NZVI@SiO2-NH2 nanoparticles were synthesized under optimal conditions with the uniform core-shell structure (80–100 nm), high loading of amino functionality (~0.9 wt%), and relatively large specific surface area (126.3 m2/g). The result demonstrated that well-dispersed NZVI@SiO2-NH2 nanoparticle with nFe0-core and amino-functional silicon shell can effectively remove 2,4,6-trichlorophenol  in the neutral condition, much higher than that of NZVI. Besides, the surface-modified nanoparticles (NZVI@SiO2-NH2) in anaerobic granule sludge system also showed a positive effect to promote anaerobic biodechlorination system. More than 94.6% of 2,4,6-TCP was removed from the combined NZVI@SiO2-NH2-anaerobic granular sludge system during the anaerobic dechlorination processes. Moreover, adding the appropriate concentration of NZVI@SiO2-NH2 in anaerobic granular sludge treatment system can decrease the toxicity of 2,4,6-TCP to anaerobic microorganisms and improved the cumulative amount of methane production and electron transport system activity. The results from this study clearly demonstrated that the NZVI@SiO2-NH2/anaerobic granular sludge system could become an effective and promising technology for the removal of chlorophenols in industrial wastewater.A novel amino-functionalized silica-coated nanoscale zerovalent iron (NZVI@SiO Chlorophenols (CPs), a group of toxic and suspected carcinogenic pollutants, have been widely identified in chemical industrial wastewater. CPs are considered resistant to biodegradation and may cause adverse effects on human health and the receiving environment –3. The d0 treatment process also increased when compared with a control anaerobic reactor [During the last decade, nanoscale zerovalent iron (NZVI) has been widely applied as a promising tool for the treatment of contaminated groundwater and soil . NZVI ca reactor . Thus, i reactor , 26. For reactor .2 has characteristics of water solubility, nontoxicity, and biocompatibility features. Additionally, the SiO2 coating shell has an abundance of surface hydroxyl groups, which makes it easy for surface modification and grafting functional groups [3O4@SiO2, have recently been prepared for potential biomedical applications [3O4@SiO2 core-shell magnetic nanomaterial has been prepared as a novel adsorbent for aqueous heavy metals removal [To overcome these problems, surface modification is recommended for synthesizing more stable and efficient NZVI. The surface modifying agents have been reported including carboxymethyl cellulose, chitosan, silica, and polymeric electrolyte membrane , which cl groups , 30. Silications . Besides2-NH2) by using a one-step Stöber method with the surface functionalization of NZVI. Batch microcosm experiments were executed to investigate the degradation efficiency of 2,4,6-TCP in the NZVI@SiO2-NH2/anaerobic granular sludge system and to assess nanoparticles influence on the anaerobic microbial activity. In addition, the effect of NZVI@SiO2-NH2 dosage and the role of Fe2+ on the removal of 2,4,6-TCP were also evaluated. The obtained results in this study will be useful to better understand the feasibility of using NZVI@SiO2-NH2-anaerobic granular sludge system for the remediation of industrial wastewater with CPs contamination.The objectives of this study were to develop a novel amino-functionalized silica-coated nanoscale zerovalent iron (NZVI@SiO4·7H2O), ferric chloride hexahydrate (FeCl3·6H2O), ferrous chloride tetrahydrate (FeCl2·4H2O), isopropanol, tetraethyl orthosilicate , and methyl alcohol  and other chemical reagents were purchased from the Jinke Chemical Reagent Co., Ltd. . 3-Aminopropyl trimethoxysilane , poly(ethylene glycol) , and 2,4,6-trichlorophenol  were purchased from Aladdin Chemistry Co., Ltd. (China). All chemical solutions and reagents used in the experiments were of analytical grade without further purification.Chemicals ferrous sulfate heptahydrate  of this sludge was 0.79. In order to avoid the interference of the residual contaminants in the growth environment for the removal experiments, the anaerobic granular sludge was washed with N4 with an excess of NaBH4 [2) was prepared based on a Stöber method using silicon alkoxide as a silica source. In brief, three grams of FeSO4·7H2O was diluted in the 150 mL ultrapure water/isopropanol  solution and stirred in a three-neck flask at room temperature. Then, 30 mL of freshly prepared NaBH4 solution (1 mol/L) was added dropwise under nitrogen protection, resulting in a suspension of iron nanoparticles. Finally, 4 mL of TEOS and 1 mL NaOH solution were added to the above iron nanoparticle solution to generate the NZVI@SiO2.Nanoscale zerovalent iron (NZVI) was firstly prepared using conventional liquid-phase reduction by reducing FeSOof NaBH4 , 32. The2-NH2) was conducted on-site by APTMS as the modified agent. After the above coating process for 4 h, 0.8 mL of APTMS was then added to the suspension, and the mixture was preserved at room temperature with continuous stirring (150 rmp/min) for 12 h under nitrogen flow (40 mL/min). After the reaction, the fresh NZVI@SiO2-NH2 nanoparticles were separated and washed several times with deionized water and ethanol to remove associated polysiloxane and dried overnight at 60°C under vacuum.Amino-functionalized silica-nanoscale zerovalent iron  was firstly spiked into the N2-sparged deionized water, and then iron samples (0.5 g/L) including NZVI, NZVI@SiO2, and NZVI@SiO2-NH2 were added to these TCP solutions, respectively. The reaction solution was deoxygenated by N2 stream for another 15 min and kept sealed with a rubber stopper during the reaction. The final volume of reaction solution was 200 mL at pH value of 7.0 which was adjusted by HCl or NaOH. The serum bottles were performed in a thermostatic incubator at 35°C with a rotation speed of 150 rpm. At given time intervals, an aliquot of reaction solution was sampled for the measurement of residual 2,4,6-TCP concentration and pH value.The removal experiments of 2,4,6-TCP by NZVI, NZVI@SiO2 system (AGS + NZVI@SiO2), and the combination of anaerobic granular sludge/NZVI@SiO2-NH2 system (AGS + NZVI@SiO2-NH2). The synthetic nutrient solution in each system was deoxygenated at pH 7.0 including a certain amount of 2,4,6-TCP, 10 g VSS anaerobic granular sludge, and the same dose of nanoparticles of 0.5 L−1. Two control experiments were also conducted to clarify the intimal activity of anaerobic microorganism and the toxicity of TCP on the biodegradation: a single anaerobic granular sludge system (AGS) was the same as the mixed system, but without any iron nanoparticle; another common anaerobic biodegradation system (AGS only) was the same as AGS, except without the 2,4,6-TCP. The composition of synthetic nutrient solution in biodegradation experiment included 3000 mg/L glucose, 286 mg/L ammonium chloride, and 65 mg/L monopotassium phosphate as nutrient and energy source to facilitate growth of the biomass, with C/N/P ratio of 200 : 5 : 1. In addition, 1 mL stock solution liquor of necessary trace elements was added to the above solution containing the following composition (mg/L): CaCl2·2H2O, 330; EDTA, 5000; NiCl2·6H2O, 190; H3BO4, 14; ZnCl2, 205; MnSO4, 500; CuSO4·5H2O, 250; CoCl2·6H2O, 240; MnCl2·4H2O, 205; (NH4)6MoO4·4H2O, 9. The saline bottles were maintained in a constant temperature shaker at 35°C with a rotation speed of 150 rpm. At various time intervals, 2 mL of suspensions was withdrawn from each reactor and control bottle for the analysis of degradation rate of 2,4,6-TCP. Besides, the removal rate, methanogenic activity, and electron transport system (ETS) activity were measured in duplicate.Three parallel nanobiodegradation hybrid systems for 2,4,6-TCP removal were performed by using 250 mL saline bottles with rubber stoppers as anaerobic microcosm reactors: the combination of anaerobic granular sludge system/NZVI  (AGS + NZVI), the combination of anaerobic granular sludge/NZVI@SiOα radiation (λ = 0.15418 nm) at 40 kV and 40 mA at 20°C. The scan rate was set at 1.2°/min, and the range was set from 10 to 80 (2θ). FT-IR spectra were measured by a Spectrum One-B FT-IR spectrophotometer (Nicolet Magna 550) under dry air at 20°C by a KBr pellets method. Each sample was scanned from 4000 to 400 cm−1 with a resolution of 4 cm−1. The sampled 2,4,6-TCP reaction solution was firstly centrifuged for 10 min at 10000 g, and the supernatant was filtered through a 0.45 μm hydrophilic polyethersulfone (PES) syringe filter for high-performance liquid chromatography (HPLC) analysis. 2,4,6-TCP was analyzed on a Shimadzu LC-2010A HPLC equipped with a UV detector. A C18 column  was used for the separation. The column temperature was set at 30°C. Methyl alcohol and ultrapure water  were used as the mobile phase. The flow rate was set at 0.7 mL/min. The UV wavelength for 2,4,6-TCP detection was 290 nm. Electron transport system (ETS) activity was measured by an INT method for assessing metal influence on anaerobic sludge [The prepared iron nanoparticles were characterized as follows. The surface morphology and size distribution were determined with an S-3700N scanning electron microscopy (SEM) characterization . The elemental composition was performed by energy-dispersive spectrometry , with energy resolution 123 eV. The crystallographic structures of these nanoparticles and oxides were determined by a D8 Advance X-ray Diffraction system and Bruker AXS with a Cu target and Kc sludge , 34. A S2-NH2 were presented in 2-NH2 nanoparticles showed a smoothly spherical shape . Besides, the hydrophobic surface properties of NZVI@SiO2-NH2 were also improved by the introduced functional groups, resulting in good dispersibility, which could effectively improve the chance to contact with the active sites in surface of the nanoparticles and contaminants, thereby increasing the removal efficiency. At the initial reaction, removal of 2,4,6-TCP by NZVI, NZVI@SiO2, and NZVI@SiO2-NH2 under different reaction time could be described by first-order rate equation in different reaction time. The result was presented in kobs value was only 0.011 h−1 for the NZVI reaction with 2,4,6-TCP. The degradation efficiencies of modified NZVI@SiO2 and NZVI@SiO2-NH2 were 0.031 h−1 and 0.043 h−1, much higher than that of NZVI, indicating that introducing SiO2 cladding layer and NH2-functional groups could enhance their ability of reductive dechlorination.The reaction reactivity of the iron nanoparticles on 2,4,6-TCP dechlorination could depend on the physical-chemical characteristic of nanoparticles. The effect of modified process on reaction reactivity was evaluated by the degradation efficiency of 2,4,6-TCP in aqueous phase. The results of removal of 2,4,6-TCP by NZVI, NZVI@SiOng media . The fre coating . On the 2-NH2 on anaerobic granular sludge treating CPs. The influences of NZVI, NZVI@SiO2, and NZVI@SiO2-NH2 on the removal of 2,4,6-TCP in the anaerobic biochlorination system were compared in 2-NH2 was added to anaerobic microbial system, more than 90% of 2,4,6-TCP was removed from the system in 120 h. Consistent with the earlier studies, adding NZVI in anaerobic system was a promising approach to promote anaerobic microbial biochlorination [2-NH2 and NZVI@SiO2) to anaerobic granule sludge system was also capable of providing positive effect to promote anaerobic biochlorination processes. At the same time, the degradation efficiency of anaerobic microbial system for adding NZVI@SiO2-NH2, 94.6% of 2,4,6-TCP removal, was substantially higher than that from adding NZVI@SiO2 and NZVI, 88.1% and 78.3% of 2,4,6-TCP removal, respectively. The experimental results showed that surface-modified and amino-functionalized NZVI@SiO2-NH2 could effectively maintain more particle surface activity and improve the dispersibility, thereby improving the ability of the combination system to remove 2,4,6-TCP.Application of anaerobic microbial processes for the treatment of chlorinated organic compounds had drawn considerable attention in recent decade , 39. Undrination . It is n2-NH2 further in anaerobic granule sludge system, the concentration of Fe2+ and pH were analyzed. Free ferrous iron were stared as the important iron reagents in practical application of environment. It can be found in 2+ was very low in NZVI/AGS system. In contrast, the concentration of Fe2+ went up sharply from 0 to 38.7 mg/L and 32.4 mg/L in NZVI@SiO2-NH2/AGS and NZVI@SiO2/AGS system, respectively. The concentration of Fe2+ was released from the Fe0 reaction, which could reflect the activity of the iron nanoparticle. However, if the freshly prepared NZVI was exposed in the atmosphere, air and water would trigger a rapid reaction with a large amount of iron corrosion product which formed the oxide layer on the surface of NZVI, identified by the earlier reports as magnetite, maghemite, and lepidocrocite [0 reduction reaction would occur, leading to little of Fe2+ being released into the reaction medium. Thus, the aminopropyl modification of SiO2 shell could effectively slow down the surface passivation of nanoparticles, and ferrous ion could migrate almost freely in the functionalization of layer.In order to explore the effect of NZVI@SiOocrocite . Once th2-NH2 added, a gradual pH rise was observed in the AGS/NZVI@SiO2-NH2 system, which could be explained by the fact that H+ was required for the dissolution of iron and the iron hydroxide was positively charged by adsorbing H+ [2-NH2 could stabilize the reaction environment and maintain the activity of microorganism in the nFe0-microbial system. Considering that the reaction medium was unbuffered when the anaerobic granule sludge was exposed to 2,4,6-TCP, the relative change of pH in the nanobiosystem was directly affected by the reaction activity of nanoparticle: the NZVI@SiO2-NH2 induced increase in pH and was in excess of 0.21 and 0.74 pH units compared to NZVI@SiO2 and NZVI at the 120 h of experiment process. Thus, these results proved that NZVI@SiO2-NH2 had a higher activity than NZVI and NZVI@SiO2. Consequently, it is confirmed that the NZVI@SiO2-NH2 would contribute to the higher removal rate of 2,4,6-TCP, the plenty of electron donors, and the stable environment in the anaerobic granule, which further augmented the function of NZVI in the removal of chlorinated organic compounds.Meanwhile, it has been widely reported that pH was one of the significance factors for the growth of anaerobic microbial activity and the degradation efficiency of chlorinated organic pollutants in anaerobic microbial system. As shown in rbing H+ . Adding 2-NH2 particles on methane production, the yield of biogas and the content of methane on the combination of AGS/NZVI@SiO2-NH2, AGS/NZVI@SiO2, and AGS/NZVI system and the AGS system were shown in 2-NH2, NZVI@SiO2, and NZVI, respectively, in contrast with 168.3 mL in the control system. In all cases, the yield of biogas was the highest in AGS/NZVI@SiO2-NH2 system, increasing 12.5% and 33.7% when NZVI@SiO2 and NZVI were supplied, respectively. Apparently, the higher activity of NZVI@SiO2-NH2 available led to plenty of electron donors and a stale and low toxic environment in anaerobic dechlorination process, which could stimulate methanogenesis dramatically. Moreover, the subdued period of methane production was shortened in the anaerobic system enhanced by NZVI@SiO2-NH2 than AGS/NZVI@SiO2, AGS/NZVI, and the AGS system. The highest methane production increased gradually to a daily maximum of 42.2 mL/g VSS d on the 20th hour during the experiment, with methane content increasing 65.4%. Therefore, the present study provided a clear demonstration that the AGS-NZVI@SiO2-NH2 exhibits better performance in terms of higher methane production and shorter subdued period.To estimate the actual effect of NZVI@SiO2-NH2 system was also further analyzed by the INT method to describe the influence of NZVI@SiO2-NH2 on dehydrogenase activity of anaerobic sludge, as shown in 2, and AGS/NZVI@SiO2-NH2 system, respectively. NZVI@SiO2-NH2 did not exert much stimulation and inactivation on the ETS activity of the anaerobic bacteria at the initial process of the experiment, because the core nFe0 was avoided directly contacting with microorganism by the surface-modified SiO2 shell. It can be observed that the addition of NZVI@SiO2-NH2 to anaerobic biodegradation system could enhance the activity of sludge and reduce the adverse influence of 2,4,6-TCP.Electron transport system (ETS) activity of the combined AGS/NZVI@SiO2-NH2 dosage increased in the anaerobic granular sludge system. For example, the removal rate of 2,4,6-TCP was found to be 80.7%, 87.4%, 95.4%, and 96.6%, respectively, when the addition dosage of NZVI@SiO2-NH2 to the anaerobic system was 0.1, 0.2, 0.5, and 1 g/L. In addition, the concentration of 2,4,6-TCP in combined system was obviously deceased in the first 10–12 h of the experiment, which indicated that 2,4,6-TCP can be directly chemically reduced and/or be adsorbed on the surface layer of NZVI@SiO2-NH2. The adsorptive and active sites on the surface of NZVI@SiO2-NH2 increased when the amount of NZVI@SiO2-NH2 increased. Moreover, adding NZVI@SiO2-NH2 dosage to anaerobic granule sludge system is capable of providing more electron donors to promote anaerobic metabolic processes, and the corrosion process of NZVI@SiO2-NH2 can produce Fe2+/Fe3+ and hydrogen which can be used as minerals for the anaerobic microorganisms; thereby, the remnants of pollutants and toxic intermediate products could be further removed biologically by the attached microorganism in the combined system.Four identical batch microcosm experiments were operated at different dosages of 0.1, 0.2, 0.5, and 1 g/L in parallel for 120 hours to evaluate the removal rate of 2,4,6-TCP. The removal rate of 2,4,6-TCP as a function of time was presented in 2-NH2 dosage on the anaerobic sludge activity in various anaerobic processes. 2-NH2 in anaerobic methanogenic process. The accumulative production of biogas of the seed granular sludge at 35°C was 268.1 mL in the AGS only system. When the anaerobic system was exposed to 50 mg/L of 2,4,6-TCP, the cumulative production of biogas decreased to 157.7 mL, far lower than the AGS only. The reason may be that chemical 2,4,6-TCP had a significant inhibitory effect on methanogenic activity of anaerobic microorganism, which affected the 2,4,6-TCP biological dechlorination and final mineralization by anaerobic microorganism. Maintaining a sufficient microbial activity was essential for a stable anaerobic treatment system on unfavorite condition. With increasing the dosage of NZVI@SiO2-NH2 from 0.1 g/L, 0.2 g/L, and 0.51 g/L to 1 g/L, obvious increase of the cumulative production of biogas in the integrated AGS/NZVI@SiO2-NH2 system during the operation was observed. And among these dosages of NZVI@SiO2-NH2, the cumulative production of biogas (0.2 g/L and 0.5 g/L) increased by 31.7% and 62.4%, respectively, more than that of the AGS system at 120 h. That is to say, the methanogenic activity of the seed sludge increased with the increasing dosage of NZVI@SiO2-NH2. The results implied that the activity of anaerobic microorganisms was significantly influenced by the function of the added iron nanoparticles. However, the results of cumulative biogas productions in anaerobic dechlorination processes with the different dosage of NZVI@SiO2-NH2 were largely different from the removal rate of 2,4,6-TCP  which might be attributed to increasing Fe2+ reducing ability. The result obviously indicated that the ferrous ion was not the major source on the removal of 2,4,6-TCP. When 50 mg/L to 200 mg/L Fe2+ was added to the AGS/NZVI@SiO2-NH2 system, the removal rate of 2,4,6-TCP decreased from 87.8% to 75.2%, indicating that the ferrous ion in the mixed system of NZVI@SiO2-NH2 and anaerobic sludge did not enhance the 2,4,6-TCP degradation. The higher content of Fe2+ had a negative effect on the 2,4,6-TCP degradation in the integrated AGS/NZVI@SiO2-NH2 system. Besides, 2+ had a significant inhibitory effect on methanogenic activity of AGS/NZVI@SiO2-NH2 system. The specific methanogenic activity decreased from 48.7 mL/g VSS d to 37.5 mL/g VSS d at 200 mg/L of Fe2+. This may be because higher concentration of Fe2+ (200 mg/L) decreased very quickly in the reaction system with a large amount of amorphous colloidal Fe(OH)3. These iron precipitates can be attached to the surface of iron nanoparticles and anaerobic granular sludge, which would decrease the reduction ability of iron nanoparticles and the activity of anaerobic microorganisms. Thus, this would further affect the degradation efficiency and methanogenic activity of anaerobic microorganisms. Besides, the concentration of Fe2+ gradually raised and remained at a certain concentration in this combined system  in the combined AGS/NZVI@SiO2-NH2 were remarkably adverse on the performance and methanogenic activity of AGS-NZVI@SiO2-NH2. The novel of modified nanoparticle could be an effective and promising material in the anaerobic treatment system for removal of CPs from industrial wastewater. However, further study should be carried out to control the efficiency and activity of microbial system in application of the novel NZVI@SiO2-NH2 in situ remediation of industrial wastewater.NZVI@SiO


Cell lines recapitulate cancer heterogeneity without the presence of interfering tissue found in primary tumor. Their heterogeneous characteristics are reflected in their multiple genetic abnormalities and variable responsiveness to drug treatments. In order to understand the heterogeneity observed in Asian gastric cancers, we have performed array comparative genomic hybridization (aCGH) on 18 Asian gastric cell lines. Hierarchical clustering and single-sample Gene Set Enrichment Analysis were performed on the aCGH data together with public gene expression data of the same cell lines obtained from the Cancer Cell Line Encyclopedia. We found a large amount of genetic aberrations, with some cell lines having 13 fold more aberrations than others. Frequently mutated genes and cellular pathways are identified in these Asian gastric cell lines. The combined analyses of aCGH and expression data demonstrate correlation of gene copy number variations and expression profiles in human gastric cancer cells. The gastric cell lines can be grouped into 2 integrative clusters (ICs). Gastric cells in IC1 are enriched with gene associated with mitochondrial activities and oxidative phosphorylation while cells in IC2 are enriched with genes associated with cell signaling and transcription regulations. The two clusters of cell lines were shown to have distinct responsiveness towards several chemotherapeutics agents such as PI3 K and proteosome inhibitors. Our molecular integrative clustering provides insight into critical genes and pathways that may be responsible for the differences in survival in response to chemotherapy. Gastric cancer is the second leading cause of cancer death worldwide, and is particularly common in East Asia Traditionally, classification of gastric cancers is based on histopathological findings. The widely used Lauren’s classification divides gastric cancers into two major histological types, namely intestinal type or diffuse type Cell lines formed the foundation of cancer biology and the quest for drug treatments. Common alterations in cell lines, which include gains and losses of entire arms of chromosomes, are often the same ones found in primary tissue Many Asian gastric cell lines are available commercially and they have contributed to the progress in gastric cancer biology and treatment. Despite being relatively homogenous and devoid of tissue complexity, these Asian gastric cell lines are found to have heterogeneous response or susceptibility to drug treatment . We reasoned that subtle genomic variations may contribute to the underlying differences observed among these gastric cell lines. Unlike tumor tissue, these differences would be intrinsic and a signature to the biology of the particular gastric cell line since there is no interference from other cell types.We analyzed the gene copy number and LOH in 18 Asian gastric cell lines. Coupling our results to gene expression profiles of these cell lines available from CCLE, we identified two distinct genomic signatures based on genetic aberrations among these Asian gastric cancer cell lines. The clustering was further validated by in vitro chemotherapy sensitivity study done in our lab and with data publicly available from CCLE and Sanger COSMIC databases. A molecular classification on gastric cancer patients would have great clinical impact as it leads to more accurate prediction of prognosis, allowing targeted therapy based on the underlying biology of each subgroup.Asian gastric cell lines SNU-1, SNU-5, SNU-16 and Kato-III, were purchased from the American Type Culture Collection. MKN1, MKN7, MKN45, MMKN74, Fu97, AZ-521, SCH, OCUM-1, NUGC-3, NUGC-4, IM95 and IM95 m were purchased from Japan Health Sciences Foundation. SNU-216 was purchased from Korea Cell Line Bank. YCC-3 6 cells were used for the aCGH hybridization. The genomic DNA quality, hybridization signal strengths and internal controls satisfied Affymetrix required standard at each step before proceeding to the next. Data from the 18 gastric cell lines on Affymetrix Cytoscan HD array were pre-processed using Affymetrix Chromosome Suite 1.2.2.271 using the single sample analysis workflow and default settings. Hidden Markov model segmentation was applied to call DNA copy number gain, loss or loss of heterozygosity (LOH) status. For matching purposes, the Entrez Gene ID was assigned to each gene name in this data set using the NCBI’s Entrez Gene Database CGH array was performed by Origen Labs (Singapore) using the Affymetrix CytoScan HD array platform. Cell pellets containing 1×10Affymetrix U133 Plus2 DNA microarray gene expressions of 27 gastric cancer cell lines  were downloaded from Cancer Cell Line Encyclopedia (CCLE) Unsupervised hierarchical clustering  was performed on the cell lines using the aCGH data. Putative driver genes of which copy number aberrations correlated to mRNA gene expression were identified to determine subtypes or clusters that are driven by different mechanisms. This was done using Mann Whitney U-test with p<0.05, and Spearman Correlation Coefficient test with Rho >0.6. We then performed consensus clustering ssGSEA was used to estimate pathway activities of the gastric cancer cell line in the Molecular Signature Database v3.1 (Msigdb v3.1) 50). P-values were computed from Mann Whitney U-test.A panel of 39 compounds against various cellular targets was obtained from Selleck Chemicals  and reduced secretion of gastric acid due to atrophic mucosa is observed in gastric cancer.We found that 72% of the Asian gastric cell lines have gene copy number gain in  kinases . AmplifiThe NCI-Nature Pathway Interaction Database Additionally, we investigated the correlation between copy number aberrations and gene expression data of these cell lines in the public domain. The integrated analysis of DNA copy number variations and corresponding gene expression data would allow identification of significant genes and cellular pathways critical to the gastric cancer pathophysiology. Of the 18 gastric cell lines that we have performed aCGH, only 14 cell lines have corresponding mRNA expression data in CCLE. CCLE has a total of 27 Asian gastric cell lines at the time of analysis (March 2013). We used Mann Whitney U-test and Spearman Correlation Coefficient test to identify 1762 putative driver genes of which copy number aberrations correlate to mRNA gene expression  were obtained from CCLE  . Similar50 ∼100 µM) towards XAV939, a tankyrase inhibitor Since we observed gene loss and LOH in the nuclear beta-catenin pathway, we postulate that targeting this pathway may have a therapeutic effect in gastric cancer Cells in IC1 have enrichment of genes associated with oxidative phosphorylation and mitochondria functions. We found that cells in IC1 are more resistant to proteosome inhibitors bortezomib and MG132 . ProteosInterestingly, we found that a subset of gastric cells within the IC1 are more sensitive towards the MEK/ERK inhibitor PD0325901  (p = 0.027), GSK269962A (Rho kinase inhibitor) (p = 0.019), and midostaurin (Flt3 and multiple kinase inhibitor) (p = 0.012).In support of our findings that the gastric cells in IC2 are enriched for genes involved in cell signalling, we found that cells in IC2 are generally more sensitive to kinase inhibitors than cells in IC1. Cell lines in IC2 are more sensitive to treatment with PI3 K inhibitors BEZ235, ZSTK424, PI-103 and PIK-75  . This reThe absolute magnitude of the differential drug sensitivities ranges from 2–10 fold in the gastric cell lines based on our clustering. The modest differences may still be clinically meaningful given the small therapeutic windows associated with cytotoxicity even in targeted chemotherapy. A large patient cohort study will be needed to confirm the value of the molecular clustering strategies by us or others in predicting chemosensitivity and prognosis.In conclusion, combination of aCGH and gene expression analysis to identify potential candidate oncogenes or tumor suppressor genes is a powerful and proven approach that has been reported in other cancer studies. This study provides insight into DNA copy number variations and their correlation to gene expression profiles in Asian gastric cell lines. A schematic diagram of the overall workflow is shown in Fig. S1 located in File S1Table S1: A panel of target-specific compounds for chemosensitivity study in the Asian gastric cell lines. Table S2: Putative driver genes selected using correlated genes between copy number aberration and mRNA gene expression . Table S3: Integrative cluster signature (Copy number and mRNA correlated genes). Table S4: Integrative cluster-specific pathway. Table S5: Compounds showing significant differences in sensitivity between the two integrative clusters of Asian gastric cell lines. Figure S1: A schematic diagram of the analysis workflow.(XLSX)Click here for additional data file.


MYCN oncogene, which is found in approximately 25% of cases (Huang and Weiss, The prognostic value of cancer stem cell markers in various cancer subtypes is a well documented research area. Our findings show that the stem cell marker Lgr5 is associated with an aggressive phenotype in neuroblastoma. Here, we discuss these findings within the context of recent studies in several cancers such as lung, colorectal and intestinal cancer, glioblastoma and ewing's sarcoma. Neuroblastoma continues to be an elusive disease, due to its heterogeneous presentation ranging from spontaneous regression to aggressive metastatic disease and intertwined genetic variability. Currently, the most significant prognostic marker of high risk disease and poor prognosis is amplification of the  Within recent years there has been an increased focus on cancer stem cells as important drivers in both cancer relapse and in chemoresistance, thereby determining cancer aggressiveness. Elucidation of the mechanisms of how cancers stem cells arise in conjunction with the identification of stem cell markers constitute fundamental steps in understanding oncogenesis, metastasis, and resistance to therapy which are often observed in high risk neuroblastoma   . Analysis was performed using the ABI Prism 7900 HT sequence detection system , gene expression was quantified relative to GAPDH.To investigate this potential link between elevated LGR5 expression and aggressiveness in neuroblastoma we performed Q-Real Time PCR to determine LGR5 mRNA expression in a panel of 11 neuroblastoma cell lines displaying a wide range of reported aggressiveness. Expression of LGR5 was quantified using Taqman® gene expression assay primers and probes p < 0.001)  = 20.20, p < 0.0001. Dunnett's post-hoc test revealed a significant difference in the expression of Lgr5 in cell lines SKSNH (p < 0.0001), SKNAS (p < 0.01), and BE2C (p < 0.05) relative to SHEP cells. The SHEP cell line was established from a primary tumor from patient it is a non-aggressive cell line that is MYCN non-amplified and p53 wildtype . To look for a correlation between cells harboring MYCN amplification, the cell lines were then categorized by their status. Although differences in the overall mean values were observed, no statistical correlation was observed between MYCN amplification status and elevated LGR5 expression  and (NCBI- Geo) show high expression of LGR5 in MYCN amplified tumors compared with non-amplified tumors (ANOVA p < 0.0001), high expression of LGR5 was also associated with disease recurrence/progression (ANOVA p = 0.0013) as well as stage 4 and 4s disease (ANOVA p = 0.02). Stage 4 disease is characterized by metastatic aggressive disease types whereas Stage 4s although metastatic at diagnosis affects infant >18 months (http://r2.amc.nl). Microarray data from the mutated ALK driven neuroblastoma mouse model showed an increase in LGR5 expression in aggressive MYCN amplified tumors as well as aggressive tumors with both MYCN amplification and mutated ALK which is in agreement with the data presented in our study that LGR5 is highly expressed in human cell lines derived from aggressive relapsed cell lines (http://www.ncbi.nlm.nih.gov/geoprofiles/101299025). This data further support the hypothesis that LGR5 may be a potential biological driver of disease progression and relapse in neuroblastoma.To further investigate the potential role of LGR5 in neuroblastoma publically accessible data bases were probed. Recent microarray data deposited in the R2 Neuroblastoma tumor database (Our study shows high LGR5 expression in SKNSH cells, which have been reported to be highly chemoresistant (Blaheta et al., Our findings are important and in agreement with Scannell et al.  who showMYCN amplified neuroblastomas (Balamuth et al., It is conceivable that there is a link between Lgr5 expression and maturation status of neural crest embryonic stem cells and that high LGR5 expression is associated with more aggressive phenotypes and increased relapse rate. Interestingly, in neuroblastoma, Balamuth et al.  identifiTaken together these studies indicate a potential role of LGR5 in neuroblastoma cancer stem cell development, warranting further investigation into this protein in this disease.MYCN amplified BE2C cell line, which previous studies reported to be the most aggressive cell type in the panel of cells under investigation (Keshelava et al., Our findings support a growing body of evidence associating LRG5 with aggressiveness in several cancer subtypes. Our investigation shows high expression of LGR5 in the In summary, we have shown elevated expression of LGR5 in a subset of neuroblastoma cell lines which are derived from patients at relapse and display an aggressive phenotype. These data, in combination with available microarray data study, which strongly support our findings, suggest a pressing need for further investigation of the role of LGR5 and other stem cell markers in tumor progression and as a prognostic biomarker in pediatric tumors such as neuroblastoma, as well as other cancer sub-types. Furthermore, these data imply that stem cell markers including LGR5 may be a potential biomarker for aggressive disease or important drivers of disease relapse and drug resistance. Importantly, we demonstrate that this is an important area of investigation and it is clear that a better understanding of the role of stem cell related genes is required. Further understanding would herald the potential for novel therapeutic intervention to increase patient survival for childhood tumors, particularly aggressive high risk neuroblastoma.University of Sunderland Research Beacon Grant.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.


Canine bladder cancer closely resembles human bladder cancer in many aspects. In this study, we isolated and characterized four primary transitional cell carcinoma (K9TCC) cell lines to be used for future in vitro were characterized by doubling time. The expression profile of cell-cycle proteins, cytokeratin, E-cadherin, COX-2, PDGFR, VEGFR, and EGFR were evaluated by immunocytochemistry (ICC) and Western blotting (WB) analysis and compared with established human bladder TCC cell lines, T24 and UMUC-3. All tested K9TCC cell lines were assessed for tumorigenic behavior using athymic mice in vivo.Four K9TCC cell lines were established from naturally-occurring canine bladder cancers obtained from four dogs. Cell proliferation rates of K9TCC cells Four established K9TCC cell lines: K9TCC#1Lillie, K9TCC#2Dakota, K9TCC#4Molly, and K9TCC#5Lilly were confirmed to have an epithelial-cell origin by morphology analysis, cytokeratin, and E-cadherin expressions. The tested K9TCC cells expressed UPIa , COX-2, PDGFR, and EGFR; however they lacked the expression of VEGFR. All tested K9TCC cell lines confirmed a tumorigenic behavior in athymic mice with 100% tumor incidence.The established K9TCC cell lines  can be further utilized to assist in development of new target-specific imaging and therapeutic agents for canine and human bladder cancer. Bladder cancer is the fourth most common cancer in men and the eighth most common malignancy in women in the US according to the ACS. An estimated 74,690 new cases of bladder cancers are expected to occur in 2014 in the US. An estimated 15,580 bladder cancer-related deaths will occur in 2014 in the US. Up to 7Canine transitional cell carcinomas K9TCC) closely resemble human invasive urinary bladder cancers. The uriTCC closeThere are numerous studies that show the correlation of the expression profiles of tumor markers in K9TCC with human TCC. The uroplakins are species-conserved integral membrane proteins that are present on the apical membrane of the terminally-differentiated superficial urothelial cells of normal bladder and preserving their expressions in neoplastic bladder TCC-15. On tin vitro. In addition, we confirmed tumorigenic behavior of all tested K9TCC cell lines using athymic mice model in vivo. New K9TCC cell lines might be used to further evaluate novel imaging and therapeutic agents for human and canine bladder cancers.The purpose of this study was to characterize primary K9TCC cell lines to better understand the mechanisms of canine and human bladder cancers. Here, we reported the characterization of four new primary K9TCC cell lines using Western blotting (WB) and immunocytochemistry (ICC) analysis Antibody for COX-2 was obtained from Cayman Chemical Corporation ; antibodies for vimentin, PDGFR, VEGFR, E-cadherin, cyclin D1, p27, p-ERK1/2, UPIa, actin, and secondary anti-goat were purchased from Santa Cruz Biotechnology ; antibodies for cytokeratin and Ki67 were obtained from Dako ; antibody for p65 (NF-κB) was purchased from BD Biosciences ; and antibody for EGFR, secondary anti-rabbit, and anti-mouse antibodies were obtained from Cell Signaling . All other chemicals and reagents were purchased from Thermo Fisher Scientific , unless otherwise specified.Human transitional cell carcinoma cell lines T24 and UMUC-3 were purchased from American Type Culture Collection . Human T-24 cells were maintained in DMEM:Ham’s F12 mixture (1:1) and human UMUC-3 cells were maintained in EMEM media; respectively, supplemented with 10% fetal bovine serum, 100 I.U. penicillin, and 100 μg/mL streptomycin. Cells were grown in an atmosphere of 5% CO2 at 37°C.Primary K9TCC cells were isolated from biopsy specimens obtained by cystoscopy from client-owned dogs diagnosed with bladder TCC. The cystoscopy was performed as a part of diagnosis with best practice of veterinary care through the Center for Minimally Invasive Procedures at the College of Veterinary Medicine of the University of Tennessee. Establishment of primary K9TCC cell lines procedure was in accordance with approved protocol by the University of Tennessee IACUC committee with client consent to use non-utilized tissue specimen for our research. The canine patients with bladder cancers had at time of diagnosis advance stages of TCC. The non-utilized biopsy tissues were washed, trypsinized (0.25% trypsin-EDTA for 2–5 min), and cultured in RPMI-1640 media with L-glutamine supplemented with 10% fetal bovine serum, 100 I.U. penicillin, and 100 μg/ml streptomycin in an atmosphere of 5% CO2 at 37°C for 24 hours. Colonies of epithelial cells identified under microscope were transferred into new culture dishes and expanded. K9TCC cells that progressed through 6 to 9 passages were characterized. K9TCC cells were cryo-preserved and recovered for tissue culture to confirm their viability. All cell lines have been maintained in the laboratory for longer than 15 passages.K9TCC cells were plated in triplicate in 6-well plates. Cells were trypsinized and counted using a hemocytometer 24, 48, and 72 hours after plating. The doubling time for the K9TCC cells was calculated using the formula dt = t X [ln2/ln(Ct/Co), where dt = doubling time, t = time between cell counts Ct and Co, Co = initial count, Ct = count after time t, and ln = natural log. Time (t) was expressed in hours.K9TCC cells were grown in RPMI-1640 media and allowed to reach 60-70% confluence. The morphology was examined under phase-contrast microscope with 20 × objective magnification  and images were captured using Moticam camera (VWR) with Motic 5.0 software.K9TCC cells were plated on 4-chamber slides  and cultured until they reached 80% to 90% confluence within 24–48 hours and followed the ICC protocol previously described. Cells wDissected tissues from athymic mice and primary tumor samples from dogs diagnosed with TCC were formalin-fixed and paraffin-embedded and sectioned at 7 μm. Hematoxylin and eosin (H&E), and IHC staining was performed following standard protocols. After d3VO4; 1 mM NaF)  and kept at -80°C until WB analysis performed as previously described[Human and K9TCC cells were cultured in media with or without serum for 24 hours. After incubation, the cells were lysed in ice-cold RIPA buffer supplemented with protease and phosphatase inhibitors cocktail  to confirm tumorigenic behavior of tested TCC cells. Human UMUC-3 cells were used as positive controls (3×106 cells/mouse with 1:1 Matrigel/PBS). The tumors lengths were measured by a digital caliper once per week for 3 weeks. After 3 weeks, the xenograft K9TCC tumors were dissected from mice, fixed, and evaluated by IHC.All animal studies were performed in accordance with approved protocols by the UT IACUC committee as previously described in details. The priPrimary K9TCC cell lines were established from four female dogs with confirmed diagnosis of urinary tract TCC. The canine patients with bladder cancers already had advance stages of TCC at the time of diagnosis.Primary K9TCC#1Lillie cell line was established from biopsy sample obtained from urethra of a 16-year-old female Pointer dog. The representative histology of the K9TCC#1Lillie by H&E staining is shown in Figure Primary K9TCC#2 Dakota cell line was established from biopsy sample of urinary bladder of a 13-year-old female Bichon Fries dog. The representative histology of the K9TCC#2Dakota by H&E staining is shown in Figure Primary K9TCC#4Molly cell line was established from biopsy sample of urinary bladder of a 10-year-old female Maltese dog. Unfortunately, the immunohistochemistry analysis of above mentioned markers were not performed in K9TCC#4Molly due to an insufficient size of biopsy sample obtained during the cystoscopy.Primary K9TCC#5Lilly cell line was established from biopsy sample of urinary bladder of a 13-year-old female mixed-breed dog. The representative histology of the primary tumor of K9TCC#5Lilly by H&E staining is shown in Figure Cell proliferation of established primary K9TCC cells was further characterized using doubling time. The doubling time of the K9TCC cell lines was calculated by counting trypsinized cells each 24 hours for 3 days. The doubling time (dt) for K9TCC#1Lillie was dt = 47.4 hours . The average age of the affected dogs with spontaneous cancers is 8.4 years, which corresponds to an average age of 50 years for humans. This suggests that as in humans, spontaneous carcinomas in dogs are influenced by age and environmental factors. Cancer heterogeneity and the ability to study the responses of naturally occurring cancer to therapy in a timely manner are further advantages of a canine model[Dogs with spontaneous tumors are still an underexploited tool to make rapid advances in human cancer prevention, diagnosis, and therapy. Dogs with naturally occurring cancers provide an important step for successful translation of novel imaging and therapeutic agents from rodents to human clinical applications . The decision of which model of cancer to use depends on the stage of drug discovery. However, the final proof of concept for efficacy and safety of novel therapeutic and imaging drugs lies in humans.The animal models  that recapitulate the nature of human cancers are major prerequisite for rapid bench-to-bedside translation of novel anti-cancer drugs and imaging agents that showed promised in cancer cells Spontaneously occurring cancers in pets share similar molecular and clinical characteristics with human cancers. Four new primary K9TCC cell lines  were characterized as epithelial-cell origin with confirmed cytokeratin, E-cadherin, and UPIa expressions. The positive expression of COX-2, PDGFR, and EGFR markers were also confirmed using ICC and WB analysis. The established primary K9TCC can be used to test novel targeted imaging and therapeutic agents for bladder cancers in dogs and people.COX-2: Cyclooxegenase-2; EGFR: Epidermal growth factor receptor; ERK: Extracellular regulated kinase; H&E: Hematoxylin and eosin; ICC: Immunocytochemistry; IHC: Immunohistochemistry; K9TCC: Canine transitional cell carcinoma; PDGFR: Platelet-derived growth factor receptor; RTK: Receptor tyrosine kinases; TCC: Transitional cell carcinoma; UPIa: Uroplakin Ia; VEGFR: Vascular endothelial growth factor receptor; WB: Western blotting.The authors have no competing interest.in vivo mince experiments; performed the statistical analysis; and drafted the manuscript. MC conceived and designed the study; performed part of IHC staining; performed in vivo mice experiments; acquired digital images of IHC and ICC staining; and assisted with writing of manuscript. Both authors have read and approved the final version of the manuscript.KR performed the laboratory experiments; performed WB, ICC, and cell proliferation assays; acquired images of cells in culture; assisted with The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/14/465/prepub


The crisis of Misidentified and contaminated cell lines have plagued the biological research community for decades. Some repositories and journals have heeded calls for mandatory authentication of human cell lines, yet misidentification of mouse cell lines has received little publicity despite their importance in sponsored research. Short tandem repeat (STR) profiling is the standard authentication method, but it may fail to distinguish cell lines derived from the same inbred strain of mice. Additionally, STR profiling does not reveal karyotypic changes that occur in some high-passage lines and may have functional consequences. Single nucleotide polymorphism (SNP) profiling has been suggested as a more accurate and versatile alternative to STR profiling; however, a high-throughput method for SNP-based authentication of mouse cell lines has not been described.We have developed computational methods  for cell line authentication and copy number analysis based on a cost-efficient SNP array, and we provide a reference database of commonly used mouse strains and cell lines. We show that CLASP readily discriminates among cell lines of diverse taxonomic origins, including multiple cell lines derived from a single inbred strain, intercross or wild caught mouse. CLASP is also capable of detecting contaminants present at concentrations as low as 5%. Of the 99 cell lines we tested, 15 exhibited substantial divergence from the reported genetic background. In all cases, we were able to distinguish whether the authentication failure was due to misidentification , the presence of multiple strain backgrounds (five cell lines), contamination by other cells and/or the presence of aneuploid chromosomes (nine cell lines).Misidentification and contamination of mouse cell lines is potentially as widespread as it is in human cell culture. This may have substantial implications for studies that are dependent on the expected background of their cell cultures. Laboratories can mitigate these risks by regular authentication of their cell cultures. Our results demonstrate that SNP array profiling is an effective method to combat cell line misidentification.The online version of this article (doi:10.1186/1471-2164-15-847) contains supplementary material, which is available to authorized users. For decades, misidentified and contaminated cell lines have been a high-profile cause of wasted research effort and funding, and false claims in the literature –3. In reMultiplex short tandem repeat (STR) profiling is the current standard for authentication of human cell lines , and alsWe genotyped 117 samples from 99 commonly used mouse-derived cell lines  were significantly higher  and less variable  than for cell line samples .Considering only the 6,212 SNP markers in common between the two arrays, reference samples had a mean call rate of 94.6%. As expected, call rates varied widely (range: 52.1% – 99.5%) and were dependent on the specific and subspecific origin of the sample  Additio. When coWe measured genotype reproducibility as the fraction of markers that was fully consistent across replicates. The mean reproducibility was 0.968  and 0.986  for MUGA and MegaMUGA, respectively. We chose the conservative value of 0.032 for the error rate that we used in Probability of Incorrect Assignment (PIA) computations (see Methods); however, we expect that the true error rate is much lower (<0.001) when considering only the markers that pass the strict quality thresholds described below.r2 < 0.25) or a different strain distribution pattern (SDP) from any linked marker. This yielded a set of 3,552 high-quality informative markers. The markers were evenly spaced across the entire autosome. Inter-SNP distances followed a Poisson distribution with median of ~500 kb . The 11 pairs with fewer than 10 differences consisted of groups of substrains , which only differ due to genetic drift, and two wild M. m. castaneus mice (IN17 and IN47) that were trapped at nearby sites. We note that the ability to differentiate between closely related substrains was a key consideration in the design of the MegaMUGA array. Using the full set of MegaMUGA markers, we compared two or three different substrains from each group that our assay had trouble differentiating and found that any pair differed at a minimum of 45 markers . Instead, we identified a subset of 1,652 markers that were consistent across replicates of outbred lines. The distribution of alignment scores for comparisons between the outbred stocks and the inbred/wild mice  suggest that alignment scores of 0.96 or greater are indicative of identical samples. We found that two-thirds of cell line samples matched a reference sample with an identity of at least 0.96  that appear to have been backcrossed prior to derivation of the cell line Figure . This leThom = 0.02 and Thet = 0.46).A cell culture that is contaminated by cells of a different genetic background Figure A can be r = 0.988).We modeled the effect of contamination at different proportions on allelic ratios using a dilution series (ratios from 1:1 to 200:1) between Phoenix (an uncontaminated cell line of 129S6/SvEvTac origin) and a feeder cell line of unknown origin (which we treat as the contaminant). We found that the pairwise alignment scores between the pure Phoenix cells and the mixed samples decreased exponentially with the concentration of the contaminant , the alleles on the duplicated chromosome will be present at twice the frequency as those on the unduplicated chromosome; therefore, at heterozygous markers, a 2:1 ratio would be observed (BAF = 0.33 or 0.66). Detection of copy number variation in cell lines is complicated by two factors. First, a cell culture may be heterogeneous for a copy number variant, leading to a less intense signal than if the variant was fixed. A variant that is present only in a small fraction of cells may not produce a change in the intensity signal that is distinguishable from noise. Second, genomic regions in cells that have not undergone G1 arrest prior to DNA extraction may vary in the number of alleles depending on the cell cycle phases they are undergoing.p = 0.02, Figure We used the genoCNA algorithm  to identWe verified all of the predicted CNAs by visual inspection of intensity plots Figure . In mostOf the nine unmatched cell lines, five had multiple obvious copy number change events . These five lines were also those that had the least evidence for contamination. Two of these were derived from tumors (Ehrlich-Lettre Ascites Strain E and Y-1), and so the presence of aneuploidy was not surprising. The other three lines  were derived from primary tissue, thus the observed aneuploidy likely occurred in culture. In summary, the combination of genotype and intensity-based analysis enabled us to discriminate between multiple possible reasons for failures to verify cell line backgrounds.We have compiled a database of SNP profiles for hundreds of commonly used mouse inbred strains and cell lines based on the MUGA genotyping array, which is commercially available and affordable (~US$100). The current version of the array (MegaMUGA) has an order of magnitude greater density than the initial version. Although data from both arrays are compatible, regenotyping of the samples used in this study on MegaMUGA would provide at least 1–2 orders of magnitude greater discriminatory power. We provide software (CLASP) that includes functions to manage the genotype database, perform fast and accurate identification of strain background and authentication of cell line identity, and recognize contaminants and CNAs. Although we developed this method for the purposes of cell line authentication, we expect that it will have other applications, such as the monitoring of mouse stocks used in biomedical research  and foreWe found that cell lines have higher call rate variability and lower reproducibility compared to matched reference samples. This variability may be due to karyotypic rearrangements or other mutations accumulated in culture, which can alter hybridization intensities  and thusWe found that, while most cell lines correctly matched their reported strain(s) of origin (98/99), a substantial fraction of cell lines had evidence of cross-contamination and/or aneuploidy. Our finding that 42% of cell lines tested showed evidence of aneuploidy Figure  was consWe acknowledge that there are inherent limitations in applying to our data set algorithms that were designed to model human tumor data sets of 100,000 or more markers. First, the relatively low density of the MUGA platform amplified the effect of noise. The genoCNA algorithm identified many more transitions between different copy number regions in cell lines genotyped on MUGA than in the same cell lines genotyped on MegaMUGA. Second, mouse cell lines may be derived from inbred strains, which have significantly greater homozygosity than what is expected in humans. Third, no algorithm is currently capable of simultaneously modeling sample heterogeneity, cross-contamination and copy number aberration, all of which may be present in a non-clonal cell culture. While the output from genoCNA corresponded well with our visual scoring of intensity plots Figure , we did Misidentification and contamination of mouse cell lines is potentially as widespread as it in human cell culture. This may have substantial implications for studies that are conditioned on the expected genetic background of their cell cultures. Laboratories can mitigate these risks by regular authentication of their cell cultures. Our recommendations for future use of SNP profiling in cell line authentication are as follows. Laboratories should test their cell cultures periodically and report on changes that occur between passages. Ideally, a central database will be maintained and made accessible using the client–server capabilities of CLASP. Database maintainer(s) should obtain multiple samples of each cell line from independent labs and determine the natural variability in each cell line in order to establish cell line-specific thresholds for identity. Journals and funding agencies should require proof of authenticity for each cell line used in a study as a prerequisite for consideration. In addition, we encourage increased study of the functional impact of aneuploidy and structural variation in cell lines. While establishing the authenticity of cell lines by genetic means is an important and necessary step in establishing the validity of research findings, we hypothesize that it may not be sufficient and that verifying the structural integrity of the genome may also be necessary.With the wide availability of STR- and SNP-based profiling, the scientific community has to the tools to resolve the long and hard-fought campaign to end the “scandalous” use of misidentified human cell lines . We expeWe obtained 117 samples from 99 different cell lines in pelleted form . Briefly, cell pellets were incubated overnight in 300 μl of lysis buffer at 65°C. The supernatant was transferred into a mixture of 300 μl of isopropanol and 0.5 μl of glycogen and DNA was isolated by centrifugation. After discarding the supernatant, the DNA pellet was washed in 70% ethanol and then resuspended in deionized water, vortexed and incubated at 55°C for one hour. The DNA concentration was determined using an ND8000 (Nanodrop) and adjusted to between 50–150 ng/μl. Samples were then randomized, and 10 μl from each sample was loaded into a 96 well plate for genotyping.http://neogen.com) as previously described  and are normally distributed (with standard deviation determined by the sample noise) around 0, 0.5 and 1 when alleles are present in the expected ratios of 2:0, 1:1 or 0:2 for AA, AB and BB genotypes, respectively. When a contaminant introduces additional alleles and has a different genotype than the host sample, it changes the allelic ratio. If the degree of contamination is high enough, the deviation can be distinguished from background noise.Thom and Thet representing the normal range of homozygous and heterozygous BAF values, we first transform the BAF value for each marker. Next, we compute the deviation of each sample from the expected BAF distribution as the sum of the transformed, non-zero BAFs divided by the total number of markers (M):Given thresholds We use the genoCNA function of the genoCN R package  to identhttp://cran.r-project.org). Supporting data has been deposited in the figshare repository (http://dx.doi.org/10.6084/m9.figshare.1185417).The CLASP R package is available in CRAN  results of analysis by 611 CLASP and 2) column annotations for the first worksheet (XLSX 116 KB)Additional file 1:Analysis of 503 reference samples. Four worksheets list reference samples obtained from 1) inbred strains, 2) outbred stocks, 3) F1 hybrids between two inbred strains and 4) wild-caught mice. A fifth worksheet summarizes the level of consistency between biological/technical replicates. (XLSX 89 KB)Additional file 2:Call rates depend on type and taxon of samples. Heterozygous (H) call rate (x-axis) and No-call (N) rate (y-axis) out of 6,212 markers for 620 samples. Color represents sample type: primary (blue) or cell line (orange). Shape represents sample taxonomy: M. musculus (circle) or other (square). Inset: primary samples have higher variability in H call rate, but generally lower N call rates (excluding non-M. musculus samples). (PDF 3 MB)Additional file 3:Overview of CLASP software. Closed rectangle: processes; cylinder: database; open rectangle: input data sets; trapezoid: output data set. (PDF 46 KB)Additional file 4:MUGA has sub-chromosome resolution to detect contamination and copy number variation. Density plot of distances between all adjacent pairs of 3,552 informative markers. Most inter-SNP distances are < 2 Mb, meaning MUGA can detect contaminations and copy number variants on the order of tens-of-megabases. (PDF 240 KB)Additional file 5:Most MUGA marker pairs are unlinked. Histogram of r2 values for all adjacent pairs of 3,552 informative markers. Typical thresholds used to classify linkage disequilibrium are 0.3 – 0.7. (PDF 185 KB)Additional file 6:Most reference samples are uniquely identified by SNP profile. Heatmap of pairwise comparisons between reference samples. Each point represents the number of genotype differences  between the pair. (PDF 140 KB)Additional file 7:Pairwise comparison of sister strain differences in MegaMUGA genotypes.(XLSX 38 KB)Additional file 8:Outbred stocks are genetically distinct from inbred strains. Histogram of pairwise alignment scores between nine outbred individuals and 156 inbred strains. (PDF 5 KB)Additional file 9:Cell lines from introgressed backgrounds have non-random differences from best matching reference sample. Physical locations of markers for which cell line genotypes don’t match the reported strain of origin for A) CAKB3, a cell line derived from an animal of a mixed  genetic background, and B) W4129, an apparently contaminated cell line. (PDF 2 MB)Additional file 10:Alignment score is negatively correlated with level of contamination. Top panel shows relative concentrations of Phoenix cell line (blue) and a contaminating feeder line (yellow) in a dilution series experiment. Bottom panel shows H (gray) and N (green) call rates (left y-axis) and alignment scores  between the mixtures and the pure Phoenix cell line sample . (PDF 326 KB)Additional file 11:BAF deviation accurately predicts fraction of contaminant. A) In the dilution series, there is a direct relationship between the fraction of contaminant (i.e. the feeder line) and the shift in BAF from the expected distribution. B) A model derived from the dilution series (red circles) enables prediction of the fraction of contamination in cell lines genotyped on MUGA (blue diamonds) and MegaMUGA (green squares) based on BAF deviation. (PDF 300 KB)Additional file 12:


We detected frequent mutations, high expression and high copy number variations of certain genes in both drug resistant cell lines and sensitive cell lines. It was observed that a few drugs, like Panobinostat, are effective against almost all types of cell lines, whereas certain drugs are effective against only a limited type of cell lines. Tissue-specific preference of drugs was also seen where a drug is more effective against cell lines belonging to a specific tissue. Genomic features based models have been developed for each anticancer drug and achieved average correlation between predicted and actual growth inhibition of cell lines in the range of 0.43 to 0.78. We hope, our study will throw light in the field of personalized medicine, particularly in designing patient-specific anticancer drugs. In order to serve the scientific community, a webserver, CancerDP, has been developed for predicting priority/potency of an anticancer drug against a cancer cell line using its genomic features ( Due to advancements in the field of sequencing technology, whole genome of different types of tumor cells have been sequenced. This flood of genomic information of tumors has broadened our understanding and provided valuable insights related to molecular and genetic characteristics of cancer types14in silico models to predict response of cancer cell lines to anticancer drugs. Papillon-cavanagh et al., have used two large pharmacogenomics datasets derived from CGP, and CCLE, to build and validate genomic predictors of drug responseet al., have developed machine learning models to predict the response of cancer cell lines to drug treatmentet al. have focused only on homozygous deletions from chemogenomics data of TCGA and CCLE, which may lead to therapeutic vulnerabilities specific to cancer cell as compared to normal cellet al. utilized the CCLE data for predictive modeling of tumor sensitivity of anticancer drugs based on integrated functional and genomic characterizationset al. fitted models for whole genome expression for three drugs , using large panel of cell lines from CCLE databaseIn the recent past, few large-scale pharmacogenomics studies, namely the cancer genome project (CGP)in silico models using various techniques for all 24 anticancer drugs  shows highest difference, it has 38.6% higher frequency of mutation in drug resistant (PF2341066) cell lines as compare to sensitive cell lines  for each gene . In othell lines . It is iWhile focusing on different biologically relevant functions, we found that DNA damage related proteins like MSH3 and UBR5 genes have >= 12% higher frequency of mutation in TAE684 resistant cell lines as compare to sensitive cell lines . SimilarPRKCB  has 35.7% higher frequency of variation in AZD0530 resistant cell lines as compared to sensitive cell lines (SLC22A3 (Solute Carrier Family 22) in drug transmembrane transport activity genes showing higher frequency of variations in TAE684 (18%) and Paclitaxel (13%) resistant cell lines as compared to sensitive cell lines, respectively  and KDM6A  show as much as 16% more variations in RAF265 and AZD6244 sensitive cell lines, respectively  and PLK3 (polo-like kinase 3) were found to be harboring more variations (21% and 18% respectively) in TAE684 resistant cell lines than sensitive ones  may lead to Topotecan sensitivity (CCND1 (Cyclin D1) and CDC14B (Cell Division Cycle 14B) have higher average expression in PD0325901 resistant cell lines. EYA1 (Eyes Absent Homolog 1) was also found to have higher average expression in PD0325901 sensitive cell lines (ERBB2 and ERBB3 show high average expression in Topotecan sensitive cell lines (EFNA1 (Ephrin-A1), ERRFI1 (ERBB Receptor Feedback Inhibitor 1), LATS2 (Large Tumor Suppressor Kinase 2) and PLK2 (Polo-Like Kinase 2) also show higher average expression in PD0325901 resistant cell lines and thus may be contributing to PD0325901 resistance  in AZD0530 resistant cell lines as compare to sensitive . Similarsitivity . Moreovell lines . Among tll lines . The tumsistance .In order to understand genomic factors responsible for drug resistance, we group drugs based on biological mechanisms. All the 24 drugs can be broadly divided in three categories; (a) Kinase inhibitors, (b) Cytotoxic drugs and (c) other targeted therapies. These kinase inhibitors can be further divided based on inhibition of a kinase like EGFR inhibitors, c-MET inhibitors, ALK inhibitors, Raf kinase B Inhibitor, MEK1 and MEK2 Inhibitors and Multi-kinase inhibitors. The group of cytotoxic drugs includes inhibition of DNA Topoisomerase-I and Beta tubulin. Other targeted therapies involved in inhibition of MDM2, HSP90, Gamma Secreatase and HDAC. We analyzed mutation/variation of genes in both drug resistant and sensitive cell lines  and S12.Similarly, we examined the expression and CNV of different class of inhibitors and identified genes (top 10 and bottom 10 genes) that exhibit maximum correlation between expression/CNV and IC50 . Here welight (http://svmlight.joachims.org/).In order to develop models for prediction drug prioritization, we developed support vector machine (SVM) based models. In this study, we implemented SVM based regression models using popular software package SVMSince the machine learning approaches required fixed length data, therefore, we took the mutation in a gene in the form of binary where mutated and wild type gene was presented as 1 and 0 respectively. Since the number of features or the vector size for mutational input was 1650, we used cfsSubsetEval algorithm of WEKA package to reduce the number of featuresSimilar to the mutational feature, we have taken general variations in a gene as binary input for developing models. The feature selection methods cfsSubsetEval and F-stepping reduced the average number of required genes per drug up to 54 and 25 respectively. After F-stepping filtering on variation data, model development for 24 drugs required a total of 425 unique genes . As per SVM-based models developed on gene expression as input features performed very well for all the 24 drugs. The initial feature selection with cfsSubsetEval algorithm reduced the average number of required genes for model development up to 66. The total number of unique genes required for 24 drugs, were 1296 in number . We furtSince the copy number variation (CNV) deals with alteration of genome with large regions resulting in deletion or duplication of many genes, we have also taken CNV as one of the features for predictive modeling. The average number of genes required for all 24 drugs were 45 and 21, before and after F-stepping respectively. After F-stepping, the final models required a total of 877 unique genes . With anIn order to improve performance of our predictive models, we used hybrid features. In case of hybrid features, we combined two or more than two types of genomic features. First, we generated hybrid features by combining genomic features based on mutation, expression and CNV. These hybrid features were reduced using feature selection techniques cfsSubsetEval and F-stepping and achieved average number of genes up to 80 and 34 respectively . These r50 between mutated and normal cell lines for a given gene. For this, we minimized the number of drugs to as few as 7 drugs, which led us to 30 genes with significant difference  between IC50 of mutated and normal cell lines. Further, we performed feature selection on significant genes, by F-stepping. Our SVMlight based models achieved a maximum correlation of 0.22 using selected features , NCI-60 and Cancer Genome Project. As shown in In order to serve the scientific community working on cancer biology, we have developed a user-friendly webserver for predicting growth inhibition of anticancer drugs against a cancer cell using its genomic features. In addition, various useful tools are also integrated which assist users in identifying most effective drugs. Brief description of major modules is given follows:This module is built on the basis of our machine learning models developed based on mutations, variations, expressions, CNVs and their hybrid as input features. User may submit related genomic information or raw NGS data (VCF/ANNOVAR input file)50 > 0.5 μM) and sensitive (IC50 <= 0.5 μM) cell lines for each of 24 anticancer cells. Then, we developed modules based on various genomic features as shown below.This module has been built in order to find out the contribution of each gene in drug prioritization. This module is based on probabilistic approach. First, we have divided the cell lines into resistant  of finding the mutations/variations in a particular gene in both resistant and mutant cell lines. User has to enter the HUGO symbol of mutated/variant gene(s) and this module will return the probability values of finding mutations/variations in both resistant and sensitive cell lines.In these modules, we have calculated the average expression/CNV of a particular gene in resistant and sensitive cell lines together with their differences  for 24 anticancer drugs. By looking at these values user can identify, which drug will be more effective for cell line having high/low expression/CNV of the query gene.50 of a drug for each gene in mutant & wild cell lines to understand effect of mutation on drug sensitivity or resistance. In case of expression, we computed average expression of each gene for resistant & sensitive cell lines for a given drug.The purpose of this module is to identify important genes corresponding to each drug. In case of mutation, we computed average ICIn these modules, server displays each gene used in this study and average growth inhibition of selected drug in cell lines contains mutated and wild form of a gene. This module also displays difference in growth inhibition with significance  in difference. These modules provide comprehensive information about a selected drug in terms of effect of mutation or variation of a gene on its growth inhibition.The aim of this tool is to find out the difference between average expression/CNV of a particular genes in resistant as well as sensitive cell lines. It allows the user to select a desired drug; then these modules display a list of all genes with their average expression/CNV in resistant and sensitive cell lines. These modules are very useful in understanding effect of expression/CNV of a gene on growth inhibition of a drug.In the era of high-throughput technologies, several efforts have been made to unveil the complex relationship between the genetic features and the drug response. In this direction, Broad Institute carried out a magnificent study, where they generated huge amount of pharmacogenomics data. This study paved the path for researchers to understand relation between genomic features of a cell and growth inhibition abilities of a drug on this cell. In this study, we also used this data for identification important genomic characteristics responsible of drug resistance. In addition, we also developed prediction models for predicting growth inhibition of each anticancer drug against a cell lines from genomic features of cancer cell line. As shown in Results section, certain drugs are highly promiscuous as they kill almost all types of cell lines. It seems that their growth inhibition ability was hardly affected by alteration of genomic profile of cells. In contrast, drugs like Erlotinib, LBW242, Nutlin3, PD0332991 are effective against very limited types of cell lines; resistant to most of cell lines.SMARCA4 gene have been associated with lung cancer, medullablastoma and pancreatic cancers etc.1617We examined the tissue-specific response of different drugs and observed that certain drugs are tissue specific where they inhibit almost all cell lines belongs to specific tissue. As shown in in vitro studies have shown a positive correlation between increased expression of CCND1 and resistance to Cisplatin in head, neck and colon cancer cells21Similarly, the difference in the average gene expression of resistant and sensitive cell lines may be associated with drug resistance; for example ATP8B1 belongs to transport proteins, have a higher average expression in PD0325901 resistant cell lines as compared to sensitive cell lines. ATP8B1 (ATPase class I type 8B member 1) is an ATP-dependent aminophospholipid transporter, which has a major role in the transport of endogenous chemicals across the biological membranes19Our study with grouped drugs based on underlying mechanism, lead us to different genomic factors associated with drug resistance. As shown in the results that PDE4DIP mutation is more frequent in cMET inhibitors, which means mutations in PDE4DIP may lead to resistance towards cMET inhibitors. Similarly, DUSP6 and ETV4 expression showed a negative correlation with MEK1/2 inhibitors and thus an increased expression of these genes may lead to MEK1/2 inhibitors’ resistance. Previous reports of genome wide screening for biomarkers for MEK1/2 sensitivity also reported DUSP6 and ETV4 as markers for MEK1/2 sensitivity23We made an attempt to develop prediction models based genomic profile of cell lines. After various feature selection techniques, the machine learning-based models for each drugs required very few number of genes for prediction . Among the selected genes for model, some of the genes were targets for that particular drug. One of the major problems in developing prediction model is identification of features or biomarkers. Unfortunately, there is no clear-cut biomarker, which can discriminate drug resistant and sensitive cell lines. If we develop models using large number of features then it has limited use in real life. Ideally models should be developed using very limited features. In this study we have used cfsSubsetEval algorithm and F-stepping techniquehttp://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-36139/). Similarly normalized CNV data for 21217 genes of 418 cell lines was obtained from Broad institute’s CCLE portal (http://www.broadinstitute.org/ccle/home). The drug sensitivity information for 504 cell lines was captured from CCLE browse data section. The data includes IC50 (μM) for 24 anticancer drugs on 504 cell lines. Out of these 24 anticancer drugs, 9 drugs are launched, 2 are in phase III trial, 3 are in phase II trial and 7 are in preclinical trial.In this study, we used CCLEIn order to understand the role of gene mutation in drug resistant cell lines, we computed biasness of mutated genes in sensitive and resistant cell line. In order to compute biasness, we computed difference in fraction of mutant in resistant and sensitive cell lines. Firstly, we computed total number of resistant and sensitive cell lines of each drug. Secondly, we computed fraction of cell lines having mutated gene for a given gene. Following equation has been used for computing difference in fractionfres is the fraction of number of resistance cell lines having mutated genes with total number of resistant cell lines. Similarly, fsen is the fraction of number of sensitive cell lines having mutated genes with total sensitive cell lines.where, 50 and gene expression or copy number variation.Similar approach has been used in case of variation for assessing role of variation in drug resistant. We computed difference in fraction of variant gene in resistant and sensitive cell lines using per se, we had to convert those into binary format. The mutated gene and normal gene (not mutated) were represented as ‘1’ and ‘0’ respectively. The binary (1 or 0) status for every gene was used as machine learning input. For example mutated gene G1 and normal gene G2 were presented as ‘1’ and ‘0’ respectively.It is well known that mutations play important role in cancer progression and also affects the sensitivity of drug molecules. Thus, we used mutation as an input features for developing prediction model using machine-learning approaches. The mutations in a gene were given in IUPAC nomenclature standard, as a mutation annotation file (MAF). Since the IUPAC mutation annotations, cannot be used in machine learning as input feature As we know that the rate of variation is high in cancerous cells and among these raw variations, some rare deleterious alterations are called mutations. We, for the first time, have considered general variations as players in the drug sensitivity. The presence and absence of such variations was taken as binary input and represented as ‘1’ and ‘0’ respectively. For example, gene G1 having variation was presented as ‘1’ and gene G2 not having variations was presented as ‘0’.The expression profile of 488 cancer cell lines was obtained from CCLE database. The authors of CCLE database obtained the mRNA expression data using Affymetrix Human Genome U133 Plus 2.0 arrays as per the manufacturer’s instructions. The background correction was accomplished by RMA (Robust Multichip Average) and quantile normalization. The provided expression values were given in log2 of expression value. In this study we used these normalized value of expression as input vector for developing prediction model.Similar to the expression data, CNV information for 418 cell lines was obtained from CCLE database. According to the authors, the raw Affymetrix CEL files were converted to a single value for each probe set representing a SNP allele or a copy number probe. CNV values are given in log2 of ratio of copy numbers of cancer vs. normal gene, where positive and negative values denote increase and deletion in copy numbers of the gene respectively. These values were used as input features for developing SVM based prediction model.50, are selected. We applied this algorithm through WEKA [Version 3.6.6]. We applied cfsSubsetEval algorithm on whole mutation, variation, expression, CNV and hybrid data.This algorithm calculates the significance of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. Finally, subsets of features, which are having low inter-correlation and are high correlation with the ICThis method has been applied in the past for feature selection50 with expression of genes. We selected top 50 most correlated genes irrespective of positive or negative relation. We used expression of these genes as input feature for machine learning methods.To reduce the number of features we computed the correlation between IC50 between mutated cell lines and not-mutated cell lines for a particular gene.In case of mutation of genes, where the mutations were either present (denoted as 1) or mutations were absent (denoted as 0), we selected those genes which were showing significant difference  of ICIn addition to the list of mutated/variant genes as described above, we have provided the option of submitting raw Variant Calling Format (VCF) file, which is one of the most commonly used format for storing mutations/variations after NGS. Since the size of VCF files may be problematic in uploading because low speed internet network, we have also provided option for submitting ANNOVAR input file. VCF file can be easily converted to ANNOVAR input file by given link of downloadable PERL script. The smaller sized ANNOVAR file can easily be uploaded via internet. The ANNOVAR input or VCF submission files are processed with ANNOVAR package (Version Revision: 527). The steps include the processing of ANNOVAR input/VCF file, extraction of annotated variation in different genes, filtering of SNPs .As shown above each type of model has their own merits and demerits, thus we implement all models in our web server. This will allow users to select best model for predicting sensitivity of drugs. In this study, for the first time, we also made an attempt to develop a webserver-based anticancer drug prioritization tool, which is a initial step towards personalized drug therapy for cancer. Scientific community can used our webserver at least for studying drugs on cancer cell lines .


Veliparib with cisplatin or etoposide or with both cisplatin and etoposide showed greater delay in tumor growth than chemotherapy alone in H146 but not H128 xenografts. The potentiating effect of veliparib was associated with in vitro cell line sensitivity to cisplatin  and DNA-PKcs protein modulation. Gene expression profiling identified differential expression of a 5-gene panel  in cell lines with relatively greater sensitivity to platinum and veliparib combination. Veliparib potentiates standard cytotoxic agents against SCLC in a cell-specific manner. This potentiation correlates with platinum sensitivity, DNA-PKcs expression and a 5-gene expression profile.Poly (ADP) ribose polymerase (PARP) plays a key role in DNA repair and is highly expressed in small cell lung cancer (SCLC). We investigated the therapeutic impact of PARP inhibition in SCLC. In vitro cytotoxicity of veliparib, cisplatin, carboplatin, and etoposide singly and combined was determined by MTS in 9 SCLC cell lines . Subcutaneous xenografts in athymic nu/nu mice of H146 and H128 cells with relatively high and low platinum sensitivity, respectively, were employed for in vivo testing. Mechanisms of differential sensitivity of SCLC cell lines to PARP inhibition were investigated by comparing protein and gene expression profiles of the platinum sensitive and the less sensitive cell lines. Veliparib showed limited single-agent cytotoxicity but selectively potentiated (≥50% reduction in IC Absorbance was measured at 490 nm using a microplate reader . The 50% inhibitory concentration (IC50) of the cytotoxic agent in the absence and presence of veliparib was determined using GraphPad Prism software . Combination Index (CI) analysis for drug interaction  could not be performed due to the limited activity of veliparib as a single agent in the tested cell lines. For accurate estimation of the CI using the method of Talaly and Chou, each agent in the combination is required to show single-agent activity and the dose–response curve should be sigmoidal , groups of tumor-bearing mice (approximately six mice per group) were matched for body weight and tumor volume and randomly assigned to treatments: vehicle, veliparib (5 and 25 mg/kg o.g. daily), cisplatin , etoposide (20 mg/kg i.p. weekly), cisplatin plus veliparib, etoposide plus veliparib, cisplatin (2.5 mg/kg i.p. weekly) plus etoposide (20 mg/kg i.p. weekly), cisplatin (2.5 mg/kg i.p. weekly) plus etoposide (20 mg/kg i.p. weekly) plus veliparib (25 mg/kg o.g. daily). At the end of the experiments, subcutaneous tumors were harvested and weighed following animal sacrifice by cervical dislocation.All animal experiments were conducted in accordance with the humane treatment of animals under an animal protocol approved by the Institutional Animal Care and Use Committee (IACUC) of Emory University. Tumor xenografts were raised in 6-week-old athymic (nu/nu) mice  housed under pathogen-free conditions in microisolator cages, fed with laboratory chow and water ad libitum. H146 , cisplatin (2.5 mg/kg or 5 mg/kg), and combinations. Treated animals were sacrificed either at 1 or 24 h posttreatment by cervical dislocation. Plasma and tumor samples were collected and immediately stored in liquid phase nitrogen or at −70°C until ready for analysis. Tissues were homogenized in approximately 1 mL of PBS. Veliparib concentrations in plasma and tissue homogenates were quantitated by LC-MS as reported previously . Concentμmol/L), cisplatin (determined IC50 concentration for each cell line), ionizing radiation (2 Gy) or cisplatin plus veliparib combination for 24 h. Total RNA was isolated from frozen specimens using RNeasy  according to the manufacturer's instructions. Total RNA sample quality and concentrations were determined using NanoDrop and Agilent 2100 Bioanalyzer. Each sample was prepared for Illumina Human HT-12 v4 Expression BeadChips  according to the manufacturer's protocol. The HT-12 platform contains over 47,000 probes that cover well-characterized genes, gene candidates, and splice variants. BeadChips were scanned on the Illumina HiScan instrument to determine probe fluorescence intensity. Raw probe intensities for all treatment conditions were normalized by the quantile normalization algorithm  [P < 0.05 considered significant.Differences in mean IC) × 100] . We asseμmol/L) in the panel of SCLC cell lines tested  was combined with two different doses (2 and 4 Gy) of ionizing radiation in two representative cell lines (DMS153 and H146); Figure Short-term MTS cytotoxicity assay was performed as described in the methods section to characterize veliparib activity in a panel of 9 SCLC cell lines. We wanted to establish the single-agent activity as well as ability of veliparib to enhance the cytotoxic effect of standard chemotherapy agents employed for the treatment of SCLC patients in the clinic. Veliparib induced limited growth inhibition over a wide concentration range 0–128 28 μmol/Lnes Fig. . There w50 concentration H146 (5.2 μmol/L) and H128 (14.5 μmol/L) from the in vitro assay for this in vivo experiments. There was greater tumor growth inhibition with the veliparib and cisplatin combination than with cisplatin alone in H146 xenografts  but not in the H128 xenograft . The potentiating effect of veliparib when combined with cisplatin appeared dose dependent  and induced objective tumor regression while the doublet only reduced tumor growth. Moreover, the triplet regimen significantly delayed tumor regrowth over the cisplatin and etoposide doublet when treated animals were observed off treatment for up to 4 weeks  will result in greater antitumor effect in vivo. The triplet combination of veliparib, cisplatin, and etoposide was more potent than the doublet  and veliparib concentrations (50 μmol/L), revealed a panel of 24 genes and pseudo genes (27 probe sets) with differential expression between the two cell clusters , 9 were restricted to the insensitive cell lines  and the remaining genes were common to both groups , which were mostly the same cell lines that displayed increased sensitivity to cisplatin and to PARP inhibition  correlates with the degree of potentiation by veliparib in our cell line panel. Clinical observation in ovarian cancer patients treated with olaparib also showed that the clinical benefit of this PARP inhibitor correlated with prior platinum sensitivity of the disease [The selective potentiation of cytotoxic chemotherapy by veliparib in vitro and in vivo is similar to the work of other groups , 41. Thi disease . Further disease . Subchro disease . Further disease –49. Cont disease , 37, 50. disease , 52. NonIn conclusion, our preclinical investigation showed that PARP inhibition with veliparib potentiates the activity of DNA damaging agents in SCLC both in vitro and in vivo. The selectivity of this potentiation correlates with platinum sensitivity of the cell lines as well as the level of DNA-PKcs expression and modulation. An ongoing phase I/II clinical trial, E2511 (NCT01642251), is already translating these findings in patients with newly diagnosed SCLC. The expression profile of a 5-gene panel identified in this work may also predict both platinum sensitivity and PARP inhibitor efficacy in SCLC and potentially other tumor types, but this would have to be confirmed.