Aim of this study was to analyze VOCs in the headspace of breast cancer cell lines in order to ascertain the potentiality of VOCs signatures in giving information about these cells and set-up a new sensor system able to detect breast tumor-associated VOCs. We identified by Gas Chromatography-Mass Spectrometry analysis a VOCs signature that discriminates breast cancer cells for: i) transformed condition; ii) cell doubling time (CDT); iii) Estrogen and Progesterone Receptors  expression, and HER2 overexpression. Moreover, the signals obtained from a temperature modulated metal oxide semiconductor gas sensor can be classified in order to recognize VOCs signatures associated with breast cancer cells, CDT and ER expression. Our results demonstrate that VOCs analysis could give clinically relevant information about proliferative and molecular features of breast cancer cells and pose the basis for the optimization of a low-cost diagnostic device to be used for tumors characterization.The efficacy of breath volatile organic compounds (VOCs) analysis for the screening of patients bearing breast cancer lesions has been demonstrated by using gas chromatography and artificial olfactory systems. On the other hand,  In Situ Hybridization (FISH)Breast tumor is the first cause of death for cancer in woman worldwideTaken together all these aspects stimulate the research of new diagnostic tools that could promptly support the clinicians in breast cancer treatment decision-making.Among them, the analysis of volatile organic compounds (VOCs) emerges as a new frontier for cancer diagnosis in particular because it is non-invasive and potentially inexpensive. The concept has been demonstrated with different types of cancer4567891011121314151617181920212223567891011121314Breath analysis could be applied in the early identification of tumors, but in order to provide histological and molecular information useful to clinicians for the selection of the specific treatment of a neoplastic lesion, VOCs analysis should be applied directly to tumor.in vitro experiments demonstrate that artificial olfactory systems can discriminate between tumor and normal cell lines and also among tumors-derived cells bearing different molecular alterations1617181920To this regard, In this study we searched with GC-MS for VOCs signatures that can be associated to the headspace of culture medium of tumor breast cell lines and that can signal the occurrence of specific breast cancer prognostic markers.The analysis of GC-MS data shows that the abundance of 13 VOCs changes between different breast cancer cell lines and is sensitive to the cell doubling time (CDT) and to the expression of specific prognostic factors such as Estrogen Receptor (ER), Progesterone Receptor (PgR) and HER2.The same samples were also measured with a novel approach that considers a metal-oxide semiconductor gas sensor operated in a thermal modulation condition. Actually this solution differs from other approaches that consider a sensor arrays working at room temperature131920212526A statistical analysis of the sensor signals results in a good classification of the breast cell lines based on transformed condition, replication time and ER expression. All these results confirm that the volatile compounds might provide a cost-effective diagnostic tool useful for the characterization of the breast tumor lesion aimed at the optimization of the therapeutic treatment.The VOCs associated with breast cell metabolism have been analyzed with GC-MS by comparing culture medium headspace of six breast cell lines with medium without cells , Table 1−16) , the abundances of 13 selected VOCs have been correlated with the different cell lines showing for each of them specific patterns . Collect−16) . The det−16) .Among the 13 VOCs identified, the abundance of nine of them significantly increases in the headspace of samples related to breast cell lines . Eight o−3)  . To iden−3) . The VOCA further step in the study was to investigate the relationship between the VOCs and some cancer cell features such as the growth rate and the expression of specific proteins. In particular, the cell doubling time (CDT) and the expression of the three main prognostic markers of breast cancer, such as ER, PgR and HER2, have been considered .−4), ER positive from ER negative (p = 3.5 × 10−5), PgR positive from PgR negative (p = 7.4 × 10−4) and HER2 overexpressing cells (p = 0.03)  .Considering the behavior of each compound, we observed that seven VOCs showed significant differences between high and low replicating breast cells. Six of them were more abundant in cells with short CDT  and 2-Dodecanone only in cells with long CDT .The abundance of five compounds was significantly different between ER negative and ER positive cell lines, four among PgR negative and PgR positive cells and, one in HER2 overexpressing cell lines . InteresThese results suggested that the identified VOCs signature could furnish information regarding the replication rate and the expression of breast cancer prognostic molecular markers.Chemical sensors are expected to provide a full exploitation of the GC-MS findings in order to develop effective diagnostics toolsIn this paper we investigated the discrimination properties of metal-oxide semiconductor gas sensor operated in self-temperature modulation mode as described in A first set of experiments was aimed at distinguishing among the headspaces of the culture media of MCF-10A control cell line, MDA231 breast cancer cell line, culture media without cells and distilled water. The discrimination among the four above-mentioned classes is shown by the scores plot of the first two principal components of the PCA model built with the features of the thermal modulation profile .In order to characterize the specific VOCs fingerprint of breast control and cancer cell lines, the analysis was extended to all the breast cell models. The PCA scores plot in A PLS-DA model was calculated from the sensor signals in order to discriminate the breast cancer cells according to the cell features previously discussed .The PLS-DA model shows that the sensorial system discriminates with high accuracy breast cell lines with low CDT  and positive for ER expression  . PgR expThe analysis of VOCs provides an elegant alternative approach to cancer diagnosis. However, the practical use is still limited by a lack of validated cancer-derived metabolites, and by a consequent lack of sensing technologies optimized to their detection. In this work we have analyzed the VOCs emitted by the culture media of different breast cancer cell lines with the GC-MS and a temperature modulated metal oxide gas sensor highlighting the potentialities of this approach to obtain diagnostic information with high impact in the clinical management of breast cancer patients.in vivo and in vitro studies, specific VOC signatures are associated with the presence of neoplastic lesions and with the molecular alterations in oncogenes and tumor suppressor genes in tumor cells20As demonstrated in Different studies analyzed the potentiality of sensors for the screening of breast cancer patients by breath VOCs analysis567891011121314In our study we analyzed the specific odor print of breast cancer cells by analyzing a simpler and less contaminated sample: the headspace of conditioned culture medium of cell lines plated in proliferative conditions.in vitro model for VOCs modifications occurring in vivo between tumor cells and body fluids, like blood, interstitial or lymphatic fluids.This kind of sample allows detecting specific VOC exchanges that take place during normal and tumor breast cell proliferation and could be also considered an We demonstrated, by GC-MS analysis, that a pattern of 13 VOCs discriminates the headspace of breast cell lines growth media. Similar VOC patterns were reported for other cell lines16303233in vitro studies. This could be due to the specific cell lines analyzed in this study and the peculiar culture conditions used in our experiments. In fact, in contrast to the conditions used in previous studies, we plated our cell lines at low densities in order to collect, during the incubation time, all compounds exchanged in the proliferative phase.The remaining compounds are never been previously observed in et al. by using different technical and methodological conditionsThe identified set of VOCs is also able to significantly discriminate breast cancer cells suggesting that non-transformed and tumor cell lines could be characterized by a specific VOCs signature by which it is possible differentiate them, as also recently demonstrated by He Among compounds increased in tumor breast cancer cells the more represented classes are the hydrocarbons, and some of these  have been previously detected in breath samples of breast and lung cancer patients6767891011121314This enhanced production of alkanes has been supposed to be due to the increased oxidative stress correlated with tumor progressionAs reported for other types of cancer cells, our results demonstrate that breast tumor cells can be classified by specific VOCs signatures, providing the rationale for the set-up of technologies aimed to the detection of these biomarkers.To this purpose, we tested the performances of a commercial metal-oxide semiconductor gas sensor to discriminate the VOCs patterns released in the headspaces of culture medium of breast-derived cell lines. Results highlight that from the sensor data we can classify with high accuracy cancer-derived samples. Despite the small number of non-transformed and tumor breast cell models analyzed, our results encourage the application of this kind of sensors for these applications. It is straightforward that the system performance can be further improved increasing the number and the kinds of sensors.in vitro studies provided evidences for the existence of VOCs signatures characteristic of genetic mutations associated with lung cancer cells or with the metastatic potential of hepatocarcinoma cell lines2021Recent This analysis demonstrated that CDT elicits changes in the VOCs profile that can be measured both with the GC-MS and the gas sensor. These interesting preliminary evidences of the correlation of VOCs with cancer cell proliferation necessitate of further experiments to fully exploit VOCs as proliferation markers of cancer cells.The same analysis of GC-MS data also demonstrated the relationship between the VOCs profiles and the molecular expression of ER, PgR and HER2. Gas sensor data discriminate between cell lines with different ER molecular status but low discrimination accuracy has been observed with respect to PgR and HER2 molecular status. This is likely due to the small difference in the VOCs signature identified by GC-MS analysis among PgR and HER2 positive and negative cells .in vitro breast cancer cell line models to discriminate the VOCs specifically emitted by tumor cells, without interferences from other cell types that are normally present in a tumor lesion. Actually the main scope of the work is not related to early diagnosis of the tumor lesion but to support the medical staff in the choice of the best therapeutic treatment of specific kind of cancer. However, a validation on biological specimens  obtained from breast cancer patients will be performed in a future investigation to understand the diagnostic potentials of the presented results.In this study we performed our analysis using in vitro and in vivo studies using many other cell models are necessary to validate the VOCs pattern and to optimize the sensor system for a future extension to clinical tests.In this paper a study about the potentialities of the associated VOCs of breast tumor cell lines to identify new proliferative and molecular biomarkers has been presented. To this regard the headspace of the culture media of cancer cell lines has been analyzed with the GC-MS and with a gas sensor operated under thermal modulation. The GC-MS has evidenced a list of potential VOCs whose contemporaneous presence is correlated with specific characteristics of the breast cancer cells. The same characteristics can also be captured by a simple gas sensor. The promising results obtained with a commercial sensor foreshadow the possibilities to improve the system performances optimizing the sensor selection or considering an ensemble of sensors. Nevertheless, further in vitro passages. The immortalized, non-transformed human mammary epithelial cell line MCF-10A was grown in DMEM/F12 medium (Sigma-Aldrich) supplemented with 5% fetal bovine serum, 20 ng/ml epidermal growth factor (EGF), 10 μg/ml insulin, 0.5 μg/ml hydrocortisone (Sigma-Aldrich), 100 units/ml penicillin and 100 μg/ml streptomycin (Sigma-Aldrich), as previously described2. The CDT of the different cell lines in the experimental culture conditions has been evaluated by cell count , BT474, ZR75-1 and MCF-10A 383940ll count . A doublll count .2) with 5 mL of its specific culture medium for 24 h. The number of plated cells was chosen based on the specific doubling time of each cell line, in order to maintain each cell lines in a proliferative phase and to obtain a comparable cell number at the end of the incubations.For VOCs analysis, each breast-derived cell line was seeded in six separated culture flasks (25 cm6 cells/flask). After this incubation period, the DMEM culture medium was harvested, centrifuged at 1200 rpm for 5 min to remove detached cells, and collected in sterilized glass vials. At the end of the incubation, cell number and viability were evaluated respectively by cell count and Trypan Blue exclusion, in order to control cell density in all flasks and assess the effect of any cell stress during the incubation time. The control medium was obtained by incubating DMEM culture medium in the same conditions as the cell samples, but without seeded cells. The experimental set-up used for the cell culture headspace analysis is shown in After 24 h, the specific culture medium was removed and replaced with 5 mL of the DMEM culture medium. Cells were grown in these conditions for 96 hrs, up to a confluence of 50%-60% (around 1.5 × 10A volume of 5 ml of the sample (control or conditioned medium) was closed in a 20 ml vial  sealed with a PTFE/Silicone crimp seal  and stored at 4 °C until the analysis.Volatile organic compounds composing sample headspace were pre-concentrated onto a SPME fiber coated with 50/30 μm Divinylbenzene/Carboxen/PDMS . Before each analysis session the fibers were conditioned at 270 °C for 1 h.Filled vials were placed in a water bath equilibrated at 40 °C. SPME fiber was then manually exposed to sample headspace for 1 h.The fiber with sampled VOCs was transferred to the GC–MS and desorbed at the injection port of the GC with an inlet temperature of 250 °C for 3 minutes. The analyses were conducted in the same day of the sample collection.The analyses of SPME sampled VOCs were performed with a GCMS-QP 2010 Shimadzu series Gas Chromatograph Mass Spectrometer, equipped with EQUITY-5 (poly(5% diphenyl/95% dimethyl siloxane) phase, SUPELCO, Bellefonte, PA, USA) capillary column, 30 m length × 0.25 mm I.D. × 0.25 μm thickness, and conducted in split-less mode using ultra-high purity helium as carrier gas. The instrument was controlled in linear velocity. Pressure was 24.9 kPa, flow parameters were 5.9 ml/min of total flow, 0.7 ml/min of column flow and linear velocity of 30.2 cm/s. The oven temperature was programmed as follow: 40 °C for 5 min, increased by 7 °C/min to 220 °C, then the oven was programmed to reach 300 °C at 15 °C/min, this temperature was held for 3 min . The injection port was held at 250 °C.The mass spectrometer was used with a single quadrupole analyzer in electronic ionization mode, scanned over a mass range of m/z 40–450 amu in the full scan mode. The detector voltage was 0.7 kV. The temperature of interface and ion source were kept constant at 250 °C.The GC-MS data were analyzed using the section GCMS Post-run Analysis of the GCMS solution software . Preliminary identification of compounds was done using both NIST 127 and NIST 147. The identification of the selected list of VOCs was then confirmed spiking the analyzed samples with 0.5 μL of authentic specimens and observing the GC-MS peak overlaps.From the experimental measurements, 5 replicas for each of the seven kind of samples .GC-MS data were analyzed using Welch’s t-test and MANOVA to evaluate the statistical difference between the patterns of VOCs related to the cancer cells48The gas sensor data (32 semi-periods of the pulse pattern for each measurement) was analyzed with Principal Component Analysis (PCA). Finally the Partial Least Square Discriminant Analysis (PLS-DA) classification model has been used to test the discrimination capabilities of the sensor data to identify the cancer cells or the proliferation rate of the different cell lines (high vs low proliferation)How to cite this article: Lavra, L. et al. Investigation of VOCs associated with different characteristics of breast cancer cells. Sci. Rep.5, 13246; doi: 10.1038/srep13246 (2015).


We demonstrate that matrix density modulated E2-induced transcripts, but did not alter the growth response. However, matrix density was a potent determinant of the behavioral outcomes of PRL/E2 crosstalk. High density/stiff matrices enhanced PRL/E2-induced growth mediated by increased activation of Src family kinases and insensitivity to the estrogen antagonist, 4-hydroxytamoxifen. It also permitted these hormones in combination to drive invasion and modify the alignment of collagen fibers. In contrast, low density/compliant matrices allowed modest if any cooperation between E2 and PRL to growth and did not permit hormone-induced invasion or collagen reorientation. Our studies demonstrate the power of matrix density to determine the outcomes of hormone actions and suggest that stiff matrices are potent collaborators of estrogen and PRL in progression of ERα+ breast cancer. Our evidence for bidirectional interactions between these hormones and the extracellular matrix provides novel insights into the regulation of the microenvironment of ERα+ breast cancer and suggests new therapeutic approaches.Breast cancers that express estrogen receptor alpha (ERα+) constitute the majority of breast tumors. Estrogen is a major driver of their growth, and targeting ER-mediated signals is a largely successful primary therapeutic strategy. Nonetheless, ERα+ tumors also result in the most breast cancer mortalities. Other factors, including altered characteristics of the extracellular matrix such as density and orientation and consequences for estrogen crosstalk with other hormones such as prolactin (PRL), may contribute to these poor outcomes. Here we employed defined three dimensional low density/compliant and high density/stiff collagen-I matrices to investigate the effects on 17β-estradiol (E2) activity and PRL/E2 interactions in two well-characterized ERα+/PRLR+ luminal breast cancer cell lines  The cell/collagen mixture (300 µl) was plated onto transwell permeable supports  and allowed to polymerize for 30 min at room temperature. Phenol red free RPMI 1640 medium containing 10% gelding-horse serum was placed in the lower chamber and the system was incubated at 37°C for 24 h. Traversed cells were stained with Giemsa, and quantified.Invasion assays were performed as previously described . Briefly5 cells) or MCF-7 (2×104 cells) were plated in compliant or stiff type-I collagen in 24-well non-tissue culture plates  and allowed to polymerize as described above. After releasing the gels, 800 µl phenol red-free RPMI 1640 + 5% CSS was added to each well with treatments as indicated. MCF-7 experiments were harvested after 72 h. T47D cells were allowed to grow for 7d, and half of the media was exchanged with fresh hormone(s) every 72 h. Experiments utilizing inhibitors were pretreated for 1 h with vehicle (DMSO 1:1000 or EtOH 1:1000), ER antagonist 4-OHT (100 nM), SFK inhibitor PP-1 (500 nM), or JAK2 inhibitor BMS-911543 , prior Immunofluorescence was performed as described , except Multiphoton microscopy and second harmonic generation imaging (20x objective) of collagen  and NADH  was performed at the Laboratory for Optical and Computational Imaging (LOCI) as previously described , 51. QuaStatistical analyses were performed using GraphPad Prism v.4.0. Independent experiments were analyzed via one-way ANOVA with the Tukey comparison post-test or unpaired t-test for collagen alignment experiments. Significance was defined as a p-value less than 0.05.Estrogen regulates multiple transcriptional enhancers, including the canonical estrogen responsive element (ERE). In order to investigate the effect of matrix density on estrogen regulated promoter activity, we cultured MELN or T47D-KBluc cells in low density/ compliant or high density/ stiff collagen-I matrices and treated +/- 17β-estradiol (E2) for 24 h. E2 robustly induced ERE-activity in both compliant and stiff matrices compared to vehicle controls (p<0.001). However, stiff matrices reduced E2-induced ERE activity compared to compliant matrices in both MCF-7 (p<0.05)  and T47DTFF1 transcripts compared to compliant matrices (CATD (PGR (UGT2B15 [TFF1 transcripts (CATD (PGR mRNAs (UGT2B15 mRNA in T47D cells were strongly decreased in stiff matrices (p<0.01), and further decreased by E2-treatment in compliant matrices (p<0.001) ts (CATD  and PGR GR mRNAs . In contp<0.001) . The decEstrogen is a potent mitogen for these luminal breast cancer cell lines, but the effect of matrix density on this activity has not been reported. We therefore cultured these cell lines +/- E2 in low density/ compliant or high density/ stiff collagen-I gels for 72 h (MCF-7 cells) or 7 days (T47D cells). E2 treatment alone significantly increased cell number, as expected. However, matrix density had no effect on E2-induced growth Figs. , 4a, in Estrogen crosstalk with hormones is a well-recognized feature of breast biology, yet the effect of matrix density on these interactions is poorly understood. To elucidate the effect of matrix density on estrogen and PRL crosstalk, we first examined the net effect on expression of ERα and the long isoform of the PRLR (L-PRLR) by western analyses. After 72h of hormone treatment, E2 had strikingly down-regulated ERα in both cell lines regardless of matrix density Figs. , 4b. PRLIn contrast to the modest PRL-induced increase in growth of breast cancer cells cultured on tissue culture plastic , 41, PRLHigh density/ stiff matrices also enhanced PRL and E2 crosstalk to increase growth of T47D cells, but the outcome and underlying processes differed. In contrast to MCF-7 cells, E2 and PRL co-treatment significantly increased cell number in both low density/ compliant and high density/ stiff matrices, compared to E2 alone p<0.05, . This rePRL activates downstream signaling cascades through proximal kinases, including JAK2 and SFKs –34. We hWhile the JAK2/STAT5 cascade is the major physiological pathway for PRL actions in mammary function , SFKs meCancer progression is characterized by local invasion of tumor epithelia, which may lead to distant metastases . In ordeOne of the hallmarks of aggressive clinical ERα+ breast tumors is modification of the alignment of collagen fibers, so that they become more perpendicular to the cell surface . We utilCharacteristics of the ECM increasingly are recognized as critical players in the progression of breast cancer. The desmoplastic response increases the stiffness of the matrix, which is implicated in metastatic progression , 12. TumEven though the ability of estrogen to activate a canonical ERE was reduced in high density/ stiff collagen-I matrices, the net effect on endogenous transcripts was gene and cell line specific. These observations indicate that the various estrogen-responsive enhancers present in the complex promoters of endogenous target genes are tuned to matrix density, which may alter the outcome of estrogen action on some aspects of tumor phenotype. However, estrogen-induced growth and its responsiveness to 4-OHT, proliferation and invasiveness were unaffected by matrix density.in vivo would be substantial. Possible underlying mechanisms, such as increased 4-OHT agonist activity at ERα [in vitro [Although interactions between the ECM and PRL/PRLR signals in breast cancer have been examined , 68, 69,y at ERα  deserve in vitro , 59, 71.in vitro , 72–74.Interestingly, although the combination of PRL and estrogen augmented cell growth in high density/ stiff matrices in both cell lines, the underlying processes were different. However, despite these differences and the multiple reported mechanisms by which PRL and estrogen have been shown to interact, T47D and MCF-7 cells shared a common dependence on SFKs for PRL enhanced estrogen-induced growth in stiff matrices. We previously showed that a stiff collagen-I environment strengthens PRL signaling through the FAK-SFK-ERK1/2 cascade, associated with increased co-localization of FAK and PRLR . In contT47D and MCF-7 cells are two of the most studied ERα+/PRLR+ luminal breast cancer cell lines with respect to both estrogen and PRL actions , 79, 80.In summary, we have shown that high density/ stiff collagen matrices enhance pro-tumorigenic crosstalk between estrogen and PRL in ERα+/PRLR+ luminal breast cancer cells. Our findings elucidate one mechanism by which the desmoplastic response around advancing ERα+ breast cancers may permit estrogen and PRL to accelerate tumor growth and invasion, and resist endocrine therapy. The differences in the two cell lines examined underscore the importance of examining multiple cell lines in preclinical studies. Together, these studies provide novel insights into the power of ECM stiffness to control the outcome of hormone signals in ERα+ breast cancers, and suggest new avenues for therapeutic approaches.S1 TableForward and reverse qRT-PCR primers utilized in this study.(PDF)Click here for additional data file.


Lack of AgNP interference with the cytotoxicity assays  and their simultaneous application point to the compatibility and usefulness of such a battery of assays. The RTH-149 and RTL-W1 liver cell lines exhibited similar sensitivity as primary hepatocytes towards AgNP toxicity. Leibovitz’s L-15 culture medium composition (high amino acid content) had an important influence on the behaviour and toxicity of AgNPs towards the RTL-W1 cell line. The obtained results demonstrate that, with careful consideration, such an in vitro approach can provide valuable toxicological data to be used in an integrated testing strategy for NM-300K risk assessment.Among all classes of nanomaterials, silver nanoparticles (AgNPs) have potentially an important ecotoxicological impact, especially in freshwater environments. Fish are particularly susceptible to the toxic effects of silver ions and, with knowledge gaps regarding the contribution of dissolution and unique particle effects to AgNP toxicity, they represent a group of vulnerable organisms. Using cell lines  and primary hepatocytes of rainbow trout (Oncorhynchus mykiss) as  Among the plethora of nanoparticles (NPs) already on the market, silver nanoparticles (AgNPs) are being used in more products >400) than any other manufactured nanomaterial (MN) . AgNPs’ 00 than aFish are particularly susceptible to the toxic effects of metal ions such as silver ,9 that cin vitro testing system to meet the high number of substances to be tested, including MNs. A large number of fish cell lines exist from different species, tissues and cell types, however they have only been used in a limited number of studies for nanoparticle toxicity assessment  + min, where max is the maximal response observed, b is the slope of the curve and min the minimal response. Normality of the data distributions was checked by means of a Shapiro-Wilk test. The homogeneity of variances was checked automatically by the program. Since the data were normal and homoscedastic, a one-way repeated measures analysis of variance (RM-ANOVA) followed by a Dunnett’s post hoc test was performed, allowing the detection of significant difference from control values and the establishment of the lowest observed effect concentration (LOEC) (The size distribution of silver nanoparticles in the aqueous dispersion and culture media (MEM(-), L-15, EMEM(pyr), EMEM(NEAA) and M199) was characterised using DLS directly after preparation and after 24 h to check stability . HydrodyDLS analysis shows that aqueous dispersions of NM-300K (93.45 µg/mL) have a stable but polydisperse size distribution (PdI 0.473) with two distinct populations according to intensity distributions with average hydrodynamic diameters of 6 ± 1 nm and 54 ± 6 nm. However, when volume and number distributions are taken into consideration, NM-300K aqueous dispersions show a population averaging 25 ± 3 nm and 4 ± 1 nm, respectively, with a maximum hydrodynamic diameter. These distributions appear as stable after 24 h. In contrast, in serum free MEM(-), the silver nanoparticles aggregate and sediment out of solution over time. DLS analysis of particles that remain in suspension after 24 h reveal that bigger populations, 467 ± 56 nm in diameter, are present. Volume and number measurements show that, together with these particles (570 ± 30 nm), there are also particles present with maximum hydrodynamic diameters of 29 ± 3 and 24 ± 3 nm, respectively.i.e., EMEM (pyr), EMEM(NEAA) and M199) used for the cell lines and primary hepatocytes exposures the NM-300K dispersion is stable over 24 h and the frequency size distribution showed no important differences among media  at the concentrations corresponding to the exposure assays did not provoke any cytotoxic effect in any of the assay systems used.3 and NM-300K are presented in 3 and NM-300K are presented in  while the other cells used in these experiments exhibited much lower IC50 values (1.1–2 µg/mL). Taking LOEC values into account, the primary hepatocytes showed increased sensitivity (LOEC = 0.34 µg/mL) with respect to the other cell lines  than the other cells (19.8 µg/mL to 41.7 µg/mL). Primary hepatocytes, RTH-149 and RTL-W1 exhibited similar LOEC values (23.4 µg/mL). The LOEC value recorded for the RTG-2 gonadal cell lines was 46.8 µg/mL  towards rainbow trout liver cell lines , a gonadal cell line (RTG-2) and primary hepatocytes. For chemicals in general, in vitro systems are still being validated and their regulatory applicability is still being explored, taking into account, in particular, their value in predicting in vivo responses. In the case of NMs, that possibly present additional confounding factors, the use of in vitro systems raises new concerns. We sought to address these issues.In this study, we have tested the toxicity of AgNOin vitro assay systems and nanomaterials [Prior to nanoparticle cytotoxicity assessment, it is essential to take into consideration the possible interference between aterials ,40,41,42According to DLS analysis, NM-300K suspensions prepared in EMEM  and M199 (primary hepatocytes) were stable over the 24 h exposure period. Taking volume distributions as the most accurate representation, due to intensity masking of smaller particles and number distributions favouring smaller particles, the NM-300K size distribution in culture medium is composed of 95% of particles with an average diameter of 7 ± 1 nm and 5% 25 ± 2 nm in diameter. This guarantees a real exposure to nanoparticulate material. All media were supplemented with FBS which appeared to ensure stability. In contrast to other media suspensions, particles prepared in L-15 medium (RTL-W1 cells) experienced aggregation formation and precipitated out of solution over the 24 h exposure timeframe. L-15 has high levels of thiol containing amino acids including cysteine and methionine which are strong Ag+ complexing ligands known to form Ag–S covalent bonds with AgNPs. It also has a higher chloride content that could favour the formation of silver chloride compounds that could precipitate out of solution . Therefo3 was included as a Ag+ ion source to investigate differences in the toxicity of Ag+ ions and AgNPs and also if differences in sensitivities to Ag+ exist between different cell lines and hepatocytes. Previous studies with CuNPs and ZnO NPs demonstrated an increased sensitivity of mammalian cell lines with respect to fish cell lines to nanoparticle toxicity, possibly due to differences in their sensitivity to ions released from the nanoparticles rather than to nanoparticle effects [3 than NM-300K, which suggests a higher toxicity of ions.In this study, AgNO effects ,20. Ther effects ,48. In t3 for RTH-149, RTG-2 and primary hepatocytes ranged from 0.4 to 3.8 µg/mL across all toxicity endpoints. This is in agreement with Farkas and colleagues [3 exposures using rainbow trout primary hepatocytes and the AlamarBlue assay system. In contrast, the RTL-W1 liver cell line was distinctly the least sensitive, and the toxic potency of AgNO3 was around 10-fold lower (IC50 ranging from 10.9 to 32.2 µg/mL) than in the rest of the cells used in this study. As previously discussed, the difference in sensitivity and susceptibility in this cell line is most likely related with decreased bioavailability and complexing of the Ag+ ions in L-15 medium. This important observation has also been made by Dayeh and colleagues [4) and copper chloride (CuCl2) in fish cells, where toxicity was absent but appeared when using amino acid free minimal medium (L15/ex). This draws attention to the important influence of medium composition on metal ion toxicity in cell lines in vitro. Kermanizadeh and co-authors reported IC50 values of 14.06 and 31.25 µg/mL for NM-300K toxicity in human renal proximal tubule epithelial cells (HK-2) following exposure in different mediums [in vivo towards rainbow trout fry with LOEC values ranging from 0.7 to 25 µg/mL, increasing with increasing water salinity [IC50 values corresponding to AgNO mediums . A similsalinity . Therefo3, the RTL-W1 cell line proved particularly susceptible to toxic effects of NM-300K when measured as lysosomal damage  or membrane disruption . However, the IC50 values according to the AlamarBlue assay were the highest recorded (IC50 75.9 µg/mL). Similarly Gaiser and co-authors [vs. 64 µg/mL respectively) using the same NM-300K particles in the human hepatic cell line C3A. This may point to a specific mechanism of toxicity for the NM-300K suspensions targeting membranes, particularly those of lysosomes. It is possible that the bigger aggregates present in RTL-W1 culture medium (L-15) can cause lysosomal damage at lower exposure concentrations. In fact, lysosomal perturbation has been reported for a variety of NMs (reviewed in [A concentration dependent reduction in viability following exposure to NM-300K (0.73 to 93.5 µg/mL) in all cell lines and primary hepatocytes was observed. IC50 values ranged from 10.7 to 75.9 µg/mL according to the specific cell line and toxicity assay endpoint used. Despite its apparent increased resistance to AgNO-authors  showed liewed in ) and is iewed in , using t3 and not when exposed to AgNPs suggesting that possessing full metabolic processes (e.g. cytochrome P450–mediated oxidation) may not be particularly significant for the toxicology of NPs. This point has also been made by other authors [in vitro and in vivo.When comparing the responses of the continuous cell lines to the primary hepatocytes in general primary cells showed comparable sensitivity both to Ag+ and AgNPs. However, when LOEC values were taken into account, primary cells showed increased sensitivity to Ag+ according to the AlamarBlue assay system which measures metabolic activity. The lack of any noticeable difference in the physico-chemical properties of AgNPs in media used for primary hepatocyte exposures suggests that the exposure environment did not contribute to this increased sensitivity. Primary cells exhibit metabolic pathways and capacities quite similar to the cells in original tissues, while continuous cell lines have lost some of them. This can be associated with higher detoxification capabilities (and therefore lower sensitivity to toxic action of substances) but also with higher sensitivities (to the toxic action of substances) due to alterations of any of the very closely related metabolic pathways or due to a favored uptake rate of Ag+. Any or a combination of these factors could explain the differences in sensitivity that would need to be addressed in future studies. Interestingly, the increased sensitivity is only seen following exposure to AgNO authors  when comIn vitro studies are always limited with respect to in vivo approaches by not being able to represent different tissue specific toxic insults, adaptive responses at whole organism levels and toxicokinetics [in vivo systems [in vivo LC50. Apart from the obvious metabolic and complexity differences between in vitro and in vivo systems, one factor that helps to explain the higher IC50 values is that the actual concentrations of bioavailable AgNPs in this study are most likely much lower due to the presence of serum proteins in the culture media. Massarsky and co-authors, also using rainbow trout primary hepatocytes, only report subtle responses to 31 µg/mL of AgNPs when bovine serum albumin was present in the exposure medium [in vitro tests in order to increase the predictive value of in vitro studies for in vivo responses. For now, working with well-defined representative NMs, choosing cell lines from relevant target tissues and using a battery of cytotoxicity assays, as in the present study, can be very useful in toxicity ranking and as pre-screening to help regulators in making the decision to avoid particular in vivo assays. It is also evident that the applied approach represents an invaluable tool for mechanistic studies.kinetics . As a co systems ,57. The  systems . In our e medium . In conte medium . In our Up until now, the cytotoxicity of the NM-300K silver representative nanomaterial has not been tested in liver cells of fish. In this comparative study, liver cell lines  proved as sensitive test systems when compared to primary hepatocytes following exposure to NM-300K. Considering the ease of maintenance and culture of cell lines compared with primary hepatocytes, this has important practical implications for the integration of such a tool into intelligent testing strategies for the hazard assessment of nanoparticles. However, further work is necessary to determine the applicability of this tool to other NPs using other cellular systems that could be more representative of particular conditions or environments.in vitro testing approach was easily applicable careful consideration must be made regarding the influence of cell culture medium composition when using cellular in vitro systems for observing toxicity of NPs. Choosing culture media with similar compositions for exposures will ensure the same NM presentation to respective cell lines being tested and facilitate an accurate comparative toxicity assessment.Using the simultaneous application of three different assay systems  proved a valuable means to assess cytotoxicity according to different targets of toxic action. The lowest IC50 was recorded using the NRU assay and the RTL-W1 cell line (10.7 µg/mL) and, thus, points to lysosomal damage as an important indicator for detecting nanoparticle specific effects. It must be noted, however, that cell culture medium composition had a strong influence on the behaviour and toxicity of Ag+ and AgNPs, causing precipitation or limiting their bioavailability and leading to a reduction of or increase in toxicity according to particular tests applied. Therefore, while such an


L aspotential nodes of resistance. We then characterized a panel of ABT-199-resistantmyeloid leukemia cell lines derived through chronic exposure to ABT-199 and foundthat acquired drug resistance is indeed driven by the upregulation of MCL-1 andBCL-XL. By targeting MCL-1 and BCL-XL, resistant AML celllines could be resensitized to ABT-199. Further, preemptively targeting MCL-1 and/orBCL-XL alongside administration of ABT-199 was capable of delaying orforestalling the acquisition of drug resistance. Collectively, these data suggestthat in AML, (1) the selection of initial therapy dynamically templates thelandscape of acquired resistance via modulation of MCL-1/BCL-XL and (2)appropriate selection of initial therapy may delay or altogether forestall theacquisition of resistance to ABT-199.ABT-199, a potent and selective small-molecule antagonist of BCL-2, is beingclinically vetted as pharmacotherapy for the treatment of acute myeloid leukemia(AML). However, given that prolonged monotherapy tends to beget resistance, wesought to investigate the means by which resistance to ABT-199 might arise in AMLand the extent to which those mechanisms might be preempted. Here we used apathway-activating genetic screen to nominate MCL-1 and BCL-X L, andBCL-w, demonstrated remarkable single-agent efficacy against AML in preclinicalstudiesL antagonism1278L and MCL-1 as the main mediators ofresistance to ABT-199 in AML and demonstrate that combinatorial inhibition ofBCL-2/BCL-XL/MCL-1 can be used to delay or altogether forestall theacquisition of cell-autonomous drug resistance.Acute myeloid leukemia (AML) is a hematopoietic malignancy defined by clonal expansion ofmyeloid precursors. Among the molecular characteristics that typify this cancer, severalstudies have highlighted the dependence of AML cells on the anti-apoptotic protein BCL-2and subsequently established how that specific dependency can be exploited fortherapeutic effect using BH3 mimetics150 values previously reported to be below10 nM50 assay  and/or NOXA , indicating acorresponding shift in dependency  while scaling up the dose (to2.5 μM ABT-199) of a separate population to produce a“high resistance” line. Characterization of the parental anddifferentially resistant THP-1 lines suggests that (1) the degree of drugresistance acquired is proportional to the background dose of drug  and thatesistant .L in resistant versus parental lines  revealed a modest 1.4–1.6 fold increase in MCL-1 transcriptlevels in resistant THP-1 and NOMO-1 lines relative to parental  and 1%penicillin/streptomycin. OCI-AML2, NOMO-1, and OCI-AML3 cell lines weregenerously gifted by Anthony Letai (Dana Farber Cancer Institute). THP-1 andHL-60 cell lines were purchased from Duke University Cell Culture Facility(CCF). ABT-199-resistant and control cells were grown in media described abovesupplemented with [0.5 μM to 2.5 μM] ABT-199 orDMSO, respectively. Drugs were purchased from Selleck chemicals and were used atthe following doses: 3 μM and 5 μM for ABT-199(apoptosis assays), 1 μM for WEHI-539 (background dose forGI50 assay).All cell lines were cultured at 37 °C in 5% CO50 of the cell line. A second plate of parental cells wassimultaneously plated with an equivalent quantity of DMSO as a paired control.Cells in both dishes were subsequently observed and counted weekly in parallel.For cells cultured in drug, ABT-199 doses were increased in increments of500 nM as soon as the cell population stabilized. Cell lines wereconsidered fully resistant when they could maintain their population in mediacontaining 2.5 μM ABT-199.Cell lines resistant to ABT-199 were generated through chronic drug exposure aspreviously describedLentivirus particles were produced through transient transfection of293 T cells using a three-plasmid system: expression clone + VSVG +δVPR as previously described50 assay, described below.Candidate genes/pathways that shifted the GI50 of both respectivecell lines to at least 1 μM were selected as candidates forfollowup.We infected discrete populations of OCI-AML2 and MOLM13 cells with lentivirusencoding the expression of each of 39 individual constitutive activatorconstructs, each driven by a moderate PGK promoter, from a previously describedcDNA libraryPathway activating constructs were previously cloned and sequence verified bymembers of our labOCI-AML2, THP-1, NOMO-1, OCI-AML3 cells were BH3-profiled as previouslydescribed50 values were calculated byfitting each individual experiment to a 4-parameter logistic curve usingGraphPad/Prism 6 software and selecting the dose at which cell viability equals50% of DMSO-treated viability.Cells were seeded in 96-well plates at 5000 cells per well. After24 hours, cells were treated, by row, with a 10-fold serial dilution ofindicated drug in DMSO to yield final drug concentrations of 20, 2, 0.2, 0.02,0.002, 0.0002, 0.00002, and 0.000002 μM. A final well wastreated with only DMSO. CellTiter-Glo luminescent viability assay (Promega) wasused to measure cell viability 72 hours after addition of drug. Eachtreatment condition was represented by three individual experiments. Relativeviability was calculated by normalizing raw luminescence counts to DMSO-treatedwells. For experiments involving two drugs, a second background drug was kept ata constant concentration across all wells except for the DMSO control. Viabilityin two-drug experiments was normalized to luminescence of secondary drug-onlywell. For pathway-activating screen, GI2; BD Biosciences). Phosphatidylserine externalization wasmeasured using APC -conjugated Annexin V (BD Biosciences).7-AAD (BD Biosciences) was used as the viability probe. Experiments wereanalyzed at 20,000 counts per sample using BD FACSVantage SE. Gating strategywas defined using stained/unstained cells.250,000 cells were seeded into each well of a six-well plate and treated withindicated quantity of drug or DMSO. Cells were incubated for 48 hours,washed twice with phosphate-buffered saline (PBS), and resuspended in Annexin Vbinding buffer  and5′-CAGTCATGCCCGTCAGGAAC-3′ (reverse). Average cycle thresholds(Ct) were calculated for each gene normalized to the referencegene GAPDH. Relative gene expression was determined using theΔΔCt method.RNA extraction, cDNA synthesis and quantitative real-time PCR was performed aspreviously describedL expression between parental and allthree derivatives were detected using one-way ANOVA. Subsequently,Dunnett’s multiple comparisons test was applied to evaluate significantdifferences in expression for each resistant derivative relative to parentalcontrol. For NOMO-1 and THP-1, differences between means of MCL-1 orBCL-XL expression in resistant relative to parental cell lineswas examined using Student’s t-test.qRT-PCR data was compiled as means and standard deviations. For OCI-AML2,differences in MCL-1 and BCL-XL, Bcl-2,BIM, BID, BAX, BAK, BAD p-S112, BAD p-S136, total BAD at a 1:1000 dilution andβ-actin at 1:5000. Secondary goat anti-rabbit IgG-HRP was applied at1:5000. All primary antibodies were purchased from Cell Signaling Technology;secondary antibodies were purchased from Santa Cruz Biotechnology.Immunoblotting was performed as previously describedTRC shRNA clones were acquired from the Duke RNAi Facility as glycerol stocks.Constructs were prepared as lentivirus and used for viral transduction asdescribed above.How to cite this article: Lin, K. H. et al. TargetingMCL-1/BCL-XL Forestalls the Acquisition of Resistance to ABT-199 inAcute Myeloid Leukemia. Sci. Rep.6, 27696; doi: 10.1038/srep27696 (2016).


Supplemental Digital Content is available in the text The mutation status of cancer driver genes may correlate with different degrees of malignancy of cancers. The doubling time and multidrug resistance are 2 phenotypes that reflect the degree of malignancy of cancer cells. Because most of cancer driver genes are hard to target, identification of their synthetic lethal partners might be a viable approach to treatment of the cancers with the relevant mutations.The genome-wide screening for synthetic lethal partners is costly and labor intensive. Thus, a computational approach facilitating identification of candidate genes for a focus synthetic lethal RNAi screening will accelerate novel anticancer drug discovery.We used several publicly available cancer cell lines and tumor tissue genomic data in this study.APC, KRAS, BRAF, PIK3CA, and TP53 by comparison of their gene phenotype values in cancer cell lines with the relevant mutations and wild-type background. Further, we experimentally validated some of the synthetic lethal relationships we predicted.We compared the doubling time and multidrug resistance between the NCI-60 cell lines with mutations in some cancer driver genes and those without the mutations. We identified some candidate synthetic lethal genes to the cancer driver genes APC, KRAS, or PIK3CA might correlate with cancer proliferation or drug resistance. We identified 40, 21, 5, 43, and 18 potential synthetic lethal genes to APC, KRAS, BRAF, PIK3CA, and TP53, respectively. We found that some of the potential synthetic lethal genes show significantly higher expression in the cancers with mutations of their synthetic lethal partners and the wild-type counterparts. Further, our experiments confirmed several synthetic lethal relationships that are novel findings by our methods.We reported that mutations in some cancer driver genes mutations in some cancer driver genes such as We experimentally validated a part of the synthetic lethal relationships we predicted. We plan to perform further experiments to validate the other synthetic lethal relationships predicted by this study.Our computational methods achieve to identify candidate synthetic lethal partners to cancer driver genes for further experimental screening with multiple lines of evidences, and therefore contribute to development of anticancer drugs. It was estimated that approximately 90% of cancer genes show somatic mutations and 20% show germline mutations.1 To investigate gene mutations in cancers and develop targeted anticancer drugs, human cancer cell lines are being widely used.2 For example, the National Cancer Institute's NCI-60 cell lines have been extensively characterized and frequently used as a screening tool for discovery of anticancer drugs.3 Based on the panel of NCI-60 cell lines, the frequently-mutant genes in cancers have been identified, such as APC, BRAF, CDKN2A, KRAS, PIK3CA, PTEN, TP53, etc.4 For the NCI-60 cell lines, the phenotypes such as doubling time (the period of time required for cell lines to double in size) and multidrug resistance (MDR) may indicate the degree of malignancy.8 With the gene mutation and cancer cell phenotypes information, an important question arises: to what degree the mutations of some genes correlate with high malignancy of cancers? In the present study, we initially investigate the correlation between gene mutations and cancers’ malignancy based on the gene mutation and malignancy phenotypes (doubling time and MDR) data for the NCI60 cell lines since so far such investigation is lacking.Numerous studies have shown that gene mutations underlie the development of all types of cancers.9 The RNAi screening technology can be used for identification of synthetic lethal partners. Two genes are synthetic lethal if dis-regulation of either alone does not result in cell death but dis-regulation of both leads to death of cells.10 Thus, abrogation of gene X that is synthetic lethal to gene Y should selectively kill Y-mutant cells and spare the cells without Y mutations. The synthetic lethality concept has been used for discovery of anticancer drugs, which may target some genes whose synthetic lethal partners are frequently mutated in cancers but are hardly druggable such as the tumor suppressor genes APC and TP53, or have severe drug resistance such as the oncogene KRAS and BRAF.12 The standard approach to systematical identification of synthetic lethal genes is based on genome-wide or kinome-wide RNAi screening.13 However, large-scale synthetic lethal RNAi screening strategy is laborious and time consuming. Its efficiency could be improved by first identifying differentially expressed genes between isogenically paired cell lines (hereafter refer to single gene mutant versus wild-type), and then performing a focused RNAi screening on the differentially expressed genes to examine their synthetic lethality to the mutant gene.12 Wang and Simon12 proposed a method to computationally prescreen synthetic lethal genes to p53 using gene expression profiles. They identified 98 kinase genes that are potential therapeutic targets for p53-mutant cancers. A limitation of that study is that the candidate synthetic lethal genes to p53 identified may harbor many false positives because their underlying presumption is not necessarily true that the gene expression difference is a result of altered gene mutation status.RNAi screening is an approach that facilitates the systematic assessment of the effect of gene deregulation on cell phenotypes such as cell death.9 In (9) the authors proposed a computational method to quantify gene-specific suppression phenotype. They generate a per-gene value for each sample—gene phenotype value (GPV), quantifying the suppression effect for a specific gene in an individual cell line by siRNA reagents. Furthermore, the authors affirmed that the GPV reflects the degree of dependency of an individual cell line's viability on a specific gene, with a lower GPV representing high viability dependency of a cell line on the gene. If we perform the 2-class comparison between a group of cell lines with mutations of some gene and another group of cell lines without mutations of the gene, we could identify the genes with significantly lower GPVs in the mutant cell lines than in the wide-type cell lines. It means that the mutant cell lines have higher viability dependency on the identified genes than the wide-type cell lines. In the other words, the identified genes could be synthetic lethal to the mutant gene. On the basis of the approach, we identified the potential synthetic lethal genes to APC, KRAS, BRAF, PIK3CA, and TP53, respectively. Then we performed an examination of the literature to evaluate other evidence for the putative synthetic lethality relationships. Furthermore, we compared expression of the identified genes in between mutant cancer cells/tumor samples and wide type, and identified a portion of genes that show the significantly higher expression level in the mutant cancer cells/tumor samples. In addition, we examined the drug sensitivity differences between NCI-60 cell lines with gene mutations and NCI-60 cell lines without the mutations for the compounds that target the kinases encoded by the genes among the potential synthetic lethal genes identified. Finally, we performed experiments to validate some of the synthetic lethality relationships we computationally identified.RNAi screening uses a short interfering RNA (siRNA) to suppress expression of specific genes. The degree of suppression of the targeted gene is often highly variable due to on-target and off-target effects of siRNA.14 and the gene mutation data of NCI-60 cancer cell lines from the publication4 . The GPVs for the 102 Achilles cancer cell lines are from the publication,9 whereas the gene mutation information for the 102 Achilles cancer cell lines is from the Cancer Cell Line Encyclopedia (CCLE) project.2 The microarray gene expression dataset for the 102 Achilles cancer cell lines is also from the CCLE project. The microarray gene expression dataset for the NCI-60 cell lines (Affymetrix U95A data from Novartis) is downloaded from the Developmental Therapeutics Program NCI/NIH website https://wiki.nci.nih.gov/display/NCIDTPdata/Molecular+Target+Data. We downloaded the RNA-Seq gene expression dataset for glioblastoma multiforme (GBM) and the microarray gene expression dataset for breast invasive carcinoma (BRCA) from the TCGA website https://tcga-data.nci.nih.gov/tcga/. All of the gene expression datasets were used for the gene differential expression analysis. Ethical approval was waived since we used only publicly available data and materials in this study.We obtained the NCI-60 cell lines’ phenotype data (doubling time and MDR) from the CellMiner database,t-test statistics . We performed the class comparisons based on the mutant status of NCI-60 cell lines for genes APC, BRAF, CDKN2A, KRAS, PIK3CA, and PTEN, respectively. A detailed description of mutation status of these genes in each NCI-60 cell line is shown in the supplementary Table S1. The numbers of the mutant and wide-type NCI-60 cell lines used for the class comparisons are given in the supplementary Table S2.We compared doubling time and MDR between the mutant NCI-60 cell lines and the wide-type NCI-60 cell lines using t-test at a 2-sided significance level of 0.001. We also performed univariate permutation tests with 10,000 permutations of the class label (mutant or wide-type) to measure the significance of individual genes. The proportion of the permutations that gave a t-test P value as small as obtained with the true class labels was the univariate permutation P value for that gene. To adjust for multiple tests, we reported the false discovery rate (FDR) for each gene identified. The FDR was estimated using the method of Benjami and Hochberg.15 This procedure was implemented with the class comparison between groups of arrays tools in BRB-ArrayTools.16 We selected the genes that showed significantly lower relative GPVs in the mutant cell lines as the potential synthetic lethal genes to the mutant genes. This procedure was carried out for APC, KRAS, BRAF, PIK3CA, and TP53 mutation status in cell lines, respectively . A detailed description of mutation status of these genes in each Achilles cell line is shown in the supplementary Table S1. The numbers of the mutant and wide-type Achilles cancer cell lines used for the class comparisons are given in the supplementary Table S3.We first identified the genes with differential GPVs between the mutant cell lines and the wild-type cell lines among 102 Achilles cancer cell lines using the univariate t test. The numbers of samples in each class for Achilles cell lines and NCI-60 cell lines are shown in the supplementary Table S3 and Table S2, respectively. The numbers of samples in each class for the TCGA tumors are summarized in the supplementary Table S4. The significantly more highly expressed genes in mutant samples than in wide-type were identified  and further analyzed.For the potential synthetic lethal genes identified, we compared their expression in between mutant and wide-type cell lines or tumors  tumor samples, respectively) using t-test statistics . GI50 is the concentration required to inhibit growth of cancer cell lines by 50%. The lower GI50 value means higher drug sensitivity.We compared drug sensitivity (GI50) between the mutant NCI-60 cell lines and the wild-type NCI-60 cell lines using 2 atmosphere. The PD-0332991 and 68C091 were from Sigma .Human cells from colorectal adenocarcinoma, LoVo, SW480, and SW620 were maintained in RPMI 1640 supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin, and 0.1 mg/mL streptomycin. The human cells from glioblastoma, TP366, and LN229 cells, and hepatocellular carcinoma, HepG2, Hep3B, and Huh7 were maintained in DMEM with 10% FBS, 100 U/mL penicillin, and 0.1 mg/mL streptomycin. All the cells were kept in a humidified incubator at 37°C and a 5% CO3 (for MTT assay) or 1 × 103 (for CCK8 assay) cells were seeded in a 96-well plate in quadruplicate or in hepatuplicate. After 24 hours, different concentrations of drugs or vehicle were added with fresh medium. Cells were incubated at 37°C for 3 days followed by an MTT assay or as indicated time points followed by CCK8 assay. The experiments were repeated at least twice.Experimental procedures for in vitro proliferation assay are as described in (5). Briefly, 3×10The scrambled siRNA and target-specific sequences synthesized by GenePharma  were as follows: siTDO2, 5’-AGU GAU AGG UAC AAG GUA UUU-3’; siCTNNB1#1, 5’-UUG UUA UCA GAG GAC UAA AUA-3’; siCTNNB1#2, 5’-UCU AAC CUC ACU UGC AAU AAU-3’; siCSNK1A1#1, 5’-GCA AGC UCU AUA AGA UUC UUC-3’; siCSNK1A#2, 5’-GCA GAA UUU GCG AUG UAC UUA-3’. Cells were transfected with siRNA using Lipofectamine 2000  according to the manufacturer's instruction. After 36 hours, the transfected cells were split into 6-well plates for knockdown confirmation and 96-well plates for cell proliferation examination.TDO2) of denaturation (94°C/15 s), annealing (55°C/15 s), and extension (72°C/30 s), and 1 cycle of final extension (72°C/5 min). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control. The amplified PCR products were separated by electrophoresis on a 2.5% agar gel containing ethidium bromide. PCR primers  were synthesized by Sangon .Total RNA was isolated from various cell lines using the TRIzol reagent . Two micrograms of RNA was transcribed into complementary DNA by PrimeScript (TaKaRa) in 20 μL, and 0.2 μL was subjected to RT-PCR. The condition for PCR was 25 cycles , and the PIK3CA mutant cell lines show stronger MDR than the wild-type . Moreover, for mutations of APC, KRAS, and PIK3CA, the less doubling time may likely correlate with stronger MDR . These genes include DOCK5, PIGL, CTNNB1, GALNT8, TDO2, ABHD2, DNAJC17, ANAPC4, and OGDH . These genes include BLMH, ABHD2, BCL2L1, and JUP .We also compared the expression of the synthetic lethal genes identified in between mutant and wide-type tumor samples from TCGA. Due to a small proportion of samples with TACC3 and MYC are much more highly expressed in TP53 mutant GBMs and TP53 mutant BRCAs compared with respective wide-types. This result indicates that TP53 mutant cancer cells could rely on the high expression of TACC3 or MYC for survival, predicting the synthetic lethality relationship between TACC3 and TP53, and between MYC and TP53. The other highly expressed genes in TP53 mutant BRCAs include CDK6 and CCT6A which are likely to be synthetic lethal to TP53 (Table CDK6 encodes a member of the cyclin-dependent protein kinase (CDK) family. This gene has been found to be upregulated in several types of cancers.31Table 32 Among the potential synthetic lethal genes we identified, some encode kinases as shown in Table Discovery of cancer-related kinases and development of kinase inhibitors have been an active research field of cancer biology. Currently, kinase inhibitors are among the key class of anticancer drugs.http://dtp.nci.nih.gov/dtpstandard/dwindex/index.jsp, we compared their drug sensitivity (GI50) between the cell lines with mutations in their synthetic lethal partners and the cell lines without such mutations using t-test statistics . Although few of the drug sensitivity differences were statistically significant , the compound sunitinib targeting the kinase CSNK1A1 shows higher sensitivity in the APC mutant cell lines than in the APC wide-type cell lines , indicating that there exists a synthetic lethality relationship between CSNK1A1 and APC.For some of the compounds whose GI50 values against NCI-60 cell lines are available in the National Cancer Institute's developmental therapeutics program (DTP) database website TP53 and CDK4/6, we treated the TP53 wild-type cells TP366, as well as TP53 mutant cells LN229, with the inhibitor of CDK4/6 PD-0332991. We found that PD-0332991 had less effect on the proliferation of TP366 cells with wild-type TP53, but induced greater proliferation reduction in LN229 cells with mutant TP53  and Huh7 (TP53 mutant) cells, compared with HepG2 with wild-type TP53  led to significant decrease of cell proliferation for APC mutant cells SW620 and LoVo compared with APC wild-type cells SW480 , and CTNNB1 has significantly higher level of expression in APC mutant cancer cell lines than in wide-type cell lines . These computational results strongly indicate that the APC mutant cell lines might depend on the CTNNB1 gene for survival much more than the APC wild-type cell lines and therefore there may exist a synthetic lethal relationship between CTNNB1 and APC. Indeed, our experiments verified the synthetic lethal relationship between them. CTNNB1 has been shown to play an important role in regulation of cell adhesion and gene transcription, and its deregulation has been associated with many cancers.34 Thus, CTNNB1 inhibitors are useful in treatment of many cancers including APC mutant cancers. Another gene TDO2 has significant lower GPVs in the APC mutant cell lines than in wide-type cell lines , and significantly higher level of expression in APC mutant cancer cell lines than in wide-type cell lines . Our experiments also verified the synthetic lethal relationship between TDO2 and APC to be true. TDO2 encodes a protein that may play a role in cancer through the suppression of antitumor immune responses.35 Thus, the associations of APC, TDO2 and antitumor immune responses are worthy to be further investigated since cancer immunotherapy could be a fundamental breakthrough in cancer treatment.36CDK6 has significant lower GPVs in the TP53 mutant cell lines than in wide-type cell lines , and significantly higher level of expression in TP53 mutant cancer cell lines than in wide-type cell lines . Again, our experiments confirmed the synthetic lethal relationship between CDK6 and TP53.Our results show that the mean of In this study, we found that only a small portion of the potential synthetic lethal genes we computationally identified showed significantly higher expression in the mutant cell lines or tumors. There are several reasons to explain this result: first, sample sizes of the cell lines and the GBM tumors are relatively small; second, only 2 types of tumors were examined ; third, the synthetic lethal interaction does not necessarily mean the expression difference between mutant and wild-type tumors. Here, we present the highly expressed genes in mutant cell lines or tumors to show that these genes are more likely to have synthetic lethal interactions with the cancer driver genes. However, we cannot exclude the synthetic lethal relationships for the genes which do not show significantly higher expression in the mutant cell lines or tumors relative to the wild-type counterparts. The synthetic lethal relationships need to be validated by experiments finally.CDK6 (with TP53), TDO2, CTNNB1, and CSNK1A1 (with APC). Interestingly, 3 of them  show significantly higher expression in the tumors or cell lines with mutations of their synthetic lethal partners than in those without such mutations. In addition, 2 of them (CDK6 and CSNK1A1) encode kinases for which small molecule inhibitors may be utilized to treat tumors with mutations of their synthetic lethal partners (TP53 or APC).Furthermore, we selected some pairs of the synthetic lethal genes we computationally identified to perform experimental validation. Our experiments verified 4 genes to be truly synthetic lethal to the cancer driver genes including The concept of RNAi synthetic lethality screening can be extended to drug synthetic lethality screening. It is known that single-agent targeted therapy is often in-efficacious or prune to relapse due to drug resistance caused by other genes’ mutations and bypassing the cell signaling pathway targeted by such single-agent at a system level. If we can find drug combinations that target these synthetic lethal partners simultaneously, we could repropose some drugs on personal precise medicine to obtain therapeutic solution to drug resistance.


Because hundreds to thousands of individual assays are needed to scratch the surface of human genetic diversity, sophisticated high throughput automation technologies or pooling and deconvolution strategies are required. Characterization of each model needs to be extensive to enable non-biased informatics based modeling. Such approaches will enable deep understanding of genetic to pharmacological response and result in new methods for patient stratification in the clinic. Oncology medicines and cancer genetics have been paving the way for these approaches and we expect to see continued expansion to other fields such as immunology and neuroscience.The drug discovery process classically revolves around a set of biochemical and cellular assays to drive potency optimization and structural-activity relationship models. Layered on top of these concepts is the inclusion of molecular features that affect final drug use, things like: bioavailability, toxicity, stability, solubility, formulation, route of administration, etc. Paradoxically, most drugs entering clinical trials are only tested in a handful of human genetic backgrounds before they are given to people. Here we review efforts and opine on the use of large scale  When a drug discovery project starts, a project team must conduct an important thought experiment: if a perfect molecule that meets all of the team’s criteria was suddenly available, what patients would be selected and what clinical assays would be used to demonstrate efficacy. In genetically well-defined diseases this can be a conceptually straight-forward task. For example in monogenic, recessively inherited diseases, like sickle cell anemia or spinal muscle atrophy, patients are readily identifiable by symptoms in the clinic and confirmed genetically. A homogenous population of patients provides the best chance of achieving a high signal to noise readout in the clinical trial. This is because all the patients should be similar in their molecular pathology and patients treated with a targeted drug should, in an ideal scenario, be obligate responders.Cancer, at a fundamental level, is more heterogeneous, but patient selection strategies have made tremendous strides in the last decade. An example is the use of Gleevec and other Abl kinase inhibitors in chronic myelogenous leukemia (CML) patients that harbor the Philadelphia Chromosome translocation creating a BCR-Abl gain of function fusion protein. Prior to the use of Abl inhibitors, approximately 10,000 people in the US died each year due to CML; that number has dropped to less than 500 people per year since the introduction of these drugs to the market. Other examples of therapies “targeted” to selected cancer types are abundant and include B-RAF, C-KIT, p53-MDM2, c-MET, JAK1, and EGFR. In many cases these therapies lead to a remission, not a cure, and this is likely a reflection of the genetic heterogeneity of the original tumor and a result of the subsequent selective pressure under drug treatment that enable resistant cells to continue to grow. Ultimately, this will lead to drug resistance. New next gen sequencing (NGS) technologies are tremendously impacting the field’s understanding of the spectrum of mutations and underlying heterogeneity in a tumor.Other “common” diseases, like Alzheimer’s disease, Type II Diabetes, and Schizophrenia are thought to be at least partially driven by common genetic variants. The genetics are complicated but the prevailing theory is that small changes in gene regulation, likely at the level of mRNA, slightly predispose an individual for a disease. As subtle genetic variations accumulate in an individual, that person’s risk of developing the disease also increases. Due to this, genetic stratification in common disease populations is extremely difficult. Nonetheless, common variant research has identified examples where genetic testing of patients can be used to select patients for clinical trials. These include people who are homozygous for a common variant in APOE, ApoE4, whom are 10 times more likely to develop Alzheimer’s Disease relative to persons not harboring the ApoE4 variant. An excellent review by in vitro clinical trials” will be to change the arc of drug development, from a paradigm where many therapies are dropped between optimization and candidate selection . Human cellular models will help bridge this gap , speeding the development of new drugs and our understanding of human disease. In this Technology Report, we aim to highlight past examples of cell panel screens and point to future applications of the approach that will extend past cancer to rapidly evolving areas like neurobiology.Cancer therapeutics have already benefitted from the use of genetically defined human cell panels. This approach should be applicable to monogenic neurological disorders and eventually to other indications where predictive cellular models can be generated. The goal of “TM as a proteasome inhibitor. Additionally, the recognition that cells could pump out a small molecule via the multidrug resistance pump  and that this could be modeled across cell lines could only be appreciated in the context of a panel of cell lines; the NCI-60 effort was truly pioneering in this approach.In the late 1980’s the national cancer institute (NCI) began profiling a set of approximately 60 human cancer cell lines in what became known as the NCI-60 panel . The proThe evolution of approaches to measure genomic chromosomal aberrations led Garraway and Sellers to look for genetic amplifications across the NCI-60 panel of cell lines as a potential means to identify new oncogenic drivers . This woThe approach taken by Garraway and Sellers was further extended in a report by Pushing the cancer cell panel approach further, Many of the same challenges that were faced by the NCI-60 team in the 1980’s still exist today. First among these is the quality control (QC) of cell line identity. As has been the case since the inception of the cancer cell line screening panel, knowing the absolute identity of each cell line screened is critical for all subsequent analyses. Early on, HeLa cell contamination was mitigated by cytogenetic testing of cell lines via chromosome banding . This prMuch like the technology for cell line QC, assay readouts have also advanced over the last 30 years. NCI’s pioneering work led to the development of a high throughput screening assay using sulphorhodamine B as a read out . This asWhile proper QC is essential for yielding good data, selection of the ideal metrics for comparing compound response profiles across cell line panels is central to turning good data into impactful data. Today’s myriad methods for genetically characterizing cell lines offer many possibilities for dissecting the details of compound activity and cell-line responsiveness. Early efforts in this area include COMPARE for NCI-60 compound profiles  and CellFurther insight can be gained by collecting more complex genetic information. Recently http://www.ncbi.nlm.nih.gov/pubmed/20881279 and unpublished results). Another example are IDH1 and IDH2 gain of function mutations that are commonly found in Glialblastoma and AML in clinical settings, yet very few, one or two, cell lines harbor these activating mutations. Therefore, the new generation of IDH1 inhibitors cannot be tested in standard cell proliferation models.Even at the earliest stages of the NCI-60 cell panel project, the importance of transitioning from cancer cell lines to animal models of cancer was recognized. Limitations to cancer cell lines include inherent biases towards specific cell signaling and growth pathways that favor growth in cell culture. For example PI3Kalpha, RAS, and BRaf mutations are well represented in cancer cell lines. However, some pathway mutations are not well represented in cell lines, for example the hedgehog pathway, via smoothened (SMO) and GLI1/2/3 mutations do not appear in any known cell line and no cell line is responsive to SMO antagonists  in parallel with on-going human clinical trials as a way of anticipating response in the human arm of the trial . SeveralBeyond oncology indications, immunological profiling of patient derived blood cells represents a fruitful and relatively straightforward method to profile drug candidates across human genomic variation. From blood draws, peripheral blood mononuclear cells (PBMCs) can be isolated and are commonly used in experiments; large numbers of patient samples can be profiled with this ready source of cells. A key issue to using PBMCs is that they are a mixed cell population representing many of the various immunological lineages, including B and T cells, macrophages, etc. Until recently this represented a significant hurdle because only a few bulk attributes could be used to monitor response to compounds thus the cellular and mechanistic resolution was not nearly high enough. Advances in single cell analytical techniques have changed the landscape and now enable researchers to examine individual cellular responses.ex vivo response of PBMCs to various stimuli and drug candidates. Differential response between subjects could be explored in more depth with the goal of correlating and attributing genetic or environment factors  to mechanistic changes in cell specific signaling pathways. Additionally, improvements are being made in single cell mRNA detection technologies and these are an attractive alternative to the CyTof technology due to enhanced flexibility of nucleic acid detection technologies  have successfully automated many of the processes associated with culturing stem cells and offer hope on the path to achieving assays scale with iPS derived cells.Most iPS and neuronal disease modeling to date has focused on highly penetrant monogenic diseases. In these experiments researchers collect cells from diseased and normal control patients, reprogram them to iPS states and differentiate them into various neuronal subtypes. Typically characterization of the neurons leads to discovery of a difference in phenotype that reflect the disease state and that, in turn, enables a platform for further characterization and functional screening. Examples here include Pheland–McDermid syndrome , TimothyOne criticism of the this approach is that it is conceptually limited to severe monogenetic disease where penetrance is very high in patients and the expectation is that distinctive iPS derived neuronal phenotypes are related to the disease. Polygenic diseases with high heritability, like schizophrenia, present a much greater challenge. The genetic variants associated with these “common” diseases are thought to exert only a small change to disease risk, and presumably cellular phenotype. Defining what the disease associated variants do at a functional level is a key driver for current research . In the last 5–7 years it has been shown by the CCLE and similar efforts that expanding the numbers of cellular models by 10-fold dramatically improves the resolution of the genetic models and enables discovery of the fundamental biology of tumor growth. Similar strategies can be used for immunological profiling of blood cells or neurological profiling of iPS derived neurons. Scaling up these processes will involve significant investments in infrastructure, methods, and QC to achieve reliable models, especially in the nascent iPS derived neuron field. Improvements in sequencing and genetics technologies are far exceeding the ability to build animal models and we believe that these human models will be more predictive by reflecting the patient diversity presented by people in the clinic.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.