SNCA gene encoding for alpha-synuclein protein lead to familial Parkinson’s disease (PD). The generations of libraries of familial human ALS iPSC lines have been described, and the iPSCs rapidly became useful models for studying cell autonomous and non-cell autonomous mechanisms of the disease. Here we report the generation of a comprehensive library of iPSC lines of familial PD and an associated synucleinopathy, multiple system atrophy (MSA). In addition, we provide examples of relevant neural cell types these iPSC can be differentiated into, and which could be used to further explore early disease mechanisms. These human cellular models will be a valuable resource for identifying common and divergent mechanisms leading to neurodegeneration in PD and MSA.Induced pluripotent stem cells (iPSCs) are becoming an important source of pre-clinical models for research focusing on neurodegeneration. They offer the possibility for better understanding of common and divergent pathogenic mechanisms of brain diseases. Moreover, iPSCs provide a unique opportunity to develop personalized therapeutic strategies, as well as explore early pathogenic mechanisms, since they rely on the use of patients’ own cells that are otherwise accessible only post-mortem, when neuronal death-related cellular pathways and processes are advanced and adaptive. Neurodegenerative diseases are in majority of unknown cause, but mutations in specific genes can lead to familial forms of these diseases. For example, mutations in the superoxide dismutase 1 gene lead to the motor neuron disease amyotrophic lateral sclerosis (ALS), while mutations in the  SNCA gene encodes for aSYN. When SNCA is mutated or multiplied, it leads to an early onset familial PD.2 Interestingly, while aSYN aggregates are found in neurons in dementia with Lewy bodies and PD, they are located in oligodendrocytes in MSA.3The group of synucleinopathies consists mainly of three neurodegenerative diseases: dementia with Lewy bodies, Parkinson’s disease (PD) and multiple system atrophy (MSA), the last being divided into Parkinsonian type MSA with degeneration of nigro-striatal dopamine neurons, and cerebellar type MSA with ataxic symptoms. The diseases share the same hallmark: intracellular aggregates composed in majority of a protein called alpha-synuclein (aSYN). The SNCA is expressed in neurons. A recent study from our laboratory showed that SNCA is also expressed at early stage of oligodendrocyte development.4 The role of aSYN in oligodendrocytes largely remains to be clarified. It is thought that under pathological condition, aSYN protein can aggregate in neurons and over time form Lewy bodies.5 Moreover, it was shown in experimental models that aSYN aggregates are toxic to neurons,7 and that a neuroblastoma cell line over-expressing human recombinant SNCA releases factors including aSYN, leading to glial reactivity,8 suggesting aSYN aggregates may cause neuronal injury. However, recent work evidenced that neuronal dysfunction and protein aggregation may be two independent events.9 Thus, although the progression of Lewy pathology throughout the brain may be due to a prion-like mechanism of cell-to-cell transfer of aSYN,10 it remains unclear what the initial molecular cascades leading to neuronal dysfunction are, and how they differ depending on the genetic background of the patients.Naturally, 11 has revolutionized our way to approach scientific problems related to human diseases. Importantly, this discovery offers unlimited access to patient cells, which can subsequently be differentiated into relevant cell types to study early pathogenic mechanisms of neurodegeneration.12–17 Such iPSC-based research strategies could lead to the discovery of new therapeutic targets, biomarkers, and the development of humanized high-throughput models for drug discovery and environmental chemical safety assessment.12–17 Thus far, several studies utilizing iPSC-based models reported neuronal dysfunction reminiscent to mutations in PD-linked genes LRRK2 (leucine-rich repeat kinase 2), PINK (PTEN-induced putative kinase 1), and PARK2 (encodes PARKIN),18–21 or in the acid beta-glucocerebrosidase gene (GBA1), which encodes a lysosomal enzyme that is deficient in Gaucher’s disease, and which renders a risk of developing PD.23 Therefore, iPSCs appear to be robust models for understanding early pathogenic events occurring in familial forms of PD. Importantly, iPSCs may provide an ideal platform for studying diseases where no genetic cause has thus far been identified, such as MSA and idiopathic PD.12–17The reprogramming of human somatic cells using “stemness” transcription factors into induced pluripotent stem cells (iPSC)25 to date, none have been described for PD. Here, we report the generation of iPSCs carrying various mutations in PD-associated genes, as well as iPSCs generated from patients diagnosed with MSA and healthy controls  or human OCT3/4, KLF4, SOX-2, and c-MYC (for UEF-3A and UEF-5G lines). The lines UEF-1A , UEF-3A and UEF-5G were generated with a polycistronic lentivirus carrying all the reprogramming factors in the same viral vector (STEMCCA) while the lines UEF-2A, B and C, -3B, -4A and B, and -5B, E, F and G, were transduced with four separate Sendai viruses, encoding the same pluripotency genes  lines: number=patient and letter=clone; e.g., UEF-2A→line generated at the UEF lab, patient 2, clone A. The skin biopsies were collected from individuals with confirmed diagnosis and mutation, and the fibroblast populations were transduced with viruses carrying genes encoding mouse cy genes . Followicy genes . An averSC lines , and conSC lines . All iPSSC lines , and theSC lines . ChromosSC lines  and 3 exSC lines . In addiSC lines .All lines formed embryoid bodies when plated in suspension dishes . Immunoc32 to generate iPSC-derived mesencephalic dopaminergic neurons. The differentiation of iPSCs was started on human feeder layer. On day 14, small pieces of colonies were plated in an adherent culture. On day 20, the neuroepithelium differentiated into cells resembling neural cells. The neuroprogenitors were positive for LMX1A and FOXA2, confirming they acquired a midbrain floor-plate identity  lab, patient 2, clone A. The lines CSC-1, -2, -3, -4, -6, -7, 8-, and -9 were generated by retroviral transduction of 3  or 4 (the 3 plus c-MYC) factors; while the lines CSC-10, -11, -13, -14, -16, -18, -19, -20, -21, and -22 were generated by Sendai virus transduction of the four factors  as positive control. In some assays, e.g., determination of telomerase activity, both embryonic stem cells and parent fibroblasts can be used as positive or negative controls, as we previously reported,4 as well as heat-inactivated samples.We commonly thaw 3 putative iPSC clones for each patient lines generated, grow all clonal cell lines in 6-well plates for up to 10 (some times more) passages (with one simultaneous passage per week), and perform within the latest passages, in addition to finger print analysis, a battery of tests allowing us to determine the efficiency of reprogramming from putative to well-established/mature iPSC lines. The tests comprise: (1) expression of nuclear and cell surface markers reminiscent to pluripotent stem cell stage , (2) alkaline phosphatase activity, (3) telomerase activity, (4) embryoid body formation, (5) loss of viral agents or downregulation of viral transgenes, and (6) differentiation towards the three germ layers. The lines can further be tested for their ability to generate midbrain neural cells types: LMX1A/FOXA2 co-expressing neural floor-plate progenitors, FOXA2/tyrosine hydroxylase-expressing neurons, glial fibrillary acidic protein-expressing astrocytes, or O4-positive oligodendrocytes, which can be used for studies of development and disease phenotypes. All these tests are performed 35 Interestingly, two clonal cell lines (CSC-11C at passage 13 and CSC-13A at passage 8) showed resistance to Sendai virus elimination over passages , regardless whether they were generated using three or four factors and if they carried karyotype abnormalities , strong expression of reprogramming factor due to transgene insertion (line CSC-7B), and persistence of Sendai virus (lines CSC-11C and CSC-13A). In summary, we obtained 8 “good” control iPSC lines and 33 “good” disease iPSC lines .P-value of <0.05 was considered significant.All quantitative data was analyzed using Prism 6.0 . Sample groups were subjected to one-way analysis of variance and unpaired t-test. A Details methods are described in


TP53 gene accounts for 50% of all human cancers. In the remaining malignancies, non-genotoxic activation of wild-type p53 by small molecule inhibition of the MDM2-p53 binding interaction is a promising therapeutic strategy. Proof of concept was established with the cis-imidazoline Nutlin-3, leading to the development of RG7388 and other compounds currently in early phase clinical trials. This preclinical study evaluated the effect of Nutlin-3 and RG7388 as single agents and in combination with cisplatin in a panel of ovarian cancer cell lines. Median-drug-effect analysis showed Nutlin-3 or RG7388 combination with cisplatin was additive to, or synergistic in a p53-dependent manner, resulting in increased p53 activation, cell cycle arrest and apoptosis, associated with increased p21WAF1 protein and/or caspase-3/7 activity compared to cisplatin alone. Although MDM2 inhibition activated the expression of p53-dependent DNA repair genes, the growth inhibitory and pro-apoptotic effects of p53 dominated the response. These data indicate that combination treatment with MDM2 inhibitors and cisplatin has synergistic potential for the treatment of ovarian cancer, dependent on cell genotype.Ovarian cancer is the fifth leading cause of cancer-related female deaths. Due to serious side effects, relapse and resistance to standard chemotherapy, better and more targeted approaches are required. Mutation of the  Ovarian cancer is the most lethal of all gynecological malignancies and was reported to be responsible for approximately 152,000 deaths worldwide in 2012 ).TP53 tumor suppressor gene is referred to as the most frequently altered gene in human cancers. It has been substantially established that p53 protects cells against environmental and intra-cellular stress stimuli by playing a central role in regulating cell cycle control, differentiation, proliferation, DNA repair and apoptosis . TP53 mWAF1, BAX and BBC3 (PUMA) [de novo mutations [MDM2 is the main negative regulator of p53, regulating p53 through ubiquitin dependent degradation. The imidazoline Nutlin-3, was the first non-genotoxic specific small-molecule antagonist of the MDM2-p53 binding interaction to be developed  and has 3 (PUMA) , 10. Usiutations  and (revutations ). It is The aim of the present study was to test a panel of established ovarian cancer cell lines for their response to MDM2-p53 antagonists, Nutlin-3 and RG7388, alone and in combination with cisplatin and examine the mechanistic basis of these responses in relation to the genotype and induced gene expression of the cells.TP53 ovarian cancer cell lines derived from tumours of different histological subtypes [50) showed that wild-type TP53 ovarian cancer cell lines were significantly more sensitive to Nutlin-3/RG7388 compared to mutant, which is consistent with their mechanism of action (p<0.0001 Mann-Whitney test). Also, RG7388 was more potent compared to Nutlin-3 (p<0.0001 Mann-Whitney test). The GI50 values for wild-type TP53 cell lines for RG7388 and Nutlin-3 were in the nanomolar range (253.3 ± 73.1 (SEM) nM) and micromolar range (1.76 ± 0.51 (SEM) μM) respectively. In contrast, TP53 mutant cell lines had GI50 values greater than 10 μM (17.8 ± 2.9 (SEM) μM) for RG7388 and range 21.2->30 μM for Nutlin-3 (Table Growth inhibition by Nutlin-3/RG7388 was investigated using the sulforhodamine-B (SRB) assay for a panel of wild-type and mutant subtypes –16  their respective GI50 concentration for 24, 48 and 72 hours. They were then analyzed by flow cytometry for cell cycle phase distribution changes and evidence of apoptosis in response to treatment. The 48 and 72 hour treatment data is presented in the Wild-type TP53 cell lines, Nutlin-3 only showed a modest increase in the proportion of cells in G0/G1 phase in a dose and time dependent manner. Nutlin-3 also increased the percentage of SubG1 events, a surrogate marker of apoptosis, in A2780 and IGROV-1 cell lines in a treatment time and dose-dependent manner  μM); however, IGROV-1 (LC50= 11 ± 2.1(SEM) μM) and OAW42 (LC50= 6.25 ± 0.50 (SEM) μM) were substantially less sensitive to Nutlin-3 . Changes in the expression of cell cycle/apoptosis-related genes as well as those involved in nucleotide excision repair (NER) and mismatch repair (MMR) for the three wild-type CDKN1A, SESN1 and GADD45A gene expression in all three cell lines, with CDKN1A consistently showing the highest level of induction (p<0.05)  genes in all three cell lines, with increases of BBC3 mRNA being highest in the IGROV-1 cell line (p<0.05)  Figure . Both tr-1 genes .TP53BP1, DDB2, ERCC1, XPC, MLH1, MSH2, RAD51 and RRM2B genes in response to Nutlin-3 and RG7388 was investigated. A significant increase was measured in the expression of DDB2 in response to Nutlin-3 and XPC in response to both Nutlin-3 and RG7388 for A2780 cells (p<0.05). In the case of IGROV-1, XPC and MSH2 gene expression levels were significantly induced and reduced respectively in response to both Nutlin-3 and RG7388 (p<0.05). For OAW42 cells there was a significant increase in the expression of DDB2 and XPC genes and a significant decrease in the MLH1 and MSH2 expression levels in response to Nutlin-3 and RG7388 treatment (p<0.05). With Nutlin-3 treatment, the TP53BP1 gene expression decreased in all three cell lines, although statistically this trend was not significant (p>0.05).To study the effect of Nutlin-3 and RG7388 on the expression of genes implicated in the response to DNA repair induced by cisplatin, the expression of CDKN1A and BAX genes was consistent with the western blot analysis for p21WAF1 and BAX proteins  and RG7388 was kindly provided by Professor Herbie Newell and made available by the Newcastle Anticancer Drug Development Initiative. Both were dissolved in dimethyl sulfoxide (DMSO). Cisplatin  was dissolved in distilled water.TP53 status and histological subtype are listed in Table TP53 mutation  [MLH1-corrected CP70+ cell line was grown in RPMI-1640 supplemented with 10% (v/v) FBS and Hygromycin B  [TP53 mutation . SKOV-3 and MDAH-2774 cell lines were cultured in DMEM supplemented with 10% and 5% (v/v) FBS and penicillin/streptomycin respectively. As information on the TP53 status of SKOV-3 in the literature was contradictory, sequencing was performed and a frame shift deletion  was confirmed  [The ovarian cancer cell lines used in this study, their l172Phe) . The MLHs, Inc.) . This ceonfirmed . The MDAg273His) .50 values, the required concentrations of each compound leading to 50% growth inhibition, were determined by Sulforhodamine B (SRB) growth inhibition assays for drug exposure over 72 hours and the absorbance of the re-dissolved SRB protein stain was measured at 570 nm using a 96-well plate spectrophotometer (Spectramax 250 Molecular Devices) [TP53 cell lines were treated for 72 hours with each agent alone and in combination simultaneously at constant 1:1 ratios of 0.25x, 0.5x, 1x, 2x, and 4x their respective GI50 concentrations. Median-effect analysis was used to calculate Combination Index (CI) and Dose Reduction Index (DRI) values [The GIDevices) . Growth ) values  using CaTM C membrane . Monoclonal Mouse Anti-Human primary antibodies Actin 1:1000 , MDM2 1:300 , p21WAF1 1:100 , p53 1:500  and Polyclonal Rabbit Anti-Human primary antibody BAX 1:1000  were used. Secondary goat anti-mouse/rabbit HRP-conjugated antibodies  were used at 1:1000. All antibodies were diluted in 5% milk/1XTBS-Tween (w/v). Enhanced chemiluminescence (GE Life Sciences) and X-ray film (Fujifilm) were used to visualize the proteins.Lysis buffer  was used to harvest whole-cell lysates, followed by sonication. The concentration of protein in the cell lysates was estimated by using a bicinchoninic acid (BCA) assay. Novex® 4-20% Tris-Glycine 12-well polyacrylamide gradient gels  were used to separate proteins. The separated proteins were transferred by perpendicular electrophoresis to a nitrocellulose Hybond50 concentrations for 24, 48 and 72 hours. Cells harvested by trypsinisation were washed with PBS and resuspended in 500 μL PBS with 1mg/mL sodium citrate , 100 μg/mL propidium iodide (Sigma), 200 μg/mL RNAse A (Sigma) and 0.3% Triton-X (Sigma). Samples were analyzed on a FACSCaliburTM flow cytometer using CellQuest Pro software . Cell cycle distribution was determined using Cyflogic .Cells were treated with Nutlin-3, RG7388 and cisplatin alone and with combinations of Nutlin-3 or RG7388 with cisplatin simultaneously at constant 1:1 ratios of 1x and 2x (A2780 and IGROV-1) or 0.5x and 1x (OAW42) their respective GI50 concentrations, depending on the cell line and its single agent LC50 values, for 48 hours. Then, the medium including drug was removed and drug-free medium was added. The cells were incubated for 2 to 3 weeks to form colonies for counting (a colony was defined as a focus of ≥50 cells). After that, the plates were washed with PBS, fixed with methanol/acetic acid (3:1) and stained with 0.5% (w/v) crystal violet. Plates were again washed and then left at room temperature to dry. The LC50 values were calculated using GraphPad Prism statistical analysis software version 5.04. DRI and CI values were calculated by median-effect analysis [Based on the drug concentration and sensitivity of cell lines, 100 to 100,000 cells were seeded into triplicate six-well plates. Cells were treated with Nutlin-3, RG7388 and cisplatin alone and in combination at constant 1:1 ratios of 0.25x, 0.5x, 1x, 2x and 4x or 0.25x, 0.5x and 1x their respective LCanalysis  using CaCaspase 3/7 activity was measured using a Caspase- Glo 3/7 assay following the manufacturer's instructions .GAPDH was used as endogenous control and the DMSO solvent control sample used as the calibrator for each independent repeat. Data analysis using the ΔΔCt Method was carried out using SDS 2.2 software (Applied Biosystems).Total RNA was extracted using an RNeasy Mini Kit . RNA purity and concentration were estimated with an ND-1000 spectrophotometer . Total messenger RNA was converted to cDNA using the Promega Reverse Transcription System  as described by the manufacturer. Validated primers used (Sigma-Aldrich UK) are listed in All statistical tests presented were carried out using GraphPad Prism version 5.04 software. A p-value of <0.05 was considered to be statistically significant based on at least n=3 experimental repeats.


Osimertinib is a third-generation EGFR-TKI with a high selective potency against T790M-mutant NSCLC patients. Considering that osimertinib can lead to enhanced HER-2 expression on cell surface and HER-2 overexpression is a mechanism of resistance to osimertinib, this study was addressed to investigate the potential of combining osimertinib with trastuzumab emtansine (T-DM1) in order to improve the efficacy of osimertinib and delay or overcome resistance in NSCLC cell lines with EGFR activating mutation and with T790M mutation or HER-2 amplification.The effects of osimertinib combined with T-DM1 on cell proliferation, cell cycle, cell death, antibody-dependent cell-mediated cytotoxicity (ADCC), and acquisition of osimertinib resistance was investigated in PC9, PC9-T790M and H1975 cell lines. The potential of overcoming osimertinib resistance with T-DM1 was tested in a PC9/HER2c1 xenograft model.T-DM1 exerted an additive effect when combined with osimertinib in terms of inhibition of cell proliferation, cell death and ADCC induction in PC9, PC9-T790M and H1975 cell lines. Combining osimertinib and T-DM1 using different schedules in long-term growth experiments revealed that the appearance of osimertinib-resistance was prevented in PC9-T790M and delayed in H1975 cells when the two drugs were given together. By contrast, when osimertinib was followed by T-DM1 an antagonistic effect was observed on cell proliferation, cell death and resistance acquisition. In xenograft models, we demonstrated that HER-2 amplification was associated with osimertinib-resistance and that T-DM1 co-administration is a potential strategy to overcome this resistance.Our data suggest that concomitant treatment with osimertinib and T-DM1 may be a promising therapeutic strategy for EGFR-mutant NSCLC. In patients with advanced Non-Small Cell Lung Cancer (NSCLC) harbouring Epidermal Growth Factor Receptor (EGFR) activating mutations, disease progression occurs after a median of 10–12 months of treatment with EGFR-Tyrosine Kinase Inhibitors (TKIs), such as gefitinib, erlotinib, and afatinib. The acquisition of the EGFR-T790M secondary mutation is the main mechanism of acquired resistance, occurring in 50–60% of the cases  with ImageJ software and the Fold Increase (FI) index was calculated as the ratio between the spheroid volume after 6 days and the volume at T0.Spheroids were generated using LIPIDURE®-COAT PLATE A-U96  as previously described . BrieflyOne million of NSCLC cells were incubated, for one hour at room temperature, with Isotype control Monoclonal Mouse IgG1/R-PE , mouse anti-Human HER-2/PE or mouse anti-Human EGFR/PE  to determine EGFR or HER-2 protein membrane levels as previously described . The anaPC9-T790M and H1975 cells were plated in 96-well plates at a density of 350 cells/well and treated with osimertinib and/or T-DM1 following the indicated schedules in the experiment. Medium with drugs was changed every 7 days. Wells were followed daily over time. Taking into account the different sensitivity to osimertinib, cells were scored as resistant when colonies with at least 20 cells were detected in PC9-T790M cell cultures or when >80% confluence was reached in H1975 cell cultures .Highly purified CD56+ natural killer (NK) cells were obtained by magnetic separation and ADCC was measured with the CytoTox 96 non-radioactive cytotoxicity assay  as previously described .Tyr1068, HER-2 (29D8), p-HER2 Tyr1221/1222 (6B12), AKT, pAKTser473, p-p44/42 MAPK (D13.14.4E), MAPK (137F5), Bim (34C5) and Mcl-1 (RC-13) were from Cell Signaling Technology ; antibody against Actin was from Sigma. HRP-conjugated secondary antibodies were from Pierce  and chemoluminescence system (Immobilion™ Western Chemiluminescent HRP Substrate) was from Millipore . Reagents for electrophoresis and blotting analysis were from BIO-RAD . The chemiluminescent signal was acquired by C-DiGit® Blot Scanner and the spots were quantified by Image Studio™ Software, LI-COR Biotechnology .Procedures for protein extraction, solubilization, and protein analysis by 1-D PAGE are described elsewhere . Antibod6 PC9/HER2c1 cells were subcutaneously injected on the flank of Balb/c-Nude female mice . The animals were housed in a protected unit for immunodeficient animals with 12-h light/dark cycles and provided with sterilized food and water ad libitum. When tumor volume reached an average size of 200 mm3, animals were randomly allocated into four groups (n = 8 tumors per treatment group): control, osimertinib, T-DM1 and osimertinib combined with T-DM1. Osimertinib (10 mg/kg in 1% Tween 80) was given once per day, five times per week, by oral gavage. T-DM1 (15 mg/kg in 0.9% NaCl) was given intraperitoneally twice per week. Tumors were measured as previously described . However, also for this agent, resistance is expected and new therapeutic approaches are needed to prolong its benefit [Osimertinib is an effective treatment in NSCLC patients with T790M EGFR mutation, progressed after first-line therapy with first- or second-generation EGFR-TKIs . MoreoveHER-2 was shown to be overexpressed (IHC 3+) in 2–6% of NSCLC, with gene amplification found in 2–4%, mainly in the adenocarcinoma histotype . In partWe tested T-DM1 in preclinical experiments suggesting its activity against HER-2-driven lung cancer cell lines in vitro . Only reAs previously reported by Scaltriti et al. for lapatinib in breast cancer cells  and by oHere we demonstrated that, differently from trastuzumab, T-DM1 showed anti-proliferative effects in PC9, PC9-T790M and H1975 cells and exerted an additive effect when combined with osimertinib in terms of inhibition of proliferation, cell death and ADCC induction. Because osimertinib is highly effective in mutated NSCLC cell lines, the addition of T-DM1 caused a modest benefit when evaluated in short-term growth assay, by contrast the combination significantly delayed the acquired resistance to osimertinib in PC9-T790M and in the less osimertinib-sensitive model H1975. The combination was effective either when the cells were treated continuously with osimertinib and a simultaneous treatment with TDM1 was cyclically performed  anticipated the appearance of resistance to osimertinib compared to cells continuously treated with osimertinib alone, by contrast in the protocols in which cells were simultaneously exposed to both drugs (osimertinib→osimertinib + T-DM1 or osimertinib + T-DM1→osimertinib), no resistant colonies were detected up to 50 days of treatment. In H1975 cells, all schedules delayed the acquisition of resistance; however, also in this model the less effective was the intermittent schedule (osimertinib→T-DM1) both in terms of percentage of resistant wells and of median time to resistance.Overall, these data may support the use of a combination of osimertinib with T-DM1 using a specific schedule to delay or prevent the acquisition of resistance to the EGFR-TKI, similarly to other combinations of third-generation EGFR-TKIs (with MEK or MET inhibitors) tested in clinical trials currently recruiting patients .In the last part of this study we investigated the role of HER-2 amplification in the responsiveness to osimertinib and the efficacy of T-DM1 in vitro and in vivo to overcome osimertinib resistance. HER-2 amplification, indeed, has been reported as a mechanism of acquired resistance to EGFR-TKIs with a frequency of 12–13% .PC9/HER2c1 cells were exposed to osimertinib in vitro and in vivo in xenotransplanted mice. These cells were significantly more resistant to osimertinib than the parental cells in vitro and tumors grew as the controls indicating intrinsic resistance to osimertinib. T-DM1 significantly reduced tumor growth but the combination of osimertinib with T-DM1 showed a complete suppression of tumor growth, suggesting an important role of T-DM1 in overcoming osimertinib resistance. These data confirm the role of HER-2 amplification as a potential cause of intrinsic resistance to osimertinib, as demonstrated by Ortiz-Cuaran S et al. , and indIn conclusion, the combination of osimertinib with T-DM1 exerted an additive effect on cell growth inhibition when given as a concomitant administration. However, when cells exposed to osimertinib were subsequently treated with T-DM1, an antagonist interaction was observed. The combination of T-DM1 with osimertinib retarded the appearance of resistance in PC9-T790M cells and overcome osimertinib-resistance in cells with HER-2 amplification. Our results may contribute to define new potential future drug-combinations in the treatment of EGFR mutated NSCLC patients.


KRAS- and BRAF-mutant tumors are often dependent on MAPK signaling for proliferation and survival and thus sensitive to MAPK pathway inhibitors. However, clinical studies have shown that MEK inhibitors are not uniformly effective in these cancers indicating that mutational status of these oncogenes does not accurately capture MAPK pathway activity. A number of transcripts are regulated by this pathway and are recurrently identified in genome-based MAPK transcriptional signatures. To test whether the transcriptional output of only 10 of these targets could quantify MAPK pathway activity with potential predictive or prognostic clinical utility, we created a MAPK Pathway Activity Score (MPAS) derived from aggregated gene expression. In vitro, MPAS predicted sensitivity to MAPK inhibitors in multiple cell lines, comparable to or better than larger genome-based statistical models. Bridging in vitro studies and clinical samples, median MPAS from a given tumor type correlated with cobimetinib (MEK inhibitor) sensitivity of cancer cell lines originating from the same tissue type. Retrospective analyses of clinical datasets showed that MPAS was associated with the sensitivity of melanomas to vemurafenib (HR: 0.596) and negatively prognostic of overall or progression-free survival in both adjuvant and metastatic CRC (HR: 1.5 and 1.4), adrenal cancer (HR: 1.7), and HER2+ breast cancer (HR: 1.6). MPAS thus demonstrates potential clinical utility that warrants further exploration. A clinical score based on the activity of genes that regulate cell signaling can predict drug sensitivity and patient outcomes across a range of cancer types. Marie-Claire Wagle, Daniel Kirouac and their colleagues at Genentech in South San Francisco, California, USA developed an index that aggregates expression levels of 10 genes involved in modulating the mitogen-activated protein kinase (MAPK) pathway. This “MAPK Pathway Activity Score”, or MPAS, performed as well or better than other more complicated, genome-based tools at predicting whether drugs that inhibit MAPK-related enzymes were active against tumor cell lines. Retrospective analyses of clinical datasets also showed that MPAS correlated with survival outcomes in patients with melanoma, colon cancer, and breast cancer. The authors suggest that MPAS should be evaluated more broadly and perhaps implemented as a clinically informative biomarker. MAP2K1/2), and extracellular signal-regulated kinase 1/2 (ERK1/2) (MAPK3/1). Nuclear translocation of activated ERK1/2 then triggers the transcriptional activation of multiple target genes involved in modulating the cellular processes of differentiation, proliferation, survival, migration, and angiogenesis.1 Enhanced MAPK pathway activity can be driven by genetic alterations (including amplification and genetic mutations) in RTKs and RAS/RAF family members, as well as aberrant growth factor signaling.2 The high prevalence of aberrant MAPK signaling in cancer has motivated the clinical development of inhibitors targeting critical pathway nodes, including BRAFV600-mutant selective inhibitors, pan-RAF inhibitors, MEK1/2 inhibitors, and ERK1/2 inhibitors.6 Currently, there are at least 20 different MEK, RAF, and ERK inhibitors being tested alone or in combination with other drugs in active clinical trials according to www.clinicaltrials.gov. While single genetic events may not always correspond to pathway hyperactivation in tumors,7 in some instances, these drugs have demonstrated therapeutic benefit alone or in combination in pre-selected patient populations defined by genetic mutations. For example, treatment of BRAFV600E or BRAFV600K mutant melanoma patients with BRAF inhibitors (vemurafenib or dabrafenib) increases overall survival,8 and the combination with MEK1/2 inhibitors (cobimetinib or trametinib) achieves deeper MAPK pathway inhibition, resulting in improved clinical efficacy.11 However, clinical responses to MAPK pathway inhibition in mutant BRAF populations outside of melanoma are more variable,13 and the therapeutic benefit of MEK1/2 inhibitors trametinib, selumetinib, and refametinib does not appear to be associated with KRAS mutation status in lung cancer or pancreatic cancer.16 ERK phosphorylation also varies extensively across cell lines and does not predict MEK inhibitor sensitivity.17 Together, these observations suggest that neither BRAF/ KRAS mutation status nor ERK phosphorylation status alone are sufficiently reliable biomarkers of MAPK pathway activity in multiple cancer indications and that additional, clinically translatable biomarkers are needed.The mitogen-activated protein kinase (MAPK) pathway is a conserved developmental pathway that regulates organ development and tissue homeostasis by transmitting signals through a series of phosphorylation events emanating from receptor tyrosine kinases (RTKs), RAS family members, RAF family members, mitogen-activated extracellular signal-regulated kinase 1/2 (MEK1/2)  pathway activity and GLI1 transcript levels have been measured in SHH inhibitor clinical trials as a pharmacodynamic biomarker to assess on-target activity.21 For the MAPK pathway, while multiple predictive transcriptional gene signatures have been identified from in vitro data, clinically translating these large gene sets to multivariate biomarkers is challenging due to many technical and practical issues. However, genes that are common between these signatures include direct targets of the MAPK pathway.24 We hypothesized that a simple aggregated gene signature consisting of direct, biologically conserved transcriptional targets of MAPK/ERK signaling may sufficiently capture the activation status of the pathway across different tumor types.Developmental pathways such as MAPK often regulate the activity of a conserved set of transcriptional targets across multiple tissues.BRAFV600E melanoma.9 Furthermore, we demonstrate that MPAS is associated with poor prognosis in adjuvant and metastatic colorectal cancer (CRC), adrenal cancers, and HER2-positive breast cancer. These findings illustrate that MPAS could potentially serve as a predictive and/or prognostic biomarker, thus paving the way for further exploration of the clinical utility of MPAS in the near future.Here we show that a MAPK Pathway Activity Score (MPAS), derived from the transcript levels of 10 MAPK target genes, correlates exclusively with MAPK inhibitor sensitivity in multiple cell lines and primary tumor samples. Retrospective analysis of CoBRIM clinical trial data shows that high MPAS correlates with improved progression-free survival (PFS) of vemurafenib-treated patients in 24 Drugs used in these studies include the MEK inhibitors selumetinib, PD0325901, CI-1040, and RDEA-119 and the BRAF inhibitors, vemurafenib, dabrafenib, and AZ628. These genes also correlate with the extent of tumor response to cobimetinib and the ERK inhibitor, GDC-0994, observed in both pancreatic ductal adenocarcinoma and non-small cell lung cancer (NSCLC) genetically engineered mouse models.25 Furthermore, these genes were the most consistently and dose-dependently inhibited by cobimetinib in three cobimetinib-sensitive cell lines: MEL-JUSO (melanoma), NCI-H2347 (NSCLC), and HUP-T4 (pancreatic cancer), compared to other MAPK pathway targets  that have also been identified as responsive genes in the literature  that have been reported in multiple gene signatures predictive of sensitivity to MAPK inhibition.50 values of cell growth inhibition and MPAS inhibition of three cell lines were similar , MPAS was calculated at each dose of cobimetinib in the three aforementioned cell lines treated with increasing doses of cobimetinib. As shown in Fig. lar Fig. .Fig. 1Ag26 Across all cell lines combined , as well as in breast, lung, colorectal, and melanoma subgroups, cell line sensitivity correlated with MPAS for both MEK and BRAF inhibitors but not inhibitors targeting phosphoinositide-3 kinase (PI3K) (GDC-0941), AKT (GDC-0068), or other pathways (data not shown). Interestingly, there was a modest positive correlation between resistance to AKT inhibition (GDC-0068) and MPAS in breast cancer, supporting reports that, in certain subsets of this disease, the MAPK signaling pathway may mediate resistance to AKT inhibition.27 Of note, hierarchical clustering separated the cancer indications into two classes: those in which MPAS predicted MEK1/2 inhibitor sensitivity , and those in which it did not (pancreatic and melanoma).Given the difference in baseline MPAS values between these three cell lines, we next queried whether such differences in baseline values are predictive of MAPK inhibitor sensitivity. To do so, we examined sensitivity to various signaling pathway inhibitors as measured by mean viability (MV) across a large cancer cell line collection Fig. .26 AcrosBRAFV600-positive melanoma, there was greater variance in sensitivity to this drug across the melanoma cell lines leading to a significant correlation of sensitivity with MPAS.As the pancreatic and melanoma cell lines were broadly sensitive to MAPK pathway inhibition they showed minimal variance in both MPAS and in sensitivity to MEK pathway inhibitors , leading to the absence of significant correlations  model to predict the sensitivity of a large panel of cell lines to the clinically approved MEK1/2 inhibitors cobimetinib and trametinib.26 Cross-validation of the E-Net model for cobimetinib and trametinib gave R values of 0.65 and 0.7, respectively , comparable to results of other such models.29 Predictive features that were identified by the model included a cluster represented by high PHLDA1 . However, the correlation between MPAS and measured MV was more significant .To validate model predictions and compare accuracy of the E-Net model to MPAS, 40 NSCLC cell lines that had not been used for model training were tested for cobimetinib sensitivity .30 Predicted sensitivities using either MPAS or E-Net gene predictors were likewise categorized as “sensitive” or “resistant” using a median cut-off. True positive and false positive rates were then calculated for MPAS, the E-Net model, and KRAS mutation status. A negative control score (CTRL score) derived from four housekeeping genes  was established as a reference  was only slightly better than using a CTRL score, which was equivalent to random chance (50%). In the 40 cell lines that were tested, all the KRAS-mutant cell lines were sensitive to cobimetinib . However, there was a high false negative rate given that many KRAS wild-type cell lines were equally sensitive to cobimetinib . MPAS and the E-Net model had identical accuracies (72%), therefore predictions derived from either applying advanced statistical models to whole transcriptome expression data (E-Net model) or from compiling known biological knowledge into a simple score (MPAS) converge on a surprisingly similar outcome.To further assess the accuracy of the different approaches in classifying MEK1/2 inhibitor sensitive vs. resistant cell lines, we categorized cell lines with ICnce Fig. . The acc28 While the set of cell lines and MEK1/2 inhibitor used were different, the results from DREAM still serve as a reasonable benchmark to compare MPAS against a variety of world-class methodologies. The cumulative distribution of Spearman correlation coefficients for all 45 teams’ predictions of MEK1/2 sensitivity are shown as a solid line in Fig. ρ = 0.55) mapping to the 36th percentile and MPAS (ρ = 0.65) to the 28th percentile. Accordingly, MPAS appeared to perform better than two-thirds of the predictions submitted to this competition. Overall, these results show that MPAS, despite being derived from the expression of only 10 genes, and without the use of regression coefficients or more advanced statistical techniques, predicted sensitivity to MEK inhibition with better or comparable accuracy to using KRAS mutational status or to other genome-based, multivariate predictive models, respectively.To further benchmark the predictive accuracy of MPAS against other gene expression-based predictors of drug sensitivity, we used data generated in the DREAM Drug Sensitivity Prediction Challenge, which engaged 45 bioinformatics teams using gene expression data from 32 breast cancer cell lines to predict sensitivity of 18 blinded cell lines to drugs (including the MEK1/2 inhibitor PD184352).BRAF (blue) or RAS  (red) mutation status for any indication analyzed . Median MPAS of each tissue type was indeed strongly correlated with the median cobimetinib sensitivity of cell lines from the same tissue type . Skin cancer (melanoma) had the highest median MPAS of all the clinical samples, and this was associated with the greatest sensitivity to cobimetinib in cell line studies , the strength of the correlations appeared to diminish, though still significant, with skin cancer remaining the most sensitive indication to cobimetinib treatment  tissues that were analyzed from TCGA varied widely (−1.5 to 2) and independently of mutational status ) than those patients with tumors who had an “immune” signature, characterized by high expression of immune-regulatory genes and CD8+ T-cell infiltration (median PFS: 9.05 (6.21–15.01)).37Approximately 50% of tus Fig. , suggestBRAF-mutant patients, we used the gene expression data to develop a multivariate Cox proportional hazard model with MPAS and the published cell cycle vs. immune categorization as two features and PFS as the measure of outcome. The “cell cycle” vs. “immune” tumor subtype was indeed a significant determinant of responsiveness to vemurafenib as published (hazard ratio (HR) = 2.15; P = 0.0085; Fig. 37 However, BRAF-mutant tumors with higher MPAS  did significantly better than those with low MPAS across all patients on vemurafenib  and resected tumor specimens from CRC patients enrolled in the AVANT phase III adjuvant trial (bevacizumab as a single agent or in combination with either FOLFOX4 or XELOX standard adjuvant therapies40). Of note, the AVANT trial demonstrated no significant difference in overall survival between bevacizumab as a single agent or in combination with adjuvant therapies. As such, data were combined for subsequent analysis. For both mCRC and AVANT datasets and across all patients, high MPAS was associated with significantly worse overall survival, whereas KRAS/BRAF mutational status, microsatellite instability status, and treatment regimens were not ; however, this prognostic effect was not significant in the KRAS-mutant or KRAS/BRAF-wild-type patients . Notably, HRs based on individual MAPK genes rather than the composite MPAS were lower, supporting the use of the aggregate signature as a more robust clinical biomarker  than any single constituent gene.MAPK signaling is known to play an important role in the development of CRC and in responsiveness to EGFR inhibitor treatments.not Fig. . FocusinP = 0.003 at an 85th-percentile cutoff)  patient populations, we performed a Kaplan–Meier scan by computing HRs for MPAS-high vs. -low patients and using a threshold varying from the 10 to 90 percentile. In mCRC patients, the relationship evidently increased monotonically, whereby patients with increasingly higher MPAS did progressively worse  Fig. . Such a ff) Fig. .41 poor overall survival in adrenocortical carcinoma patients has been correlated with MAPK pathway activity based on a recently developed 100-gene MAPK transcriptional signature.24 Likewise, despite the low prevalence of RAS/RAF family mutations in breast cancer, MAPK signaling is thought to be active in some breast cancer subtypes, e.g., triple-negative breast cancer (TNBC), due to KRAS or BRAF amplification.42 In addition, a recent study showed that a significant fraction of HER2+ cell lines are dependent on MAPK signaling for proliferation and survival.43 We therefore assessed whether MPAS is prognostic in these indications in which the activation state of the MAPK pathway seems to be independent of RAS/RAF mutational status.Although adrenal cancers have a low prevalence of RAS/RAF family mutations,P = 0.02) ; P = 0.008), suggesting that variable MAPK signaling dependence in this subgroup has an impact on prognosis  Fig. . Next, wsis Fig. .45 Whilesis Fig. .Fig. 5MP43 We therefore assessed associations between MPAS and EGFR, ERBB3, or CDKN1B transcript levels . High EGFR expression in the HER2+ breast cancers was significantly associated with high MPAS , while HER3 and p27 expression were negatively associated with MPAS  in this subgroup, implying that higher relative expression of EGFR to ERBB3 may be responsible for the higher MAPK signaling and poorer survival outcomes.A previous report demonstrated that cellular dependency on PI3K or MAPK signaling in HER2+ breast cancer could be predicted by RNA and protein expression levels of EGFR, ERBB3/HER3, and CDKN1B/p27.46 In addition, the functional activity of RAS/RAF mutations can be modified by the cellular context, including compensatory feedback through the PI3K pathway, which could alter cellular dependence on MAPK signaling. Therefore, a downstream readout of pathway signal flux based on direct transcriptional targets might better represent the extent of pathway activity. As such, we assessed whether the expression of a core set of MAPK genes would be sufficient to capture the extent of MAPK pathway activation and therefore be of use in the clinic. The 10 genes that constituted MPAS have already been identified in other published gene expression signatures that correlate with MAPK inhibitor sensitivity.24 Some were also identified by the E-Net prediction model of cobimetinib sensitivity described in this report.The MAPK pathway may be activated by mutations in RTK and RAS/RAF oncogene family across many human cancer types. While these mutations are often used as predictive biomarkers for MAPK targeted therapies, other genetic alterations  and epigenetic mechanisms may also contribute to hyperactivation of this oncogenic pathway.BRAF mutational status, supporting the idea of a potential disconnection between mutation status and the activity of the MAPK pathway  population, there existed a wide range of, and perhaps dependency on, MAPK signaling that may partially explain some instances of unresponsiveness to vemurafenib in melanoma.Melanoma had the highest levels of MAPK activity relative to other tissue types (measured by median MPAS) in our study, consistent with the robust clinical activity of BRAF and MEK1/2 inhibitors observed in this indication Fig. . However40 high MPAS was significantly correlated with poorer outcomes, with a trend for wider separation in patients within the BRAF-mutant subgroup, albeit less statistically significant due to smaller patient number ,cer Fig. , which mIn adrenal and breast cancers, RAS and RAF mutations are rare. However, high MPAS in adrenal tumors Fig.  and in a47 Note that the absolute value of the score depends on not only the cell biology but also the assay characteristics and normalization. Thus it is likely not possible currently to directly translate absolute MPAS thresholds from preclinical experiments or between different clinical studies. This is exemplified by examining the variation in the median and distribution of MPAS scores from the five clinical studies reported . The PD0325901 MEK inhibitor was purchased from Selleck Chemicals .26 and cultured according to the supplier’s specifications. The NCI-H2347 cell line  and the MEL-JUSO cell line  were routinely cultured in a growth medium of RPMI-1640 with 10% fetal bovine serum (FBS). The HUP-T4 cell line ACC 223  was routinely cultured in a growth medium of Minimum Essential Medium with 10% FBS.Cell lines were obtained from the Genentech cell line repository (gCSI)48 With this method, cell growth after treatment can be compared to that before treatment . Three replicate wells per treatment were used, such that, at 5% standard deviation, viability differences of >9% should be detectable with a power of 80%. Data shown represent three independent experiments.Cells were seeded in 96-well plates and treated in triplicate for 72 h with 0–10 μM concentrations of the relevant drug diluted in the appropriate growth medium (please see above). Cell viability was measured using CellTiter-Glo® reagent per the manufacturer’s instructions  and ATP measurements were read using an Envision reader . Cell viability measurements were corrected to the day 0 viability (day of treatment) as measured by CellTiter-Glo® and plotted relative to the dimethyl sulfoxide control (100% viability).n = 50), CRC (n = 50), and lung patients (n = 100)  tissue blocks were procured from melanoma . For the Nanostring data, gene expression levels were normalized by sample, using a set of housekeeping genes, and across samples for the RNA-Seq data. The score is thus a relative metric of MAPK gene expression, the absolute values of which will depend on the assay characteristics and choice of normalization, in addition to the underlying biology. As such, MPAS values should be interpreted as relative to other samples within an experimental or clinical context.MPAS for each cell line, tumor, or patient sample was derived from expression data for 10 MAPK-specific genes . Gene expression was measured using either RNA-Seq  sequencing (reads per counts per million)49 This model was trained on gene expression features with alpha = 0.5 and optimal lambda chosen by 5-fold cross validation. After applying a variance stabilizing transformation, gene expression was normalized to a normal distribution  to remove absolute expression bias.49 To cross-validate the model, predicted MVs of the cell lines used to train the model were correlated with experimentally derived MVs to both trametinib and cobimetinib , cell lines in the validation set (40 NSCLC cell lines) were categorized as either cobimetinib sensitive (IC50 < 1 µM) or resistant (IC50 > 1 µM) based on the average clinical serum concentration of cobimetinib (0.1–1 µM).30 Based on numbers of True Positive (TP) True Negative (TN), False Positive (FP), and False Negative (FN) calls, the true positive rate (TPR = TP/(TP + FN)), false positive rate (FPR = FP/(FP + TN)), and accuracy (ACC = (TP + TN)/N) were then computed using a median-based classifier of the E-Net predictions and MPAS.To assess the overall accuracy of each predictor  approval and informed consent were analyzed using a custom melanoma-specific Nanostring expression panel consisting of 800 genes including the 10 MAPK-specific genes constituting MPAS   and from 1262 curatively resected stage III CRC patients enrolled in the AVANT phIII adjuvant trial44mRNA expression (RPKM) data for the 10 MAPK genes, as well as patient survival and subtype classifications (for Breast Cancer) were obtained from the BROAD Firehose TCGA portal. Breast cancer data were from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) study.Rank-sum tests were used to compute all correlation coefficients. Cox proportional hazard regression models were developed using MPAS as a continuous univariate predictor or as part of a set of multivariate predictors, i.e., mutational status or treatment. Log-rank tests were performed for all Cox hazard regression models. For the CRC data, a Kaplan–Meier scan was performed using MPAS as a discrete predictor, varying the threshold for calling positive/negative over a range from the 10th to 90th percentiles, and HRs were computed along this range. For the melanoma data, a sliding 30th-percentile window was used, and HRs were computed in comparison to the unselected population  vs. the entire population, and increasing the window by 2 points successively across the range to the 70–100th percentile (median 85%)). The E-Net regression model was developed in R, and all computational analyses were performed in the MATLAB v2015b software .Expression data for Figs. Supplementary figures and tables


Neuroblastoma (NB) is a devastating disease. Despite recent advances in the treatment of NB, about 60% of high-risk NB will have relapse and therefore long-term event free survival is very minimal. We have reported that targeting glycogen synthase kinase-3 (GSK-3) may be a potential strategy to treat NB. Consequently, investigating LY2090314, a clinically relevant GSK-3 inhibitor, on NB cellular proliferation and may be beneficial for NB treatment.The effect of LY2090314 was compared with a previously studied GSK-3 inhibitor, Tideglusib. Colorimetric, clonogenic, and live-cell image confluency assays were used to study the proliferative effect of LY2090314 on NB cell lines . Western blotting and caspase glo assay were performed to determine the mechanistic function of LY2090314 in NB cell lines.LY2090314 treatment exhibited significant growth reduction starting at a 20 nM concentration in NGP, SK-N-AS, and SH-SY-5Y cells. Western blot analysis indicated that growth suppression was due to apoptosis as evidenced by an increase in pro-apoptotic markers cleaved PARP and cleaved caspase-3 and a reduction in the anti-apoptotic protein, survivin. Further, treatment significantly reduced the level of cyclin D1, a key regulatory protein of the cell cycle and apoptosis. Functionally, this was confirmed by an increase in caspase activity. LY2090314 treatment reduced the expression levels of phosphorylated GSK-3 proteins and increased the stability of β-catenin in these cells.LY2090314 effectively reduces growth of both human MYCN amplified and non-amplified NB cell lines in vitro. To our knowledge, this is the first study to look at the effect of LY2090314 in NB cell lines. These results indicate that GSK-3 may be a therapeutic target for NB and provide rationale for further preclinical analysis using LY2090314. Neuroblastoma (NB) is the most common extracranial solid tumor found in children with over 650 cases diagnosed each year in the United States and it is responsible for 15% of pediatric cancer deaths . Timely Several intracellular signaling pathways have been demonstrated to play a key role in embryonal tumor biology, including growth factors controlling tumor proliferation, survival, differentiation and metastasis –14. Furt2 in air at 37 °C. LY2090314 and Tideglusib  were dissolved in dimethyl sulfoxide . An equivalent amount of DMSO alone in the treatment group served as a control.Human NB cell lines SK-N-AS (CRL-2137) and SH-SY-5Y (CRL-2266) were purchased from American Type Culture Collection  and NGP cells were a kind gift from Dr. Thiele at the National Cancer Institute  and were authenticated before receipt and use. NGP, SH-SY-5Y, and SK-N-AS cell lines were maintained in RPMI1640 medium , supplemented with 10% fetal bovine serum , and a combination of 100 IU/mL penicillin and 100 μg/mL streptomycin  in a humidified atmosphere of 5% COCell viability of each cell line was measured using a 3--2, 5-diphenyltetrazolium bromide colorimetric (MTT) assay. A 96-well plate was seeded with each cell line and incubated overnight for adhesion. Tideglusib treatment in increasing micromolar concentrations (0 – 30 μM) was added to each well, and for drug comparison, LY2090314, was added separately in increasing nanomolar concentrations, (0 – 1000 nM). Control wells were treated with DMSO. All wells were made in quadruplicate, and all plates were incubated for 48, 72, or 96 h. Cell viability was measured as described , 26. TheClonogenic (CFU) assays were performed to determine the ability of NB cells to form colonies after treatment with LY2090314 using the standard protocol as described previously , 26. BriIncuCyte Live-cell Imaging system (Essen Bioscience) was used to measure cellular proliferation/confluence of NB cell lines as described previously , 26. AppS9 and S21 , and active GSK-3Y279 and Y216 . Next day, the membranes were washed three times in a 1× phosphate buffered solution with .05% Tween-20 buffer and incubated with horse-radish peroxidase linked anti-mouse or anti-rabbit secondary antibody  depending on the source of the primary antibody. The membranes were developed using Supersignal West Dura or West Femto (Thermo Fisher Scientific) and imaged using the Molecular Images Chemi-Doc XRS+ imager (Bio-Rad).Cell lysates were prepared and quantified as described previously , 26. Bri+ imager (Bio-Rad). The relative number for the band intensities were calculated by normalized against GAPDH.After visualizing proteins bands on the membrane, the band intensities were quantified using Image Lab software version 5.2.1, Molecular Images Chemi-Doc XRSA Caspase-Glo 3/7 Assay  kit was used to measure cleaved caspase-3 and -7 activities from cell lysates after LY2090314 treatment as described previously , 26. Brip value of < 0.05 was considered significant. Data were represented as ± SE.One-way ANOVA analysis was performed using a statistical analysis software package (IBM SPSS Statistics version 22). A Several assays and imaging techniques were utilized to determine cellular growth patterns of 3 NB cell lines  treated with LY2090314 or Tideglusib. Cells were plated and treated with LY2090314 in increasing nanomolar concentrations , and proliferation was recorded using a colorimetric, MTT assay at 48 h, 72 h, and 96 h Fig. . Lastly,To determine the pathway mechanism behind the reduction in cell growth, western blotting was performed, proteins of interest were sorted, and the band intensities were measured compared to GAPDH control Fig. . LY20903Neuroblastoma is a rare developmental cancer of the sympathetic nervous system found primarily in infants and young children under 5. Its unique presentation and tumor biology present challenges for effective treatment regimens, notably in patients diagnosed as high risk (>MYCN expression) , 27, 28.Our results confirm that inhibition of GSK-3 by LY2090314 results in NB growth suppression. Importantly, low concentrations of LY2090314 significantly reduced NB cellular growth compared to Tideglusib, another GSK-3 inhibitor. This is the first study in NB, and LY2090314 is a potential agent for the treatment of NB in future.


GR50) and efficacy (GRmax) that are analogous to the more familiar IC50 and Emax values. In this work, we report GR-based, proliferation-corrected, drug sensitivity metrics for ~4,700 pairs of breast cancer cell lines and perturbagens. Such data are broadly useful in understanding the molecular basis of therapeutic response and resistance. Here, we use them to investigate the relationship between different measures of drug sensitivity and conclude that drug potency and efficacy exhibit high variation that is only weakly correlated. To facilitate further use of these data, computed GR curves and metrics can be browsed interactively at http://www.GRbrowser.org/.Traditional means for scoring the effects of anti-cancer drugs on the growth and survival of cell lines is based on relative cell number in drug-treated and control samples and is seriously confounded by unequal division rates arising from natural biological variation and differences in culture conditions. This problem can be overcome by computing drug sensitivity on a per-division basis. The normalized growth rate inhibition (GR) approach yields per-division metrics for drug potency ( IC50, the drug concentration at which cell count is half the control value, which is a traditional measure of potency, and Emax, the maximum effect at the highest concentration tested, which is a measure of efficacy. Although in use for many decades, this calculation is sensitive to the number of cell divisions taking place over the course of the assay, which differ when cell division rates vary. As a consequence, it is possible for IC50 values for a single drug in a single cell line to vary 100-fold or more simply as a consequence of exogenously imposed changes in the rate of division2. Natural cell division rates vary from one cell line to the next, often in a systematic manner depending on tissue of origin3, media composition, culture conditions, and plating density1. Because division rate is a powerful confounder in the calculation of IC50 and Emax values, and because it is often poorly controlled, we speculate that it is a contributor to the observed irreproducibility of drug dose-response data2–5.The prioritization of compounds for lead discovery, identification of pharmacogenomic associations, and study of cellular machinery requires reproducible and accurate data on drug response in cultured cell lines. In the case of anti-cancer drugs, which act by slowing cell proliferation or increasing the rate of cell death, the traditional approach is to expose cells to a drug over a range of concentrations, measure the number of cells at the end of a fixed period of time  and then compare that number to the number of cells in a vehicle-only control. The ratios are usually fitted to a sigmoidal curve from which it is possible to compute et al.2 corrects for variation in division rates by estimating the magnitude of drug response on a per-division basis. The GR value in the presence of drug at concentration c is GR(c)=2k(c)/k(0)−1 where k(c) is the growth rate of drug-treated cells and k(0) is the growth rate of untreated control cells. Growth rates can be estimated using time course data or, as in this paper, the number of cells prior to and at a fixed time after the addition of drug. Metrics of drug potency, GR50, and efficacy, GRmax, can then be calculated from the dose-response curve. The sign of the GR value is related directly to response phenotype: negative values reflect cytotoxicity, a value of 0 corresponds to complete cystostasis, and positive values reflect partial growth inhibition. GR calculations are described in detail at http://www.GRbrowser.org/, and a detailed method for performing the calculations with web-based software and an R pipeline is also available7.The normalized growth rate inhibition (GR) method recently introduced by Hafner 9 as well as substantial new data and comprise, after filtering low-quality data points, GR-based dose-response data for 71 breast cancer cell lines from all three major subtypes of breast cancer  and 107 perturbagens . We found that low growth was a good criterion for filtering out data with high relative error, as judged by standard error of the mean (SEM) for GR values, presumably because: (a) a very low division rate (fewer than one division per 72 h) is an indicator that the experimental setup is not optimized for the growth of a particular cell line; (b) fixed error in Cell Titer-Glo (CTG) assays makes measurement of smaller differences in cell number between control and experimental samples unreliable; and (c) partial cell cycle synchronization introduced during plating of cells with division times longer than the assay duration violates the assumption in GR calculations that growth is homogenous and constant10. As a quality control filter, we therefore eliminated from further consideration all samples in which the vehicle-only controls underwent too few divisions. Despite the need to filter out some data, the 4,700 unique cell line/perturbagen pairs described in this paper represent one of the largest collections of growth-corrected drug-response measurements currently available.The datasets presented here make use of drug dose-response data that have been described previously11. This required adjusting passage and plating conditions for each line.The original dataset  comprised the effects of 139 perturbagens  on 73 breast cancer cell lines, some in multiple cell growth conditions, determined by plating cells at a density such that they remained in logarithmic growth during the assayCells were allowed to attach overnight onto 96-well plates before being treated in technical triplicate for 72 h with nine doses of each perturbagen in a 1:5 serial dilution. The concentration range was optimized on six cell lines, and, for most cell line/perturbagen pairs, the highest concentration tested fell between 33 μM and 167 μM.12. A background CTG value was obtained from wells containing culture medium but no cells . Dataset DS0 [Data Citation 1] comprises all raw CTG values.Cell count was estimated using the Cell Titer-Glo (CTG) Luminescent Cell Viability Assay (Promega) after three days in drug (the endpoint of the assay). Cell count was also measured in untreated plates at the time of treatment. The raw CTG assay readout values were considered proportional to the number of viable cells and used as surrogates for cell count values, which is a widely used assumption with some caveatsDataset DS1 [Data Citation 2] comprises data for the 107 perturbagens  and 71 cell lines that could be unambiguously identified , representing 87% of the original dataset selected for further analysis (see Data Records). First, background CTG value was subtracted from the raw CTG values  in drug-treated wells. Background-corrected values below 1 or negative, which represented <0.1% of the data, were set to 1 to eliminate artefacts from near-background CTG values. Then, we calculated the following values:x(c), a robust average of three technical replicates for the background-subtracted CTG value following drug treatment at concentration c; x(c)=mean({xi∈x | abs(log10(xi)–log10(mean(x)))<1}) where x is the vector of treated values.–xctrl, the robust average background-subtracted CTG value in DMSO-treated control wells from the same plate; xctrl=mean({xi∈x | abs(log10(xi)–log10(mean(x)))<1.5)}) where x are all DMSO-treated control values.–x0, the background-subtracted median value from the untreated samples  measured at the time of treatment.–1:Dataset DS1 [Data Citation 2] comprises the GR value for each treated condition calculated as followset al.6. Given the data points and fitted equation, we extracted the response parameters:GR values for a specific drug across multiple concentrations were fitted to a sigmoid curve with the equation GR50: the primary metric of drug potency; the concentration of drug c at which GR(c)=0.5. If the value for GRinf (see below) is above 0.5, GR50 cannot be defined and we set its value to +∞. If GRinf is below 0.5 but the inferred GR50 value is half an order of magnitude (3.16-fold) above the highest tested concentration, we set the GR50 value to +∞ to avoid artefacts due to extrapolation (this affected 0.6% of conditions). In plots of GR50 values, GR50 values were capped at 100 μM for visualization purposes.GRmax: the primary metric of drug efficacy; the GR value at the highest tested dose of the drug. GRmax lies between –1 and 1; negative values correspond to a cytotoxic response (i.e. cell death), a value of 0 corresponds to a fully cytostatic response (no increase or decrease in cell number), and positive values less than one correspond to partial growth inhibition. GRmax can only be compared across drugs or cell lines when the highest tested doses are the same.GRAOC: the integrated effect of the drug across a range of concentrations as estimated from the ‘area over the curve’. A value of 0 means no effect of the drug across the full dose-response range. GRAOC can only be compared across drugs or cell lines when the dose range is the same.GRinf: a measure of drug efficacy extrapolated to an infinitely high drug concentration as determined from the asymptote of the dose-response curve; GRinf is similar to GRmax.hGR: the Hill coefficient of the fitted curve; it reflects the steepness of the dose-response curve. We constrained its value between 0.1 and 5.GEC50: an analogy to EC50 corresponding to the concentration of drug at half-maximal effect. GEC50 is relevant for drugs that have poor potency and that do not reach a GR value below 0.5. In the fitting procedure, GEC50 is constrained to lie within two orders of magnitude of the highest and lowest tested drug concentrations.Emax values, but fewer than 1% of the responses in the overall dataset were sufficiently cytotoxic that Emax values would not have been biased by the low division rate. Following filtering, we obtained 4,788 unique cell line/perturbagen pairs, the majority of which were recorded in biological duplicate for a total of 8,882 cell line/perturbagen pairs, each tested at nine doses in technical triplicate. Before averaging biological replicates when available, we further filtered the data to exclude measurements for which division time was greater than 80 h (corresponding to 0.9 division over the course of a 72 h assay). We included this cutoff based on preliminary data showing that GR values obtained from slow-growing cell lines had high uncertainty . The final dataset comprised 4,650 cell line/perturbagen pairs corresponding to a median of 88 perturbagens per cell line .We excluded from further consideration cell line/perturbagen pairs in which fewer than 0.3 cell divisions (a 23% increase in cell number) were recorded for the no-treatment control over the course of a 3-day assay . This was done because GR values quantify the effects of drugs on cell growth. If untreated control cultures are not growing, the response to drug is generally weak, and GR values become highly variable and biologically meaningless. In principle, it is possible to quantify the effects of highly cytotoxic drugs under these conditions using traditional 6 and found on the GitHub repo at https://github.com/datarail/gr_metrics. Other analysis was performed using MATLAB standard functions.GR calculation, curve fitting, and extraction of metrics was performed using scripts published in Hafner et al.8 and Daemen et al.9 and additional data generated subsequently (Data Citation 1). DS1 reports relative cell counts estimated from the available CTG values as well as computed GR values and nominal division rates for all cell line/perturbagen pairs from the raw dataset for which the cell line and perturbagen reagents used could be unambiguously identified  and for which the cell line demonstrated sufficient growth over the course of the assay to exceed our cutoffs (see Methods). DS2 reports the calculated GR metrics for the same set of cell line/perturbagen pairs. DS3 and DS4 report the median, upper quartile, and lower quartile GR metrics per perturbagen and per perturbagen class, respectively, across all cell lines in DS2. DS0 consists of a downloadable zip file containing multiple reference files necessary for annotating the raw data file with reagent identifiers and perturbagen concentration details, reference file metadata in the form of data column lookup tables (LUTs), experimental metadata including a detailed protocol, and a ReadMe file. DS1, DS2, and DS3 each consist of a downloadable zip file containing a data file, data file metadata in the form of a data column LUT, experimental metadata including a detailed protocol and reagent metadata tables, and a ReadMe file. DS4 consists of a downloadable zip file containing a data file, data file metadata in the form of a data column LUT, experimental metadata including a detailed protocol, and a ReadMe file. All files are provided in their original format and, where relevant, also in a non-proprietary, preservation-friendly format . All dose-response curves and metrics corresponding to DS2 also are available for interactive viewing at http://www.GRbrowser.org.This manuscript describes 4 new datasets  that are publically available as a single data package in the Dryad Digital Repository (Data Citation 2) alongside a fifth file bundle (DS0) consisting of the reference files used to process the file containing the raw dose-response data described in Heiser et al.1, and the code has been validated on multiple datasets. Use and installation instructions, as well as bug fixes, are available at our GitHub repo (https://github.com/datarail/gr_metrics). Methods for plating cells, adding drugs, and performing CTG assays have also been described in Heiser et al.8 and elsewhere. We have recently published an in-depth protocol10 for performing drug dose-response experiments in cell lines that includes advances in methodology not available at the time the current data were collected. We recommend that future studies follow these more recent guidelines.The GR method used to perform calculations in this paper has previously been describedx(c), xctrl, and x0 are more sensitive to fixed measurement noise in the case of slow growing cultures. Low division number can arise because some cells are inherently slow growing, but sporadic examples of slow growth are more likely to reflect defects in the plate, excess evaporation of the medium, or pipetting error. Regardless, eliminating data for these slow growing conditions improved the SEM of the GR values and metrics.One of the key biological premises implicit in computing GR metrics is that cells in untreated wells are proliferating in a uniform, exponential, and continuous manner. In principle, this should be true for all established cell lines, but in a large-scale drug-response experiment it is inevitable that some variability will be encountered. For this reason, we discarded any plate in which control cells underwent fewer than 0.3 cell divisions. When we examined cell line/perturbagen pairs in which cells had undergone more than 0.9 divisions in the absence of drug, we observed a substantially lower standard error of the mean SEM; . The 95tGR50 across all perturbagens and cell lines was low , but this estimate is biased by the many cases in which cells are resistant for which GR50 is set at infinity. For the subset of cell line/perturbagen pairs in which a substantial biological response is observed (GR50 <10 μM), 77% of the data have SEM values below half an order of magnitude. This is consistent with the GEC50 values, whose SEM is below an order of magnitude for 75% of the cell line/perturbagen pairs. Overall, the sigmoidal fit was good (r2>0.75) for 90% of the cell line/perturbagen pairs that show a substantial biological response (GRmax<0.5). Taken together, these results show that, for this dataset, differences in GR value above 0.2 or in GR50 values above half an order of magnitude should reflect substantial differences in sensitivity.The SEM of the GR values was below 0.1 for 75% of the data in dataset DS1, but 10% of the conditions in DS1 remained substantially variable across replicates (SEM > 0.2). Among the GR values with SEM > 0.2, we observed little bias by cell line. We therefore concluded that the data were impacted primarily by sporadic rather than systematic error (see Methods). With respect to estimation of drug potency, the SEM for 2: (1) it ensures that enrichment analysis is not biased by differences in division rate, and (2) it makes it possible to distinguish genes associated with drug-induced cytotoxicity  from cytostasis . In the current study, we first compared potency, as measured by GR50 values, and efficacy, as measured by GRmax values, by drug class across cell lines (see GR50), whereas HDAC inhibitors and DNA cross-linkers have the highest efficacy (low GRmax). Many drugs induced a cytotoxic response at the highest concentrations , but most MAPK inhibitors and the majority of the ErbB, PI3K, and RTK inhibitors had positive GRmax values, showing that such drugs are partially cytostatic in most breast cancer cell lines. When results were sorted by clinical subtype, we found, for example, that tubulin-binding drugs were generally less efficacious in non-malignant (NM) and hormone-receptor-positive cell lines (HR+) than in HER2-amplified (HER2amp) and triple-negative (TNBC) cells; such drugs are used most widely in the treatment of TNBC.Uses of our data include identifying cell lines or subtypes that are particularly sensitive or resistant to a class of drugs (or the converse) for follow-up investigation. Using GR metrics for systematic pharmacogenomics studies has two advantages relative to standard approachesIC50 and Emax values for the Heiser et al.8 dataset are highly variable across drugs and cell lines13. When we repeated this analysis using the expanded dataset and GR-based drug metrics described in this work, we came to the same conclusion, namely that values for GR50, GRmax, GRAOC, and hGR are variable across cell lines and drugs , we observed no correlation between GR50 and GRmax (Spearman’s p > 0.05).Contemporary analysis of tool compounds and therapeutic drugs focuses almost entirely on comparing differences in drug potency, with relatively little attention paid to efficacy as measured by maximum drug effect. We have previously shown, however, that both nd drugs . For somGR50 values for tubulin-binding drugs are very similar across cell lines; variation in response primarily involves GRmax values, many of which are negative .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Plasmablastic lymphoma (PBL) is a very aggressive variant of diffuse large B-cell lymphoma initially described in the oral cavity of HIV-infected individuals. PBL represents a diagnostic challenge given its characteristic morphology and lack of CD20 expression, and also a therapeutic challenge, with early responses to therapy, but with high relapse rates and poor prognosis. In recent years, our understanding and clinical experience with PBL has increased in both HIV-positive and -negative settings. However, given its rarity, most of the data available rely on case reports and case series. The main goal of this article is to systematically review the most recent advances in epidemiology; pathophysiology; clinical, pathologic, and molecular characteristics; therapy; and prognosis in patients with PBL. Specific covered topics include new pathological markers for diagnosis, its association with Epstein-Barr virus, and the need of more intensive therapies.


In this study, we report a miRNA122a based targeted gene therapy for hepatocellular cancer stem cells (CSCs). First, we assessed the levels of miRNA122a in normal human hepatocytes, a panel of hepatocellular carcinoma (HCC) cell lines and hepatocellular CSCs observing its significant downregulation in HCC and CSCs. The miRNA122a binding site was then incorporated at the 3’-UTR of reporter genes gaussia luciferase (GLuc) and eGFP which resulted in significant hepatocyte detargeting. Using this strategy for the delivery of gene directed enzyme prodrug therapy (GDEPT) utilizing the cytosine deaminase/5-fluorocytosine (CD/5-FC) system, we showed significant killing in cells with low or no miRNA122a while those cells, such as hepatocytes with high miRNA122a were largely spared. Next, we showed that CSC enriched tumorspheres exhibit a significant downregulation of miRNA122a expression providing a rational to exploit its binding site for targeted gene delivery. Using plasmids harboring reporters GLuc and eGFP with or without miR122a binding sites, we showed high reporter expression in the CSCs and little reported expression in the non-enriched cultures. Finally, we demonstrate the efficacy of miRNA122a based post-transcriptionally targeted GDEPT for hepatocellular CSCs. Hepatocellular carcinoma (HCC) is the most prevalent form of primary malignancy in the liver and is the third leading cause of cancer related death worldwide , 2. CurrCancer stem cells (CSCs) represent a subpopulation of cancer cells with innate resistance to radio and chemotherapies and are vital for tumor progression and recurrence after therapy , 7. AccumicroRNAs (miRNAs) are small non-coding RNA molecules which post-transcriptionally regulate the expression of genes that are involved in a variety of cellular functions including development, maintenance and/or elimination of cancer stem cells by binding to its complementary site usually present at the 3′-UTR of the corresponding gene. A number of miRNAs have been reported to be dysregulated in HCC derived CSC . miRNA12In this study, we demonstrate the utility of a miRNA122a based strategy to target HCC and hepatocellular CSCs for gene delivery which is pivotal for targeted cancer gene therapy. We show that hepatocellular CSCs exhibit significant downregulation of miRNA122a and that including the miRNA122a binding site into the 3’-UTR of a vector results in significant hepatocyte detargeting and subsequent targeting of HCC and hepatocellular CSCs.We quantified the levels of expression of miRNA122a in primary human hepatocytes, and a panel of HCC and non-HCC cell lines. The copy number of miRNA122a was reported as copies per 1000 copies of human positive control. Using this method, primary human hepatocytes were found to contain an average of 5655 copies of miRNA122a per 1000 copies of control while the expression of miRNA122a was not detected in HCC cell lines Hep3B, SKHep1, PLC/PRF/5, and SNU423    was significantly inhibited in primary human hepatocytes after transfection with CMV-GLuc-miR122a*3  Figure . Similar) Figure . In cont) Figure  and 2E.p < 0.001). The addition of miRNA122a inhibitors did not alter the luciferase expression in the low/non-expressing HCC cell lines (data not shown).In order to confirm that miRNA122a expression was responsible for the detargeting of our vectors, we utilized miRNA122a inhibitors and mimics to alter miRNA expression in hepatocyte and HCC cell lines. Using the miRNA122a inhibitor, we showed a significant increase in luciferase expression in both primary human hepatocytes Figure  and Hepap < 0.005). Luciferase expression decreased from 89.8% to 27.3%, 75% to 1.8%, 70% to 1.7%, and 101% to 12% (p < 0.005) in HCC cell lines Hep3B, PLC/PRF/5, SKHep1 and SNU423 respectively  whereas the miRNA122a negative HCC cell lines showed no significant difference in proliferation  and examined their ability to inhibit cellular proliferation and induce cytotoxicity. CD functions by converting the prodrug 5-FC into a cytotoxic metabolite. In the proliferation study, cells transfected with CMV-CD-miR122a*3 and incubated with 5-FC were normalized against proliferation observed with cells transfected with an untargeted CMV-CD construct. The HuH7 cell line, expressing miRNA122a at similar levels to that of normal hepatocytes, transfected with CMV-CD-miR122a*3 had a significantly higher proliferation than those transfected with the untargeted CMV-CD constructs , whereas no significant difference in cell death was observed between groups CMV-CD and CMV-CD-miR122a*3 in HCC cell lines Hep3B, PLC/PRF/5, SKHep1 , CD133 (p < 0.005), CD90 (p < 0.005), and Oct4 (p < 0.005)  in HuH7 cultures enriched for CSCs  Figure  normalizs Figure . These rP < 0.05).Next, we investigated whether the miRNA122a binding site could be utilized for targeting CSCs derived from HCC. To do this, we transfected HuH7 and HuH7 CSCs with CMV-eGFP-miR122a*3, and CMV-GLuc-miR122a*3 and normalized the expression of reporters with CMV-eGFP and CMV-GLuc respectively. We showed that HuH7 cultures, enriched for CSCs, had a significant increase in the expression of GLuc Figure  and eGFPP < 0.05) with a 1.91 fold higher cell death observed  by including disease specific regulatory elements like tumor specific promoters and miRNA binding sites , 23. In et al. it was shown that CD133+ CSCs isolated from the human HCC cell line, PLC/PRF/5 also exhibited a significant reduction in miRNA122a compared to non-CSCs [The well-known intra and inter-tumoral heterogeneity of HCC means that not all subtypes may have a reduced miRNA122a profile , 30. Whenon-CSCs . In thisnon-CSCs . Given tExploiting the differential expression of miRNA122a between functional hepatocytes, HCC and hepatocellular CSCs, we designed vectors incorporating 3 copies of the binding site of miRNA122a with the goal of limiting expression of transgenes to those cells with reduced miRNA122a expression, i.e. HCC and hepatocellular CSCs. Using luciferase as a marker transgene, we showed that cells expressing miRNA122a (primary hepatocytes and HuH7) had a significant decrease in luciferase expression. Conversely, HCC cells with little or no miRNA122a had significantly higher luciferase expression. These results were in line with previous studies exploiting the miRNA122a binding site for efficient hepatocyte detargeting , 33. AftTo explore the potential therapeutic benefit of using miRNA122a binding sites in HCC, we constructed vectors expressing the suicide gene cytosine deaminase (CD) with or without the miRNA binding sites (pCMV-CD-miR122a*3 and pCMV-CD). The CD gene produces an enzyme that converts the non-toxic 5-FC prodrug to the cytotoxic 5- fluorouracil (5-FU). We found that the use of post-transcriptional targeting of HCC and the subsequent detargeting of hepatocytes using miRNA122a was both feasible and effective, with significantly more killing and the inhibition of proliferation in HCC cells. Cell death and inhibition of proliferation was directly related to the presence or absence of miRNA122a in the cells. Using the pCMV-CD-miR122a*3 vectors in the CSC enriched cultures, we also showed significant cytotoxicity in line with miRNA122a expression and confirmed its utility in targeting CSCs. By targeting CSCs with a suicide gene therapy approach, such as the CD/5-FC system used in this model, there is the potential for a double bystander effect. The first induced by the CD/5-FC itself, where the non-toxic 5-FC is processed by the CSCs expressing CD into the toxic anabolite 5-fluorouracil (5-FU) . 5-FU thThe increasing knowledge of the genetic makeup of HCC and the CSCs derived from them allows identification of specific expression patterns which make them different from the normal liver cells. Initial work on utilising this knowledge for targeted gene therapy centred on the use of HCC restricted tumour specific promoters, such as that for alpha- fetoprotein (AFP), to drive transgene expression . The usemiRNA122a negative HCC cell lines Hep3B, PLC/PRF/5, SKHep1, and SNU423 were obtained from ATCC and maintained in DMEM  media supplemented with 10% fetal bovine serum (FBS)  and 1% penicillin/streptomycin (P/S) (Gibco). miRNA122a positive HCC cell line HuH7 was provided by Dr. Kim Bridle (University of Queensland). Cell line ID service was provided by the Australian Genome Research Facility (AGRF) and cell line genotyping was performed to confirm identity of cell lines. Breast cancer cell line T47D and prostate cancer cell lines LNCaP and DU145 were maintained in DMEM media with 10% FCS and 1% P/S. Melanoma cell lines 92.1, Mel202, Mel270 (gifted by Nicholas Hayward) and ovarian cancer lines SKOV3, CAOV3, TOV21G were cultured in RPMI media (Thermo Fisher Scientific) supplemented with 10% FCS and 1% P/S. Human retinal pigmental epithelium cell line ARPE-19 was maintained in DMEM/F12 media (Thermo Fisher Scientific) with 10% FCS and 1% P/S. Cryopreserved human hepatocytes (HUCS10P)  containing pooled hepatocytes from 10 donors were cultured as per the manufacturer's recommendations in human hepatocyte maintenance media.3D tumorspheres of HuH7 were maintained in a serum free, stem cell conditioned NSA media as previously described . The NSAEndogenous expression levels of miRNA122a was quantified by quantitative real time PCR (qRT-PCR) with Bioline Lo-Rox Sybr  using the ViiA7 RT-PCR machine (Thermo Fisher Scientific) at following conditions: 95° C- 2mins followed by 40 cycles of 95° C- 5 s, 60° C -15 s and 70° C -15 s on the cDNA which was synthesized from total RNA isolated with trizol using the MystiCq microRNA cDNA Synthesis Mix (Sigma Aldrich) as per the manufacturer's recommendations. MystiCq Universal PCR  and 5′- TGGAGTGTGACAATGGTGTTTGT- 3′ primers were used and the levels if miRNA122a was calculated as number of copies per 1000 copies of human positive control using formula (2^(Ct control-Ct sample))*1000. Similarly, HCC stemness markers CD44, EpCAM, and Oct4 were quantified as number of copies per 1000 copies of GAPDH housekeeping gene using primers listed in Table The gene encoding gaussia luciferase (GLuc) with three miRNA122a binding sites (ACACCATTGTCACACTCCAAC*3) at the 3′-UTR was artificially synthesized (Thermo Fisher Scientific). The gene with and without miRNA binding sites was then cloned in the pscAAV-GFP  using enzymes EcoRI, StuI and EcoRI, EcoRV to obtain CMV-GLuc and CMV-GLuc-miR122a*3 (Supplementary Information) respectively. Similarly, artificially synthesized cytosine deaminase (CD) was cloned in the above mentioned plasmids replacing GLuc to obtain CMV-CD and CMV-CD-miR122a*3.All transfection studies for studying GLuc and eGFP expression were performed with Lipofectamine 3000 (Thermo Fisher Scientific) as per the manufacturer's protocol. Briefly, 30,000 cells were seeded in a 24 well plate and transfection was performed with 500 ng of plasmids. 72 hours post-transfection, the amount of secreted GLuc was quantified with the Pierce gaussia luciferase glow assay kit (Thermo Fisher Scientific) as per the manufacturer's recommendations. The chemiluminescence measurement was done with the Infinite 200 Pro NanoQuant . For primary human hepatocytes, the transfections were performed an hour after seeding and the amount of secreted GLuc was measured after 24 hours. In order to account for the difference in transfection efficiencies across cell lines, chemiluminescence detected with CMV-GLuc-miR122a*3 was normalized against that with CMV-GLuc for individual cell type. Tumorspheres of HuH7 were transfected at day 3 after seeding in the NSA media and secreted GLuc was quantified after 48 hours. Similarly, to quantify the percentage of eGFP positive cells, flow cytometry was performed with the FACS Canto II and data was analysed with FCS express version 3 . Percentage of GFP positive cells after transfection with CMV-eGFP-miR122a*3 was normalized with CMV-eGFP.miRNA122a inhibition experiments in primary hepatocytes were performed by co-transfecting miRNA122a inhibitor  (5pmol) and either CMV-GLuc or CMV-GLuc-miR122a*3 in a 24 well plate using Lipofectamine 3000 as per the manufacturer's protocol. 24 hours post transfection, secreted GLuc was quantified with Pierce gaussia luciferase glow assay kit. Similarly, HCC cell lines Hep3B, SKHep1, PLC/PRF/5, and SNU423 were co-transfected with miRNA122a mimic  (5 pmol) and either CMV-GLuc or CMV-GLuc-miR122*3. The amount of secreted GLuc for each group was reported as a percentage of CMV-GLuc.For cell proliferation assays, 10,000 cells were transfected with CMV-CD or CMV-CD-122a*3 in a 96 well plate and media containing 10 μm 5-FC was added after 24 hours. CellTiter96 Aqueous One Solution Cell Proliferation Assay kit  was used to quantify cell proliferation after 48 hours of incubation with 5-FC containing media. Absorbance was measured at 540 nm with the Infinite 200 Pro NanoQuant. Percentage proliferation was calculated for CMV-CD and CMV-CD-122a*3 for each cell line and percentage proliferation with CMV-CD-122a*3 was reported relative to CMV-CD in order to account for the difference in transfection efficiencies across cell lines.To quantify cell apoptosis, flow cytometry based on Annexin/PI staining was used (Life Technologies). 30,000 cells were transfected with CMV-CD and CMV-CD-122a*3 in a 24 well plate and incubated for either 24 hours (for tumorspheres) or 48 hours (adherent cells) with 10 μm 5-FC containing media. Cells were then washed with PBS, collected and co-stained with Annexin V and PI as recommended by the manufacturer. Annexin V/PI positive cells were quantified with the FACS Canto II and data analysis was performed with FCS express 3. The percentage of apoptotic cells after transfection with CMV-CD-122a*3 was normalized with CMV-CD.t-test was performed to test the difference between groups (GraphPad Prism v7) .All experiments were repeated at least thrice in triplicates and results were presented as mean ± SD. Two-tailed student's