Emerging evidence shows that the pituitary tumour-transforming gene (PTTG)-binding factor (PBF) functions as a proto-oncogene in some tumors. However, the precise functions of PBF in tumorigenesis and its action mechanisms remain largely unknown. Here for the first time we demonstrated that PBF was associated with a tumor-related cell phenotype in esophageal carcinoma (ESCA) and identified the involved signaling pathways. PBF was up-regulated in ESCA tissues (Data from GEPIA) and cells. Then we down-regulated PBF in ESCA cell lines, Eca-109 and TE-1, by using RNAi technology. Cell function analysis suggested that down-regulation of PBF could inhibit tumor-related cell phenotypes, including proliferation, motility, apoptosis and cell cycle, in Eca-109 and TE-1 cells. Mechanism investigation suggested that apoptosis induced by PBF knockdown may be mediated by the activation of the mitochondrial apoptosis pathway and cell cycle arrest. AKT/mTOR and Wnt3a/β-catenin, key pathways in regulating tumor proliferation and metastasis, were found to be inactivated by the down-regulation of PBF in ESCA cells. In conclusion, our study demonstrates that PBF functions as a proto-oncogene in ESCA in vitro, which may be mediated through AKT/mTOR and Wnt3a/β-catenin pathways. At present, ESCA is the eighth most common cancer in the world and the sixth leading cause of cancer-related deaths . ESCA isThe pituitary tumour-transforming gene (PTTG)-binding factor (PBF), gaining its name due to directly interacting with PTTG1, is also known as PTTG1 interacting protein (PTTG1IP). The main role of PBF is to mediate the transfer of cytoplasmic PTTG1 into the nucleus, where it further regulates a series of biological processes, including metaphase-anaphase transition in mitosis, DNA repair and activation of gene transcription , 6, 7. Din vitro, including proliferation, motility, apoptosis, cell cycle and so on. In addition, we also explored the regulating mechanisms of PBF in ESCA by focusing on AKT/mTOR and Wnt3a/β-catenin pathways.In this study, using Eca-109 and TE-1 cell lines as a model, we first investigated the effect of PBF on the cell phenotype of ESCA 22.12 at 37°C.Human normal esophageal epithelial cell line HEEC and ESCA cell lines Eca109, TE-1, EC9706, EC8712 and KYSE30 cells were purchased from the Cell Bank of the Chinese Academy of Sciences . Cells were maintained in DMEM medium plus 10% fetal bovine serum in humidified 5% CO2.2The sh1-PBF (5’-GAGCTGCTTGTTCTCAGAA-3’), sh2-PBF (5’-CCTGTGAAGAGTGCCTGAA-3’) and sh3-PBF (5’-GAACGTCTCCGCCTGTTTA-3’) were synthesized by oligobio (China) and transfected into ESCA cells by using Lipofectamine 6000 . A scrambled shRNA was used as a negative control.After transfection for 48 h, the total RNA of ESCA cells was collected by using TRIzol reagent (USA) and then reverse transcribed to cDNA using the Rever Tra Ace qPCR RT kit . The mRNA expression of PBF was examined by a FTC-3000 Real-Time Quantitative Thermal Cycler . GAPDH was used as an internal control.2.33 per well. Cells in each well were incubated with 10 μl of CCK8 reagent  for 2 h at 24 h, 48 h and 72 h time point, respectively. Absorbance at 450 nm was detected by using an enzyme standard instrument.CCK8 assay：ESCA cells transfected with shNC or sh2-PBF for 24 h were seeded in a 96-well plate at a density of 1×10Colony formation assay: After transfection for 24 h, ESCA cells (about 200) were seeded into a 6 cm dish with 5 ml of DMEM medium. Cells were normally cultured until forming large enough clones. After staining with 0.1% crystal violet, cell clones were imaged and counted under a microscope.2.4Scratch assay: ESCA cells were transfected with shNC or sh2-PBF for 24 h and normally cultured until the cell monolayer was 95% confluent. Then a single-line wound was created using a pipette tip. After washing the detached cells with PBS, the ESCA cells were normally cultured in a serum-free medium for 24 h. The cell monolayer was imaged at 0 h and 24 h under a microscope and the wound closure (%) was calculated.Transwell migration assay: The transfected ESCA cells (8×104) were resuspended in 500 μl of serum-free medium and were added in the transwell inserts (8 μm)  placed in a 12-well plate. The lower chamber was added with 500 μl of complete medium as a nutrition inducer. Forty-eight hours later, cells that migrated through the membrane were fixed, stained and counted under a microscope. For the transwell invasion assay, a BioCoat Matrigel Invasion Chamber  was used following the instructions.2.5Apoptosis was detected using the Annexin V-FITC/PI Apoptosis detection Kit  according to the manufacturer’s instructions. After transfection with shNC or sh2-PBF for 24 hours, ESCA cells were continually cultured in a serum-free medium for 24 h of starvation. Then the cells were collected and washed by cold PBS. Resuspend the cells by 1X binding buffer and adjust the cell density to 1-5 x 106/ml. Take 100 μl of cell suspension in a 5 ml flow tube, add 5 μl of Annexin V/FITC and incubate in the dark for 5 min. Add 10 μl of PI dye solution and 400 μl PBS for flow cytometry analysis. Flowjo software was used for processing results.For the cell cycle, the transfected ESCA cells were digested with 0.25% trypsin, washed once with PBS and collected at a concentration of 1×106/ml. 1ml of cell suspension was centrifuged and incubated in 70% cold ethanol overnight at 4 °C. After being washed twice with PBS, cells were incubated with a pre-prepared 500 μL PI/RNase: A staining working solution at room temperature for 30-60 min in the dark. Then the cell cycle was analyzed using flow cytometry and Flowjo software.2.6ESCA cells transfected with shNC or sh2-PBF were lysed by RIPA Lysis Buffer  for protein extraction. The concentration of proteins was determined using a BCA method. Then cell proteins were separated by a SDS-PAGE at 20 μg per lane and transferred onto a PVDF membrane. The membrane was then blocked, incubated with primary antibodies overnight at 4 °C. The primary antibodies, including anti-Bcl2 , Bax , cleaved-caspase 3 , cleaved-caspase 9 , AKT , CDK4 , CDK6 , cyclin D1 , AKT , p-AKT , mTOR , p-mTOR , p-p70S6K , Wnt3a , β-catenin , E-cad , N-cad , Snail1 , Snail2 , Vimentin  and GAPDH  were used in the present research. HRP sheep anti rabbit/mouse secondary antibodies (1:5000) were purchased from the PTG Company (USA). Protein bands were then developed by an ECL system and density-quantified by an ImageJ software.2.7t-test. P<0.05 was considered statistically significant.All the experiments were performed three times. GraphPad Prism 7 was used for all statistical analysis. The comparisons between the two groups were analyzed using a student 33.1P<0.05). In addition, the survival curves from GEPIA analysis . Moreover, we investigated the expression of PBF in a normal esophageal epithelial cell line HEEC and ESCA cell lines using western blot. The results indicated that PBF was highly expressed in Eca109, TE-1, EC9706, EC8712 and KYSE30 cells, compared with HEEC cells  is a cancer-related RNA expression analysis website, data of which are from TCGA and GTEx databases . On GEPI3.2P<0.5, P<0.05, in vitro, suggesting that PBF might play an oncogenic role in ESCA.To evaluate the effect of PBF on cell phenotype of ESCA, Eca-109 and TE-1 cells were transfected with shRNA-PBF to establish PBF silenced ESCA cell models. The results in 3.3P<0.05). Cell invasion and migration detected by transwell assay also suggested a significant inhibition when Eca-109 and TE-1 cells were transfected with shPBF . Taken together, down-regulation of PBF promotes cell apoptosis and induces cell cycle arrest in G1 phase in both Eca-109 and TE-1 cells.3.5In order to further determine the mechanism of the pro-apoptosis effect of PBF knockdown in ESCA cells, we investigated the status of apoptosis-related mitochondrial pathways, including Bcl2, Bax, cleaved-Caspase 9 and cleaved-Caspase 3, by using western blot. As shown in 3.6Previous studies have discovered that the AKT/mTOR pathway plays a key role in the regulation of numerous biological processes, which could promote cell growth, survival and movements . MoreoveThe Wnt/β-catenin pathway is reported to be involved in regulating the process of epithelial-mesenchymal transition (EMT), including decreasing cell adhesion and increasing cell migration and invasion , 18. HerTaken together, PBF knockdown-induced cell phenotypic changes, including inhibition of proliferation, mobility and so on, may be mediated by the inactivation of AKT/mTOR and Wnt3a/β-catenin pathways.4in vitro.PBF is a ubiquitous protein in human tissues and highly conserved in numerous species, suggesting that it plays an important role in the evolution of species , 19. HowIn order to determine the action of mechanism of PBF in ESCA, we investigated the status of related signaling pathways by using western blot. Mitochondrial apoptotic pathway is one of the main pathways of apoptosis. Under stimulation of apoptotic signals, Bax translocates to the outer mitochondrial membrane and multimerize, forming membrane channels that stimulate mitochondria to release cytochrome C (Cyt C) , 26 . RePBF could be associated with some pathways to exert a proto-oncogenic role in tumors. For example, it is found that overexpression of PBF in thyroid and colorectal carcinoma causes degradation of the tumor suppressor p53 by ubiquitination, leading to a p53 dysfunction related cell phenotype , 22. TheIn conclusion, our study suggested that PBF functions as a proto-oncogene in ESCA in vitro, which may be mediated through AKT/mTOR and Wnt3a/β-catenin pathways. Our finding will contribute to the therapy of ESCA in the future. However, our study also has some limitations. For example, PBF gain-of-function experiments in ESCA are not carried out in this study. Moreover, no rescue experiments are performed to further verify the oncogenic effects of PBF on ESCA cells via AKT/mTOR and Wnt/βcatenin signaling pathway indeed. These experiments will be investigated further in the following studies.


Lung cancer patients face a dismal prognosis mainly due to the low efficacy of current available treatments. Cisplatin is the first-line chemotherapy treatment for those patients, however, resistance to this drug is a common and yet not fully understood phenomenon. Aiming to shed new light into this puzzle, we used established normal and malignant lung cell lines displaying different sensitivity towards cisplatin treatment. We observed a negative correlation between cell viability and DNA damage induction upon cisplatin treatment. Interestingly, drug sensitivity in those cell lines was not due to either difference on DNA repair capacity, or in the amount of membrane ion channel commonly used for cisplatin uptake. Also, we noted that glutathione intracellular levels, and expression and activity of the transcription factor nuclear factor erythroid 2-related factor 2 (NRF2) were determinant for cisplatin cytotoxicity. Remarkably, analysis of gene expression in non-small cell lung cancer patients of the TCGA data bank revealed that there is a significant lower overall survival rate in the subset of patients bearing tumors with unbalanced levels of NRF2/KEAP1 and, as consequence, increased expression of NRF2 target genes. Thus, the results indicate that NRF2 and glutathione levels figure as important cisplatin resistance biomarkers in lung cancer. In 2018, it is estimated that different types of cancer will cause 610 thousand deaths in the United States alone, and the most common cause will be cancers of the lung, with 84 thousand deaths. Lung cancer can be divided in two main types – non-small-cell lung carcinoma (NSCLC) and small-cell-lung carcinoma (SCLC) – with the former accounting for 85% of the cases and being relatively insensitive to chemotherapy, and the latter being highly aggressive and invasive through the metastasis process. As a consequence, lung tumor patients face a very poor prognosis. For instance, in the U.S., only approximately 18% of patients diagnosed with lung cancer survive more than five years2.Cancer is one of the main causes of morbidity and mortality worldwide, with a total annual economic costs of approximately US$ 1.16 trillion, and the number of cases are expected to rise 70% over the next two decades3. Cisplatin is an alkylating agent that reacts with water when inside cells, replacing its chloride atoms with water molecules. This process results in highly reactive molecules that can covalently bind to DNA to form cisplatin-DNA adducts – including monoadducts, intrastrand crosslinks and interstrand crosslinks (ICL). ICLs are the most toxic DNA lesions caused by cisplatin, due to severe distortions in the DNA double helix that blocks replication and transcription, and, as a consequence, inducing cell death4. Another mechanism that has been described as responsible for cisplatin cytotoxicity is the induction of reactive oxygen species (ROS), compounds that interact with macromolecular components, such as lipids, proteins and DNA, generating lesions5.Commonly, chemotherapy for lung cancer patients uses platinum-based compounds, especially cisplatin, which is also used for ovarian, testicle, and head and neck tumors11.Several mechanisms have been reported as responsible for cisplatin resistance, such as reduced drug uptake by a decrease in CTR1 expression, increased efflux by ABC transporters, augmented induction of stress response chaperones, induction of autophagy, inactivation of apoptosis pathway signaling, inactivation by thiol-containing proteins, and functional changes in DNA repair pathways12. The mismatch repair pathway (MMR) is also involved in cisplatin resistance, mainly as it recognizes post-replicative G/T mismatches induced by cisplatin adducts. MMR removes the base opposite to the adduct, which may cause a new mismatch, starting the pathway again and leading to the so-called futile cycle, generating double stranded breaks. Moreover, this process can inhibit the removal of cisplatin lesions by NER and ultimately leading to cell death. Thus, contrary to what is seen in NER, patients with tumors deficient in MMR show resistance to cisplatin13.Since cisplatin cytotoxicity is mainly due to its ability to cause DNA damage, DNA repair capacity is expected to be one of most important, and therefore studied cisplatin-resistance mechanisms. The most relevant DNA repair pathways responsible for repair cisplatin-induced DNA damage are nucleotide excision repair (NER) and homologous recombination (HR). Importantly, ERCC1 (Excision Repair Cross-Complementing group 1) and XPF (Xeroderma Pigmentosum complementation group F) are essential proteins for both the NER and HR pathway. ERCC1 forms a heterodimer with the endonuclease XPF, and this protein complex is responsible for the incision of the DNA strand processing the damage. Not surprisingly, several reports described an association between ERCC1/XPF increased expression and activity with resistance to platinum compounds in many types of tumors15. The enzymes responsible for GSH synthesis, such as glutamate-cysteine ligase modifier subunit (GCLM) and the glutamate-cysteine catalytic subunit (GCLC), and enzymes related to GSH utilization, such as glutathione reductase, glutathione peroxidase and glutathione S transferase (GST), have their transcription regulated by the transcription factor NRF2 , known as the master regulator of antioxidant response16. Normally, NRF2 is attached to KEAP1 (Kelch-like ECH associated protein 1), promoting NRF2 proteasomal degradation. During oxidative stress situations, KEAP1 is oxidized and then NRF2 goes to the nucleus, promoting the transcription of many genes. The importance of NRF2 to cisplatin resistance has been demonstrated recently, and its overexpression has been correlated with higher resistance to several chemotherapeutic drugs in different types of cancer17.Other important resistance mechanisms prevent the drug from reaching the DNA and causing lesions. One of these mechanisms is the reduction in the intracellular accumulation of cisplatin due to an increase in the thiol-containing protein-mediated inactivation, such as glutathione (GSH), a highly abundant and low-molecular-weight tripeptide (Glu-Cys-Gly), well known antioxidant in cells. GSH can bind to and inactivate cisplatin through its highly reactive thiol group, preventing the drug from binding to DNA and cause damage18. There is a cross-talk between AhR and the NRF2 pathways, with NRF2 being capable of inducing AhR transcription, and also with the AhR pathway inducing NRF2 activation by ROS generation, highlighting the importance of NRF2 in lung cancer21.The aryl hydrocarbon receptor (AhR) signaling pathway, activated by polycyclic aromatic hydrocarbons (PAHs) present in tobacco, is another important important mechanism, particularly in cigarette smoke induced lung cancer. This pathway is involved in xenobiotic metabolism, inducing the expression of several detoxification enzymes by binding in xenobiotic response elements (XRE) containing promotersIn this work, the molecular mechanisms of resistance to cisplatin were investigated, focusing on DNA repair and NRF2/GSH pathways. Using either cisplatin sensitive or resistant established lung cancer cell lines, we showed that cisplatin induced cell death correlated with increased DNA damage induction, and although DNA repair shows important contributions to cellular resistance, it can not explain the cell viability differences among these cell lines. On the other hand, increased NRF2 induction and GSH levels were shown to correspond to increased tumor cell resistance to cisplatin. Thus, this work clearly indicates that NRF2/GSH pathway plays a primary role in cisplatin resistance in lung cancer cells.The cell sensitivity to cisplatin treatment was evaluated in one normal lung fibroblast cell line (IMR-90) and two NSCLC cell lines (A549 and NCI H23). The cells were incubated with increasing doses of cisplatin, and after 72 h of treatment, the cellular viability was measured. As showed in Fig. In situ immunofluorescence for γH2AX was also performed for.cisplatin treated A549 and NCI H23 cells, with a clear increase of γH2AX foci in the damaged cells, particularly in NCI H23 cells  on the DNA during its replication or repair process. In turn, DSBs lead to phosphorylation of the histone H2AX (γH2AX), which is widely used as a marker for genotoxic stress. Analysis of γH2AX-positive cells after cisplatin treatment showed that A549 cells had a low induction of DNA damage, while the NCI H23 lineage had a high induction at 6 h of treatment and displayed a threefold increase after 24 h. Again, the IMR-90 cell line showed an intermediary phenotype, with a significant increase in γH2AX staining only after 24 h  assay was performed. In this assay a damaged plasmid expressing a fluorescent protein reporter gene is transfected into the cells and the recovery of fluorescence detected by flow cytometry. The levels of fluorescence are directly affected by the DNA repair capacity of the cells. HCR analysis showed that A549 shXPF cells lose their capacity to remove UV  using shRNA lentiviral system. The silencing resulted in a substantial decrease in XPF protein levels, and, interestingly, also in the protein levels of its heterodimer partner ERCC1, suggesting that XPF is needed to maintain the stability of ERCC1 and prevent its degradation  is one of main mechanisms involved cisplatin cellular uptake. It has been observed that lower CTR1 expression leads to a decreased accumulation of intracellular cisplatin decreasing the amount of DNA lesions and conferring resistance to treatmentWith the possibility of each cell line receiving different amounts of the drug being discarded, we focused in understanding if the DNA repair capacity, as showed by the XPF knockdown in the most resistant lineage, could explain the different sensitivity to cisplatin among them. To address that, the protein levels of the heterodimer partners XPF and ERCC1 Fig.  were invAnother factor that could explain the different sensitivity between A549 and NCI H23 cell lines is an increased cisplatin detoxification, which can reduce cisplatin DNA damage, and as consequence, lower drug cytotoxicity. It is well established that one of the main factors that promote intracellular cisplatin detoxification is GSH, since it can bind covalently to the drug and prevent it to reach the DNA. Indeed, quantification of the intracellular ratio between reduced and oxidized GSH, an index of the intracellular amount of useful GSH, showed that A549 exhibited a higher ratio, indicating higher levels of reduced GSH and therefore showing an inverse correlation with the amount of DNA damage induced by cisplatin in both cell lines Fig. . To confIn situ immunofluorescence of NRF2 also indicated this protein is highly expressed and present in the nucleus of A549 cells, with lesser expression in the nucleus of H23 cells  and xCT (a subunit of an antiporter that exports glutamate while importing cysteine for glutathione synthesis) was assessed through real time PCR (RT-PCR). The results show that the mRNA levels of both genes are significantly decreased in the NCI H23 cell line, which may explain the reduced GSH levels observed in this cell line Fig. . ImportaBased on these results, we hypothesized that levels of the transcription factor NRF2 may determine cisplatin sensitivity in lung cancer cell lines by regulating GSH production. In order to test this hypothesis A549 NRF2 knockdown (A549 shNRF2), and NCI H23 NRF2 overexpressing (H23 NRF2) cell lines were established. As shown in Fig. Notably, and confirming our hypothesis, A549 shNRF2 showed a higher sensitivity to cisplatin treatment, as shown by the XTT cell viability assay Fig. . In contin vitro analysis, since as we have shown, higher amount of NRF2 and GSH mediate tumor resistance to cisplatin treatment, ultimately leading to a worse prognosis for lung cancer patients.Based on the evidence that NRF2 and GSH are determinants for cisplatin resistance in cell culture of lung cancer lines, we aimed to investigate if this is reflected in the prognostic of patients diagnosed with non-small cell lung cancer. For this purpose, we analyzed the gene expression data from TCGA for NRF2 and its inhibitor, KEAP1, observing that there is a strong positive correlation between them Fig. . However23.Cisplatin, the main chemotherapeutic drug used to treat lung cancer, was the first FDA-approved platinum compound for cancer treatment, in 1978. It is clinically proven to combat different types of cancer, such as testicular, ovarian, cervical and head and neck, included on the World Health Organization’s list of essential medicines. Since its approval, 13 analogs have been evaluated in clinical trials, but only one, carboplatin, has achieved worldwide acceptance, due to its reduced side-effects, although being less effective, with the clinical standard dosage of carboplatin being a 4:1 ratio compared to cisplatin13. Indeed, herein, the results show that lung cancer cell lines that are more sensitive to cisplatin treatment, as determined by XTT cell viability assays, presents higher levels of DNA damage, represented by increased phosphorylation of the H2AX histone, an important genotoxic stress marker24. Additionally, the DNA damage induction results in increased apoptosis, as indicated by a higher number of cells with degraded nuclei (sub-G1 population) and increased activation of Caspase-3.DNA is considered the critical target for cisplatin’s cytotoxicity. Upon entering the cytoplasm, the chloride atoms on the drug are displaced by water molecule, resulting in a compound that binds to the N7 reactive center on purines and causes DNA damage. This process blocks cell division and results in apoptotic cell death11. Some studies show a correlation between the increase in the expression of these proteins and the repair of ICLs25. Cisplatin is highly effective in the treatment of testicular cancer, mainly due to the low levels of both proteins, and therefore ICL repair, in these tumors26. Also, patients with non-small cell lung carcinomas (NSCLC) with low levels of these proteins are more benefited by cisplatin chemotherapy. In melanomas and ovarian carcinomas, high levels of ERCC1 mRNA were observed after treatment with cisplatin27. Several inhibitors of XPF-ERCC1 have been shown to reduce NER activity and sensitize different types of cancer cells to cisplatin13. Furthermore, ERCC1 expression has been negatively correlated with survival after cisplatin chemotherapy in patients with non-small cell lung cancer (NSCLC) and is being exploited as a prognostic factor in the clinic25. In this study, a significant decrease in cell viability after XPF knockdown, which also reduces ERCC1 levels, was observed in the most resistant tumor cell line, confirming that DNA repair has a fundamental role in cisplatin resistance and variation in the protein levels of XPF and ERCC1 can explain, at least partially, the acquired resistance in tumors when compared to a normal fibroblast. Yet, this is not true for all cases, since the XPF/ERCC1 heterodimer levels were increased in the most sensitive cell line and no difference in DNA repair capacity was detected comparing the resistant and sensitive tumor cell lines.However, the drug resistance of tumor cells limits the clinical use of cisplatin, which can be intrinsic or acquired. DNA repair activities of cancer cells clearly participate as a mechanism for cisplatin resistance. Particularly the heterodimer XPF-ERCC1, a rate-limiting factor for the NER pathway, that has single-strand DNA endonuclease activity and incises DNA on the 5’ side of cisplatin adducts, and also participate in the repair of double-strand breaks induced by ICLs28. One of these nucleophilic species is glutathione, a tripeptide that is synthesized in the cytosol from glutamic acid, cysteine and glycine, that exists in thiol-reduced (GSH) and disulfide-oxidized (GSSG) forms, with the former representing around 98% of intracellular glutathione under physiological conditions29. GSH can bind to cisplatin through its reactive thiol group and prevent the drug from reacting with the DNA, thus, preventing damage induction. Indeed, the GSH/GSSG ratio is greatly reduced in NCI H23 cell line, explaining the higher DNA damage induction in these cells causing higher cell sensitivity to cisplatin. Furthermore, by using BSO − a chemical inhibitor of glutamate cysteine ligase (GCL), an enzyme responsible for glutathione synthesis − we depleted glutathione levels in A549 cells and observed significant increase in sensitivity to cisplatin, confirming the importance of glutathione in determining cisplatin resistance.Increased inactivation of cisplatin by thiol-containing proteins is an important mechanism for tumor resistance, as showed for cervical cancers, osteosarcoma and glioblastoma cells30. Importantly, the expression of these genes is controlled by the transcription factor NRF2, and several studies have shown that high amounts of NRF2 can induce cisplatin resistance in ovarian, bladder, and head and neck cancers33. Under physiological condition, NRF2 is constantly being targeted for proteasomal degradation by KEAP1, a process that is interrupted upon oxidation situations. In that sense, loss-of-function mutations in KEAP1 gene – leading to higher NRF2 activation – have been identified in several human adenocarcinoma, with lung cancer being the type where this gene is most mutated34. These mutations changed KEAP1 residues necessary for binding NRF2, reducing its affinity and promoting NRF2 nuclear accumulation. The A549 cell line, resistant to cisplatin treatment, shows a somatic mutation at the first Kelch domain of KEAP1, necessary for its interaction with NRF2, and also methylated promoter, both features that could explain the high levels of NRF2 protein in this cell line35. On the other hand, the NCI H23 cell line, sensitive to cisplatin, shows a mutation at the IVR (intervining-region) domain of KEAP1. This domain is necessary for the interaction with Cullin 3 (Cul3), a subunit of the E3 ligase complex, that promotes ubiquitination of NRF236. We hypothesize that this mutation could promote a more stable interaction of KEAP1 with Cul3, increasing NRF2 degradation and explaining the lower levels in this cell line. In this work, both enzymes responsible for glutathione synthesis and NRF2 protein levels were highly reduced in a cisplatin sensitive cell line and, moreover, overexpression of this transcription factor induces an increase in GSH levels and resistance to the treatment. In agreement, knockdown of NRF2 in a cisplatin resistant cell line depleted GSH and sensitized the cells to cisplatin. Also, the analysis of non-small cell lung carcinomas in the TCGA data bank showed that, although there is a strong correlation between NRF2 and KEAP1 gene expression, there is a subset of patients with median expression of NRF2 and very low expression of KEAP1. As expected, these tumors have in fact increased expression of NRF2 target genes, including enzymes necessary for GSH synthesis. Interestingly, the patients with this NRF2/KEAP1 unbalanced expression show a worse prognosis when considering their overall survival.The intracellular glutathione pool is regulated by a series of enzymes involved in its synthesis, utilization and recycling, and high expression levels of these genes have been seen to promote cisplatin resistance in lung adenocarcinoma cell lines, with the glutamate cysteine ligase catalytic subunit (GCLC) expression being proposed as a potential predictor of treatment failureTogether, these results strongly indicate that the NRF2/GSH antioxidant defense pathway plays an important role in conferring cisplatin resistance in lung cancer cell lines. Although DNA repair is an important marker for cisplatin resistance, we have shown that neither repair proteins expression nor DNA repair capacity analysis alone can predict patient response to the treatment. On the other hand, analysis of NRF2 and GSH amounts showed to be a valid and potent parameter to presume the lung cancer sensitivity to cisplatin treatment. These observations are in line with the fact that tumors of the lung are highly mutated in KEAP1, the inhibitor of this pathway. Altogether, these findings support the evidence that NRF2 is a relevant prognostic factor for lung cancer patients, since its activity status can predict the treatment outcome. Finally, we propose the use of BSO plus cisplatin combinatory therapy as a potential alternative treatment regimen that may overcome cisplatin resistance in lung cancer patients.2 atmosphere. The culture media were supplemented with 1% antibiotic-antimycotic and 10% FCS . The A549 and NCI H23 cell lines have missense substitutions  in the KEAP1 gene.Human lung carcinoma cell lines A549 and NCI H23, and human normal lung cell line IMR-90 were kindly provided by Prof. Mari Sogayar . A549 and IMR-90 cell lines were cultivated in DMEM , and NCI H23 cell line was grown in RPMI medium (Invitrogen), at 37 °C in a humidified, 5% CO4 cells were plated in a 12 multi-well plate and incubated with different doses of cisplatin  for 72 h. In some experiments cells were pre-treated, for 16 h, with 500 μM BSO . After that, cells were washed with phosphate-buffered saline (PBS) and incubated with XTT reagent kit as recommended by the manufacturer´s instructions .2 × 105 cells were plated in a 12 multi-well plate and incubated with 3 μM cisplatin for 6, 24 and 72 h. Supernatant and attached cells were collected, fixed with 1% formaldehyde and then with 70% ethanol. Afterwards, cells were blocked, permeabilized and incubated with a primary mouse monoclonal antibody to γH2AX (Ser-139)  diluted 1:500 for 16 h at 4 °C, followed by incubation with anti-mouse FITC secondary antibody (Sigma-Aldrich) diluted 1:200 for 2 h at room temperature, or with a mouse anti-active caspase 3  diluted 1:50 for 16 h at 4 °C. In both cases, cells were then stained with propidium iodide (PI) at room temperature for 1 h, in PBS containing 20 μg/mL PI (Sigma-Aldrich), 200 μg/mL RNase A and 0.1% Triton X-100.1.5 × 1037.Cells were lysed and the protein concentration was quantified using the Pierce BCA Protein Assay kit . Proteins were separated by electrophoresis on a SDS-polyacrylamide gel and transferred to a nitrocellulose membrane . Membranes were blocked with 5% (w/v) milk powder in PBS for 1 h and incubated for 16 h at 4 °C with one of the following primary antibodies: rabbit anti-SLC31A1/CTR1 , mouse anti-GAPDH , mouse anti-XPF , mouse anti-ERCC1 (Santa Cruz Biotechnology), mouse anti-Tubulin (Abcam), rabbit anti-NRF2 (Abcam). Afterwards, the membranes were incubated with the correspondent secondary antibody and a chemiluminescent HRP substrate  was used to develop the membranes. Each blot was performed twice, and quantification of protein levels was done with the ImageJ software39. Briefly, pHIV-dTomato  plasmid was irradiated with 600 J/m2 of UVC light or incubated with 750 nM cisplatin for 3 h at 37 °C to induce DNA damage. In a 6 multi-well plate, 1.5 × 105 cells were plated and transfected with 900 ng of pHIV-dTomato and 100 ng of pIRES2-EGFP , a transfection efficiency control, using Lipofectamine 3000 Transfection Reagent . One day after plasmid transfection, cells were collected and red and green fluorescence were measured by flow cytometry.The experiments of host cell reactivation assay were performed as previously describedRNA from cells was extracted using PureLink RNA Mini kit (Invitrogen) and cDNA was prepared using a High Capacity cDNA Reverse Transcription kit . Afterwards, 3 μL of diluted cDNA, 6 μL of SYBR green master mix, 0.5 μL of 10 mM reverse and forward primers and 2.5 μL of nuclease-free water were combined for each reaction to determine gene expression by quantitative PCR (Q-PCR), which was carried out using the 7500 Real-Time PCR System (Applied Biosystems). Relative expression of GCLM (Fwd: 5′-CCACCAGATTTGACTGCATTTG-3′/Rev: 5′-GCTTCTTGGAAACTTGCTTCAG-3′), GPx1 (Fwd: 5′-GACTACACCCAGATGAACGAG-3′/Rev: 5′-TCGAAGAGCATGAAGTTGGG-3′), GPx2 (Fwd: 5′-GCTTCCCTTGCAACCAATTTG-3′/Rev: 5′-TTCTGCCCATTCACCTCAC-3′), GPx3 (Fwd: 5′-CTGCTTTCCCTGCTCCTG-3′/Rev: 5′-GCTCCGTACTCGTAAATGGTG-3′), HO1 (Fwd: 5′-AACTTTCAGAAGGGCCAGGT-3′/Rev: 5′-GTAGACAGGGGCGAAGACTG-3′), NQO1 (Fwd: 5′-CAGCGGCTTTGAAGAAGAAAG-3′/Rev: 5′-GGTCCTTCAGTTTACCTGTGAT-3′) and xCT (Fwd: 5′-CCTGGCATTTGGACGCTACAT-3′/Rev: 5′-TCAGAATTGCTGTGAGCTTGCA-3′) was calculated using the relative standard curve method, and normalized against GAPDH (Fwd: 5′-ACCCACTCCTCCACCTTTGA-3′/Rev: 5′-CTGTTGCTGTAGCCAAATTCGT-3′) as the housekeeping gene.4 cells were plated in white, opaque, 96 multi-well plates and, after 24 h, they were lysed by using either a total glutathione lysis reagent or an oxidized glutathione lysis reagent. Then, luciferin generation reagent was added to the wells and the plate was incubated 30 min at room temperature, followed by addition of a luciferin detection solution and incubation of 15 min at room temperature. Luminescence was measured using a Glomax-Multi+ Luminometer (Promega), and serial dilutions of a GSH standard solution was used to generate a standard curve.Reduced and oxidized glutathione levels were quantified using the GSH/GSSG-Glo Assay , following the manufacturer’s instructions. Briefly, 1 × 10http://xena.ucsc.edu) in July, 2018. Gene expression data were generated using the Illumina HiSeq. 2000 RNA sequencing platform, quantified using RSEM, upper quartile normalized, log2(x + 1) transformed, and z-normalized. Analysis of survival was performed using Kaplan-Meier curves and the log-rank test. We used the two-sided Student’s t-test to perform two-group comparisons and the Pearson’s method to calculate correlations.TCGA gene expression and clinical data from 1017 non-small lung carcinomas were downloaded from the UCSC XENA Browser (Data is presented as the mean plus standard error of the mean (SEM) of at least two independent experiments and statistical significance among data sets were accessed by applying two-way ANOVA followed by Bonferoni post-testing, always in comparison to the resistant cell line (A549), using Prism 6 software . Differences were considered significant for p-values lower than 0.5.The balance between NRF2/GSH antioxidant mediated pathway and DNA repair modulates cisplatin resistance in lung cancer cells


Colorectal cancer (CRC) is the second leading cause of cancer deaths in the US with the majority of deaths due to metastatic disease. Current chemotherapeutic regimens involve highly toxic agents, which limits their utility; therefore, more effective and less toxic agents are required to see a reduction in CRC mortality. Novel fluorinated N,N’-diarylureas (FND) were developed and characterized by our group as potent activators of adenosine monophosphate-activated kinase (AMPK) that inhibit cell cycle progression. The purpose of this study was to determine the effect of a lead FND compound, FND-4b, either alone or combined with PI-103  or SN-38 (active metabolite of irinotecan) on cell cycle arrest and apoptosis of CRC cell lines . Treatment with FND-4b for 24h resulted in a marked induction of phosphorylated AMPK expression and a concomitant reduction in markers of cell proliferation, such as cyclin D1, in all CRC cell lines. Apoptosis was also notably increased in CRC cells treated with FND-4b. Regardless of the genetic profile of the CRC cells, FND-4b treatment alone resulted in decreased cell proliferation. Moreover, the combination of FND-4b with PI-103 resulted in increased cell death in all cell lines, while the combination of FND-4b with SN-38 resulted in increased cell death in select cell lines. Our findings identify FND-4b, which activates AMPK at micromolar concentrations, as a novel and effective inhibitor of CRC growth either alone or in combination with PI-103 and SN-38. Colorectal cancer (CRC) is the second leading cause of cancer deaths in the United States , 2. A muDeregulation of cellular metabolism and cell proliferation is a major mechanism of tumor cells. When cells are metabolically stressed, the intracellular ratio of adenosine monophosphate (AMP) to adenosine triphosphate (ATP) is increased, which in turn, activates AMP-activated protein kinases (AMPKs). AMPK activation then regulates various cellular processes, such as cell proliferation, cell polarity, autophagy, and apoptosis , 6. SpecNovel fluorinated N,N’-diarylureas (FNDs) were developed and characterized by our group as potent activators of AMPK that inhibit cell cycle progression . These FTo better characterize the pharmacologic potential of FND-4b as a novel chemotherapeutic agent, we investigated the effect of FND-4b, either alone or in combination with PI-103, a dual inhibitor of Class IA phosphatidylinositide 3-kinase (PI3K) and mTOR –18, or SFND-4b was synthesized as previously described. PI-103 , p-AMPKα , cyclin D1 ; p-AKT ; AKT ; β-actin ; anti-rabbit and anti-mouse . Activation of the mTORC signaling pathway was evaluated by expression of p-AKT and p-AMPKα. Cell proliferation was evaluated by cyclin D1 expression. Apoptosis was evaluated by PARP cleavage.2. DLD1 mutant and wild type cells were grown in RPMI-1640 medium  containing 10% FBS and 1X penicillin-streptomycin and cultured at 37°C and 5% CO2. At the time of experimentation, cells were in a passage range of 15–20 and cells were seeded at 8x105 cells per well.The human CRC cell lines  were obtained from ATCC  and authenticated in February 2016 . DLD1 PI3KCA mutant cells were obtained from ATCC, while DLD1 PI3KCA wild type cells were a gift from Dr. Jing Wang . Genetic mutations with potential implication for treatment resistance to one of the studied drug agents include a PI3K mutation in all four main cell lines tested  Tables. 2 chamber or decapitation, consistent with the 2000 Report of the American Veterinary Medical Association panel.This research utilized human CRC tissues collected from consented patients who had undergone surgical resection at UK Medical Center and was approved by the UK Institutional Review Board (IRB # 16-0439-P2H). Mice used in this research were housed in the UK Division of Laboratory Animal Resources, which is fully accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care. All procedures involving mice were prospectively approved by and performed under supervision of the University of Kentucky’s Institutional Animal Care and Use Committee  and according to regulations stipulated by the Animal Welfare Act and the Guide for the Care and Use of Laboratory Animals. Euthanasia was conducted using the COscid gamma mice (NSG™)  to establish the PDX. The resultant primary CRC cell lines  were established after three sequential generations in mice and authenticated as unique human cell lines in February 2016 (Genetica DNA Laboratories). After this, we established two other primary CRC cell lines derived from PDX tumors (Pt.2377-Primary Tumor (1°), Pt.2377-Liver Metastasis (LM)) in the same manner and used Next Generation Sequencing to compare the genetic profile of 198 oncogenes to original patient tumors (University of Kentucky Oncogenomics Core). Genetic mutations with potential implication for treatment resistance to one of the studied drug agents include a PI3K mutation in Pt.2377-1° and Pt.2377-LM , followed by an overnight incubation in primary antibodies at 4°C. Membranes were washed in TBST and incubated with horseradish peroxidase-conjugated secondary antibodies for 30 min. After two additional washes, immune complexes on the membrane were visualized using Immobilon Western Chemiluminescent HRP substrate  or Amersham ECL . Activation of the mTORC signaling pathway was evaluated by expression of p-AKT and p-AMPKα. Cell proliferation was evaluated by cyclin D1 expression. Apoptosis was evaluated by PARP cleavage.3 cells/well, PDX-derived cell lines at 8x103 cells/well; 100 μL). After treatment with respective drug or combination of drugs, cell proliferation was evaluated at 48h treatment duration  or 144h duration (PDX-derived cell lines) using the CytoScan™ SRB Cytotoxicity Assay  according to the manufacturer’s protocol. This assay utilizes sulforhodamine B (SRB) to bind to viable cellular protein in order to directly measure the total protein mass, which correlates to viable cell number. Cell viability was plotted as a percentage relative to the untreated control .Cells were seeded in 96-well plates in antibiotic-free media 24h prior to treatment  and then treated with test drugs alone or in combination for 48h. DNA fragmentation was evaluated by examination of cytoplasmic histone-associated-DNA-fragments after induced cell death using the Cell Death Detection ELISAPLUS Kit  according to the manufacturer’s protocol. DNA fragmentation was plotted as absorbance (mU).Cells were plated in 96-well plates 24h prior to treatment . Significance was pre-determined at p<0.05.in vitro concentration and duration for FND-4b treatment, commercially-available CRC cell lines (HCT116 and HT29) were treated with FND-4b at different dosages  for 48 h and 72 h , which supports our western blot findings . HT29 ceTo determine the impact of FND-4b treatment on CRC apoptosis, we first probed western blots for PARP cleavage. In all cell lines, we found increased PARP cleavage in cells treated with FND-4b compared with the untreated control . In HCT1Next, we quantitated DNA fragmentation in each cell line 48h after drug treatment as a measure of apoptosis. Interestingly, in the HCT116 cell line, DNA fragmentation was more pronounced following treatment with FND-4b alone or in combination with either PI-103 or SN-38, compared to control treatment or PI-103 and SN-38 as single agents . More spTo extend our findings regarding the effectiveness of FND-4b treatment on CRC cell growth and cell death, we next utilized four PDX-derived CRC cell lines, which were established from resected colon cancers at the University of Kentucky. In all four PDX-derived CRC cell lines, treatment with FND-4b, alone or in combination, caused almost complete inhibition of cyclin D1 expression . Pt.93 aTo next evaluate the effect of FND-4b treatment on cell cycle arrest, PDX-derived CRC cell lines were treated with FND-4b alone or in combination with PI-103 or SN-38 for 144h and analyzed by CytoScan SRB Cytotoxicity assay . There wTo evaluate the impact of FND-4b treatment on apoptosis of the PDX-derived CRC cell lines, we first probed western blots for cleaved PARP. In all cell lines, PARP cleavage was increased following treatment with FND-4b compared with the untreated control . In Pt.1To better delineate the effect on cell death, PDX-derived CRC cell lines were treated with combinations of FND-4b, PI-103, and SN-38 for 144h and DNA-fragments were quantitated using a Cell Death Detection ELISA. All cell lines demonstrated increased DNA fragmentation when treated with FND-4b compared to untreated control  . FurtherFND-4b represents an AMPK activator that arrests cell cycle and induces apoptosis in colon cancer cells at micromolar concentrations. Previously, we reported the ability of FND-4b to inhibit growth in CRC stem cell lines at significantly lower doses than were required by metformin treatment , 13. In et al. [et al. [et al. [AMPK plays a major role in the regulation of cell metabolism and growth in both normal and neoplastic tissues , 6. AMPKet al.  found th [et al.  demonstr [et al.  has showWe selected PI-103 as a comparison compound in order to include a drug that targets molecules surrounding AMPK, specifically PI3K and mTOR. Given that FND-4b activates AMPK, which in turn inhibits mTOR signaling, the upstream peptides in the mTOR signaling pathway, PI3K and AKT, would accumulate and potentially eventually override the FND-4b effect on AMPK, ultimately allowing the re-initiation of cell proliferation. We found that although FND-4b treatment did cause an accumulation of upstream pAKT, this did not have a negative impact on the ability to suppress cancer cell growth or enable apoptosis. We also showed that a dual drug regimen of FND-4b with PI-103 achieved greater pro-apoptotic impact and inhibited cell proliferation more significantly than the dual therapy of FND-4b with SN-38, or any mono drug therapy. Importantly, these effects were also noted in cell lines with known PI3K mutations, which lacked cytostatic activity when treated by PI-103 monotherapy. We speculate that the robust impact of dual FND-4 + PI-103 therapy on cell cycle inhibition is related to the target of FND-4b being downstream from the target of PI-103, as such FND-4b is not impacted by a PI3K mutation. Our findings align with those of another group that studied dual therapy of a PI3K-inhibitor (LY294002 10μM) and an AMPK activator (metformin 10mM) in ovarian cancer cells; however, in contrast, we were able to demonstrate cell cycle arrest and induction of apoptosis in CRC cells using a significantly lower dosage of FND-4b . To our We studied SN-38 as the second comparison treatment compound in order to compare the pro-apoptotic effects of FND-4b to an agent that functions like irinotecan, a Category 1A recommended systemic chemotherapy agent used to treat advanced or metastatic colon cancer but with a lower side effect profile than irinotecan –37. We dIn summary, we have shown that despite a variety of mutation profiles in the studied CRC cell lines, treatment with FND-4b consistently results in cell cycle arrest and apoptosis. Furthermore, these results are noted with treatment of FND-4b at micromolar concentrations, much lower concentrations than have been shown effective for other drugs, such as metformin. Importantly, we demonstrate that FND-4b is a novel and effective inhibitor of CRC growth either alone or in combination with PI-103 and SN-38. Given its efficacy at micromolar concentrations, FND-4b should be the focus of future CRC treatment studies—particularly those that evaluate its efficacy in an in vivo model.S1 Table(DOCX)Click here for additional data file.S2 Table(X) Denotes a damaging mutation. Six of the cell lines studied have potentially damaging PI3K mutations: HCT116, HT29, LS174T, DLD1, Pt.2377-1°, and Pt.2377-LM.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Although we tentatively identified several PS and PI species contributing to cell line clustering, full PI and PS profiles appeared to be a key to the successful cell line discrimination. In parallel, we compared PL composition of primary epithelial (EpCAM-positive) cells, isolated from tumor and adjacent non-tumor tissues of colon cancer patients, with PL profiles of cell lines derived from normal colon mucosa (NCM460) and from colon adenocarcinoma  cells, respectively. In general, higher total levels of all PL classes were observed in tumor cells. The overall PL profiles of the cell lines, when compared with the respective patient-derived cells, exhibited similarities. Nevertheless, there were also some notable differences in levels of individual PL species. This indicated that epithelial cell lines, derived either from normal colon tissue or from CRC cells, could be employed as models for functional lipidomic analyses of colon cells, albeit with some caution. The biological significance of the observed PL deregulation, or their potential links with specific CRC stages, deserve further investigation.Identification of changes of phospholipid (PL) composition occurring during colorectal cancer (CRC) development may help us to better understand their roles in CRC cells. Here, we used LC-MS/MS-based PL profiling of cell lines derived from normal colon mucosa, or isolated at distinct stages of CRC development, in order to study alterations of PL species potentially linked with cell transformation. We found that a detailed evaluation of phosphatidylinositol (PI) and phosphatidylserine (PS) classes allowed us to cluster the studied epithelial cell lines according to their origin: i) cells originally derived from normal colon tissue ; ii) cell lines derived from colon adenoma or less advanced differentiating adenocarcinoma cells ; or, iii) cells obtained by  The colorectal cancer (CRC) development is a complex multi-step process, which involves a gradual progression from adenomatous polyp to adenoma, and then to malignant carcinoma . The colSeveral studies have pointed to significant changes in fatty acid (FA) composition occurring in colon mucosa during cancer development, already three decades ago –7. The dde novo lipogenesis have been shown to alter PL composition, and these might take place well in advance of morphological changes observed during tumorigenesis [More recent studies have also indicated that increased levels of total phospholipids (PLs) and, in particular, of some specific PL classes/species could be associated with CRC development . The bioigenesis . Interesigenesis . HoweverRecent developments in lipid analytical techniques (lipidomics) provide a unique opportunity for more sophisticated studies of the lipid alterations and their role in cancer development/therapy , 19, 20.In the present study, we analyzed lipid composition of nine human colon epithelial cell lines derived from normal colon mucosa or from tumor tissue(s) at various stages of malignant transformation, with the aim to identify key lipid species, which would allow to discriminate among individual cell lines. We particularly focused on their PL profiles, as these have been recently shown to provide a suitable tool e.g. for discrimination between non-small cell lung cancer and normal lung tissue . We then™; Rockville, MD, USA), were cultured in a 1:1 mixture of Ham's F12 and DMEM , supplemented with HEPES , 10% FBS, cholera toxin , insulin , transferrin  and hydrocortisone . Human adenocarcinoma cell lines, HT-29 (ATCC® HTB-38™), HCT-116 (ATCC® CCL-247™), SW480 (ATCC® CCL-228™) and corresponding lymph node metastatic SW620 (ATCC® CCL-227™) cell line, were cultured in McCoy's 5A medium (Sigma-Aldrich), supplemented with 10% FBS and penicillin/streptomycin (50 mg/l). Human colon adenoma AA/C1 [in vitro transformation of AA/C1 cells [2; 95% humidity). All cell lines were routinely checked for Mycoplasma infection.Human epithelial colon NCM460 cells , derived from normal colon mucosa, were cultured according to the company specifications, in M300F medium (Incell) supplemented with 10% fetal bovine serum (FBS)  and penicillin/streptomycin . Human fetal colon FHC cells, obtained from American Tissue Type Collection  in cell culture dishes . Upon reaching sub-confluency, cells were harvested and counted using Coulter Counter model ZM . For each cell line, 8×106 cells were frozen in 1 ml methanol (at -20°C) in glass tubes for lipid analyses. The tubes were previously degreased by sulfuric acid and washed with ethanol.Cells were seeded , based on informed consent signed by patients. The study was approved by the Ethical Committee of the University Hospital Brno and all experimental procedures were in compliance with the Declaration of Helsinki, as well as with the law of the Czech Republic.utto smaller pieces with scissors under sterile conditions, and then incubated for 15 min at room temperature (RT) in dithiothreitol: Hank´s balanced salt solution (HBSS)  mix (1:9 v/v), in order to remove potential contamination. Samples were then washed with HBSS (37°C), centrifuged  and digested in culture medium and TM liberase  at 37°C for 2 h. The culture medium RPMI 1640  contained HEPES, antibiotic/antimycotic mix , NaHCO3, insulin, hydrocortisone and 5% FBS. After incubation, the rest of digested tissue was filtered through 50 μm filter, washed with culture medium and 20% FBS, centrifuged , and finally washed with HBSS (37°C). Contaminating erythrocytes were lysed for 5 min on ice in a lysis solution , and then washed with HBSS. Cell pellet was then re-suspended in a mixture of 300 μl of cold MACS buffer , 100 μl FcR reagent , for blocking of non-specific binding of the antibody, and 100 μl magnetic beads conjugated with the EpCAM  antibody . This mixture was incubated for 30 min at 4°C in the dark, and then washed again with MACS buffer. The re-suspended cell pellet was then loaded onto magnetic column. Epithelial cells bound to magnetic beads were captured in the magnetic column and then displaced with a piston. Isolated epithelial cells were counted using Coulter Counter model ZM  and stored in 1 ml of methanol at -20°C until further analyses.The surgically removed tissue was cCell pellets were thawed and transferred from Eppendorf tube into degreased glass tubes (100 x 10 mm) using 3 x 0.5 ml of methanol. After each addition of methanol, cell pellet was homogenized by sonication in a water bath. Following this procedure, 0.75 ml of chloroform and 0.3 ml of water were added into tube; its content was dispersed using a probe sonicator and extraction process continued at RT overnight. The monophase extraction system was dried by nitrogen at room temperature. For reconstitution of lipids, 0.3 ml of solvent mixture (methanol/chloroform 1/1 vol. %) was used. This concentrated extract was then used for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of lipids, performed using Agilent 1200 high performance liquid chromatography (HPLC) System  and mass spectrometer TripleQuad 6410, equipped with electrospray ionization (ESI) and atmospheric pressure photoionization (APPI), respectively (Agilent). Methanol, chloroform, acetonitril, hexane, isopropanol, formic acid, ammonium formate were purchased from Sigma-Aldrich; HPLC columns Supelcosil LC SI–, Supelcosil LC DIOL  were used for lipid separation.Phospholipid classes were separated by normal-phase HPLC using methanol-acetonitril-water gradient mobile phases and detected by tandem mass spectrometry with ESI. Non-polar lipid classes were separated by normal-phase HPLC using hexane-isopropanol gradient mobile phases and detected by mass spectrometry with APPI.Phospholipids: ESI ionization was set in positive mode, drying nitrogen flow at 10 l/min, and fragmentor voltage and collision energy voltage were optimized for each lipid class. Neutral loss scan mode and precursor ion scan mode, respectively, were used for specific detection of respective lipid classes: precursor scan at m/z 184 for phosphatidylcholines (PC); neutral loss scan at m/z 115 for phosphatidic acids, at m/z 141 for phosphatidylethanolamines (PE), at m/z 185 for phosphatidylserines (PS), and at m/z 277 for phosphatidylinositols (PI).Triacylglycerols (TAGs) and cholesteryl-esters (CholE): APPI ionization was set in positive mode, drying nitrogen flow at 11 l/min, and fragmentor voltage was optimized for each lipid class. Detection by full scan mode were used for detection of fully separated lipid classes.m/z species were then assigned and calculated. Levels of individual lipid species were based on their corresponding chromatographic peak areas. Results were normalized to cell numbers (per one million of cells). For this comparative method, the volume of cell extract and analyzed aliquot (i.e. injection volume) were maintained the same for all samples.MassHunter software (Agilent) was used for evaluation of mass spectra of all lipid classes. Peak areas of individual Each PL species (represented by peak area) has specific molecular weight (MW), number of carbons (C) and number of double bonds (DB). We first verified acceptable experimental variability as the repeatability of the estimates of the content of lipid compounds in analyzed cell lines. All analyses were based on original peak area values (direct outcome from chromatograms) or on transformed peak area values , reflecting the content of individual lipid compounds (represented by specific molecular weight species). All compared individual lipid compounds  as well th percentile) were in the same color in all PL classes. The most discriminating species were selected using ANOVA and Tukey post-hoc test.The results are shown for all PL classes separately, since peak areas here represent unstandardized amount across different PL classes; nevertheless, the values were fully comparable within a given PL class. In order to better display and compare amount of all PL species, peak areas in particular classes were transformed to percentiles and heat maps were built. All percentiles of amount of PL including median  and non-tumor (N) tissues, the heat maps show tumor/non-tumor (T/N) cell ratio (expressed as LOG) for different PL species. Clustering using Wards method on matrix of Euclidean distances was adopted for the description of similarity of lines according to peak areas.Principal component analysis (PCA) followed by a varimax rotation was applied for the dimension reduction and the visual description of relationship among cell lines. Position of the cell lines in the space of lipid species was computed based on their log peak values in given cell lines and lipid class. The principal components are linear combinations of the original variables (log peak areas) weighted by their contribution to explaining the variance in a particular orthogonal dimension. Principal components with eigenvalue > 1 were selected and visualized for each lipid class. PCA was performed using IBM SPSS Statistics 23 (IBM Corporation) and Statistical Software R (version 3.4.2).in vitro transformed variant of AA/C1 cell line, AA/C1/SB10 cells; and cell lines derived from colon adenocarcinoma—HT-29, HCT-116, SW480, as well as SW620 cell line, which has been derived from a lymph node metastasis of the same patient as SW480 cell line. A detailed description of all cell lines used in the present study is provided in We first evaluated the content and profiles of individual PL classes, TAGs and CholE in nine human colon epithelial cell lines derived both from normal colon mucosa and from colon tumors at distinct stages of colon cancer development. These included: NCM460 cells, derived from normal adult colon mucosa; FHC cells derived from normal fetal human colon; cell lines derived from colon adenoma, RG/C2 and AA/C1 cells, as well as All lipid analyses were based on peak area values (directly derived from chromatograms) reflecting the content of individual lipid compounds (represented by specific molecular weight—MW species). A wide range of MW species was detected within each PL class in each cell line. Here, our analyses aimed to identify those MW species with specific discrimination properties in particular classes. The cumulative peak areas (primary data) according to lipid species MWs and number of DB within individual PL classes, as well as neutral lipids, are shown in . Contributions of specific lipid MW species are graphically depicted in form of heat maps , where color intensity matches a relative contribution of a given species to the particular PL class content. Here, we were able to identify PL species with the highest discrimination power (i.e. with the largest differences observed among the cell lines), that are marked in red in We then focused on a more detailed evaluation of individual PL classes profiles . A summary of suggested FA patterns in all evaluated PL species is presented in . Contribeat maps , where cPCA in The same analytical approach, which was used for colon cell lines, was then applied for analysis of PL content of epithelial cells isolated from tumor (T) and adjacent non-tumor (N) tissues from eight CRC patients. The heat maps, based on the T/N ratio values for individual lipid MW species of PC, PI, PE or PS classes, are presented in in vivo with a high efficiency [Based on the above results, we then compared individual PL class composition (PL profiles) that were determined in patient’s non-tumor and tumor cells, with PL profiles of model cell lines derived from normal colon mucosa (NCM460 cells) and malignant adenocarcinoma (HCT-116 cells). HCT-116 cell line was selected for this comparison, because it is a widely used colon carcinoma model, with a number of available transgenic variants, which is capable to form tumors and metastases ficiency . Relativet al. established correlations of increased PI and PC with CRC genesis, as well as the relationship between increased PE and hepatic metastasis in colorectal carcinoma [An altered lipid composition can have a major impact on survival, proliferation or communication of tumor cells, due to the multiple roles of lipids in cellular signaling, build-up of cell membranes or in cell-to-cell interactions , 38. CRCarcinoma . Nevertharcinoma , 44 havearcinoma .in vitro transformation of adenoma cells and/or advanced colon adenocarcinoma cells  on the other side. A notable exception among the studied cell lines was RG/C2 cell line that has been originally derived from adenoma cells and which co-segregated with adenocarcinoma HCT-116 cells. However, this adenoma cell line has been previously shown to exhibit a relatively high malignant potential [The results of this study seem to suggest that the commonly used human colon epithelial cell models can be discriminated based on their PL (in particular PS and PI) profiles. A detailed analyses of individual MW species within given both PS and PI classes revealed that cell lines can be segregated together in an order corresponding with their origin, i.e. into cells originally derived from normal colon tissue , from colon adenoma or less advanced differentiating adenocarcinoma cells  on one side, and, into cells obtained by otential , unlike in vivo conditions. For example, it can be expected that in vivo, colon cancer metastatic cells could also increase FA import from metastatic niche and consequently also PL production, e.g. through CD36 FA transporter up-regulation [Significant differences in the abundance of lipid species have been found to be associated not only with formation of primary tumors, but also with further cancer progression and formation of metastases. For example, increased plasmanylcholine and TAG levels and decreased plasmenyl-ethanolamine lipids have been observed in metastatic SW620 cells, as compared to the primary colon cancer SW480 cell line . In the gulation . PL profRegarding the mechanisms responsible for the observed alterations of PL profiles, several hypotheses have been proposed. Recently, the differences in lipid profiles and key lipid metabolism enzymes have been compared between DNA mismatch repair-proficient and -deficient colon cell lines. In mismatch repair-proficient cell lines , higher levels of PC 16:0/18:1) and phosphatidic acid (18:0/18:0) were detected, as compared with mismatch repair-deficient cell lines  . Neverth:1 and phOur subsequent analyses of isolated primary colon epithelial cells isolated from CRC patients, detected higher levels of PLs of all classes in tumor than in non-tumor samples. This was most evident for PC, the principal PL component of cell membranes. Our results are mostly in a good accordance with earlier studies using complete colon tumor tissues , 40, 41.Some discriminating species in our study also contained similar saturated and monounsaturated acyl chains. Increased levels of such PLs could be attributed to increased endogenous FA synthesis due to overexpression of key enzymes (FA synthase and stearoyl-CoA desaturase) in cancer tissue, including colon cancer microenvironment . NeverthAlterations of PL profiles in cancer cells have been shown to promote activity of pro-survival signaling pathways or to modulate their invasivity , 60. Thiin vitro cell lines should be interpreted with caution and, ideally, these experiments should be combined with analyses of primary colon cells for validation of the cell line-derived data.Finally, we found that the most abundant PL species in primary epithelial cells were also the most abundant PL species found in model colon cell lines. Thus, when we compared individual PL profiles of the two studied cell lines (NCM 460 with HCT-116 or SW480 cells) with patient’s derived primary non-tumor and tumor epithelial cells, we observed, in spite of overall higher PL levels in cell lines, significant similarities between overall PL profiles of a cell lines and the respective clinical material. Thus, epithelial cell lines derived either from normal colon tissue or from CRC cells could be employed as models for functional lipidomic studies in colon cells. Nevertheless, it should be stressed that, although we identified several specific PL species to exhibit similar ratios in tumor vs. non-tumor cells, as in NCM460 vs. adenocarcinoma cell lines, there were also some PL species exhibiting opposite trends in primary colon cells and in permanent colon cell lines, including PC MW 731.7, PI MW 862.6 or PS MW 787.6. This suggests that colon cell lines as models for lipidomics may also have some limitations (as discussed above in case of metastatic SW620 cell line). For example, changes associated with immortalization of non-tumorigenic colon cells (such as NCM460 cells) could also partly alter their PL profiles. Therefore, the data from In conclusion, PL profiling was found to allow for a successful discrimination among colon epithelial cell lines derived from normal colon tissue, or, at various stages of cell transformation. The present results confirm that cell transformation may lead to a significant deregulation of some PL species, which is reflected also in their differences among various colon cell lines. Importantly, for the first time, we performed a detailed analysis of PL profiles in isolated primary epithelial (EpCAM-positive) colon tumor cells from CRC patients and compared them with adjacent non-tumor epithelial cells isolated from the same donor. Here, we found increased levels of all PL classes in primary tumor cells, and we identified some specific lipid species within their profiles, with a potential to distinguish between tumor and non-tumor cells. The overall similarities of PL profiles between colon cell lines and isolated colon epithelial cells also suggest that permanent colon cell lines could serve as suitable models for lipidomic analyses. Nevertheless, when investigating the PL-dependent mechanisms regulating colon cancer cell survival, proliferation and differentiation, or, when evaluating therapeutic approaches targeting lipid metabolism, the CRC-derived cell models certainly have also some limitations. In future studies, it will be important to establish both biological significance and the mechanisms responsible for the observed up-regulation or down-regulation of individual PLs in colon tumor cells.S1 FigBaseline description of primary data profiles . The variability of the primary estimates is associated (is growing) only with the value of peak area (A) but still with a small coefficient of variance. The analysis proved that standard error of the repeated estimates is fully random, not specifically associated with measured compounds or their characteristics, such as e.g. molecular weight (B).(PDF)Click here for additional data file.S2 FigCumulative peak areas (primary data) according to lipid species molecular weights and number of double (D) bonds within phosphatidylcholines (PC), phosphatidyletanolamines (PE), phosphatidylinositols (PI), and phosphatidylserines (PS) are shown for all colon epithelial cell lines. Molecular weight is the main covariate of the total PL content in all the cell lines; the biggest differences in PC, PE and PS amount among the cell lines are generated by the content of species with molecular weight in the range 680–830. Within this range, the differences among the lines in the total PL content are established and species with higher molecular weight than 830 do not further contribute to the differencing. On the contrary, the most discriminating PI species are those with MW>830. Increment in number of double bonds did not significantly influence changes in the total amount of PC, PE and PS classes—their content in all compared cell lines significantly increases due to contribution of PL compounds with low degree of saturation, i.e. with ≤ 1 double bond. Only PI species with higher double bond number (2–5) are also contributing significantly to total lipid mass.(PDF)Click here for additional data file.S3 FigCumulative peak areas (primary data) according to lipid species molecular weights and number of double (D) bonds within triacylglycerols (TAG) and cholesterol-esters (CholE) are shown for all colon epithelial cell lines.(PDF)Click here for additional data file.S4 FigRelative distribution  of specific PL species in non-tumor and tumor primary epithelial cells  as well as non-tumor (NCM460) and tumor (SW480) derived cell lines. Carbon and double bond (DB) numbers are shown in parentheses. Only PL species, which were above detection limit both in patient’s samples and in cell lines are shown here.(PDF)Click here for additional data file.S1 Tablep = 0.184) which enables a direct comparison of lipid profiles among lines.A detailed analysis of repeated estimates (3 independent repeats) of phospholipid profiles confirmed a high degree of repeatability of the experimental outcomes. Analysis of variance  revealed coefficient of variance in the range of 16.1–22.1% which confirms effective normalization of the peak data base on logarithmic transformation. Random error exhausted only 0.09% of the overall experimental variance . Furthermore, test of homogeneity of variance among compared cell lines proved acceptable homogeneity ((PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Meta-analysis of datasets available in public repositories are used to gather and summarise experiments performed across laboratories, as well as to explore consistency of scientific findings. As data quality and biological equivalency across samples may obscure such analyses and consequently their conclusions, we investigated the comparability of 85 public RNA-seq cell line datasets. Thousands of pairwise comparisons of single nucleotide variants in 139 samples revealed variable genetic heterogeneity of the eight cell line populations analysed as well as variable data quality. The H9 and HCT116 cell lines were found to be remarkably stable across laboratories , in contrast to the highly variable HeLa cells (89.3%). We show that the genetic heterogeneity encountered greatly affects gene expression between same-cell comparisons, highlighting the importance of interrogating the biological equivalency of samples when comparing experimental datasets. Both the number of differentially expressed genes and the expression levels negatively correlate with the genetic heterogeneity. Finally, we demonstrate how comparing genetically heterogeneous datasets affect gene expression analyses and that high dissimilarity between same-cell datasets alters the expression of more than 300 cancer-related genes, which are often the focus of studies using cell lines. Comparisons of in-house data and public datasets enable researchers to contrast their results to existing information in a biologically meaningful way, while meta-analyses of public datasets can yield biologically and technically relevant information that the individually analysed constituent datasets cannot2. The scientific context of different studies vary greatly, but the chosen context does not, however, preclude the possibility of subsequently investigating other scientific questions, making re-analysis of previously published data an important venture to achieve novel insights3. Indeed, some of the earliest “Big Data” articles’ citations have been mainly attributed to novel results from re-analyses of the data rather than the original conclusions themselves4. Re-analyses are also an efficient use of scientific resources, as new conclusions can be drawn without needing to perform new and costly sequencing experiments. Integration of different data types (e.g. transcriptomics and genomics) is also becoming more relevant, as they can reveal a more complete biological picture5. Integration of data collections are especially important, since they can yield insights into biological questions difficult to assess by direct experiment6. Before any such project can start, however, investigations need to be made to ensure that included datasets are comparable. There are several reasons why a dataset might be disqualified from inclusion, such as low data quality or non-equivalent biological sources2.As the number of gene expression experiments continue to increase, so do the availability of datasets in publicly available data repositories, such as the Gene Expression Omnibus (GEO)in vitro models for cancer and drug testing, but a considerable problem is that of cell line authenticity: whether or not the cells used are biologically equivalent to their original source7. While cell lines are excellent, easy-to-use sources of unlimited experimental materials that side-steps the ethical and practical issues related to using human samples, authenticity remains a major concern. Unauthentic cells can arise because of several reasons, such as cross-contamination by another cell line, mislabelling at the lab or genetic drift due to long-term culturing. It has been shown that between 15% and 20% of all cell lines are misidentified or contaminated8. Mycoplasma infections also affect the cells, but can be avoided by performing routine tests and using proper culturing techniques9. The HeLa cell line is among the most frequent sources of cross-contamination due to its ubiquitous use in laboratories across the globe. Not only do problems arise during culturing and experimentation on cell lines, but it is now apparent that many have become contaminated at the time of their creation10.Cell lines are commonly used as de facto standard recommended by the American Type Culture Collection (ATCC), but analysis of single nucleotide variants (SNVs) is also becoming increasingly used12. There are, however, problems with using STR profiling as the basis for cell line authenticity, such as microsatellite instability and genetic heterogeneity14. Researchers have recently shown that a batch of the MCF7 cell line possessed genetic heterogeneity that affected its phenotype, while still yielding a perfect STR match to the ATCC reference15.Analysing short tandem repeats (STRs) in the cell line of interest and comparing the results to a database is the 16, we previously developed a method to analyse RNA-seq for cell line authentication17. The method uses the vast amounts of sequence information available from RNA-seq experiments to compare variants with the Catalogue of Somatic Mutations in Cancer (COSMIC) database on a larger scale than conventional STR or SNV profiling does18. While SNVs are traditionally analysed with genomic methods, it has previously been shown that 40% to 80% of variants discovered using whole genome sequencing are also found by RNA-seq19. There are numerous studies empirically proving that RNA variant analysis can yield novel biological insights22. This highlights the ability of RNA-seq to also be used for variant analysis (in addition to standard gene expression studies), greatly increasing its utility. One of the strengths of the method is its capacity for re-analysis of existing sequencing data, allowing it to investigate any publicly available RNA-seq datasets as well as novel data. Another advantage is its potential to analyse variants across the entire transcriptome, rather than a preset number of STRs or SNVs, thus greatly increasing its statistical power. In addition to filling the need for new and robust methods for cell line authentication highlighted by Freedman et al.23, the method both authenticates cell lines to a high degree of certainty as well as provides detailed information about deviations from known variants in the cells. We also demonstrated that our method could potentially be used for transcriptome-wide authentications, taking the totality of overlapping SNVs in each dataset into account. This would represent an improvement over e.g. STR panels, as the global analysis of individual mutations is important for many diseases24.As RNA sequencing (RNA-seq) has been shown to be highly robust across both platforms, laboratories and experimental designsHerein, we present one of the largest studies of genetic heterogeneity and comparability in public datasets to date using the previously published method, performing several thousands of pairwise comparisons of 139 samples across 85 RNA-seq datasets and eight cell lines in the GEO. We characterise the varying degrees of genetic heterogeneity present in the different cell lines and confirm that this heterogeneity has an effect on gene expression and cellular functions, reveal that H9 and HCT116 are remarkably stable while HeLa possesses a high degree of variation, and highlight a mislabelled MCF7 dataset. We thus demonstrate the importance of checking that public data used for new analyses are based on biologically equivalent sources and provide a general workflow demonstrating how this can be achieved.6 reads on average across one or more samples in order to control for biases in sequencing depth, contain a mix of single- and paired-end data and to be of “wild type” origin; samples that contained gene knockouts, genetic transformations, treatments or other perturbations (according to the metadata) were ignored, as such samples could confound the underlying biology being compared. A total of 139 samples from 85 different datasets and eight cell lines were selected to be analysed  with the exception of HCT116, which has 2,777 in total (Table overlap as the number of variants that are present in both samples for any given pairwise comparison (i.e. the RNA-seq datasets and the COSMIC database); between tens and hundreds of the COSMIC SNVs overlap with those found in the analysed datasets  relative to the overlap . Six out of seven cell lines (discounting H9) have a median concordance above 90% (a previously employed threshold for cell authenticity)12, while MDAMB231 has 87.6%  more clearly reflect the biology of the SNVs. The distribution of matched and mismatched SNV impacts is comparable to previous cell line data . However, a large proportion of the mismatched COSMIC SNVs are homozygous variants  ÷ (n + a + b), where s is the number of matching variants, n the number of overlapping variants, a = 1 and b = 5. The variables a and b were selected to yield a cutoff equivalent to the one used by Yu et al.12, resulting in a lower bound of 44 perfectly matching variants yielding a score of 90; this better utilises the increased statistical power from the greater number of variants in transcriptome-wide analyses and better highlights potential problems with data quality.There are three datasets from the H9, HeLa and MCF7 cell lines that have a low number of identified SNVs in total , compared to the other transcriptome-wide datasets SFigure . The pai25. No such bias can be seen in the datasets in the present study  are listed as “total RNA” and “RNA-seq”, respectively, but details in the corresponding article reveals that this data is of small RNA-seq origin, which explains the low number of variants resulting from this dataset. The GSE68471 dataset (HeLa) is likewise listed as “total RNA”, but is in fact from low complexity histone-specific libraries from a method for sequencing 3′-ends of RNA, while the GSE77033 dataset (MCF7) uses nanoCAGE26, a method for identifying 5′-ends of transcripts and discovery of new promoters. These three datasets, in addition to the mislabelled MCF7 dataset, are not included in any subsequent analyses.The three datasets that showed mixed concordances are seen as mostly blank columns/rows, highlighting their deviation from the bulk of datasets and need for further investigation. The reason for the low number of variants in the GSE39872 dataset (H9) could be due to the imprecise manner in which metadata is submitted and stored in the GEO: the Taken together, these results demonstrate that transcriptome-wide variant analyses efficiently capture the biological variation needed to investigate genetic heterogeneity and dataset comparability, as well as its capacity to highlight problems in data quality and metadata storage.Kallisto software for isoform abundance estimation27. These abundances were subsequently summed to the gene-level by the TXimport R package and analysed with edgeR for differential expression, as it has been shown that such workflows have high accuracy30. Only datasets with at least two replicates were considered for this analysis, where genes with a two-fold expression change at a significance level of 0.01 were considered as differentially expressed genes (DEGs). The similarity score per comparison was correlated with its total number of DEGs and its median fold change at a significance level of 0.01. Figure The transcriptome-wide analyses demonstrate that the different cell lines have distinct degrees of heterogeneity: H9 and HCT116 are highly stable across datasets, while the others are more variable. These results raise the important question as to whether comparability of multiple datasets as defined in our study obscures or alters the results of common transcriptomic analyses. We thus performed differential gene expression analysis on each pairwise, same-cell dataset in order to evaluate the effect of the genetic heterogeneity present in public cell line datasets. As abundance measures  are commonly available in many expression databases, this analyses was performed using the Human Protein Atlas project31. The H9, K562 and U2OS cells were not used for this analysis, as they do not have a corresponding HPA tissue. It is clear that the same relationship between the similarity score and DEGs still holds even for prognostic markers  way of measuring gene expression similitude is the global correlation of all genes between pairwise samples. While the statistic rigorousness of a differential expression analysis (as above) is dismissed, such an analysis has the potential to give a wider perspective, as not only samples with replicates can be included. Indeed, the same pattern as above can be seen not only for four of the cell lines, but U2OS as well - the same holds true for correlations of only the prognostic markers SFigure  and 16. In order to more fully investigate the possible origins of the genetic heterogeneity, we hypothesised that it might be due to genetic drift through either genomic instability, many years of culturing in and between different laboratories, or both. If so, a general accumulation of mutations is to be expected, particularly for low impact variants. As can be seen in Fig. 33. In order to further investigate the presence of CIN and aneuploidy, we analysed the GEO data with an orthogonal RNA-seq method, which examines the allelic ratio  across the entire transcriptome34. The mean allelic ratio of H9 and HCT116 (approximately 1.7) is significantly different from that of the other cell lines  and abnormal chromosomal numbers (aneuploidy), both of which are recognised as hallmarks of cancer and are often found together8. We thus sought to evaluate the comparability, authenticity and heterogeneity of RNA-seq cell line population data deposited in the GEO database using the methodology previously described17 on a large scale, performing several thousands of pairwise comparisons of high-quality SNVs across 139 samples and 85 datasets from eight different cell lines of varying origin.A growing number of researchers use publicly available expression data to compare with their own results, but the accuracy of such analyses have yet to be assessed on a large scale. An increasingly apparent problem is that of the biological source of the samples used, as the results may be inaccurate or skewed if the datasets being compared are not genetically equivalent. While this applies to any kind of biological sample studied it is especially evident in cell line research, where the prevalence of contaminated, misidentified and otherwise unauthentic cells is increasingi.e. variants that have been found in more than one dataset  example of this. Any result based on a small number of variants is less reliable than one based on many variants; the A549 cell line, for example, has below 50 overlapping COSMIC variants in four out of seven datasets. The reasons for the low COSMIC overlap for the H9 cell line are unclear; it has the third most SNVs of all studied cell lines, and the transcriptome-wide analysis indicate that they are highly similar. An analysis of various COSMIC metadata for each cell line highlights H9 and HeLa as having an almost zero proportion of “verified” variants,  SFigure . While tIn order to alleviate these problems, we performed transcriptome-wide analyses on all the datasets investigated in this study in a pairwise manner, thus including the totality of SNVs and possible biological variation in each dataset. The most striking result is as previously mentioned for the H9 cell line, which has the highest median transcriptome-wide concordance of all the cell lines investigated , clearly showing that its datasets most likely originate from biologically equivalent sources; similar results were found for HCT116. While the concordance is a useful measure of similarity, it doesn’t account for cases with few variant overlaps, such as for the three datasets from H9, HeLa and MCF7. By unfavourably weighing the concordances of comparisons with few overlaps these datasets are more easily incorporated into the analysis, enabling consideration of a single parameter (the similarity score) rather than two (concordance and overlap). The discrepancy between the GEO metadata and the detailed information available in the corresponding publications of these datasets demonstrate the difficulty of performing a bioinformatic analysis across experiments, but also highlights how important dataset assessment is; such information is vital to any researcher wishing to analyse publicly available data.e.g. a baseline for MCF7 cell lines against a drug treatment or for direct comparisons with other breast cancer cell lines the results would be meaningless at best and potentially disastrous at worst. Our analysis does not dismiss the data itself in its entirety, far from it: seeing as this dataset so clearly comes from HCT116 cells it could potentially be incorporated into experimental designs where this would be appropriate.Another striking finding is that a single MCF7 dataset is unequivocally more similar to HCT116 than to the other MCF7 datasets. There is no published article associated with this dataset, making a thorough investigation of the reasons for this disparity difficult to perform. It does, however, present a clear-cut case where use of public datasets without proper evaluation can lead researchers to draw erroneous conclusions. If such a dataset was to be used for 38. The variation seen in HeLa is unsurprising, given its history of producing stable, heterogenous cell populations39. While variations in HeLa’s karyotype have been inconsistently reported, it is clear that such changes have an effect on the phenotype of the cells40. Indeed, HeLa, MCF7 and MDAMB231 have been established to be genetically unstable42. While the datasets analysed herein were not chosen with suitability for differential expression as primary criteria, the correlations of similarity score versus the number of DEGs and fold change indicate that the genetic heterogeneity observed has a significant effect on gene expression in the H9, HCT116, MCF7, MDAMB231 and U2OS cell lines. This was also seen for cancer-related genes, as upwards of several hundreds of prognostic markers were differentially expressed between genetically dissimilar same-cell datasets. The magnitude of the DEGs are also significantly affected, as the median fold change for the same datasets can reach as high as well above five. While neither date of submission nor number of overlapping variants were significant confounding factors for this analysis, there might be others. As this analysis is performed on the SNV-level, larger copy number variations (CNVs) might affect the analysis. The mean allelic ratios for H9 and HCT116 does, however, indicate that this is likely not the case. Experimental factors not included in the GEO metadata might also play a role, such as RNA quality or the polymerase used for library preparation43. These results demonstrate that the genetic heterogeneity also affects genes that are likely to be key components of cancer research, thus representing a critical confounding factor for such studies. While the number of datasets and replicates puts a limitation on the enrichment analysis, there are indications that the genetic heterogeneity also has an effect on general cellular functions. These conclusions are corroborated by gene expression correlations, both globally and for the prognostic markers.The stability of H9 and HCT116 has been previously shown, corroborating our results33. The analysis of mean allelic ratios separate H9 and HCT116 as having fewer chromosomal aberrations than the others, explaining their high stability across datasets. Such biological information is highly pertinent for any researcher about to start a cell line-based study: what level of genetic stability is relevant for the scientific question examined? A stable cell line may be more suitable for studies examining a specific gene or pathway (increased comparability of datasets), while a more varied cell line might be more applicable for drug screenings . It is thus not only the patient- or organ-of-origin of a cell line that is important: careful consideration of the overall goal of the experimental design also needs to be taken into account. Additionally, scientific findings obtained using cell lines have to be carefully scrutinised in the light of the inherent intra- and inter-genetic variation encountered in cell line populations. The U2OS cell line, for example, has a higher proportion of mismatched higher impact variants than the others, meaning that special care needs to be taken to ensure that none of the variants affect genes important to the scientific question at hand. Such considerations highlight one of the major strengths of our general methodology: the ability to investigate the biological effects of any variant differing between samples. This is particularly relevant given the previously demonstrated varying phenotype for perfectly matched STR profiles of the MCF7 cell line15.We hypothesised that this heterogeneity could be due to genetic drift as a result of genetic instability, supported by a greater proportion of mismatched low impact variants. The genetic instability that leads to genetic drift and accumulation of mutations in cancer may have several causes, commonly including chromosomal instability and aneuploidyThere are several factors that need to be considered in regards to the biological equivalency of two or more samples. The biology of any sample should be investigated by calculating its global similarity to a reference (preferably transcriptome-wide), if such is available. Any mismatched higher impact variants that might affect genes related to the biological question at hand need to be examined, as well as previously known variants. The impact of variants should also be investigated, in particular for those variants present in a pathway of interest. This is all given that the variant calling itself is performed with the most appropriate methods and software available, including choice of genome assembly and quality metrics, as done here. Not only cell line data could be analysed in this manner, but also data from tissues, cancer tumours or organoids. By following these guidelines . We have demonstrated the importance of evaluating datasets by biological equivalency on both global and gene level as well as data quality and experimental conditions. Our methodology can help scientists make informed decisions E-utilities44, the GEOquery R package46 and the SRAdb R package47. The GEO was queried with the NCBI E-utilities for human RNA-seq datasets, and the metadata contained in the associated SOFT format files were parsed and filtered using a custom R script together with GEOquery. The SOFT files from GEO contain detailed metadata on each dataset not easily accessible through the online queries , making them a good choice for selection and filtering of metadata in a large-scale, programmatic manner. Only cell lines with at least ten RNA-seq datasets in the GEO and available in the COSMIC database18 were considered. As metadata specific for the raw sequencing data (such as file size and the number of sequenced bases) is only available in the SRA, the SRA-specific metadata was collated with the GEO metadata using the SRAdb R package and the NCBI E-utilities. This process was performed on November 16th, 2016; the final metadata contains 80 different metadata fields per sample  and can be found in SData Selection and acquisition of data from the GEO was performed using an array of tools and online software packages: the NCBI 17. Briefly, raw data from each sample was downloaded from the SRA using the fastq-dump utility from the SRA toolkit, followed by read alignment, variant calling (including confident homozygous reference sites) and filtering using STAR, GATK and in-house scripts, respectively49. In addition to comparisons of cell line-specific SNVs that were downloaded from the COSMIC database, the transcriptome-wide variants were also compared in a pairwise manner to each other. All parts of the analysis utilised the GRCh38 assembly. Files containing the results of both the COSMIC and transcriptome-wide comparisons are included in SData Analysis of SNVs and cell line authentication of the biological samples analysed in the selected datasets was performed as previously describedKallisto (0.43.0)27 and TXimport (1.2.0)28 software, followed by differential gene expression analysis with edgeR (3.16.1)29. Genes with fold changes greater than two and FDR ≤ 0.01 were counted as differentially expressed. The list of prognostic markers was downloaded from table 31. Gene expression correlations were performed using log-normalisation of (TPM + 1). KEGG Module enrichment was performed using the clusterProfiler (3.5.5)50 R package. Data on DEGs, expression correlations and enrichments is available in SData Gene expression estimation was performed using the 34 was performed as previously described on SNVs that passed all of the following GATK variant calling filtering criteria: Fisher strand value ≥ 30, quality by depth ≥ 2, clusters with no more than 2 variants within a 35 base pair window, total allelic depth ≥ 10 and minor allelic ratio ≥ 0.2. Moving medians of allelic ratios were calculated across all chromosomes, yielding a measure of the mean allelic ratio across the entire transcriptome. All statistical testing was performed at significance levels of 0.01; a detailed RMarkdown document for reproducing all figures is available in the supplementary information.The eSNP-Karyotyping analysisSupplementary information