Salmonella can survive for long periods under extreme desiccation conditions. This stress tolerance poses a risk for food safety, but relatively little is known about the molecular and cellular regulation of this adaptation mechanism. To determine the genetic components involved in Salmonella’s cellular response to desiccation, we performed a global transcriptomic analysis comparing S. enterica serovar Typhimurium cells equilibrated to low water activity (aw 0.11) and cells equilibrated to high water activity (aw 1.0). The analysis revealed that 719 genes were differentially regulated between the two conditions, of which 290 genes were up-regulated at aw 0.11. Most of these genes were involved in metabolic pathways, transporter regulation, DNA replication/repair, transcription and translation, and, more importantly, virulence genes. Among these, we decided to focus on the role of sopD and sseD. Deletion mutants were created and their ability to survive desiccation and exposure to aw 0.11 was compared to the wild-type strain and to an E. coli O157:H7 strain. The sopD and sseD mutants exhibited significant cell viability reductions of 2.5 and 1.3 Log (CFU/g), respectively, compared to the wild-type after desiccation for 4 days on glass beads. Additional viability differences of the mutants were observed after exposure to aw 0.11 for 7 days. E. coli O157:H7 lost viability similarly to the mutants. Scanning electron microscopy showed that both mutants displayed a different morphology compared to the wild-type and differences in production of the extracellular matrix under the same conditions. These findings suggested that sopD and sseD are required for Salmonella’s survival during desiccation. Salmonella enterica, a gram-negative bacterium belonging to the Enterobacteriaceae family, is a foodborne human pathogen that can cycle from the environment to animals and humans through their fecal matter .Bacterial cultures were grown overnight at 37°C with shaking at 250 rpm in LBglc. The cultures were collected through centrifugation  and washed twice with DSW to eliminate nutrient residues. For these experiments we decided to use glass beads (150–250 μm) . Our decision was motivated by the need to increase the number of cells, while still ensuring that the cells were evenly distributed and exposed to low aw 0.11 and 1.0, the samples were equilibrated for 7 days at 25°C in desiccators containing a saturated lithium chloride  solution or sterile distilled water (SDW). After 7 days, the water activity of samples was measured , and the samples were sealed. To determine the survival rate, beads for every sample were serially diluted in saline and spread plated on dTSA for cell enumeration. The recovery after every treatment was measured as cell viability in Log (CFU/g), and the survival rate was calculated as viability change in Log (CFU/g).After drying, the beads were distributed into sterile 200 μL PCR plastic tubes (Thermo Fisher Scientific). For exposure to aw 0.11 and 1.0. Immediately after collection, samples were fixed with a solution of 1% paraformaldehyde, 1% glutaraldehyde, and 0.05 M sodium cacodylate as previously described . The differences between Salmonella WT and the mutants were significant . Although the E. coli O157:H7 count decreased, it was not significantly different from Salmonella WT (p = 0.06).Cell counts decreased for all the strains after drying  . Conversely, ΔsseD decreased by 2.4 Log (CFU/g). This difference was statistically significant when compared to the WT and ΔsopD, but not to E. coli O157:H7 .After equilibration to aw 1.0 , the celw 0.11 ; ΔsopD, -1.7 Log (CFU/g); ΔsseD, -2.7 Log (CFU/g); and E. coli O157:H7, -2.2 Log (CFU/g)], although only ΔsseD had a significantly lower recovery than Salmonella WT (p = 0.012).After equilibration to aw 0.11 , the celsopD during drying lowered the cell count close to the detection limit of the experiment [2.7 Log (CFU/g)]. Therefore, it was difficult to estimate the additional decrease in viability and its significance after exposure to aw 0.11. For this reason, we also calculated the total change in cell count for both aw over the entire treatment period ; ΔsopD, -3.1 Log (CFU/g); and E. coli O157:H7, -3.7 Log (CFU/g)]. On the contrary, ΔsseD had an overall reduction of 6.7 Log (CFU/g), significantly larger than Salmonella WT and ΔsopD, but not E. coli O157:H7 . At aw 0.11 ; ΔsopD, -7.4 Log (CFU/g); ΔsseD, -7.1 Log (CFU/g); and E. coli O157:H7, -6.8 Log (CFU/g)]. The mutants and E. coli O157:H7 had significantly lower cell viability compared to Salmonella WT .The large decrease in cell count of Δ11 days, . At aw 1 aw 0.11 , we obseS. enterica serovar Typhimurium wild-type (WT), ΔsopD, ΔsseD, and E. coli O157:H7 cells on glass beads showed WT and E. coli O157:H7 cells as rod-shaped cells with an average length of 1.5 μm and an average width of 0.5 μm. In contrast, both mutants displayed different cell morphology, more coccobacillary, with markedly smaller, rounder, and shorter cells, 1 μm in length or less , we decided to perform a global transcriptomic analysis on cells exposed to extreme low aw using abiotic surfaces.The response of esponses , 47, 48,anut oil  and ther plastic , 12, 19.w has a broad impact on the expression of many genes involved in anabolic and catabolic pathways. This was expected because the rate of enzymatic reactions slows when occurring in low aw, thus causing a decrease in the metabolic rate. This phenomenon has already been suggested in other studies [w induced the expression of many genes involved in DNA replication and repair. DNA damage is known to be an effect of desiccation, in particular covalent modifications and double-stranded breaks [Deinococcus radiodurans [Bradyrhizobium japonicum [w, a portion of the replicating cells were unable to complete their replication due to either lack of energy and building blocks or water available for chemical reactions. This halt in the process would most likely be sensed as replication errors by the DNA replication checkpoints, thus inducing DNA repair mechanisms.Our transcriptomic analysis showed that exposure to low a studies . Howeverd breaks . DNA repiodurans  and Bradaponicum . We can w. tRNAs are usually difficult to observe by RNA sequencing, mainly because of their strong secondary and tertiary structures and post-transcriptional modifications [w. Similar observations—up-regulation of amino acid transport and metabolism, and transcription and translation-associated genes—led Gruzdev et al. [de novo protein synthesis is a requirement for the cell adapting to desiccation.We also observed a stark increase in many tRNAs in cells exposed to low aications . Therefov et al.  to the cosmV (2.91 fold), one of the genes involved in the transport of osmoprotectants during osmotic stress, in cells exposed to aw 0.11 for four days. However, this was the only osmolarity-related gene induced in our treatment conditions. This is interesting, because one of the main long term responses the cell deploys to counteract osmotic stress during desiccation is the intracellular accumulation of osmoprotectants [de novo synthesis [al. and Finn et al. found the ProU system up-regulated in S. enterica serovar Typhimurium desiccated on filter paper and stainless steel, respectively [Osmolarity homeostasis is important in the desiccation process and, in our study, we observed the induction of tectants . Osmoproynthesis –57. Duriynthesis –61. Li eectively .w. In particular, we washed and re-suspended the cells in pure water prior to desiccation, whereas Li et al. [al. [w. Moreover, those two studies focused on very short-term effects of desiccation , which is another important osmoprotectant synthesized by the cell [The difference we observed in the induction of the ProU system may be explained by differences in methodology, including desiccation conditions and length of exposure to low ai et al.  used celal. [al.  used celal. [al. , the use(2 hours  and 4 ho(2 hours ), whereasm genes . It is ism genes . A similthe cell –65.sseD and sopD. While sseD and sopD have been characterized for their role in virulence and infection mechanisms, to our knowledge, they have never been reported to be involved in desiccation adaptation and tolerance, and for this reason we targeted them for further analysis. SseD is a translocon component of the Salmonella Pathogenicity Island 2 (SPI-2) Type III Secretion System (T3SS) [sseABCD operon, encoding a chaperon protein and the other translocon components [sseA, encoding the chaperonin for sseB and sseD [sscA, encoding the chaperonin of sseC [w 0.11. This indicates that the entire SPI-2 T3SS translocon is likely involved during adaptation and survival to desiccation and low aw. Interestingly, although SopD was initially identified as an effector translocated by the T3SS of SPI-1 [sopD expression remains elevated during later stages of infection and is involved in survival and replication inside the macrophage [Our class of orthology gene classification of the differentially expressed genes identified two genes encoding for virulence factors, m (T3SS) , and it mponents . We alsoand sseD –42, and  of sseC , in cellof SPI-1 , it is nof SPI-1 . Even thof SPI-1 , sopD excrophage , 71.S. enterica serovar Typhimurium to survive desiccation, we compared the survival rates and morphological characteristics of the WT, ΔsopD and ΔsseD mutants, and a virulent strain of E. coli (O157:H7). We chose to include E. coli in our comparisons because of its genetic and physiological relatedness to Salmonella and lack of the same kind of virulence mechanisms encoded by the SPIs.To better understand the impact of these two genes on the ability of E. coli O157:H7, had the same cell viability of Salmonella WT before being inoculated on glass beads (data not shown). On the contrary, interesting differences were detected when observing the different cell-resuspensions with SEM. Both mutants exhibited a shorter and coccoidal shape compared to the WT. Both ΔsopD and ΔsseD mutants had decreased tolerance to desiccation compared to the WT, clearly indicating that SopD and SseD play an important role during desiccation survival. To the best of our knowledge, this is the first time that different cell shape and size have been observed for mutants in these two genes.The mutants were first compared to the WT under optimal growth conditions , and no differences were detected in generation time and in the ability to reach stationary phase. Additionally, the cell viability of the mutants in the initial cell-resuspension was checked, and the mutants, as well as sseD in that case [S. enterica serovar Typhimurium strain—ATCC14028 instead of NCTC 12023—and different growth conditions might justify the different morphologies detected in our study. Furthermore, Chakravortty et al. used specific SPI-2 inducing conditions. As a result, the induction of other SPI-2 T3SS genes could have masked defects in cell morphology caused by the mutation in sseD.In a previous study, field emission scanning microscopy (FESEM) was performed on a large number of SPI-2 effectors mutants and no changes in cell morphology were reported for Δhat case . The usemreC mutant in Salmonella [E. coli [mreBCD operon, which is responsible for the cell shape and correct formation of the cytoskeletons [E. coli, deletion in mreBCD caused the formation of spheroid-like cells defective in adjusting the rate of phospholipid synthesis [mreBCD operon in WT cells of S. enterica serovar Typhimurium equilibrated to aw 0.11, suggesting a role of the operon in the cell response to low aw, probably through the maintenance of a correct membrane composition and cell-shape. Although we cannot directly prove a connection between sopD/sseD and the mre operon, the fact that the two mutants, defective in cell-shape, are less tolerant to desiccation, suggests that the desiccation response mechanisms may include the sopD/sseD-dependent induction of the mre operon.A similar round shape was observed for lmonella  and E. c[E. coli . This gekeletons –79. For ynthesis . Interesw 0.11. The cell viability of the two mutants decreased once more. This indicates that sopD and sseD are not only involved in Salmonella adaptation to desiccation but are also essential for long period survival. It is interesting to note that drying and exposure to aw 0.11 had a dramatic effect also on E. coli O157:H7, which lacks SPI-1 and 2 genes.The effects of the mutations persisted after the additional week at aSalmonella cells under different stress conditions, including drying and desiccation [An additional possibility is that the mutants are more prone than the WT to enter a viable but not culturable state (VBNC). The VBNC state is described as a dormant state in which cells are metabolically inactive and are not culturable using standard methods . This phiccation –83.w 0.11, Salmonella WT displayed a thick layer of solid extracellular matrix. In general, production of EPS has been associated to a higher desiccation tolerance in a variety of bacterial species, such as E. coli, Acinetobacter calcoaceticus, Erwinia stewartii, and Rizhobium sullae [w 0.11 on filters—i.e. sthA, sthB, and sthE, part of the fimbrial operon sthABCDE, important for colonization in mice [Salmonella WT after the 7-day equilibration showed some signs of damage, possibly due to the desiccation of the hydrogel.Structurally, after drying and prolonged exposure to am sullae , 85. It m sullae . Moreove in mice  as well  in mice . The matE. coli O157:H7 and ΔsopD presented signs of damage as well as cell detachment, while after exposure to aw 0.11, neither ΔsopD nor E. coli O157:H7 had any kind of extra-cellular material production. Interestingly, ΔsseD did not produce any solid matrix after drying, although cells were encased in a network of ropy filaments, which appeared to connect all cells. ΔsseD did not present these filaments after exposure to aw 0.11 and the cell viability decreased compared to after drying.After drying, the extracellular solid matrices of both w. Based on the different role and characteristics of SopD and SseD, we hypothesize that the decrease in desiccation and low aw tolerance that we observed in our mutants is mainly due to lack of secretion of the effector protein SopD. As previously mentioned, SopD is an effector secreted by both T3SSs, while SseD is part of the injectosome of the SPI-2 T3SS. SPI-1 and SPI-2 T3SS are expressed during the infection process at different stages [Salmonella containing vacuole (SCV), SPI-2 is necessary at a later stage, for Salmonella induced filaments (SIF) formation and bacterial replication inside the SCV.It is possible that the mutant cells can temporarily produce extracellular structures with the role of protecting the cell from desiccation, but this response might be ineffective for long periods of exposure to low at stages . While SsopD mutant, SopD cannot be produced nor secreted, and cell viability decreases dramatically from the first treatment (desiccation). ΔsseD is less susceptible than ΔsopD to the effect of initial desiccation, but after prolonged exposure to aw 0.11, the effect of sseD depletion becomes as dramatic as sopD. These data suggest that during the initial desiccation, the SopD effector could still be secreted in the ΔsseD mutant, probably by SPI-1 T3SS, but during long-term low aw exposure, the role of SPI-2 T3SS may become fundamental, and due to malfunctioning of the injectsome in ΔsseD, SopD may no longer be secreted. Additionally, the induction in the WT of sseD, as well as sseA and sscA, in cells equilibrated to aw 0.11 strongly indicates that the correct assembly and functioning of the SPI-2 T3SS is required for survival at extreme low aw conditions.In the case of the Δw (aw 1.0) after drying, cell viability for the WT, ΔsopD, and E. coli O157:H7 increased. This may be due to the ability of the cells to utilize proteins and nutrients released by the dead cells still on the beads, similarly to what hypothesized by Gruzdev et al. [w 1.0. Additionally, it has also been suggested that residues of extracellular polymeric substances (EPS) formed during desiccation can serve as a source of nutrients [sseD mutant, instead, did not increase in cell viability after equilibration to aw 1.0. The differences in recovery from desiccation between the two mutants could be linked to the different role of the two effectors. Possibly, the lack of a completely developed needle structure in ΔsseD results in defective secretion of several other effectors required for the recovery process. At aw 1.0 no differences were detected in EPS production, confirming that the differences in the exopolymeric matrix formation between the WT and the mutants/E. coli O157:H7 are due to differences mainly in response to desiccation.When cells were exposed to high av et al. . The preutrients , 91. ThesopD and positioned in its coding region (from position +726 to +822), has been partially characterized in Salmonella [al. using a combination of differential RNA-seq and in silico analysis [al. renamed this antisense RNA STnc3140 and showed that this RNA interacted with the RNA chaperone Hfq specifically during the transition from late exponential to early stationary phase [al. thoroughly characterized the regulatory networks of 280 sRNAs in 5 different conditions  and found that STnc3140 is positively regulated by RpoS and Hfq, but is repressed by HilD and Fur [al. observed a decreased expression of STnc3140 also in a ProQ mutant of Salmonella [Very recently, the regulatory system of a 97 nt small antisense RNA, STnc3140, encoded on the negative strand of lmonella –95. Thisanalysis . A subse and Fur . In partlmonella . In our w-sensitive phenotype observed in the ΔsopD. However, this hypothesis is unlikely based on the fact that i) none of the regulators reported in the above mentioned studies are differentially expressed in our conditions; ii) the induction, under low aw conditions, of sopD and few other genes related to secretion  was described in the WT strain first and not limited to STnc3140; and iii) the phenotypes of ΔsopD and ΔsseD, although clearly distinct, they shared several similarities . Regardless, as more information about small ncRNAs becomes available, it will be interesting to unravel the effects of the SopD effector from the ones of STnc3140, possibly by reintroducing into a ΔsopD background only STnc3140 and its regulatory unit and complementing the ΔsopD mutation with codon bias-modified copy of the gene not encoding STnc3140.The absence of STnc3140 could be at least partially responsible for the low aS1 FigS. enterica serovar Typhimurium wild-type sopD and sseD gene knock-out mutations with the chloramphenicol resistance cassette and the kanamycin resistance cassette, respectively (A). The sites of λ Red-mediated homologous recombination are indicated with crossing lines, while the primers used for the creation of the cassette are indicated with arrows. UL1 and UR1 are the universal caps part of the drug-cassette kit by Dr. Roth Laboratory . Figure B shows the collocation of the primers used in the PCR reaction for the verification of ΔsopD and ΔsseD mutants of S. enterica serovar Typhimurium.Schematic drawing of the (TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Breast cancer cell lines are frequently used as model systems to study the cellular properties and biology of breast cancer. Our objective was to characterize a large, commonly employed panel of breast cancer cell lines obtained from the American Type Culture Collection (ATCC 30-4500 K) to enable researchers to make more informed decisions in selecting cell lines for specific studies. Information about these cell lines was obtained from a wide variety of sources. In addition, new information about cellular pathways that are activated within each cell line was generated.We determined key protein expression data using immunoblot analyses. In addition, two analyses on serum-starved cells were carried out to identify cellular proteins and pathways that are activated in these cells. These analyses were performed using a commercial PathScan array and a novel and more extensive phosphopeptide-based kinome analysis that queries 1290 phosphorylation events in major signaling pathways. Data about this panel of breast cancer cell lines was also accessed from several online sources, compiled and summarized for the following areas: molecular classification, mRNA expression, mutational status of key proteins and other possible cancer-associated mutations, and the tumorigenic and metastatic capacity in mouse xenograft models of breast cancer.The cell lines that were characterized included 10 estrogen receptor (ER)-positive, 12 human epidermal growth factor receptor 2 (HER2)-amplified and 18 triple negative breast cancer cell lines, in addition to 4 non-tumorigenic breast cell lines. Within each subtype, there was significant genetic heterogeneity that could impact both the selection of model cell lines and the interpretation of the results obtained. To capture the net activation of key signaling pathways as a result of these mutational combinations, profiled pathway activation status was examined. This provided further clarity for which cell lines were particularly deregulated in common or unique ways.These two new kinase or “Kin-OMIC” analyses add another dimension of important data about these frequently used breast cancer cell lines. This will assist researchers in selecting the most appropriate cell lines to use for breast cancer studies and provide context for the interpretation of the emerging results.The online version of this article (doi:10.1186/s13058-017-0855-0) contains supplementary material, which is available to authorized users. The general subtyping of breast cancer in the clinic is based on the expression of three main types of receptor: estrogen receptor (ER), progesterone receptor (PR) and the human epidermal growth factor receptor 2 . ER+ breast cancers (60% of breast cancers) express ER ± PR and can be treated with anti-estrogens, such as tamoxifen, or aromatase inhibitors to block the generation of estrogen . HER2 brAdditional gene expression analyses have allowed for a more refined subgrouping of these subtypes that often helps to predict treatment responsiveness –10. LumiThere are numerous additional genes with variable expression levels and/or mutations within breast cancer cells, which contribute to a diverse genetic background that could influence therapeutic responses. As such, it is important to appropriately select breast cancer cell lines that accurately reflect this diversity when carrying out breast cancer studies. Further, if the molecular characteristics of the breast cancer cell lines are known, their ability to influence the results of experiments can be more effectively considered.In this report, information was compiled from a variety of sources about a large panel of breast cancer cell lines that included the mutational status and mRNA expression of many important genes, and the tumorigenicity and metastatic properties in mouse xenograft models. Protein expression levels were examined for the corresponding gene products and we noted that these did not always correspond to the mRNA levels. In addition, lysates from serum-starved cells were used to carry out two types of pathway activation analyses to assess the activation of various signaling pathways within each cell line.A panel containing 40 breast cancer cell lines and 4 non-tumorigenic breast cell lines was obtained from the American Type Culture Collection . Cells Protein expression in the breast cancer cell lines was quantified by immunoblot analysis as previously described . Brieflyhttp://www.ATCC.org; accessed May 14, 2015) provided basic information regarding the cell lines, including the sources used to generate the cell line, the type of breast cancer, and in some instances, the molecular classification or subtype, and partial data on the expression or absence of some genes. The catalogue of somatic mutations in cancer  database of cell line mutations was accessed January 30, 2016 (version v75). For Additional file https://www.broadinstitute.org/software/cprg/?q=node/11; 2012 September version [The ATCC website ( version ) datasetCells were cultured under serum-starvation conditions  containing medium) for 24 hours to analyze the signaling proteins/pathways activated within these cell lines. Cells were lysed and used to probe a PathScan RTK Signaling Antibody Array  using reagents provided within the kit and according to the supplier’s instructions. These slide-based antibody arrays enable the simultaneous quantification of the extent of phosphorylation of 28 receptor tyrosine kinases and 11 key signaling proteins. The nitrocellulose-coated glass slides have highly specific capture antibodies that selectively bind target proteins within the cell lysate. A biotinylated detection antibody mixture and streptavidin-linked DyLight 680 molecule allow for the detection of bound protein using the Odyssey Infrared Imaging System . Quantification was carried out using Odyssey V3.0 software. The raw intensity data for each target (mean of duplicate target measurements on each slide) had the background subtracted (mean of the 2 negative control spots), and was reported as a percentage of positive control spots (mean of 10 positive control spots). Data for the targets are reported for each cell line as the mean ± standard deviation of at least two (but for most three) independent experiments containing duplicate measurements, each using a fresh lysate. Hierarchical clustering was performed on both the phosphoproteins and cell lines using Ward's method (minimize cluster variance) with the Euclidean distance set as the distance metric. The heatmap and dendograms were generated using the Matplotlib and SciPy libraries for Python.A customized peptide array was developed to consider cancer-associated pathways, proteins and phosphorylation events. Phosphorylation events represented on the array were selected from databases of experimentally defined phosphorylation events and from those predicted by the software program DAPPLE2 . Additio6 cells were collected, pelleted, and lysed by addition of 100 μl of lysis buffer  . Cells were incubated on ice for 10 minutes and spun in a microcentrifuge for 10 minutes at 4 °C. A 70-μl aliquot of this supernatant was mixed with 10 μl of activation mix , 60 mM MgCl2, 0.05% (v/v) Brij 35, 0.25 mg/ml bovine serum albumin) and incubated on the array for 2 hours at 37 °C. Arrays were then washed with phosphate-buffered saline (PBS) + 1% Triton X-100. Slides were submerged in phosphospecific fluorescent ProQ Diamond Phosphoprotein Stain  with agitation for 1 hour. Arrays were then washed three times in destaining solution containing 20% acetonitrile  and 50 mM sodium acetate at pH 4.0 for 10 minutes. A final wash was done with distilled deionized water. Arrays were air dried for 20 minutes and then centrifuged at 300 × g for 2 minutes to remove any remaining moisture. Arrays were analyzed using a GenePix Professional 4200A microarray scanner  at 532 to 560 nm with a 580-nm filter to detect dye fluorescence. Images were collected using GenePix software (version 6.0) and the spot intensity signal was collected as the mean of pixel intensity using local feature background intensity calculation with the default scanner saturation level.Design, construction, and application of the peptide arrays were based upon a previously reported protocol with modifications , 19. Brihttp://saphire.usask.ca/saphire/piika. For each peptide within a given array, the chi-square test was performed to determine whether the degree of variability among the technical replicates for that peptide was greater than would be expected by chance. Any peptide that had a P value according to the chi-square test of less than 0.01 was considered to be inconsistently phosphorylated among the technical replicates and was excluded from further analysis.All data processing and analysis was done using the Platform for Intelligent, Integrated Kinome Analysis (PIIKA) software , which iThe preprocessed data were subjected to hierarchical clustering and principal component analysis (PCA) to cluster peptide response profiles across cell lines. For each of the 1290 peptides in a single sample and cell line, the average was taken over the nine replicates that are transformed through variance stabilization and normalization (VSN). For hierarchical clustering, each sample/cell line vector was considered a singleton  at the initial stage of the clustering. The two most similar clusters were merged, and the distances between the newly merged clusters and the remaining clusters were updated, iteratively. The method, as described by Eisen et al. , used thP) based on the number of proteins present for a particular pathway and the degree to which they were differentially expressed or modified relative to a control condition.InnateDB is a publicly available resource which, based on levels of either differential expression or phosphorylation, predicts biological pathways based on experiment fold change data sets . PathwayFor hierarchical clustering of kinome profiles, the distance metric used was (1 - Pearson correlation), while McQuitty linkage was used as the linkage method. Colors indicate the average (over nine intra-array replicates) normalized phosphorylation intensity of each target, with red indicating increased phosphorylation and green indicating decreased phosphorylation. The intensity of the color corresponds to the degree of increase or decrease .We have compiled the currently available data and carried out further analyses to better classify and characterize a panel of 40 breast cancer cell lines compared to 4 non-tumorigenic control breast cell lines. Several studies using large groups of breast cancer cell lines have evaluated gene expression data to molecularly classify each cell line from general subtypes of normal, ER+, HER2-amplified, and TNBC and into further subgroups of luminal A, luminal B, HER2-amplified, basal A, and basal B  , 4, 6, 7In some cell lines, and in contrast to the wild-type status and positive p53 mRNA expression for HCC1428, MCF7 and ZR-75-30 cells, there was minimal detectable expression of p53 protein  pathway genes that when mutated or differentially expressed would promote PI3K pathway activation, which is critically important in tumorigenesis. As described, mutational data were obtained from the COSMIC database, mRNA expression from the CCLE, and an immunoblot analysis for protein expression was performed  of key genes, and to a lesser extent key proteins. This molecular information is fairly straightforward to obtain, but it does not provide a more integrated global readout of which cellular pathways are activated and to what extent. Therefore, we carried out two analyses to assess pathway activation within this panel of breast cancer cell lines.To determine the key receptors and pathways that were activated within each of the cell lines as a result of the protein expression profiles and mutational status, cells were cultured for 24 hours in low serum (0.5% FBS) to minimize the impact of growth factor-mediated receptor and pathway activation. Two complementary analyses were then carried out. First, lysates were used to probe a commercially available PathScan RTK Signaling Antibody Array from Cell Signaling Technology. This analysis provided the activation status of 28 receptor tyrosine kinases and 11 downstream signaling proteins  corresponding to kinase substrates within major signaling pathways involved in key cellular processes, including cell proliferation, metabolism, and apoptosis  showed a decrease in phosphorylation in many cell lines Additional file 2:Table S1. Basic expression profile and molecular classification of a panel of breast cancer cell lines. (XLSX 19 kb)Additional file 3:Figure S1. Protein expression analysis to aid in the molecular classification of breast cancer cell lines. Cell lysates containing equivalent amounts of total cell protein from the indicated non-tumorigenic breast  and breast cancer cell lines were probed with the indicated antibodies and GAPDH (loading control). The amount of total protein loaded per lane for each set of blots was as follows: ER (50 μg), PR , HER2 (10 μg), EGFR (50 μg), p53 (50 μg), and GAPDH (50 μg). The molecular weight of each protein is indicated. (PDF 259 kb)Additional file 4:Table S2. Mutations, mRNA and protein expression for PI3K pathway components in breast cancer cell lines. (XLSX 15 kb)Additional file 5:Figure S2. PI3K pathway protein expression analysis for breast cancer cell lines. Cell lysates containing equivalent amounts of total cell protein from the indicated non-tumorigenic breast  and breast cancer cell lines were probed with the indicated antibodies and GAPDH (loading control). The amount of total protein loaded per lane for each set of blots was as follows: p110α (50 μg), p110β (50 μg), p85α (25 μg), PTEN (50 μg), and GAPDH (50 μg). The molecular weight of each protein is indicated. (PDF 234 kb)Additional file 6:Table S4. Pathscan analysis of receptors and pathways intrinsically activated in a panel of breast cancer cell lines. (XLSX 67 kb)Additional file 7:Figure S3. Downstream signaling pathway activation in breast cancer cell lines. Lysates from the indicated breast cancer cell lines that had been grown under serum-starved conditions were used to probe a PathScan array to detect intrinsic activation of the indicated proteins using pan-pTyr, or the specified phosphospecific antibody. The schematic image of the array was reproduced courtesy of Cell Signaling Technology, Inc. . (PDF 390 kb)Additional file 8:Figure S4. Downstream signaling pathway activation in additional breast cancer cell lines. Lysates from the indicated breast cancer cell lines that had been grown under serum-starved conditions were used to probe a PathScan array to detect activation of the indicated proteins using pan-pTyr, or the specified phosphospecific antibody. The schematic image of the array was reproduced courtesy of Cell Signaling Technology, Inc. . (PDF 398 kb)Additional file 9:Table S5. Kinome peptide list. List of protein substrates from which peptides were derived, the 15-mer peptide sequence, and the specific amino acid(s) with phosphorylation that was detected if phosphorylated by a kinase from the cell lysate. The amino acid phosphorylation site numbering is for the human protein although some of the phosphorylations were originally identified at the equivalent sites in other species. (XLSX 106 kb)Additional file 10:Table S6. Complete kinome data for all peptide substrates and all breast cell lines evaluated. Fold change is given for the phosphorylation of each peptide substrate, relative to the mean of four non-tumorigenic breast cell lines . Site refers to the amino acid residue in the human protein. (XLSX 443 kb)Additional file 11:Table S7. Kinome peptides in array, grouped according to the major biological pathways to which they contribute. (XLSX 38 kb)


Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute “network profiles” for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles. The last decade has seen significant advances in high-throughput molecular profiling technologies113One such area of interest is the problem of drug response prediction. This involves using a patient’s molecular profile to estimate the effectiveness of a given drug on the patient. To address this problem, extensive patient drug screening projects need to be performed in order to discover significant patterns of response. However, it is not feasible to treat large populations of cancer patients with numerous drugs. To circumvent this issue in the context of cancer, two large drug screening projects have been carried out using cancer cell lines in place of patient samples. These are the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE) projects5et al., tested over 100,000 models based on well-known machine learning algorithms using combinations of molecular data from either the GDSC or CCLEOne of the largest works in drug response prediction, the NCI-DREAM Drug Sensitivity Prediction Challenge, obtained data on breast cancer cell lines and compiled 44 drug response prediction algorithms from participating researchersWhile machine learning approaches have led to significant progress in drug response prediction, they also face important challenges in the context of this problem. The first is that of high dimensionality. Namely, the number of genes profiled is orders of magnitude larger than the number of samples, making feature selection difficult and vulnerable to overfitting. Second, since it is more difficult to learn from binary or discrete-valued features, effective use of mutation or copy number information in drug response prediction is challenging.et al. constructed a dual-layered network consisting of cell lines and drugs where weighted edges between two nodes of the same type represented similarity and edges between cell lines and drugs contained the response data1112To overcome the challenges associated with applying machine learning to drug response prediction, network and pathway information can be utilized. The idea behind this approach is to provide a functional context for the features that are being used in classification, thereby improving the biological relevance, robustness, and reproducibility of resulting models. This has been done implicitly for certain machine learning algorithms via a modification to the training phase. The objective function optimized during training in order to learn feature weights for the final predictive model is altered by adding a network-constraint termIn this work, we propose an alternate perspective to using network data in drug response prediction by formulating it as a link prediction problem. For this purpose, we integrate cell line mutation data and drug responses from the GDSC with a protein-protein interaction (PPI) network, creating a large, heterogeneous network consisting of genes, cell lines, and drugs. For each cell line and drug pair, we compute network profiles (representing the proximity of mutated genes in a cell line to every other node in the network) by performing a random walk with restart (RWR) on variations of the network. Network proximity utilizes functional links between proteins. Using this measure, we can smooth out the mutations to see how they may influence the overall biology of a cell. The “mutation profile” for a cell line provides information on the genes that are mutated in a given cell line. In contrast, the network profile computed by smoothing the mutation profile across the network provides information on the genes that are functionally associated with genes that are mutated in a given cell line. To this end, the incorporation of network proximity provides a functional context for the interpretation of mutations. We compute one network profile for each cell line and two network profiles for each drug, reduce the dimensionality of these profiles, and use these profiles to assess the likelihood of the existence of a “link” between each drug and cell line. Here, a link between a drug and cell line represents either the sensitivity or resistance of the cell line to the drug. In other words, based on the premise that different biological pathways may be underlying the sensitivity or resistance to a specific drug, we compute separate profiles for each drug that represent the functional bases of sensitivity or resistance to the drug. Subsequently, for each cell line-drug pair, we assess the similarity between the network-smoothed mutational landscape of the cell line and the resistance and sensitivity profiles of the drug to assess the likelihood of the sensitivity and resistance of the cell line to the drug. In this way, we score each cell line-drug pair, where the score is a prediction of sensitivity. We perform parameter analysis to investigate the value added by the use of the PPI network, and observe that the utilization of network information drastically improves prediction of response, as compared to mutational profiles alone. We also validate the resulting prediction method on the CCLE dataset to assess the ability of our method to predict response for unseen samples. Finally, we functionally annotate the sensitivity and resistance profiles of selected drugs to understand whether separate biological processes underlie sensitivity vs. resistance to a drug.Our method uses existing heterogeneous datasets ) to predict the effect of a drug on a given cell line. This is achieved by building a series of heterogeneous networks based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Given a new cell line, these networks serve as a model used to predict the response of this cell line to tested GDSC drugs. Similarly, given a new drug, these networks can be used as a model to score tested GDSC cell lines for their potential response to this drug.The workflow of the proposed method is shown in Our approach is based on the premise that a cell line that is sensitive (resistant) to a given drug will have a network profile that is more similar to the network profiles of other cell lines that are also sensitive (resistant) to the drug, as compared to those that are resistant (sensitive). This hypothesis is grounded in the notion that protein-protein interaction networks provide a functional context for molecular perturbations, including cancer mutations. Indeed, integration of mutations with protein interactions has been used to identify sets of genes important for different cancers and tumorigenic pathways141517In order to evaluate the accuracy of our method, we perform leave-one-out cross validation (LOOCV) using the GDSC data. For every cell line-drug pair, the corresponding nodes in the network are connected by exactly one edge, either in the sensitivity or resistance network for the respective drug. By removing this edge during profile calculation, we create a cell line-drug pair for testing. We then use the remaining GDSC data, via the network, to score this pair. This effectively reduces the problem to link prediction between the nodes representing the cell line and drug pair. To comprehensively assess the prediction performance of the proposed method, we repeat this process for all cell line-drug pairs in the dataset.We compare the scores for each test pair  with normalized IC50 values from the GDSC drug screening data to evaluate classification performance for different values of parameters . We use area under the receiver operating characteristic curve (AUC) to measure accuracy. The two response classes for cell line-drug pairs are sensitive and resistant, which are assigned to pairs with IC50 values respectively less than or greater than the maximum used concentration of the drug in the screening assay. Overall, we observe that the prediction performance of the method is quite robust to the selection of parameters, and there is a combination of parameters that leads to an AUC of 0.8813. To visualize how cell line-drug pairs are scored, we plot the relationship between predicted sensitivity scores, predicted resistance scores, and normalized IC50 values for each cell line-drug pair . In thisα , and the profile sparsity threshold, ε . In order to understand the affect of α and ε on prediction accuracy, we perform LOOCV analysis with varying values of each parameter  improves classification accuracy to a certain degree. This implies that somatic mutations alone may not be strong predictors of drug response, but significant value is added when they are incorporated into a network of functional associations. Finally, the prediction performance for large α and ε is similar to that provided by machine learning approaches that utilize only mutation data, which again demonstrates the value of the information added by the PPI network.In the LOOCV, values of formance . We obsely large . These rTo simulate making predictions for an unseen sample, we evaluated our method using the CCLE dataset. For each of the 235 cell lines obtained from CCLE (i.e. not included in the GDSC project) a representative node and mutation edges are added to the existing GDSC-based network. A final prediction score is then calculated for each of the 11 drugs shared by the CCLE and GDSC datasets. We then evaluated these prediction scores by using the IC50 values in the CCLE response data. In total, 2,585 new cell line-drug pairs are scored. Similar to our observation for the LOOCV analysis, we observe that sensitive and resistant pairs are separated well by the two correlation scores for the cross-classification analysis as well, leading to an AUC of 0.8474 . This reAs discussed in the previous section, the method proposed is highly accurate in both a LOOCV (within GDSC) and a cross-classification  framework in terms of classifying sensitive and resistant cell line drug pairs. In practice, the problem can be posed in two different settings: (i) predicting effective drugs for a given cell line vs. (ii) predicting resistant cell lines for a given drug. The former setting is more likely to be relevant in a clinical setting, in that the clinician may be interested in identifying drugs that are likely to lead to better outcomes for a given patient. The latter setting, on the other hand, can be useful in research and drug development, in which researchers identify  cancers that can be targeted for a new or an existing drug. In this section, we assess and compare the performance of the proposed method in these two settings, with a view to characterizing the practical applicability of the method, as well as gaining insights into the behavior of our method.For this purpose, we comprehensively compute performance figures for all cell lines in both the GDSC LOOCV and CCLE validation . SimilarTo understand the underlying reasons for the performance gap between the two different settings, we examine the characteristics of the predicted scores in the context of LOOCV on GDSC. The results of this analysis are shown in e − 11 for cell lines and 0.0165 for drugs. The equivalent comparison is performed for CCLE validation . e − 07 for cell lines and 0.967 for drugs. Together, these results show that in a cross validation problem, our method significantly outperforms the KBMTL algorithm for both prediction tasks (classifying drugs for cell lines and classifying cell lines for drugs). Additionally, when making predictions for new samples (cell lines), which is the clinically relevant setting, we significantly outperform KBMTL.In order to better understand the accuracy of our method, we compare it against the top performing approach in the DREAM Drug Sensitivity Prediction Challenge, Gonen and Margolin’s kernelized Bayesian multitask learning (KBMTL) algorithmε score threshold of 1e − 5 and restart probability of 0.7. PF-562271 inhibits FAK and terms relating to the function of FAK have much higher enrichment in the genes that are recruited into the resistance profile ). In doing so, network profiles of cell lines and drugs could then be compared for different data used. Such comparisons may show what information each data type adds and how it influences response prediction. Similarly, optimal parameter values may be different between the types of data used, which could provide insights into the influence or importance of the network for each data type.It is important to note that the set of real functional connections in a given cancer cell line will be different from the PPI network used here, since the latter includes connections that have been validated in any tissue or cell type. For this reason, the effectiveness of the methods proposed here can be improved by using networks consisting of interactions that are specific to each cell line, such as tissue-specific networks that are inferred by using tissue-specific gene expression data21In cases such as cancer, the same disease may present quite differently in a group of patients according to their molecular profiles. For this reason, the best therapy for two individuals diagnosed with the same disease may not be equivalent. By utilizing patient molecular data, we can link these biological differences among patients to make accurate and personalized choices for drug treatment. Following this idea, our method employs a functional association network with a drug screening dataset to construct an accurate model for predicting drug response for a sample/patient with a given set of genetic mutations. This model could be extended to other applications in biology, such as disease-drug prediction and drug-target prediction, while the sparse network profile correlation procedure can be a valuable contribution to works attempting to score proximity/similarity of nodes in a network. Network-based prediction methods can incorporate large amounts of existing data while offering an alternative way of answering biological problems. In this work, we were able to explicitly show the value of the PPI network and improve upon previous approaches in the area of drug response prediction.Cell line response data was downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) project website. This data includes log-normalized IC50 values for 707 cancer cell lines treated by 140 drugs. As suggested in the GDSC documentation, IC50 values were further normalized by the maximum drug concentration used in the screening assay. Cell lines with an IC50 value less than the maximum applied concentration of a drug were labeled as sensitive and the rest (those whose IC50 is greater than the maximum concentration) as resistant.Cell line mutations were obtained from COSMICCell lines used in this work were limited to those with mutation information and those showing sensitivity to a least one drug. Similarly, drugs were not considered if they were not effective against at least one cell line. Application of these filters resulted in a final set of 686 cell lines, with 170,164 mutations, and 138 drugs.Finally, the protein-protein interactions included in the network were obtained from BioGRIDG = , where V denotes the set of vertices (proteins) and E denotes the set of edges (interactions). To calculate the network profile of a given cell line ci, denoted x(ci) in gk that is mutated in ci, an edge with unit weight is added between the node representing ci and the node representing the protein coded by gk. Ideally, edges linking cell lines to mutations would be weighted based on a score that represents the mutation’s importance to the phenotype (i.e. driver vs passenger mutations)ci and the products of genes that are mutated in ci. The profile of ci is then computed by performing a random walk with restarts (RWR) on ci. RWR is a network proximity measure that is defined as follows:We represent the PPI network as W denotes the stochastic matrix derived from the adjacency matrix of the network, ci and zeros for all other nodes, and α denotes the restart probability . The restart probability parameter configures the relative importance of the network context vs. identity of individual mutations in a cell line, where a larger α weighs individual mutations more than network proximity to mutations. As presented in the Results section, we comprehensively assess the effect of this parameter on prediction performance. Defined this way, Here, H = , where C is the set of all cell lines and EC is the set of edges representing mutations for all cell lines, i.e. s(dj) for drug dj, we add to the PCM network edges connecting dj to the cell lines that are sensitive to dj and assign weights to these edges representing the corresponding normalized IC50 values. In other words, the sensitivity network for drug dj is defined as dj to cell lines that are sensitive to dj. We then compute s(dj) by performing a RWR on dj. Similarly, to compute the resistance profile r(dj) for drug dj, we add to the PCM network edges connecting dj to the cell lines that are resistant to dj, and assign weights to these edges representing the corresponding normalized IC50 values. The resulting resistance network for dj is denoted dj to cell lines resistant to dj. We then compute r(dj) by performing RWR on dj.To compute network profiles for drugs, we add all cell line nodes and corresponding mutation edges to the network to obtain a Protein-Cell Line-Mutation (PCM) network. We represent this network as ci and dj, we compute a sensitivity score, σij = corr(s(dj), x(ci)), and a resistance score, ρij = corr(r(dj), x(ci)). Since these vectors are very high-dimensional (each dimension represents a node in the network and there are tens of thousands of nodes in the network) and sparse , we perform dimension-reduction before computing the correlations. For this purpose, we use a sparsity (or dimension reduction) parameter ε. Namely, while computing the correlation between s(dj) and x(ci), we remove any gene with a proximity score less than ε in both s(dj) and x(ci) from both profiles. A final prediction score, δ, is then calculated for a cell line-drug pair by subtracting the sparse sensitivity correlation from the sparse resistance correlation, or δij = ρij − σij. This prediction score is designed to be proportional to the maximum concentration-normalized IC50 values. A cell line that is sensitive to a drug is expected to have a higher correlation with that drug’s sensitivity profile than its resistance profile, making δij < 0. Correspondingly, a cell line sensitive to a drug will have an IC50 value less than zero after normalization by the drug’s maximum concentration used in the assay and then by log-normalization . Therefore, our prediction score δij and the real response value are negative for sensitive cell line-drug pairs and positive for resistant pairs.Our objective is to compute a prediction score for the sensitivity of a given cell line to a given drug based on the network profiles computed as described in the previous section. For this purpose we use the similarities between the network profiles of cell lines and the sensitivity/resistance profiles of drugs. To measure the similarity between cell line and drug profiles, we employ the Pearson correlation coefficient. Namely, for a given cell line and drug pair, denoted respectively as It is important to note that during the GDSC leave-one-out cross validation, if there is an edge between the cell line and drug nodes of the current pair being tested, it is removed before the drug profile calculations. This ensures that the prediction on the test pair is done without the pair’s response data. During validation with other datasets, this is not an issue as the scores are calculated for new drugs or cell lines, meaning there cannot be an edge in the network connecting the pair.CCLE drug response and cell line mutation data was downloaded from the Broad Institute website. Cell lines not present in the GDSC project were scored against the drugs shared between both datasets using the GDSC-based network. For each new cell line, a corresponding node and mutation edges were added to the network for calculation of cell line and drug profiles. A score was obtained for all combinations between the 235 CCLE-only cell lines and the 11 drugs shared by CCLE and GDSC. These scores were then compared to the reported IC50 values in the CCLE dataset to determine prediction accuracy.ε parameter. Genes shared between these two sets were removed to produce sensitive-only and resistant-only sets of proximal genes per drug. These were individually used as input lists for the web-based tool DAVID for functional enrichmentFrom the two profiles of a selected drug, node sets representing sensitivity and resistance were generated by extracting genes having RWR scores greater than the How to cite this article: Stanfield, Z. et al. Drug Response Prediction as a Link Prediction Problem. Sci. Rep.7, 40321; doi: 10.1038/srep40321 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


In our study, we estimate an effect from chromosome aberrations and genome mutations on changes in microRNA expression profiles in cancer cell lines demonstrating different radiosensitivity. Here, cell viability and microRNA spectrum have been estimated 1, 4, and 24 h after irradiation. MiSeq high-throughput sequencing system  is employed to perform microRNA spectrum estimation. In the K562 cell line, the number of expressed microRNAs in chromosomes demonstrates a more pronounced variation. An analysis of microRNA effects on signaling pathway activity demonstrates differences in post-transcriptional regulation of the expression of genes included into 40 signaling pathways. In the K562 cell line, microRNA dynamics analyzed for their dependence on chromosome localization show a wider scattering of microRNA expression values for a pair of chromosomes compared to the HL-60 cell line. An analysis of microRNAs expression in the K562 and HL-60 cell lines after irradiation has shown that chromosome abnormalities can affect microRNA expression changes. A study of radiation-induced changes of microRNA expression profiles in the K562 and HL-60 cell lines has revealed a dependence of microRNA expression changes on the number of chromosome aberrations and genome mutations. Radiation therapy is widespread for cancer treatment . HoweverThe main mechanism regulating gene expression at the posttranscriptional stage is the regulation of mRNA degradation by microRNAs, which are non-coding RNAs of 20–24 nucleotides in length. In the human genome, more than one thousand microRNAs have been discovered. Each microRNA is able to regulate thousands of microRNAs. Therefore, they play a determining role in many cellular processes including cell radiosensitivity and radioresistance. It has been shown that microRNAs regulate the expression of about 30% of protein-coding genes . In receExpression of protein-coding genes and microRNA can change in the presence of chromosome abnormalities associated with the changes in chromosome number and structure of cancer cells . ChromosThus, estimating microRNA expression and its involvement in radioresistance mechanisms, chromosome abnormalities, and genetic mutations has to be taken into account. In this study, we perform an analysis of the radiation-induced dynamics of the microRNA expression profiles of radiosensitive and radioresistant cell lines with different chromosome abnormalities.Thus, the most pronounced changes in microRNA expression profiles are recorded 1 h after irradiation. Changes in the number of expressed microRNAs have been observed for all chromosomes. Variations in the number of expressed microRNAs in chromosomes are more noticeable in the K562 cell line.IR/SNEC). Negative values of log2(SNEIR/SNEC) show an increase of microRNA effect on a particular signaling pathway, i.e., signaling pathway activity decreases. Positive values of log2(SNEIR/SNEC) show a decrease of microRNA effect, so the signaling pathway activity increases. For convenience, each signaling pathway has been assigned with a code. Signaling pathways with the log2(SNEIR/SNEC) value higher or lower than 0.5 in at least one experimental point have been taken for analysis.Regulation of gene expression by microRNA is a complex process. Note, one microRNA can regulate the expression of many genes and the expression of one gene can be regulated by many different microRNAs. So, the study of microRNA differential expression is uninformative for understanding of radioresistance mechanisms. Thus, for more comprehensive study of radioresisitance, we have analyzed the distribution of microRNA sum of normalized expression (SNE) for intracellular signaling pathways. In IR/SNEC) change; (2) signaling pathways with an opposite dynamics of log2(SNEIR/SNEC) change; (3) signaling pathways with a partially opposite dynamics of log2(SNEIR/SNEC) change.IR/SNEC) value is 2–3 times less than in the HL-60 cell line. On the contrary, Shigellosis (hsa05131) and Bacterial invasion of epithelial cell (hsa05100) signaling pathways (IR/SNEC) value differs by 2–3 times compared to the HL-60 cell line.The first group includes 4 signaling pathways , code 2 IR/SNEC) value, indicating the decrease of microRNA effect on these signaling pathways. In the K562 cell line, microRNA effect on these signaling pathways is increased. The rest of the signaling pathways (IR/SNEC) values are −0.39 and 0.78 in the HL-60 and K562 cell lines, respectively.The second group includes 12 signaling pathways  values are −1.68 and −1.52 in Amoebiasis (hsa05146)  includes 24 signaling pathways (sa05146) , code 8 sa05146) , code 14Thus, analysis of the dynamics of microRNA effect on signaling pathways has revealed significant differences in 40 signaling pathways. After radiation exposure, the activity of these signaling pathways can differ significantly between the radioresistant K562 cell line and radiosensitive HL-60 cell line.There are significant differences in the number of chromosomes between the HL-60 and K562 cell lines. The K562 cell line has monosomy or trisomy in 14 pairs of chromosomes, whereas the HL-60 cell line has only 4 pairs of chromosomes with such abnormalities. Changes in the amount of DNA in the cell can significantly affect gene expression. The diagrams in The differences in scattering of the log2fc values are clearly shown in Thus, analysis of the dynamics of microRNA expression depending on chromosomal localization has shown a greater scattering of log2fc values for each pair of chromosomes in the K562 cell line compared to the HL-60 cell line.IR/SNEC) values less or more than 0.5 . Our analysis reveals a difference in the microRNA pressure obtained for the radiation-resistant and radiation-sensitive cell lines. Fourty signaling pathways with high differences in log2(SNEIR/SNEC) have been revealed. Among them, one can see the signaling pathways already associated with radiation resistance (IR/SNEC) values in radioresistant and radiosensitive cell lines are significantly different. We believe a large number of chromosome and genome mutations in the K562 cell line are responsible for these differences. Chromosome and genome mutations lead to change in the amount of genetic material and can mechanistically influence gene expression and signaling pathway function [The effect of microRNA on signaling pathways is evaluated as the binary logarithm of a ratio between the sum of the normalized microRNA expressions in irradiated cells and a similar sum in cells of the control group log2 values in radioresistant and radiosensitive cell lines. However, comparing hsa-mir-146a-5p gene localization in the K562 and HL-60 cell lines and its expression (IR/SNEC) values are related to mechanistic effects of chromosome abnormalities and are specific for our cell lines only.For further analysis, microRNAs located in chromosomes with monosomy and trisomy have been selected. Chromosomes with this type of mutation are not subjected to large-scale functional rearrangements and can preserve mechanisms for the regulation of gene expression typical for normal chromosomes. Commonly, the expression of genes located in chromosomes with trisomy is increased  and in cpression , one canThus, we can explain partially an absence of single-valued data on microRNAs, genes, and signaling pathways associated with radioresistance of cancer cells. If the location of chromosomes and the presence of genomic mutations and chromosomal aberrations are not taken into account in the analysis of microRNA expression, one can conclude that these microRNAs are markers of radioresistance in cancer cells. However, in reality, the difference in the microRNA expressions is due to chromosomal aberrations in the studied cell lines.(1)the HL-60 cell line, a radiosensitive human promyelocytic leukemia cells ,del(7)(p?),der(7)t,t,add(8)(q?),der(9)del(9)(p2?),t,del(10)(p?),ins,der(14)t,−15,der(16)t,der(16)t,+18) ;(2)the К562 cell line, a radioresistant chronic myeloid leukemia cells add(2)(q33),+4,+5,der(?)t,dup(6)(p12~p22),−7,inv(7),del(7)(p15),der(7)readel(7),+8,−9,−9,dup(9)(q34),del(9)(p12),der(10)t,dert,+11,der(?)t,−12,der(12)t,der(12)t,der(?)t,−13,der(13)t,+15,+16,−17,der(17)t×2,−19,der(19)t,der(?)t,der(22)(q11.2),del(X)(p11)) .In the experiments we used:2. Cells were maintained in RPMI-1640 culture medium containing 10% FBS  that was supplemented with 50 μg/mL gentamycin  and L-glutamine .Cells were grown at 37 °C in a humidified atmosphere of 5% COCell lines were exposed to X-rays at a dose of 4 Gy (photon energy of 10 MeV) using the Elekta Synergy linear accelerator . Cells were irradiated for 55 s at room temperature. Cancer cells were in 6 well plates. Transportation of the plates with cell cultures to the site of irradiation and back was performed in a thermostated container at a temperature of 37 °C. All experiments with the cells were carried out during the logarithmic growth phase. Manipulation with the control group was similar to the experimental group, except for irradiation.Cell viability was evaluated using a mixture of fluorescent dyes: Propidium iodide and acridine orange. Analysis of stained cells was carried out using fluorescence microscopy methods .The total number of microRNA-containing RNA was isolated from the cells using Absolutely RNA miRNA kit  1, 4, and 24 h after irradiation. Using the Agilent 2100 Bioanalyzer capillary electrophoresis device, the quality of the isolated RNA was evaluated by the ratio of 18S/28S RNA. For further studies, samples were taken with the RNA integrity number (RIN) > 8.0. Using the NEBNext Small RNA Library Prep Set , cDNA libraries were prepared from microRNA. Purification of the libraries was performed by electrophoresis in 6% polyacrylamide gel. The cDNA regions corresponding to the microRNA between 145 and 160 bp were cut from the gel and isolated. In all the microRNA libraries obtained, concentration of cDNA was measured using a Qubitfluorometer . Equimolar amounts (2 nM) of sample were taken from each library and a pool of libraries was prepared, which was sequenced using the MiSeq System  high-throughput sequencing system employing a 150-bp single-ended reading kit.5 reads. During the data processing, the number of differentially expressed microRNAs and the number of the same differentially expressed microRNAs in two cell lines were calculated. Further, a search was carried out for genes, the expression of which was regulated by the microRNAs obtained during the sequencing. To continue the analysis, microRNAs were taken with normalized expression > 100 pieces. As a result, lists of genes were created, the expression of which regulates microRNA with normalized expression > 100 pieces, for each cell line 1, 4, and 24 h after irradiation. The expression of microRNA was determined by the log2 ratio of the sums of normalized microRNA expressions in the experiment to the control value: log2(SNEIR/SNEC).After sequencing, FASTQ files were obtained. Data were processed using the GenXPro omiRas services. As a result, tables were created with data that included the microRNA name and the number of microRNA normalized for 10http://www.genome.jp/kegg/). Since each microRNA can have an effect on different genes, the normalized expression of microRNA for each gene (NEG) was calculated by the formulai-microRNA for 105 reads, AThe search for signaling pathways comprising these genes was performed in the KEGG database  (the binary logarithm of the SNE ratio between experiment and control). Signaling pathways demonstrating the log2(SNEIR/SNEC) > 0.5 and the log2(SNEIR/SNEC) < −0.5 in at least one experiment were selected. The obtained data are shown in diagrams.The sums of normalized expression of the experiment (irradiated cells) and control (unirradiated cells) were compared and given as the log2(SNEhttps://www.ncbi.nlm.nih.gov/).The localization of microRNA in chromosomes was determined using the National Center for Biotechnology Information Search database (2) was calculated by the formula Dispersion of the log2fc values for each individual pair of chromosomes . Differences between irradiated and control cells are regarded as statistically significant when Cell cultures used in the research were obtained from RCCC of V (Russian cell culture collection of Vertebrates) . No human/animal directly participated in biomaterial sampling process during this research. Local Russian regulations require no approval for using cell line biomaterials for scientific research purposes .An analysis of radiation-induced change in the profiles of the microRNA expression of the K562 and HL-60 cancer cell lines allows for the conclusion that the change in expression in response to the radiation exposure in our experiments can depend on the number of chromosome aberrations and the genome mutations of cell lines. microRNA localization and the presence of chromosome aberrations and genome mutations have to be taken into account in estimation of the relation between the radioresistance of cancer cells and profiles of microRNA expression.


Squamous cell carcinoma (SCC) is well-known for its high rate of metastasis with poor prognosis. CD109 is a glycosylphosphatidylinositol-anchored cell-surface glycoprotein. Recently, CD109 emerges as a potential biomarker and a therapeutic target for SCCs. Accumulating studies have reported that CD109 is highly expressed in human SCCs of multiple organs, and may contribute to the progression of SCCs. In this review, we summarized the findings on expression pattern of CD109 in SCCs, and discussed the molecular mechanisms underlying the roles of CD109 in pathogenesis of SCCs. Squamous cell carcinoma (SCC), also known as prickle cell carcinoma, is one of the leading causes of cancer-related death in worldwide. SCC is a malignant epithelial tumor , arisingCD109 gene is located in chromosome 6q, constituting approximately 3.3% of the total CD109 genomic sequence with its 33 exons [Cell surface antigen CD109 is a glycosylphosphatidylinositol (GPI)-linked glycoprotein of approximately 170 kDa and a member of the a2 macroglobulin (a2M)/C3, C4, C5 family of thioester-containing proteins [+ positive bone marrow mononuclear cells, activated T lymphoblasts, activated platelets, endothelial cells, mesenchymal stem cell subsets and several human tumor cell lines [CD109 was first identified as a cell-surface antigen by a monoclonal antibody raised against the primitive lymphoid/myeloid cell line KG1a . Previoull lines , 12, esoll lines , 13, utell lines , skin [1ll lines , penis [ll lines , oral call lines  and gallll lines . CD109 ill lines . Loss anll lines –18. In tIn tumor tissues, CD109 was immunohistochemically detected in SCCs –18 as weLung squamous cell carcinoma (LSCC) is one of the major subtypes of non-small-cell lung cancer (NSCLC), and accounts for approximately 20–30% of cases of NSCLCs . CD109 eEsophagus squamous cell carcinoma (ESCC) accounts for one-sixth of all cancer-related mortality, occurring at a higher incidence in Asian countries . The proCervical (uterine cervix) cancer accounts for about 12% of cancer-related death of women , 31. SCCCutaneous squamous cell carcinoma (CSCC) is the second most common type of non-melanoma skin cancer (NMSC) with a constantly increasing incidence . AlthougOral squamous cell carcinoma (OSCC) accounts for 2–3% of all cancers worldwide . Loss ofGallbladder cancer (GBC) is the most aggressive of the biliary cancers with shortest median survival . GallblaThe TGF-β signaling pathway is involved in many cellular processes including cell growth, cell differentiation apoptosis, and cellular homeostasis , 41. TheReceptor endocytosis is a pivotal regulatory mechanism in signal transduction. TGF-β receptors are internalized via both clathrin- and caveolae-dependent pathways. Internalization of the TGF-β receptors via the clathrin-coated pits has been linked with signaling via Smad2/3 and receptor recycling. In contrast, TGF-β receptor localization in caveolae is associated with downregulation of Smad2/3 signaling and receptor degradation following ubiquitination by the E3-ubiquitin ligase Smurf2 . HoweverTGF-βR1 gene have also been found in SCCs of the skin, suggesting that the inactivation of TGF-β leads to the initiation of SCCs [Dysregulation of the TGF-β pathway has been implicated in multiple types of cancer . Studies of SCCs .CD109 is a TGF-β co-receptor , and modHowever, Vorstenbosch et al.  reportedBesides, TGF-β1 is a potent inhibitor of growth in most epithelial cells . HagiwarSignal transducer and activator of transcription factor 3 (STAT3) is critical to cell proliferation, differentiation, migration, survival, and oncogenesis , 55. LitAlthough knockdown of CD109 in human keratinocytes and lung adenocarcinoma cells downregulates STAT3 signaling in vitro , 57, theThe discrepancy of the results from in vitro and in vivo studies might be caused by the systematic changes of microenvironment in the tissues of CD109 deficient mice. Loss of CD109 in all the cells in mice might modify the subcutaneous microenvironment which activates STAT3 signaling in keratinocytes. In addition, CD109 may exert distinct regulatory effects in different cell types, leading to cell-type specific modification in STAT3 signaling. In addition to keratinocytes, CD109 is expressed in endothelial cells, epithelial cells, and fibroblasts , 19, 42,Epidermal growth factor receptor (EGFR) is a member of the ErbB family of receptors. Upon ligand binding by EGF, EGFR forms dimers, either homodimers or heterodimers with another member of the ErbB family HER2 . The dimTo date the broad picture of tumorigenesis and development of SCC remain incomplete. There are cross-talks between TGF-β and STAT3 or EGFR signaling pathways. CD109 is likely one of the key effectors of the signaling network regulating SCCs. However, no direct evidence has been reported to delineate the roles of CD109 in pathogenesis of SCCs.Studies from human tissue samples indicate that CD109 is highly expressed in SCCs of multiple organs, particularly in well-differentiated malignant squamous cells , 17. By CD109 is a GPI-linked glycoprotein, which enables it to be released from the membrane . The solExosomes communicate primary tumor lesion and its niche via its package containing selected proteins or other molecules . The isoIn addition to the potential as a biomarker, CD109 might be a target for therapeutic approaches. CD109 is a membrane protein , which cSquamous cell carcinoma (SCC) is one of the most common cancers of epithelial origin. CD109 is a cell-surface antigen and belongs to the α2-macroglobulin-C3, C4, C5 family of thioester-containing proteins. Studies from human tissue samples indicate that CD109 is highly expressed in SCCs of multiple organs, particularly in well-differentiated malignant squamous cells. Besides, CD109 is a co-receptor of TGF-β and negatively regulate TGF-β signaling via formation of a receptor complex with TGF-βR1 and TGF-βR2. In addition, CD109 interact with JAK/STAT3 and EGFR signaling pathways in other cell types. However, there is no direct evidence that CD109 regulate the development of SCCs by these signaling pathways. Further studies are needed to investigate the role of CD109 highly expression in pathogenesis of SCCs and the underlying molecular mechanisms.


We demonstrated that activation of tTreg using the CD4-binding human immunodeficiency virus-1 protein gp120 leads to a substantially increased suppressor activity of tTreg without the need for additional expansion. Gp120-activated tTreg prevent GvHD development in a preclinical humanized mouse model. In addition, gp120 is not only effective in prevention but also in therapy of GvHD by suppressing all clinical symptoms and improving survival of treated mice. These data indicate that tTreg activation by gp120 is a feasible and potent strategy for significant functional improvement of tTreg as cellular therapeutic for GvHD treatment without the need of complicated, time-intensive, and expensive in vitro expansion of isolated tTreg.Allogeneic hematopoietic stem cell transplantation is the only curative treatment option for several hematological malignancies and immune deficiency syndromes. Nevertheless, the development of a graft-versus-host disease (GvHD) after transplantation is a high risk and a severe complication with high morbidity and mortality causing therapeutic challenges. Current pharmacological therapies of GvHD lead to generalized immunosuppression followed by severe adverse side effects including infections and relapse of leukemia. Several novel cell-based immunomodulatory strategies for treatment or prevention of GvHD have been developed. Herein, thymus-derived regulatory T cells (tTreg), essential for the maintenance of peripheral immunologic tolerance, are in the focus of investigation. However, due to the limited number of tTreg in the peripheral blood, a complex, time- and cost-intensive  Allogeneic hematopoietic stem cell transplantation (aHSCT) is a potentially curative therapy for many patients with hematological malignancies or immune deficiencies. However, the development of graft-versus-host disease (GvHD) dramatically limits the efficacy of aHSCT, and is the leading cause of long-term morbidity and mortality . The rep+ donor T cells in the graft which differentiate into IFN-γ and IL-17 producing T effector cells  as the naïve CD4+ T cells preferentially differentiate into T effector cells, and inflammatory cytokines block the suppressive function of tTreg  recognize the recipient’s cells and tissues (the host) as foreign, leading to a complex interaction between donor and recipient adaptive immunity followed by massive host tissue destruction . The cliof tTreg –11. TherUp to now, systemic corticosteroid therapy remains the first line treatment for GvHD. The need for new or improved therapies based on manipulating immune responses has extremely increased in the last decades, especially in cases of steroid-refractory GvHD patients. Targeting and modulating T cell responses, the etiological factors in GvHD induction, seems to be a promising strategy.Thymus-derived Treg, comprising 2–5% of all peripheral blood cells in humans , are keyA significant challenge in the development of efficient tTreg cellular therapies is the low rate of these cells in the peripheral blood. Since in murine models high numbers of tTreg are needed for a marked reduction of GvHD, it has been postulated that ratios of nearly 1:1 tTreg to T effector cells are necessary for successful GvHD prevention , 20. How+CD25+CD127low) and their efficacy in vivo  stimulation and costimulation for functional activation. Without this stimulation, only the few percent alloreactive tTreg are functionally activated and effective suppressor cells. Reagents that allow a polyclonal activation of tTreg without stimulation of conventional T cells would shift the balance in favor of the tTreg and significantly reduce the necessary number of tTreg needed for GvHD suppression. CD4-mediated activation of tTreg is such a possibility .+ T cells into induced Foxp3+ Treg. The induced tolerance by such treatment is “dominant, transferable to naïve recipients, and transferred CD4+ T cells have the ability to “infect” naïve T cells to acquire a tolerant state”  blocked the activation, differentiation, and expansion of CD4+ T cells and subsequently prevented all signs of GvHD, demonstrating that this human/mouse chimeric animal model is applicable for the analysis of human tTreg function, and tTreg cellular therapeutics in vivo. In accordance with published observations, transferred tTreg ratios lower than 10:1 were not effective in GvHD prevention. These results confirm the necessity of high tTreg numbers for successful suppression of GvHD.To investigate the potential tTreg-stimulating properties of gp120 red PBMC , 41. GvHin vitro experiments showed that CD4-mediated stimulation of tTreg by gp120 significantly improves their suppressor function. Consequently, significantly lower tTreg ratios were sufficient to inhibit a mixed leukocyte reaction in vitro. Therefore, we postulated that lower tTreg ratios should also be sufficient to prevent GvHD development in vivo after CD4-mediated activation of Treg. We investigated this possibility by directly using gp120 as tTreg activator in the humanized GvHD mouse model. Indeed, a single injection of 5 µg gp120 with 5 × 106 PBMC, without additional transfer of tTreg, was able to completely suppress the formation of GvHD  and EP2332991 (A3)] that are in connection with the use of CD4-dependent activation of human tTreg and their potential clinical use.


Pancreatic ductal adenocarcinomas are among the most malignant neoplasms and have very poor prognosis. Our understanding of various cancers has recently improved the survival of patients with cancer, except for pancreatic cancers. Establishment of primary cancer cell lines of pancreatic ductal adenocarcinomas will be useful for understanding the molecular mechanisms of this disease.KRAS and TP53 sequencing or SMAD4 and p53 immunohistochemistry. Xenografts using AMCPAC cell lines were established.Eighty-one surgically resected pancreatic ductal adenocarcinomas were collected. Six novel pancreatic cancer cell lines, AMCPAC01–06, were established and histogenetic characteristics were compared with their matched tissues. The clinicopathologic and molecular characteristics of the cell lines were investigated by KRAS in codons 12, 13, or 61 and TP53 in exon 5 as well as showed aberrant p53  or DPC4  expression. AMCPAC cell lines demonstrated homology for the KRAS mutation and p53 expression compared with matched primary cancer tissues, but showed heterogeneous DPC4 expression patterns.From the 81 pancreatic ductal adenocarcinomas, six (7.4% success rate) patient-derived primary cell lines were established. The six AMCPAC cell lines showed various morphologies and exhibited a wide range of doubling times. AMCPAC cell lines contained mutant The novel AMCPAC01–06 cell lines established in this study may contribute to the understanding of pancreatic ductal adenocarcinomas.Trial registration Retrospectively registeredThe online version of this article (doi:10.1186/s12935-017-0416-8) contains supplementary material, which is available to authorized users. Ductal adenocarcinomas are the most common malignant neoplasms of the pancreas and account for approximately 85–90% of malignant neoplasms arising from the pancreas (known as pancreatic cancers). Pancreatic cancers are the ninth most common cancer in Korea , and theRecently, several trials were conducted to examine customized cancer treatment using patient-derived pancreatic cancer tissues –13. HoweIn addition, some different clinicopathologic features were observed in pancreatic cancer patients of different ethnicities. Therefore, establishing novel primary pancreatic cancer cell lines derived from Korean pancreatic cancer patients and applying various chemotherapeutic regimens to the developed primary pancreatic cancer cell lines can help in the determination of highly sensitive chemotherapeutic regimens for individual pancreatic cancer patients, particularly when regional recurrence or distant metastasis develops.3 in size were obtained from 81 surgically resected pancreatic ductal adenocarcinomas, immediately soaked in RPMI640 media , and transferred in a laminar flow biosafety cabinet.After approval (2015-0480) from the institutional review board, fresh tumor tissues measuring 0.5 × 0.5 × 0.2 cm3 fragments, and digested with 0.1% (W/V) of collagenase type 1  at 37 °C for 20 min. The resulting fragments were centrifuged at 200×g for 5 min, washed thrice with phosphate-buffered saline, plated onto RPMI1640 media (GIBCO) containing 10% fetal bovine serum (GIBCO) and 1% penicillin/streptomycin (GIBCO), and allowed to adhere. After incubation for several days, mixed growth of cancer cells and fibroblasts was observed in the tissue fragments. To overcome fibroblast overgrowth, periodic trypsinization was conducted by incubation with 0.005% trypsin/EDTA (GIBCO) at 37 °C for 3 min during 2–3 passages to remove fibroblasts, and unwanted fibroblasts were detached by pipetting. The primary cell culture was monitored with a phase-contrast microscope. Cancer cells were grown at 37 °C in a humidified atmosphere with 5% CO2.Pancreatic tumor tissues were rinsed with Hank’s balanced salt solution in clean bench, minced into <2 mm4 cells were seeded into 96-well plates, 0.5 mg/mL MTT was added over consecutive days for violet pellet formation by living cells. The pellets were solubilized in 200 μL of dimethyl sulfoxide. The optical density of each sample was measured at 570 nm using a microplate reader . Growth rate was measured as a percentage of control growth. Cells from passage 15 were used to determine population doubling time, and all experiments were repeated twice in triplicate.The cell growth rate was measured using 3--2,5-diphenyltetrazolium bromide  at 24-h intervals. After 1 × 106 cancer cells with a Cytorich Red fixative solution  for 48 h, the supernatant was removed after centrifugation. The pellets were additionally fixed with 95% ethanol for 60 min then embedded in paraffin. Each cancer cell block was selected as a donor, and the designated areas for each cell block were punched with a 5-mm diameter cylinder by a Manual Tissue Microarrayer  and transferred to a recipient block, and cell microarrays (CMAs) were constructed.After fixation of 5 × 102O2 for 10 min, and heat-induced antigen retrieval was performed. Primary antibodies with Benchmark autostainer  were used as per the manufacturer’s protocol. Primary antibodies for cytokeratin 19 , p53 , and DPC4  were incubated at room temperature for 32 min, and the sections were labeled with an automated immunostaining system with the I-View detection kit . Immunostained sections were lightly counterstained with hematoxylin, dehydrated in ethanol, and cleared in xylene.Immunohistochemical labeling was performed by the immunohistochemical laboratory of the Department of Pathology, Asan Medical Center. Briefly, 4-μm tissue sections from the CMA and matched formalin-fixed paraffin-embedded (FFPE) primary cancer tissues of ductal adenocarcinomas were deparaffinized and hydrated in xylene and serially diluted with ethanol, respectively. Endogenous peroxidase was blocked by incubation in 3% HTP53 gene with intragenic primers flanking these exons as previously described [TP53 gene sequencing was performed with BigDye 3.1 and a 3730xl DNA analyzer .The genomic DNA of the established cell lines was extracted using the QIAamp DNA Micro kit  following the manufacturer’s protocol. Polymerase chain reaction (PCR) amplification was performed with 10 ng of DNA covering exons 5–8 of the escribed . PCR-ampKRAS at codons 12, 13, and 61. Primer sequences of KRAS are summarized in Table KRAS. The biotin-labeled PCR products were immobilized on Streptavidin Sepharose HP beads  and the immobilized PCR products were sequenced using a pyrosequencer, PyroMark Q96MD System (Qiagen) according to the manufacturer’s protocol. Primer sequences used to amplify TP53 and KRAS are shown in Table Similarly, pyrosequencing was performed to detect 6 cancer cells from culture bilaterally into each flank of male NSG (5 weeks old) mice. Xenograft tumor growth was monitored twice per week for 3 months. Tumor volume (TV) was calculated according to the formula: TV (mm3) = length × width2 × 0.5. When a tumor size of 100–200 mm3 was reached, tumors were explanted from the mice. The explanted tumor xenografts were used for reimplantation and FFPE to confirm the histology.Tumorigenicity in mice was confirmed by subcutaneous injection of approximately 2 × 10t test and Mann–Whitney U test for unpaired observations.The significance of differences between experimental conditions was determined using the student’s Fresh tissues from 81 patients diagnosed with pancreatic ductal adenocarcinomas were used to establish primary cancer cell lines. Among the 81 cases, 75 cases (93%) failed to establish cell lines because of fungal or mycoplasmal contamination in 30 cases (37%) and overgrowth of cancer-associated fibroblasts in 45 cases (56%). Six (7.4%) patient-derived pancreatic cancer cell lines were established and named, AMCPAC01–AMCPAC06. The first sub-culture was performed 7–15 days after initiating primary culture for attachment and spreading, and the AMCPAC cell lines were passed a minimum of 10 times.Clinicopathologic characteristics of the established cancer cell lines are summarized in Table To more efficiently investigate cytological characteristics, CMAs were constructed with the AMCPAC cell lines. The CMA block was composed of 6 wells containing each AMCPAC cell line, and CMA sections were stained with hematoxylin and eosin; matched tissue samples of pancreatic ductal adenocarcinomas were included Fig. b. CytoloKRAS was detected in all AMCPAC cell lines. Mutations in KRAS codon 12 were detected in AMCPAC01, AMCPA02, AMCPA03, and AMCPA06, while a mutation in KRAS codon 13 was detected in AMCPAC04. And two mutations in KRAS codons 60 and 61 were detected in AMCPAC05  based on Clinvar database analysis  from Korean patients with pancreatic ductal adenocarcinomas. The success rate of cancer cell line establishment was 7.4%. The main reason for failure was overgrowth of cancer-associated fibroblasts, accounting for 56% of samples. Ductal adenocarcinoma of the pancreas is characterized by extensive desmoplastic stromal reactions; therefore, the density of cancer cells in limited areas was very low. This low cellularity of cancer cells may prohibit the establishment of primary pancreatic cancer cell lines. To overcome these limitations, several primary cell culture methods have been developed such as patient-derived tumor xenografts or three-dimensional tumor organoid models –24. In tKRAS is the most commonly activated oncogene in pancreatic cancer. More than 90% of pancreatic cancer patients show KRAS mutations, including a low grade of pancreatic intraepithelial neoplasm, a pancreatic cancer precursor [KRAS mutations were reported to commonly occur as G12D  and G12 V  on codon 12 by Sanger sequencing [KRAS mutations were evaluated by pyrosequencing, which is more sensitive compared to conventional Sanger sequencing. All 6 pancreatic cell lines contained various KRAS mutations, 4 of them had mutation in codon 12, one in codon 13, which was observed in previous whole exome sequencing studies [KRAS mutations detected in AMCPAC cell lines were reported in various cancers, such as pancreatic carcinoma, non-small cell lung cancer, stomach cancer, colorectal cancer, and leukemia [KRAS mutation in AMCPAC01 are rare, but have been previously reported in one pancreatic and one colorectal cancer patients [KRAS mutations were detected throughout proximal region of codon 61. Q61L KRAS mutation is also rarely reported in pancreatic cancer or neoplasm [KRAS has not reported any previous studies and variation databases, including ClinVar and COSMIC . Finally, AMCPAC cell lines harbor various and noble KRAS mutations and these are useful resources for pancreatic cancer research when associated with KRAS mutation type.recursor , 26. In quencing . In the  studies –30. The leukemia . Nucleotpatients , 38. In neoplasm , 40. HowTP53 was analyzed because genetic alternations in KRAS, TP53, and SMAD4 play key roles in the tumorigenesis of pancreatic ductal adenocarcinoma [TP53 mutations are frequently mutated in pancreatic cancer; approximately 38% of Korean pancreatic cancer patients contain TP53 alternations [TP53 mutations only in exon 5, and AMCPAC02 and AMCPAC03 showed no TP53 gene alterations. TP53 mutations in AMCPAC04 and AMCPAC06 cells are known pathogenic mutations, while those in AMCPAC01 and AMCPAC05 were newly identified [The mutational status of arcinoma –43. TP53rnations . Four AMentified .CMAs are novel multiplex array techniques for immunocytochemistry evaluation to detect the molecular composition and function of cell lines. The use of an agarose matrix or agarose mold for cell microarray construction has been reported. In this study, a cell block was used for cell microarray by directly paraffin embedding the cell pellet, resulting in the highest cell density in an array core compared to other methods .KRAS, p16/DCKN2A, GNAS, and BRAF are mutated early in pancreatic cancer progression, while SMAD4/DPC4 and TP53 are mutated at later stages [TP53 [TP53 sequencing revealed a deletion (TGCCCTCAAC) on exon 5 of TP53 which was associated with the loss of p53 protein expression. Several studies reported worse prognosis of cancer patients with a lack of p53 expression compared to those with missense TP53 mutations in lung and ovarian cancers [It is known that r stages , 46. To es [TP53 . Our TP5 cancers , 49.SMAD4/DPC4 mutation, immunohistochemical staining for DPC4 protein is used as a diagnostic criteria of pancreatic cancer with lower cost than sequencing of SMAD4/DPC4 [Because DPC4 protein loss is correlated with AD4/DPC4 . Loss ofAD4/DPC4 –53. In tGenetic heterogeneity occurs in various situations, such as between separate cancers with the same histologic subtypes, primary and metastatic cancers from the same individuals, and separate regions from the same cancers –58. AddiTP53 and DPC4 signaling pathways. In total, 3 cell lines have been previously established from Korean pancreatic cancer patients, including SNU213, SNU324, and SNU410, which are available from the Korean Cell Line Bank. We have added 6 newly established pancreatic cancer cell lines from Korean patients. Different molecular pathologic characteristics of our new established AMCPAC cell lines may provide diversity and help in determining the molecular pathological basis of pancreatic cancer in different ethnicities from other previously established pancreatic cancer cell lines.AMCPAC cell lines can provide a resource for pancreatic cancer studies, including the basic and translational research fields. For example, comparison of DPC4(+)/P53(−) (AMCPAC01) and DPC4(+)/P53(+) (AMCPAC02–06) cell lines will be helpful in understanding the interactions of We established and characterized 6 novel pancreatic cancer cell lines (AMCPAC01–AMCPAC06). These novel cell lines may contribute to the understanding of pancreatic ductal adenocarcinomas, including the molecular basis of tumorigenesis, progression, and metastasis of pancreatic cancers.