may be their sensitivity to insulin. To test this, we compared the response of PHH, HepG2 and McA cells to combinations of glucose and fatty acids in the presence of varying concentrations of insulin. All cell types showed an increase in lipid accumulation in response to insulin (independent of the presence of fatty acids or glucose concentration). Again, differences were seen in PHH from different individuals with only two of the three preparations appearing insulin sensitive. The response in McA cells was considerably higher than HepG2, suggesting that the differences in response in these two cell lines may be due to insulin sensitivity. This difference was subsequently confirmed when we compared AKT phosphorylation across the three hepatoma cell types, with McA cells appearing much more insulin sensitive than the other two cell types.The main findings of this study are that the 3 hepatoma cell lines  all showed increased lipid accumulation with fatty acid treatment, but there were no effects of fructose in any cell line and only McA cells showed an increased lipid accumulation with glucose treatment (and only in the presence of fatty acids). In contrast, there were significant effects of fatty acids, glucose and fructose (including interactions) in the PHH, but the responses varied across the 5 livers used. Due to the low numbers of livers, it was not possible to identify what factors might be responsible for this observed variability between livers. However, both HepG2 and Huh7 cells showed lower insulin sensitivity (measured as Akt phosphorylation) compared to McA cells, which could be involved in the differences observed. Importantly, the results from this study suggest that the hepatoma cell lines are not a good model for PHH and therefore their continued use is questionable (particularly HepG2 and Huh7 cells). Future work needs to integrate nutritional and genetic factors in physiologically relevant models.


Acute myeloid leukemia (AML) represents an aggressive hematopoietic malignancy with a prognosis inferior to that of other leukemias. Recent targeted therapies offer new opportunities to achieve better treatment outcomes. However, due to the complex heterogeneity of AML, its prognosis remains dismal. In this study, we first identified the correlation between high expression of BRD4 and overall survival of patients with AML. Targeted degradation of BRD2, BRD3, and BRD4 proteins by dBET1, a proteolysis-targeting chimera (PROTAC) against the bromodomain and extra-terminal domain (BET) family members, showed cytotoxic effects on Kasumi (AML1-ETO), NB4 (PML-RARa), THP-1 (MLL-AF9), and MV4-11 (MLL-AF4) AML cell lines representing different molecular subtypes of AML. Furthermore, we determined that dBET1 treatment arrested cell cycling and enhanced apoptosis and c-MYC was identified as the downstream target. Collectively, our results indicated that dBET1 had broad anti-cancer effects on AML cell lines with different molecular lesions and provided more benefits to patients with AML. Acute myeloid leukemia (AML) is an aggressive hematopoietic cancer characterized by blocked differentiation of myeloid lineage -3. Due tNPM1, FLT3, IDH1/2, KIT, BCL-2, TP53, and MDM2) [AML is highly genetically heterogenous. Cytogenetic analysis revealed major mutations, including chromatin translocation, deletions, insertions, and inversions, among others, in approximately 55% of AML cases. Based on cytogenetic analyses, patients with AML were classified into the favorable, intermediate, and unfavorable groups. More recently, progress in genetics of AML has discovered several recurrent mutations in the remaining 45% of patients with AML, with being prognostic factors  -13. Morend MDM2) , 15. Thein vitro and in vivo [The BET family members  are epigenetic readers that recognize acetylated proteins, including histone and nonhistone proteins  -18. The  in vivo . Further in vivo , 23, 25.Recently, a new technology, proteolysis-targeting chimera (PROTAC), has been developed, consisting of a ligand for a protein of interest, a linker, and an adaptor to recruit an E3 ubiquitin ligase. A PROTAC would bring its target protein in close proximity to a E3 ligase, and initiate polyubiquitination and subsequent proteasome-mediated degradation . There aIn this study, we explored the cytotoxic effects of dBET1 on several AML cell lines , representing different molecular subtypes  of AML. Moreover, we identified the inhibition of cell cycling and enhancing apoptosis as being the downstream effector of the degradation of BRD2, BRD3, and BRD4 by dBET1. Our results shed new light on the molecular mechanism of dBET1’s cytotoxicity and expanded the therapeutic potential of dBET1 to more molecular subtypes of AML.2.NB4, MV4-11, Kasumi, and THP-1 cells were purchased from National Collection of Authenticated Cell Cultures and were grown in culture in RPMI 1640 medium  supplemented with 10% heat-inactivated FBS, 2 mM L-glutamine, 100 units/ml penicillin G sodium, and 100 μg/ml streptomycin sulfate at 37°C in 5% COhttps://depmap.org/portal/) and shown as a box plot by different tissue/organ. Survival curves of adult patients with AML from TCGA with high/low expression of BRD4/c-MYC were generated from GEPIA2 (http://gepia2.cancer-pku.cn/#index).Public expressions data of BRD4 in 1389 cell lines was downloaded from DepMap . After 48 h of treatment, cell viability was tested with Cell Counting Kit-8 (CCK8) kit (Dojindo Molecular Technologies). Absorbance at 450 nm was applied to estimate the cell viability, which was normalized to DMSO control. Cell viability assay was performed in triplicate, and each assay was repeated at least three times. The half-maximal inhibitory concentration (IC50) of dBET1 was calculated using Graph Prism version 8 .AML cell lines were plated at a density of 2 × 103 cells per well) in six-well plate of soft agar supplemented with variable concentrations of dBET1. After 10 days, cells were first fixed with 100% methanol for 15 min, followed by Giemsa staining for 1 h. Images were acquired using an optical microscope, and the number of colonies were manually counted.NB4, MV4-11, Kasumi, and THP-1 cells were seeded with the same density (4 × 10Short hairpin RNA (shRNA) targeting CRBN (the sequences: CCGGGCCCACGAATAGTTGTCATTTCTCGAGAAATGACAACTATTCG) was cloned into pLKO.1 plasmid , and cDNp value <0.05 and absolute log2 (fold change) > 1. Gene Ontology (GO) and gene set enrichment analysis (GSEA) with Hallmarks gene sets (version 7.1) were conducted using R/Bioconductor package cluster Profiler [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE196693). The data can be accessible with a token upon request.Total RNA was extracted using the RNeasy Mini kit . Library preparation, transcriptome sequencing (Illumina NovaSeq 6000) and clean data filtering were carried out by Novogene Bioinformatics Technology Co., Ltd. . The 150 bp paired-end reads were aligned to hg38 (Ensembl) genome using HISAT2 software (version 2.2.0). Transcriptome assembly and abundance analysis were performed with StringTie software (version 2.1.2). Differentially expressed genes were identified by R/Bioconductor package DESeq2 with adjusted Profiler . HeatmapNB4, MV4-11, Kasumi, and THP-1 cells were treated with dBET1 for 24 h and were then fixed with 70% ethanol and incubated at 4°C for overnight, followed by washing with cold 1× phosphate-buffered saline (PBS) buffer. Fixed cells were then incubated with 1.5 μM propidium iodide (PI)  solution supplemented with RNase A (25 μg/ml) at room temperature for 1 h. Cell cycle distribution was determined using flow cytometry and analyzed using MultiCycle AV DNA analysis software .Cell apoptosis was analyzed as previously described . BrieflyTotal RNA was purified using the RNeasy Mini Kit . First-strand cDNA was generated using a mix of 2 μg of total RNA, 500 ng of random primers , 200U of M-MLV reverse transcriptase , and 20U of RNase inhibitor  in a total volume of 20 μl. qRT-PCR was performed using LightCycler® 480 SYBR Green I Master mix  on a Light cycler 480 Real-Time System  according to the standard protocol. Quantitative mRNA expression of MYC  was calculated using the Ct method using glyceraldehyde 3-phosphate dehydrogenase (GAPDH) expression  as an internal reference.Cells were lysed using RIPA buffer containing protease and phosphatase inhibitor cocktail  for 30 min on ice. The supernatant was collected by centrifugation as total protein and the concentration of total protein was quantified using the Pierce BCA Kit (Thermo Fisher Scientific). Blotting was performed as previously described . Primaryp values were estimated by two-tail t test and <0.05 was regarded as statistically significant . Error bar was indicated by Means ± Standard Deviation (SD).All experiments were performed in triplicates and independently repeated at least three times. Statistical analyses were performed using GraphPad Prism (version 8) . p = 0.044)  .50: 0.1483 μM, NB4 IC50: 0.3357 μM, THP-1 IC50: 0.3551 μM, and MV4-11 IC50: 0.2748 μM)  using Western blotting . BRD4 wa2748 μM) .50 values of dBET1 in these four cell lines: Kasumi (0.1483 μM), followed by MV4-11 (0.2748 μM), NB4 (0.3357 μM), and THP-1 (0.3551 μM)  has cytotoxic effects on AML cell lines, we determined the protein levels of the BRD family in four AML cell lines treated with increasing concentrations of dBET1. Efficient degradation of the BRD family in a dose-dependent manner was observed in all four cell lines, with the most efficiency observed in Kasumi cells, followed by MV4-11, NB4, and THP-1, which conforms to the IC3551 μM) . Besides3551 μM) .To characterize the effects of dBET1 on cell proliferation, we conducted soft agar colony formation assay with incremental dosages of dBET1. The numbers of colonies significantly decreased after treatment with dBET1 in a dosage-dependent manner in the four AML cell lines  . These rTo further define the effects of dBET1 on cell proliferation, we first assessed cell cycle distribution after dBET1 treatment. Treatment with dBET1 significantly decreased the percentage of cells in the S phase compared with that treated with DMSO control in a dosage-dependent manner. In contrast, the percentage of cells in the GO/G1 phase significantly increased after dBET1 treatment. Collectively, these results showed that inhibiting BET family by dBET1 arrested the cell cycle of the four AML cell lines tested. Moreover, we performed flow cytometry using Annexin V and PI to detect apoptosis at 24 h after dBET1 treatment (1 μM and 8 μM) in the four AML cell lines. Treatment with dBET1 induced more apoptotic cells in a dosage-dependent manner (The BRD family plays important roles as a modulator of chromatin remodeling and transcriptional regulation of critical genes in cellular homeostasis and disease progression . Thus, lThe OS rate of AML is poor compared with those of other acute leukemias. Except for APL and CBF AML, the prognosis of AML remains poor, with only approximately 60% of patients progressing to remission and approximately 30% of patients achieving long-term remission , 38. TheBRD4 has emerged as a therapeutic target in AML. In this study, we explored the expression of BRD4 across different cancer cell lines and found that BRD4 was highly expressed in AML cell lines, indicating the dependence of AML cell lines on BRD4 for survival and the usage of BRD4 as a potential therapeutic target. Besides, high BRD4 expression was significantly associated with worse OS of adult patients with AML than low BRD4 expression. These results suggested that high BRD4 expression conferred a survival advantage of AML cell lines and represented as a vulnerability. As expected, inhibiting the BET family proteins using dBET1 demonstrated efficient cytotoxic effects on AML cell lines. Previously, dBET1 has shown cytotoxic effects on MV4-11 cells, without knowing its underlying molecular mechanism . In thisTo further explore the downstream pathways affected by dBET1 treatment, we compared the transcriptomes of AML cell lines with or without dBET1 treatment using RNA-seq. Of note, cell cycle and apoptosis are two enriched pathways because of dBET1 treatment which agrees with the results of the colony formation assay, cell proliferation, cell cycle, and apoptosis analyses. Besides, c-MYC was a well-known downstream target of the BET family, which was also confirmed in this study. Given that c-MYC was an oncogene and played important roles in promoting cell proliferation of cancer cells , the dowThe BCL6 gene was essential for the survival and self-renewal of AML cell lines, especially those with high expression of BCL6 . InteresIn conclusion, we tested the cytotoxic effects of dBET1 on several AML cell lines representing different molecular subtypes of AML and mechanistically explored the downstream effector of dBET1. Our results revealed the therapeutic potential of dBET1 for AML with different molecular lesions.


The cell lines were derived from patients with Sjogren’s disease (SjD) and were compared to the results from cell lines from females lacking autoimmune disease.Shaw et al.   . XIST bio of 9:1 . A minorInterestingly, despite the majority of patients experiencing disease onset around the time of menopause, SjD is characterized by autoantibodies (anti-SS A and anti-SS B) that may precede the onset of clinical SjD by many years . FinallyExtensive genome-wide association studies (GWAS) have been published in SjD, and none of the associations comes anywhere close to the female predominance. The usual explanation to medical students is that female preponderance in autoimmune disease is due to Darwinian natural selection that has shaped the female immune system to accept the implantation and gestation of a semi-allogeneic fetus. The hypothesis dates back to the time of Sir Burnet , who invAdditional evidence has been provided to support this hypothesis by many studies that demonstrated skewed X-linked genes that promote escape from tolerance induction during thymic development, and subsequently play a role in triggering autoimmune disease . Other snew potential therapeutic targets [However, this study by Shaw and co-workers makes us ask this question more critically. The potential escape of certain X-linked genes from inactivation in patients with autoimmune disease leads not only to clues for pathogenesis (triggers) but also to  targets .Toll-like 7 or Toll-like 8 receptors which are known to be located on the X-chromosome [One immediate candidate might be the romosome . Howevernot observed in 4 control female cell lines. Interestingly, the set of silenced genes was not identical in all the SjD cell lines, although overlap was noted . Also, skewing was noted in samples from Systemic Lupus Erythematosus patients  in comparison to control females.This unequal silencing was not an artifact of a single cell line, but was found in 4 differently established salivary mesenchymal cell lines derived from SjD subjects, while They have excluded artifacts introduced in maintaining cell lines by allele-specific PCR .These intriguing studies have identified a new batch of candidate deregulated genes that have emerged in at least 1 SjD patient . Other findings such as alteration in H3K27me3 deposition and decreased level of miR6891-5p (a HLA expressed miRNA) may provide further candidates. The role of miR6981 in this inhibition leading to H3K27me3 and the allelic skewing are new challenges for understanding pathogenesis of the female-predominant autoimmunity.In summary, this article points out possible mechanisms by which environmental triggers can lead to clinically significant autoimmune manifestations in genetically suspectable individuals. In fact, it focuses our attention on the often overlooked observation that these autoimmune diseases occur predominantly in women.


Hyrtios erectus sponge led to the annotation of eleven major compounds (1–11). H. erectus-derived crude extract (HE) was tested in vitro for its antiproliferative activity against three human cancer cell lines, Hep-G2 (human liver cancer cell line), MCF-7 (breast cancer cell line), and Caco-2 (colon cancer cell line), before and after encapsulation within niosomes. Hyrtios erectus extract showed moderate in vitro antiproliferative activities towards the studied cell lines with IC50 values 18.5 ± 0.08, 15.2 ± 0.11, and 13.4 ± 0.12, respectively. The formulated extract-containing niosomes  increased the in vitro antiproliferative activity of the entrapped extract significantly . A subsequent computational chemical study was performed to build a sponge–metabolite–targets–cancer diseases network, by focusing on targets that possess anticancer activity toward the three cancer types: breast, colon, and liver. Pubchem, BindingDB, and DisGenet databases were used to build the network. Shinygo and KEGG databases in addition to FunRich software were used for gene ontology and functional analysis. The computational analysis linked the metabolites to 200 genes among which 147 genes related to cancer and only 64 genes are intersected in the three cancer types. The study proved that the co-occurrence of compounds 1, 2, 3, 7, 8, and 10 are the most probable compounds possessing cytotoxic activity due to large number of connections to the intersected cytotoxic genes with edges range from 9-14. The targets possess the anticancer effect through Pathways in cancer, Endocrine resistance and Proteoglycans in cancer as mentioned by KEGG and ShinyGo 7.1 databases. This study introduces niosomes as a promising strategy to promote the cytotoxic potential of H. erectus extract.In this study, the LC-HRMS-assisted chemical profiling of  Hyrtios are classified within Kingdom: Animalia, phylum: Porifera, class: Demospongiae, order: Dictyocerat-ida, and family: Thorectidae [Hyrtios erectus considered a rich reservoir of pharmacologically active metabolites [Hyrtios were previously tested against several cell lines such as Dipuupehedione, which exhibited a potent cytotoxic activity on KB cells  [Hyrtios erectus against human breast cancer through inhibition of MCF-7 cells viability with the IC50 value of 13.04 ± 1.03 µg/mL after 48 h via a mechanism that involves ROS production and either extrinsic or intrinsic apoptosis pathways. Moreover, hyrtiosone A as an example of chromanone derivative was furthermore analyzed for in vitro anticancer activity in hepatocellular carcinoma HepG2 cells. Hyrtiosone A demonstrated cell cycle arrest at S and G2/M phase, and resulted in elevated p53 and p27 gene expressions, which signified the chromanone derivative as a prospective therapeutic lead to attenuate hepatocellular carcinoma [The second cause of death all over the world after ischemic heart disease is cancer . Cancer rectidae ,5. The mabolites ,7. Vario3 μg/mL) . Hyrtina3 μg/mL) . Moreove3 μg/mL) . Additio3 μg/mL) . Furtherarcinoma .This provoked us to test the ability of the total extract against the three cancer cell lines. Moreover, HR-LCHRMS analysis was carried out for the crude extract to putatively identify its major compounds. Subsequently, these compounds were subjected to a number of in-depth in silico investigation to highlight the most probable anticancer compounds. Although marine products are pharmacologically treasured, their optimum use has been hindered due to the high fragile nature of their commodities and rapid deterioration . TherefoHyrtios erectus ex-tract was performed in order to identify chemical compounds responsible for its cyto-toxic activity. Dereplicated compounds, as mentioned in 1) [2) [3) [4) [5) [6) [7) [8) [9) [10) [11) [Metabolic profiling  using HRed in 1) , xiamenmn 1) [2) , linieod [2) [3) , rhopalo [3) [4) , crambin [4) [5) , norcram [5) [6) , nebrost [6) [7) , iriomot [7) [8) , cytoglo [8) [9) , hipposp[9) [10) , and tru10) [11)  . Previously, compound 8 and 11 have been considered as a potent anticancer agents [As explained in r agents ,29.1, 8−11) were subjected to an inverse docking-based screening using the idTarget inverse docking platform . This software utilizes a docking protocol, divide-and-conquer, which builds small overlapping grids in order to confine the searching space on the protein surfaces, allowing it to perform a large number of precise dockings in a short time. The query structure can be docked against almost all proteins structures hosted in the Protein Data Bank (PDB) . The retrieved results were listed as scores from the highest negative values to the lowest one. To identify the best protein targets for each query structure in HE, with binding affinity of 7 kcal/mol as a cut-off score. As key proteins in the cancer pathogenesis and progression, Pim1 kinase [1 and 9 with binding affinity scores of −8.7 and −8.3 kcal/mol, respectively. To support the previous preliminary pharmacophore-based virtual screening step and to obtain further insight into the most probable molecular targets of the predicted anticancer compounds  were able to achieve stable bindings over the course of simulation producing low binding deviations in comparison with the initial binding modes  with average RMSDs of 2.1 and 1.1 Å, respectively. Accordingly, their estimated ΔG were found to be considerably low  indicating good affinity toward each corresponding protein target.To further validate the docking results of the previous step, we subjected the re-trieved docking pose of each compound to 50 ns MDS experiments. As depicted in 9, it established two H-bonds with THR-220 and ASN-329 similarly to the co-crystalized ligand, and was also able to interact with PHE-214, LYS-326, and PHE-351. According to the previous in silico and structural findings, both compounds 1 and 9 are likely linked to the anticancer properties of HE.Looking into the binding modes of each compound inside the binding sites of the corresponding proteins at the end of MDS revealed that they were achieved their stable bindings by establishing a number of strong H-bonds (<2.5 Å) and hydrophobic interactions. As shown in Hyrtios erectus extract was successfully prepared as re-vealed by TEM image . Furthermore, we investigated the influence of entrapment in the niosomal formulation on the enhancement of HE extract’s antiproliferative activity. Therefore, MTT assay was carried out for the ex-tract-containing niosomes with the IC50 determination against the three tested cell lines. The formulated HE niosomes  significantly enhanced the in vitro anti-proliferative activity of the encapsulated HE extract . This could be at-tributed to the small particle size of the prepared niosomes which promotes cellular in-take and internalization. This is incontinence with previous results that identified niosomes as potential delivery systems in anticancer therapy. Moreover, recent study has revealed the impact of niosomal formulation in preferential targeting of the uploaded cargo to the tumor tissues [In silico analysis results confirmed, the powerful anticancer potential of HE. Sub-sequently, in vitro investigation for its anti-proliferative activity was performed against the three-studied cells .  tissues . Evaluat tissues .H. erectus using HR-LCMS, metabolic profiling was identified. The identified 11 metabolites were connected to the sponge extract.Based on chemical analysis of Red sea sponge The target genes related to the identified metabolites were predicted by BindingDB database and Swiss Target Prediction database. The active metabolites were connected to 200 genes as illustrated in Target genes were connected to different classes of three cancer types  using the DisGenet online database and FunRich software. This result in 147 genes among the resulting gene list related to the three types of cancer in different classes. The network of the target genes——is composed of 150 nodes and 333 edges, with a characteristic path length of 2.004 and network centralization of 0.929.There are 147 nodes represented by the target genes and 3 nodes represent the three cancer types of liver, colon, and breast; the intersected genes  are 64 genes as illustrated in H. erectus), 11 nodes corresponding to the active metabolites, and 64 nodes represent the intersected genes to the studied cancer types. The network contains 100 edges representing the number of interactions of each compound and intersected genes among our gene set, as illustrated in Figure B. The network showed that compound iriomoteolide 1b has the top number of edges at 14 edges, followed by nebrosteroid C with 11 edges, then hippospongide A and linieodolide A with 10 edges each, and finally lorneamide and xiamenmycin with 9 edges each as illustrated in The network of the sponge–metabolites–intersected genes related to the major studied genes of breast, colon, and hepatocellular cancers consists of 76 nodes . The fresh sponge Hyrtios erectus (100 g) was collected, freeze dried, and then reduced to fine powder (40 g). The powdered sponge was then macerated in MeOH: DCM (1:1) to create 6 g of the total extract.The marine sponge LCMS was performed using a Synapt G2 HDMS quadrupole time-of-flight hybrid mass spectrometer . BEH C18 column was adjusted to 40 °C then connected to the guard column to be ready to inject the sample (2 μL). The mobile phase with gradient elution was used. Mzmine 2.12 was maintained for differential analysis of MS data. ProteoWizard was employed for convention of the raw data into positive and negative files in mzML format.Hyrtios extract were dissolved in 2.5 mL ethanol (absolute) forming the organic phase. The organic phase was loaded in a closed spraying device. The organic phase was then finely sprayed in a closed system containing 2 mL 9% w/v sucrose solution (2 mL per 10 s). The whole system was heated to 60 °C and continuously stirred at 1200 rpm. Stirring at 1200 rpm was continued for 20 min until the complete evaporation of ethanol was reached and spontaneous formation of niosomes occurred. The formulated niosomal suspension was kept for 24 h at 4 °C to complete the strengthening of the formed bilayer. Prepared niosomes were stored in the refrigerator for further investigations. Niosomes were prepared by adopting the spraying technique reported previously . BrieflyHyrtios extract (1:150 v/v) were visualized using  adjusted at 80-kV. Niosomes were dried at 40 °C on copper grids left for 15 minutes before imaging [Diluted samples of the prepared niosomal suspension of  imaging .v/v) were used to determine the hydrodynamic diameter and zeta potential, respectively.Size and poly-dispersity index of prepared niosomes were evaluated with a Zetasizer-Nano-ZSP . Dynamic light scattering and electrophoretic mobility of the diluted samples . Dried samples were heated from 30 to 400 ºC using a heat flow rate of 20 °C/min and nitrogen (flow rate 20 mL/min). Moreover, the possible interaction among Span 60, cholesterol, and Hyrtios extract was studied using Fourier-transform infrared (FT-IR) spectroscopy for the empty niosomes and Hyrtios erectus extract-containing niosomes along a range of wavenumber from 4000 to 400 cm  following dispersing the samples in discs of KBr. For further estimation of the stability of the prepared formulation, the change in the weight with changing temperature of free 2, and MCF7 cell lines were kept separately in RPMI-1640 , which were enhanced with fetal bovine serum (FBS) (10%) , 100 mL U/mL penicillin G, 100 μM/mL streptomycin, and 2 mmol/L glutamine. Cells were cultured in DMEM  and enhanced with FBS (Hyclone) (10%), insulin (10 μg/mL) (Sigma), and penicillin-streptomycin 1%.HepG2, CACO2 at 37 °C. Then, 96-well cell culture plates were used to seed the tested cell lines (2 × 104 cells/well in the case of MCF-7 and HepG-2 and 6 × 104 cells/well in the case of Caco-2 cells). The anti-proliferative potential of the empty niosomes and HE—either free or encapsulated within the niosomal formulation—was evaluated. Concentrations of 0, 5, 12.5, 25, and 50 µg/mL of the respective extract were included. Cell viability was determined using 3--2,5-diphenyltetrazolium bromide (MTT) assay 8. Plates including triplicate of untreated cells were considered as 100% viability. Plates with triplicates of cells managed by cytotoxic reagents  sodium azide 20%) were supposed as 0% viability. Average of triplicates were analyzed using Graph Pad Prism 5 software .Breast cell lines (MCF-7), liver cell lines (HepG-2), and colorectal cell lines (Caco-2) were maintained in earth RPMI 1640 medium with heat-inactivated FBS (10%) then grown in 5% COPASS software  was emplhttp://idtarget.rcas.sinica.edu.tw/, accessed on 22 July 2022) [The highly probable anticancer compounds predicted in the previous neural networking pharmacophore-based screening were subjected to an inverse docking-based virtual screening using the idTarget platform (ly 2022) ,37,38. TH. erectus using HR-LCMS, metabolic profiling was identified. The identified 11 metabolites were connected to the sponge extract in a simple network.Based on chemical analysis of Red Sea sponge https://pubchem.ncbi.nlm.nih.gov/, accessed on 20 July 2022) [https://www.bindingdb.org/rwd/bind/index.jsp, accessed on 20 July 2022) [http://www.swisstargetprediction.ch/, accessed on 20 July 2022) [Homo sapiens) was selected.The target genes related to the identified metabolites were predicted by PubChem (ly 2022) , Bindingly 2022)  and Sawily 2022)  databasely 2022)  using cahttps://www.disgenet.org/, accessed on 20 July 2022) [The DisGenet (ly 2022)  online dThe network of the sponge–metabolites–intersected genes related to the major studied genes of breast, colon, and hepatocellular cancers.https://cytoscape.org/download.html, accessed on 8 August 2022) [The complete network was constructed between the sponge and the active metabolites and the target genes that have influence on the three main types of cancer: colon cancer, breast cancer and hepatocellular carcinoma. The networks visualization was performed using the software Cytoscape 3.9.0. (st 2022) .http://www.funrich.org, accessed on 10 August 2022) [https://www.genome.jp/kegg/, accessed on 12 August 2022) [http://bioinformatics.sdstate.edu/go/, accessed on 13 August 2022, a graphical gene set enrichment tool) [The gene ontology and enrichment analysis was performed on all the targets of the active metabolites to discover the cellular components, molecular function, and biological processes that are affected by this set of genes using FunRich version 3.1.3. (st 2022) . The enrst 2022)  and Shinnt tool) .Hyrtios erectus (HE) led to the annotation of 11 compounds of different chemical classes, e.g., polyketides, terpenoids, steroids, and indole alkaloids. Upon in vitro anticancer testing, HE showed a moderate antiproliferative effect against the MCF-7, HepG2, and Caco-2 cell lines; IC50 ranged from 13–18 µg/mL. The improved uptake by the studied cell lines was obtained by the encapsulation of HE within niosomal formulation. The results reveal the impact of entrapment of the HE extract as a promising approach to enhance the anti-proliferative potential of the extract. However, further in vivo studies should be implemented to confirm that concept. PASS software-assisted virtual screening highlighted a number of annotated compounds  that are associated with the anticancer potential of HE compounds. This study also confirmed the preferability of niosomes as a nanocarrier for improvement of the anticancer activity of the HE extract. The network pharmacology analysis that focused on target genes involved in the three types of studied cancer cell lines  revealed that compounds 1, 2, 3, 7, 8, and 10 are the major compounds responsible for the anticancer potential of the three cell lines and that the pathways in cancer are the top pathways of the target genes. The docking study results and pharmacology networking study revealed that compounds 1, 8, and 10 have a high binging score and contain more anticancer genes so the co-occurrence of Lorneamide A, Iriomoteolide 1b, and hippospongide A in the HE extract might be responsible for the anticancer potential of the extract.Herein, the LC-HRMS-assisted chemical profiling of Red Sea


The receptor tyrosine kinases (RTKs) family is well-recognized as vital targets for the treatment of hepatocarcinoma cancer (HCC) clinically, whereas the survival benefit of target therapy sorafenib is not satisfactory for liver cancer patients due to metastasis. EGFR and MET are two molecules of the RTK family that were related to the survival time of liver cancer patients and resistance to targeted therapy in clinical reports. However, the mechanism and clinical therapeutic value of EGFR/MET in HCC metastasis are still not completely clarified. The study confirmed that EGFR/MET was highly expressed in HCC cells and tissues and the phosphorylation was stable after metastasis. The expression of EGFR/MET was up-regulated in circulating tumor microemboli (CTM) to accelerate IL-8 production and resistance to the lethal effect of leukocytes. Meanwhile, highly expressed EGFR/MET effectively regulated the Ras/MAPK pathway and stabilized suspended HCC cells by facilitating proliferation and inhibiting apoptosis. Moreover, EGFR/MET promoted phosphorylation of hetero-RTKs, which was dependent on high-energy phosphoric acid compounds rather than their direct interactions. In conclusion, highly expressed EGFR/MET could be used in CTM identification and suitable for preventing metastasis of HCC in clinical practice. Liver cancer is one of the most lethal cancers, causing the 6th carcinoma incidence and 4th cancer mortality in the world . HepatocReceptor tyrosine kinases (RTKs) are currently considered as critical roles in malignant transformation and cancer metastasis . The humIn the present study, we determined the expression of RTKs proteins in CTM and cells and verified the important roles of EGFR/MET and the possibility of being used as a clinical treatment target. Furthermore, we identified the mechanism that EGFR/MET promotes metastasis and stabilizes HCC cell lines in hematogenous metastasis. These findings provide mechanistic evidence for us to design HCC medications targeting EGFR/MET, thereby benefiting patients with HCC in reducing the incidence of metastasis and prolonging survival time.We firstly explored the expressions of RTKs in HCC tissues and HCC cell lines. Interestingly, the expressions of RTKs in HCC tissues and HCC cell lines varied greatly. Certain RTKs namely M-CSFR were highly expressed in HCC tissues whereas poorly expressed in cell lines. However, other RTKs namely the HER family were highly expressed in both HCC tissues and cell lines. Compared to the other RTKs we detected, the concentration of EGFR/MET was high expressional RTKs both in HCC tissues and HCC cell lines. Especially in HCC cells, expressions of EGFR and MET in MHCC97H were 54,548.83 and 607,470.8 pg/mg, respectively, and those of SK-HEP1 were 103,099.9 and 38,652.84 pg/mg, respectively, higher in expression than other RTKs Fig. . AdditioNext, we verified whether the expressions of EGFR/MET using a xenograft model for lung metastasis of mice were stable during metastasis. In this model, Hepa1-6 cells were successfully metastasized from the left lobe of the liver to the lungs Fig. . CompareSince metastasis of CTM was closely related to anoikis resistance, we next observed the role of EGFR/MET in HCC cell lines of suspension. Phosphorylation RTKs array reported that phosphorylation of EGFR/MET in suspended HCC cell lines was enhanced and Western blot assays indicated that peak point appeared at 24–48 h Fig. . IL-8 syAs the Ras/MAPK pathway regulated by RTKs played an important role in tumor metastasis, the function of EGFR/MET in regulating the Ras/MAPK pathway will be further investigated in our subsequent research. Firstly, like phosphorylation of EGFR/MET, we found Ras-GTP was up-regulated in HCC cell lines in suspension Fig. . Both EGNext, we analyzed the activation of ERK in suspended HCC cell lines. The findings were identical to Ras-GTP indicating that the phosphorylation level of ERK was up-regulated in suspended HCC cell lines Fig. . FollowiBased on the high expression of EGFR/MET in HCC tissue/cell lines and stable expression in metastasis, the regulatory effects of EGFR/MET on the phosphorylation level of other RTKs were investigated. In this study, Lenvatinib, as an inhibitor of PDGFRβ/VEGFR/FGFR/RET, was effective in suppressing EGFR/MET and targeting RTKs in MHCC97H and HCCLM3, but less effective on PLC/PRF/5. In contrast, Sorafenib, as an inhibitor of PDGFRβ/VEGFR/Raf, had less effect on EGFR/MET, and barely suppressed the function of PDGFRβ in MHCC97H and LM3 Fig. . Also, wNext, we focused on possible mechanisms related to the phosphorylation of EGFR/MET. Interestingly, we found that phosphorylation of MET was affected by MET amplification Fig. . PhosphoRTKs nowadays are considered to be critical in tumorigenesis, intrahepatic metastasis, and distal metastasis of liver cancer . The corExtrahepatic metastasis is easily observed in patients with HCC . BecauseThe Ras/MAPK pathway is considered a survival factor for carcinoma cells, and RTKs produce a similar mechanism in regulating the Ras/MAPK pathway , 36. IncRTKs have a similar effect on the activation of downstream signaling pathway Ras/MAPK pathway . Our preIn conclusion, we have demonstrated that EGFR/MET are as the two of the most expressed in the RTKs family in both HCC tissue and cells. The study also clarifies the relationship between EGFR/MET and CTM. Activation of EGFR/MET caused by complicated factors helps suspended HCC cell lines to maintain cell stability and distal metastasis. Targeting EGFR/MET may provide opportunities for the prevention of metastasis of HCC.2. 100 mg/mL penicillin G and 50 μg/mL streptomycin  were added. All cell lines were identified by STR and mycoplasma tests and all were used within 3 months after thawing early passage cells.Human HCC cell lines MHCC-97H , PLC/PRF/5 , HCCLM3, SK-Hep1, and mouse HCC cell line  were cultured in DMEM containing high glucose  with 10% fetal bovine serum  at 37 °C in a humidified atmosphere containing 5% COOf 3% Poly-HEMA  was dissolved in ethanol for preventing HCC cell lines adhesion. Tissue culture plates were coated with 1 ml of Poly-HEMA solution and dried overnight at room temperature in a laminar flow hood. All cells were sub-cultured, re-plated in suspension condition, and harvested at indicated time points for detection.For EGFR/MET knockdown, siRNAs were transfected into MHCC97H/HCCLM3/PLC-PRF-5 cells using Lipofectamine 3000 . Cells were cultured for further experiments after 48 h. The sequences of siRNAs were listed in Table Sorafenib (VEGFR and PDGFRβ), Lenvatinib , Erlotinib (EGFR), Afatinib (EGFR), Crizotinib (MET), and AZ628 (Ras/MAPK) were purchased from Selleck  and dissolved in DMSO. Recombined EGF, HGF, FGF2, PDGF-BB, and VEGF were purchased from PeproTech , and recombined insulin was bought from Selleck .The composition of Co-IP buffer was 50 mM Tris, 150 mM NaCl, 1% NP40, 0.5% sodium deoxycholate, 1 mM EDTA, 1 mM PMSF and 1× protease inhibitor cocktail . Co-IP and Western blot were performed as described [Luminex Kit  was coated with RTKs  and RTKs ligands (EGF and HGF). ELISAs  were used to detect the concentration of RTKs which Luminex Kit didn’t contain. Phosphorylation of RTKs was performed by Human/Mice Phospho-RTKs Array Kit  according to the manufacturer’s instructions. Image pro plus 6.0 was used for intensity quantification. Ras Activation Assay Kit  was used for the detection of Ras activation.5/ml. Trypsin digestion was performed in cells with or without suspension at the same time to dissociate clusters into single cells and reduce interference from trypsin. PI/RNAse was used for detecting cell cycle and Annexin V/PI was used for detecting apoptosis of HCC cell lines. Flow cytometry was performed according to the manufacturer’s instructions.All HCC cell lines were harvested and resuspended at a concentration of 10Paraffin specimens from patients with HCC and carcinoma tissues of mice were performed according to the manufacturer’s instructions. Immunofluorescence assays were conducted in a manner blinded to the sample identity. Cells cultured with or without suspension were harvested and dried on coverslips with Poly-L-lysine  coated. EDU assay  was used for detecting DNA synthesis of HCC cell lines.Leukocytes were collected from healthy donor blood by gradient centrifugation with leukocytes separation . After being cultured with DMSO or inhibitors for 24 h, HCC cells lines were re-suspended and the supernatant was discarded. Then, equal leukocytes were added to the culture medium with MHCC97H cell line and were co-cultured for 24 h in suspension. Cell mixtures were collected, dried on coverslips with Poly-L-lysine coated, and observed by immunofluorescence with EpCAM + EGFR and CD45 staining.5 Hepa1-6 cells were injected into the left lobe of the liver in each mouse. The animals were sacrificed after 60 days and the presence of any extrahepatic metastasis except lungs was considered to be a failure, even lung metastasis model was successfully established. Tumor tissues from the liver and lung in the same mouse were collected for phosphorylation detection, immunohistochemistry, immunofluorescence, and scRNA-seq.5-week-old C57 male mice  were employed to construct xenograft tumor models. The mice were anesthetized with 50 mg/kg Zoletil®50  combined with 5 mg/kg Xylazine hydrochloride  by intraperitoneal injection, and 10p-value < 0.05; min. pct > 0.1) was used for marker gene identification.ScRNA-seq was performed by Annoroad Gene Tech . Carcinoma tissues were separated from the xenograft model in 48 h and a library was constructed as manufacturer’s instructions. CellRanger was applied to identify cell barcodes in reads1 and UMI marks in different transcripts, and the corresponding reads2 were compared referring to genome through STAR to determine source gene of the reads and complete quantification of gene expression. RBC in cells containing <200 expressed genes or mitochondria UMI rate >80% were excluded. Then, mitochondrial genes were removed in the expression table. After normalizing UMI data of gene expression in each cell by Seurat analysis and UMI data dimension reduction by PCA, the top 20 principal components were selected for cell classification and UMAP construction based on graph-based clustering method. FindAllMarkers function by the Wilcox rank-sum test algorithm  was used for data analysis. The statistical significance of differences between different experimental cell groups was determined by SupplementSupplement 1Supplemental Table 1Supplemental Table 2Supplemental Table 3Supplemental Table 4Reproducibility ChecklistAuthor Contribution FormWestern Blot Origin


Colorectal cancer (CRC) is one of the most prevalent cancers, driven by several factors including deregulations in intracellular signalling pathways. Small extracellular vesicles (sEVs) are nanosized protein-packaged particles released from cells, which are present in liquid biopsies. Here, we characterised the proteome landscape of sEVs and their cells of origin in three CRC cell lines HCT116, HT29 and SW620 to explore molecular traits that could be exploited as cancer biomarker candidates and how intracellular signalling can be assessed by sEV analysis instead of directly obtaining the cell of origin itself. Our findings revealed that sEV cargo clearly reflects its cell of origin with proteins of the PI3K-AKT pathway highly represented in sEVs. Proteins known to be involved in CRC were detected in both cells and sEVs including KRAS, ARAF, mTOR, PDPK1 and MAPK1, while TGFB1 and TGFBR2, known to be key players in epithelial cancer carcinogenesis, were found to be enriched in sEVs. Furthermore, the phosphopeptide-enriched profiling of cell lysates demonstrated a distinct pattern between cell lines and highlighted potential phosphoproteomic targets to be investigated in sEVs. The total proteomic and phosphoproteomics profiles described in the current work can serve as a source to identify candidates for cancer biomarkers that can potentially be assessed from liquid biopsies. Colorectal cancer (CRC) is one of the world’s most prevalent cancers with an estimated 1.4 million new cases diagnosed annually. CRC is described as a “silent” disease, and few patients with CRC exhibit clinical symptoms until late stages of the disease when the cancer has metastasised to other organs in the body .Small extracellular vesicles (sEVs), ranging from 50–200 nm in size, are secreted from cells in both physiological and pathological conditions . They caPost-translational modifications (PTMs), such as phosphorylation, are well-described events governing intracellular signalling, which are known to be central in cancer biology. The phosphorylation status of proteins is frequently altered due to protein mutations ,16,17. GThe aim of this study was to examine to what extent the proteomic content of sEVs reflect their paternal cell and whether the proteome of sEVs would uncover features of cancer cells that could allow for improved insight into cellular processes such as disease-associated signalling aberrations. We additionally wanted to uncover activated signalling pathways or molecular signatures through global phosphoproteomics analysis of whole-cell lysates. To investigate these questions, we selected the commonly used colorectal cancer cell lines HT29, HCT116 and SW620. SW620 is isolated from a metastasised colon cancer while HT29 and HCT116 are isolated from the colon. Some of the well-described pathogenic mutations found in these cell lines are as follows: SW620 contains KRAS, APC and TP53; HCT116 contains BRCA2, CTNNB1, KRAS, PIK3CA and TGFBR2; and HT29 contains APC, BRAF, SMAD4 and TP53 [Our investigation detected a broad overlap of proteins packaged in sEVs compared to their cell of origin. We identified characteristic protein expression patterns for the cell lines known to be key players in cellular signalling cascades including MAPK, Wnt, TGF-β, p53 and mTOR signalling and further investigated these through phosphoproteomic analysis of CRC cells. A subset of proteins involved in the PI3K-AKT signalling pathway was enriched in sEVs. Further investigation into CRC-biology-associated proteins reveals TGF-β signalling proteins, TGFB1 and TGF-β receptor 2 (TGFBR2), to be enriched in sEVs only. sEV proteomic cargo was also found to contain several specific proteins playing key roles in intracellular signalling. These results may allow for a better understanding of sEVs in CRC cell biology and serve as a source to investigate candidates for cancer biomarkers.2 air atmosphere.Human colorectal carcinoma cells HCT116, HT29 and SW620 were directly acquired from the National Cancer Institute (NCI) via the NCI-DCTD repository in Frederick, MD (MTA #1-5578-17). No cell line identity verification was performed in house, but the NCI characterises the cell lines using ~120,000,000 SNP arrays and oligonucleotide-based HLA typing as well as other approaches . The cel6), HT29 (8 × 106) and SW620 (5 × 106) cells were seeded and cultured in a 23 mL medium (175 cm2 flask) for 72 hrs to allow sEV secretion. The average number of cells from a 1xT175 flask was 25 × 106 (HCT116), 28.5 × 106 (HT29) and 42 × 106 (SW620), respectively. The media were collected (eight flasks per cell line) and centrifuged at 500× g for 5 min to remove cellular debris. The sEVs were isolated from the eight flasks using a standardised ultracentrifugation protocol as described by Peinado et al. [g ultracentrifugation. The sEV fraction collected was further isolated by ultracentrifuging at 100,000× g for 70 min. One isolation resulted in an sEV fraction, which was then washed in either (i) 21 mL PBS for nanoparticle tracking analysis (NTA) or (ii) an added 200 µL of 2% paraformaldehyde for transmission electron microscopy (TEM) analysis before (iii) suspension in a 20 μL lysis buffer  in Thermo Scientific Pierce™ RIPA Buffer). Next, the sEVs were gently rocked at 4 °C for 3 h before measuring the protein concentration using a Bio-Rad assay for proteomic analysis. All of the above work was carried out at 4 °C cooled settings. HCT116  in Thermo Scientific Pierce™ RIPA Buffer). Cell lysates were continuously swayed at 4 °C for a period of 3 h before measuring protein concentration using Bio-Rad assay .Cell lysates for all three cell lines were collected simultaneously as the media were removed for sEV isolation. For cell lysis, media were removed and the cells were washed with cold PBS, trypsinised and then centrifuged at 500× 8 to 1 × 109 particles/mL. Triplicate video recordings (60 s) were obtained and analysed by the NTA software . Mean particle numbers/mL of 2.56 × 108 ± 4.92 × 107 particles/mL (HCT116), 158 nm 8.06 × 108 ± 3.34 × 107 particles/mL (HT29) and 2.12 × 109 ± 1.67 × 108 particles/mL (SW620) were acquired. The parameters for detection included a detection threshold equal to 5 and camera level equal to 10 as per the NanoSight NS300 for all measurements. For TEM examination, the sEVs were resuspended and fixed in cold 2% paraformaldehyde in PBS. Droplets of sEVs were put on Formvar-carbon coated copper grids (200 mesh) for 5 min, fixed in 2.5% Glutaraldehyde (GA) in 0.1 M Sorensen’s phosphate buffer for 10 min, washed in H2O reverse osmosis water, stained and embedded in 2% uranyl acetate (UA) for 10 min. The excess liquid on the grids was removed using filter paper and then air-dried. The grids were examined using a transmission electron microscope , at 100,000× magnification and a voltage of 80 kV. Images were captured with a Morada digital camera with iTEM software—BoRAS.The sEV pellets were suspended in PBS 1:500 and examined using the NanoSight NS300 analyser allowing the particles to fall in the instrumental linear range of detection, which is approximately 1 × 10Equal amounts of protein from both the cellular and sEV lysates were loaded into 4–12% Bis-Tris gels (the protein concentrations are specified in m/z 400–1800) was acquired at a resolution of 12,000 FWHM, followed by 15 MS/MS scans triggered for AGC targets above 2E3, at a maximum ion injection time of 50 ms for MS and 100 ms for MS/MS scans.All the experimental conditions for mass spectrometry were run with three biological replicates and one technical replicate. For shotgun analysis, 50 µg of protein extract was reduced, alkylated and digested on magnetic HILIC beads as described by the producer . For phosphoproteomic analysis, 500 µg of protein extract was reduced, alkylated and digested prior to phosphopeptide enrichment using TiIMAC magnetic beads according to the protocol . Following desalting using C18 StageTips , liquidshttp://www.thermoscientific.com/content/tfs/en/product/proteome-discoverer-software.html, accessed on 1 September 2022) version 2.3 (PD) was used to quantify protein levels from the raw MS data. The raw files were inspected to determine optimal search criteria using Preview version 2.3.5 from Protein Metrics Incorporate .The following parameters for the search were used: precursor mass tolerance of 10 PPM and fragment mass tolerance of 0.02 Dalton; phosphorylation of Serine/Threonine/Tyrosine, acetylation of Protein N-terminal, oxidation of Methionine and deamidation of Asparagine/Glutamine as a dynamic post-translational modification and carbamidomethylation of Cysteine as static; enzyme specified as Trypsin with a maximum of two missed cleavages allowed. Only unique peptides with high confidence were used for the final protein group identification by setting downstream analysis of the peptide spectra matches (PSM) and false discovery rate (FDR) for both protein and peptide to 1%. The integration of the area under the peak curve was used to calculate abundance. Each protein group abundance was normalised by the total abundance of all identified peptides at FDR <1%. The summed up median values for all unique peptide ion abundances were mapped to respective proteins using the label-free quantification algorithm . The resulting values were scaled for all averages with the Precursor Ion Quantifier node  for MQ and PD, respectively. The values for all samples were log2-transformed and subjected to principal component analysis (PCA). In order to calculate phospho-site occupancies, PTM was set to True in the MQ experiment design for all the enriched shotgun proteomics samples.The software Thermo Scientific™ Proteome Discoverer™  of the data was carried out using the R package DEP . Prior tBos taurus proteins, 42 Homo sapiens proteins and 9 Mus musculus proteins were filtered out. Venn diagrams were created with the R-package eulerr [HUGO Gene Nomenclature Committee (HGNC) gene symbols were associated with the accession numbers of proteins from the shotgun proteomics analysis. Proteins without the HGNC gene symbol were filtered out as these accession numbers are outdated or belong to other species. Out of 7306 unique accession numbers, 95 e eulerr  and heate eulerr .This study aimed to characterise and compare the proteomic signatures and signalling configurations in sEV cargo and cells of origin for three CRC cell lines: HCT116, HT29 and SW620 A. The EVTo characterise their proteomic content, lysates from whole cells and sEVs from the three CRC cell lines HCT116, HT29 and SW620 in three biological replicates were subjected to LC-MS/MS analysis. We noted that similar numbers of proteins, approximately 4500, were detected in each of the CRC whole-cell preparations A. AbundaWhen comparing the proteome of the sEVs from the three cell lines B, we obsNext, we compared the proteomic content of sEVs to the proteins extracted from their cells of origin. Most of the sEV proteins were also detected in their whole-cell counterpart, with an overlap of 81% in HCT116 and 87% in HT29 and in SW620 C. IntereTo further assess the similarity between the sEVs and their cell of origin, we performed PCA analysis on proteins detected both in sEVs and in cells. This analysis revealed that the sEV profiles are more similar to their cell of origin than to other sEV profiles or other cell lines D. The vaIn order to further investigate functional aspects of the whole-cell and sEV proteomes, we performed KEGG pathway enrichment analysis using DAVID GO term analysis of the following four different sets of proteins: (i) all proteins from whole cells, (ii) all sEV proteins, (iii) proteins observed in both whole cells and sEVs and (iv) proteins observed in sEVs only  or “PI3K-AKT pathway” (hsa04151) as depicted in Cluster analysis of abundancies of proteins participating in the chosen pathways and observed in both whole cells and small EVs identified in three main clusters A. ProteiFrom the heat map analysis of “Colorectal Cancer” and “PI3K-AKT”pathways proteins detected in whole-cell lysates only B, we comTGFB1 and TGFBR2 were detected only in sEV lysates and not in any of the whole-cell lysates C. The TGTaken together, we conclude that the proteomic data displayed in The activity of a protein is not only affected by its abundance but is also dependent on its phosphorylation state. To complement our previous proteomics data, we therefore investigated the phosphoproteomic profiles of the cell lysates from the three CRC cell lines included in this study with three biological replicates for each cell line. Together, this gives a broader view of the status of these cells. Mass spectrometry analysis of phosphopeptide-enriched preparations from whole-cell lysates captured approximately 2600 phosphoproteins in each of the three cell lines. Of these, 85% were detected in all cell lines while a small subset was found in only one of the cell lines A. PearsoGiven that we observed phosphopeptides associated with cellular processes relevant for CRC, we were next interested in taking a closer look at the phosphorylation patterns of proteins known to play a role in the proteins represented in the KEGG pathways “Colorectal Cancer” (hsa05210) or “PI3K-AKT pathway” (hsa04151) . The heaMost phosphopeptides in cluster I display very low reproducibility between the biological replicates. However, RPTOR serine (S) (868–894), known for its role in mTOR signalling, and EIF4B S/Threonine (T) (445–472), which plays a role in protein transcription, appeared across all three cell types with highest expression noted in HCT116 and SW620 cells. Cluster II identifies phosphopeptides that display higher abundance in in both HCT116 and HT29 cells or in only HT29 cells compared to SW620. HT29 cells encompass greater levels of TP53 T/S (307–319) and PDPK1 S (239–257) phosphopeptides where a high total protein abundance is also observed in Taken together, our data show that the cell lines studied here display phosphorylation statuses that are relevant in cancer and highlight the individual phosphorylation states of the CRC cell lines used in this study. Colorectal cancer is one of the most prevalent cancers worldwide. Currently, the clinical management of colorectal cancer is directed by the histological and molecular analysis of biopsies of tumours. Extracting biopsies can be invasive and measurements originating from a small part of a tumour do not necessarily reflect the overall state of the disease. sEVs are a potential source of disease-associated biomarkers without any of these issues. They can be isolated from biofluids, which are easier to acquire than biopsies, and small EVs from biofluids originate from the whole tumour . ExtensiIn this study, we find that most of the proteins detected within sEVs overlap with those in their cell of origin. However, the overlap is not complete, as a subset of the proteins is only detected in sEVs and not in their cell of origin. The presence of these sEV-specific proteins indicate that proteins of certain processes are enriched in sEVs and therefore easier to detect in sEVs than in cell lysates. However, we cannot establish with certainty why these proteins were not detected in the cell lysate fraction. These proteins should be present in the cell lysate samples as the sEV proteins originate from the same cells. Studies of breast cancer cells and lung cancer cells also find that sEVs broadly reflect their cell of origin, while only a subset of the proteins is enriched in sEVs ,37. ThesThe mechanisms guiding the packaging of proteins into sEVs is not completely understood, but ESCRT -dependent and independent systems, tetraspanins, and lipid-dependent mechanisms are reported to be involved ,39. The From the proteins annotated to the KEGG pathways colorectal cancer and PI3K-AKT signalling, one peptide of the TGF-β signalling proteins TGFB1 and TGFBR2 was detected in sEVs but not in the whole-cell lysates. Interestingly, these proteins are not listed as associated with sEVs isolated from CRC in the Exocarta.org database or found in EVs isolated from HCT116, HT29 or SW620 according to the deposited data in another study , emphasiChanges in the amount of a protein may not necessarily be accompanied by parallel changes in their activity status. Although we do not always know whether phosphorylation is associated with activation or inactivation, phosphorylation levels can be a more accurate measurement of protein activity compared to protein amounts. This makes the phosphorylation data included here an important reference. The phosphorylation state of our proteins showed a large overlap between our cell lines whereby less than 10% of phosphopeptides were detected exclusively in one of the cell lines. The differential expression of the HCT116, HT29 and SW620 cell lines revealed specific differences, which were further shown through GO term and KEGG pathway enrichment analysis. The differences in phosphopeptides included changes in a multitude of proteins normally associated with important changes in colorectal cancer. We found a greater abundance of phosphorylated MAPK1/ERK2 Y (173–191) in HCT116 cell lysates compared to other cell lines. This is in agreement with the cell line’s KRAS mutation, which is linked to increased ERK activity  and has sEVs extracted from a bodily fluid do not solely originate from cancer cells, but also from other cells of the body. In this study, we therefore chose to study sEVs extracted from cell lines as we can clearly know what cells the sEV originate from. Cancer cell lines have different characteristics from primary tumours but do have cellular and molecular traits that resemble tumours . The stuTogether, our results show specific enrichment for each of our three cell lines in terms of key signalling proteins driving CRC carcinogenesis while also highlighting the whole cell’s representation within the matching sEV. They additionally confirm the value of studying the proteome within sEVs as potential sources for further biomarker research ,63. It iHaving a broader overview of signalling pathway proteins in cells and sEV cargo allows for opportunities to further study potential CRC biomarkers and advance novel diagnostics and prognostics as well as understand potential combinatorial treatment targets to investigate. While we argue that this study presents a greater overview of signalling pathway proteins in cells and sEVs through the investigation of their proteomic landscape, we also recognise some important challenges existing still in the field of sEVs. The isolation of sEVs gives limited material, which can hinder certain types of analysis, such as phosphoproteomic analysis, which requires large amounts of input material. Furthermore, there is inherent variability in sEV proteomic isolation and the release of sEVs between cell types can vary ,68. HowesEVs represent their cell of origin, and their release into biofluids makes them an attractive diagnostic target. Here, we have used proteomics to globally compare the content of sEVs to their parental cell lysates in three CRC cell lines. We found an association between proteins present in cell lysates and sEVs, thus highlighting that they do represent their parental cell. However, this overlap is not complete, and specific pathways are enriched in sEVs, such as those involved in packaging sEVs. Interestingly, we also found the enrichment of proteins relevant in cancer, such as proteins involved in the PI3K-AKT pathway. These results highlight how sEVs can be used as a readout for processes in the parental cells, but, because certain pathways are enriched, the targets that are chosen as cancer biomarkers must be chosen with care. We hope that the data presented here can be an important reference for further research identifying potential cancer biomarkers from sEVs.


Here we report the extensive profiling and comparison of 21 kinase inhibitors approved by the FDA for oncology indications since June 2018 and 13 previously approved comparators on panels of 255 biochemical kinase assays and 134 cancer cell line viability assays. Comparison of the cellular inhibition profiles of the EGFR inhibitors gefitinib, dacomitinib, and osimertinib identified the uncommon EGFR p.G719S mutation as a common response marker for EGFR inhibitors. Additionally, the FGFR inhibitors erdafitinib, infigratinib, and pemigatinib potently inhibited the viability of cell lines which harbored oncogenic alterations in FGFR1-3, irrespective of the specific clinical indications of the FGFR inhibitors. These results underscore the utility of in vitro kinase inhibitor profiling in cells for identifying new potential stratification markers for patient selection. Furthermore, comparison of the in vitro inhibition profiles of the RET inhibitors pralsetinib and selpercatinib revealed they had very similar biochemical and cellular selectivity. As an exception, an NTRK3 fusion-positive cell line was potently inhibited by pralsetinib but not by selpercatinib, which could be explained by the targeting of TRK kinases in biochemical assays by pralsetinib but not selpercatinib. This illustrates that unexpected differences in cellular activities between inhibitors that act through the same primary target can be explained by subtle differences in biochemical targeting. Lastly, FLT3-mutant cell lines were responsive to both FLT3 inhibitors gilteritinib and midostaurin, and the PI3K inhibitor duvelisib. Biochemical profiling revealed that the FLT3 and PI3K inhibitors targeted distinct kinases, indicating that unique dependencies can be identified by combined biochemical and cellular profiling of kinase inhibitors. This study provides the first large scale kinase assay or cell panel profiling study for newly approved kinase inhibitors, and shows that comprehensive in vitro profiling of kinase inhibitors can provide rationales for therapy selection and indication expansion of approved kinase inhibitors.During the last two decades, kinase inhibitors have become the major drug class for targeted cancer therapy. Although the number of approved kinase inhibitors increases rapidly, comprehensive  Her) and stored as solids at 4 °C. Before experiments, compounds were dissolved in 100% dimethyl sulfoxide (DMSO).All kinase inhibitors were purchased from commercial vendors  of every individual kinase (50) on primary and secondary kinase targets were determined in duplicate 10-point dilution series of compounds in MSA for most kinases. For MEK1 and MEK2, inhibition of enzymatic activity was measured in enzyme-linked immunosorbent assays (ELISA).Compounds were profiled on a panel of 255 wild-type kinases in either mobility shift assays (MSA) or immobilized metal ion affinity particle (IMAP) assays at a compound concentration of 1 µmol/L and an ATP concentration within 2-fold of the affinity for ATP . Kinome trees were generated using Coral .Cell lines were purchased from the American Type Culture Collection (ATCC) , German Collection of Microorganisms and Cell Cultures (DSMZ) , Japanese Collection of Research Bioresources (JCRB) , or RIKEN BioResource Research Center , as indicated in 50 values of kinase inhibitors were calculated by fitting a 4-parameter logistic curve to the percentage viability values using IDBS XLfit5 . All curves were visually inspected and submitted to an F-test as implemented in XLfit5. Curves with F > 1.5 were invalidated. For some compounds, biphasic curves were measured in one or more cell lines, indicating a dual mechanism of action in these cell lines (10log(IC50 [nmol/L]) values were used. The IC50 was limited to the maximum tested inhibitor concentration when the IC50 exceeded the maximum tested concentration. Reported average IC50 values indicate the geometric average.The effect of compounds on cell viability was determined by measuring intracellular ATP content as an indirect readout of cell number . Cells wll lines . In thes10logIC50 values of the kinase inhibitors on 134 cancer cell lines were compared by hierarchical clustering with the Ward method, using 1 – Pearson correlation (r) as clustering distance, as described before  , Cancer hibition . J82 wasGene expression profiles of 19,177 genes were retrieved from the DepMap database (version 22Q1) . Cell lisynergyfinder R package (version 3.2.10)  method, using the  3.2.10) . The ZIP 3.2.10) . A ZIP sp-value < 0.05 was considered significant. Multivariate analysis of variance (MANOVA) was performed for multi-group comparison. Benjamini-Hochberg corrected p-values < 0.2 were considered significant.All statistical analyses were performed in R (version 4.1.2), unless otherwise indicated. A two-sided Mann-Whitney test was performed for two-group comparisons. A NTRK) fusion-positive solid tumors. The progress towards tissue-agnostic indications underscores the importance of identifying suitable biomarkers for successfully advancing the development of kinase inhibitors.From June 2018 to February 2021, a total of 20 small molecule kinase inhibitors were approved for clinical use in cancer Table 1.50 values of the compounds on their primary target and relevant secondary kinase targets were determined in 10-point dose-response curves which allows for comparing the potency of similar inhibitors on their primary or secondary targets  and p.L858R), and EGFR harboring the p.T790M substitution which is associated with resistance (the single mutation p.T790M and the double mutants p.ex19del/T790M and p.L858R/T790M)  or p.L858R (11–18) mutations. NCI-H1975 harbors the sensitizing p.L858R mutation in combination with the p.T790M resistance mutation . The colon carcinoma cell line SW48, which harbors an EGFR p.G719S mutation, was the most sensitive cell line for gefitinib and dacomitinib, and was also sensitive to osimertinib . In line with these findings, the indication of another second-generation EGFR inhibitor, afatinib, was recently expanded to also include NSCLC harboring p.G719X mutations . The endometrial carcinoma cell line RL95-2, harboring a p.A289V mutation, was among the most sensitive cell lines for all three inhibitors . The p.A289V mutation lies within the extracellular domain, which is frequently mutated in glioblastoma , which suggests that the p.A289V mutation is not a strong predictive biomarker for sensitivity to the three EGFR inhibitors in endometrial cancers. SNU-C2B, harboring a p.R165Q mutation, which also lies in the extracellular domain, was sensitive to gefitinib, but not to dacomitinib or osimertinib, while the endometrial cell line HEC-1-B harbors a p.A864V mutation and was relatively sensitive to all three inhibitors .To determine how the response of the different fitinib ;. The colutations . Furtherfitinib ;. The colblastoma . Interesfitinib ;. The colfitinib ;. The col50 profiles of the three EGFR inhibitors between EGFR-mutant and wild-type cell lines shows preferential targeting of EGFR-mutant cell lines  and osimertinib (33-fold)  or fusions involving either FGFR2 or FGFR3  (FGFR3-TACC3) and SW780 (FGFR3-BAIAP2L1) are representative models for the therapeutic indication of erdafitinib. Due to the lack of available FGFR2 fusion-positive cholangiocarcinoma cell lines, no representative models for the clinical indications of pemigatinib and infigratinib could be included in the study. The other six FGFR-altered cell lines harbored FGFR alterations or had a tissue origin other than those included in the indications of the three inhibitors  over the other FGFR-altered cell lines , and the colorectal adenocarcinoma cell line NCI-H716 (FGFR2-COL14A1 fusion) were also potently inhibited by all three inhibitors, with IC50 values ranging between 1.8 and 28 nmol/L  translocation  Table 4. cancers , there aar brake . This mugratinib . All thrgratinib , 54, whiFGFR-altered cell lines over FGFR wild-type cell lines in the cell line viability assays . This mutation is associated with lorlatinib resistance in both ALK-positive NSCLC and ALK-mutant neuroblastoma and may bypass ALK inhibition by reactivating MEK/ERK signaling  and four ALCL cell lines  harboring an e fusion Table 5.blastoma , and in blastoma , but is ignaling –61. For ALK-altered cell lines compared to the ALK wild-type cell lines, a MANOVA was carried out. This showed that all five ALK inhibitors were significantly more potent in the ALK-altered cell lines  . For gilteritinib, potential application in lorlatinib-resistant cancer has been proposed   . RoughlyNTRK1-3) . NTRK geer types . On the  than 1% .;;50 on all three TRK kinases compared to larotrectinib  was included in our cell line panel  (FLT3 ITD-mutant cell lines MOLM-13 and MV4-11 showed some response to larotrectinib (50)  Table 2,tant AML .RET gene fusions or mutations. Both inhibitors are investigated in tissue-agnostic clinical trials . The inhibitors showed sub-nanomolar potency on RET in biochemical assays, but selpercatinib was almost two-fold more potent on RET compared to pralsetinib  and the thyroid carcinoma cell line TT (p.C634W). These cell lines are representative models for the therapeutic indications of the RET inhibitors and were among the most sensitive of the complete panel for both inhibitors  by pralsetinib , which results in constitutive, ligand-independent activation of the receptor. Gilteritinib was, on average, eight times more potent in the FLT3 ITD-mutant cell lines compared to midostaurin, which is in agreement with the difference in biochemical potency. Unsurprisingly, the FLT3 ITD-mutant cell lines were among the most sensitive for both inhibitors , the two FLT3 ITD-mutant cell lines were the most potently inhibited of the eight AML cell lines, suggesting that FLT3 ITD-mutant cell lines are especially sensitive to duvelisib. This finding is in line with the observed activation of the PI3K/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway by constitutive FLT3 activation in FLT3 ITD-mutant AML  were approved for treatment of diverse B-cell leukemias . All thrin vitro activity of BTK inhibitors. BTK inhibitors exert their therapeutic activity by promoting egress of malignant B-cells from lymph nodes  amplified cell lines (44-fold), as did ibrutinib (254-fold) and zanubrutinib (45-fold), suggesting HER2 is a common off-target of the currently approved BTK inhibitors . Sunitinib was approved for treatment of imatinib-resistant GIST in 2006, and in 2013 regorafenib was approved for the treatment of imatinib- and sunitinib-resistant GIST. Approximately 80% of GIST have activating mutations in utations . Treatmehibitors . AvapritPDGFRA and the PDGFRA p.D842V mutant with sub-nanomolar potency in our biochemical assays (KIT p.D816V mutant. This mutation is frequently observed in systemic mastocytosis, which is an additional indication for avapritinib. Ripretinib was also active on these mutants, although to a lesser extent (Avapritinib inhibited wild-type l assays Table 2.FIP1L1-PDGFRA fusion-positive CEL cell line EoL-1 and the KIT p.N822K mutant AML cell line Kasumi-1. Both inhibitors were most active in these cell lines, suggesting that both avapritinib and ripretinib could be beneficial for treatment of PDGFRA- and KIT-altered hematologic malignancies (In line with their overlapping primary targets, the two inhibitors cluster together based on their cellular inhibition profile Figure 1In addition to avapritinib and ripretinib, other inhibitors potently inhibited the viability of EoL-1 and Kasumi-1 Table S3;;KIT-mutant GIST and systemic mastocytosis (Based on the dual sensitivity of EoL-1 and Kasumi-1 to PDGFRα/KIT and MEK inhibition, we sought to determine whether the combination of either avapritinib or ripretinib with a MEK inhibitor acted synergistically in these cell lines. Interestingly, all tested combinations had additive effects in EoL-1, which may indicate that EoL-1 is fully dependent on MEK signaling through the FIP1L1-PDGFRα fusion protein, and further inhibition of MEK does not provide an additional inhibitory benefit compared to inhibition of PDGFRα alone ;. Strikinocytosis . Our resAs the spectrum of approved kinase inhibitors for oncology indications expands, it is essential to determine and compare the potency and selectivity of these inhibitors to make the most informed decisions about therapy selection and label expansion. This study provides a head-to-head comparison of 21 newly approved kinase inhibitors and 13 previously approved comparators by extensive biochemical and cell panel profiling. For 11 of the newly approved inhibitors this is the first large-scale kinase profiling study in the public domain, and for seven this is the first profiling study on a large cancer cell line panel. We find that the biochemical characteristics of these kinase inhibitors do not always translate to a cellular context, indicating that cell panel profiling can provide additional insights into kinase inhibitor activity compared to biochemical profiling alone. Lastly, the patient stratification markers currently described in the FDA labels of kinase inhibitors were confirmed to a high degree in our assays, and we additionally identified potential predictive drug response biomarkers which may warrant further investigation for label expansion of the approved kinase inhibitors.The original contributions presented in the study are included in the article/Conceptualization, JK, GZ, and JU; Resources, GZ and YK; Investigation, JK, WR, JD, MP, YG, TB, AD, JM, JU, YN, YK, JR, NW-S, and GZ; Visualization, JK; Writing – original draft, JK and GZ; Writing – review and editing, WR, YG, and TB; All authors contributed to the article and approved the submitted version.GZ is managing director and shareholder of Oncolines B.V. JK, WR, JD, YG, TB, AD, JM, JR, NW-S are employees of Oncolines B.V. MP is employee of NTRC Therapeutics B.V. JU is employee of Acerta Pharma B.V., a member of the AstraZeneca Group. YN, YK are employees of Carna Biosciences, Inc.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.