Mesenchymal Stem Cells (MSCs) are a promising mammalian cell type as they can be used for the reconstruction of human tissues and organs. MSCs are shown to form bone, cartilage, fat, and muscle-like cells under specific cultivation conditions. Current technology of MSCs cryopreservation has significant disadvantages. Alternative technologies of mammalian cells preservation through lyophilisation or desiccation (air-drying) are among the upcoming domains of investigation in the field of cryobiology. Different protectants and their combinations were studied in this context. Loading of the protectant in the live cell can be a challenging issue but recent studies have shown encouraging results. This paper deals with a review of the protectants, methods of their delivery, and physical boundary conditions adopted for the desiccation and lyophilisation of mammalian cells, including MSCs. A hybrid technique combining both methods is also proposed as a promising way of MSCs dry preservation. Mesenchymal Stem Cells (MSCs) are multipotent stromal cells able to differentiate into different cell types, including chondrocytes, adipocytes, and osteoblasts –3. MSCs The usage of MSCs requires their preservation in a way allowing them to be rapidly available for an application. The commonly used approach for MSCs storage is cryopreservation using liquid nitrogen . This teThis paper discusses the fundamental principles, mechanisms, and advantages of the lyophilisation and desiccation usage for MSCs preservation. It also briefly describes an application and possible crosstalk of these technologies, usage of different lyoprotectants, and physiological factors involved in the cell response.Lyophilisation is a process of drying of the frozen sample when frozen water is removed in two steps: primary drying (sublimation), followed by secondary drying (desorption). Since lyophilisation is performed at low temperature, it is used to stabilize and prolong shelf life of thermolabile products and those products which are otherwise unstable in aqueous state and hence need to be dried. The principle involved is sublimation of water at temperature and pressure below its triple point, that is, 611 Pascal and 0.0098 degree Celsius. Sublimation takes place after the sample is frozen by supplying heat through conduction or radiation or both. The driving force behind the sublimation or removal of water is the water vapour concentration gradient between the drying front and the condenser . SimilarDesiccation is a nature-driven process, widely used by plants and lower animals as a way to survive the water deficiency. In response to desiccation, such organisms switch to the specific state of suspended animation, anhydrobiosis, allowing them to revive when water returns . OrganisA variety of protectants were tried for the dry preservation of mammalian cells. Among them, nonreducing disaccharides trehalose and sucrose are the most popular because they are used by almost every anhydrobiotic animal. There are two main hypotheses explaining the protective effect of sugars in desiccation: (i) water-replacement hypothesis and (ii) vitrification hypothesis . The watNonreducing sugars were preferred as protectants for desiccated objects since they are less reactive than reducing monosaccharides or disaccharides like maltose. Maltose has proven to be an effective lyoprotectant for DNA , but itsTg) than sucrose which means that when a small amount of water is added, Tg for sucrose goes below the storage temperature and hence the sample dried with sucrose degrades more rapidly as compared to trehalose dried product (whose Tg remains above storage temperature). In addition, antioxidant effect of trehalose was shown [Trehalose is widely assumed as the protectant superior to others in the desiccation context. The hydration radius of trehalose is 2.5 times greater than that of sucrose, which implies that 2.5 times the concentration of sucrose is required to provide the same protein protection properties as trehalose . Trehaloas shown . Hence, Different combinations of protective agents were tried for the desiccation of cells, since it is clear from existing studies that anhydrobiotic animals typically use the interplay of several mechanisms. Similarly to the case of trehalose, these combinations were mostly inspired by their naturally existing prototypes in different anhydrobiotic organisms. Study shows that more than 40 mM trehalose could effectively maintain the viability of MSC more than 92% for 7 days at 4°C storage temperature . Polypedilum vanderplanki [Expression of small heat-shock proteins (HSPs) is tightly linked to the stress tolerance, including the desiccation tolerance in African chironomiderplanki . Probablerplanki . This imerplanki . Polypedilum vanderplanki was reported to ensure the same level of protection as intracellular trehalose, as judged by membrane integrity after rehydration [Another promising way to improve desiccation tolerance of cells is the use of LEA proteins. LEA proteins stabilize vitrified sugar glasses which are important in the dried state  and protydration . Since eydration , 25, theCombination of trehalose with other molecules has also been tested for an increase in cell viability compared to usage of trehalose alone. A study involving desiccation of bovine sperm using sorbitol as a substitute for trehalose proves that in bovine sperm during dehydration it can be used as an excellent osmolyte and it also offers improvement in motility of sperm during desiccation. Sorbitol was found to be enhanced protection on permeating through the plasma membrane, whose mechanism is yet to understand properly . GlyceroRecent studies involve the components of stress alleviating pathways such as chelators. For instance, sperm exhibited improved desiccation tolerance and enhanced survival rates when they were coupled with either sucrose or intracellular trehalose along with 1 mM Desferal to the desiccation buffer. Although sperm motility at postdrying stage did not show significant improvement, an increase in membrane integrity of sperm was observed . FurtherSynergistic effect of trehalose and Arbutin (antioxidant) associated with an induction of HSPs in human MSCs was also shown . These fPresence of protectants such as trehalose on both sides of the cell membrane is important for adequate protection of intracellular components and cell membrane. However, the key protectants trehalose and sucrose are not membrane permeable. In typical case, MSCs do not have transporters for those protectants. The possible use of native membrane pores such as P2X7  is very The number of proteins were shown to transport trehalose or other protective sugars into the cells, such as MalEFGK2, MAL11/AGT1, or TRET1 . The advDirect methods of trehalose loading have also been used for the delivery of trehalose. Electropermeabilization was first tried as a trehalose delivery mechanism by Shirakashi et al. in mouse myeloma cells in isotonic trehalose substituted medium . Microinπ–π bond, was successfully loaded into the Mouse Embryonic Fibroblasts (MEFs) [ Staphylococcus aureus  α-hemolysin to create pores and to load trehalose into the cytoplasm of fibroblast cells [A novel technique of trehalose loading involves the use of basic amino acid rich cell penetrating peptides (CPPs), such as peptide KRKRWHW . This pes (MEFs) . This CPst cells . In thisRecently, a successful trehalose loading into cells was achieved through modification of its molecule . IntraceThe physical conditions of the macroenvironment and microenvironment of cells play a major role in the desiccation tolerance of desiccated cells. The beneficial aspect of this approach is an elimination of genetic engineering of the cells. A thorough study of the effects of various physical conditions should be carried out to make conclusions for optimization of favourable limits for all the physical parameters. Primarily, vacuum conditions improve the cell tolerance to desiccation in case of lyophilisation compared to air-drying. In case of MSCs, lyophilisation can ensure the cell viability up to 70% even without trehalose . HoweverElectromagnetic cryopreservation is undergoing a lot of research as a way of cryopreservation of MSCs, since static and oscillating electric fields and magnetic fields influence the ice formation . Dental We have studied also the effect of drying rate on cell viability. Cultured cells were dried in tissue culture plates in the airflow of a biosafety cabinet. The drastically increased cell-death was observed in an absence of trehalose (data not shown). Ceasing laminar airflow caused a reduction of the rate of desiccation and an increase of cell recovery. Interestingly, clearly different cell viability was observed among different wells and within different areas of the same well. A better cell survival was observed at the well periphery, whereas reverse occurred at the centre of the well (data not shown).In a different set of experiments we demonstrated that MSC-like cells from third molar tooth germs maintained their biological properties, including expression of MSC surface antigens CD29, CD73, CD90, CD105, and CD166, expression of pluripotency associated genes, proliferation, and differentiation ability . ImportaLight causes deleterious effect on the cells during desiccation, probably because of the increase of the free radicals concentration. Fluorescence light was shown to generate free radicals within hamster of human cells, thereby facilitating oxidative DNA lesion and single-strand breaks. Therefore, cells desiccated and maintained at dark condition have greater viability than those exposed to the fluorescence light.In order to optimize the ability of cell culture to withstand desiccation, the effects of desiccation temperature and cell culture confluency were also investigated. Optimal desiccation tolerance was found at higher cell density. Cell aggregation decreases their tolerance to desiccation. The temperature of operation for cell desiccation greatly influences the viability of cells, and the optimal survival point was found to be slightly lower than room temperature (20°). Recovery of desiccated cells was sufficient at this optimum temperature only. Therefore, it can be summarized that vacuum encapsulates the cells during the process of desiccation thereby improving the cell viability. Also, it had been found that cells can survive without addition of carbohydrates (or polyols) if the slow process of desiccation is followed by vacuum storage. A consistent outcome of the procedure can be obtained by defining a desiccation process which preserves cellular structures. Even in this case, gradual loss of viability may be found because of destruction with time of the desiccated state due to release of free radicals .Survival of cells in dry and desiccated states is few of the most fascinating phenomena of nature and is lot to explore. The ability of MSCs to withstand these conditions for sustaining high degree of viability could have applications for wide spectrum of biological sciences, including tissue engineering. From above discussion it can be concluded that the lyophilisation and desiccation are two competing formulators for long-term storage of MSC. Both these technologies have been quite successfully applied in a wide range of fields including tissue engineering. Preservation of MSCs through desiccation in the dry state is used to meet the growing demand for MSCs storage and transport in regenerative medicine. Desiccation provides significant advantages over lyophilisation because of simplicity and higher energy efficiency. However desiccation may cause cell stress such as changes in cell volume, osmotic pressure, shrinkage of membrane/cell organelles, changes in activity of enzyme, metabolism downregulation, increased salt concentrations, cell viscosity changes, and stress protein production . LyophilThe potential of MSCs in regenerative medicine increases the importance of development of optimized protocols for their desiccation and lyophilisation. Uptake of protectants by MSCs is required for successful desiccation or lyophilisation. However, uptake of protectant by cells can cause cellular response specific to the cell type. Since HSPs are able to improve the cell viability and protect cells from apoptosis during freeze-drying , some MSThermodynamic aspects of the above two processes of preservation of MSC are interestingly competitive. Each process can involve quantization of temperature and pressure for transition from one state to another in order to improve cellular viability. In the figure, black arrowed path indicates the normal process of lyophilisation which involves vitrification followed by creation of extensive vacuum at a temperature of −79 degrees centigrade. This process is expensive as well as time consuming. On the other hand, desiccation involves greater cellular mortality due to rapid dehydration of cellular masses by overshooting the base temperature of the cellular medium to a higher point. A brief description of the heat transfer accounted during the process of lyophilisation is described below.λ, cp, and x is (1 × 10−3). Since the diameter of cellular suspension is much smaller than the length of UTS, therefore, heat transfer along the axis can be neglected. Additionally, thermal property within the UTS can be assumed to be uniform and temperature-independent. Thus, the above equation can be simplified asα is the thermal diffusivity.Bioheat equation for heat transfer in a system of suspended cells in ultrathin straw (UTS) undergoing for slow cooling or vitrification can be realised by zero-dimensional heat transfer equation and it is given asT0 and let the final temperature be T∞. Let the dimensionless form of temperature be expressed asJ0 and J1 are Bessel functions and are positive roots of the transcendental equation defined by ζn:h” and often it evaluates the quality of the cryopreservation system.Let the initial temperature of the suspension be x) is governed by the equationTm, Q, R, L, and rf refer to temperature at the end of the freezing process, activation energy, gas constant, latent heat, and radius of ice when x is equal to 1, respectively. The thickness of transition layer between liquid and solid (“mushy zone”) is λ, and n is the kinematic viscosity.The production of volume fraction of ice  is the projected path for the process of desiccation followed by lyophilisation. Sample is at room temperature at point 1; then, it is mildly heated with a gradual decrease in pressure to reach a state of point 2. Point 2 is defined as step-down transition point: slope and intercepts of this point are optimized in order to achieve maximum pressure drop, with minimum increase in temperature and maximum dehydration of the cell. Thus maximum cellular viability can be maintained. Once this state is achieved, the cell mass will be allowed to freezing state while maintaining the temperature to be constant, and point 3 will be reached. Point 3 is a critical point of freezing at low pressure when crystalline state of fluid present in intracellular and extracellular regions will be converted to glassy state. Then, desiccation will be followed by freeze-drying to reach point 4. Since vitrification of cell is done at lower pressure, it might happen that vitrification temperature will be much higher than −80 degrees centigrade. Therefore, pressure drop required to achieve vapour state from freeze state is much higher, reducing the cost of the combined process. Last step is sample dehydration by rapid temperature increase (up to room temperature) at constant pressure to reach point 5. This entire path of crosstalk is able to ensure two vital aspects of preservation of cells: (i) enhancement of cellular viability and (ii) decreasing lyophilisation (freeze-drying) cost. The scheme of hypothesized processes in the cells undergoing desiccation-lyophilisation in accordance with proposed method is depicted in the following .Cell preservation using crosstalk technique explained in the previous section can be achieved in a controlled chamber. Rate of dehydration of cell can be monitored continuously by a ultrasonic transducer measuring the humidity in the chamber. Cell viability can be continuously monitored by a droplet based remote microscopic system and real-time processing of obtained images. Output of this image-processing system can be used to control temperature and pressure in the chamber.Enhanced measures of protection are required for successful anhydrobiotic engineering of MSCs since existing protocols are not able to ensure robust cell recovery. Although the lyophilisation and desiccation are found to be efficient in some cases, the limitations of individual methods impart certain rigidity of their implementation. At the same time, maintenance of the dried cells viability in long-term storage is another critical issue which needs to be addressed. Continuous monitoring and control of preservation process can ensure successful MSCs preservation, but optimization for all the physical parameters is also needed.


The research of carcinogenetic mechanisms of breast cancer in different ethnic backgrounds is an interesting field, as clinical features of breast cancers vary among races. High premenopausal incidence is distinctive in East-Asian breast cancer. However, human cell lines derived from Asian primary breast tumor are rare. To provide alternative cell line models with a relevant genetic background, we aimed to establish breast cancer cell lines from Taiwanese patients of Han-Chinese ethnicity. Fresh tissue from mammary tumors were digested into organoids, plated and grown in basal serum-free medium of human mammary epithelial cells (HuMEC) with supplements. Cells were further enriched by positive selection with CD326 -coated micro-magnetic beads. Two breast cancer cell lines derived from premenopausal women were successfully established by this method, and named Chang-Gung Breast Cancer 01 (CGBC 01) and 02 (CGBC 02). These two cell lines had a similar phenotype with weak expression of estrogen receptor (ER), progesterone receptor (PR), and without amplification of receptor tyrosine protein kinase erbB-2 (HER2/neu). Genome-wide Single Nucleotide Polymorphism (SNP) array showed multiple copy number alterations in both cell lines. Based on gene expression profiles, CGBC 01 and 02 were clustered into basal-like subtype with reference to the breast cancer cell line gene expression database. The tumorigenicity of both cell lines was extremely low in both anchorage-independence assay and transplantation into the mammary fat pads of nude mice. CGBC 01 and CGBC 02 are low tumorigenic breast cancer cell lines, established from Han-Chinese premenopausal breast cancer patients, which serve as in vitro models in studying the biological features of Asian breast cancer.The online version of this article (10.1007/s13577-017-0197-3) contains supplementary material, which is available to authorized users. Breast cancer is the most common malignancy affecting women globally. It is the second common cause of cancer-related death in women and is one of the most extensively studied cancer types. Among the in vitro breast cancer research models, cell lines are the most widely used tools in laboratory studies of mechanisms of cancer growth, metastasis, tumor microenvironment crosstalk, signaling pathways, drug screening, and functional genomics , 2. CurrLuminal A  and B  breast cancers are characterized by estrogen receptor (ER) expression with or without progesterone receptor (PR) –8. ExampMost of the cell lines used commonly in breast cancer research, including the abovementioned, were derived from Western countries. Most of continuous breast cell lines derived from tumors at metastatic sites such as pleural effusion represent cancers at more advanced stages of tumor development , 6–9, 11The study was approved by the Institutional Review Board (IRB) of Chang Gung Medical Foundation . Informed consents were obtained from all patients prior to study participation. Total 50 tissue specimens were collected for establishment of primary cell cultures. Fresh breast cancer tissues were collected at surgery from primary tumors in 27 patients with untreated breast cancer , and 3 patients with neoadjuvant chemotherapy-treated breast cancer. Normal breast tissues were collected from 3 patients receiving surgery for benign fibroadenoma, and 1 healthy patient receiving reduction mammoplasty. Malignant pleural effusion was collected in another 3 patients with metastatic breast cancers. The demography data of enrolled patient are shown in supplementary Table 2. Patients participating in the study underwent standard preoperative screening according to institutional guidelines, including a detailed medical history, complete physical examination, and whole breast ultrasound. Mammography, chest radiographs, bone scan, and abdominal ultrasound were checked if malignancy was diagnosed.2. The final concentrations of the components in the supplemented medium were: 0.4% BPE, 5 μg/mL bovine insulin, 0.5 μg/mL hydrocortisone, and 3 ng/mL recombinant human epidermal growth factor (hEGF). The confluent cells were harvested with 0.05% trypsin–EDTA  and subcultured at a split ratio of 1:3, after treatment with trypsin inhibitor (Sigma-Aldrich). 17-β-Estradiol (0.3–1 nM) (Sigma-Aldrich) was added if ER was detected in the primary cancer tissues. The successfully established primary breast cancer cell lines were further enriched with micro-magnetic beads coated with CD326  antibody   was maintained in minimum essential Eagle’s medium (MEM) with 2 mM 6 cells/mL in buffer containing 1× PBS, 2 mM EDTA and 0.5% bovine serum albumin (BSA), and incubated at 4 °C in dark for 10 min with fluorescein isothiocyanate (FITC) conjugated CD326 antibody . After centrifugation and washing, cells were resuspended in 200 μl buffer and analyzed for the expression of EpCAM with FACSCalibur flow cytometer , and data were analyzed using ModFit software . At least 20,000 cells were analyzed per sample.The cells were resuspended at a density of 2 × 10For immunohistochemistry, formalin-fixed and paraffin-embedded tissues were sectioned to 4 μm thickness, deparaffinized, rehydrated, and prepared for antigen retrieval. The slides were then incubated at room temperature for 1 h with the following antibodies at appropriate dilution: anti-ER , anti-PR , anti-HER2 , anti-cytokeratin 18 (CK 18) , anti-cytokeratin 19 (CK19) , anti-CK 5/6 , anti-EGFR , anti-E-cadherin , and anti-EpCAM . After incubation, the slides were washed thrice with PBS, incubated with horseradish peroxidase (HRP) conjugated antibody (Zymed) at room temperature for 10 min, and developed by adding 3,3′-diaminobenzidine tetrahydrochloride (DAB) reagent (DAKO) as the chromogen and hematoxylin as the counterstain. Expression of ER and PR was rated using Allred score. All specimens were independently reviewed by two pathologists blinded to the clinical origin of the specimens.The cells were treated with radioimmunoprecipitation assay (RIPA) lysis buffer, and 25–50 μg of the extracted protein was used for western blotting. The proteins of interest were detected by using the enhanced chemiluminescence system (ECL) (Millipore) as the manufacturer’s instructions. Expression of beta-actin was used as an internal control in this experiment. Antibodies used in this experiment included anti-ER , anti-PR , anti-EGFR , anti-E-cadherin (Millipore), anti-HER2 , anti-CK18 (Abcam), anti-CK19 , anti-CK6 , anti-CK5 (Abcam), and anti-beta-actin (Sigma-Aldrich).The breast cancer cell lines were seeded in six-well culture plates at a density of 5000 cells/per well in 1.5 mL DMEM containing 10% FBS and 1% UltraPure™ Low Melting Point Agarose (GIBCO) on bottom layer, on top of which was layered 1.5 mL DMEM containing 10% FBS and 0.5% UltraPure™ Low Melting Point Agarose. The final layer consisted of 1 mL of DMEM containing 10% FBS. The cells were incubated at 37 °C for 21–28 days. Colonies were stained with 0.05% crystal violet and colony forming ability of cell lines in soft agar was assessed by the size and the number of colonies.http://www.affymetrix.com) in GeneChip Hybridization oven 645 . Following hybridization and washing, gene chip was scanned by GeneChip Scanner 3000 7G4C .RNA was extracted from the breast cancer cell lines using TRizol reagent  according to the manufacturer’s instructions, and enriched with RNeasy MinElute kit (Qiagen). The quality and quantity of RNA were analyzed by Agilent Bioanalyzer 2100 . Ten micrograms of total RNA was reverse transcribed to labeled cDNA using SuperScript II reverse transcriptase (Invitrogen). Twenty-five micrograms of the labeled cDNA was hybridized to probes on GeneChip Human Genome U133A 2.0 Array (Genomic DNA (gDNA) was extracted from CGBC 01 (P32) and CGBC 02 (P14), respectively, according to the manufacturer’s recommendations . The extracted genomic DNA was sent to Genomic Medicine Research Core Laboratory of Chang-Gung Memorial Hospital, Linkou, for quality assessment, DNA fragmentation, labeling and hybridization to Affymetrix SNP 6.0 array according to the manufacturer’s protocol . In brief, about 250 ng of gDNA was digested with two restriction enzymes (NspI and StyI). The fragments were ligated to adaptors and amplified by PCR. The PCR products were purified using Agencourt AMPure XP system  and quantitated by labeling and hybridization to Affymetrix SNP 6.0 array. The chips were washed, stained, and scanned to generate .cel files for further analysis after overnight hybridization. The .cel files were converted into copy number data file (cnchp) via Genotyping Console™ Software , which was further analyzed to detect chromosome copy number changes via Chromosome Analysis Suite .Genomic DNA from cell lines was sent to BCRC of Taiwan for STR profiling and analysis. Amelogenin and 15 STR marker loci were analyzed and compared. The STR profile of each cell line was blasted with the database of other known cell lines, including Hela cells.Female BALB/c nude mice (BALB/cAnN.Cg-Foxn1nu/CrlNarl) were purchased from the National Laboratory Animal Center  and maintained in specific pathogen-free housing and husbandry following institutional reviewed animal protocols. Cancer cell xenografts were injected into the 4th mammary fat pads of mice between the ages of 6 and 8 weeks.http://www.bcrc.firdi.org.tw) and CGBC 02 is under administrative review process for depositing.Two cell lines, named CGBC 01 and CGBC 02, were successfully established from their primary cell cultures of treatment-naïve breast tumors out of the 33 cell cultures prepared from primary tumors or metastatic cancer cells in pleural effusion (Supplementary Table 2). CGBC 01 (BCRC No. 60610) was deposited in BCRC of Taiwan , PR (3 +) and HER2 (−) by IHC scoring (Table Concentric growth of cells around the organoids that attached to the culture dish (P0) Fig. a could bSince these two cells lines are enriched by positive EpCAM selection, EpCAM was expressed in 96.07% of CGBC 01 and 99.65% of CGBC 02 Fig. a. The exShort tandem repeat (STR) profiles of CGBC 01 and CGBC 02 (Supplementary Table 3) were not identical to each other, but were identical to the profiles of their original tumor tissue. The cell lines were further authenticated by the absence of a match of their profile with any other cell lines such as Hela cells in ATCC public database. Genome-wide Affymetrix SNP 6.0 array was used for additional genotyping of CGBC 01 and CGBC 02. The ethnic origin of these two cell lines was revealed based on the reported race-distinguishing single nucleotide polymorphisms (SNPs); including rs11051  to distinguish between “CHB and YRI”, rs489095 (A/G) for “CHB and CEU”, rs6546753 (G/T) for “JPT and YRI”, rs6437783 (C/T) for “JPT and CEU”, and rs735480 (C/T) for “YRI and CEU , 37. TheGene expression profiles of CGBC 01 and CGBC 02 were assessed with Affymetrix Human Genome U133 Plus 2.0 Array. Gene expression profiles of commonly used human breast cancer cell lines were downloaded from GEO (Gene Expression Omnibus) dataset accession GSE15376  Fig. 4bb4b.Colony formation was evaluated in the soft agar seeded with CGBC 01 and CGBC 02 cells after being cultured for 21–28 days. There were a few tiny colonies observed in CGBC 01 culture, whereas CGBC 02 barely grew into any colony. The colony size of CGBC 01 was significantly smaller than those of MCF7, T-47D, and MDA-MB-231 Fig. a. The nu6 cells from each cell line were injected into 4th mammary fat pad of female BALB/c nude mice at the age between 5 and 8 weeks. Approximately 4 weeks after injection, bumps due to local reaction subsided and growth of tumor nodules became apparent. Tumor formation was prominent only in mice injected with MDA-MB-231, but not in any of the mice injected with other cell lines including CGBC 01, CGBC 02, and MCF 10A . Among the 33 cancer samples enrolled, only CGBC 01 (from patient M_17) and CGBC 02 (from patient M_18) were able to propagate spontaneously after the 10d method , 32 was d method , 33 and d method . AlthougThe expression of PR, CK18, and CK19 was significantly different between primary tumors and the established cell lines, especially after EpCAM selection Figs. . The losGenome-wide copy number alteration map showed that chromosome 20 was amplified in both cell lines Fig. a. Amplifhttp://tcr.cph.ntu.edu.tw/main.php?Page=A5B2). The incidence is expected to rise in the next 10 years, and the effect of birth cohort reflecting Westernized lifestyle bears the blame [Asian breast cancer is different from Western breast cancer by earlier age at onset, affecting more premenopausal women. The postmenopausal incidence maintains a plateau instead of steadily increasing after the age of 50, which was common in Caucasian women. The distinctive epidemiological trend was widely observed across Eastern Asia with high to progressively lower incidence along the south-to-north gradient , 22, 24.he blame , 46. Howhe blame . In Taiwhe blame . We are Two cell lines, CGBC 01 and CGBC 02, were established from premenopausal breast cancer patients in Taiwan with Han-Chinese genetic background. The molecular signatures of both cells were categorized into basal-like subtype and expressed basal markers. Their proliferation rate was moderate in vitro and showed low tumorigenicity when transplanted into the mammary gland of nude mice.Supplementary material 1 (DOCX 79 kb)Below is the link to the electronic supplementary material.


Such methods may overlook DRC features and do not simultaneously leverage information about drug response patterns across cell lines, potentially increasing false positive and negative rates in drug response associations. We consider the application of two methods, each rooted in nonlinear mixed effects (NLME) models, that test the relationship relationships between estimated cell line DRCs and factors that might mitigate response. Both methods leverage estimation and testing techniques that consider the simultaneous analysis of different cell lines to draw inferences about any one cell line. One of the methods is designed to provide an omnibus test of the differences between cell line DRCs that is not focused on any one aspect of the DRC . We simulated different settings and compared the different methods on the simulated data. We also compared the proposed methods against traditional IC50-based methods using 40 melanoma cell lines whose transcriptomes, proteomes, and, importantly, BRAF and related mutation profiles were available. Ultimately, we find that the NLME-based methods are more robust, powerful and, for the omnibus test, more flexible, than traditional methods. Their application to the melanoma cell lines reveals insights into factors that may be clinically useful.Cancer cell lines are often used in high throughput drug screens (HTS) to explore the relationship between cell line characteristics and responsiveness to different therapies. Many current analysis methods infer relationships by focusing on one aspect of cell line drug-specific dose-response curves (DRCs), the concentration causing 50% inhibition of a phenotypic endpoint (IC Cancers have proven notoriously difficult to treat because of their cellular complexity and heterogeneity, their ability to coopt a number of naturally-occurring molecular processes and factors to sustain their growth, their capacity to evade and adapt to treatments through a rapid evolution, and a general lack of available drugs that can combat different mechanisms contributing to their initiation and growth. The identification of specific factors and processes that reveal points of vulnerability in cancers should reveal therapeutic targets, but relevant strategies for doing so are far from trivial. In addition, there is now growing consensus that the factors contributing to the initiation and growth of a tumor are nuanced and often very individual-specific, so that the best way to treat cancer is to identify the potentially unique determinants of an individual's tumor and therapeutically target those determinants. Such “personalization” efforts for cancer treatments are receiving a great deal of attention , 2.Strategies for identifying the very heterogeneous determinants of cancer that might be used in the tailoring therapies to an individual are varied. Many large-scale epidemiological and tumor characterization efforts, such as the TCGA initiative , have prin vitro responses to actual in vivo settings [Alternatives to large-scale epidemiological and focused clinical studies on individual patients to find ways of identifying connections between drugs and tumor characteristics include tumorgraft (or xenograft) models and tumor cell line-based studies. Implanting tumors in mice or other organisms and studying the engrafted tumor's response to drugs has been used to great effect, but depends critically on the tumor environment in mice mimicking that in humans, which is not often the case , 7. Cellsettings , but thesettings . In addisettings .Unfortunately, the reproducibility of cell line-based HTS experiments have been called into question . Differe50”) of a phenotypic endpoint, such as cell growth, from those curves [50 values are then tested for association with other factors, like the expression levels of genes measured on those cell lines, to identify markers of response. Analyzing each cell line and drug combination in isolation is highly problematic since it ignores variation exhibited across the cell lines that might inform the response profile of any single cell line. Leveraging variation across all the cell lines when making claims about drug responsive in any single cell line can reduce noise and lead to more reliable inferences.Traditional statistical methods for analyzing cell line-based HTS data involve fitting  dose response curves (DRCs) to each cell line's dose response data in isolation and then extracting a single estimated parameter, the concentration causing 50% inhibition  formulation of an omnibus test in order to detect differences between DRC profiles among cell lines subsets and is thus theoretically much more flexible than IC50-based tests since it can capture differences between the DRCs among subgroups of cell lines beyond IC50 values.In this light, we considered the use of non-linear mixed effects (NLME) models in analyzing HTS dose-response data, which considers variation across the data on all or subsets of the cell lines in order to estimate and assign a metric of drug responsiveness to each cell line. We considered two different NLME-based tests to compare against traditional methods. Both tests considered the evaluation of the cell lines collectively when estimating IC50-based data analytic methods to the proposed NLME-based methods. We also considered analyses with the available Cancer Cell Line Encyclopedia [50 values. We find that the proposed NLME-based tests are more powerful, sensitive and, for the omnibus test, flexible than traditional methods. In fact, the LR-based NLME test can be used to uncover associations among various factors collected on the cell lines, such as gene expression levels, and drug responsiveness in a very robust and compelling way, and that are simply beyond the reach of analyses focusing exclusively on IC50 values. Both proposed NLME-based methods may reveal factors mitigating cancer drug response that could inform “personalized” or targeted therapeutic approaches to cancer in actual patient care. We also consider a few extensions of the proposed methods in the Discussion section.We applied the proposed NLME-based models and tests to 40 available melanoma cells lines with genomic, transcriptomic, and proteomic profiles available that had been screened against 120 drugs Figure . We compclopedia  (CCLE) d50 values, the IC50 NLME test and Omnibus LR NLME tests) we calculated the false positive rate or type I error rate  and the false negative rate, power or type II error rate . Power analysis revealed that 100 simulations for these analyses would suffice . This was to be expected, since the traditional IC50 test estimates IC50 values for each cell line independently and then explores the relationship between those estimated IC50 values and some other factor on those cell lines. Thus, it essentially ignores several important cell line properties. The NMLE IC50 test, although focused on the IC50 values, is influenced by the other DRC parameter differences between the two groups since it estimates parameters for the groups using all the data. Therefore, it is able to detect differences in DRCs beyond IC50 values.As noted, we simulated DRCs assuming triplicate cell line evaluations  for sample sizes of 34, 50, and 100 triplicate cell lines Figures . Based o suffice . For theer Table . We note50 values  exhibited a type 1 error rate consistent with the null model  in cases where differences in DRCs were simulated that involved changes in left asymptote, right asymptote, and scale  parameters  exhibited much less power.When exploring specific settings, the association tests using traditionally derived ICt-tests and found that the traditionally derived IC50 association tests performed worse than the NLME  and LLR tests . Even upon increasing the number of sample sizes to 50 and 100 cell lines, the Omnibus LR NLME tests had greater statistical power than IC50 NLME and traditional IC50-based association tests  values against the IC50 NLME test, we performed association tests using drugs with NLME curve fits to cell lines that had at least one cell line with cell viability less than 20%. Since a subset of the drugs in our study target the BRAF-mediated pathway, we suspect that differential drug response may be observed based on the BRAF associated genes’ expression patterns. However, based on analyses of these 15 drugs, no BRAF associated genes were significantly associated when using IC50 NLME test, and only four significant associations were found with traditional IC50 calls: HSPA9  and KSR1  with Vorinostat; RAF1  and HSPA9  with Crizotinib. Scatterplots comparing these correlations highlighted the differences in the nature of the associations between the traditional IC50 and IC50 NLME-based methods  with the drug Thioguanine Bonferroni adjusted p-value = 0.026). However, using traditional methods, we found seven significant associations in the first RPPA set and eleven significant associations in the second RPPA set. Interestingly, when we applied simulation models, the NLME models yielded less false positives. Thus, we suspect that the increased associations identified using traditional methods may be false positives rather than true hits.Similar to the gene expression analysis, we compared the associations between proteins and drug responses defined by the traditional ICll lines . As withe Figure . Across 50 values, we stratified cell lines based on their gene expression levels for those genes in the BRAF pathway. For each gene, the samples were either labeled as over-expressed or under-expressed based on the median intensity level across all cell lines. LLRs were calculated for each gene and drug combination, and permutation-based tests were performed to assess statistical significance . However, there were several other cases that yielded strong (but not beyond multiple hypothesis testing correction thresholds) association between gene expression and drug response  conferred resistance, whereas the increased expression of phosphorylated PKC (Phospho-PKCα/βII: Thr638) and PKCα (anti-PKC: Ser657) were associated with sensitivity to the drug  values against the available IC50 values computed by the CCLE research team  defined on an assumed associated cell line characteristic like gene expression level, are different.HTS studies using tumor cells lines can illuminate the relationship between drug responsiveness and various factors collected on those cell lines . Such relationships can be exploited clinically to “personalize” cancer treatments. However, there have been recent concerns surrounding the reliability HTS studies using tumor cell lines , 10, 11.50 NLME test, can be used.We pursued simulation studies to assess false positive rates and the statistical power of NLME methods relative to traditional methods. Our simulations showed that NLME-based tests, in particular the proposed Omnibus LR NLME test, and are more powerful than standard tests. However, these simulations are very computationally expensive, especially for the Omnibus LR NLME test. Due to the computational burden, we suggest that if adequate computational resources are not available, then the use of more efficient tests that still leverage NLME models, such as the IC50-based analytical methods. We found evidence for many associations that could not be attributed to chance. Upon mining gene and protein interaction databases, we observed that many of the associations we observed several were consistent with previously published data. For example, we found that the differential expression of the MAPK3 gene stratified melanoma cell lines into Cladribine responders and non-responders, which is consistent with what is known about the mechanism of action of Cladribine. Based on this finding, and in conjunction with the interrogation of the DGIdb database, we found that kinase inhibitors 5-Iodotubercidin and Purvalanol are likely drug candidates for these cell lines.We applied the NLME-based tests in order to see if they could identify differential gene expression and protein level associations with drug response in more powerful and compelling ways than traditional individual DRC ICp-values for the Omnibus LR NLME test, and may not be optimal in every context; e.g., when one wants to search the entire HTS space for gene by drug interactions which could be very computationally demanding. While traditional association tests require the least computational resources and could be run locally, the NLME association tests require slightly more computational resources (approximately 4 hours and 8 GB of RAM per drug tested) and the Omnibus LR NLME test required even more computational resources (greater than 24 hours and 8GB of RAM per drug tested). In an ideal setting, every association among genes, proteins, and drugs could be explored. Given the computational burden, we opted for a biologically-guided search  that ultimately led to compelling associations between genes and drug responses. The use of NLME-based analysis approaches that we document here can be extended to test associations between other important factors  and drug response.Our analyses and observations suggest that NLME-based models for fitting and analyzing DRCs could complement current HTS strategies and infrastructure. However, these NLME methods require, as noted, a fairly heavy computational burden, especially if one wants to rely on permutation-based methods for obtaining In addition, there are some very useful statistical modeling extensions of the proposed methods that could be exploited. First, bootstrap methods could be used to obtain standard errors for the parameter estimates . This maWe made use of a number of resources in our evaluation of different methods for analyzing cell line-based DRC data generated from HTS data. We briefly describe these resources before providing greater detail about the construction and execution of the two proposed NLME-based models and tests.Thirty-three melanoma cell lines from the University of Arizona Cancer Center (UACC) repository were used for our analyses. Additionally, the SK-MEL-2 cell line was received directly from Memorial Sloan Kettering Cancer Center through the Cancer Cell Line Encyclopedia (CCLE) repository and MeWo cell line from the Developmental Therapeutics Program's NCI-60 repository. The Translational Genomics Research Institute (TGen) had access to all other UACC-cell lines through the UACC . All celEach of the 40 cell lines was used in a nine-point  HTS study  across 120 drugs Figure  at the SThe cell line samples were subjected to nucleic acid extraction, verification, amplification, and hybridization per the protocol regarding the use of the Affymetrix HG-U133 plus 2.0 arrays . Affymetrix HG-U133 Plus 2.0 microarrays were normalized  using robust multichip averaging  in R.Cell lysates from the cell lines were printed in triplicate onto nitrocellulose-coated slides  using a 2470 Aushon arrayer equipped with 185 mm pins  and subjected to Reverse Phase Protein Array (RPPA) analysis at George Washington University. For quality control purposes, standard curves were printed on each array along with the samples. To quantify the amount of total protein in each sample, selected arrays were stained with Sypro Ruby staining solution  pursuant to manufacturer recommendation. Before proceeding with immunostaining, arrays were first incubated in Reblot Stripping solution  for 15 minutes, washed twice with PBS  and incubated in I-Block  for one hour. Using an automatic system , arrays were then incubated with commercially available 3% hydrogen peroxidase solution, avidin-biotin blocking system, and protein block . The expression/activation level of a panel of FDA-approved and/or under investigation drug targets and their downstream effectors was measured using a single primary antibody targeting the protein and phosphorylation site of interest. Each antibody used on the array was previously validated using western blot to confirm its specificity. A commercially available tyramide-based avidin/biotin amplification system  and fluorescent detection  were used to quantify the amount of protein present in each sample. Antibody stained slides were scanned using the Tecan laser scanner . Images were analyzed with MicroVigene 5.1.0.0  as previously described [To provide another comparison and evaluation of the performance of different analysis methods, we downloaded and processed raw CEL files from the CCLE. We obtained gene expression profiles and genome-wide mutation calls for melanoma cell lines from the CCLE web portal.50 values were significantly associated with the gene expression, we used Pearson correlation tests with the paired gene expression and estimated IC50 values across the cell lines.For each of the 40 cell line's 9-point dose response data generated for each of 120 drugs from the melanoma drug screen, we fit DRCs using a traditional four parameter sigmoidal curve. The parameters governing these curves were: left asymptote of the curve, right asymptote of the curve, midpoint of the curve , and a scale parameter . All ana50 analyses discussed in the previous section. For the first NLME-based model, which we will refer to as the “IC50 NLME” model, we fit four parameter sigmoidal curves to all the cell lines simultaneously allowing for variation in IC50 values across the cell lines. We leveraged the lme4 [50) parameter while using the cell line as a grouping factor to do so. Note that the IC50 NLME analysis model produces IC50 values for each cell line that can be used in Pearson correlation tests to determine their association with other factors collected on those cell lines, such as gene expression levels, but just estimated simultaneously with the other cell lines.As noted, we considered two different NLME-based models and tests for the DRC data, each assuming the same four parameter sigmoidal curve as in the traditional ICthe lme4  package For the second test, which we will refer to as the “Omnibus LR NLME” model and test, we fit four parameter sigmoidal curves to stratified subsets of the cell lines based on grouping factors, such as the cell lines expressing a gene above and below the median gene expression value  or cell lines with and without a mutation. The likelihoods obtained from each group's curve fits were then used to calculate a log-likelihood ratio statistic comparing the DRCs between the two groups as:50 value, it can in theory identify differences between DRCs obtained from two (or more) groups that could involve the left or right asymptote, the scale parameter, the x-mid parameter or any combination of these [i and each permutation j, we calculated Cij as:Note that since the Omnibus LR NLME test does not focus on a single parameter, like the x-mid or ICof these . To detep-value for the observed stratification was:Then, based on these permutations, for n total permutations, the p-values were going to exceed 0.05 based on the number of permutations we set , we stopped the permutations for that setting and proceeded to the next test setting. Additionally, we employed adaptive permutations [p-value, we increased the number of permutations to meet multiple hypothesis testing criterion .To save computational resources, when association test utations ; i.e., fp-values using the RPPA values stratifying the call lines at the median intensity value. We also ran tests on all BRAF associated genes for which we had gene expression data and the top five most significant phosphoproteins using Pearson correlation tests for the IC50 NLME and traditional IC50 test. As a check on the robustness of the analyses, we also assessed significance on five random analytes for each drug. Bonferroni corrections were used to account for multiple comparisons given the number of genes, proteins and mutations we tested for association with drug responses across the cell lines. For the analyses involving the CCLE data set, we compared BRAF mutant and wild type cell lines, and compared the results against analyses using the IC50 values provided the Broad Institute for the CCLE data set [We explored the utility of the proposed Omnibus LR NLME tests relative to the traditional tests in a number of scenarios with the melanoma cell lines. We computed statistics and data set . We const-test of over-expressers vs. under-expressers with traditionally called IC50, t-test of over-expressers vs. under-expressers with NLME model-derived IC50 values, and the Omnibus LR test with the NLME-fitted DRCs. Keep in mind that differences in the test results between the IC50 values from the traditional analysis and the IC50 NLME called IC50 values reflect the influence of analyzing all the cell lines together rather than individually.We simulated High Throughput Screen (HTS) nine-point Dose Response Curves (DRCs) data assuming a four parameter sigmoidal model. We note that each simulation required heavy computational resources (30–120+ hours and 8GB of RAM per simulated parameter set). We assumed that the standard deviation of the response for each concentration followed that of the observed drugs’ standard deviations calculated from the actual melanoma cell line data. We simulated null models in which there was no assumed relationship between a hypothetical factor and drug response. In addition, we simulated cell line DRCs assuming sample sizes of 34, 50, and 100 in triplicate with a left asymptote of 100, right asymptote of 10, scale of -0.5, and x-mid values following a normal distribution with mean -0.5 and standard deviation of 0.3 ) Figure . These v50 values, we simulated dose response curves under different alternative models. In these simulations, under-expressed genes were simulated with different sigmoidal curve parameters described in Figure 50 values. To create realistic simulations, we explored various combinations  of the parameters and performed 100 simulations for each set of parameters.To evaluate the power of the association tests by exploring the impact of changes in DRCs not associated with IC


Recent literature suggests that most widely used ovarian cancer (OVCA) cell models do not recapitulate the molecular features of clinical tumors. To address this limitation, we generated 18 cell lines and 3 corresponding patient-derived xenografts predominantly from high-grade serous carcinoma (HGSOC) peritoneal effusions. Comprehensive genomic characterization and comparison of each model to its parental tumor demonstrated a high degree of molecular similarity. Our characterization included whole exome-sequencing and copy number profiling for cell lines, xenografts, and matched non-malignant tissues, and DNA methylation, gene expression, and spectral karyotyping for a subset of specimens. Compared to the Cancer Genome Atlas (TCGA), our models more closely resembled HGSOC than any other tumor type, justifying their validity as OVCA models. Our meticulously characterized models provide a crucial resource for the OVCA research community that will advance translational findings and ultimately lead to clinical applications. Ovarian cancer (OVCA) ranks as the fifth deadliest cancer affecting women in the United States . The disin vitro derived findings have limited potential to translate to in vivo systems in the early stages of preclinical OVCA research [et al. compared genomic profiles of clinical HGSOC tumors with commonly used HGSOC cell lines and revealed striking dissimilarity [To evaluate the biological significance of molecular alterations in OVCA tumorigenesis, clinically and genetically characterized cell models that recapitulate clinical OVCA are required , 6. Withresearch . Recentlmilarity . Only 12milarity , which imilarity –11. Moremilarity , 12, 13.milarity , 14–17.To address this need, we sought to generate a new panel of genomically characterized OVCA cell lines that accurately model clinical tumors. Unlike another recent group , we focuEPCAM and the characteristic OVCA markers MUC16, WT1, and PAX8 were high in tumors, cell lines and xenografts, demonstrating that our models maintain an epithelial phenotype and typical OVCA marker expression (MUC16) in corresponding models were also high, suggesting plasma CA125 in xenograft bearing mice could be used as an indicator of therapeutic efficacy [We generated and characterized 18 cell lines, 15 patient-matched lymphoblastoid lines, and 3 patient-derived xenografts from 18 treatment naïve OVCA tumor ascites, providing a new panel of suitable models for preclinical research. We achieved a success rate of 41%  with standard media and culture conditions. Of these 18 lines, 6 (33%) were capable of growth as tumor xenografts in mice: HCC5006, HCC5012, HCC5023, HCC5044, HCC5048, and HCC5076  are hallmark features of HGSOC , 25, andTP53 [TP53 mutations were identified in 16/18 cases, and BRCA1, BRCA2, CSMD3, CDK12, NF1, RB1, and MECOM mutations were also detected among the models, illustrating the spectrum of genetic heterogeneity we captured. Furthermore, we observed significant positive correlations between our cell lines and xenografts with the mean copy number (ρ range: 0.12-0.62), methylation (ρ range: 0.39-0.70), and gene expression profiles (ρ range: 0.43-0.61) of TCGA OVCA tumors  in cell lines, xenografts, and tumors were comparable to those of TCGA tumors . However, this is not surprising given that cell lines are excellent vehicles for predicting response to relevant targeted therapies, but less predictive for response to non-targeted chemotherapeutic agents, as has been described before [Furthermore, we characterized the response of 8 cell lines to standard chemotherapeutics used to treat OVCA, cisplatin and paclitaxel. Adherent cell lines were assessed because they generate reproducible results in dose-response assays. The range in cell line sensitivities was consistent with those reported in the Supplementary Material of Ince et al.   performed according to the manufacturer's instructions.Detailed methods describing OVCA model establishment and genomic profiling and analyses can be found in the Supplementary Material. Cell lines and patient-derived xenografts were generated from malignant peritoneal effusions of 18 treatment naïve OVCA patients with informed patient consent at the University of Texas Southwestern (UTSW). Subsequent pathological review revealed that one tumour (HCC5032) was of endometrial origin after manuscript submission (Table https://tcga-data.nci.nih.gov/docs/publications/ov_2011/) [Genomic profiling including genome-wide copy number (Affymetrix SNP 6 arrays), gene expression (Illumina HT-12v4 BeadChips), DNA methylation (Illumina HM450K arrays) and exome-sequencing (SureSelect Target Enrichment System for Illumina Paired-End Sequencing) was conducted on genomic DNA or RNA extracted from tumors, cell lines, xenografts, or constitutional DNA from lymphoblastoid lines. All genomic data are available at the Gene Expression Omnibus (GSE71525) and Sequence Read Archive (PRJNA291290). Spectral karyotyping (SKY) was performed on a subset of samples as previously described . Genomicv_2011/) , 39, andv_2011/) . Detaile


Activation of the Wnt signaling in some types of cancer and its relation with chemotherapy resistance is a very interesting issue that has been emphasized in recent years. Although, it is known that increase in the activity of β-catenin is important in blast transformation and drug resistance, the underlying mechanisms are still unclear. In this study, changes in the expression levels of 186 genes that are thought to be important in drug resistance and Wnt signaling pathways were determined by using qPCR method in doxorubicin-sensitive and -resistant HeLa and K562 cell lines. It has been observed that the genes involved in the Wnt signaling pathways are involved in more changes in HeLa/Dox cells (36 genes) than in the K562/Dox cells (17 genes). Genes important for the development of cancer resistance have been found to be significantly different in expression levels of 18 genes in HeLa/Dox cells and 20 genes in K562/Dox cells. In both cell lines, the expression of ABCB1 gene was significantly increased to 160 and 103 fold, respectively. However, despite the resistance to same drug in HeLa and K562 cell lines, it appears that the expression levels of different oncogenes and genes involved in Wnt signaling pathways have been altered. It has been found that although resistance develops to the same drug in both cell lines, the expression levels of different genes have changed. If functional analysis of these genes is performed on patient population groups, these molecules may become candidates for novel therapeutic target molecules. Wnt signaling pathway is involved in both embryonic and adult stages in an organism . This iThe multidrug resistance 1 gene (MDR1) encodes an ABC transporter called P-glycoprotein (P-gp), which is the direct target of the Wnt/β-catenin signaling pathway. Overexpression of P-gp actively pumps out cytotoxic drugs from the cell and by reducing drug concentration in the cell cause drug resistance. The Frizzled 1 protein (FZD1), an important member of the Wnt/β-catenin pathway, and P-gp have been shown to increase in the adriamycin resistant MCF-7 breast cancer cell line. It has been shown that when the FZD1 gene expression is silenced via siRNA, cytoplasmic and nuclear β-catenin levels fall, as well as the level of P-gp decreases and sensitivity to chemotherapeutic agents is regained . Activation of Wnt signaling is known to play a role in the pathogenesis of some solid tumors and leukemia types . IC50 values of both original and drug resistant cells were determined. Doxorubicin resistant variants of original HeLa and K562 cell lines were developed in our laboratory by applying the drug in dose increments. The cells were grown in 25 cmRNA isolation was performed from HeLa and K562 cell lines, which are sensitive and resistant to doxorubicin using TRI Reagent  according to the manufacturer's instructions. RNA samples were assessed qualitatively on an Agilent 2100 Bioanalyzer. RNA samples with sufficient concentration (at least 200 ng/µl) and free of genomic DNA and protein contamination were used for cDNA synthesis. cDNAs were synthesized from total RNA by using random primers of Transcriptor High Fidelity cDNA Synthesis Kit (Roche Life Sciences) according to manufacturer's instructions.PCR-arrays  were performed on 96-well plates using the quantitative RT-PCR method by using Roche Light Cycler 480 instrument. It has been attempted to determine the expression levels of the genes which have a role in Wnt signaling pathways and genes which are thought to be important in resistance development. Genes on the "Wnt Signal Transduction PCR Array" and "Drug Resistance PCR Array" are given in Tables 1RT-PCR analyzes were performed in dublicates using a total of 16 PCR arrays from both the "Wnt Signal Transduction PCR array" and the "Drug Resistance PCR array" from all original and doxorubicin resistant cell lines. Relative amount changes were determined by calculating the Ct values (2-ΔΔCt) obtained for each gene. According to this method; from a Ct value relative to a control gene (beta-actin) in a gene-resistant drug line; the value of ΔΔCt [ΔΔCt = ΔCt resistant-ΔCt original] was obtained by subtracting the Ct value relative to the control gene in the same cell line. The fold change in the amount of gene was obtained by substituting ΔΔCt in the 2-ΔΔCt. The results obtained with the two replicates were evaluated with the SPSS 16.0 program and the differences less than the p-value of 0.05 and those with more or less 2 folds changes in gene expression levels were considered as "statistically significant".50 values at 72 h by XTT assay. In the original HeLa cell line, doxorubicin killed 50 % of the cells at a dose of 2,664 μM  whereas in doxorubicin-resistant HeLa cells this value was found to be 5,470 μM  , while in doxorubicin-resistant K562 cells this value was found to be 0.996 μM (IC50 = 0.996 μM). These results show that doxorubicin-resistant K562 cell line is approximately 32 times more resistant than the original K562 line . The role of Wnt signal transduction in the emergence of many cancer types and its relationship with chemotherapy resistance has been a subject of interest in recent years. There are three types of Wnt signaling pathways; Wnt/β-catenin (canonic), non-canonic and Wnt/calcium. Especially, the mechanisms between Wnt/β-catenin signal activation, blast transformation and drug resistance are being explored . In this study, attempts were made to identify changes in the expression levels of 186 genes which have important roles in drug resistance and Wnt signaling pathways in original and doxorubicin resistant HeLa and K562 cells. The selection of the model cell lines to two different types of cancer is to demonstrate possible similar and/or different effects of the same drug in different types of cancer.Resistant variants of HeLa and K562 cell lines were obtained and resistance development were determined by XTT cytotoxicity analysis. According to the results, the doxorubicin resistance in the HeLa cell line was about 2-folds higher than that of the original HeLa line; and the doxorubicin-resistant K562 cell line developed about 32 times more resistance than the original K562 line. It is seen that the amount of resistance developed to the same drug is significantly different in different cancer cell lines. The expression of the ABCB1 gene encoding MDR1 protein in both cell lines was significantly increased to 160 and 103 folds, respectively, in doxorubicin resistant HeLa and K562 cell lines. In many studies, it has been shown that overexpression of MDR1 in cancer cells is responsible for resistance to chemotherapy drugs Haber, 1992); the in92; the iIt is known that oncogen activation is related to the development of cancer formation and drug resistance. For this reason, many cancer drugs have been developed to target oncoproteins . It hasThere are also significant differences between the doxorubicin-resistant HeLa and K562 cells in the expression of the genes involved in the Wnt signaling pathway. Among 93 genes included in this study; expression levels of 36 genes in HeLa/Dox and 17 genes in K562/Dox were observed to change with respect to drug-sensitive variants. Activation of Wnt signaling pathways begins with the binding of the Wnt protein to the FZD and LRP5/6 receptors in the target cell membrane. Some of the beta-catenin that accumulates in the cytoplasm enters the nucleus, causing transcription of target genes . In HeLal., 2017). In anoal., 2017). Three In conclusion; this study provides important information on the level of gene expressions in terms of determining the relationship between doxorubicin resistance and changes in the Wnt signaling pathway in HeLa and K562 cell lines. However, in order to investigate these relations in more detail, functional analyzes at the protein level are required in the following studies.This work was supported by TÜBİTAK 3001 project (Project No: 214S634).The authors declare that they have no conflict of interest.


Rab1A expression is associated with malignant phenotypes in several human tumors; however, the role of Rab1A in lung cancer is still unclear. In this study, we attempted to establish the role of Rab1A in major human lung cancer subtypes. Rab1A expression in different histological types of human lung cancer was analyzed in lung cancer tissues with paired adjacent noncancerous tissues and a large panel of lung cancer cell lines. The effect of Rab1A expression on multiple cancer-associated signaling pathways was also examined. The results demonstrated that Rab1A was significantly overexpressed in the different histological types of lung cancer as compared to non-cancerous tissues, and Rab1A expression was correlated with tumor volume and stage. In a large panel of lung cancer cell lines, high Rab1A expression was observed as compared to a normal lung/bronchus epithelial cell line. However, Rab1A protein levels were not correlated with mTORC1 (P-S6K1), mTORC2 (P-AKT), MEK (P-ERK), JNK (P-c-Jun) or p38MAPK (P-MK2) signaling. Rab1A knockdown had no effect on mTOR signaling or cell growth. These data suggested that Rab1A may be involved in the pathogenesis of human lung cancer in an mTOR- and MAPK-independent manner. Lung cancer is the leading cause of cancer-related death worldwide, with 1.8 million new cases and 1.6 million deaths reported in 2012 . Only 16Rab1A, a small GTPase, has been well established to mediate vesicular trafficking between the endoplasmic reticulum (ER) and Golgi apparatus . It is aRab1A has been identified as a colorectal oncogene, and elevated expression of Rab1A has been reported in multiple cancer types, including colorectal cancer (CRC) , hepatocImmunohistochemical staining was performed to evaluate Rab1A expression in 60 lung cancer tissues and paired adjacent noncancerous tissues. Positive staining appeared brown. Rab1A was predominantly expressed in the cytoplasm, and the percentage of nuclear expression is very low (less than 5%) in both noncancerous tissues and cancerous tissues. In contrast to the noncancerous tissues, Rab1A was significantly highly expressed in tumor cells Figure . On an iTo further delineate the clinical significance of Rab1A expression, we examined the relationship between Rab1A staining and widely recognized clinicopathologic factors. Cases were divided into high and low Rab1A staining groups based on the median H score of 180 in cancer tissues. The Kruskal-Wallis test showed that Rab1A staining is significantly correlated with tumor size (P = 0.002), and T stage P = 0.039), as shown in Table 9, as shoWe further examined Rab1A expression in a panel of lung cancer cell lines. Compared with the immortalized lung/bronchus epithelial cell line BEAS-2B, Rab1A is highly expressed in all lung cancer cell lines with great variability, ranging from a 6-fold increase in the A549 and H1650 cell lines to a 1.5-fold increase in H460 and SK-LU-1 cells lines Figure . These fIt has been reported that Rab1A knockdown dramatically attenuated mTORC1 signaling and cell growth in CRC and HCC. Therefore, we examined whether Rab1A knockdown had similar effects in human lung cancers. Although Rab1A was successfully knocked down in a panel of lung cancer cell lines, no changes were observed with regards to mTORC1 (P-S6K1(T389)) or mTORC2 (P-AKT (S473)) signaling Figure . ConsistElevated levels of Rab1A DNA, mRNA or protein have been reported previously in human CRC , gliomasRecent studies have shown that Rab1 is a regulator of several central signal transduction pathways, particularly the mTOR pathway ,21. Rab1We also analyzed a panel of lung cancer cell lines for additional key signaling pathways including mitogen-activated protein kinase (MAPK) pathways, which are often deregulated in human cancers -24. ThreIn light of the present data considered in the context of previous findings, we can hypothesize that Rab1A overexpression is associated with clinical pathological parameters of human lung cancer. Because this study examined limit number of cell lines from different histological subtypes, to achieve a sound conclusion, correlation between major cancer signaling pathways and the malignant role of Rab1A needs to be further evaluated in a larger sample set of cancer tissues/cancer cell lines from specific histological subtype of lung cancer. The mechanisms by which Rab1A overexpression contributes to lung cancer, including cell cycle progressing and promoting migration and metastasis, are under investigation.Sixty de-identified lung cancer tissues with paired adjacent noncancerous tissues were collected from the hospital between January 2007 and March 2008 and developed into tissue arrays by Shanghai OUTDO Biotech . Twenty adenocarcinomas, 10 squamous carcinomas, 10 adenosquamous carcinomas, 10 large cell carcinomas and 10 small cell carcinomas were included. The study protocols were approved by the SJTUSM (Shanghai Jiao Tong University School of Medicine) Ethics Committee. All procedures adhere to the BRISQ Guidelines for reporting research on human biospecimens.Immunohistochemical detection of Rab1A was performed using a streptavidin-biotin complex method as described previously . The pri2 incubator. Cell lines were immediately expanded upon receipt and aliquots frozen to allow the cell lines to be restarted every three to four months from the same batch of cells. Cell phenotypes were verified in every experiment.Immortalized bronchial epithelial cells (BEAS-2B) and nine human lung cancer cell lines  were purchased from ATCC (American type culture collection). All cell lines were confirmed to be free of mycoplasma contamination. BEAS-2B and H292 cells were cultured in Dulbecco's Modified Eagle Medium with 10% fetal bovine serum (FBS). A549, H1299, H1650, H1975, H2030, H460, H838 and SK-LU-1 cells were cultured in RPMI 1640 medium with 10% FBS. All cell lines were maintained at 37°С in a 5% COSmall interfering RNAs (siRNAs) targeting human Rab1A was synthesized by GenePharma  and have been validated in our previous studies . Sequenc3 cells/well. Assays were performed at 48 h post-seeding according to the manufacturer's protocols. Data represent the mean ± SD  from three independent triplicate experiments.To determine the effects of Rab1A knockdown, cell growth was analyzed using an MTT assay as previously described . Cells wCells were harvested 48 h post-transfection and subjected to immunoblotting as described previously [Statistical analyses were carried out using SPSS 20.0 software. The statistical analysis of numeration data was performed using Pearson's chi-square test or Fisher's exact chi-square test. The comparisons of continuous data between groups were performed using the Kruskal-Wallis test. A nonparametric Spearman's correlation test was performed to analyze the correlation between Rab1A and P-S6K1, P-AKT1, P-ERK, P-MK2 and P-c-Jun expression levels. All statistical tests were conducted at a two-sided significance level of 0.05.


JAK-2 dysregulation plays an important role as an oncogenic driver, and is thus a promising therapeutic target in hematological malignancies. Ruxolitinib is a pyrrolo[2.3-d]pyrimidine derivative with inhibitory activity against JAK1 and JAK2, moderate activity against TYK2, and minor activity against JAK3. Vorinostat is an HDAC inhibitor that reduces JAK-2 expression, thus affecting JAK-2 mRNA expression and increasing JAK-2 proteasomal deterioration. Here we hypothesized that the combination of ruxolitinib and vorinostat could have synergistic effects against hematological disease. We tested combinations of low doses of ruxolitinib and vorinostat in 12 cell lines, and observed highly synergistic cytotoxic action in six cell lines, which was maintained for up to 120 h in the presence of stromal cells. The sensitivity of the six cell lines may be explained by the broad effects of the drug combination, which can affect various targets. Treatment with the combination of ruxolitinib and vorinostat appeared to induce a possible reversal of the Warburg effect, with associated ROS production, apoptotic events, and growth inhibition. Decreased glucose metabolism may have markedly sensitized the six more susceptible cell lines to combined treatment. Therapeutic inhibition of the JAK/STAT pathway seems to offer substantial anti-tumor benefit, and combined therapy with ruxolitinib and vorinostat may represent a promising novel therapeutic modality for hematological neoplasms. During hematopoietic ontogenesis, cytokines play key roles by initiating the intracellular signals that govern cell fate decisions, such as proliferation and differentiation. Most cytokine receptors lose intrinsic kinase activity and, therefore, often employ Janus kinase (JAK) as a signaling intermediate to facilitate downstream signaling. Receptor-related non-receptor tyrosine kinases, such as JAK1, JAK2, JAK3, and TYK2, are triggered after cytotoxic receptor activation . HematolRuxolitinib INCB018424) is an orally administered, biologically available pyrrolo[2.3-d]pyrimidine derivative that shows inhibitory activity against JAK1 and JAK2, moderate activity against TYK2, and minor activity against JAK3 , 8. Clin424 is anHistone deacetylase (HDAC) inhibitors are a promising group of therapeutic agents for various malignancies , 18. His50) in the 12 hematological cell lines after 24 and 48 h of treatment with ruxolitinib and vorinostat as single agents. We used the MTT assay to quantify the inhibition of cell viability. In all cell lines, the IC50 of ruxolitinib ranged from 12–20 µM after 24 h of incubation, and from 1–5 µM after 48 h. The IC50 of vorinostat in all cell lines ranged from 15–25 µM after 24 h, and from 5–10 µM after 48 h.First, we evaluated the cytotoxic effects  in the WSU-NHL, Granta 519, Jeko1, U266, HUT78, and L540 cell lines after 24 h of simultaneous exposure to ruxolitinib and vorinostat. In the remaining cell lines  the drug combination induced a synergistic effect (CI < 1) after 24 h. More specifically, the CI values ranged from 0.1–0.3, indicating strong synergy according to the Chou-Talalay theory . After 24 h of single-drug treatment with ruxolitinib (5 µM) and vorinostat (10 µM), the percentage of apoptotic cells was not more than 5–10%. On the other hand, combined treatment with these drugs for 24 h led to an increase of the apoptotic fraction to 40–50% Figure . AdditioTo better clarify the apoptosis mechanism induced by ruxolitinib and vorinostat, we evaluated the expressions of some apoptosis-regulating proteins that induce programmed cell death , and of apoptosis inhibitors (BCL-2 and MCL-1). Expression levels were measured as mean fluorescence intensity (MFI) by flow cytometry after 24 h of incubation. In all cell lines, treatments with both single drugs and with the drug combination were associated with increased expressions of the BAX, BID, and BAD pro-apoptotic proteins. With regards to the anti-apoptotic proteins BCL-2 and MCL-1, the combination of ruxolitinib with vorinostat was associated with downregulation of both proteins in the six most sensitive cell lines, while single-drug treatment did not have this effect  secretion of IL-10 into the media  increase of ROS production in the six more sensitive cell lines. Co-treatment with NAC effectively blocked ROS generation in all 12 cell lines.We previously found that the combination of ruxolitinib and vorinostat triggers the mitochondria-mediated signaling pathway. Alterations in the redox state of apoptotic cells could be related to activation of the final stage of the caspase cascade , 35. ROSin vitro model with cultured cells, thus limiting fluctuations in lactate levels. Based on our results, we further examined whether cells treated with the combined drugs would show decreased expression of GLUT1, the key enzyme of the glycolytic pathway. After 24 h of incubation with ruxolitinib plus vorinostat, we detected decreased GLUT1 expression in all 12 cell lines, with no significant difference between the most sensitive cell lines and the remaining cell lines . The success of ruxolitinib has inspired the rapid development of additional JAK inhibitors, extending the armamentarium for targeted therapy of malignancies driven by JAK signaling [JAK-2 dysregulation plays an important role as an oncogenic driver, prompting increasing interest in JAK-2 inhibitors for therapy against hematological malignancies , 38. Theignaling . Howeverignaling . Since Jignaling –43. Combin vitro model of hematological disease. We found that a combination of the two drugs showed greater efficacy compared to low-dose single-agent treatment with ruxolitinib and vorinostat in 12 cell lines, including two B-cell lymphomas, two T lymphoma, one chronic B-cell leukemia, two myeloma multiple, two Hodgkin lymphoma, two mantle lymphoma, one non-Hodgkin lymphoma. The results of combination treatment were additive in both mantle cell lymphoma cell lines, in one LH cell line, in one myeloma cell line, in one T-cell lymphoma cell line, and in one B-cell lymphoma cell line. In the remaining cell lines, the combination of the two drugs had highly synergistic effects. These results led us to postulate that this combination may have the potential to overcome protective effects of a tumor microenvironment. Indeed, tumor cells treated with the combination of ruxolitinib and vorinostat for 48 h were not protected by co-culture with stromal cells. The synergistic cytotoxic action of this drug combination was maintained for up to 120 h. We also found that the ruxolitinib and vorinostat combination influenced the cell cycle distribution, increasing G2-M phase arrest after 24 h in all 12 cell lines. Correspondingly, the combined treatment affected the related cell cycle proteins. In all 12 cell lines, treatment with ruxolitinib and vorinostat for 24 h resulted in upregulation of the cell cycle regulators p21 and p27, and downregulation of CCND1 and AURORA A, which are important in regulating the cell cycle control points. These results confirmed that this combination of drugs induced tumor growth inhibition.Our present results show that combination treatment with ruxolitinib and vorinostat had improved effects on proliferation and apoptosis in an After 24 h of single-agent treatment with ruxolitinib or vorinostat, less than 10% of cells were apoptotic annexin V-positive cells. However, this proportion of apoptotic cells increased to 50% in all 12 cell lines after treatment with ruxolitinib combined with vorinostat. Caspases play a very important role in programmed cell death and are thus attractive targets for the development of new cancer therapies. We chose to study the caspase initiator caspase-8, and the caspase effectors caspase-9 and caspase-3. After 24 of combined drug treatment, we detected cleavage of caspase-8 and caspase-3 in all 12 cell lines, suggesting activation of the extrinsic apoptotic pathway. Moreover, only the six most sensitive cell lines showed increases in the cleaved form of caspase-9, suggesting caspase activation of mitochondrial events regulating intrinsic apoptosis. The most sensitive cell lines also differed from the other cell lines in terms of the regulation of anti-apoptotic proteins. After treatment with the drug combination, all 12 cell lines showed increased expression of the pro-apoptotic proteins BAX, BID and BAD, but only the six more sensitive cell lines showed downregulation of the apoptotic inhibitors BCL-2 and MCL-1. These results confirmed the possibility that ruxolitinib combined with vorinostat may differentially target signaling pathways in the six more sensitive cell lines.JAK/STAT activation has been demonstrated in hematological malignancies, providing the rationale for various therapeutic approaches involving JAK kinase inhibitors –46. HereJAK is reportedly involved in autophagy induction for immune regulation in various cancer cells –52. Thusin vitro and ex vivo conditions to clinical practice. Our present results offer evidence of synergistic interaction between ruxolitinib and vorinostat in hematological tumor cells, and provide the rationale to support clinical studies using the combination of both agents in patients.In conclusion, here we show that treatment with a combination of ruxolitinib and vorinostat led to decreased glucose metabolism, and that this partial reversion of the Warburg effect was associated with ROS production, apoptosis, and cell growth inhibition. Testing these drugs under different cell culture conditions provided information regarding drug efficacy, and could help predict pharmacological effects that may be difficult to translate from http://www.dsmz.de/human_and_animal_cell_lines/main). The B-cell lymphoma cell line WSU-NHL was kindly provided by Dr. M. Introna . GRANTA519 and MEC1 cells were cultured in Iscove’s MEM supplemented with 10% fetal bovine serum (FBS), 2 mM L-glutamine, penicillin (100 U/mL), and streptomycin (100 U/mL). All other cell lines were cultured in RPMI-1640 supplemented with 10% fetal bovine serum (FBS), 2 mM L-glutamine, penicillin (100 U/mL), and streptomycin (100 U/mL) . The human mesenchymal stem cells (hMSC) were purchased from Tebu Bio and cultured in complete MSC expansion media (Tebu Bio). Cells in the logarithmic growth phase were used for experiments.We purchased the mantle cell lymphoma cell lines GRANTA519 and Jeko1, multiple myeloma cell lines U266 and RPMI8266, B-cell lymphoma cell lines Karpas422 and RL, cutaneous T-cell lymphoma cell line HUT78, anaplastic cell lymphoma cell line Karpas 299, chronic B-cell leukemia cell line MEC1, and Hodgkin lymphoma cell lines LH540 and LH1236 from Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH. Cell line characteristics are available online  to create 1050) using Calcusyn software  applying the median-effect method. Viability was assessed by exclusion assay with 0.2% Trypan Blue (Euroclone). DMSO was directly added to control samples and used as a drug solvent for drug-treated samples, and the DMSO concentration was kept constant (0.1%) among treatments for all experiments. The maximum final concentration of DMSO (< 0.1%) did not affect cell proliferation and did not induce cytotoxicity in the tested cell lines.In dose-response experiments, cell lines were treated with increasing concentrations of ruxolitinib and vorinostat for 24 and 48 h. Relative viable cells were determined by MTT assay . The absorbance at 550 nm was measured using an ELISA reader. We determined the drug concentration required for 50% inhibition of cell proliferation , following the manufacturer’s instructions. At least 20,000 events were acquired using a FACSCalibur cytometer , which were then analyzed using FlowJo Software .In preparation for intracellular marker staining, the cells were washed with 1% v/v FBS-PBS (staining buffer), fixed in 4% w/v paraformaldehyde (20 min at 4°C), and permeabilized with 0.1% saponin in PBS. Then the cells were incubated for 1 h at 4°C with primary antibodies against p-BAD (Ser112), BID, BAX, MCL-1 and BCL-2 . Finally, the cells were incubated with fluorophore-tagged secondary antibodies for 45 min at 4°C, and the cells were analyzed using a FACSCalibur flow cytometer. The results were analyzed using the CellQuest program. Whenever possible, the immunophenotype results were expressed as the percentage of positive cells.We measured caspase-3, caspase-8, and caspase 9 activity using colorimetric assay kits purchased from Enzo Life Sciences, in accordance with the manufacturer’s instructions. Samples were analyzed using an ELISA reader.For cell cycle distribution analysis, cells were harvested after 24 h of drug treatment. These cells were fixed in ethanol (95%) containing RNase (10 g/mL), stained for 15 min with propidium iodide , and then analyzed with a FACSCalibur cytometer. We calculated the percentages of cells in the subG1/G0 (dead cells), G1/G0, S, and G2/M phases of the cell cycle  using Modifit LT software .We quantified autophagy in cell lysates using the quantitative immunometric detection method provided by the p62 (sequestosome 1) ELISA kit  following the manufacturer’s instructions.Reactive oxygen species (ROS) production was analyzed by incubating cells with 2′,7′-dicholorodihydrofluorescein diacetate  in complete medium for 30 minutes at 37°C. Fluorescence was quantified with a FACSCalibur cytometer (Becton Dickinson), and these data were analyzed using FlowJo Software (Tree Star).We measured lactate and ATP levels using an L-lactate assay colorimetric kit and an ATP assay colorimetric kit (Abcam), following the manufacturer’s instructions. In the L-lactate assay kit, lactate is oxidized by lactate dehydrogenase to generate a product that interacts with a probe, producing a color (OD = 450 nm). The ATP assay kit is based on the phosphorylation of glycerol, generating a product that can be colorimetrically quantified (OD = 570 nm).6 cells were pelleted and then lysed using the Mammalian Cell Extraction Kit  following the manufacturer’s instructions. Proteins (100 µg/lane) were electrophoresed on 4–20% (w/v) Miniprotean TGX Precast Gels , and then transferred to nitrocellulose membranes . Membranes were immunoblotted using the following primary antibodies: total JAK2, p-JAK2, total STAT3, p-STAT3, total STAT5, p-STAT5, CCND1, AURORA A, p21, p27 and GLUT-1. Next, the membranes were incubated with species-specific horseradish peroxidase (HRP)-conjugated secondary antibody. All above-mentioned antibodies were purchased from Euroclone. Blots were developed using SuperSignal West Pico Chemiluminescent Substrate , and images were acquired using Chemidoc XRS+ and analyzed with Image Lab Software v.3.0 (Bio-Rad Laboratories).After drug treatments, 1 × 105 cells/mL for 24 h with ruxolitinib and vorinostat, alone and in combination. Then the supernatants were collected and analyzed for levels of the cytokines IL-6, IL-10, and IL-17a using a Human Magnetic Luminex Assay (R&D Systems), following the manufacturer’s instructions.Cells were cultured at a density of 5 × 10P values less than .05 were assigned significance. Data were analyzed using the Stata 8.2/SE package .All experiments were independently repeated three times, with multiple replicates within each run. Data are expressed as mean ± standard error. We analyzed statistical differences between control and drug-treated cells using one-way ANOVA, and