Connectivity map data and associated methodologies have become a valuable tool in understanding drug mechanism of action (MOA) and discovering new indications for drugs. One of the key ideas of connectivity map (CMAP) is to measure the connectivity between disease gene expression signatures and compound-induced gene expression profiles. Despite multiple impressive anecdotal validations, only a few systematic evaluations have assessed the accuracy of this aspect of CMAP, and most of these utilize drug-to-drug matching to transfer indications across the two drugs.http://www.broadinstitute.org/CMAP/).To assess CMAP methodologies in a more direct setting, namely the power of classifying known drug-disease relationships, we evaluated three CMAP-based methods on their prediction performance against a curated dataset of 890 true drug-indication pairs. The disease signatures were generated using Gene Logic BioExpress™ system and the compound profiles were derived from the Connectivity Map database  performs better than the standard Kolmogorov-Smirnov (KS) statistic in terms of the area under curve and can achieve a four-fold enrichment at 0.01 false positive rate level, with AUC = 2.2E-4, Connectivity map can significantly enrich true positive drug-indication pairs given an effective matching algorithm.The online version of this article (doi:10.1186/s13073-014-0095-1) contains supplementary material, which is available to authorized users. Identifying the correct indications for a drug is important as it is often a surrogate for deciding which patients to treat with the drug, and several computational methods have been described in the literature to aid in this process . TranscrOne of the most important techniques in the space of drug repositioning is connectivity map (CMAP) . CMAP isHowever, despite numerous impressive anecdotal validations, very few systematic evaluations have been done to estimate the prediction performance of drug-disease relationship. This is because a lot of effort is needed to compile true drug-disease associations and generate large numbers of disease gene signatures across these different disease areas. Most of the earlier research was largely focused on evaluating CMAP methodologies to predict drug-drug relationships . This apet al. [et al. computed the early retrieval performance of their approach and showed that their proposed approach performs better than previous methods [Iskar et al.  presente methods . Early ret al. [et al. [Cheng et al.  also uti [et al.  further The current study is based on the conclusions drawn from the previous studies. Instead of predicting drug-drug relationships, we evaluate the CMAP performance in a more direct drug repurposing setting: predicting drug-disease relationships. We compiled a set of 890 true drug-disease pairs from two different sources see  as a benWe downloaded the Connectivity Map  data, wIn our previous work , we evalProbe level data (CEL files) from CMAP were processed using Array Studio . Briefly, microarray datasets were grouped based on the cell line. For each microarray dataset, the probe set intensities were normalized using Robust Multi-array Average (RMA) procedure. Next, all scaled probe sets with values less than primary threshold values (set to 128) for all treatments and control samples were set to that threshold value (128). The intensity values for each probe set are then log2 transformed. Finally, the log2 intensities of each probe set from all vehicle control samples within the same batch and cell line are averaged and subtracted from the treatment sample to generate the corresponding treatment-to-control values. These treatment-to-control values are referred as expression values in this paper.The preprocessing step resulted in 6,100 gene expression instances. Multiple instances for the same compound in a particular cell line were averaged to generate cell line specific compound profiles. There are 1,294, 1,182, and 1,078 compound profiles for MCF7, PC3, and HL60 cell lines, respectively.et al. [et al. [Cheng et al.  show tha [et al.  and use An example of ESS histogram of compound profiles and the ESS threshold is shown in Additional file In the final step, we merged the cell-line-specific compound profiles into the compound level profiles by averaging the expression values across cell lines. This step resulted in 496 compound expression profiles, which were used to calculate different expression similarity metrics  for oveAll data analyses, including preprocessing and normalization, and summarization of the AffyBatch probe level data, were carried out using R package ‘Bioconductor 2.9’ . To geneA total of 238 disease signatures from human clinical disease samples were generated. Each disease gene signature consists of 500 upregulated probe sets and 500 downregulated probe sets selected by fold change between disease samples and normal samples. Among the 238 diseases, 145 diseases have at least one true positive compound in our compiled benchmark standard and were further evaluated  was downloaded from the FDA website , where tDifferent similarity metrics have been used to match gene signatures (profiles), such as Kolmogorov-Smirnov (KS) statistic  and the KS: The initial CMAP approach utilized a non-parametric, rank-based Kolmogorov-Smirnov (KS) statistic defined as follows.For this study, N is set to 500 in all metrics.XSum: The eXtreme Sum score is calculated as follows.UpInDisease and DownInDisease) to query compound profiles and then measure the difference between the two corresponding scores. Instead of using KS score to check whether the queried genes are concentrated at the top or bottom of the sorted compound profile, XSum simply sums up the compound gene expression values of the queried genes.XSum has some similarity with KS - they both use two gene lists  across all disease signatures, and OneAUC is calculated by combining the prediction scores across all the disease signatures and then calculating a single overall AUC.We use multiple areas under the curve (AUC) of the receiver operator characteristic (ROC) measures to evaluate CMAP performance, focusing on early retrieval performance where the false positive rate is low: FPR = 0.01 and FPR = 0.1 . The rationale for using this approach is we desire high specificity because we can only pursue a limited number of drug-disease indication hypotheses in a drug repositioning exercise. We calculate two types of AUC numbers: P values by performing 10,000 runs with random permutations of the 890 disease mappings of the compounds.To determine the statistical significance of the AUC results, we calculate non-parametric In total, 145 of the 238 diseases have at least one true positive compound in our compiled benchmark standard and thus can be evaluated . In fact, the performance for non-neoplastic disease does not achieve significance; this suggests that perhaps better samples or methods might be needed for the non-neoplastic diseases.We also grouped the disease signatures into two categories: neoplastic (65 disease signatures) and non-neoplastic (80 disease signatures) and evaluated the AUC performance of XSum for each category  from Broad Institute (available online at ). The reeXtreme Sum (XSum) we were able to achieve a significantly improved metric with the AUC around 0.57 and having a significant permutation P value (P = 0.03). In an earlier study and on a smaller set of the drugs and diseases (19 disease signatures and 69 drugs), the best metric obtained had an AUC of 0.45. The result of this study confirms two factors identified in our earlier work [We systematically evaluated multiple CMAP methodologies by assessing their performance on 152 drug profiles across a curated dataset consisting of 145 disease gene signatures. This large comprehensive study can serve as a benchmark to assess any new methodologies in the future. The results from this study show that by using a simple scoring algorithm called ier work ,17 whiche P = 0.0. In an eier work .Connectivity map can significantly enrich true positive drug-indication pairs given an effective matching algorithm especially for neoplastic diseases. In combination with the numerous anecdotal examples of successful experimental validation of CMAP hypotheses, this increases confidence in the use of CMAP for drug repurposing. With better methods and stronger validation datasets, it is our belief that CMAP will prove to be an effective method to repurpose drugs across a broad range of diseases.


According to the recent critical assessments of structure prediction                    methods held in 2010 and 2012, this server was found to be superior or                    comparable to other state-of-the-art programs in the category of                    ligand-binding-site prediction. A strong merit of the GalaxySite program is that                    it provides additional predictions on binding ligands and their binding poses in                    terms of the optimized 3D coordinates of the protein–ligand complexes,                    whereas other methods predict only identities of binding-site residues or copy                    binding geometry from similar proteins. The additional information on the                    specific binding geometry would be very useful for applications in functional                    studies and computer-aided drug discovery.Knowledge of ligand-binding sites of proteins provides invaluable information for                    functional studies, drug design and protein design. Recent progress in                    ligand-binding-site prediction methods has demonstrated that using information                    from similar proteins of known structures can improve predictions. The                    GalaxySite web server, freely accessible at                              Proteins perform their biochemical functions by interacting with other biomolecules                such as small ligands, other proteins or nucleic acids. The detection of binding                site on a protein makes it possible to infer the function of the protein and                provides information on binding pockets crucial for computer-aided drug discovery                    ,2. Ligan            Recently, methods that use experimental structures of similar protein–ligand                complexes have been successfully applied in binding-site predictions in critical                assessment of structure prediction (CASP) experiments . In such            However, a few difficulties have to be overcome to apply molecular docking to                binding-site prediction methods. First, docking requires prior knowledge of the                protein structure and binding ligand. Second, docking results can be sensitive to                structural details, and the prediction accuracy may decrease if the protein                structure is not sufficiently accurate or if conformational changes occur upon                binding . In the             http://www.cameo3d.org/lb/)                released between 16 August and 8 November 2013. In these tests, the performance of                GalaxySite was superior or comparable to other state-of-the-art prediction                methods.GalaxySite has been tested on the following non-metal ligand-binding-site prediction                test sets in addition to the blind prediction test sets of CASP9 and CASP10: 644                nucleotide-binding proteins with known experimental structures, 46 holo/apo pairs of                proteins with experimentally resolved structures and 480 targets of the                ligand-binding-site prediction category from the continuous automated model                evaluation server  deviating from                            ijrk)( (the                    corresponding distance in the kth template) with                    template-dependent weight factor ijkω, and                    the total restraint energy is expressed as follows: jkd is the position deviation of the                        Cα atom of the residue to which the jth                    atom belongs in the target structure from that in the kth                    template when target and template structures are superimposed. The weight factor                    is expressed as k is the structural similarity                    between the kth template and the input structure. The second                    term, (Residue score)jk, is 0 if the corresponding                    template residue is not of the same amino acid type as the target residue or if                            jkd > 2 Å. Otherwise, the                    residue score represents side-chain orientation similarity calculated using the                    dot product of the normalized vectors connecting Cα atoms and                    the side-chain centroid (of the residue to which the jth atom                    belongs) for the input and kth template structures. The third                    term accounts for the optimality of the template distance estimated by the ratio                    of the AutoDock3 energy value at that distance to the optimal energy. The                    relative weight of the AutoDock3 energy to the restraint term in Equation The energy function used for docking is expressed as follows: function  except t                                        GalaxySite has been extensively tested on various types of binding-site                    prediction test sets. See Supplementary Information for details on the test                    results. Tests on 644 nucleotide-derived ligand-binding proteins  and 46 h                                                            The GalaxySite server runs on a cluster of seven Linux servers of 2.33-GHz Intel                    Xeon 8-core processors. The web application uses Python and the MySQL database.                    The ligand-binding-site prediction pipeline is implemented using Python. Open                    Babel 2.2.3 is used to prepare the ligands for the molecular docking procedure.                    The molecular docking algorithm for binding-site prediction is implemented in                    the GALAXY program package ,17,25–28                                        The required input is a protein sequence in FASTA format or a protein structure                    in PDB format. The number of residues in the input file is limited to 500 for                    computational efficiency. The average run times are 2 h for a structure input                    and 4 h for a sequence input. Predictions for up to three non-metal ligands and                    their template complexes are provided with links to the RCSB PDB website. For                    each predicted ligand, predicted binding pose and ligand-binding residues can be                    viewed and downloaded from the website Figure .                                                            GalaxySite is a web server for the prediction of binding sites of non-metal ligands                that employs molecular docking. The method is applicable to experimentally resolved                structures, model protein structures and protein sequences. In addition to                information on binding residues provided by previous binding-site prediction                methods, GalaxySite predicts specific binding ligands and binding poses that can be                useful for further applications, e.g. in computer-aided drug discovery.                            Supplementary Data are available at NAR Online, including                [1–7].                            National Research Foundation of Korea ; Asan Institute for Life Sciences, Seoul, Korea (2013–591                and 2014–607). Funding for open access charge: Seoul National                University.            Conflict of interest statement. None declared.


Preterm birth, low birth weight, and infant catch-up growth seem associated with an increased risk of respiratory diseases in later life, but individual studies showed conflicting results.We performed an individual participant data meta-analysis for 147,252 children of 31 birth cohort studies to determine the associations of birth and infant growth characteristics with the risks of preschool wheezing (1-4 years) and school-age asthma (5-10 years).First, we performed an adjusted 1-stage random-effect meta-analysis to assess the combined associations of gestational age, birth weight, and infant weight gain with childhood asthma. Second, we performed an adjusted 2-stage random-effect meta-analysis to assess the associations of preterm birth  and low birth weight (<2500 g) with childhood asthma outcomes.P < .05). The inverse associations of birth weight with childhood asthma were explained by gestational age at birth. Compared with term-born children with normal infant weight gain, we observed the highest risks of school-age asthma in children born preterm with high infant weight gain . Preterm birth was positively associated with an increased risk of preschool wheezing  and school-age asthma  independent of birth weight. Weaker effect estimates were observed for the associations of low birth weight adjusted for gestational age at birth with preschool wheezing  and school-age asthma .Younger gestational age at birth and higher infant weight gain were independently associated with higher risks of preschool wheezing and school-age asthma (Younger gestational age at birth and higher infant weight gain were associated with childhood asthma outcomes. The associations of lower birth weight with childhood asthma were largely explained by gestational age at birth. Respiratory diseases have at least part of their origins in early life. It has been hypothesized that adverse exposures in fetal and early postnatal life might influence lung growth and development, which could lead to persistently smaller airways and impaired lung function. These developmental adaptations might predispose the subject to asthma and chronic obstructive pulmonary disease in childhood and adulthood.Most children with low birth weight show catch-up growth in infancy.Therefore we conducted a meta-analysis of individual data from 147,252 children up to the age of 10 years participating in 31 European cohort studies to assess the strength, consistency, and independence of the associations of gestational age, birth weight, and infant weight gain with the risk of preschool wheezing and school-age asthma. We specifically explored the combined effects of gestational age, birth weight, and infant growth.www.chicosproject.eu, www.birthcohortsenrieco.net, www.ga2len.org, and www.birthcohorts.net assessed until May 29, 2012). We invited the 52 potentially eligible cohorts, of which 41 responded to our invitation. From those, 31 cohorts agreed to participate, leading to 147,252 children with information on at least 1 early growth characteristic and respiratory outcome (see the flow chart shown in www.jacionline.org). All original cohort studies were approved by their local institutional review boards and provided written informed consent for using their data. Anonymized data sets were stored on a single central secured data server with access for the main analysts  only.European population-based birth and mother-child cohorts participated if they included children born between 1989 and 2011, had information available on at least gestational age and weight at birth and preschool wheezing (1-4 years) or school-age asthma (5-10 years), and were willing and able to exchange original data. We identified 52 European cohorts selected from the existing collaborations on childhood health or asthma-related outcomes (www.jacionline.org) and used as continuous and categorical variables. Infant weight gain in the first year was defined as the difference between weight at 1 year  and weight at birth divided by the exact number of months between those 2 measurements. We created gestational age–adjusted birth weight standard deviation scores (SDS) based on a North-European reference chart.www.jacionline.org.Information about birth weight, gestational age at birth, and weight in the first year of life per cohort was obtained by using measurements, medical registries, or parental questionnaires .www.jacionline.org). We considered breast-feeding status , lower respiratory tract infections , and eczema  in the first 2 years of life as potential intermediates (description of available intermediates per cohort is shown in www.jacionline.org).We included covariates based on known associations with childhood asthma from previous studies.First, we performed a 1-stage random-effect meta-analysis on an individual participant's data to examine the separate and combined associations of gestational age, birth weight, and infant weight gain with preschool wheezing and school-age asthma. For this analysis, individual participants' data from all cohorts were included in 1 multilevel analysis and were analyzed, simultaneously taking into account clustering of participants within studies.Q and I2 values, which varied per analysis.Second, we performed a 2-stage random-effect meta-analysis to examine the associations of gestational age at birth, birth weight, and infant weight gain and dichotomized preterm birth and low birth weight with the risks of preschool wheezing and school-age asthma. For this analysis, which was used for the clinically relevant associations of preterm birth and low birth weight, we first used logistic regression models to calculate effect estimates per cohort and then used calculated pooled odds ratios (pORs) from the per-cohort effect estimates.The cohort-specific information about the main exposures and outcomes are shown in A and B). Almost all children born before a gestational age of 40.0 weeks had an increased risk of preschool wheezing and school-age asthma. Birth weight SDS were not consistently associated with childhood asthma outcomes (C and D). Results for birth weight in grams without taking gestational age into account are shown in www.jacionline.org, showing an inverse association. We observed a positive association of infant weight gain with preschool wheezing and school-age asthma. Compared with children with a weight gain of between 500 and 600 g/mo (largest group), children with a mean infant weight gain of between 900 and 1000 g/mo had the highest risk of preschool wheezing  and school-age asthma . The overall results for the linear associations of gestational age at birth, birth weight, and infant weight gain from the 1-stage meta-analysis of individual participants' data were similar to those from the 2-stage meta-analysis of individual participants' data (results are shown in www.jacionline.org). The results from the confounder model were not materially different from those from the crude model. Also, additionally adjusting the confounder model for potential intermediates  did not materially change the effect estimates (results are shown in www.jacionline.org). Also, we observed similar effect estimates for preschool wheezing and school-age asthma after excluding cohorts one by one, indicating no disturbing effect of any particular population (data not shown). After exclusion of the Danish National Birth Cohort, the largest cohort in our meta-analysis (COPSAC) and a high-risk asthma and atopy cohort, we also did not observe major changes in effect estimates (data not shown).In the 1-stage meta-analysis of individual participants' data, we observed consistent inverse associations of gestational age at birth with the risk of preschool wheezing and school-age asthma. Compared with term-born children, children born before 28 weeks of gestation had the highest risk of preschool wheezing  and school-age asthma , between gestational age and infant weight gain , and between birth weight and infant weight gain . We performed stratified analyses and an overall test for interaction. In each analysis the largest group was used as the reference group. For the combined effect analysis of gestational age at birth and birth weight SDS, we observed a higher risk of preschool wheezing among children born at an earlier age with higher birth weight SDS, but the overall interaction term with birth weight SDS was not significant (A). Similarly, we observed a tendency toward a higher risk of school-age asthma in children born at an earlier gestational age with higher birth weight SDS . The highest risks for school-age asthma were observed for children born before 32 weeks of gestation with a moderately high birth weight SDS  and with a high birth weight SDS  compared with children born at term with a normal birth weight SDS. The P value for interaction between gestational age at birth and infant weight gain for the associations with preschool wheezing and school-age asthma were 0.05 and 0.23, respectively (C and D). We observed the highest risk of preschool wheezing and school-age asthma among children born before 32 weeks of gestation with an infant weight gain of more than 700 g compared with children born at term with a normal weight gain . The interactions between birth weight SDSs and infant weight gain for the associations with preschool wheezing and school-age wheezing were not significant (E and F). As a sensitivity analysis, we performed our analysis in Northwest European cohorts only and observed similar results (results are shown in www.jacionline.org). The results of complete case analyses were similar (data not shown). Also, we observed similar effect estimates for preschool wheezing and school-age asthma after excluding cohorts that used parental reports of birth weight or non–ISAAC-based questions on wheezing. With this exclusion, only the youngest and lowest weight-for-gestational-age groups tended to show stronger effects (see www.jacionline.org). This indicates that differences in data collection did not lead to systematic differences in effect estimates.Next, we explored the combined effects of gestational age at birth, birth weight SDS, and infant weight gain. The significant correlations were between gestational age and birth weight . These associations were independent of birth weight. The population-attributable risk of preterm birth was 1.96% for preschool wheezing and 2.14% for school-age asthma. Compared with children with a normal birth weight, those with a low birth weight (<2500 g) had an increased risk of preschool wheezing  and school-age asthma . These associations were stronger without adjustment for gestational age at birth (results are shown in Results from the 2-stage meta-analysis focused on the associations of preterm birth and low birth weight with childhood asthma outcomes are shown in Results from this large-scale meta-analysis of individual participants' data suggested that younger gestational age at birth and higher infant weight gain were associated with increased risks of preschool wheezing and school-age asthma. The associations of low birth weight with childhood asthma outcomes were largely explained by gestational age at birth. The highest risk for childhood asthma outcomes was observed among children born before a gestational age of 32 weeks with high infant weight gain.Adverse exposures in fetal and early postnatal life can lead to developmental lung adaptations, such as persistently smaller airways and impaired lung function. These developmental adaptations might predispose the patients to obstructive pulmonary diseases in childhood and adulthood.Jaakkola et alCombining childhood asthma outcomes from different age periods is not easy. Asthma is a difficult clinical diagnosis and cannot easily be diagnosed in children younger than 5 years. Many studies used asthma-related outcomes, such as wheezing and shortness of breath, as main outcomes in children. Wheezing seems to be the strongest risk factor for childhood asthma.Mechanisms underlying the associations of factors in early life with asthma outcomes in later childhood might include smaller airways and lungs.We performed a large meta-analysis of individual participants' data from many birth cohorts throughout Europe. We did not rely on published data, which limits any potential publication bias. The large number of participants enabled us to assess small effects and to adjust for various potential confounders. We presented results from random-effects models, which allow heterogeneity in the true effect estimates between different populations and take between-study variation into account. Another strength is that information on exposures in early life was collected from records and did not depend on long-term participant recall. Misclassification of gestational age is always possible because of the large number of pregnant women who did not know their exact gestational duration.Children born at a younger gestational age at birth or higher infant weight gain have increased risks of childhood asthma.Younger gestational age at birth and higher weight gain in infancy were associated with childhood asthma outcomes. The association of lower birth weight with childhood asthma outcomes was largely explained by gestational age at birth. Further studies are needed to evaluate the effects of early-life characteristics on specific asthma-related outcomes, such as lung function, airway size, and airway inflammation.


PTEN tumor suppressor, but also a number of targets that are less frequently mutated in specific cancer types, including ABL1 oncoprotein in acute myeloid leukemia. An application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns, such as those indicative of their receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) status and co-addiction partners, which may lead to clinically actionable, personalized drug treatment developments. To promote its application to the future drug testing studies, we have made available an open-source implementation of the TAS calculation in the form of a stand-alone R package.Deconvoluting the molecular target signals behind observed drug response phenotypes is an important part of phenotype-based drug discovery and repurposing efforts. We demonstrate here how our network-based deconvolution approach, named target addiction score (TAS), provides insights into the functional importance of druggable protein targets in cell-based drug sensitivity testing experiments. Using cancer cell line profiling data sets, we constructed a functional classification across 107 cancer cell models, based on their common and unique target addiction signatures. The pan-cancer addiction correlations could not be explained by the tissue of origin, and only correlated in part with molecular and genomic signatures of the heterogeneous cancer cells. The TAS-based cancer cell classification was also shown to be robust to drug response data resampling, as well as predictive of the transcriptomic patterns in an independent set of cancer cells that shared similar addiction signatures with the 107 cancers. The critical protein targets identified by the integrated approach were also shown to have clinically relevant mutation frequencies in patients with various cancer subtypes, including not only well-established pan-cancer genes, such as  Drug Discovery Collection: The authors take a pan-cancer approach that provides functional insights into molecular vulnerabilities across various cancer subtypes and patient-specific target addiction patterns that could lead to actionable treatment strategies. With the emerging challenges of translating the cancer genome sequencing information into clinically actionable, targeted drug treatment strategies, it has become evident that we also need complementary strategies for functional investigation of druggable vulnerabilities and cellular addictions of cancer cells. Advances in cell-based high-throughput compound screening have made it possible to profile the cellular activity of an extensive collection of bioactive small molecules, thereby enabling a functional phenotype-based approach to unbiased identification of candidate compounds for individual cancer patients  is an important aspect of the rational phenotype-based drug discovery approach . Knowledin vitro and to patient-derived samples ex vivo; for a given cancer cell model, it provides a functional importance scoring of the drug targets, according to an estimate of how much the cell sample is addicted to the particular target protein. In an application to >100 cell lines, we deconvoluted the target signal patterns across the heterogeneous cancer cell collection, hence revealing pan-cancer correlations in their addiction signatures between both shared and unique genetic and tissue backgrounds. A specific application to leukemia patient primary cancer cell models demonstrates how the target deconvolution approach can yield functional and actionable insights into patient-specific addiction patterns towards clinical translation.BackgroundCell-based drug sensitivity testing is becoming a popular approach to provide a functional profile not only for cancer cell lines but also for primary patient cells to map pathway dependencies in specific cancer subtypes and to select optimal personalized treatments for cancer patients. However, several critical challenges remain; in particular, how to deconvolute signals of addiction – a phenomenon describing the dependency of certain tumors on a single oncogene for growth and survival – behind the drug sensitivity and resistance phenotypes, which is a critical step in the phenotype-based drug development process and an important prerequisite for many translational applications.ResultsIn this study, the authors implemented a network-based experimental-computational approach that makes use of polypharmacological effects of drug compounds to identify pharmacologically actionable target signals in individual cancer cell subtypes and in patient-derived cell models. When applied to >100 cancer cell lines, this approach mapped target signal profiles across heterogeneous cancer cell models; these profiles revealed pan-cancer correlations in the addiction signatures of different cancer cell subtypes that have both shared and unique genetic and tissue backgrounds. Specific application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns that might lead to clinically actionable treatment strategies.Implications and future directionsThis computational target deconvolution method represents an open-source and extendable approach that can be used to identify target addiction profiles across heterogeneous cancer cell lines and leukemia patient primary cell models. This approach should thus provide a valuable resource for the community to enable translation of drug sensitivity testing results into many exciting drug development and repurposing opportunities.In the present study, we applied an experimental-computational target deconvolution approach in a diverse collection of cancer cell models. Our computational target addiction scoring (TAS) algorithm integrates cell-based compound sensitivity profiling with global drug-target interaction networks that capture a broad spectrum of both on- and off-target effects of the compound panel, comprising not only kinase inhibitors but also other important drug target classes. The integrated approach is applicable both to cell line models We implemented an experimental-computational platform that identifies critical drug targets in individual cancer cell lines or patient-derived cell models . The appsupplementary material Table S1). As the primary drug response metric, we used the drug sensitivity score (DSS), which was recently shown to improve the prediction of drugs' mechanism of action, as well as the identification of drug-responsive cell lines and patient-derived cancer cells (50), as well as the area under the dose-response curve (AUC), as available in the GDSC resource. The pairwise addiction signatures between the cell lines were compared with those derived from the genomic signatures of the selected cell lines, using congruence analysis, and later the predictive power of the TAS signatures was assessed in an additional set of 20 cell lines from GDSC.We first systematically tested the TAS approach using the comprehensive cell line data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, which is the largest public resource for information on drug sensitivity in cancer cells along with their comprehensive genomic and molecular characteristics . We calcer cells . We alsoP=0.0001, permutation test of the Kendall coefficient). In contrast, the cell line clustering based on the target addiction profiles was effectively independent of the tissue type . The same was also true when using the DSS alone, although not as prominent as with the DSS-TAS transformation . For instance, gastrointestinal (GI) tract cell lines formed a clear sub-cluster in the expression-based clustering, whereas these cells were scattered in various sub-clusters of the TAS-based functional classification. This indicates that the pan-cancer functional relationships in the addiction patterns carry information beyond the tissue origin alone, which may therefore be more closely connected to the underlying biology shared by multiple cancer subtypes.We first derived unsupervised molecular and functional classifications of the 107 cancer cell lines based on their transcriptional, drug response and target addiction profiles. As expected, the cancer cell line clustering based on the gene expression profiles was largely driven by the tissue origin of these cells . The novel GFA analysis resulted in both unique and common genes in the shared active components, including, for instance, well-known cancer oncogenes, such as MYCN and CDKN2A, which were common to all the three data sets, whereas TP53 and KRAS were unique to the MUT data set only , copy number (CN) and mutation status (MUT) across the genetically heterogeneous cancer cells, we used a sparse extension of the Bayesian group factor analysis GFA; , which eset only A.Fig. 3P=0.003, permutation test). Likewise, the AUC response metric showed slightly improved concordance after its TAS transformation, whereas the IC50 metric showed a totally opposite trend in concordance. Surprisingly, the combination of the CN and MUT components resulted in a lower concordance with the addiction signatures, compared with that when using the mutation status alone, perhaps owing to the relatively high dependency (anti-correlation) of the genomic information captured by the copy number alterations and somatic point mutations, as observed in recent pan-cancer analyses .When comparing the target addiction signatures and the combined genomic signatures from the GFA analyses, it was found that the TAS version calculated with DSS led to the overall best concordance . Although the average robustness values over the whole spectrum of sub-cluster structures were relatively similar between the three response metrics, the DSS-TAS combination resulted in the most consistent cluster solution over the bootstrap samples. Notably, each of the cluster solutions made without the TAS transformation had sub-clusters with 0% robustness also at the upper levels of hierarchy in the corresponding dendrograms , indicating that these larger sub-clusters at higher levels were in fact very sensitive even to small changes in the drug response input data. In contrast, the TAS-based cluster solutions resulted in notably higher robustness values for each sub-cluster, especially at the upper levels of the dendrogram , compared with their TAS-free counterparts .To verify that the TAS-based cell line cluster solutions were not obtained by chance, for instance, owing to the specific data sample only or a single run of the clustering algorithm, we repeated the cell line clustering on 10,000 bootstrap samples of the original 107 cell lines. Strikingly, the robustness of the cell line classifications based on each drug response metric could be improved by using the TAS transformation A, most ssupplementary material Table S1). Within each of the detected eight addiction sub-clusters, we compared the GE, CN and MUT profiles of the newly added cell lines with those of the original cell lines, often representing different tissue types . As expected, the gene expression profiles were highly correlated among all the cancer cell line pairs, leading to a shifted background distribution with a positive mean (Spearman correlation 0.8). However, the expression correlations of the new cell lines with those predicted to share similar addiction patterns were significantly higher . Likewise, the TAS-based clusters were fairly predictive of the CN profiles, whereas the mutation status of the new cell lines could not be predicted accurately (data not shown). This is likely to result from the relatively small number of genes whose mutation status was available .To evaluate whether the DSS-TAS profiles could also carry a predictive signal for the genomic characteristics of the cancer cell lines, we combined 20 additional cell lines from GDSC (tested with fewer than 138 compounds) with the original analyses done on the basis of the 107 cell lines  and CN  and none with the mutational profiles; although this surprisingly small overlap may be attributed in part to the rather sparse mutation panel, it also demonstrates that the functional drug/target profiling provides added value to the genomics-only-based analyses and that the targets deemed functionally critical by the addiction scoring are not necessarily overlapping with those detected by genomic analyses of the same cancer cells.Next, we combined the target addiction signatures of the cancer cell lines into GFA in order to study the similarities and differences in the active components revealed by the genomic and functional profiles. Overall, the shared active components had only a few common genes, indicating the complementary nature of these profiles A. In parABL1, ABL2, GPRC5A and CTNNB1). An interaction network was constructed by combining these genes with each other using direct, experimentally validated and manually curated interactions from the Ingenuity Pathway Analysis knowledgebase . In addition to novel links, the network analysis also identified missing players between these four genes, including RAS oncogene, EGFR and BCR, all of which are well established as having a role in the pathogenesis and treatment of many cancer types, including breast, colorectal, pancreatic and lung cancer; these molecules are either serving as biomarkers for cancer prognosis and/or prediction of treatment efficacy .As a specific example, we focused more closely on the four common genes that were included in the shared active components between DSS-TAS, GE and CN  and HOXA1 in lung adenocarcinoma  resource. In addition to pan-cancer recurrent genes, such as arcinoma B. In leuleukemia .in silico personalized medicine’ case study. Specifically, we applied the TAS approach to the cell-based drug response data obtained from a recent study where the sensitivity of primary leukemia patient cell samples was assessed to a panel of 66 kinase inhibitors ex vivo . Based on our previous analysis of ex vivo drug response patterns in AML patients, where receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) mutation status was found to be associated with functional classification of the AML subtypes . PCR analysis revealed one of the patient samples (ID 08024) to exhibit FLT3-ITD with loss of the wild-type FLT3 allele in the original study . The calculation was based on a number of compounds with both on- and off-targeting of FLT3-ITD, including sunitinib, sorafenib and ponatinib . Whereas VEGFR2 and RET shared a number of common inhibitors with FLT3-ITD, partly explaining their correlated addiction profiles, GAK displayed a more distinct inhibitor panel, with only one potent inhibitor common with FLT3-ITD  or gain of function . In practice, only proteins with activating mutations can be targeted directly with small-molecule inhibitors. In contrast, loss-of-function cancer genes, including ethality . Howeverhenotype ; hence, FLT3 (FLT3-ITD), which are detected in approximately 20% of AML patients associated with a poor prognosis  mutations in rognosis . Clinicaassembly . In addil growth , highly l growth , as welll growth . GAK is assembly , and patassembly . Given ssupplementary material Table S3), because the observed drug response (that we want to capture) is elicited not only through the direct binding targets, but also through secondary and downstream effects . Even though we made our best effort to include only the most reliable targets, through stringent filtering criteria and manual curation, it is likely that the target mappings from these databases include both false-positive and false-negative interactions , future improvements in both the compound and target coverage of the quantitative bioactivity mappings should lead to further improvements in the performance of computational target deconvolution methods.To model the polypharmacological effects and mechanism of action of multitargeting compounds, we wanted to consider here a wide spectrum of both direct and indirect compound-target interactions  response parameters. We had access also to the raw dose-response data , enabling us to compute our drug sensitivity score ; more specifically, we also collected secondary and downstream targets for the compounds using a number of additional resources. For the kinase targeting compounds, we extracted quantitative bioactivity data from the large-scale kinase inhibitor selectivity profiling assays by 50, Kd, Ki and potency ≤35 nM to focus on the most potent targets only. We also manually filtered out genes involved in cellular metabolism, such as cytochrome (CYP) and ATP-binding cassette (ABC) genes. This resulted in an interconnected network among 138 compounds and 749 protein targets, capturing both direct and indirect interactions, which was used for modelling the comprehensive spectrum of not only on-target but also off-target and downstream effects of the compounds .For the system-level modeling of drugs' mode of action and polypharmacological effects, we extended the compound-target mappings beyond the direct binding interactions and the primary targets listed at Genomics of Drug Sensitivity in Cancer (GDSC) database of the Sanger Institute  for a particular target t is calculated by averaging the observed drug response (DR) over all those tn compounds that are known to target protein t, as follows:The target addiction score gives an estimate of the sensitivity of a cell to the inhibition of a particular protein target, thereby extending the concept of kinase addiction maps introduced by https://bitbucket.org/BhagwanYadav/target-addiction-score-tas-calculation.2 scale. Supplementary material Fig. S3A shows the standard deviation of all genes in the expression data. The genes with high enough standard deviation (above the red line) were used in GFA. All the data sets were scaled to unit standard deviation, giving them equal weight. GFA was run with a varying number of components (K) to model the total variation in the three data sets. Supplementary material Fig. S3B shows the activity of the components in GFA. A component is present for a data set when shown in black, and otherwise absent. The run resulted in components absent in all the data sets, indicating that K=30 was large enough to model the data.Factor analysis is a statistical method, which allows description of the dependencies among many observed variables using a few factors. Group factor analysis (GFA) is a recent extension of the standard factor analysis to capture joint variability across multiple data sets . Here, GW) is a non-parametric statistic to assess the agreement between two rankings . The statistical significance of the congruence was assessed by performing 10,000 permutations. We also performed Mantel's randomization test using ‘ade4’ R package . We retrieved mutation information of the selected genes in various cancer patients from The Cancer Genome Atlas (TCGA) resource. The TCGA data were downloaded and extracted using the ‘cgdsr’ R package from cBioPortal . We investigated the cancer relevance of the selected genes using QIAGEN's IngenuityPathway Analysis . Within each sub-cluster, we performed Spearman correlation analysis between the newly added cell lines and the original cell lines within the sub-cluster to evaluate their average similarity in gene expression and copy number. To assess the statistical significance of the correlation coefficients, we computed the background null distribution as pairwise Spearman correlation coefficients of every pair of the cell lines.Beyond the 107 cell lines in our original analysis, we later selected 20 additional cell lines, which were tested with 134-137 compounds. We added the TAS profiles of the 20 new cell lines to the original clustering based on the TAS profile of the 107 cell lines, and used the same distance function and clustering algorithm to re-compute new clusters. The complete panel of cell lines was divided into eight sub-clusters (FLT3-ITD.For the leukemia case study, we extracted the drug screening and clinical data for 151 primary leukemia patient samples . In this


Prostate cancer begins as an androgen-responsive disease. However, subsequent accumulation of multiple sequential genetic and epigenetic alterations transforms the disease into an aggressive, castration-resistant prostate cancer (CRPC). The monoallelic Androgen Receptor (AR) is associated with the onset, growth and development of Prostate cancer. The AR is a ligand-dependent transcription factor, and the targeting of androgen- and AR-signaling axis remains the primary therapeutic option for Prostate cancer (PCa) treatment. A durable and functional disruption of AR signaling pathways combining both traditional and novel therapeutics is likely to provide better treatment options for CRPC. Recent work has indicated that expression of AR is modulated at the posttranscriptional level by regulatory miRNAs. Due to a relatively long 3’ untranslated region (UTR) of AR mRNA, the posttranscription expression is likely to be regulated by hundreds of miRNAs in normal as well as in disease state. The main objective of the article is to offer a thought-provoking concept of “andro-miRs” and their potential application in AR gene expression targeting. This new paradigm for targeting constitutively active AR and its tumor specific splicing isoforms using andro-miRs may pave the way for a novel adjunctive therapy and improved treatment of CRPC. Prostate Cancer (PCa) is the most commonly diagnosed malignancy among the male population in North America and continues to be the second leading cause of cancer-related deaths . Recent Acquired resistance to anti-androgen therapy remains a major obstacle in the treatment of advanced stage metastatic cancer. Although the exact molecular mechanisms responsible for the progression of the disease from androgen-dependent state to androgen-refractory state is unclear, CRPC tumors display revived capability of androgen signaling through AR, a ligand dependent transcription factor belonging to nuclear receptor super-family ,4. TestoThe AR protein structure is composed of three major functional domains: N-terminal transactivation domain (TAD), central DNA-binding domain (DBD) and C-terminal ligand-binding domain (LBD) ,8. MolecIn this review, we briefly remind the reader of potential mechanisms mediated through AR; though we mainly intend to focus on the implication of miRNAs mediated regulations of PCa, with AR-centric approach. We discuss the regulation of AR activity through andro-miRs and their potential application as therapeutic agents in treatment of CRPC patients.Discovery of a crucial role for AR in PCa dates back to the 1940s when Huggins and Hodges demonstrated the effectiveness of surgical castration in men with PCa . Since tMajorities of PCa are dependent on AR signaling—even as they progress into castration-resistant state; AR continues to remain the favorite target for cancer therapy. Overexpression of AR at both the mRNA and protein levels has been reported in CRPC tumors ,17. AR ghttp://androgendb.mcgill.ca). Over 800 of these mutations appear to promote androgen -independence [Accumulation of a significant number of AR mutations is another molecular mechanism exploited by PCa cells to acquire androgen-independence and become castration-resistant. AR gene is one of the most abundant mutated of the nuclear steroid receptor family with 1,029 mutations have been reported in the AR gene mutations database (pendence . The incpendence .The first AR gene mutation (T877A) was identified in hormone-dependent LNCaP PCa cells derived from lymph-node metastasis . InteresSeveral AR splice variants have been identified and altered splicing has emerged as another significant molecular mechanism potentially contributing to the recurrence of PCa. AR splice variants AR3, AR4, AR4b, AR5 and AR8 lacks the LBD and have been found to be over expressed in CRPC tumors . The absAR protein is known to interact with a plethora of diverse proteins including other transcription factors as well as AR coregulators with distinct coactivating or corepressing properties. Interaction of AR with coactivator proteins is important for assembly of the transcriptional complexes to modulate transcriptional activities of its target genes. A recent update of AR-database registers over 300 proteins as AR coregulators. As of February 2013, the list of proteins that have been identified as AR-coregulators in the Nuclear Receptor Signaling Atlas (NURSA) contains 283 entries. These coregulators are involved in a multitude of functions. The first AR co-activator to be identified was SRC-1 . The steAlthough the list of proteins functioning as coregulators for AR is vast, AR may regulate the expression of most of the coregulators, since modulation of expression of the cofactors are dependent on an AR feedback mechanism to regulate the coregulator complex formation . AlteratIn the last few years, there have been numerous reports supporting the impact of microRNAs (miRNAs) in the progression of androgen-sensitive PCa to CRPC. Only recently, however a couple of reports have shown that the AR is a direct target of miRNAs ,36. ReguC. elegans and D. melanogaster in the 1990s have shed light on the role of miRNAs in carcinogenesis [miRNAs are short (~20 – 24 nucleotides) noncoding RNAs that regulate gene expression by facilitating cleavage of its target mRNA in plants; and mostly by translational repression in animals. In animals, miRNAs target mRNAs by imperfect complimentary base-pairing to the 3’ untranslated region (3’ UTR) to downregulate target’s protein synthesis in actively translating ribosomes . Currentogenesis . In the ogenesis . Studiesogenesis .Our knowledge of miRNA mediated regulation of PCa still presents an unclear picture owing to the heterogeneity of the disease and the complexity of cellular signaling presumably involved in the conversion of androgen-sensitive to CRPC. Expression profiling of miRNAs in androgen-dependent versus androgen-independent cell lines reveal that miRNA expression is significantly increased in metastatic invasive cell lines such as the PC3, DU145, and MDA PCA 2B indicating the importance of miRNAs in the progression of the disease to androgen-independence . Of the *, miR-330, miR-148a and miR-449. Each of these affects the expression of growth regulatory genes in PCa [Several other miRNAs have been reported in PCa including miR-126s in PCa –61. Upres in PCa . Converss in PCa . Similars in PCa . CollectGreater than 50% of miRNA genes are coded in the introns of protein coding genes. Some studies have shown that there appears to be a coordinated expression of the host genes and the miRNAs that are coded in the introns, because most intronic miRNAs must be transcribed from the host gene’s RNA polymerase II type promoters . NeverthNumerous miRNAs express differentially in PCa cells and large number of these miRNAs have potential to target androgen-centric signaling pathways in addition to targeting of AR expression directly. An experimental undertaking to identify all the miRNAs, which have potential to directly interact with AR 3’ UTR is clearly lacking. A validation of all the miRNAs targets found within the 3’ UTR of AR and fine-tuned influence of these andro-miRs in AR expression is of paramount importance to realistically understand the progression of disease to metastatic stages. It is interesting to note that a modest 3–4 fold increase in expression of AR expression is sufficient for tumors to develop androgen-independence , and dowet al., using a tour de force approach analysis of miRNAs targeting AR expression, revealed 71 potential andro-miRNAs that down-regulated endogenous AR protein levels in multiple AR positive PCa cell culture models [* can directly bind to the 3’ UTR of the AR to downregulate AR expression both at the mRNA and protein levels resulting in decreased cell proliferation and increased apoptosis in LNCap and C4-3B cells [As noted earlier, due to a relatively large 3’ UTR, the AR is likely to be targeted by a plethora of miRNAs. It is logical that all the predicted andro-miRs may not express in prostate cells or in tumors. Nevertheless, it is expected that experimental introduction of miRNAs into PCa cell or tumors is likely to downregulate AR expression. An elegant recent study by Östling e models . Among t3B cells . Our recA comprehensive study to biochemically catalog miRNAs that have the potential to bind to the AR 3’ UTR would help the PCa and AR research community to start thinking towards potential application of miRNA based adjunctive therapy of PCa. However, off-target effect of miRNAs should also be considered cautiously in order to establish a comprehensive picture of miRNA mediated PCa therapy.We propose that miRNAs which have been validated to interact directly with the AR 3’ UTR by base-pairing and downregulate AR mRNA and protein levels, can be conveniently termed as andro-miRs, albeit in PCa context only. A list of target-validated andro-miRs is shown in AR mRNA species of length 8.5 kb and 11 kb with variable 6.6 to 6.9 kb to 3’ UTR lengths have been identified ,57,74, hDecades of research have presented us with complex scenarios for the development of CRPC; however, the role of androgen and AR in this progression is irrefutable. The heterogeneity and inherent complex molecular circuitry of disease continues to rely on androgen and its cognate receptor, leading to drug-resistance and failed-treatments. Because of these intrinsic biological properties, the current practice of targeting the AR protein activity does not appear to be completely effective. Development of curative therapies for the treatment of CRPC is urgently needed; especially when resistance to AR-antagonist Enzalutamide (MDV3100) poses a grim prognosis. Discovery of andro-miRs has opened up a new avenue, which must be further explored to establish the efficacy of miR-based adjunctive therapeutics in CRPC treatment. This approach would require the development of an efficient systemic delivery method for andro-miRs to directly target the expression of AR mRNA isoforms, in addition to conventional therapeutics which are designed to target androgen and the AR protein. Last but not least, the 3’ UTR of the AR remains an uncharted territory needing further exploration to understand the regulatory role of this region: the important cis-acting RNA motifs and structural elements, as well as the alternative polyadenylation events responsible for the activation of multiple isoforms. Understanding the RNA-processing events and their potential role in AR function would comprehensively enhance our knowledge of this very fascinating, yet under studied molecule.


The clear cell sarcoma of tendons and aponeuroses (CCSTA) is a rare soft tissue sarcoma in the head and neck region and parapharyngeal space. Over 95% of CCSTAs present in the extremities, with the head and neck region (1.9%) being an unusual site. This study presents an additional case of CCSTA of the head and neck region involving the parapharyngeal space in a 48-year-old men and review of the literature on CCSTA. Clear cell sarcoma of tendons and aponeuroses, also referred to as malignant melanoma (MM) of soft parts, is a rare malignancy derived from neural crest cells. There is evidence for melanocyte differentiation by immunohistochemical, ultrastructural, and genomic profiling studies , 2. BecaA 48-year-old man presented with a 14-month history of progressive right hearing loss associated with headache and mastication pain. Intraoral examination and rhinoscopy revealed right-sided pharyngeal swelling with normal mucosa on the mass. On intraoral palpation, a relatively well defined and elastic hard mass with no fluctuation was observed. No cervical lymphadenopathy was detected. Computerised tomography (CT) and magnetic resonance imaging (MRI) of the head and neck revealed a well-defined mass measuring approximately 60 mm x 40 mm in its greatest dimension in the right parapharyngeal space . A biopsDuring 24 months follow up there is a good clinical response with desperation of headache and pain masticatication. Radiologically, MRI showed significant tumor reduction . Six monDescribed by Enzinger in 1965, the clear cell sarcoma is a rare melanin-producing soft tissue sarcoma . Clear cClear cell sarcoma of the parapharyngeal space is an extremely rare tumor for which it has been difficult to amass outcome and therapeutic data in a large patient population.


The RIG-I-like receptors (RLRs) play a major role in sensing RNA virus infection to initiate and modulate antiviral immunity. They interact with particular viral RNAs, most of them being still unknown. To decipher the viral RNA signature on RLRs during viral infection, we tagged RLRs  and applied tagged protein affinity purification followed by next-generation sequencing (NGS) of associated RNA molecules. Two viruses with negative- and positive-sense RNA genome were used: measles (MV) and chikungunya (CHIKV). NGS analysis revealed that distinct regions of MV genome were specifically recognized by distinct RLRs: RIG-I recognized defective interfering genomes, whereas MDA5 and LGP2 specifically bound MV nucleoprotein-coding region. During CHIKV infection, RIG-I associated specifically to the 3’ untranslated region of viral genome. This study provides the first comparative view of the viral RNA ligands for RIG-I, MDA5 and LGP2 in the presence of infection.DOI:http://dx.doi.org/10.7554/eLife.11275.001 An immune system can protect against disease-causing microbes such as viruses. Human cells contain three different receptors that can recognize and respond when a virus enters and begins to replicate inside. These receptors include RIG-I, MDA5 and LGP2, and they are collectively known as the RIG-I-like receptors. RIG-I-like receptors specifically recognize viruses that store their genetic material in the form of molecules of RNA. However, the specific viral parts that trigger RIG-I-like receptors to respond remain almost completely unknown.RNA viruses include well-known and re-emerging viruses such as polio and measles, as well as chikungunya – a virus that is spread by mosquitoes and causes illness worldwide. This means that understanding how RIG-I-like receptors identify RNA viruses and then trigger an immune response to eradicate them has the potential to inform the development of vaccines and antiviral therapies for many diseases.Sanchez David et al. now describe and validate a new experimental approach to determine the distinct viral regions that are recognized by human RIG-I-like receptors. The approach involves purifying the RIG-I-like receptors out of infected cells and then working out the sequence of RNA fragments that bind to the receptors. This approach revealed that each of the three human RIG-I-like receptors detected different viral RNA sequences during a measles infection. On the other hand, only RIG-I could recognize a specific part of the chikungunya virus genome.All together, the experiments illustrate how to identify the RNA sequences recognized by any of the three human RIG-I-like receptors during infection by a RNA virus. With the ability to gain this kind of insight, it may soon be possible to develop ways of using the RIG-I-like receptor pathway to control viral infections and enhance the body’s immune response to vaccination.DOI:http://dx.doi.org/10.7554/eLife.11275.002 Because of increasing global population and worldwide exchanges, infections by RNA viruses are expanding with a huge impact on public health. New infections are emerging almost every year , tropical fevers are expanding  and even previously controlled diseases are re-emerging . Although several efficient vaccines against RNA viruses are used in human medicine, we still lack potent therapeutic treatments for the majority of viral infections as well as rational strategies to create efficient new vaccines. Recent progress in our understanding of cellular pathways controlling viral replication suggests that modulating host cell functions early upon viral infection could inhibit a large panel of RNA viruses . The RIGvia binding to pathogen-associated molecular pattern (PAMP) motifs. To date three RLR members have been identified: RIG-I (Retinoic acid-Inducible Gene-I), MDA5 (Melanoma-Differentiation-Associated gene 5), and LGP2 (Laboratory of Genetics and Physiology 2) (reviewed in 3) Further bioinformatic assays of the current data are helpful to understand the commons and differences of RNAs recognized by individual RLRs. At least, further description of the detected RNA profiles and the comparison to previous studies is necessary.In the revised manuscript, we performed additional bioinformatic analysis of the NGS data. Our studies concluded that not specific RNA sequences but rather structural elements are more likely to be linked to RLRs functionality. The relevant results are presented and discussed.4) For the confirmation of the detected RNA signatures, it can be performed in alternative permissive cells that naturally respond to the viruses using the enriched RNAs. First, we would like to provide a short introduction about MV and CHIKV permissive cells.For CHIKV the main virus-permissive cells are dermal fibroblasts, migrating monocytes/macrophages, and endothelial cells . Using our RLR-tagged approach we have found that RIG-I specifically interacts with CHIKV 3’-UTR. CHIKV 3’-UTR contains multiple 21 nucleotide-long repeated sequence elements forming dsRNA structure. The evolutional pressure exerted by mosquito vectors on this region and the length of CHIKV 3’-UTR are directly linked to the viral pathogenesis . We are currently performing additional studies in various cell types in which 3’-UTR of different CHIKV strains and mutant genomes are used. These studies will be later published elsewhere.via aerosols or droplets. Further, MV enters local lymphatic tissues and during the systemicphase of infection MV spreads by mononuclear cells that carry the virus to various organs . So in the late stage of the infection epithelial cells are the main targets of MV.Monocytes and dendritic cells of respiratory tract are the first targets of MV that is transmitted Taking into account that immune cells  harbour TLR7/8 that recognise viral single- stranded RNA in endosomes of infected cell and lead to activation of the transcription factor NF-κB and activation of type I IFN production, we used in our validation experiments MV-permissive epithelial cells (A549) naturally lacking TLR 7/8 receptors.In the revised manuscript, a validation of MV RNA signatures for RLR has been added . Thus, due to this gradient of transcription, promoter-proximal genes are expressed more efficiently than promoter-distal genes, a differential level that corresponds to the virus needs. To provide a clear vision of MV transcriptome, we now introduced a figure with NGS analysis of total RNA from MV-infected cells , nor to compute this specificity because we do not know which positions are truly not enriched (no reference population). However, we used statistical methods that are well adapted to analyse count data with a low number of replicates. These methods have been shown to be among the most reliable  and to control the false positive rates to expected levels.It is true that read counts are low in the L region, but this is the situation where the statistical analysis has less power. So the fact that read counts in this region are significant over the whole L region gives even more confidence about the significance of the difference.In We modified MV genome scheme to show that V region was deleted. We thank the reviewer #1 for this remark .In  This global enrichment in sequencing reads corresponding to the C-E2-E1 region has also its biological reason. Indeed, previous studies have shown that alphaviruses subgenomic RNA is more abundant than genomic RNA in cytoplasm of infected cells To provide a better vision on CHIKV NGS results, Reviewer 2: 1) This study is based on a HEK293 cell model after overexpression of tagged RLRs. This cell line is different in expression of the three endogenous RLRs. So could these differences affect the enrichment of RNAs for NGS? Moreover, single cell clones were used to perform the infection and RNA isolation, which may also bias the enriched RNAs. In fact, it is better to use a RLR-deficient cell (cell line) with rescue of respective RLR. I may also encourage the authors to use a typical virus-permissive immune cell model instead of HEK293.http://www.proteinatlas.org/). Indeed, we did not detect any of the three RLRs by western blot in the background HEK293 cells when using RLR specific antibodies  Previously, some studies have revealed the RNA features of several RNA viruses using different approaches. Despite the proposals of advantages of current approach by the authors, I am more interested in the similarities and differences identified by these researches for RNA viruses. Did common sequence or structural similarities exist for recognition by individual RLR? Otherwise, did common sequence or structural similarities exist in RNA viruses that could be recognized by RLRs?RLR specific PAMPs were previously detected for two paramyxoviruses (MV and Sendai virus) by immuno-precipitation of RIG-I and MDA5 and NGS of specific RNA ligands . This was already discussed in the previous version of the manuscript. In the revised manuscript we provide further information on the similarities and differences of our results and previous data (Discussion).Additionally, did the authors find particular self-RNA in the enriched RNAs?6 reads per sample, whereas in order to analyse human transcripts we need at least x108 reads/per sample. In the future, our protocol will be adapted to identify RLR-specific cellular partners that will include less or no multiplexing upon NGS.Unfortunately, in the current experimental setup we used samples multiplexing for NGS that allowed an appropriate analysis of viral -derived RNA but not cellular RNA molecules. Indeed, the mean number of reads aligned to the human genome in our NGS results were 16x10More bioinformatics analysis data should be performed, and these concerns may need further discussions.In the revised manuscript, we performed additional bioinformatic analysis of the NGS data obtained for the three RLRs upon infection with MV. We evaluated AU bias of the RLR sequencing results  as well The Results, Discussion, Materials and methods sections as well as the Figure legends were modified accordingly.3) To evaluate the stimulatory effects of the enriched RNAs, total pooled RNAs were used to activate the IFNβ reporter. Since RNA signatures in MV and CHIKV have been revealed by the study, it may be better for the authors to use these individual "RNA signatures" to examine the effects in IFNβ induction. Considering that IFNβ expression is rather complicated than transactivation, I suggest the authors to use an immune cell to examine the IFNβ levels after transfection of the identified signature RNAs.Immune cells  express TLRs capable of recognizing viral RNA (TLRs 3/7 and 8), and secrete type-I IFN upon ligand binding. In order to assess the immunostimulatory activity of RLR-specific RNA fragments synthesised in vitro and containing the RLR specific signatures, human PBMCs were transfected ex vivo. Indeed, we observed an increased secretion of type-I IFN. However, we were unable to discriminate what percentage of the response was TLR-dependent or RLR- dependent. In the presence of TLR7/8 inhibitor, the cytokine response was strongly reduced (Data not shown). Therefore, using immune cells represented a technical bias that makes it complicated to address this question in the allotted period. However, we believe that studying the RLR response in non-immune cells that do not respond through the TLR pathway is highly pertinent. Indeed, RLRs are ubiquitously expressed, and any cell is susceptible of recognizing viral RNA in their cytoplasm. In addition, non-immune cells are classically used as a model to study the RLR response . Consequently, validation experiments were done using MV-permissive epithelial cells naturally lacking TLR7/8 .The Results, Discussion, Materials and methods sections as well as the Figure legends were modified accordingly.


Although the prognosis of ischemic stroke is highly dependent on the lesion location, it has rarely been quantitatively utilized. We investigated the usefulness of regional extent of ischemic lesion (rEIL) predicting the improvement of visual field defect (VFD) in patients with posterior cerebral artery infarction.The rEILs were measured in each individual cortex after transforming the lesions to a standard atlas. Significant improvement of VFD was tentatively defined as 20% improvement at 3 months after stroke. The performances of clinical and imaging variables predicting significant improvement were measured by support vector machine. The maximum performance of variables predicting the significant improvement was compared between subgroups of variables  and the effect of adding rEIL to those subgroups of variables was evaluated.A total of 35 patients were enrolled in this study. Left PCA infarct, MR-time from onset, rEILs in the lingual, calcarine, and cuneus cortices were good prognostic indicators of hemi-VFD . A combination of the rEILs of each cortical subregions demonstrated a better predictive performance for hemi-VFD (83.8±9.5%) compared to a combination of clinical variables , baseline severity , or lesion volume . Adding a rEIL to other variables improved the prognostic prediction for hemi-VFD .An estimation of rEIL provides useful information regarding the ischemic lesion location. rEIL accurately predicts the significant improvement of VFD and enhances the prediction power when combined with other variables. Understanding the natural course and prognosis helps to guide the management of ischemic stroke.  The seveThe topographical lesion location determines the type and severity of neurological deficits. The location of the ischemic lesion was associated with functional outcome  and moreUnlike other impairments caused by stroke (e.g. motor or language dysfunction), the lesion location causing visual field defect (VFD) is less diverse. Usually, the lesion causing VFD is located at the area supplied by posterior cerebral artery (PCA) and the lesion location well-correlates with the severity of VFD objectively measured by perimetry.  Therefore.g., hemorrhagic transformation or recurrent ischemic stroke) between enrollment and final follow-up, and/or (4) had visual field test results that were unreliable due to being clinically inconsistent and incomplete Ischemic stroke patients with VFD caused by PCA infarction were prospectively enrolled between September 2011 and July 2014. Patients were included if they were older than 20 years of age and if they were admitted to Asan Medical Center within one week of symptom onset. Patients were excluded if they (1) received thrombolysis, (2) had a previous history of stroke, (3) had new vascular events  and at 3 months of symptom onset (follow-up). The visual field score was calculated according to the results of perimetry (see below). A higher score represented a less severe VFD.Enrolled patients were assigned into one of two groups to perform a classification test. A visual field score increase of more or less than 20% from baseline to three months was arbitrarily regarded as a significant or poor VFD improvement, respectively. This cut-off value was chosen because it was close to the median value of improvement among all patients. The Institutional Review Board of the Asan Medical Center approved this study. Written informed consent was obtained from the patients or their legally authorized representatives.. Patients were asked to respond when the short light stimuli appeared with their head fixed in position. The subjects were asked to fixate a fixation spot to reduce the influence of eye movements. From the visual field test, whole visual field consists of 76 points (19 points for each quadrant) for each eye. Each point is presented as a standard deviation from the mean score of an age-matched normal population . First, the average visual field score of a particular point was calculated by averaging the scores of both eyes. Then, by summating the average visual field score of each point in individual hemi-field and upper- or lower-quadrants of those who had a defect at the particular field were regarded as the visual field score of hemi- and upper- or lower-VFD, respectively. The whole visual field was categorized into four quadrants, upper-affected side (upper-VFD), upper-unaffected side, lower-affected side (lower-VFD), and lower-unaffected side. Defects on the affected side of the upper- and lower quadrants were considered as defect on hemi-field.VFD was quantitatively measured by a Humphrey Field Analyzer . DWIs were obtained by a single-shot spin-echo echo-planar imaging technique with repetition time of 3000 ms, echo time of 86 ms, 0.651 × 0.651 × 5 mm3 resolution with 2 mm gap, 384 × 384 in-plane voxels. Additionally, patients underwent 3-T MRI scanning  at 1 week after symptom onset to obtain high-resolution T1-weighted MR images .Patients underwent 1.5-T MRI scanning An ischemic lesion mask was extracted using FSLView toolbox in FMRIB Software Library (FSL) in its native DWI space . The iscRegistration of the lesion mask from its native space to a standard Montreal Neurological Institute (MNI) space was performed via the FMRIB Linear or Non-linear Image Registration Tool implemented in FSL . In detail, co-registration was performed to estimate the rigid body transformation parameter between an individual apparent diffusion coefficient scan and a high-resolution individual T1 scan via FLIRT with normalized mutual information cost function. Spatial normalization was performed to estimate affine transformation and local deformation parameters between a high-resolution T1 scan and the standard MNI T1 scan via FLIRT and FNIRT with excluding lesion mask option in the non-linear registration stage. The lesion mask in diffusion space was transformed into standard MNI space by applying the rigid body transformation, affine transformation and local deformation parameters. The rEIL in each individual cortex was calculated on the automated anatomical labeling (AAL) atlas in the standard MNI space  to quantp < 0.05 was considered statistically significant. To compare the voxel-wise frequency difference of lesion between the significant and poor improvement groups, Bernoulli model based two sample t-test was performed for each voxel. (12) The false discovery rate correction with significance level at q < 0.05 was employed with considering the multiple comparisons in the voxel-wise test. and and25] ae.g., motor weakness and aphasia) should also be evaluated in this model to expand its clinical application. Finally, though most VFD following stroke has largely been attributed to cortical lesions, infarct at the different visual areas such as optic radiation fibers was not considered in the present study. Further studies using diffusion tensor image or functional connectivity MRI to predict the improvement of VFD may be needed. [There are some additional noteworthy limitations to this study. First, sample size was small. However, we performed a test-retest classification to ensure the reliability of our results. Second, recanalization of the PCA was not checked, which may influence the functional recovery. Third, while this lesion location-based prognosis prediction model accurately predicted the improvement of VFD, other stroke symptoms Click here for additional data file.


Owing toGiven the high costs of drugs to treat multi-drug resistant tuberculosis (MDR-TB), the Green Light Committee (GLC) initiative enables TB programs to procure quality-assured drugs at reduced prices. Despite price reductions, internationally quality assured (IQA) drugs can be more expensive than locally procured drugs. There is little evidence to inform decision-makers about whether IQA drugs are more effective than local drugs. This is the first study to compare outcomes between MDR-TB patients treated using IQA, and locally procured drugs in the same hospitals during the same time period.A retrospective cohort study was conducted in three hospitals across Pakistan. Data on baseline characteristics and treatment outcomes during the first six months of treatment were extracted from hospital records of adult culture-positive pulmonary MDR-TB patients starting treatment between January 2011 and June 2012. Two cohorts were defined: patients receiving IQA drugs, and patients receiving locally procured non-IQA drugs. Data were analysed using Kaplan-Meier curves and Cox proportional hazards regression. The primary outcome compared between cohorts was time to culture conversion. Of 231 patients, 90 were in the IQA and 141 in the non-IQA cohorts. Baseline characteristics were similar except for higher frequency of quinolone resistance in the IQA cohort. Overall, 193 patients (84%) culture converted. Culture conversion was not faster in the IQA cohort; the median time was 81 and 68 days in the IQA and non-IQA cohorts, respectively. Unadjusted and adjusted hazard ratios for culture conversion in IQA verses non-IQA cohorts were 0.82  and 0.95  respectively.Use of good quality, locally procured drugs can be effective in treating MDR-TB, may involve lower costs than using IQA drugs and could strengthen developing country drug quality assurance systems. This may be a suitable alternative in lieu of or whilst awaiting arrival of internationally procured medicines. Mycobacterium tuberculosis resistant to both isoniazid and rifampicin, poses several challenges for National Tuberculosis Programs (NTPs). Expensive and sophisticated diagnostic methods are needed to confirm MDR-TB cases and effective treatment requires patients to take costly second-line anti-tuberculosis drugs for more than 18 months. Furthermore, the number of MDR-TB cases notified to NTPs in high burden countries is rising every year and is expected to keep growing. While 94,000 MDR-TB cases were diagnosed in 2012, reports suggest that less than 25% of the people who are estimated to have MDR-TB were identified[Multi-drug resistant TB (MDR-TB), which is caused by strains of A major barrier to provision of timely and effective treatment by NTPs is the high cost of quality-assured second line drugs (SLDs). In order to promote access to and rational use of SLDs for the treatment of MDR-TB, the WHO formed a multi-institutional partnership called the Green Light Committee (GLC),3. The GEvidence from existing literature on patient benefit through use of IQA drugs is limited. An analysis of the first five projects approved by the GLC, involving data from 1768 MDR-TB patients reported treatment success in 65%, default in 14%, failure in 7% and death rates of 11%[Pakistan ranks fifth in terms of global MDR-TB burden. In 2010The objective of this study was to compare response to treatment in patients treated using IQA drugs through the GLC initiative with those treated according to standard local protocols using non-IQA drugs in the same hospitals during the same time period. The question being addressed is whether IQA drugs are more effective in treating MDR-TB than locally procured drugs.A retrospective cohort study was conducted in three hospitals in different regions of Pakistan: OICD (Karachi), Leprosy Hospital  and Institute of Chest Diseases Kotri (Hyderabad). Hospitals were included in the study if they were treating MDR-TB patients using both IQA and non-IQA drugs simultaneously. Hospitals that were treating patients with exclusively IQA or non-IQA drugs during the study period were not included as patient populations may differ.Culture-positive adults who were enrolled on to treatment for pulmonary MDR-TB at the study hospitals between January 2011 and June 2012 were included in the study. Exclusion criteria were: age under 15 years, only extra-pulmonary TB diagnosed, culture positive at diagnosis but negative/missing culture result at treatment initiation and default immediately before initiating treatment. Patients who had received less than 30 days of non-IQA drugs before starting on IQA drugs for their entire treatment were included in the study and classified as receiving IQA drugs. Any patients who had been switched from non-IQA to IQA drugs more 30 days after treatment initiation were excluded from the study.All patients received free individualized treatment regimens at the study hospitals. Sputum smears and cultures were performed at diagnosis, initiation of treatment and at monthly follow-up visits over the course of treatment. Patients were followed up for six months after treatment initiation.Time to conversion of sputum mycobacterial cultures, which is recognized as the most important interim indicator of the efficacy of anti-TB treatment for MDR disease,8, was tSecondary outcomes were time to sputum smear conversion, weight gain six months after treatment initiation and occurrence of drug-related side effects during the first six months after treatment initiation. Time to sputum smear conversion was defined as the time between treatment initiation and the first of two negative sputum smear microscopy results. This analysis restricted to patients who had a positive sputum smear result at the time of treatment initiation. If a patient’s sputum remained smear-positive, observations were censored as previously described.Among the explanatory variables, the initial drug regimen was defined as the number of drugs prescribed in the first month of treatment. Drugs that were removed from the regimen within 30 days of initiation were not counted. Drugs to which patient isolates is sensitive was defined as drugs given to the patient for at least one month to which there was no resistance detected during drug sensitivity testing. Period of treatment initiation was classified into three categories based on the calendar month during which a patient was started on treatment: January—June 2011, July – December 2011 and January 2012 – June 2012. Treatment initiation delay was defined as the time between a positive diagnostic culture for MDR-TB and treatment initiation. Previous first and second line TB treatments were defined as the most recent first and second line treatments received prior to MDR-TB treatment initiation at the study site, respectively. Initial sputum microscopy grading results were taken for the specimen submitted at the time of treatment initiation or within two weeks of treatment initiation if no sputum microscopy was conducted on the day of treatment initiation.Data on patients’ baseline characteristics, TB treatment history, current disease severity and treatment progress indicators were extracted from hospital records for a period of six months following treatment initiation and directly entered into a database. Individual patient data were checked by a data supervisor on a weekly basis, and any data entry errors identified were corrected by referring to the hospital records. Two cohorts of patients were defined: those who received IQA drugs and associated social support through the GLC initiative (IQA cohort) and those who received locally procured drugs (non-IQA cohort).The sample size was determined by the number of MDR patients being diagnosed and treated using IQA or non-IQA drugs during the study period; a power calculation was conducted at the beginning of the study. Using NTP records it was estimated that 109 patients had initiated treatment on IQA drugs and200 patients had initiated treatment on non-IQA drugs at the study hospitals between January 2011 and June 2012. It was expected that 20% of patients in the non-IQA cohort will culture convert by 3 months post treatment initiation, based on a previous study conducted in Pakistan. We estia priori based on literature were used in the adjusted model. The proportionality assumption of the Cox model was assessed graphically. Kaplan-Meier curves were used to show time to sputum smear and culture conversion by IQA and non-IQA cohorts. Weight gain between cohorts was compared using multiple linear regression to control for potential confounders. Frequency of reported side-effects was compared using the z-test. All analyses were conducted using Stata/ IC 11.0 .Baseline characteristics of cohorts were compared using chi—square test for categorical variables and t-test for continuous variables. Cox proportional hazards regression was used to assess the effect of IQA versus non-IQA treatment on time to sputum smear and culture conversion. Unadjusted and adjusted hazard ratios of sputum smear and culture conversion in IQA and non-IQA cohorts were calculated. Potential confounders identified This study received ethical approval from the Pakistan Medical Research Council and the London School of Hygiene and Tropical Medicine. Since pre-existing secondary patient data was analysed and there was no contact with patients during data collection, the institutional review boards did not require informed consent to be taken. All data abstracted from existing patient records were anonymised.Of 314 patients listed in the MDR treatment registers at the study hospitals during the study period, 83 were excluded because they were either not resistant to both isonaizid and rifampicin, were culture-positive at diagnosis but culture-negative by the time of treatment initiation or defaulted immediately before initiating treatment. Thus 231 patients were included in the study and of these 90 were in the IQA cohort and 141 were in the non-IQA cohort.Patient baseline characteristics were generally similar across cohorts as was TB treatment history . The medOne hundred and ninety-three patients (84%) culture converted within six months of treatment initiation. Median time to culture conversion was 81 days in the IQA cohort and 68 days in the non-IQA cohort . The unaOf the 231 culture-positive patients included in the study, 217 (94%) were also smear-positive and were included in the time to smear conversion analysis. Overall, 152 patients (70%) smear converted during the study period. Eighty-five percent (71/84) of patients in the IQA cohort and 61% (81/133) patients in the non-IQA cohort smear-converted within six months of treatment initiation. Unlike time to culture conversion, time to smear conversion was shorter in the IQA cohort  of patients had culture positive and smear negative result compared with 49% (69/141) in the non-IQA cohort. Overall 74% of the discordant results had a scanty positive smear-microscopy grading . Culture-negative, smear-positive results were not found to cluster at a particular hospital or time period.Data on weight at six months post treatment initiation was available for 84% (76/90) and 79% (112/141) of patients in the IQA and non-IQA cohorts. Median weight after six months of treatment was 44 kg and 45 kg in the IQA and non-IQA cohorts respectively. Weight gain in the IQA cohort was lower than that in non-IQA cohort even after adjustment for same factors described previously . The proportion of patients who experienced drug-related side-effects was 38% and 30% (p = 0.21) in the IQA and non-IQA cohorts respectively.Results of this study indicate that there was very little difference in the primary outcome, time to culture conversion, between IQA and non-IQA cohorts. Good culture conversion rates were achieved using both types of drugs; more than 75% of patients treated using IQA or non-IQA drugs had sputum cultures negative for mycobacterial growth by the time they had completed four months of treatment. Median times to culture conversion in this study were substantially shorter than the 196 days previously reported in a single-centre study in Pakistan for patients on non-IQA drugs. HoweverAn analysis of other treatment success indicators showed that patients in the IQA cohort did not experience greater weight gain (if anything slightly lesser weight gain) despite provision of food baskets to patients through the GLC program. Provision of food baskets may have an impact on adherence or weight gain later in treatment (ie. one year post treatment initiation); further studies to evaluate the strategy of providing nutritional support are required to draw conclusions. The frequency of self-reported side-effects did not differ between cohorts (p = 0.2). These clinical data are consistent with the culture outcome data of similar responses by IQA and non-IQA cohorts.There was, however, strong evidence of faster time to smear-conversion in the IQA cohort; on average, patients on IQA drugs became negative on smear-microscopy almost two months before patients on non-IQA drugs. The same laboratories processed all samples at each site, laboratory technicians were unaware of whether patients were receiving IQA or non-IQA drugs, and there was no evidence of issues with the quality of laboratory results. The only difference in drug regimens between cohorts was in the choice of aminoglycoside antibiotic used (Kanamycin versus Amikacin), which should not cause differences in smear-conversion. Since the majority of smear-positive, culture-negative specimens were scanty positive, it is possible that non-viable bacteria may have been detected on these specimens. The presence of smear-positive, culture-negative results has been reported in other studies,12, but The challenge of diagnosing and managing increasing numbers of MDR TB patients is forcing NTPs in high burden countries to make difficult choices on resource allocation and program planning. With the wide-spread introduction of new diagnostics such as the Xpert MTB/RIF system, it is expected that there will be increasing numbers of newly diagnosed MDR-patients waiting to be put on treatment in high burden countries, and many countries are now reporting such “waiting lists”. A major decision that programs need to make is whether to wait to receive IQA drugs or to use lower cost drugs that are available locally. Until now evidence on whether there is a benefit to patients from being treated though IQA drugs has been lacking. This study is the first to directly compare response to treatment in IQA and non-IQA patient cohorts undergoing treatment at the same hospitals during the same time period, and to find no evidence of a faster time to culture conversion in patients treated using IQA drugs.It must be considered that this was a non-randomized study and that the distribution of unidentified confounding factors may have been different across cohorts. Apart from higher fluoroquinolone resistance in GLC patients, which was adjusted for when calculating adjusted hazards ratios, there was no major difference across cohorts in the baseline variables we studied. However, there may be other confounders on which we did not collect data. As our follow-up period ended 6 months from the start of treatment, we were able to compare culture conversion between cohorts but were not able to assess overall treatment success rates and recurrence rates. We believe that evidence from this first cohort study comparing IQA and non-IQA drugs warrants a study with a longer follow-up period and, based on the evidence generated, an assessment of the costs and benefits of using internationally procured drugs for MDR-TB treatment.An important factor to consider when interpreting our findings is that the quality of local drugs used in study hospitals was checked according to hospitals’ own guidelines. We recognize that ‘locally procured drugs’ encompasses a range of products which can include counterfeit and substandard drugs, and thaIn order to ensure that patients are receiving good quality drugs, NTPs and large hospitals may need to invest in drug quality assurance systems if drugs are being procured locally. The costs and workload involved in quality assurance would need to be compared with that of international procurement and local distribution. An advantage of local procurement and quality testing would be to encourage growth of the local pharmaceutical industry and to exert pressure on manufacturers to abide by quality standards. Furthermore, if there is sufficient evidence that good quality local drugs are no less effective than internationally procured drugs, it may be unethical to delay initiation of treatment until international drugs are available in country.