One of the ways cancer cells achieve drug resistance is through the expression of efflux pumps that actively pump drugs out of the cells. To date, several studies have investigated the potential of using 3-dimensional (3D) multicellular tumor spheroids (MCSs) to assess drug resistance; however, a unified system that uses MCSs to differentiate between multi drug resistance (MDR) and non-MDR cells does not yet exist. In the present report we describe MCSs obtained from post-diagnosed, pre-treated patient-derived (PTPD) cell lines from head and neck squamous cancer cells (HNSCC) that often develop resistance to therapy. We employed an integrated approach combining response to clinical drugs and screening cytotoxicity, monitoring real-time drug uptake, and assessing transporter activity using flow cytometry in the presence and absence of their respective specific inhibitors. The report shows a comparative response to MDR, drug efflux capability and reactive oxygen species (ROS) activity to assess the resistance profile of PTPD MCSs and two-dimensional (2D) monolayer cultures of the same set of cell lines. We show that MCSs provide a robust and reliable in vitro model to evaluate clinical relevance. Our proposed strategy can also be clinically applicable for profiling drug resistance in cancers with unknown resistance profiles, which consequently can indicate benefit from downstream therapy.One of the hallmarks of cancers is their ability to develop resistance against therapeutic agents. Therefore, developing effective  MDR is considered as the major impediment to the success of chemotherapy3, and leads to an unprecedented decrease in the survival rate4 of cancer patients. The development of MDR occurs at an alarmingly high rate during the treatment phase of various cancers5 and the underlying mechanisms of MDR in cancer and subsequent relapse have puzzled researchers worldwide6. Only a small subset of tumor cells has been reported to be sufficient for progressive resistance to chemotherapy, leading to the development of MDR in at least 50% of cancer patients7. The basic underlying MDR mechanism is associated with 5 events: (i) increased drug efflux, (ii) decreased drug influx, (iii) increased drug metabolism, (iv) increased DNA repair and (v) decreased apoptosis9. The major players involved in drug efflux related MDR mechanisms are the ATP-binding cassette (ABC) transporter proteins, such as P-glycoprotein (P-gp/MDR1), multidrug resistance-associated protein 1 (MRP-1), and breast cancer resistance protein (BCRP)10. Overexpression of P-gp/MDR-1, a membrane-bound active drug efflux pump, appears to be the most prominent contributor to MDR development in cancer cell lines12.Cancer drug resistance is an unmanageable outcome of a series of cascade events that are altered in cancer cells during disease progression. Multidrug resistance (MDR) is one of the mechanisms of cancer drug resistance, which is described as the resistance to multiple chemotherapeutic drugs with different structures and functional activity14. The significance of drug transporters has been extensively studied by Dirk et al.15, the study showed the role of drug transporters for MDR in HNSCC. Subsequently, other researchers have also established a correlation between HNSCC and MDR17. Presently, there are limited well-defined in vitro models and assay systems available to classify resistance into MDR and non-MDR categories. First of the two presently employed strategies uses treatment-sensitive in vitro cell lines that are exposed to a specific therapeutic anticancer drug until the designated cell line attains a resistance genotype18. The second strategy uses a genotype-based assay to focus on the identification of genetic anomalies arising in the treatment-resistant cell lines19. These two tactics have been exploited to integrate numerous MDR pump inhibitors into cancer treatment modalities; however, the outcomes were not sufficiently effective for clinical translation20. These strategies have been associated with various discrepancies concerning the differentiation between treatment-sensitive and treatment-resistant cancer cells in vitro10.A directly proportional relationship exists between elevated ABC transporters levels and the progress of MDR, as has been previously reportedin vitro model21. MCSs are self-assembled aggregates of cancer cells, which can mimic the complex micro-environmental milieu of the tumor tissue observed in vivo22. Sutherland’s integration of in vitro three-dimensional (3D) culture methods into cancer research nearly four decades ago, triggered increased interest in the application of MCSs in drug discovery and understanding of the basic biological mechanisms underlying tumor progression and response to treatment23. MCSs show an intermediate but clinically relevant complexity between in vitro 2D cell cultures and in vivo solid tumors and they have been assigned a relevant platform for in vitro drug screening24. They mimic the complex cell-cell adhesion and cell-matrix interactions in solid tumors, which results in the metabolite gradient generation for nutrients and growth factor signals as observed in vivo22. As result of the metabolite gradient and a complex microenvironment, MCSs contain proliferating, quiescent, and necrotic zones, much like the internal milieu of human tumors25. In addition, owing to their multicellular nature, MCSs spontaneously develop MDR against many chemotherapeutic drugs27, thus making them the appropriate model system for the purpose of the present study. Recently, members of our group reported a marked treatment response difference between 2D cell cultures and MCSs of HNSCC pertaining to epithelial-mesenchymal transition and stem cell characteristics, suggesting that 3D cell cultures are a clinically relevant superior model to 2D monolayers for the investigation of new therapeutic targets28. However, there is no well-defined in vitro method or criteria for the identification of the resistance status of cancer cells. Furthermore, the integration of these two approaches into translational research is challenging and unlikely to be implemented in the near future.Multicellular tumor spheroids (MCSs) are considered to be the most relevant pre-clinical, high throughput in vitro model and assay system for the profiling of drug resistance status in cancer cells using MCSs obtained from PTPD HNSCC. This report constitutes a comparative investigation between 2D and MCSs for the assessment of drug resistance profile of the same cell. Our strategy combines drug screening, real-time fluorescence microscopy, and flow cytometry for rapid identification of drug resistance status using MCSs, so that a beneficial personalized treatment regimen can be offered to patients. The cell lines used were previously established by the members of our group29. Briefly, we have investigated the drug response profiles of LK0917 (gingiva), LK0902 (tongue), and LK1108 (hypopharynx) cells to doxorubicin, cisplatin, and methotrexate in 2D and MCSs. In order to establish the drug response profiles for these cell lines, we first investigated their efflux pump activities by assessing the differential uptake of calcein acetoxymethyl ester , a substrate for the P-gp and MRP1 efflux pumps30, using real-time live cell fluorescence imaging31. We further studied the reactive oxygen species (ROS) generation in both in vitro models using the 2′,7′-dichlorofluorescein diacetate (DCFDA) assay, in order to have a better understanding of the MCS microenvironment of the PTPD HNSCC cell lines, which we then used for further assessment of MDR status. Finally, we validated our findings with a flow cytometry-based (FACs) assay for functional detection and profiling of MDR phenotypes in 2D cell cultures and MCSs by assessing calcein-AM uptake in the presence of specific efflux pump inhibitors.In the present study, we describe a fast and robust in vitro experimental workflow used for determining the MDR profile is provided below . The research adhered to the tenets of the Declaration of Helsinki.The schematic representation of the low Fig. . The cel29, biopsies were excised from the tumors of gingiva, tongue, and hypopharynx and harvested immediately for establishing 2D cell lines in vitro. MCSs and 2D monolayer cells were developed using the same cells. Categorical segregation of MDR and non-MDR cancer cells was performed using a combination of anticancer drug screening on 2D and MCSs, differential uptake of calcein-AM in time-lapse fluorescence microscopy, monitoring of real-time ROS generation in the MCSs and 2D cultures, and a flow cytometry-based MDR assay. Finally, MCSs obtained from LK0917 gingiva tumor (referred to as MCS17 hereafter), LK0902 tongue tumor (referred to as MCS02 hereafter), and LK1108 hypopharynx tumor (referred to as MCS08 hereafter) were randomly selected for the development of a multidrug cancer resistance model system.As reported earlier2 atmosphere at a temperature of 37 °C. Once cells reached 80% confluence, single cell suspensions were prepared by detaching the cells via mild enzymatic dissociation using 0.25% trypsin and 0.02% EDTA solution (Thermo Fisher Scientific). Trypsin was inactivated by adding complete KSFM medium. The number of live cells/mL were determined by adding 10 µL of 0.4% trypan blue (Thermo Fisher Scientific) to 10 µL of single cell suspension, mounting the mixture on Luna cell counter slides, and counting the cells on the automatic Luna cell counter . For the generation of MCSs sized 300–500 µm, 200 µL of LK0917 (MCS17), LK0902 (MCS02), and LK1108 (MCS08) single cell suspensions were seeded in ultra-low attachment (ULA) plates  at varying cell densities in the range of 0.25–0.75 × 105 cells/mL. The plates were incubated at a humidified 5%CO2 atmosphere at 37 °C (48–72 hrs) for maturation and assessment of MCSs diameter variation with respect to cell density. Progression of spheroid formation was imaged on a daily basis using a bright field microscope  with 5× or 10× objectives and further image analysis was performed. Formation of MCSs was also monitored every 3 hours by live-cell imaging using Incucyte Zoom™  throughout the entire spheroid formation process with a phase-contrast set up using the 10× objective and the images were analyzed.The PTPD HNSCC cell lines LK0917, LK0902, and LK1108 were revived from frozen stocks in 10 mL complete keratinocyte serum-free growth medium , supplemented with 10% fetal bovine serum , and penicillin 50 IU/mL and streptomycin 50 µg/mL (Thermo Fisher Scientific) and incubated in a humidified 5% CO2 atmosphere for 24 hours before drug treatment. After 24 hours, the culture medium was carefully aspirated and 2D cultures of three cell lines were treated with cisplatin (3.33–333 µM), doxorubicin (170–17000 µM), and methotrexate (2.2–220 µM) prepared from their stock solutions  in complete KSFM medium. Cells were treated with drugs for 72 hours. Generation of MCSs was performed as described in the previous section. The cell density for the cytotoxicity assays was 0.7 × 105 cells/mL for both MCS17 and MCS02 and 0.5 × 105 cells/mL for MCS08. Tumor spheroids were incubated at 37 °C and 5% CO2 atmosphere for 48 hours. After 48 hours of spheroid formation, MCS17, MCS02, and MCS08 were treated with different doses of cisplatin, doxorubicin, and methotrexate at the same concentrations used for the 2D cell cultures, by replacing 50% of the culture medium with freshly prepared drug-supplemented32 medium, followed by incubation at 37 °C and 5% CO2 atmosphere for 72 hours. For each drug concentration, 8 MCSs were used in triplicates, with effective drug concentrations equivalent to those used for the 2D cell cultures. Cell cytotoxicity in the drug-treated 2D cell cultures was assessed using the CellTiter96® AQueous One Solution Cell Proliferation Assay . Briefly, at the end of 72 hours, the drug supplemented medium was replaced with 317 µg/mL MTS reagent-supplemented medium. For a total volume of 200 µL, 40 µL of the MTS reagent was added into each well and the plates were incubated at 37 °C and 5% CO2 atmosphere for 3 hours. At the end of the incubation period, absorbances at 490 nm and 650 nm were recorded using a microplate reader . Three independent set of experiments were performed with triplicates for each concentration of drugs per study.Single cell suspensions (2D) of LK0917, LK0902, and LK1108 cell lines were seeded in 96-well flat bottom plates at a cell density of 8000 cells/well in 200 µL complete medium at 37 °C and 5% CO31. In the present context, we have utilized the enhanced efflux properties of MDR tumor cells to generate separate calcein-AM uptake kinetic profiles for 2D and MCSs. We monitored the real-time calcein uptake and intracellular calcein accumulation in 2D cell cultures and MCSs of LK0917, LK0902, and LK1108 using live-cell fluorescent imaging over a period of 11 hours (images represent up to 10 hrs) with image acquisition at 20-minute intervals.Acetoxymethyl ester (AM) derivatives of fluorescent probes such as calcein are actively pumped out of cancer cells with higher MDR1 and MRP1 expression5 cells/mL (2D) and cultured for 24 hours before the start of the experiment. MCS17, MCS02, and MCS08 were generated as described in the section above. After spheroid formation, the KSFM growth medium was carefully decanted without disturbing the spheroids. 2D and MCSs were incubated in serum-free KSFM medium containing non-fluorescent calcein-AM (1 mM in Dimethyl sulfoxide (DMSO), Sigma AB, Malmo, Sweden) at a final concentration of 1 µM, for 12 hours at 37 °C and 5% CO2 atmosphere. During the 12-hour incubation period, phase contrast and green fluorescence  images of the monolayer/spheroids were acquired every 15 minutes using time-lapse fluorescent microscopy. A 10× objective was used for image acquisition . Three independent sets of experiments were performed with triplicates for each individual study.In brief, cells were seeded in 96-flat bottomed plate (2D) and ULA (for MCSs) at a density of 0.7 × 1017, MCS02, and MCS08 were prepared using various cell densities. Here, we seeded 1 × 104, 1.5 × 104, and 5 × 104 cells/well for MCSs formation and assessed the calcein-AM uptake of the generated MCSs using the same procedure described in the section above.For this experiment, MCS5 cells/mL and cultured in complete KSFM medium for 24 hours before the experiment. MCS17, MCS02, and MCS08 were generated as previously described, and the complete growth medium was replaced with serum-free KSFM containing DCFDA (20 µM). DCFDA is a hydrophobic fluorogenic dye used in the measurement of intracellular ROS activity. After cellular uptake, DCFDA is deacetylated by cellular esterases into a non-fluorescent hydrophilic compound that cannot exit cells and which is further oxidized into a highly fluorogenic product in the presence of ROS, which can be detected by fluorescence microscopy. The cell monolayers and the ULA plates containing the spheroids were immediately incubated in the Incucyte ZoomTM (Sartorius AG) live-cell imaging microscope at 37 °C and 5% CO2 atmosphere. Green fluorescence images were automatically obtained every 20 minutes for a total duration of 60 minutes. Three independent experiments were conducted for the study with triplicates for each set of experiment. 2D cells were seeded at a density of 0.7 × 106 cells/mL were prepared in complete KSFM medium. For each sample to be assayed, 4 sets of tubes were prepared and each set-in triplicates.For the experiments on 2D cell cultures, single-cell suspensions of LK0917, LK0902, and LK1108 cell lines were prepared by trypsinization and counted using an automated cell counter as described in the previous section. For each cell line, 1 × 1017, MCS02, and MCS08 was initiated 48 hours before performing the assay. After spheroid formation, single cell suspensions were prepared from MCSs using 0.25% trypsin and 0.02% EDTA solution. The trypsinization time for MCS17 and MCS02 were 10 minutes and for MCS08 was 20 minutes. Immediately following trypsinization, complete KSFM medium was added in a 1:1 ratio. The MCSs were gently pipetted several times for complete dissociation. In the following step, different MDR pathway inhibitors such as novobiocin , verapamil , and MK-571  provided with the MDR assay kit (Abcam 204534), were added to the reaction tubes. Complete KSFM medium containing 1% DMSO was used as control. The reaction tubes were then incubated at 37 °C for 5 minutes after gentle mixing, followed by the addition of efflux green detection reagent, gentle mixing, and incubation at 37 °C for 30 minutes. Following 30 minutes of incubation, 5 µL of propidium iodide (PI) provided with the kit was added to the reaction mixture before performing flow cytometry. The cellular green fluorescence signal of efflux green detection reagent was measured using BD FACSARIA III  in the PI-negative cell population using identical PMT voltage settings. Median fluorescence intensity (MFI) values were calculated for each triplicate set of reaction tubes using the DIVA software (BD Biosciences) and FlowJo 2.0 (BD Biosciences).For the experiments on MCSs, the spheroid formation for MCSTo obtain the automated real time drug uptake information, accurate segmentation of MCSs is important for quantitative analysis of red and green channel fluorescence of the spheroids. Intensity inhomogeneity over the spheroids and poor contrast in the boundary of spheroids are the major bottlenecks for acceptable segmentation. Traditional image segmentation techniques such as thresholding, region growing, and level set methods are unable to segment the spheroid with sufficient accuracy.33, which takes an entire image as input and give a dense segmentation. The detailed architecture of P-Net is shown in Fig. 34 was used in P-Net to preserve the resolution of feature maps and enlarge the receptive field to incorporate larger contextual information. The images are resized to 512 × 512 pixels to reduce the time of segmentation. Several augmentation techniques such as flip and rotation were performed to increase the number of training images. A stochastic gradient-based optimization ADAM35 was applied to minimize the cross-entropy based cost function.Therefore, we have used the P-Net Fig.  based fu36. The background and foreground weights were maintained at 1:10 ratio and training was performed up to 20 epochs.The learning rate for the ADAM optimizer was set to 0.0001 and over-fitting was reduced by using dropoutThe hyper-parameters were determined based on the validation dataset. The qualitative segmentation results are shown in Fig. 2D cell culture and MCS image analyses were performed using MATLAB 2016b . ANOVA and Tukey’s multiple comparison test was performed in GraphPad Prism 8  for comparing different data sets. Values are presented as mean ± S.D. A p value < 0.05 was considered as statistically significant. All experiments were performed in triplicates.We studied the drug response profiles of 2D and MCSs obtained from LK0917, LK0902, and LK1108, to doxorubicin Fig. , cisplat38. In 2D and MCSs of all cell lines, we observed lowest sensitivity to doxorubicin (LK0917 displayed lowest IC50 in comparison to the other two cell lines i.e. LK0902 and LK1108) for both the in vitro model, followed by cisplatin and methotrexate. In terms of drug resistance, LK1108 appeared to be the least sensitive cell line to treatment having the highest IC50 values for all the three drugs tested, followed by LK0902, and LK0917. However, the IC50 in case of methotrexate (2D) remains the same for all the tested cell lines. Interestingly, although large differences could not be observed between the drug responses of 2D cell cultures and MCSs obtained from LK0902 and LK0917 cell lines to cisplatin and methotrexate, significant differences were observed between the drug responses of LK1108 2D cell cultures and MCSs to doxorubicin. In addition, the difference in response of LK1108 2D cell cultures to cisplatin and methotrexate could be observed in the LK1108 MCSs , MCS02 (middle panel), and MCS08 (lower panel) as exemplified in Fig. 17 followed by MCS02 and MCS08. This suggests that the efflux pump activity was lowest in MCS17 followed by MCS02 and MCS08, which is consistent with the drug response profiles provided in Fig. 08 was the most resistant to drug treatment, indicated by significantly higher IC50 values  and indeed, the MCSs from these cells showed the highest efflux of calcein-AM over time, indicated by lowest green fluorescence over time, without penetration to the spheroid core. Likewise, MCS17 was the least resistant to drug treatment, indicated by significantly lower IC50 , which showed the lowest efflux of calcein-AM over time, indicated by the highest green fluorescence over time, with penetration into the spheroid core and without complete expulsion over a period of 12 hours. These findings suggest that MCS17, MCS02, and MCS08 can be characterized as treatment sensitive, moderately resistant, and highly resistant, respectively, based on their efflux pump activity  values for the 2D cell cultures and MCSs obtained from LK0917, LK0902, and LK1108 cell lines in the presence and absence of the specific efflux pump inhibitors verapamil, novobiocin, and MK-571 against MDR1, BCRP, and MRP1, respectively, using flow cytometry analysis44, out of which 90% are HNSCCs. Early-stage disease progression is curable by surgical removal of tumor tissue and radiotherapy, but the prognosis for recurrent disease onset is still challenging and puzzling45. Presently, chemotherapeutic drugs such as cisplatin, 5-fluorouracil (5-FU), and taxanes such as paclitaxel and docetaxel, are the standard treatment options for recurrent or advanced HNSCC. However, the variability and robustness of these treatment modalities are not very well understood47. MDR against cytotoxic drugs is regarded as the main clinical impediment in using chemotherapy in HNSCC. MDR is a result of the interplay between a diversity of factors, which include overexpression of the transporter molecules MDR1, MRP1, and BCRP54. In spite of this well-known phenomenon, effective detection methods are still lacking for correct characterization of MDR status in cancer cells. Development of strategies that enable this characterization can prove to be highly effective in devising targeted treatment regimens against sensitive and resistant cancer cells. Presently, commonly used detection methods include polymerase chain reaction (PCR), in-situ hybridization (ISH), and RNase protection assays (RPAs) for the quantification of MDR1 mRNA levels. Western blotting and immunohistochemistry have also been used for the detection of MDR proteins59. Previously, researchers have shown that the expression of MDR1, MRP1 and MRP7 are refractory factors in head and neck cancer chemotherapy58. In another study, it has been shown that the expression of hypoxia-inducible factor 1α (HIF-1α) is significantly correlated with MDR1 expression in human laryngeal cell carcinoma (LSCC) and that these two proteins might serve as potent biomarkers for predicting the progression of malignant and metastasis of human LSCC59. In the present study, we have attempted to simplify the identification of MDR status in PTPD HNSCCs by combining drug screening, measurement of the difference in calcein-AM uptake studied using live-cell fluorescence imaging, fluorescence-based assessment of ROS activity, and flow cytometry-based prediction of ABC transporter involvement. We performed a comparative assessment of 2D cell cultures and MCSs and observed noticeable differences between the two in vitro systems. Tumor spheroids were introduced as model systems by Sutherland et al., owing to their resemblance to solid tumors in many structural and microenvironmental aspects, and they serve as the most reliable in vitro model for investigating therapeutic and mechanistic approaches60.The global annual occurrence of head and neck cancers exceeds 0.5 million50 values compared to the 2D cultures. Interestingly, LK1108 cells required the highest dose for all three tested drugs in order to achieve 50% inhibition, irrespective of the culturing method, which indicated that among the three cell lines, LK1108 showed the highest resistance to treatment. On the other hand, the lowest IC50 values were observed for LK0917, indicating this cell line as the most drug sensitive among the three cell lines. These findings suggested the MDR status of LK0917 < LK0902 < LK1108.In the present study, we have used 2D cell cultures and MCSs obtained from the PTPD LK0917, LK0902, and LK1108 HNSCC cell lines. Firstly, we performed a treatment response cell viability assay in the presence of cisplatin, doxorubicin, and methotrexate to assess drug cytotoxicity in both 2D cell cultures and MCSs (Fig. 64. We chose live-cell fluorescence imaging because dynamic cellular changes can be observed using this method, unlike fixed-cell imaging. In addition, real-time fluorescence microscopy is less prone to experimental artifacts, rendering the outcomes more reliable. We did not observe any difference between the calcein-AM uptake profiles of 2D cell cultures obtained from LK0917, LK0902, and LK1108, with total accumulated calcein trapped inside the cells remaining comparable for the three cell lines, hence making it extremely problematic to detect differences in drug resistance activity of these cell lines. However, using MCSs as the in vitro model system we were able to detect differences in drug resistance, indicated by calcein accumulation over time. Over a 12 h time window, we observed the highest calcein accumulation in MCS17, followed by MCS02 and MCS08, which indicated that transporter activity was lowest in the MCS17, followed by MCS02 and MCS08. A comparison of the mean green fluorescence intensities between the 2D cell cultures and MCSs confirmed these findings. In order to assess whether spheroid diameter affected calcein-AM diffusion, we performed the same experiments with varying cell seeding densities and observed the same calcein accumulation pattern, indicating that spheroid diameter did not have any effect on the calcein-AM uptake profiles of MCSs, thus eliminating the possibility of false positives. After establishing the transporter activity based MDR status in these cell lines, we further validated our findings using the DCFDA assay for the detection of ROS activity.Next, we performed live-cell fluorescence imaging of calcein-AM uptake in the 2D cell cultures and MCSs, in order to assess transporter activity, as calcein-AM is a substrate of MDR1 and MRP117, indicated by green fluorescence and no ROS activity indicated by a lack of fluorescence in the MCS02 and MCS08 spheroids that we identified to have higher drug resistance compared to the MCS17. ROS activity is indicative of oxidative stress. It is expected that drug resistant cell lines that actively pump drugs out of the cell have acquired higher survival capacity compared to those that cannot. In this context, lack of ROS activity in the untreated MCS02 and MCS08 spheroids, indicates lack of ROS activity in these cells, which could be attributed to another mechanism of survival, as oxidative stress is detrimental to cell survival, thus indirectly supporting our finding that these cell lines are highly drug resistant, one of the mechanisms being high transporter activity and the other being acquired lack of ROS activity. On the other hand, the presence of ROS activity in the non-resistant MCS17 spheroids shows that these cells are more prone to oxidative stress and therefore they are more responsive to drug treatment. These findings indicate that by assessing drug transporter activity and ROS activity, the MDR status of patient cancer cells can be further characterized based on their drug transporter and ROS activities, which could potentially help determine patients that can benefit from a particular treatment regimen.We observed highest ROS activity in MCSWe have investigated drug resistance in 2D and MCSs to establish an assay system for the determination of cancer cell MDR status in cancers with unknown drug resistance profiles. Using our assay system, we were able to predict the efflux pump activities of three different PTPD-HNSCCs, which is important for determining cytotoxic drug vulnerability and the potential of developing MDR as a result of repeated drug exposure. The methods we described here could potentially be integrated into translational research for obtaining the MDR status of cancers and aiding in the determination of the optimal treatment strategy.Dissecting multi drug resistance in head and neck cancer cells using multicellular tumor spheroids


A shorter duration of PDX growth was observed during F0 to F2 transplantation; concomitantly, increased Oct-3/4 and Sox2 were evidenced in 50% and 33%, respectively, of serial PDXs. Only four cell lines were established. The cell lines exhibited either bile duct (KKK-D049 and KKK-D068) or combined hepatobiliary origin (KKK-D131 and KKK-D138). These cell lines acquired high transplantation efficiency in both subcutaneous (100%) and intrasplenic (88%) transplantation models. The subcutaneously transplanted xenograft retained the histological architecture as in the patient tissues. Our models of CCA PDX and PDX-derived cell lines would be a useful platform for CCA precision medicine.Cholangiocarcinoma (CCA) is a deadly malignant tumor of the liver. It is a significant health problem in Thailand. The critical obstacles of CCA diagnosis and treatment are the high heterogeneity of disease and considerable resistance to treatment. Recent multi-omics studies revealed the promising targets for CCA treatment; however, limited models for drug discovery are available. This study aimed to develop a patient-derived xenograft (PDX) model as well as PDX-derived cell lines of CCA for future drug screening. From a total of 16 CCA frozen tissues, 75% (eight intrahepatic and four extrahepatic subtypes) were successfully grown and subpassaged in Balb/c Rag-2 Cholangiocarcinoma (CCA) is a rare subtype of liver cancer for which the highest incidence and mortality have been reported in northeastern Thailand ,2. The pOpisthorchis viverrini (Ov), through ingestion [Ov-associated CCA is increased xenobiotic metabolizing gene expression [Ov-associated CCA identified a distinct signature with higher rates of TP53, SMAD4, and ARID1A mutations but lower frequencies of IDH1/2 and BAP1 mutations when compared to other Asian and Western populations [Ov-associated CCAs were gathered in cluster 1 and cluster 2 in Jusakul’s study [CCA has high heterogeneity in nature . Severalngestion ,10. A unpression . Mutatioulations ,13,14. Tulations ,16,17. Hulations ,19. The ’s study . The mol’s study  and cyclThere are several models for CCA, including a previously established cell line, a cell line-transplanted xenograft, and a genetically engineered mouse model. The limitation of these models is disease homogeneity ,21. The The patient-derived xenograft (PDX) model is a promising tool for the propagation of a patient’s tumor in an immunodeficient mouse. This PDX is an invaluable asset for the advancement of cancer precision medicine, particularly for rare and aggressive cancers . A highe2 atmosphere.Four CCA cell lines—KKU-055, KKU-100 , KKU-213Sixteen CCA tissue samples were obtained from the Department of Pathology, Faculty of Medicine, Khon Kaen University, Thailand, after the clinical specimens were obtained from the operating room and the pathological specimens were taken as the standard protocol for pathological staging. All tissue samples were histologically diagnosed; 10 were of intrahepatic CCA (ICC), and six were of the extrahepatic subtype (ECC). The study protocol was reviewed and approved by the Ethical Committee for the Human Research of Khon Kaen University (HE571283), based on the Declaration of Helsinki of 1975. Written informed consent was obtained from each subject. Demographical and pathological characteristics of patients and CCA tissues are listed in 3 pieces (2–3 mm each dimension). Non-viable cells were removed. Each tissue was divided into two parts: (1) for transplantation and (2) for molecular characterization and paraffin-embedded tissue preparation. Tissues were transplanted into flank areas of Balb/c Rag-2/Jak3 double-deficient  [For transplantation, frozen CCA tissues were thawed and vigorously washed with PBS three times. Tissues were cut into 8–27 mmlb/c RJ)  or Balb/lb/c RJ)  subcutan2 atmosphere. Cells were maintained in vitro culture system at least 6 months to ensure the immortalization properties.For cell line development, fresh xenograft tissues were prepared as previously described . Cells wAll four newly established cell lines were deposited into the Japanese Cancer Research Resources Bank (JCRB), National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan.To demonstrate the liver cell origin of the cell lines, the expression profile of bile duct or hepatocyte-related genes including cytokeratin 7 (CK7), CK19, γ-glutamyl transferase (GGT), α-fetoprotein (AFP), and albumin (ALB) were determined as previously described . RNA wasPCR products were separated in 1.5% agarose gel in Tris-Borate-EDTA (TBE) buffer. A gel was stained with ethidium bromide solution  and the images were captured by Bio-Rad Gel Doc 2000 .® Identifiler® Plus PCR Amplification Kit . DNA was extracted by the QIAamp® DNA Micro Kit . PCR products were analyzed using ABI Prism 3130 Genetic Analyzer and GeneMapper® ID Software v3.2 (Applied Biosystems).To determine the genetic stabilities of the cell lines, 16 short tandem repeats (STR) of cell lines, original tumor tissues, and patient’s white blood cells (WBC) were compared using AmpFℓSTRTP53 gene mutation was analyzed as previously described in [ribed in . Briefly4 cells were injected intrasplenically as previously described [The xenograft transplantation ability of the cell lines was determined. One to two million cells of each cell line were transplanted subcutaneously into both flanks of Balb/c RJ mice. For intrasplenic transplantation, 5 × 10escribed . Tissues® DAB substrate kit .Hematoxylin and eosin staining of the original CCA tissues and transplanted tumors was performed regularly. For immunohistochemistry staining, a standard protocol using citrate buffer retrieval buffer was used. Signals were enhanced by EnVision-system-HRP  or the Vectastain Elite ABC standard kit . Detection was performed using the HistofineThe sources of antibodies were as follows: anti-CK19  was from Sigma-Aldrich, anti-Ki-67 (MIB-1) was from Dako, anti-epithelial cell adhesion molecule  and anti-Oct-3/4 (C-10) were from Santa Cruz Biotechnology , anti-Sox2 (D6D9) was from Cell Signaling Technology , and biotinylated goat anti-mouse IgG and biotinylated goat anti-rabbit IgG were from Vector Laboratories.The comparison of tissue architecture between the original CCA tissue and transplanted tissue was made by the pathologists. The images were taken by the BZ-8100 Biozero fluorescent microscope. For the quantitation, the immunoreactivity signals were quantified by BZ-II Analyzer  as previously described .r) was calculated using GraphPad Prism version 6.07 .For the correlation study, Pearson’s correlation coefficient  while six were of the extrahepatic subtype (ECC) based on the 7th edition of the AJCC cancer staging classification. The most extended storage duration with successful transplantation was 134 days (19–134 days). After defrosting and cleaning, tissues were transplanted into both flanks of Balb/c RJ mice. Thirteen tissue samples (eight ICC and five ECC) successfully grew in the subcutaneous areas of the mice . No massA similar experiment was carried out in nude RJ mice. Three samples were transplanted into both flanks of mice. The transplantation success rate was comparable to those in Balb/c RJ, but F0 growth in nude RJ took approximately 2 weeks longer. Thus, we selected the Balb/c RJ mice for our PDX generation and further testing.From our protocol, we successfully established the method for generation of the CCA-PDX model, which is highly efficient (67–80% success rate) . The durAmong 12 serially transplantable tissues, four tissues were successfully developed into cell lines. All cell lines were of intrahepatic origin; three were histologically characterized as well-differentiated subtypes  and one was characterized as a mixed adenosquamous subtype . KKK-D068 and KKK-D131 were established from the F0-transplanted tumor while KKK-D138 and KKK-D049 were established from the F1 and F2 tumors, respectively. The morphologies of the cell lines are presented in The authentications of cell lines were performed by STR analysis . DNA froFurther characterization of cell lineage marker was performed using RT-PCR b. ComparTP53 mutation analysis revealed CCC to CGC at codon 72, which will cause missense P72R mutations in KKK-D138 but is not detected in KKK-D068 and KKK-D131. The TP53 mutation of KKK-D049 has not yet been analyzed.n = 4). Tumors were grown in the mice for a month, and tumor masses were observed twice a week. We observed tumor masses from all injected sites . Spleen, liver, and lungs were removed at 1 month after injection. Tumors were observed in 88% of transplanted livers and spleens (7/8 mice)  b. No tum/8 mice) . No tumoThe subcutaneous tumor masses were prepared for the histological comparison with the tumor tissues from the patients . These tOpisthorchis viverrini (Ov) infection has been demonstrated by distinctive molecular signatures [Ov-related CCA might be vulnerable to distinct treatment. Several attempts were made to provide opportunities for Ov-related CCA treatment [Cholangiocarcinoma (CCA) is a rare, aggressive tumor of liver found worldwide ,2. The wgnatures ,13,18,19Patient-derived xenograft (PDX) models are preclinical models that are ideally developed for the implementation of personalized medicine. PDX models are a powerful tool for cancer propagation that retain disease complexity and heterogeneity . MoreoveThe newly developed CCA-PDX model is highly efficient, with 75% transplantable efficiency compared to 6–35% engraftment rates in the previously described models ,28. ThisPDX-derived cell lines developed in this study show some degree of heterogeneity in vitro and in vivo, yet keep the characteristics of tissue organization, which are common in PDX models ,26,27. TWe propose the platform for anti-cancer drug screening using a PDX-derived cell line, and a PDX mouse model as demonstrated in In conclusion, in this study we established a highly effective CCA PDX model and highly transplantable PDX-derived cell lines. Cell authentications and characterization have been demonstrated. The cellular heterogeneity and preserved tissue architecture have been confirmed in PDX-derived cell lines and cell line xenografts. These PDX-derived cell lines and PDX models might be a promising platform for anti-CCA development. The advantage of our PDX model for personalized medicine seems to be limited by the long transplantation duration in some cases.


Brassica napus CMS  lines with their corresponding maintainer lines. By comparing anther developmental stages between CMS and maintainer lines, we identified that in the Nsa CMS line abnormality occurred at the tetrad stage of pollen development. Phytohormone assays demonstrated that IAA content decreased in sterile lines as compared to maintainer lines, while the total hormone content was increased two-fold in the S2 stage compared with the S1 stage. ABA content was higher in the S1 stage and exhibited a two-fold decreasing trend in S2 stage. Sterile lines however, had increased ABA content at both stages compared with the corresponding maintainer lines. Through transcriptome sequencing, we compared differentially expressed unigenes in sterile and maintainer lines at both (S1 and S2) developmental stages. We also explored the co-expressed genes of the three sterile lines in the two stages and classified these genes by gene function. By analyzing transcriptome data and validating by RT-PCR, it was shown that some transcription factors (TFs) and hormone-related genes were weakly or not expressed in the sterile lines. This research work provides preliminary identification of the pollen abortion stage in Nsa CMS line. Our focus on genes specifically expressed in sterile lines may be useful to understand the regulation of CMS.The interaction between plant mitochondria and the nucleus markedly influences stress responses and morphological features, including growth and development. An important example of this interaction is cytoplasmic male sterility (CMS), which results in plants producing non-functional pollen. In current research work, we compared the phenotypic differences in floral buds of different  A vacuum chamber was used to evacuate the air and volatiles from the sample bottles. Fixed floral buds were dehydrated by a graded series of ethanol  for one hour. Pre-infiltration and penetration by Technovit 7100 resin steps were undertaken to produce semi-thin sections ~3 μm thick. Samples were stained by 1% toluidine blue  for 3 min, and 5 specimens of each stage were observed to take images under an optical microscope . Pol CMS, Ogu CMS, Nsa CMS, and their corresponding maintainer lines were quantified for ABA and IAA phytohormones. Samples were collected and extracted using methanol compounds [About 60 floral buds  of ompounds . The extOgu CMS, Nsa CMS, and their corresponding maintainer lines) at the same time. Samples collected from each line were pooled, frozen in liquid nitrogen, and stored at −70 °C for RNA preparation. Total RNA from two stages of floral buds (<2.5 mm and >2.5 mm) of pol CMS, Ogu CMS, Nsa CMS, and their corresponding maintainer lines were extracted by using RNA kits  in accordance with the manufacturer’s protocol. The integrity of the total RNA was checked by 1% agarose gel electrophoresis. The concentration was detected by Nano-Drop  and purity of RNA was determined by Agilent 2100 Bio-analyzer . RNA (10 μL) was sequenced using the Illumina HiSeq 2000  and 150 bp of data collected per run. After removing adapters and low-quality data, the resulting clean data was aligned to the B. napus reference genome [About 60 floral buds were harvested from the plants of each line . Specific primers for differentially expressed genes were designed to amplify 600–750 bp sequences . RT-PCR Pol and Ogu CMS lines. Conversely, the pollen abortion stage in the Nsa CMS line had not been determined clearly. From this study, we identified the tetrad stage of Nsa CMS for pollen abortion by using the semi-thin sectioning of floral buds. This information will help support the application of Nsa CMS in plant breeding. Higher content of ABA and lower content of IAA was observed in sterile lines when compared to maintainer lines in all male sterile systems. This result may reveal that ABA and IAA play different roles in fertile pollen development. During the two stages, genes involved in energy production were enriched in maintainer lines in comparison to sterile lines for all CMS systems investigated.Considerable effort has been taken to identify the pollen abortion stage in


BIRC5) promoter, can be used to treat several cancers. As survivin is overexpressed in ATL, we examined the effects of Surv.m-CRAs on ATL-selective replication and survival.Adult T-cell leukemia/lymphoma (ATL) is a peripheral T-cell malignancy caused by long-term human T-cell leukemia virus type I (HTLV-1) infection. Survivin-responsive, conditionally replicating adenoviruses regulated by multiple tumor-specific factors (Surv.m-CRAs), in which the expression of the adenoviral early region 1A gene is regulated by the survivin , which is important for adenoviral infection, was high in the cell lines. In contrast, we observed the absence of survivin promoter activity and a low expression of CAR in activated peripheral blood lymphocytes (PBLs) from healthy subjects. Surv.m-CRAs actively replicated and induced cytocidal effects in five out of six cell lines; conversely, we observed minimal viral replication and no marked cytotoxicity in normal activated PBLs.This is the first report demonstrating that Surv.m-CRAs constitute attractive potential anti-ATL agents. Adult T-cell leukemia/lymphoma (ATL) is an aggressive malignancy of mature peripheral T lymphocytes that results from long-term infection with human T-cell leukemia virus type I (HTLV-1). Five to twenty million individuals are estimated to be currently infected with the virus, and only 3–5% of HTLV-1-infected patients develop ATL. HTLV-1 infection can lead to the insertion of oncogenic viral genes into the host genome, which activate NF-κB and Akt signaling, as well as cyclin-dependent kinases, and silence TP53 in host cells.The prognosis for patients with ATL after treatment with classical cytotoxic agents is still poor. New therapeutic approaches, distinct from standard cytotoxic chemotherapy treatments, are expected to lead to the eradication of ATL , 2. For E1A), the first transcribed gene after infection with wild-type adenoviruses, transactivates the viral and cellular genes essential for generating transmittable adenoviruses. m-CRAs replicate in a tumor-specific manner by replacing the innate E1A promoter with a tissue- and tumor-specific promoter [Conditionally replicating adenoviruses (CRAs), also termed oncolytic adenoviruses, which selectively replicate in and eradicate cancer cells, are smart anti-cancer agents . We devepromoter –15.Survivin (BIRC5), a member of the inhibitor of apoptosis gene family, is overexpressed in most cancers, but not in normal cells. Clinical trials have demonstrated a positive correlation between high survivin expression levels and poor prognosis, enhanced rate of recurrence, and increased resistance to chemotherapy in patients with malignancy .We developed survivin-responsive m-CRAs (Surv.m-CRAs), which express E1A under the transcriptional control of the survivin promoter. Surv.m-CRAs can kill a variety of cancers more efficiently and specifically than telomerase-responsive m-CRAs in vitro and in vivo . MoreoveAs survivin is overexpressed in ATL, especially in acute-type ATL , we exam2. Written informed consent was obtained from healthy subjects to participate of this research.The human ATL cell lines, S1T and Su9T01, and the HTLV-1-infected T-cell lines, Oh13T, K3T, F6T, and MT-2, were sustained in RPMI 1640  supplemented with 1% penicillin/streptomycin (Gibco/Invitrogen) and 10% fetal bovine serum (Gibco/Invitrogen). All the cell lines, except MT-2, were established from patients in our laboratory . MT-2 waE1A under the transcriptional control of the survivin promoter, E1B55KD under the transcriptional control of the CMV promoter, and EGFP under the transcriptional control of the CMV promoter; two types of E1-deleted, replication-defective adenoviruses expressing EGFP under the transcriptional control of the CMV promoter (Ad.CMV-EGFP) or LacZ under the transcriptional control of the survivin promoter (Ad.Surv-LacZ) were also proliferated and purified.The following viruses were proliferated and purified as described previously : Surv.m-5/plate) were infected with Ad.Surv-LacZ at a multiplicity of infection (MOI) of 30 for 24 h. We measured cellular β-gal activity using the β-Galactosidase Enzyme Assay System , after harvesting. The AGTE for each cell type in vitro was examined 48 h after infection with Ad.CMV-EGFP at an MOI of 30.Promoter activities and in vitro adenoviral gene transduction efficiency (AGTE) were examined as described previously with some modifications . Cells . Using flow cytometry, we assessed the expression of coxsackie and adenovirus receptor (CAR), which is recognized as the primary cropping receptor for coxsackie B viruses and members of the adenovirus family JM Bergelson, JA Cunningham, G Droguett, EA Kurt-Jones, A Krithivas, JS Hong, MS Horwitz, RL Crowell and RW Finberg . Cells wCells in 96-well plates were infected with Surv.m-CRA at MOIs of 0.3, 3, and 30 or Ad.CMV-EGFP at an MOI of 30. Cell viability was determined by MTT assay  after 3 and 5 days, according to the manufacturer’s protocol.t-test. P < 0.05 was concluded statistically significant.Data are shown as the mean ± standard errors (s.e.). Statistical significance was determined using Student’s We tested 2 ATL cell lines and 4 HTLV-1-infected T-cell lines. We infected the cells with Ad.CMV-EGFP or Ad.Surv-LacZ at an MOI of 30. All the cell lines had high AGTE and strong survivin promoter activation  gene in multiple cancers, especially in ATL. PD-L1 disruption could serve as a genetic marker to identify cancers that actively evade anti-tumor immunity through PD-L1 overexpression [Recently, immunotherapies using antibodies against programmed cell death 1 (PD-1) and its ligand (PD-L1) have proven to be powerful and effective treatments for patients with various advanced cancers in many clinical trials –30. The pression . CombinaThis is the first report demonstrating that Surv.m-CRAs represent a striking potential anti-leukemic agent that could efficaciously and specifically treat ATL. Because our experiments did not include any in vivo therapeutic data, we will focus on the methods of gene administration in future extensive studies.


Colorectal cancer remains a deadly cancer due to metastatic disease. To understand the molecular mechanisms of metastasis in colon cancer, we investigated whether the copper chaperone antioxidant-1 (Atox1) protein plays a role in this process. Recent findings indicate that Atox1 protein has transcription factor activities and plays a vital role in cell proliferation in cancer cells. However, the role of Atox1 in metastasis has not been examined.Atox1 expression was determined by immunofluorescence in a tissue microarray generated from a spectrum of CRC patients. Subcellular fractionation of colon cancer cell lines SW480 and SW620 cells was used to examine the cellular location of Atox1 in the face of activin A, a cytokine that stimulates colon cancer metastasis. Atox1 expression was genetically manipulated and cellular migration measured through trans-well assay and proliferation measured by colony formation assays.Here we demonstrate that in patients with metastatic colon cancer, there is a significant increase in the expression of nuclear Atox1. Interestingly, the metastatic CRC cell line SW620 has increased nuclear localization of Atox1 compared to its related non-metastatic cell line SW480. Further, inhibition of endogenous Atox1 by siRNA in SW620 decreased colony formation and reactive oxygen species generation via decreased expression of Atox1 targets cyclin D1 and NADPH oxidase subunit p47 phox, respectively. Additionally, overexpression of nuclear-targeted but not copper binding domain-mutated Atox1 in SW480 cells increased colony formation and cell migration that was further augmented by activin A stimulation, a known enhancer of colon cancer metastasis.Our findings suggest that nuclear Atox1 might be a new therapeutic target as well as a new biomarker for metastatic colorectal cancer. Colorectal cancer (CRC) is a common and deadly cancer due to its metastatic nature . AlthougAntioxidant protein 1 (Atox1) is known to play a key role in copper homeostasis . It reguThe present study explored the role of Atox1 in CRC using metastatic and non- metastatic CRC cell lines and a patient tissue microarray (TMA). Interestingly, we found that Atox1 nuclear location was significantly correlated with the severity of metastatic CRC in a TMA of colorectal tumors from patients. Consistent with this, Atox1 is highly expressed in the nucleus in metastatic SW620 colon cancer cell line, while Atox1 is expressed more in the cytosol in non-metastatic SW480 colon cancer cell lines. Activin A treatment promotes Atox1 nuclear translocation in both colon cancer cell lines. Endogenous inhibition of Atox1 in metastatic SW620 human colon cancer cells decreased cellular migration, viability and colony formation, which was associated with decrease in cyclin D and p47 phox expression. Importantly, we found that nuclear-targeted Atox1 induces migration and colony formation in non-metastatic SW480 colon cancer cells that was further increased upon activin A stimulation. Our findings represent a functional role for Atox1 in CRC metastasis.PKKKRKVD) was derived from the SV40 large T antigen fused to the C terminus of Flag-tagged w-Atox1 (wild type) as previously described [The nuclear-targeted Atox1 plasmid (Flag-Atox1-NLS) construct containing a tripartite NLS  sequence  Institutional Review Board and tissues collected only after written informed consent was administered and signed. There are no individual case notes or any patient private health information included in this manuscript. Briefly, tumor tissue derived from colon was obtained from patients diagnosed with colorectal carcinoma at the University of Illinois Medical Center. A total of sixty colorectal resection specimens cores from the formalin fixed paraffin embedded tumor were placed in a tissue micro-array. The quality and structural integrity of the tissue blocks were appraised and evaluated before inclusion into the study as described previously . A totalFormalin-fixed and paraffin-embedded TMAs were de-paraffinized in xylene and then rehydrated in descending ethanol series. The sections were blocked in 3% BSA for 60 min to block nonspecific binding sites. Immunostaining was performed at 4°C overnight with a rabbit Atox1 antibody (1:500 dilution). FollowiThe colon cancer specimens on the TMA slides were examined and scored. A staining index for each tissue core was obtained based on published methods , 22. The2 chamber supplemented with 10% FBS and 1% Pen Strep containing media and were serum starved for 24 hours prior to treatment. To limit variability between experiments, cells were cultured to a maximum of 30 passages to minimize mutations in the cell lines. In addition, cell cultures under identical conditions of treatment were done in parallel to validate our findings. Further, all the cell lines were tested for mycoplasma prior to starting experiments. The cells were authenticated once a year and validated by 9 STR (short tandem repeat) profiling using Cell Check 9Plus and tested for mycoplasma .SW480 and SW620 cell lines were acquired from the American Type Culture Collection (ATCC). The cell lines were cultured in a 5% COCell lysates were treated with bicine chaps buffer containing 1x protease cocktail inhibitor  and prepared for western blot analysis as previously described . Blots wColon cancer cell lines were subjected to subcellular fractionation using NE-PER nuclear and cytoplasmic extraction reagents  according to manufacturer’s instruction. Fractionation efficiency was determined by western blot analysis using GAPDH as cytoplasmic and p84 as nuclear protein controls respectively.6 cells according to the manufacturer’s instruction. After 48 hours of transfection cells were left either untreated or treated with activin A  for an additional 24 hours. Protein expression of transfected cDNAs was confirmed by western blot analysis. Specific siRNA for Atox1  was transiently delivered at a final concentration of 30 nM via electroporation using AMAXA Nucleofector . Transfection efficiency was confirmed by fluorescent microscope using the pmaxGFP control vector. Forty-eight hours post transfection cell lysates were prepared for western blot analysis.Colon cancer cells were transfected with 1 ug of plasmid DNA encoding Flag tagged WT Atox1, Atox1 NLS, Atox1 CBD using lipofectamine 3000 in 12-well plates at a density of 1x104 cells/well) were plated into Transwell 12 well plates  that were coated with fibronectin (Sigma) to facilitate migration. After 6 hours, non-migrated cells at the membrane top were removed and the cells that have migrated through the membrane were fixed with 4% PFA and stained with DAPI (nuclear stain). Images were captured by fluorescent microscopy and the number of migrated cells per well was determined by counting the cells at the center of each well using image J software to digitized images. Data are expressed as number of particles counted per high powered field (hpf).Colon cancer cell lines  as previously described . The celFor colony formation assay, cells were seeded into six-well plates at a density of 100 cells per well and incubated for two weeks in DMEM containing 10% FBS at 37°C. After two weeks, colonies were fixed by cold methanol for 15 minutes and stained with 0.5% crystal violet for 1 hour. The number of colonies (>50cells/colony) was counted using a stereomicroscope and analyzed by image J software . All theAll data are expressed as means ± SD from at least three independent experiments. Statistical analyses for differences were performed via Student’s t test when two groups are analyzed and by one-way ANOVA followed by Newman-Keul’s multiple comparison analysis using Graphpad Prism 5.0 when more than two groups were analyzed. The criterion for statistical significance was p > 0.05.To characterize the role of endogenous Atox1 in CRC metastasis, immunofluorescent staining of Atox1 protein was examined in a tissue microarray (TMA) consisting of adenocarcinoma and adjacent normal epithelial mucosa from 60 CRC patients . We obseThe distinct nuclear localization of Atox1 protein in the colon cancer TMA prompted us to verify its subcellular distribution in CRC cell lines by cellular fractionation followed by immunoblotting. The nuclear localization of Atox1 protein was assessed in two cell lines derived from the same colon cancer patient. The SW480 cell line was derived from the primary colon tumor and is non-metastatic, while the SW620 colon cancer cell line was derived from the lymph node of the same patient when the cancer recurred with wide-spread metastases . EfficieTo investigate the fate of Atox1 localization after activin A stimulation, SW480 and SW620 cells were treated with or without activin A for 24 hours. Effect of Activin A was assessed by subcellular fractionation  and immuTo further investigate the mechanism by which Atox1 may regulate metastasis in CRC, the endogenous expression of Atox1 was depleted by Atox1 siRNA in the metastatic SW620 cell line . Since cRecent evidence has suggested a positive role for reactive oxygen species (ROS) in regulating EMT in cancer cells through influencing extracellular matrix remodeling, cytoskeleton remodeling, cell-cell junctions and cell mobility –29. TherTo explore whether nuclear accumulation of Atox1 has a functional consequence, we assessed cell migration of SW620 cells following Atox1 KD with and without activin A treatment. KD of Atox1 decreased SW620 cell migration when compared with cells transfected with scrambled siRNA. Interestingly, Atox1 KD attenuated activin A-induced cellular migration compared to scrambled control cells . KD of Atox1 also reduced SW620 cell number, as demonstrated by MTT assay , which wBecause our findings point to Atox1 as a positive regulator of metastasis, we over-expressed a nuclear-targeted Atox1 in the non-metastatic CRC cell line SW480 and assessed metastatic potential. To efficiently target Atox1 to the nucleus, an Atox1 nuclear localization signal (NLS) cDNA construct with a C-terminus Flag-tag was used as previously described . In addiThe wAtox and NLS Atox1 cell lines also had increased colony formation compared to control vector cells, which again were further increased by activin A treatment. Colony formation was not changed in CBD Atox1 cell lines , S3 Fig.Atox1 is a copper (Cu) chaperone protein involved in maintaining copper homeostasis. In addiIn the metastatic SW620 cells, there is more Atox1 in the nucleus compared to the non-metastatic SW480 cells. In line with the nuclear enrichment of Atox1 in the SW620 cell, there is Atox1-dependent expression of growth promoting cyclin D1 and NADPH oxidase p47 phox subunit indicative of increased ROS accumulation. Treatment with activin A stimulates translocation of Atox1 into the nucleus of SW480 cells as well as inducing a further increase in nuclear Atox1 in SW620 cells. The functional significance of nuclear Atox1 was examined first in SW620 cells where knock-down of Atox1 expression resulted in decreased cell migration and colony formation which was unaffected by activin A. Further, we demonstrate that Atox1 knockdown by siRNA induced the most prominent proliferation suppression in metastatic SW620 colon cancer cell lines. This inhibitory effect was associated with inhibition of intracellular signaling as revealed by a decrease in cyclin D1 protein expression. In contrast, transfection of wild type Atox1 or nuclear localizing Atox1 into non-metastatic SW480 cells resulted in increased cell migration and colony formation which was further enhanced by activin A. The Cu-binding domain mutated Atox 1 was unable to increase either cell migration or colon formation and interestingly, decreased the activin A stimulation of those processes. This may indicate that Cu-bound Atox1 plays a role in the activin A stimulation of cell migration and colony formation. Although the presence of Atox1 in the nucleus has been reported in different cancers –17, thisWe report that Atox1 is localized both in cytoplasm and nucleus and its nuclear localization is enriched in metastatic CRC where it influences cell migration and colony formation. Activin A, an inflammatory cytokine increased in metastatic CRC, increases the nuclear localization of Atox1. In addition, Cu-bound Atox1 appears to enhance activin A-induced cell migration. Taken together, understanding the combined actions of activin A and Atox1 may improve the current staging of colorectal cancer patients.S1 FigThe adjacent normal colon tissue on the colon cancer tissue microarray was also stained for DAPI (blue) and Atox1 (red). Shown in a representative image indicating very little staining for Atox1 in normal colon tissue.(EPS)Click here for additional data file.S2 FigImage of SW620 colon cancer cell colonies formed after treatment with either scrambled or Atox1 siRNA and an additional treatment with either 25 ng/ml of activin or control PBS (Con). The number of colonies were quantified as indicated in the methods and show in (EPS)Click here for additional data file.S3 FigImage of SW480 colon cancer cell colonies formed after transfection with the indicated vectors and an additional treatment with either 25 ng/ml of activin or control PBS (Con). The number of colonies were quantified as indicated in the methods and shown in (EPS)Click here for additional data file.S1 FileThis is the file of complete western blots used to created (PDF)Click here for additional data file.


Although metabolism is profoundly altered in human liver cancer, the extent to which experimental models, e.g. cell lines, mimic those alterations is unresolved. Here, we aimed to determine the resemblance of hepatocellular carcinoma (HCC) cell lines to human liver tumours, specifically in the expression of deregulated metabolic targets in clinical tissue samples.P < 0.05) were used for pathway enrichment and gene ontology (GO) analyses. Further, we compared the topmost gene expression, pathways, and GO from poorly differentiated cell lines to the pattern from four human HCC datasets (623 tumour tissues). In well- versus poorly differentiated cell lines, and in representative models HLE and HUH7 cells, we specifically assessed the expression pattern of 634 consistently deregulated metabolic genes in human HCC. These data were complemented by quantitative PCR, proteomics, metabolomics and assessment of response to thirteen metabolism-targeting compounds in HLE versus HUH7 cells.We compared the overall gene expression profile of poorly-differentiated  to well-differentiated  HCC cell lines in three publicly available microarray datasets. Three thousand and eighty-five differentially expressed genes in ≥2 datasets , and amino acid metabolism . In contrast, HUH7 cells showed a higher expression of 98 metabolic targets upregulated in tumours . Metabolomics revealed that the genomic portrait of HLE cells co-exist with profound reliance on glutamine to fuel tricarboxylic acid cycle, whereas HUH7 cells use both glucose and glutamine. Targeting glutamine pathway selectively suppressed the proliferation of HLE cells.We found that poorly-differentiated HCC cells display upregulated MAPK/RAS/NFkB signaling, focal adhesion, and downregulated complement/coagulation cascade, PPAR-signaling, among pathway alterations seen in clinical tumour datasets. In HLE cells, 148 downregulated metabolic genes in liver tumours also showed low gene/protein expression – notably in fatty acid β-oxidation , urea cycle , molecule transport  contains supplementary material, which is available to authorized users. TP53, TERT promoter and CTNNB1 mutations are well known [Hepatocellular carcinoma (HCC) is the most common type of liver cancer, which also include cholangiocarcinoma as well as secondary liver cancer that metastasized from distant organ-sites, e.g. colon and pancreas. HCC is a highly heterogeneous and poorly understood tumour entity. While a lot of its molecular alterations, e.g. ll known , the cliSimilar to human HCC, the corresponding cell lines used for in vitro studies are heterogeneous in their molecular and phenotypic portraits. For example, HCC cell lines show differential proliferative response to Src/Abl inhibitor dasatinib  as well We recently identified 284 metabolic genes upregulated in at least 6 of 8 human HCC microarray datasets, and also 350 downregulated metabolic genes under the same criteria. Two hundred and one of these genes were highlighted as predictive of overall survival in a cohort of HCC patients, underscoring the potential clinical significance of the genes . Here, wP < 0.05) were separated into two categories – i.e. the upregulated and downregulated genes in poorly differentiated cell lines. Using Venn diagram tool (http://bioinfogp.cnb.csic.es/tools/venny/), genes exclusively upregulated or downregulated in ≥2 datasets (unless otherwise indicated) were selected for comparison with human HCC tissues genomic profile. Comparison with human HCC profile was in three parts. First, examining how the topmost 100 significantly upregulated or downregulated genes in all three cell line datasets are expressed in four patients datasets . Second, examining the cell lines' expression of previously published list of consistent metabolic genes (n = 284 upregulated and 350 downregulated HMGs) that were derived from ≥6 human HCC microarrays, including GSE14520 (used in this study) [P < 0.05) and in ≥2 cell line datasets (P < 0.05). The second and third parts are discussed next. For the first part, the z-score of expression fold was calculated for each human HCC dataset. Then, the topmost genes identified from cell line data were extracted from each human dataset, and subsequently ranked by their average z-score across the four human datasets. With the + or – z-score values determined for each gene in all four human datasets, a gene significantly upregulated/downregulated in poorly differentiated HCC cell lines and upregulated/downregulated in the human datasets were considered to be concordantly expressed in both settings. In contrast, all genes significantly upregulated in poorly differentiated cell lines, but downregulated in tumours, and vice versa, were considered to be discordant .Microarray datasets GSE36133 (from CCLE project) , GSE3581https://www.ebi.ac.uk/arrayexpress/) for GSE57083, and cBioPortal platform for the CCLE project. Differences in measured mRNA (i.e. HLE - HUH7) were used to determine the relative expression of HMGs by the respective cell lines in each dataset. Subsequently, HLE cells were considered to mimic the up- or downregulated HMGs it concordantly express at a higher or lower level in both datasets. HMGs that showed discordant expression pattern in both datasets are more mimicked by HUH7 cells.To uncover the HMGs mimicked by the HCC cell lines, all genes significantly expressed in ≥2 of the earlier mentioned cell line datasets were overlapped with the 284 upregulated and 350 downregulated HMGs. This was followed by the selection of specific metabolic genes concordantly upregulated/downregulated in poorly differentiated cell lines as those more mimicked by the more ‘cancer-like’ cell lines. On the other hand, HMGs with discordant or opposite expression pattern were considered to be more portrayed by well-differentiated cell lines. In addition, HMGs expression was analyzed, focusing on HUH7 and HLE (two cell lines we mainly used for subsequent phenotypic assays). Specifically, gene expression data for these two cell lines were obtained via ArrayExpress database (https://david.ncifcrf.gov/). For human HCC microarrays, we have previously shown pathway enrichment [n = 2017) or downregulated (n = 1547) genes in all four human HCC datasets were separately analysed to determine their associated molecular function (MF), cellular component (CC) and biological process (BP). For the cell lines, pathway enrichment and GO analysis were performed using genes upregulated or downregulated in poorly differentiated HCC cell lines (at least in two datasets). Based on P < 0.0001 and false discovery rate (FDR) < 25%, the topmost 10 enriched pathways as well as top five MF, CC, and BP were selected.Pathway enrichment and gene ontology (GO) analysis were performed using the bioinformatics platform DAVID 6.8  supplemented with 2 mM glutamine (Gln), 10% heat inactivated fetal bovine serum, penicillin (100 U ml− 1), and streptomycin (100 μg ml− 1). For glucose or glutamine deprivation experiments, the cells were first seeded into culture plates and incubated overnight. Thereafter, the cells were washed 1× with Hank’s Balanced Salt Solution followed by further incubation with culture media lacking the respective metabolites. Compounds used to interfere with metabolism in the cell lines are listed in the Additional file Human HCC cell lines HUH7, HEPG2, HLF and HLE were obtained from the Japanese Cancer Research Resources Bank or American Type Culture Collection. The cells were cultured at 37 °C in a humidified incubator with 5% CO5 in 6 well plates (7 replicate wells for HUH7 and 8 for HLE). The cells were cultured for 48 h. Thereafter, culture media was aspirated off and the cells were washed by gentle swirling with 3 ml phosphate buffered saline per well. This step was repeated twice, after which cells were detached by scraping, and transferred into 1.5 ml vials. The cell suspension was then centrifuged at 800 rpm for 5 min at 4 °C and the supernatant discarded. The remaining cell pellets were stored at − 80 °C until analysis. Proteomic analysis was conducted by mass spectrometry as previously described [P < 0.05 after analysis of variance were considered significant in our analyses, which includes pathway enrichment, GO, and overlap with HMGs.For proteomic comparison, HUH7 and HLE cells were seeded at a density of 5 × 10escribed . ProteinP < 0.05, unless otherwise indicated. Where applicable, results were expressed as mean ± standard deviation (SD). Data were analysed with GraphPad Prism v6 . Genecards database (http://www.genecards.org) was used for crosschecking gene names and functions. The differentially expressed genes in human HCC microarrays GSE14520, GSE25097, GSE1898, and GSE55092 were determined by using NCBI GEO2R, comparing normal liver/adjacent non-tumours versus liver tumours. Information about metabolomics and other experiments performed with the cell lines are included in the Additional file Statistical significance was accepted as n = 1584 upregulated and 1501 downregulated, P < 0.05) in at least two of the three datasets  or lowly expressed  in poorly differentiated cell lines  were downregulated in most human HCC microarrays  , TTR, EPCAM, apolipoproteins , MTTP, complement 3 and 5 (C3/C5), among other, reflecting ~ 85% concordance with patients’ tumour samples. The notable exemptions were villin 1 (VIL1), VSNL1, MBNL3, KLB, AFP and glypican 3 (GPC3), which are all consistently upregulated in liver tumour datasets. It is noteworthy that while many of these downregulated genes are novel candidates in HCC . Intriguingly, where significantly altered, more than half of the top upregulated genes in the cell lines  Fig. . In the KP) Fig. , includiHCC Fig. , AFP andn = 82, Additional file P < 0.05 at least in one dataset) in poorly differentiated cell lines relative to those well-differentiated. Examples are mitogen-activated protein kinase (MAPK)/RAS family members (HRAS and NRAS), hemochromatosis (HFE), BRD7, ATM, vimentin (VIM), which are upregulated in poorly differentiated cell lines and in human HCC datasets , FAH, PNPLA3, GSTT1, ATP7B, SERPINA1, NFE2L2, CUL3 and MDM2 , the other ‘HCC-associated’ genes such as FRK, HNF1A, ARID1B, KEAP1, β-catenin (CTNNB1), FGF19, LGR5 and glutamine synthetase (GLUL) were lowly expressed in poorly differentiated cell lines (i.e. more expressed in well-differentiated cells) and consistently upregulated in human liver tumours   Fig. . The enc01) Fig. , and shohttps://david.ncifcrf.gov). Pathway enrichment of the upregulated genes indicated an activation of cancer pathways, MAPK/RAS pathway, focal adhesion, proteoglycans in cancer, nuclear factor kappa B signaling and tumour necrosis factor signaling , as well as those consistently downregulated, e.g. liver-specific glutaminase (GLS2), complement 7 and 9 (C7/9), CXCL12, CYP1A2, CYP2E1, GYS2, HAMP, IGF1, LCAT, SLC22A1, etc, n = 1547) . MF protein binding and cell adhesion-related activities, CC nucleoplasm and cytoplasm, as well as virus-related BP were within the top five of the respective GO terms for the human and cell line datasets  was cancer-oriented albeit heterogeneous  were strongly involved in metabolic processes as observed with genomic data. Thus, at proteomic level, the poorly differentiated cell lines also strongly mimic the downregulation of metabolic components as seen in tumours. Carbon metabolism surprisingly appeared in the pathway enrichment of proteins more expressed as well as those lowly expressed in HLE cells  are concordantly expressed by the surrogate cell lines has not yet been reported. We identified HMGs whose expression pattern are more mimicked by poorly differentiated HCC cell lines. These include  92 of 350 downregulated HMGs Fig. . Of noteous Fig. . In contlls Fig. . HoweverIn line with the notion that the metabolic gene profile of the cell lines is reflected at the protein level, the expression pattern of the top deregulated ‘metabolic proteins’ in HLE relative to HUH7 cells (fold difference > 2) overlapped with that of the encoding HMGs Fig. . For exaIt is noteworthy that some metabolic genes , though differentially expressed between well- and poorly differentiated cell lines, were at protein level not distinctly expressed in HLE compared to HUH7 cells. Examples are RRM1, RRM2, PAICS, ITPA, PPAT and GART (in nucleotide biosynthesis) and PGLS, TALDO1 and DERA in the pentose phosphate pathways (PPP). Along with these were over 20 candidates in ribosomal protein synthesis , hexokinase (HK1), PFKP, PGK1, ENO1, and lactate dehydrogenases (LDHA/B) are more expressed in HLE cells, but glucose transporter 6 (SLC2A6), hexokinase muscle isoform (HK2), ALDOA, BPGM, pyruvate kinase (PKM), DLAT, and MPC2 are upregulated HMGs more expressed in HUH7 cells. Similarly, asparagine synthetase (ASNS), glutamine transporters , FASN in fatty acid synthesis (lipogenesis), and PSPH (in serine pathway) are all expressed higher in HUH7 cells. Furthermore, yet unknown but consistently upregulated HMGs such as PLCB1, GNPAT, PLA2G7 and CDS1 in membrane lipids, as well as ALG3, B3GALNT1, CHPF2, DOLK and LYZ in glycan metabolism are more expressed in HUH7 cells. Other upregulated metabolic genes that showed higher expression in HUH7 cells include NADPH oxidase 4 (NOX4), phosphogluconate dehydrogenase (PGD in PPP) and AKR1B10 (among the topmost upregulated metabolic gene in human HCC datasets), as well as key players in cholesterol metabolism  and TCA cycle  , are low in HLE cells as in human HCC datasets  emerged as lowly expressed in HLE cells. This cell line also mostly mimicked downregulated small molecule transporters seen in tumours, e.g. NPC1L1, SLC25A15/20, SLC2A2, SLC1A1 and SLC7A2 , PHGDH, PSAT1, GLUL, glutamate dehydrogenase 1 (GLUD1), among others. Indeed, the corresponding proteins encoded by many downregulated HMGs showed low expression in HLE cells, thus reinforcing the gene-level data  in serine pathway, and transamination mediators (e.g. GOT1 and GPT1) as in patients’ datasets. Further, although HLE cells express a higher level of citrate synthase (CS), they lowly express MPC1 as noted earlier and also several TCA cycle enzymes as seen in patients datasets  are all lowly expressed in HLE cells. Taken together, our findings on the metabolic profiles shared between tumours and the poorly differentiated cell lines support the notion that these cells could be important surrogates for clinically-relevant HCC metabolism studies.Our observation is that the cell lines representative of well (e.g. HUH7) and poorly differentiated (e.g. HLE) HCC subclasses, both, mimic the expression pattern of human HCC-derived metabolic genes (HMGs)  are more expressed in HUH7 cells. It is therefore difficult to exclude that the well-differentiated cell lines also display tumour molecular signatures. Rather, given that these cell lines are low migrating, low proliferating and are known to be more hepatocyte-like [While poorly differentiated cell lines more closely mimic human HCC profiles, the well-differentiated cells, exemplified by HUH7 cells, also showed striking resemblance to human tumours. For example, considering gene or protein expression differences, we identified almost 100 upregulated HMGs more expressed in HUH7 than in HLE cells. These upregulated HMGs are mostly involved in lipogenesis, small molecule transport, glycan-, membrane lipid- and cholesterol metabolism. Furthermore, with the exemption of GLS (more expressed in HLE cells), few amino acid-related genes upregulated in HCC tissues  is a known metabolic feature of cancer . HoweverCDH1, ALB and EPCAM are highly expressed in HUH7 cells. This cell line also express a high level of many metabolic targets consistently upregulated in HCC patients microarrays  [HRAS, NRAS, APC, HFE, MMP9, and a high enrichment of MAPK/RAS, NFKB, and TNF signaling pathways are all evident in HLE cells as often seen in cancer. Prospective hypotheses could interrogate the potential contribution of such enriched pathways in modulating HCC metabolism and progression.Beyond identifying resemblance to tumour metabolic profile, our study uncovered other altered targets in the cell lines that could be relevant signatures in HCC. For example, BASP1, LDHB, CD44 (high in HLE) and APOA1, ASGR1, ITIH2, AGT, AFP (high in HUH7 cells) emerged as the most consistently top targets . The impact of these candidates on HCC metabolic fate is currently  unclear. Of note, besides AFP and GPC3 which are highly studied biomarkers for early HCC detection , CDH1, A, PLCB1) , thus ra. Taken together with the metabolite level changes that coexist with these gene alterations, our findings provide useful insights that could lead to future identification of HCC subclasses with unique nutrient requirements. This will facilitate the prospects of targeting metabolism for HCC therapy, and pave way for delineating metabolic enzymes that are biomarkers or also indispensable drivers of HCC. Importantly, our work will guide the selection of suitable experimental models for the translational studies of specific metabolic pathway alterations seen in human liver cancer.In light of the growing relevance of genomics in refining clinical HCC subclasses and therapeutic prospects , 2, 41, Additional file 1:Figure S1. Phenotypic and molecular characteristics of HCC cell lines. Figure S2. Overlap of genes deregulated in human HCC datasets, and the top ranked ontology of the highly expressed genes (tumours and poorly differentiated cell lines). Figure S3. Overlap of the gene and protein expression pattern in the cell lines compared with human HCC-tissue derived metabolic genes. Figure S4. Metabolic pathways targeted by the drugs used in this study and the effect of glycolysis inhibition or glutamine withdrawal on the proliferation of HCC cell lines. (PDF 1024 kb)Methods and Figures. Additional file 2:Table S1. Genes more or lowly expressed in poorly differentiated relative to the well-differentiated cell lines. Includes all differentially expressed genes . Datasets used: GSE57083, GSE36133 (CCLE) and GSE35818. (DOCX 166 kb)Additional file 3:Table S2. HCC-associated genes. Contains a set of genes often associated with HCC, including those related to epithelial–mesenchymal transition. (DOCX 19 kb)Additional file 4:Table S3. Top pathway enrichment and the specific genes involved. Enrichment analysis was performed with the genes more (3a) or (3b) lowly expressed in the poorly differentiated relative to well-differentiated HCC cell lines. (DOCX 18 kb)Additional file 5:Table S4. Consistent genes in all four human HCC datasets analysed in this study. These are the genes differentially upregulated  or downregulated  in HCC relative to non-tumour liver tissues in each of the four datasets. (XLSX 56 kb)Additional file 6:Table S5. Complete list of downregulated HMGs concordantly low in the p.d. cells . Also included are 475 proteins indistinctly expressed under the same conditions. (XLSX 260 kb)Additional file 8:Table S7. Human HCC tissue-derived metabolic genes (HMGs) mimicked by HLE or HUH7 cells upon comparison. Contains HMGs whose expression pattern in tumours (high or low expression) is reflected either in HLE  or HUH7 cells , when the cells are compared. The included HMGs showed consistent expression pattern in the respective cell lines both in GSE57083 and GSE36133 (CCLE) datasets. Those that also showed similar pattern at the protein level are indicated. (DOCX 25 kb)


SPOP is one of the important subunits for CUL3/SPOP/RBX1 complex tightly connected with tumorigenesis. However, its exact roles in different cancers remain debatable. Here, we identify CYCLIN E1, as a novel substrate for SPOP. SPOP directly interacts with CYCLIN E1 and specific regulates its stability in prostate cancer cell lines. SPOP/CUL3/RBX1 complex regulates CYCLIN E1 stability through poly-ubiquitination. CDK2 competes with SPOP for CYCLIN E1 interaction, suggesting that SPOP probably regulates the stability of CDK2-free CYCLIN E1. CYCLIN E1 expression rescued proliferation, migration, and tumor formation of prostate cancer cell suppressed by SPOP. Furthermore, we found SPOP selectively regulates the substrates’ stability and signaling pathways in prostate cancer and CCRC cell lines, suggesting that complicated mechanisms exist for SPOP to regulate substrate specificity. Altogether, we have revealed a novel mechanism for SPOP in suppressing prostate cancer and provided evidence to show SPOP has dual functions in prostate cancer and CCRC. Drosophila [SPOP was initially identified as a nuclear protein that exhibits a speckled localization pattern . Nuclearosophila –19. MostExtensive genomic studies identified SPOP as a tumor-suppressor in prostate cancer (PCa) tissues , 21. MulSPOP was also identified as an oncogene in clear cell renal carcinoma (CCRC) . SPOP isThus, a debate is currently existing in the field about SPOP’s function in tumorigenesis , 22. WheIn the present study, we have revealed a new mechanism for SPOP in repressing PCa and identified CYCLIN E1, an oncogene and important cyclin involved in G1/S transition, as a new SPOP substrate. We show here SPOP interacts with CYCLIN E1, selectively regulates its stability in prostate and bladder cancer cell lines, and regulates PCa tumorigenesis through CYCLIN E1 degradation. Furthermore, we show here SPOP has substrate specificity and regulates distinguished transcription programs in PCa and CCRC cell lines, suggesting complicated mechanisms exit for SPOP substrate selectivity in different cancers.When exploring the underlying mechanisms for SPOP-regulated tumorigenesis, we found that the exogenous expressed SPOP and CYCLIN E1 interact with each other in HEK293 cells Fig. . We furt4Cl, chloroquine (CQ) and 3-MA, inhibitors for lysosome and autophagy, respectively. Only MG132 inhibited the effect of SPOP on CYCLIN E1 stability, further suggesting the process is dependent on proteasome , one colon cancer cell line (HCT116), two liver cell lines (HepG2 and HL7702), one bone osteosarcoma cell line (U2OS), one cervical cancer cell line (HeLa), one bladder cancer cell lines (UM-UC-3) and one prostate cancer cell line (22RV1)  Fig. . QuantitIn DU145, we successfully verified the interaction between endogenous CYCLIN E1 and SPOP with co-immunoprecipitation Fig. . OverexpTo test whether CYCLIN E1 is one of the substrates for SPOP, in vivo and in vitro ubiquitination assays were performed. SPOP expression greatly increased the poly-ubiquitination of CYCLIN E1 in HEK293 cells Fig. . Then SPTo examine the effect of SPOP mutants in prostate cancer on CYCLIN E1, four constructs containing most frequent SPOP mutations were generated. When CYCLIN E1 was co-expressed with these mutants, it was much more stable than that with wild-type SPOP Fig. . Co-immuPreviously, two ubiquitin E3 ligases for CYCLIN E1 were reported, FBXW7 and RhoBTB3 –28. To c173-177, which both abolish the interaction with CDK2 [To explore the underlying mechanism how CDK2 inhibits SPOP-dependent CYCLIN E1 ubiquitination, we used a CDK2 kinase dead mutant, T160A, and found it can still inhibit CYCLIN E1 degradation by SPOP as wild-type Fig. . Meanwhiith CDK2 , 31, werSPOP stable knockdown cell line, but not that for CCNE1, probably due to the cell cycle defect without CYCLIN E1. The flow cytometry results indicated SPOP deficiency increased S phase cells after release  after nes Fig. , which fnes Fig. . The mRNnes Fig. . These gSPOP is one of the most frequent mutated genes in PCa. In the current study, we identified CYCLIN E1 as its substrate. SPOP directly interacts with CYCLIN E1, poly-ubiquitinates CYCLIN E1 in vivo and in vitro, and represses cancer-related phenotypes induced by CYCLIN E1 expression. Thus, we reveal a new mechanism for SPOP in repressing PCa tumorigenesis.An early structural study revealed a consensus sequence in the substrates for SPOP recognition . In our CYCLIN E1 is one of the important proteins for cell cycle progression and it is well-known that CYCLIN E1 couples with CDK2 and regulates G1/S transition and DNA replication , 34. MorOur data showed that SPOP regulates CYCLIN E1 stability specific in some prostate and bladder cancer cell lines, but not in the other tested cell lines. SPOP overexpression could successfully decrease CYCLIN E1 in nearly all the tested cells. In some cell lines, CYCLIN E1 even decreased after SPOP knockdown, suggesting some unknown factors involves in regulating CYCLIN E1 stability with SPOP in a cell type dependent manner. We have further showed that SPOP selectively regulates its substrates in PCa and CCRC cell lines. Importantly, the xenograft experiments showed that SPOP represses tumor growth rising from a PCa cell line and enhances tumor formation of a CCRC cell line. A recent study indicated that SPOP mutants has cancer specific preference in endometrial cancer to degrade BET family proteins . Our datHEK293, HeLa, HCT116, HepG2, DU145, UMUC3, and HL7702 cell lines were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1% penicillin and streptomycin. ACHN, 786-O, 769-P, PC3, SV-HUC-1, T24, U2OS, and 22RV1 cell lines were cultured in RPMI 1640 supplemented with 10% FBS, 1% penicillin and streptomycin. Caki-1 cell lines were cultured in McCOY’s 5A supplemented with 10% FBS, 1% penicillin and streptomycin. All the cell lines were purchased from Cell Bank of Chinese Academy of Science. The tests for mycoplasma contamination were performed by the vendor.Antibodies against Flag (Sigma F3165), HA (Origene TA100012), Myc , EGFP (Abmart 264076), β-Actin , CUL3 (Epitomics #2489-1), RBX1 (Epitomics #5296-1), CYCLIN E1 , ERG , DEK (BD 610948), DAXX (CST #4533), PTEN , FBXW7 , RHOBTB3 (LSBIO LS-C385850) were purchased from indicated commercial sources. Rabbit anti-SETD2 was raised and now commercial available at Abclonal. Mouse anti-SPOP was raised at the Wuhan Institute of Virology, CAS.Cell proliferation and migration were analyzed with RTCA assay as described before . Cells whttp://david.abcc.ncifcrf.gov).Two biological replicates were sequenced for each sample. mRNA-seq library was performed by using Illumina Tru-Seq library construction kit. A 5 μg of total RNA was used as initiation and then prepared according to the manufacturer’s instruction. mRNA-seq libraries were sequenced using HiSeq2000 for 100-bp paired-end sequencing. Quality control of mRNA-seq data was performed using Fatsqc, and then low quality bases were trimmed. After quality control, data were mapped to hg19 genome reference by Tophat2 and allow maximum 2 mismatch. Cufflinks were used to find out differential expression genes. Gene ontology analysis was performed using DAVID (http://gepia.cancer-pku.cn/) [The disease free survival  analysis based on gene expression via GEPIA performs (pku.cn/) . GEPIA uhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100743.The original RNA-Seq data were submitted to the GEO database at the following link, Supplemental Materials