Background: There is extensive scientific evidence that radiation therapy (RT) is a crucial treatment, either alone or in combination with other treatment modalities, for many types of cancer, including breast cancer (BC). BC is a heterogeneous disease at both clinical and molecular levels, presenting distinct subtypes linked to the hormone receptor (HR) status and associated with different clinical outcomes. The aim of this study was to assess the molecular changes induced by high doses of ionizing radiation (IR) on immortalized and primary BC cell lines grouped according to Human epidermal growth factor receptor (HER2), estrogen, and progesterone receptors, to study how HR status influences the radiation response. Our genomic approach using in vitro and ex-vivo models  is a necessary first step for a translational study to describe the common driven radio-resistance features associated with HR status. This information will eventually allow clinicians to prescribe more personalized total doses or associated targeted therapies for specific tumor subtypes, thus enhancing cancer radio-sensitivity. Methods: Nontumorigenic (MCF10A) and BC (MCF7 and MDA-MB-231) immortalized cell lines, as well as healthy (HMEC) and BC (BCpc7 and BCpcEMT) primary cultures, were divided into low grade, high grade, and healthy groups according to their HR status. At 24 h post-treatment, the gene expression profiles induced by two doses of IR treatment with 9 and 23 Gy were analyzed by cDNA microarray technology to select and compare the differential gene and pathway expressions among the experimental groups. Results: We present a descriptive report of the substantial alterations in gene expression levels and pathways after IR treatment in both immortalized and primary cell cultures. Overall, the IR-induced gene expression profiles and pathways appear to be cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to HR status. Conclusions: Genomic biomarkers and gene-signatures of specific tumor subtypes, selected according to their HR status and molecular features, could facilitate personalized biological-driven RT treatment planning alone and in combination with targeted therapies. Breast cancer (BC) is the second most common cause of death from cancer among women worldwide . In receApproximately 50% of all cancer patients receive RT , and extensive scientific evidences support the use of RT as a vital component of multimodal therapy for many types of cancer. In BC, RT is an efficient treatment for controlling localized tumors, often described as the last option for patients with inoperable cancers or the first choice in cases of incompletely resected or recurrent tumors after surgery . In partBC is a heterogeneous disease at both clinical and molecular levels, presenting distinct subtypes   associatAlthough technological advances in radiation delivery have strongly enhanced the tumor killing capacity, the current clinical scenario still offers standard RT protocols for groups of patients with the same cancer type , regardless of the molecular subtypes. However, it is well established that tumor heterogeneity, in terms of clinical and molecular characteristics, strongly affects the outcome of RT. Thus, delivering the same RT to all BC patients may not be the best option. Considering that biological differences in the radio-sensitivity of tumor subtypes are not taken into account when determining the total dose and the number of fractions administered during RT schedules, we argue that more radiobiological research is needed to understand and control the response variability. For example, the triple-negative BC subtype (ER−/PR−/HER2−) is described as a highly aggressive tumor with strong metastatic abilities and poor sensitivity to treatments, including RT .RT treatment planning is currently based exclusively on radiation dose and tumor volume. However, in the post-genomic era, the possibility of including molecular information on the tumor to be treated could increase the therapeutic efficacy by enhancing the tumor control probability and better controlling the normal tissue complication probability. Molecular information would make it possible to prescribe a higher total dose for more radio-resistant tumors and a lower total dose for more sensitive tumors, with a consequent reduction of side effects .To this end, research teams need to make great efforts to help clinicians to understand the molecular IR response of different cancer subtypes, so that the best RT modality and schedule, and the potential combination with other targeted treatment modalities, can be prescribed according to the specific molecular characterization of the tumor to be treated.Specific gene signatures have been used to predict radio-sensitivity in many cancer types, including BC ,12,13. SThe delivery of IR during RT schedules induces many pathways that control the survival/apoptosis balance by activating the key genes that regulate the cell cycle, survival, DNA repair, and inflammation ,23. UndeThe aim of the present study was to assess the molecular differences, in particular at the gene expression and pathway level, induced by sub-lethal doses of IR treatment  and by 23 Gy of IR in groups of BC cell lines with different HR statuses.For this purpose, three human immortalized cell lines  and three primary cell cultures , obtained from both tumor and healthy specimens, were classified into three groups of BC: a high grade group (with ER−/PR−/HER2−), a low grade group (with ER+/PR+/HER2−), and a group originating from healthy tissue. We assessed alterations in gene expression levels and enriched pathways after the two IR treatments in both immortalized and primary cell cultures.Our findings highlight the involvement of specific gene and pathways signatures related to different HR statuses, which are probably linked to differences in IR cell responses. Our opinion is that such molecular signatures of specific tumor subtypes, selected according to histological and molecular features, could facilitate the planning of personalized biological-driven RT treatment, alone or in combination with targeted therapies, to increase cancer radio-sensitivity.For IR-treated (9 and 23 Gy) immortalized and primary cells, results of gene expression differential analysis are provided in Differential gene expression analysis of immortalized breast cell lines versus untreated cells revealed that irradiation significantly altered the expression levels of more than 400 genes selected in total by 2-fold or more  were found in common between the immortalized cell lines. Some pathways (12) were found between the tumorigenic cell lines. However, a cell line-specific response to RT is confirmed by the higher number of DEGs  in each breast cell line as different effect of RT.Overall, the results obtained for the 23 Gy-treated immortalized cell lines confirmed the results obtained from the 9 Gy-treated immortalized cell lines, showing a cell-line specific response to RT.Differential gene expression analysis revealed that more than 400 irradiated genes have expression levels significantly altered by 2-fold or more compared with the untreated reference group .CDCA5), Cell division cycle 6 (CDC6), Cell division cycle associated 7 (CDCA7), E2F transcription factor 1 (E2F1), E2F transcription factor 2 (E2F2), Polo like kinase 4 (PLK4), Minichromosome maintenance 10 (MCM10), and Minichromosome maintenance 6 (MCM6).Some DEGs were found in common between normal and tumorigenic cell cultures . We obtained 194 common DEGs (37%) between the two primary BC cell cultures (BCpc7 and BCpcEMT) and other 84 DEGs (20%) between the three breast primary cell cultures. Among these 84 DEGs we found Cell division cycle associated 5  were activated in both the two tumorigenic primary cultures . Other 15 enriched pathways  were found in common in all the three primary breast cell cultures.A cell line-specific response to RT is shown also for the primary breast cell cultures by the high number of DEGs in each breast cell line  as a different effect of RT. Specifically, among the 25 pathways found for ER/PR positive BC cell lines (BCpc7), we identified transcription and degradation of mitotic protein pathways. The apoptosis pathway was uniquely activated in the triple-negative BC primary cultures (BCpcEMT).Overall, the primary breast cultures showed a more homogeneous response to IR, since RT appeared to activate a shared molecular response profile (common pathways), although the primary cells showed a distinct response to RT.Similarly to results already observed for 9 Gy, 23 Gy irradiation significantly altered the expression levels of hundreds of genes (more than six hundred) selected in total by 2-fold or more .Although many genes were deregulated only in specific breast primary cell cultures , we observed 101 common DEGs (16%) between the two primary BC cell cultures (BCpc7 and BCpcEMT) and other 203 (33%) in common between all three primary breast cell cultures. After enrichment, we identified 2 pathways (p53 and cell cycle pathways) that were activated only in BC primary cultures and other 33 pathways in common in all three primary breast cell cultures.Overall, 23 Gy RT appears to activate a shared molecular response profile, although the primary cells show a distinct IR response.In order to assess the molecular response to the two different IR doses, for each cell line and primary breast cells, we compared the pathways enriched with DEGs after 9 Gy and 23 Gy irradiation  and a substantial overlap in breast primary cell cultures. .Considering the good overlap of activated molecular pathways observed in response to 9 Gy and 23 Gy  and healthy primary cell cultures (HMEC), only 11 DEGs were found in common  , Fanconi anemia complementation group A (FANCA), MCM6, Minichromosome maintenance (MCM8), MCM10, Fas cell surface death receptor (FAS) and MDM2 proto-oncogene (MDM2), while 59 DEGs (14%) were shared in the high grade tumorigenic groups (MDA-MB-231 and BCpcEMT).More DEGs  were found in common in the low grade tumorigenic cell groups, e.g., After gene enrichment analysis, only 1 pathway (the peptide ligand binding receptor pathway) was found activated in the non-tumorigenic group samples; 6 pathways were shared between the low-grade tumorigenic group samples , and 9 pathways were in common in the high-grade group samples .Considering the gene signature consisting of (a) the 64 DEGs in common between MCF7 and BCpc7, and (b) the 59 DEGs in common between MDA-MB-231 and BCpcEMT, we performed a Principal Component Analysis.CDKN1A, involved in cell cycle progression and whose expression is controlled by p53, and MDM2, which is involved in the DNA damage pathway [In MCF10 cells, among the total 560 DEGs , we iden pathway .APAF1), BCL2 binding component 3 (BBC3), Cyclin E2 (CCNE2), Cyclin dependent kinase 6 (CDK6), CDKN1A, Cytochrome c, somatic (CYCS), damage specific DNA binding protein 2 (DDB2), FAS, Growth arrest and DNA damage inducible factor (GADD45S), MDM2, Ribonucleotide reductase regulatory subunit M2 (RRM2), and Ring finger and CHY zinc finger domain-containing 1 (RCHY1) [In MCF7 cells, among the total 777 DEGs , we iden (RCHY1) .TUBB1), MYB proto-oncogene, transcription factor (MYB) and Dynein, cytoplasmic 1, and intermediate chain 1 (DYNC1l1) are involved in the cell cycle.In MDA-MB-231 cells, among the total 416 DEGs , we idenIn HMEC healthy cells, among the 435 DEGs, we identified a network of 36 DEGs A; in BCpE2F1, E2F2), in the cell cycle , in DNA replication , in DNA repair , and in DNA damage .In these networks, we found genes involved in transcription  in nontumorigenic (MCF10A) and tumorigenic BC (MCF7 and MDA-MB-231) immortalized cell lines, and in healthy (HMEC) and BC (BCpc7 and BCpcEMT) primary cultures obtained from surgical biopsies. More precisely, as previously described, the samples were divided into three groups according to their HR status , irradiated, and analyzed by cDNA microarray technology to select and compare their differential gene and pathway expressions. Although preliminary results of partial breast irradiation with IOERT, either as an anticipated boost (9 Gy) or as an exclusive treatment (23 Gy), seem to be promising in terms of local disease control, there is little information on the biological basis of the effects of IOERT. In the context of this radiation treatment modality, this study aimed to describe the molecular response, in terms of deregulated genes and pathways, to high doses of IR according to the different BC subtypes. Our genomic approach using in vitro and ex-vivo models  is a necessary first step for a translational study to describe the common radio-resistance features associated with HR status. Such information would support clinicians in prescribing more personalized total doses or associated targeted therapies for specific tumor subtypes, thus enhancing cancer radio-sensitivity. However, this type of investigation would need to be extended to numerous panels of cell lines; thus, the present work should be considered as a pilot study.Here, we report substantial alterations in gene expression levels after RT in both immortalized and primary cell cultures.Considering the good overlap of activated molecular pathways observed in response to 9 and 23 Gy , we focuIrradiation with 9 Gy caused a change in the expression level of more than 700 genes in the MCF7 BC cell line (belonging to the low grade group) and about 400 genes in the MDA-MB-231 BC cells (high grade group) . These rAPAF1, BBC3, CCNE2, CDK6, CDKN1A, CYCS, DDB2, FAS, GADD45S, MDM2, RRM2, and RCHY1 found in the MCF7 network have a role in this pathway), specifically by the damage-specific DNA-binding protein  and by DNA damage-inducible factors .As previously described by our group and several other authors, radiation effects on cells are heterogeneous and appear to act in a cell line-dependent way. This behavior was also confirmed by the pathway analysis conducted in this study. More precisely, no common pathway was shared among the considered immortalized cell lines after IR 9 Gy . InsteadTUBB1, MYB, and DYNC1l1 found in the MDA-MB-231 network have a role in these pathways) and DNA maintenance processes  were specifically deregulated after radiation exposure.These results are in line with others reported in the literature  and alsoCDC6, CDCA5, and CDCA7), transcription factors (E2F1 and E2F2), PLK4, MCM10, and MCM6, and other cell cycle-associated proteins. Twenty-seven pathways are shared between the two tumorigenic primary cell cultures known to be activated in RT . However, a cell line-dependent RT response, clearly observed in this work in immortalized cell lines, has been found also in primary cell cultures, since 25 pathways including transcription and degradation of mitotic proteins were uniquely activated in BCpc7 and the apoptosis pathway in BCpcEMT.All of the primary cell cultures investigated here shared the activation of 15 common pathways  that areFinally, we evaluated the DEGs lists and relative pathways enriched in immortalized and breast primary cultures according to HR status in the low grade, high grade, and healthy groups. As reported in In contrast, a signature of 64 DEGs and 6 common pathways were found to be deregulated in the low grade group samples, characterized by the positive status of ER and PR receptors: Mitotic-G1-S phases, S-phase, cell cycle, activation of the pre-replicative complex, G1-S-transition, and p53 signaling pathway. Overall, these cells are able to modulate processes involved in cell cycle regulation, repairing DNA strand breaks, and cell survival/death balance through the activation of apoptosis signaling. Interestingly, these results are congruent with others reported by our group, which highlighted the deregulation of genes controlling the cell cycle process ,12,13,27A signature of 59 DEGs and 9 pathways were activated in the triple-negative cell lines of the high grade group samples. These pathways are involved in cell cycle regulation, nucleosomes, chromosome, and telomere maintenance, which could justify the more aggressive phenotypes and high rate of radio-resistance, because they are linked to chromosome instability, which is, in turn, caused by radiation exposure . InteresTo the best of our knowledge, limited data are reported in the literature regarding the IR-induced gene expression changes in relation to BC tumor grading, especially in primary cultures. Although this is a pilot study, the identification of IR-induced low grade- and high grade-specific signaling may provide a source of biomarkers that is predictive of RT outcomes or that could be useful for the optimization of targeted therapies in tandem with RT ,34,35,362 incubator and were irradiated at sub-confluence under sterile conditions.Both immortalized and primary BC cell cultures were used. Immortalized cell lines, in particular, adenocarcinoma MCF7 and MDA-MB-231, which are characterized by different tumorigenic aggressive phenotypes ,38. PrimCell irradiation treatments at different tissue depths were performed using the NOVAC7  system. The electron accelerator system was calibrated under reference conditions. Cell radiation treatments were carried out at the doses of 9 and 23 Gy to the 100% isodose at a dose rate of 3.2 cGy/pulse. The cell irradiation setup and the dose distribution were conducted as previously reported .We grouped both immortalized and primary cell lines into low grade and high grade groups, according to their histological features. More precisely, the low grade group contained immortalized MCF7 and primary BCpc7 BC cells (both ER+/PR+/HER2−), whereas the high grade group was formed by immortalized MDA-MB-231 and primary BCpcEMT BC cells (both ER−/PR−/HER2−) because of their more aggressive phenotype, as also previously described by our group ,40,41. FGene expression profiling of immortalized cell lines  and primary cell cultures  treated with electron beams using 9 and 23 Gy of IR were performed. Because IR exposure can cause time-dependent transcriptional changes, based on our previous experiences and several other studies this topic ,13,42,43Total RNA extraction and its qualitative and quantitative analyses were conducted as previously described ,13. Micrt-tests for each breast cell line and breast cell culture, using R package  if they showed a fold-change (FC) of at least 2 compared with untreated cells used as reference samples.In particular, the experiment was performed using six replicates for each configuration assayed. Statistical data analysis, background correction, normalization, and summary of expression measures were conducted with one-sample e limma ,45) and  and t-teThe data discussed in this publication have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO)  and are p-values were adjusted using the Benjamini-Hochberg procedure for multiple testing [p-value was <0.01. To display the results obtained for each DEG and enriched pathway, and all of the possible combinations between different breast cells, we constructed Venn diagrams [Given that 1077 pathways were derived from the REACTOME , BIOCART testing ,51, and diagrams . We perfdiagrams .The analysis of IR-deregulated biological networks was performed by creating protein-protein interactions for each cell line and primary culture. We downloaded protein-protein interactions from GeneMania using SpidermiR tool and identified the densest networks for each cell line and primary culture according to the networks with the highest numbers of connected DEGs find in total ,55,56,57In this pilot study, we produced a comprehensive description of the effects of 9 and 23 Gy IR doses in BC and healthy immortalized and primary cell cultures. Overall, a great amount of reported data suggest that the response to radiation is cell-line dependent and that some specific gene and pathway signatures seem to be linked to HR status.


Curcuma longa has long been used in China and India as anti-inflammatory agent to treat a wide variety of conditions and also as a spice for varied curry preparations. The chemoprofile of the Curcuma species exhibits the presence of varied phytochemicals with curcumin being present in all three species but AA only being shown in C. longa. This study explored the effect of a curcumin/AA combination on human cancer cell lines. The curcumin/AA combination was assessed by isobologram analysis using the Loewe additivity drug interaction model. The drug combination showed additive cytotoxicity toward CCRF-CEM and CEM/ADR5000 leukemia cell lines and HCT116p53+/+ and HCT116p53−/− colon cancer cell line, while the glioblastoma cell lines U87MG and U87MG.ΔEGFR showed additive to supra-additive cytotoxicity. Gene expression profiles predicting sensitivity and resistance of tumor cells to induction by curcumin and AA were determined by microarray-based mRNA expressions, COMPARE, and hierarchical cluster analyses. Numerous genes involved in transcription , apoptosis-regulation  signal transduction  DNA repair , mRNA metabolism , and transporter genes (ABCA1) correlated with cellular responsiveness to curcumin and ascorbic acid. In conclusion, this study shows the effect of the curcumin/AA combination and identifies several candidate genes that may regulate the response of varied cancer cells to curcumin and AA. Curcuma longa L. belongs to the family Zingiberaceae, which is a perennial herb that measures up to 1 m height with a short stem. It is distributed throughout tropical and subtropical regions in the world, being widely cultivated in Asian countries, mainly in India and China  in vitro. The combination of quinine with quinidine and cinchonine is 2–10 times more effective in vitro against quinine-resistant strains, and the mixture of alkaloids reveals more consistent effects than any of the alkaloids singly used  as phytochemical constituent of C. longa to investigate the additive or synergistic effects of its combination with curcumin.The activity of many medicinal plants results from the interaction action of several constituents, which may cooperatively act in an additive or synergistic manner. It has been repeatedly observed that extracts of medicinal plants reveal better activities than their isolated single compounds at comparable equivalent concentrations of the active components. This phenomenon is attributed to the absence of interacting substances present in crude extracts. For example, extracts were obtained from the fresh herb of in vivo  and the oxidized form , USA to find possible mechanistic explanations for the interaction of these two compounds.In this paper, we have chosen AA because it is widely distributed in many plants and it is also synthetically available. This allows us to investigate the cytotoxicity of the combination of curcumin and AA outside of the plant at exactly defined conditions to clarify their pharmacological effects in the combination. Our strategy was to investigate the combination by isobologram analysis and to investigate the genes which are related to the cytotoxicity induced by curcumin and AA in a broad spectrum of cancers. We postulate that the identification of genes that are specifically regulated by AA and curcumin could improve the understanding of the efficacy of All chemicals were of analytical grade. Curcumin, AA and DMSO were purchased from Sigma-Aldrich .10IC50 (M)] have been deposited at the NCI database (http://dtp.cancer.gov/databases_tools/default.htm). The mRNA microarray hybridization of the NCI cell lines has been reported and deposited at the NCI website (http://dtp.cancer.gov/databases_tools/default.htm). COMPARE analyses were performed to produce rank ordered lists of genes expressed in the NCI cell lines. The methodology has been previously described in detail as a tool to identify candidate genes for drug resistance and sensitivity. To derive COMPARE rankings, a scale index of correlation coefficients  was created from log10IC50 (M) values of test compounds and microarray-based mRNA expression values. Greater mRNA expression correlated with enhanced drug resistance in the standard COMPARE approach, whereas greater mRNA expression in cell lines indicated drug sensitivity in reverse COMPARE analyses. Pearson's correlation test was used to calculate significance values and rank correlation coefficients as a relative measure for the linear dependency of two variables.The cancer cell lines of the Developmental Therapeutics Program of NCI consisted of a series of non-small cell lung cancer, colon cancer, renal cancer, ovarian cancer cells, leukemia, melanoma, prostate carcinoma, breast cancer, and tumor cells of the central nervous system. Their origin and processing have been previously reported . Merging of objects with similar features leads to cluster formation, where the length of the branch indicates the degree of relation. Distances of subordinate cluster branches to superior cluster branches serve as criteria for the closeness of clusters. Thus, objects with tightly related features were clustered closely together, while separation of objects in the dendrogram increased with progressive dissimilarity. Hierarchical clustering and heat-map analysis were performed using Euclidean distance and ward method implemented in “dist,” “hclust,” and “heatmap” functions in R programming  in resistant cells  and knockout clones (p53−/−) derived by homologous recombination , were kindly provided by Dr. W. K. Cavenee . They were cultured in RPMI-1640 medium supplemented with 10% FBS and 1% penicillin/streptomycin . Doxorubicin (5000 ng/mL) was added to maintain overexpression of P-gp  0.01% w/v in ddH2O was added to each well and the plates were incubated at 37°C for 4 h. Fluorescence was measured on an Infinite M2000 Proplate reader  using an excitation wavelength of 544 nm and an emission wavelength of 590 nm. Each assay was done at least two times, with six replicates each. The cytotoxic effect of the treatment was determined as percentage of viability and compared to untreated cells. The calculated cell viability (y-axis) was plotted against the log drug concentration (x-axis) using Microsoft Excel. The obtained curve was used to determine the IC50-value, which represented the concentration of the test compound required to inhibit 50% of cell proliferation.Cell viability was evaluated by resazurin assay. This test is based on reduction of the indicator dye, resazurin, to the highly fluorescent resorufin by viable cells. Nonviable cells rapidly lose the metabolic capacity to reduce resazurin and thus produce no fluorescent signal. Glioblastoma and colon cancer cells were harvested with 0.25% trypsin/EDTA  and diluted to a final concentration 5 × 10The Loewe additivity model was used to calculate synergetic drug interactions between curcumin and vitamin C in inhibiting cell growth. In this model, the combination index (CI) was defined as CI = (d1/D1)/(d2/D2), where D1 and D2 were the doses of drug 1 and drug 2 that produced an response Y  when used alone, d1 and d2 were the doses of drug 1 and drug 2 in combination, which can generate the same response Y. If the CI is equal, less than or more than 1, the combination dose  is termed as additive, synergistic, or antagonistic, respectively. The drug interaction was illustrated geometrically as isobologram.50-values for curcumin and ascorbic acid.Pearson's correlation test was used to calculate significance values and rank correlation coefficients as a relative measure for the linear dependency of two variables. This test was implemented into the WinSTAT Program . Pearson's correlation test determined the correlation of rank positions of values. Ordinal or metric scaling of data is suited for the test and transformed into rank positions. There is no condition regarding normal distribution of the data set for the performance of this test. We used Pearson's correlation test to correlate microarray-based mRNA expression of candidate genes with the IC2-test was applied to bivariate frequency distributions of pairs of nominal scaled variables. It was used to calculate significance values  and rank correlation coefficients  as a relative measure for the linear dependency of two variables. This test was implemented into the WinSTAT program . The χ2-test determines the difference between each observed and theoretical frequency for each possible outcome, squaring them, dividing each by the theoretical frequency, and taking the sum of the results. Performing the χ2-test necessitated to define cell lines as being sensitive or resistant to curcumin and ascorbic acid. This has been done by taking the median IC50-value (log10 = −5.1 M) for curcumin and IC50-value (log10 = −2.7 M) for ascorbic acid as a cut-off threshold.The χCurcuma species  based on the chemical compositions of these species deposited at Dr. Duke's Phytochemical and Ethnobotanical Databases (http://www.arsgrin.gov/cgi-bin/duke/farmacy2.pl). We subjected the chemical composition of these plants to hierarchical cluster analysis . Ten compounds were found in two of the three species, whereas all other compounds were found in only one Curcuma species. This specific distribution of phytochemicals enables specific clustering and separation of the Curcuma species.As a first step, we established chemoprofiles of three s Figure . A totalC. longa against cancer cells is not solely caused by its main compound, curcumin, but that other compounds may also contribute to this activity of the plant. To prove this hypothesis, we mined the NCI database for compounds found in C. longa and six compounds were identified  toward curcumin and AA were determined. All the cell lines were treated with varying concentrations of curcumin and AA for 72 h, their growth was inhibited in a dose-dependent manner, albeit at different efficacy. The IC50-values of curcumin in combination with AA were reduced by less than half of the IC50 of curcumin alone. In the glioblastoma and colon cancer cell lines, the IC50-value of two of the curcumin concentrations (20% IC50 curcumin and 40% IC50 curcumin) decreased with increasing AA concentrations less than the IC50 of curcumin alone. However, the two other concentrations of curcumin reduced the IC50 in combination with AA by less than half of the IC50-value of curcumin alone. Dose-normalized IC50 isobolograms for all cell lines were generated by plotting the combination treatment IC50-values of curcumin against AA. Additive effects were observed in CCRF-CEM and CEM/ADR5000 . The cancer cell lines were treated with varying concentrations of AA at indicated concentrations of curcumin for 72 h. In CCRF-CEM and CEM/ADR5000 cells, the IC0 Figure  as well 10IC50 (M) values for curcumin and AA. This bioinformatical approach was performed to identify novel putative factors associated with cellular response to curcumin and AA. The top 20 genes with direct and top 20 genes with inverse correlation co-efficient are shown in Tables TFAM, TCERG1, RGS13, and C11orf31), apoptosis-regulating genes  and signal transduction genes . Curcumin induction resulted in DNA repair genes , mRNA metabolism genes , signal transduction genes , proliferation-related genes (RHOD), apoptosis-regulating genes (DFFB), and transporter genes (ABCA1).The transcriptome-wide mRNA expression of the NCI cell lines based on the Novartis microarray platform was investigated by COMPARE analyses and correlated to the logC. longa contains some 30 different phytochemicals. In the present study, we analyzed the cytotoxicity of a combination of two of these phytochemicals of C. longa, curcumin and AA. If applied alone, curcumin and AA were cytotoxic toward cell lines of different tumor types with curcumin exhibiting stronger cytotoxicity than AA. Our results are in line with other reports on the inhibitory activity of curcumin  generation, which is considered to be preferentially cytotoxic to cancer cells  represent enzymes that catalyze reversible high-energy phosphoryl transfer reactions between adenine nucleotides in the intermembrane space. During periods of metabolic stress, AK2 increases the amount of available adenosine monophosphate and therefore activates downstream ATP-sensing mechanisms—such as AMP-activated protein kinase (AMPK)—to regulate the cellular metabolism. Inhibition of AK2 expression significantly inhibited the proliferation of cancer cells  cofactor is important for proliferating cells, required for nucleotide synthesis and methylation reactions. MTHFD2 is a bifunctional enzyme, catalyzing the NAD+ dependent CH2-THF dehydrogenase and CH+-THF cyclohydrolase reactions within the mitochondria. Within the mitochondrial folate pathway, MTHFD2 is of special interest, because MTHFD2 was one of the most consistently overexpressed mRNAs genome-wide across 19 different tumor types. The MTHFD2 protein is specifically expressed in transformed cells, but not the stroma surrounding the tumor tissues. MTHFD2 by RNAi impairs proliferation in a variety of cancer cell lines, independent of the tissue of origin, and decreases invasion and migration in breast cancer cell lines. MTHFD2 is broadly required for cancer cell proliferation and viability , although the majority of genes were different in both analyses. This result points to an observation that was frequently made by many investigators: different microarray platforms deliver different results. Nevertheless, several biological functional groups were found to be common in our previous and the present paper, e.g. cell cycle, DNA damage response, cell migration, inflammation, signaling pathways, and apoptosis-regulating genes. To our opinion, microarray data are reliable, if they are used for the generation of testable hypotheses. In this respect, both of our microarray analyses were useful. Microarray data represents the starting point for the elucidation of modes of action of cytotoxic compounds rather than completed end results.Previously, we reported the mRNA expression profile induced by curcumin in the NCI cell line panel, which was merged from four different microarray platforms (Novartis, Stanford, Chiron, and Genelogic; Sertel et al., Cluster analyses were applied in the present investigation under the assumption that responsiveness of cancer cells might be predicted by using gene expression patterns and that appropriate gene expression profiles might be sufficient to predict whether a cancer cell line is sensitive or resistant to a cytotoxic compound (Sertel et al., Artemisia annua L., which also showed additive rather than synergistic interactions (Efferth et al., The fact that the mRNA expression profiles induced by curcumin and AA related to different gene expression patterns may be related to different modes of actions of both compounds. Medicinal herbs generally contain mixtures of active compounds, which may interact in an additive or synergistic manner. Synergistic interactions may need common mechanisms e.g., a common specific pathway that they inhibit. From an evolutionary point of view, synergistic interactions need co-evolutionary selection pressures to evolve. Hence, it can be speculated that synergisms are less likely to occur than additive effects. Therefore, additive drug interactions can be more frequently found in medicinal herbs. Compounds with different modes of action can efficiently and sufficiently fulfill the requirements for plants to survive under specific evolutionary selection pressure. This may explain that we found additive rather than synergistic interactions in isobologram analyses between curcumin and AA in the panel of cell lines tested. This observation is in accordance with previous data with several cytotoxic compounds from in vivo. The varied cellular functionalities represented by the genes downregulated may support additive effects observed in the isobologram analyses.In summary, we have identified some genes which were downregulated by AA and curcumin. The genes may be responsible for cellular responsiveness of the varied cancer cells to AA and curcumin treatments. The cellular activities tackled by the downregulated genes include inhibition of cell proliferation and cell cycle activities, reduction in cellular metabolism, downregulation of anti-apoptotic gene products and inhibition of angiogenic cytokines. The two natural products seem to induce cytotoxicity by different mechanisms and this may lead to achieve tumor eradication Title selection and research design: TE, EO. Laboratory experiments, results generation, data analysis, and interpretation: EO, OK. Manuscript writing and submission: EO. Manuscript writing: HG. Advising in research design, results interpretation, and manuscript writing: TE.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


HOXA7, HOXA9, and HOXA10 known to be important for the development of the anterior-posterior body axis. These results were confirmed by qPCR. Immunohistologically, clivus chordomas had no or very low levels of HOXA10 protein while sacral chordomas showed a strong nuclear positivity in all samples analysed. This differential expression of HOX genes in chordomas of the clivus and sacrum suggests an oncofetal mechanism in gene regulation linked to the anatomic site.Chordomas are rare tumours of the bone arising along the spine from clivus to sacrum. We compared three chordoma cell lines of the clivus region including the newly established clivus chordoma cell line, U-CH14, with nine chordoma cell lines originating from sacral primaries by morphology, on genomic and expression levels and with patient samples from our chordoma tissue bank. Clinically, chordomas of the clivus were generally smaller in size at presentation and patients with sacral chordomas had more metastases and more often recurrent disease. All chordoma cell lines had a typical physaliphorous morphology and expressed brachyury, S100-protein and cytokeratin. By expression analyses we detected differentially expressed genes in the clivus derived cell lines as compared to the sacral cell lines. Among these were  Chordoma therapy usually consists of surgical resection in combination with radiotherapy. There is a lack of efficient standard chemotherapy, which may be due to the generally slow tumour growth4. Therefore, there is a focus on novel therapeutic strategies for the treatment of chordoma, such as targeting the PDGF receptor7, VEGF receptor8 or mTOR-kinase10. Overexpression of epidermal growth factor receptors (EGFR) has been reported to be a frequent event in chordoma14 and inhibition of the EGF receptor 1 has a significant effect on cellular growth16. As the majority of chordomas show a deletion of the tumour suppressor gene CDKN2A (encoding for the protein p16)18, we provided evidence that the CDK4/6 inhibitor palbociclib may be a promising therapeutic option for chordoma treatment19. Up to now, none of these strategies have made it to clinical usage.Chordomas are rare, malignant bone tumours thought to arise from notochordal remnants along the spine. This tumour entity is generally considered as a low-grade malignant tumour, but metastases and recurrences occur22.A characteristic feature of chordoma is the overexpression of the transcription factor T encoding for the protein brachyury. Brachyury is essential for the development of the notochord and the formation of posterior mesodermal elementsHOX gene family, a highly conserved gene family which encodes for several transcription factors24.Regarding developmental processes, an important role in the formation of the anterior-posterior body axis and other morphogenetic processes is assumed for members of the HOX genes have been identified which are organized in four clusters, called HOXA, HOXB, HOXC, and HOXD. Each cluster is located on a different chromosome, namely 7p14, 17q21, 12q13, and 2q31, respectively. With regard to the formation of the anterior-posterior body axis, HOX gene expression follows the rules of the so-called temporal and spatial collinearity. This means that genes located at the 3′ end of the cluster are expressed earlier in the embryogenesis and have a more anterior boundary of expression than those located at the 5′ end27. As the two major predilection sites of chordoma are the cranial (clivus) and the caudal  ends of the spine, a comparison of these different entities might give new insights into the formation of chordomas. Due to the rareness of this tumour, there are a small number of chordoma cell lines and especially just two from clivus chordoma. In the following, we addressed the question whether differences between clival and sacral chordomas exist. We established a novel chordoma cell line, U-CH14, derived from a clivus chordoma, analysed this cell line together with two other clivus derived cell lines, MUG-CC1 and UM-Chor1, and compared them with nine sacral chordoma cell lines on genomic, gene expression and protein grounds to identify possible differences with focus on HOX genes. Finally, we validated these findings in our chordoma tissue bank in situ.In vertebrates, 39 TP53 variant p.P72R  with regard to eventual differences between chordomas of the clivus (n = 5) and the sacrum region (n = 24) showed that clivus chordomas were significantly smaller than sacral chordomas (p = 0.04). The median tumour size was 1.4 cm (inter quartile range (IQR): 0.65 cm; 2 cm) versus 7.0 cm . Clivus chordoma patients tended to be younger than sacral chordoma patients  versus 70 years ). Regardless of the different localisation along the spine, chordomas were of the ‘not otherwise specified’ (NOS)-subtype. The proliferation rate was lower in clivus chordoma samples, as all cases had a Ki-67 index lower than 10%. In sacral chordomas, 5/24 samples had Ki-67 indices above 10%. In p53 immunohistochemistry, 0/5 clivus and 7/23 sacrum chordoma cases showed a nuclear positivity in more than 1% of total cells . A loss of CDKN2A in the chordoma cells was found as compared to the co-hybridized centromere 12 probe  and the nine sacral chordoma cell lines (GEO accession number: GSE68497) revealed that the clivus chordoma cell lines clustered together in one group against the background of the sacral chordoma cell lines Fig. . FilteriHOX genes in our two subgroups . Compared to the sacrum lines HOXA7 and HOXA10 were downregulated in all clivus lines. For HOXA9 expression we found a broad range of expression levels within the sacral subgroup. All three clivus cell lines, however, had very low expression levels. Therefore, these 3 HOX family members were further analysed in detail.We analysed the expression of the different ups Fig. . As indiHOXA7 and HOXA10 was an invariable feature of the clivus cell lines, a low expression of HOXA9 was detected in all clivus lines and in 4/9 sacrum cell lines. The sacrum lines U-CH1, U-CH6, U-CH12, and MUG-Chor1 expressed HOXA9 at low levels comparable to the clivus lines U-CH14, MUG-CC1, and UM-Chor1 , as previously described by Fischer et al.34. Furthermore, we identified 2 additional mutations in 3 chordoma cases . These variants were graded neutral in 8/8 protein prediction tools , and cytokeratins. Chordomas may arise all along the spine, ranging from the cranial to the caudal ends. Here, we present a comparison of chordomas of the clivus and the sacrum region. Analysing the clinical aspects of our chordoma tissue cohort, no significant differences could be detected with regard to morphology of the tumours and age of onset of disease. In contrast, the tumour entities differed clearly in size, proliferation rate, and the propensity to metastasize and recur. Obviously, clival chordomas are smaller than the sacral tumours explained by the localisation itself and earlier symptoms they provokein vitro model. Hence, we were able to compare three clivus with nine sacral chordoma cell lines on the expression profile level. In an unsupervised approach the clivus chordoma cell lines clustered together in one group and clearly separated from the sacral cell lines. This confirms that, indeed, there are differences between these two groups. Taking a closer look on the differently expressed genes it is remarkable that many of the downregulated genes in clival cell lines can be assigned to developmental processes. As chordoma cell lines have shown to express typical stem cell markers35, a connection to developmental processes seems to make sense. Amongst the downregulated genes in clivus chordomas, there were genes of the HOX family. HOX genes play a fundamental role in the development of the anterior posterior body axis24. The downregulation of HOX genes was validated in qPCR and confirmed in our tissue cohort.Our results argue for biological differences between clival and sacral chordomas as two different tumour entities. To further elucidate this, we established a novel cell line (U-CH14) derived from a clival chordoma in addition to the only two further clival chordoma cell lines available, MUG-CC1 and UM-Chor1. Morphology and the expression of typical chordoma markers are conserved in U-CH14 indicating that this cell line is a suitable HOX gene expression between clival and sacral chordoma is solely oncofetal in origin or has a pathologic role in the phenotype of the more aggressive sacral chordomas is an open question. Considering the redundancy within paralogue groups and the temporal and spatial collinearity, HOX expression effects solely caused by the localisation of the tumour would result in most of the 3′- HOX genes being up and 5′-HOX genes  being downregulated in clival chordoma and vice versa. The downregulated HOX genes in clivus cell lines  compared to normal tissue revealed that 18 of the 39 genes showed an increased expression40. High expressions of HOXA7, HOXA9, and HOXA10 are frequently seen in acute myeloid leukaemia (AML) and the overexpression of HOXA9 is linked to a poor prognosis43. An increased proliferation in human hepatocellular carcinoma has been described for the overexpression of HOXA744. Here, we present the first study of HOX genes in chordomas of different localisations along the spine. As a consequence of the rareness of this tumour disease, the low number of cases (n = 29) and cell lines (n = 12) may be a limiting factor of this study. Nevertheless, the expression of various HOX genes in sacral chordoma fits well with the results observed in other tumour entities. Furthermore, it was reported that the expression pattern of HOX genes can differ between cancer subtypes of one tumour entity. Abe et al. showed that in both squamous cell carcinoma (SCC) and adenocarcinoma of the lung, HOXA5 and HOXA10 are expressed at high levels, while HOXA1 and HOXC6 were only overexpressed in SCC45. The difference in HOX gene expression between clival and sacral chordomas buttresses our hypothesis of two separated subentities of chordomas. Sacral chordomas have a higher expression of HOX genes indicating a lower differentiation status which may be linked to tumour aggressiveness presenting itself by higher proliferation, the occurrence of metastases and recurrent disease, as seen in our chordoma cohort. As HOX overexpression is a feature in various cancer types, there is a high effort in trying to take advantage of HOX transcription factors as a potential therapeutic target. It is known that PBX forms dimers with many HOX proteins and potentiates the DNA binding capacity and specificity46. The interaction between HOX and PBX proteins was reduced by using a cell-permeable 18-amino acid peptide called HXR9 which induced apoptosis in cancer cell lines49 and tumour growth reduction in melanoma xenograft models50. Hence, HXR9 may also be a potential agent for treating sacral chordomas.An altered expression of HOX genes as a discriminator between these two subtypes.In summary, we here describe the newly established clivus chordoma cell line U-CH14, to increase the number of the existing clivus chordoma cell lines to three. In addition, we compared clival and sacral chordomas with regard to clinical features of our chordoma cohort and gene expression profiles of the existing cell lines. We found the expression of various via a transnasal endoscopic approach at the Department of Neurosurgery, Günzburg, Germany. Only a partial resection was achieved and the patient received proton beam therapy hereafter. Histologically, the tumour presented as a clivus chordoma of the not otherwise specified (NOS) subtype according to the WHO. The diagnosis was confirmed as the tumour showed immunopositivity for brachyury, S100-protein, and epithelial membrane antigen (EMA). The proliferation rate determined by the anti-Ki-67 antibody was below 5%.A new clival chordoma cell line, U-CH14, was established from a 79-year-old, male chordoma patient. The tumour measured 1.7 × 1.4 × 1.2 cm and was localised in the clivus region and was resected 37. UM-Chor1 (Clivus) and the sacral chordoma cell line MUG-Chor151 were provided by the Chordoma Foundation (www.chordomafoundation.org), the other sacral cell lines U-CH1, U-CH2, U-CH3, U-CH6, U-CH7, U-CH10, U-CH11, and U-CH12 were established at the Institute of Pathology, University Hospital Ulm, as previously described52. All cell lines were cultured applying the same protocol as mentioned above. All the performed methods and experiments were in line with the guidelines of the ethics committee of the Federal General medical Council . The research was carried out in compliance with the Helsinki Declaration. All experimental protocols were conducted and approved in accordance with the local ethics committee of the University of Ulm . The patient gave his written informed consent for scientific usage of his tumour cells.The tumour tissue was immediately processed for cell culture at the Institute of Pathology at the University Hospital Ulm. The material was mechanically minced into small pieces and partially digested with collagenase. Isolated cells were cultured in Iscove’s Modified Dulbecco’s Medium/RPMI 1640  with 10% fetal bovine serum , 2 mM glutamine, and penicillin/streptomycin. Before reaching a state of complete confluence, the cells were detached using trypsin/EDTA . Another continuous chordoma cell lines, MUG-CC1 (Clivus) was contributed by Beate Rinnervia the avidin-biotin-complex-method using the K005 AP/RED Detection System . The following antibodies were used: monoclonal antibodies against brachyury , epithelial membrane antigen , vimentin , cytokeratin , Ki-67 , p53  and the polyclonal antibodies against S100-protein  and HOXA10 . Appropriate positive and negative controls were included.Immunohistochemistry was performed on formalin-fixed and paraffin-embedded tissue 53.The ratio of positive chordoma cells was characterized as follows: “no immunoreactivity detected” (−), “immunoreactivity in up to 30% (+), “immunoreactivity in more than 30% and up to 70%” (++) and “immunoreactivity in more than 70%” (+++) of the total number of chordoma cells in the sectionFor genotyping of the cell lines the GenomeLab STR Primer Set Kit  and the Amplitaq GOLD DNA Polymerase  were used, respectively. The analysis was performed according to the manufacturer’s handbook. PCR products were separated on the CEQ GeXP capillary electrophoresis system (Beckman Coulter). 12 alleles were determined using the Gene marker software .54 with regard to eventual differences between clival and sacral chordomas concerning the age of the patients, tumour size, presence or absence of metastases and/or recurrences, R-status, proliferation rate, TP53 status, and histological subtype.We analysed five clival and 24 sacral chordoma samples of our chordoma tissue bank (n = 43)Total RNA was extracted from the three clival chordoma cell lines  and from the 9 sacral chordoma cell lines  using the RNeasy Mini-Kit . Quality and quantity control was performed using the 2100 Bioanalyzer . Gene expression analysis was performed using the Human Gene Expression 4 × 44 K Microarray Kit . 200 ng of each sample was used and processed according to the standard protocol (Agilent Technologies). Each sample was analysed in duplicates at least. The signals were captured by a Microarray Scanner . The generated data was analysed using the software Genespring 12.1 (Agilent Technologies) and Ingenuity Pathway Analysis . Hierarchical clustering was performed with an euclidean similarity measure and centroid linkage rule. Differentially expressed genes with statistical significance  between the two groups (clivus versus sacrum) were identified using scatter plot filtering. Classification of the differently expressed genes was performed via gene ontology (GO) analysis.HOXA7, HOXA9, and HOXA10 using the suitable QuantiTect Primer Assays (Qiagen). β-Actin and GAPDH served as an internal control. The PCR reaction was performed as follows: the initial denaturation step was 95 °C for 5 min, followed by 60 cycles at 95 °C for 10 sec and at 60 °C for 30 sec. All experiments were performed as triplicates.Verification of the microarray results was performed using qPCR. Single-strand cDNA was synthesized from total RNA using the QuantiNova Reverse-Transcription Kit (Qiagen). qPCR was performed using the QuantiNova SYBR Green PCR Kit (Qiagen) and the Light-cycler Rotor Gene Q (Qiagen). The expression of the following genes was analysed: Array comparative genomic hybridization was carried out using the SurePrint G3 Human CGH Microarray 8 × 60 K . DNA isolation of the cell lines was performed using the QiAmp DNA Mini Kit (Qiagen). Quantity and quality of the DNA were assessed on the NanoDrop platform . DNA hybridization was performed according to the standard procedures after labelling of 500 ng of the sample DNA and the control DNA . Microarray data was analysed using Agilent Genomic Workbench (Agilent Technologies).Protein isolation was performed using TNT-buffer , 200 mM sodium chloride, 1%-Triton-X100, 1 mM DTT) containing both phosphatase and protease inhibitor cocktails . Lysates were incubated in liquid nitrogen for 3 min and then centrifuged at 14 000 rpm at 4 °C to remove debris. The isolated proteins were quantified using the Biorad protein assay  applying the Bradford method. SDS-PAGE was performed using NuPAGE 4–12% Bis-Tris protein gels (ThermoFisher Scientific). 10 µg of the protein lysate was used; after gel electrophoresis the proteins were transferred to a nitrocellulose membrane applying a standard semi-dry electro-transfer method. The membrane was blocked with TBS, 0.1% Tween 20, 10% skim milk powder (Sigma-Aldrich) for 60 min. Following, the membrane was washed thrice (each 10 min) with TBS containing 0,1% Tween 20 and then incubated with the primary antibody  overnight at 4 °C. After three washing steps the membrane was incubated for 1 hr with the appropriate secondary horseradish-peroxidase-conjugated antibody. After further washing, ECL detection was performed using the SuperSignal West Dura Extended Duration Substrate (ThermoFisher Scientific). Anti-β-actin antibody  was used to detect β-actin as a house keeping protein.FISH was performed using the commercially available ZytoLight SPEC CDKN2A/CEN9 Dual Colour Probe  according to the manufacturer’s protocol.TP53. Target enrichment, amplicon processing, and library generation were performed according to the manufacturer’s handbook. For target enrichment, we included 1 ng to 40 ng DNA. Successful target enrichment and library generation was checked using the High Sensitivity DNA kit on a bioanalyzer device (Agilent Technologies). Libraries were diluted to 10 pM solutions and the sequencing was performed on a MiSeq platform  using the V2 chemistry. Mean read depth on target region was 200–4000 fold and 99% of bases were covered at 96–100% on average. The resulting fastq files were subjected to further analysis using the GeneRead web based analysis tool , the Biomedical Workbench software package (Qiagen), and the Variant Studio software (Illumina). All identified mutations were manually re-analyzed using the Integrated Genome Viewer Software . The impact of confirmed mutations on protein functions were classified using 8 online protein prediction tools  according to the providers’ instructions.For isolation of genomic DNA from the FFPE tissue samples, 5 µm tissue slices were transferred to glass slides. To spot the area containing the tumour, HE stained FFPE tissue slices (2 µm) were marked by an expert pathologist. The tumour-harboring areas of the FFPE tissue were subjected to a DNA extraction procedure using the GeneRead DNA FFPE kit (Qiagen) according to manufacturer’s instruction. DNA isolation of the cell lines was performed using the QiAmp DNA Mini Kit (Qiagen). DNA purity and concentration was determined fluorometrically . For molecular characterization of both tumour tissue and cell line DNA, we employed a targeted re-sequencing methodology using the GeneRead V2 chemistry (Qiagen) and a custom-made re-sequencing panel including primers for all exons of For statistical analysis, student’s t-tests were performed. A p-value ≤ 0.05 was considered as significant.array CGH result table U-CH14Supplementary Information


BRAF, NRAS, or NF1 mutations had near zero mean Pearson correlation of copy number alterations per gene to tumors and also tended to have higher stromal scores. The Comet Exact Test was applied to tumors and cell lines identifying three pairs of genes mutated in a mutually exclusive pattern in tumors but not cell lines: BRAF and NRAS, BRAF and NF1, as well as NRAS and PTEN. Additionally, 31 genes were more frequently mutated in cell lines than tumors. Avoiding cell lines with co-occurring mutually exclusive mutations and the fewest differentially mutated genes within a known distribution of genetic similarity to tumors by copy number alterations may optimize selection.Insulated culture environment and prolonged propagation contribute to known limitations of cell lines, and selection is often limited to availability or favorable growth characteristics. To better characterize and improve selection of cell lines, we compared 60 melanoma cell lines profiled by the Cancer Cell Line Encyclopedia and 472 cutaneous melanoma tumors profiled by The Cancer Genome Atlas by DNA sequence and copy number alterations. All samples were scored for stromal and immune cell composition by the ESTIMATE algorithm, and 412 tumors with ≥ 60% tumor cell fraction were compared to cell lines. Uncharacterized early passage cell lines that lacked  Incidence of cutaneous melanoma in the United States occurred at a rate of 26.8 per 100,000 males and 16.6 per 100,000 females from 2009–2013 . Cutaneoin vivo by their molecular characteristics makes a first step to increasing translational efficiency of pre-clinical studies. Significant genetic alterations can be applied as a basic metric before investigating cancer biology with cell lines including significant mutations of oncogenes or tumor suppressors, karyotype similarity, and DNA methylation. The number of cell line passages before analysis also critically influences results since genomic features may change during propagation and maintenance of cell lines [Cancer cell lines continue as a pre-clinical tool for development of novel therapeutics and diagnostics. Cell lines have been widely used for drug screening and mechanistic studies in 2D and 3D cell cultures as well as xenograft models, but immortalized cancer cell lines limit the generalizability of conclusions due to clonal homogeneity and the lack of a full complement of stromal and immune cell types . Charactll lines . Recentlll lines –14. Thesll lines –18.in vivo. To address this limitation, we focus on genomic data, particularly DNA mutations and DNA copy number alterations, from 470 cases included in The Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) study and 60 melanoma cell lines profiled by CCLE.The original TCGA study that characterized cutaneous melanoma (TCGA-SKCM) included 67 primary tumors and 266 metastatic tumors, and as samples continue to be analyzed these numbers have increased to 104 primary tumors and 367 metastatic tumors included in this study . CommercAran et al. applied multiple molecular data analysis approaches to estimate the tumor cell fraction of all tumor samples profiled by TCGA and found highly variable purity, particularly in TCGA-SKCM samples, which confounds conclusions drawn from next generation sequencing data . To addrThe CCLE study included 62 cell lines annotated as being skin derived, and 60 of those are defined as cutaneous melanoma cell lines. BJHTERT, an immortalized fibroblast cell line, and GRM, a likely pancreatic cancer cell line by SNP identity according to CCLE annotation, were excluded from comparative analysis. TCGA-SKCM profiled 472 fresh frozen cutaneous melanoma tumor samples from 470 cases. Target paired-end sequencing was performed on CCLE samples using Agilent Sure-Select Target Enrichment System including 1651 selected genes . Copy nuIn order to control for variability in tumor sample composition, the tumor cell fraction was estimated for cell lines using mRNA microarray expression data and for tumors using RNA-seq expression data with the ESTIMATE algorithm . The TCGhttps://www.atcc.org/) with variable mixtures of tumor and stromal cells that were originally provided by the Naval Biosciences Laboratory (NBL). HS600T, HS834T, HS688AT, HS839T, HS934T, and HS940T are melanoma derived cell cultures made available by ATCC as uncharacterized early passage lines, and all are included in the cluster with the highest stromal score. Additionally, BJHTERT, an immortalized fibroblast cell line, and HS895T, a fully characterized cell line, both cluster with the higher stromal scoring cell lines. Many other cell lines originally sourced by NBL were further developed to fully characterized cell lines and generally cluster according to their cell type.ESTIMATE scores were also calculated for all cell lines included in the CCLE study. In the GCT file of normalized microarray expression data, 139 of the 141 genes were common with each the ESTIMATE stromal gene set and immune gene set respectively. Stromal and immune scores across cell lines are very low relative to those found in melanoma tumors, and clusters tend to form by the derived cell type Figure . Hematopn = 59), metastatic tumors (n = 310), and primary tumors (n = 101) (p < 0.05 one-way ANOVA). Cell line mean FGA (0.45 ± 0.23) was significantly higher than both primary tumors (0.33 ± 0.19) and metastatic tumors (0.38 ± 0.20) (p < 0.05 Tukey’s Method). Mean FGA was not significantly different between primary and metastatic tumors (p = 0.09 Tukey’s Method) . FGA was applied as described by Domcke et al. to measure of the proportion of copy number segments that are either amplified or deleted above a chosen threshold as described in the methods section . Mean FG) Figure . HS695T,) Figure . HS940T,In order to compare overall copy number similarity between cell lines and tumors across all gene coding regions, the average copy number per gene was calculated as described in the methods section. Sample Pearson correlation coefficients were calculated for the copy number alteration per gene between each cell line and each tumor sample to determine the distribution of similarity. Comparison of copy number across all genes between individual cell lines and tumors results in a broad range of Pearson r values with multiple outliers across cell lines Figure . HS939T n = 367) by GISTIC 2.0 analysis provided by the Broad Institute Genomic Data Analysis Center Firehose [In general, Pearson correlation between cell lines and tumors based on copy number per gene was fair to poor with the highest mean value of 0.31. Melanoma tumor samples are a very heterogeneous in terms of copy number alterations with a large range of FGA from 0.02 to 0.98. To narrow the comparison to regions of significance, Pearson r values were calculated between cell lines and tumors by copy number per gene for a subset of genes within the peak of significant focal amplifications and deletions in metastatic melanoma tumors from TCGA-SKCM (Firehose . Correlan = 53) and melanoma tumor (n = 412) MAF files. Single nucleotide polymorphisms, insertions, and deletions were included in the comparison, but synonymous variants were excluded. There were 31 genes that were mutated in significantly higher proportion of cell lines relative to the proportion of tumors  : BRAF and NRAS mutations , BRAF and NF1 mutations , and NRAS and PTEN mutations  (Table n = 53)  , primary tumors (n = 24), and cell lines (n = 53) were found to be significantly different in at least two groups (p ≤ 0.05 by one-way ANOVA) , not significantly different between cell lines and metastatic tumors (p = 0.99), and significantly higher in metastatic tumors than primary tumors (p < 0.05). Though the mutational load of primary tumors was found to be significantly different from metastatic tumors and cell lines, sequencing coverage information was only available for a small subset of primary tumors. Since the genome size may affect the total number of mutations present, log2 normalized mutations per megabase were plotted against FGA for each cell line that was profiled for copy number alterations and DNA sequence (n = 52) and tumor samples (n = 213)  Figure . The disUV radiation induced mutation signature is a common genomic feature of skin cancers like melanoma and has been previously been defined by C> T transitions at dipyrimidine sites making up ≥ 60% of all mutations or CC>TT making up ≥5% of all mutations , 28. Of In this study, commercially available melanoma cell lines profiled by CCLE were compared to tumors profiled by TCGA-SKCM according to genomic features including the number of mutations per megabase, the presence of differentially mutated genes, the presence of mutually exclusive mutations, total copy number alterations in the form of FGA, and the correlation of copy number alterations per gene.BRAF, NRAS, NF1, or triple wild type) and proliferative or invasive behavior (MITF/AXL expression ratio). However, proliferative and invasive phenotypes are not a characteristic of all melanoma cells within a single tumor, and a spectrum of MITF expressing and AXL expressing cells exist in any single tumor that can be manipulated through treatment with RAF and MEK inhibitors [MITF become a hierarchically organized mass of MITF high and MITF low expressing cells after growth in a mouse xenograft [MITF via HIF1α leading to switching of melanoma proliferative to invasive phenotype [SOX9 promoter methylation which leads to the proliferative phenotype by expression, and the overexpression of SOX9 promotes the invasive phenotype in a mouse model [BRAF, NRAS, and NF1 subtypes. MEWO and COLO792 were the only two NF1 mutants, and both have much larger numbers of mutations per megabase relative to other cell lines. However, the mutational burden for both falls within the distribution of tumor mutations per megabase.Based on these criteria, each melanoma cell line can be evaluated by its degree of similarity to a large sample of highly annotated tumors from TCGA-SKCM. Selecting a cell line model for studying cancer biology depends on multiple factors. For melanoma cell lines, the preeminent factors are the presence of the most significantly mutated genes  than tumors (64/412) . The result may be dependent on different coverage requirements for mutation calls, the number of genes compared, and the different sample sets used.Both Pollock et al. and the original TCGA-SKCM study found rrelated , 33. In melanoma . NF1 mutand NRAS . WM88, Hand NRAS . MELJUSOM tumors , 37. In BRAF, NRAS, and NF1 were defined in the TCGA-SKCM study as triple wild type. The triple wild type melanoma cell lines compared in this study have poor correlation with tumors by copy number per gene with mean Pearson r values between 0.22 for HS940T and −0.14 for CHL1. Uncharacterized early passage lines made available by ATCC including HS839T, HS600T, HS934T, HS895T, HS940T, and HS688AT had different molecular characteristics relative to tumors and other cell lines. All six of these cell lines had very low correlation of copy number per gene for focal amplifications and deletions identified by GISTIC 2.0 analysis and FGA near zero. Mutations per megabase in these samples were also lower than other cell lines (mean = 4.4 mutations per megabase). One potential reason for the lack of similarity may be due to a relatively high concentration of stromal cells relative to tumor cell lines in these early passage cell cultures. ESTIMATE scoring indicated that these cell lines had elevated stromal scores relative to other cell lines. CJM, HMCB, and CHL1 also lacked mutations in BRAF, NRAS, or NF1, but these cell lines had similar FGA and mutational burden relative to other melanoma cell lines as well as tumors. However, these three cell lines also had very low mean correlation to tumors by copy number alterations in focally amplified and deleted genes. HMCB and CHL1 were annotated as having high SNP similarity in the original CCLE study [Tumors that lack mutations in LE study . Both HMThis study has identified features of melanoma cell lines which may indicate that they are not accurate models of most human melanomas. Though a UV mutation signature is common in melanoma and occurs in 65.3% (269/412) of TCGA-SKCM tumors, only 15.1% (8/53) cell lines harbor a UV signature. The UV signature may be lost during maintenance of cell lines as more mutations are acquired making the UV signature less discriminatory and more descriptive of a cell line’s origin. Most cell lines have good correlation of DNA copy number per gene for focally amplified and deleted genes by GISTIC 2.0 analysis, but a subset of tumors was found to have low similarity across cell lines independently of tumor stage, primary or metastatic status. Cell lines which harbor mutations of interest, the fewest differentially mutated genes, and the highest Pearson sample correlation with most tumors provides criteria to select cell lines with more genetic similarity to patient tumors.in vitro studies.There are several limitations to conclusions drawn from this study. DNA sequencing data from CCLE was only available for a set of 1651 genes selected for hybrid capture sequencing. Specific mutations, mutational burden, and UV signature were drawn only from this small subset of genomic data. Additionally, all data types were not available for all cell lines, limiting the full characterization of each one. Despite these limitations, comparison of cell line molecular features may lead to more applicable pre-clinical models that can improve the translational efficiency of https://portals.broadinstitute.org/ccle/data/) as described in the original CCLE study [All files were downloaded in March of 2017. Cell line DNA copy number, DNA mutation, and mRNA expression data were obtained from the Broad Institute data portal  mapped to the hg19 genome to facilitate comparison with CCLE [SKCM_pairs.aggregated.capture.tcga.uuid.automated.somatic.maf. Since the sequencing coverage for TCGA is not made publicly available, sequencing coverage for TCGA-SKCM data was downloaded from Synapse (https://www.synapse.org/) via syn1709990 as 255 BED files containing genome regions covered with at least 14 reads (n = 255) as described by Kandoth et al. [TCGA-SKCM melanoma tumor data from 470 cases including DNA copy number, DNA mutation, and mRNA expression were downloaded with the TCGAbiolinks R package from the GDC legacy archive  by GISTIC 2.0 analysis downloaded from the Broad Institute GDAC Firehose [A reduced segment matrix was extracted from segmented copy number data for each CCLE and TCGA-SKCM sample and was used to calculate mean copy-number per gene with CNTools . The CNTFirehose .BRAF, TP53, NF1, NRAS, PTEN, MAP2K1, IDH1, and RB1. These genes were tested for the occurrence of mutations in a mutually exclusive pattern each in tumors and cell lines using cometExactTest via maftools [Differentially mutated genes were identified using Fisher’s Exact Test implemented via maftools to compare the proportion of tumors relative to the proportion of cell lines that carry a mutation in a given gene with a minimum of five samples carrying the mutation . Comparimaftools . MultiplIn order to compare mutations between CCLE data and TCGA-SKCM data, the total number of mutations was normalized by the breadth and depth of coverage for each data set. Sequencing coverage data was available for 255 melanoma tumor samples provided from the Synapse data sharing platform, and 213 of those tumor samples had tumor cell fraction greater than 60% calculated using ESTIMATE scores. The BED files contain genomic start and end positions for regions covered by ≥14 reads. The breadth of sequencing coverage for TCGA-SKCM samples was calculated by subtracting each end position from each start position in the BED file, and total breadth of coverage was calculated by summing all lengths. Coverage was available for CCLE samples as WIG files, which contain the number of reads covering each genome position. In order to have equivalent breadth of coverage for both CCLE and TCGA-SKCM samples, the total length covered for CCLE samples was calculated by counting the total positions provided in the WIG files with ≥14 reads. Total mutations were counted for TCGA-SKCM and CCLE from their respective MAF files filtered by variant allelic fraction ≥ 0.1 and ≥ 14 reads including synonymous variants, insertions, deletions, and single nucleotide polymorphisms. Total coverage-normalized mutations were calculated by dividing the sum of all coding region mutations from the MAF files divided by the sequencing coverage calculated for each of the TCGA-SKCM samples and CCLE samples.n = 412) and CCLE samples (n = 53) was determined by extracting the flanking bases surrounding mutations from MAF files using the maftools and summing the number of C>T transition mutations flanked by either C or T; then dividing the sum by the total number of substitution mutations in the MAF file. MAF files for TCGA-SKCM and CCLE were filtered by variant allelic fraction ≥ 0.1 and ≥ 8 reads including synonymous variants, insertions, deletions, and single nucleotide polymorphisms. A UV signature was defined as C>T transitions occurring at dipyrimidine sites comprising ≥ 60% of all substitution mutations or ≥5% CC>TT mutations.UV signature for TCGA-SKCM samples  were searched on June 7th, 2017 using the advanced search builder with multiple punctuation combinations including spaces, hyphens, and periods. Additionally, the search term “melanoma” was required in combination with each cell line name to avoid false-positive search hits. For cell lines with zero search hits, the number of cell line studies annotated by Cellosaurus (web.expasy.org/cellosaurus) was summed to account for publications which don't have the cell line names included in the title or abstract [PubMed titles and abstracts (abstract .


A search for new faint iron lines has been made on spectrograms taken with an arc-in-air as source. The range of observations is from 2102 to 8679 A. The reciprocal dispersion of the spectrographs used for the various spectral regions varies from 1 A/mm to 3 A/mm.Twelve new energy levels have been found, resulting in a total of 121 classified lines. A table containing 698 classified lines includes many lines whose wavelengths had been predicted as combinations among the known energy levels, and found in the solar spectrum in earlier work. Their reality has been confirmed in the present work.A list containing 1,102 newly measured unclassified lines is included. Many of the lines listed in the tables have been reported by other observers with varying degrees of accuracy. All such reference sources are indicated in the tables. As a result of the new measurements, these lines may safely be attributed to Fe I.A comparison of the new lines with the solar spectrum has resulted in the identification of 306 solar lines of Fe I unblended, and of 85 as blends to which Fe I is a contributor. When the analysis of Fe I was carried out in 1944,[HPrism: reciprocal dispersion 1 A/mm to 1/3 A/mm.XGrating: Wood, 21 ft. concave grating 30,000 lines per inch, reciprocal dispersion 1 A/mm in the second order.RGrating: Rowland, 21 ft. concave grating 20,000 lines per inch, reciprocal dispersion 3 A/mm.A search for faint iron lines has been made on spectrograms taken with an arc-in-air as source, and with exposures long enough to reveal fainter lines than have been recorded previously. The range of observations is from 2102 to 8679 A. The electrodes used in the arcs were prepared from the purest iron obtainable at the time. The observations were made by the senior author (CCK) with the spectrographs at NBS. The spectrograms that have been measured are listed in In order to obtain at least two exposures over the entire range it has been necessary to fill in gaps by using Fe comparison spectra from miscellaneous spectrograms in the collection at the Georgetown College Observatory. These were taken with the Rowland grating described above. A serious attempt has been made to eliminate all possible impurities from the spectra.J-values entered in column 2. They account for a total of 121 classified lines. Observations with a more suitable source will be required to extend this study further. One serious disadvantage encountered here is the masking of faint lines by wings of stronger ones. An electrodeless lamp doubtless would excite many new faint lines, and thus make it possible to find additional terms and assign their configurations.The present work has been carried out to confirm the predicted lines attributed to Fe I in the solar spectrum, and to extend the identifications of Fe I in the sun. It was hoped, also, that the analysis could be extended by additional observations. A search has been made among the new unclassified lines for levels combining with the known low terms of Fe I. Twelve new levels have been found, of which three are subject to some question. These levels are listed in The classified lines are listed in Some wavelengths are in italics. These were included in the original Princeton line list as unclassified and have been used for combinations with the new levels in “n” and “N”. The next two columns contain, respectively, the observed wave numbers and those calculated from the term combinations. The designations in column 5 are those used for Fe I in the 1944 Monograph [The total number of lines in onograph . The mulonograph . New linThe letters in the last column are the same ones used for reference sources in the Monograph line list. They refer to lines that are not new but are newly classified, to lines measured by earlier observers that needed additional confirmation, or to unpublished measurements from the Massachusetts Institute of Technology, kindly furnished by G. R. Harrison in 1942. References Z, BK, and ZZ have been added.In 1934 a list of faint lines of Fe I was compiled by Burns and Kiess. It contains early unpublished measurements made by Burns at Bonn and possibly at the Allegheny Observatory, and, also, measurements by Kiess of “H” plates described above. Lines in “n” and “N” in the intensity column. In the last column the letters denote the same reference sources as were used in the 1944 paper. The symbols and references are described at the end of In the last column of In conclusion, it is a pleasure to express our gratitude to those who have assisted with this laborious program. Special thanks are due Misses Eva Novotny and Janet Rountree for their work in measuring the plates. Mrs. Isabel Murray has prepared the tabular data with meticulous care. The work could not have been brought to its present stage of completion without special financial aid. The project has been carried in part by grants NR 046–136 from the Office of Naval Research and G 8193 from the National Science Foundation to the Georgetown College Observatory. Both of these are gratefully acknowledged.


HMA-resistant patients have very poor prognosis and this cohort of patients constitutes an important area of research. To understand the mechanisms underlying HMA-resistance and to overcome it, we established an azacitidine-resistant cell line, MOLM/AZA-1 and a decitabine-resistant cell line, MOLM/DEC-5 using MOLM-13. For cytogenetic characterization, we performed microarray-based comparative genomic hybridization (array-CGH), which identified a total of 15 copy number alterations (CNAs). Among these CNAs, eight regions in HMA-resistant cell lines showed CNA patterns distinct from the parental MOLM-13 genome. Single nucleotide polymorphism (SNP) microarray was also performed to obtain a more reliable interpretation of the identified CNAs, and all HMA-resistance-specific CNAs except one detected by array-CGH were successfully validated. In addition to CNAs, copy neutral loss of heterozygosity and mosaic loss events were identified in HMA-resistant cell lines. In our resistant cell lines, MDR-1 was not overexpressed, while DNMT3b was upregulated. Azacitidine and decitabine did not inhibit DNMT1, DNMT3a, or DNMT3b in both HMA-resistant cell lines, while they inhibited the enzymes in parental MOLM-13. We also developed mouse xenograft models using MOLM/AZA-1 and MOLM/DEC-5. Our  Post-mitotic modification of DNA methylation constitutes a major epigenetic regulatory mechanism for inactivating gene expression. The mechanisms controlling methylation are frequently dysregulated in cancer and aberrant methylation has been implicated in the pathogenesis of several hematologic malignancies, including acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) . Currentin vitro tools for assessing anticancer drug resistance in the clinical scenario. Cell line models with acquired resistance to anticancer drugs provide us with valuable information in elucidating the mechanisms underlying clinical anticancer drug resistance. Differential phenotypic and/or molecular changes between a drug-resistant cell line and its drug-sensitive counterpart can also suggest a locus of drug action inferred by the presence of those particular alterations [in vitro resistance to clinical doses of azacitidine and decitabine, respectively, from a human monocytic leukemia cell line, MOLM-13. The parental MOLM-13 cell line was established from the peripheral blood of a patient at relapse of AML, which had evolved from MDS, and the cell line had features of both AML and MDS [Drug-resistant cell line models can be useful erations . In this and MDS . We comp50 values for azacitidine and decitabine in MOLM-13 were 0.03804 μM and 0.06294 μM, respectively, while the IC50 value for azacitidine in MOLM/AZA-1 was 1.376 μM  and that for decitabine in MOLM/DEC-5 was 9.242 μM   and that for decitabine in MOLM/AZA-1 was 2.427 μM   and decitabine-resistant (MOLM/DEC-5) cell lines from the parental cell line, MOLM-13. The IC and deci) Figure .We performed microarray-based copy number profiling of parental and HMA-resistant MOLM-13 cell lines with pooled normal genomes as reference. The eight copy number gain on 6p, 6q, 8p, 8q, 13q, 19p, 19q, and 20p, and the one copy number loss on 9p21.3 were identified in the parental MOLM-13 genome Figure , Table 2DNMT1gene resides showed copy number gain in the parental MOLM-13 genome, but not in the MOLM/AZA-1 and MOLM/DEC-5 genomes. A 31-Mb-sized region on 19q in the MOLM/DEC-5 genome showed relative copy loss, while the MOLM/AZA-1 genome showed the same copy number as the parental MOLM-13 genome.To validate the identified CNAs, we repeated the array-comparative genomic hybridization (array-CGH) for the MOLM/AZA-1 and MOLM/DEC-5 cell lines with genomic DNA from the parental MOLM-13 cell line as a reference for each hybridization. All the CNAs identified in these array-CGH analyses can be interpreted to be acquired during development of HMA-resistance. Through this validation, all of the HMA-resistance-specific CNAs were successfully validated . In partTo obtain a more reliable interpretation, we performed whole-genome SNP microarray for the genomes of parental MOLM-13 and HMA-resistant cell lines. Through the SNP microarray, all HMA-resistance-specific CNAs except one (14q24.2–q32.33 in MOLM/DEC-5) detected by array-CGH were successfully validated . In addiNext, we compared the HMA-resistance-specific CNAs to those identified in representative myeloid neoplasm cell lines. For the comparison, we downloaded the raw SNP microarray data of three AML cell lines  from Gene Expression Omnibus database (GSE36138) and identified CNAs with the same detection criteria used in this study. Of the three cell lines, two (SKM-1 and U-937) exhibited a copy gain on 1q which was identified as HMA-resistance-specific CNA in this study . In addiSTK11, SMARCA4, and RUNX1 were located within the HMA-resistance-specific CNAs  in copy gain region on 1q21.1–q44 identified from both HMA-resistant cell lines were significantly higher in HMA-resistant cell lines compared to MOLM-13, whereas expressions of three genes  in copy loss region on 19p13.3–p12 identified from both HMA-resistant cell lines were significantly lower in HMA-resistant cell lines compared to MOLM-13. Expressions of RUNX1 and U2AF1 genes in a copy number loss on 21q11.2–q22.3 identified from MOLM/AZA-1 were significantly lower in MOLM/AZA-1 compared to MOLM-13 and MOLM/DEC-5. To infer the biological implication of the cancer-related genes, we performed ontology/pathway analyses using the KEGG and DAVID tools [P=3.6×10-11), ‘cell differentiation’ (P=0.004), ‘PI3K-Akt signaling pathway’ (P=0.013), and ‘negative regulation of cell growth’ (P=0.023)  . Taken tMDR1 gene, is an ATP-dependent efflux pump with broad substrate specificity, and hENT1, encoded by the SLC29A gene, is an equilibrative nucleoside transporter family. As a positive control of MDR1 and Pgp expression, we used an NCI/ADR-Res cell line. Expression level of MDR1 gene in MOLM-13 (1.0 fold), MOLM/AZA-1 (0.1 fold) and MOLM/DEC-5 (1.1 fold) was significantly lower than those of NCI/ADR-Res (3241.9 fold). The protein expressions of Pgp reflected the gene expressions of the cell lines  and hENT1 in HMA-resistant cell lines Figure . Pgp, ens Figure . SLC29A s Figure . These rWe also assessed the expression of DNA methyltransferase (DNMT) family proteins; DNMT1, DNMT3a, and DNMT3b. RQ-PCR assay showed that mRNA expression of DNMT family genes in MOLM/AZA-1 and MOLM/DEC-5 slightly decreased compared to parental MOLM-13 Figure . Proteinin vivo tumor models. Interestingly, HMA-resistant cell lines showed more aggressive increases in tumor volume compared to the parental MOLM-13 cell line and tumor volumes of HMA-resistant cell lines were higher than that of MOLM-13 on 22 days  to determine the establishment of s Figure . The finin vitro and in vivo HMA resistance models using the MOLM-13 cell line that has biologic features of both AML and MDS. We also characterized the phenotypic and cytogenetic properties of our HMA-resistant cell lines (MOLM/AZA-1 and MOLM/DEC-5). Overexpression of the MDR1 gene was not associated with the development of HMA resistance in our cell line models because the mRNA and protein levels of the MDR1 gene were not elevated in resistant cell lines compared to the parental cell line. Interestingly, protein levels of DNMT3b were upregulated in HMA-resistant cell lines while it was not expressed in the parental MOLM-13 cell line. Furthermore, DNMT enzymes  were not effectively inhibited by DNMT inhibitors in HMA-resistant cell lines, although the DNMT enzymes (DNMT1 and DNMT3a) were effectively inhibited by both DNMT inhibitors (azacitidine and decitabine) in the parental cell line. DNMT1 is known to methylate CpG residue and to be responsible for maintaining methylation patterns established during development [de novo DNA methylation in newly synthesized DNA molecules [de novo modifications. Thus, HMA appears to have to inhibit DNMT3a and DNMT3b as well as DNMT1 to exert anti-tumor activities. An in vitro study demonstrated that azacitidine and decitabine decreased protein expression of DNMT1 and DNMT3a in MDS-derived cell lines [The clinical course after failure of treatment with HMA is usually dismal and HMA failure is an unmet clinical need in the management of MDS and elderly AML patients , 7. Thusolecules . Howeverolecules . A recenolecules . DNMT3a ll lines . In our For cytogenetic characterization of our HMA-resistant cell lines, array-CGH and SNP microarray were performed. Eight regions showed distinct CNA patterns between parental MOLM-13 and one or both HMA-resistant cell lines Table , and add50 values compared to prior azacitidine-resistant cell lines: 36-fold (MOLM/AZA-1) and 147-fold (MOLM/DEC-5) higher IC50 than the parental cell line. We also developed in vivo xenograft models with HMA-resistant cell lines  fetal bovine serum and1% penicillin/streptomycin .Molm-13  cell line was cultured at 37°C in 5% CO® Luminescent Cell Viability Assay  in accordance with the manufacturer's instructions. Briefly, cells were plated at 1,000–3,000 per well in a 96-well opaque plate and were incubated in complete growth medium. Cells were treated with various concentrations of azacitidine or decitabine, purchased from Sigma Aldrich  and were prepared as a 50-mmol/L stock solutions in dimethyl sulfoxide (DMSO). After 48 h, cell viability was determined by measuring luminescent signals with a VICTORTM X Light luminescence plate reader . The concentrations of azacitidine or decitabine required for 50% growth inhibition were scored as IC50 values.Cell viability was assessed using the luminescence-based CellTiter-Gloin vitro. MOLM-13 cells were exposed continuously to gradually increasing concentrations of azacitidine (0.5 pM to 50 nM) and decitabine (0.05 nM to 1 μM), and the cells acquired resistance to azacitidine or decitabine. The resistant cells were isolated by a series of stepwise selections and two HMA-resistant cell lines, MOLM/AZA-1 and MOLM/DEC-5, were cloned by the limiting dilution method. The degree of resistance was calculated by dividing the IC50 value of the resistant cells by that of the parental cells.The parental MOLM-13 cell line was sensitive to both azacitidine and decitabine ® Identifiler® Plus PCR Amplification Kit  and analyzed by 3730 DNA analyzer and Peak scanner .To prove the derivation of MOLM/AZA-1 and MOLM/DEC-5 cell lines from the parental MOLM-13 cell line, short tandem repeat profiling of all three cell lines was performed and compared. Fifteen tetranucleotide repeat loci and the amelogenin gender-determining marker were amplified using AmpFℓSTRDNA copy number profiling was performed using Agilent Sure Print G3 Human CGH Microarray 180K . Array-CGH experiments were conducted according to the manufacturer's instructions. In brief, 1 μg of each genomic DNA from MOLM-13, MOLM/AZA-1, and MOLM/DEC-5 cells was labeled with Cy5-dCTP  and reference DNA from normal individuals  was labeled with Cy3-dCTP . Labeled DNA was applied to the array with hybridization buffer and human Cot-1 DNA . Array slides were incubated for 24 h at 65°C. After washing and scanning the arrays, images were analyzed with Feature Extraction Software v10.7.3.1 . Probe mapping was conducted according to its genomic location in the UCSC genome browser (Human NCBI37/hg19). The Rank Segmentation statistical algorithm in NEXUS software v7.5  was used to define copy number alterations in each sample.® HD microarray, which contains 743,304 SNP markers and 1,953,246 copy number markers , according to manufacturer's instructions. The average marker spacing is 1.1 kb. The signal intensity extraction and copy number alteration detection were conducted using Chromosome Analysis Suite (Affymetrix).Genome-wide SNP genotyping was conducted using the CytoScanbeta-actin.Total RNAs were extracted using QIAzol Lysis Reagent  and converted into cDNA using Revert Aid premium reverse transcriptase  according to manufacturer's instructions. The mRNA transcript levels were quantified by RQ-PCR on LightCycler 96  using the primer sets . After a® West Pico Chemiluminescent Substrate  and images were produced using medical X-ray film blue . Specific antibodies were as follows: p-glycoprotein antibody , hENT1 , DNMT1 , DNMT3a , DNMT3b (Abcam), and β-actin .The cells were lysed in lysis buffer  for 15 min on ice. The protein concentration of the lysates was measured using a Bradford assay . Protein extracts (50 μg) were separated by 6% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and were transferred onto polyvinyl difluoride membranes. After blocking with 2% skim milk for 1 h, the membranes were incubated with primary antibodies overnight at 4°C, then with secondary antibody conjugated with horseradish peroxidase  for 2 h. The membrane was visualized by Super Signal6 cells of parent MOLM-13, MOLM/AZA-1, and MOLM/DEC-5 were mixed with matrigel  and were injected subcutaneously through 22-gauge needles into the flank of 6-week-old BALB/c nu/nu mice  under isoflurane anesthesia. Mice were housed in a laminar flow caging system, and all food and water were autoclaved. The study was performed after obtaining the approval of the IACUC . In order to determine tumor volume by a digital Vernier caliper, the greatest longitudinal diameter (length) and the greatest transverse diameter (width) were determined. Tumor volumes based on caliper measurements were calculated by the modified ellipsoidal formula: tumor volume=1/2(length × width2).Each dose of 5 × 10The quantification of mRNA expression was performed with triplets (n≥3) and the data are presented as mean ± SEM. Statistically significant differences were estimated using P<0.01 and evaluated by one-way ANOVA. Statistical analyses were conducted using GraphPad Prism version 5 .