Using Similarity Metrics on Real World Data and Patient Treatment Pathways to Recommend the Next Treatment.

Non-small-cell lung cancer (NSCLC) is one of the most prevalent types of lung cancer and continues to have an ominous five year survival rate. Considerable work has been accomplished in analyzing the viability of the treatments offered to NSCLC patients; however, while many of these treatments have performed better over populations of diagnosed NSCLC patients, a specific treatment may not be the most effective therapy for a given patient. Coupling both patient similarity metrics using the Gower similarity metric and prior treatment knowledge, we were able to demonstrate how patient analytics can complement clinical efforts in recommending the next best treatment. Our retrospective and exploratory results indicate that a majority of patients are not recommended the best surviving therapy once they require a new therapy. This investigation lays the groundwork for treatment recommendation using analytics, but more investigation is required to analyze patient outcomes beyond survival.

Pancreatic islet-autonomous insulin and smoothened-mediated signalling modulate identity changes of glucagon+ α-cells.

The mechanisms that restrict regeneration and maintain cell identity following injury are poorly characterized in higher vertebrates. Following ß-cell loss, 1-2% of the glucagon-producing α-cells spontaneously engage in insulin production in mice. Here we explore the mechanisms inhibiting α-cell plasticity. We show that adaptive α-cell identity changes are constrained by intra-islet insulin- and Smoothened-mediated signalling, among others. The combination of ß-cell loss or insulin-signalling inhibition, with Smoothened inactivation in α- or δ-cells, stimulates insulin production in more α-cells. These findings suggest that the removal of constitutive 'brake signals' is crucial to neutralize the refractoriness to adaptive cell-fate changes. It appears that the maintenance of cell identity is an active process mediated by repressive signals, which are released by neighbouring cells and curb an intrinsic trend of differentiated cells to change.

Abemaciclib Is Active in Preclinical Models of Ewing Sarcoma via Multipronged Regulation of Cell Cycle, DNA Methylation, and Interferon Pathway Signaling.

PURPOSE: Ewing sarcoma (ES) is a rare and highly malignant cancer that occurs in the bone and surrounding tissue of children and adolescents. The EWS/ETS fusion transcription factor that drives ES pathobiology was previously demonstrated to modulate cyclin D1 expression. In this study, we evaluated abemaciclib, a small-molecule CDK4 and CDK6 (CDK4 and 6) inhibitor currently under clinical investigation in pediatric solid tumors, in preclinical models of ES. EXPERIMENTAL DESIGN: Using Western blot, high-content imaging, flow cytometry, ELISA, RNA sequencing, and CpG methylation assays, we characterized the in vitro response of ES cell lines to abemaciclib. We then evaluated abemaciclib in vivo in cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) mouse models of ES as either a monotherapy or in combination with chemotherapy. RESULTS: Abemaciclib induced quiescence in ES cell lines via a G1 cell-cycle block, characterized by decreased proliferation and reduction of Ki-67 and FOXM1 expression and retinoblastoma protein (RB) phosphorylation. In addition, abemaciclib reduced DNMT1 expression and promoted an inflammatory immune response as measured by cytokine secretion, antigen presentation, and interferon pathway upregulation. Single-agent abemaciclib reduced ES tumor volume in preclinical mouse models and, when given in combination with doxorubicin or temozolomide plus irinotecan, durable disease control was observed. CONCLUSIONS: Collectively, our data demonstrate that the antitumor effects of abemaciclib in preclinical ES models are multifaceted and include cell-cycle inhibition, DNA demethylation, and immunogenic changes.

Exaggerated CpH methylation in the autism-affected brain.

BACKGROUND: The etiology of autism, a complex, heritable, neurodevelopmental disorder, remains largely unexplained. Given the unexplained risk and recent evidence supporting a role for epigenetic mechanisms in the development of autism, we explored the role of CpG and CpH (H = A, C, or T) methylation within the autism-affected cortical brain tissue. METHODS: Reduced representation bisulfite sequencing (RRBS) was completed, and analysis was carried out in 63 post-mortem cortical brain samples (Brodmann area 19) from 29 autism-affected and 34 control individuals. Analyses to identify single sites that were differentially methylated and to identify any global methylation alterations at either CpG or CpH sites throughout the genome were carried out. RESULTS: We report that while no individual site or region of methylation was significantly associated with autism after multi-test correction, methylated CpH dinucleotides were markedly enriched in autism-affected brains (~2-fold enrichment at p < 0.05 cutoff, p = 0.002). CONCLUSIONS: These results further implicate epigenetic alterations in pathobiological mechanisms that underlie autism.

miRge - A Multiplexed Method of Processing Small RNA-Seq Data to Determine MicroRNA Entropy.

Small RNA RNA-seq for microRNAs (miRNAs) is a rapidly developing field where opportunities still exist to create better bioinformatics tools to process these large datasets and generate new, useful analyses. We built miRge to be a fast, smart small RNA-seq solution to process samples in a highly multiplexed fashion. miRge employs a Bayesian alignment approach, whereby reads are sequentially aligned against customized mature miRNA, hairpin miRNA, noncoding RNA and mRNA sequence libraries. miRNAs are summarized at the level of raw reads in addition to reads per million (RPM). Reads for all other RNA species (tRNA, rRNA, snoRNA, mRNA) are provided, which is useful for identifying potential contaminants and optimizing small RNA purification strategies. miRge was designed to optimally identify miRNA isomiRs and employs an entropy based statistical measurement to identify differential production of isomiRs. This allowed us to identify decreasing entropy in isomiRs as stem cells mature into retinal pigment epithelial cells. Conversely, we show that pancreatic tumor miRNAs have similar entropy to matched normal pancreatic tissues. In a head-to-head comparison with other miRNA analysis tools (miRExpress 2.0, sRNAbench, omiRAs, miRDeep2, Chimira, UEA small RNA Workbench), miRge was faster (4 to 32-fold) and was among the top-two methods in maximally aligning miRNAs reads per sample. Moreover, miRge has no inherent limits to its multiplexing. miRge was capable of simultaneously analyzing 100 small RNA-Seq samples in 52 minutes, providing an integrated analysis of miRNA expression across all samples. As miRge was designed for analysis of single as well as multiple samples, miRge is an ideal tool for high and low-throughput users. miRge is freely available at http://atlas.pathology.jhu.edu/baras/miRge.html.

Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism.

Recent studies of genomic variation associated with autism have suggested the existence of extreme heterogeneity. Large-scale transcriptomics should complement these results to identify core molecular pathways underlying autism. Here we report results from a large-scale RNA sequencing effort, utilizing region-matched autism and control brains to identify neuronal and microglial genes robustly dysregulated in autism cortical brain. Remarkably, we note that a gene expression module corresponding to M2-activation states in microglia is negatively correlated with a differentially expressed neuronal module, implicating dysregulated microglial responses in concert with altered neuronal activity-dependent genes in autism brains. These observations provide pathways and candidate genes that highlight the interplay between innate immunity and neuronal activity in the aetiology of autism.

RNA-Seq optimization with eQTL gold standards.

BACKGROUND: RNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking. RESULTS: To address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis. CONCLUSION: As each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one's data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments.

Defining the contribution of CNTNAP2 to autism susceptibility.

Multiple lines of genetic evidence suggest a role for CNTNAP2 in autism. To assess its population impact we studied 2148 common single nucleotide polymorphisms (SNPs) using transmission disequilibrium test (TDT) across the entire ~3.3 Mb CNTNAP2 locus in 186 (408 trios) multiplex and 323 simplex families with autistic spectrum disorder (ASD). This analysis yielded two SNPs with nominal statistical significance (rs17170073, p = 2.0 x 10(-4); rs2215798, p = 1.6 x 10(-4)) that did not survive multiple testing. In a combined analysis of all families, two highly correlated (r (2) = 0.99) SNPs in intron 14 showed significant association with autism (rs2710093, p = 9.0 x 10(-6); rs2253031, p = 2.5 x 10(-5)). To validate these findings and associations at SNPs from previous autism studies (rs7794745, rs2710102 and rs17236239) we genotyped 2051 additional families (572 multiplex and 1479 simplex). None of these variants were significantly associated with ASD after corrections for multiple testing. The analysis of Mendelian errors within each family did not indicate any segregating deletions. Nevertheless, a study of CNTNAP2 gene expression in brains of autistic patients and of normal controls, demonstrated altered expression in a subset of patients (p = 1.9 x10(-5)). Consequently, this study suggests that although CNTNAP2 dysregulation plays a role in some cases, its population contribution to autism susceptibility is limited.

Postmortem cardiac tissue maintains gene expression profile even after late harvesting.

BACKGROUND: Gene expression studies can be used to help identify disease-associated genes by comparing the levels of expressed transcripts between cases and controls, and to identify functional genetic variants (expression quantitative loci or eQTLs) by comparing expression levels between individuals with different genotypes. While many of these studies are performed in blood or lymphoblastoid cell lines due to tissue accessibility, the relevance of expression differences in tissues that are not the primary site of disease is unclear. Further, many eQTLs are tissue specific. Thus, there is a clear and compelling need to conduct gene expression studies in tissues that are specifically relevant to the disease of interest. One major technical concern about using autopsy-derived tissue is how representative it is of physiologic conditions, given the effect of postmortem interval on tissue degradation. RESULTS: In this study, we monitored the gene expression of 13 tissue samples harvested from a rapid autopsy heart (non-failed heart) and 7 from a cardiac explant (failed heart) through 24 hours of autolysis. The 24 hour autopsy simulation was designed to reflect a typical autopsy scenario where a body may begin cooling to ambient temperature for ~12 hours, before transportation and storage in a refrigerated room in a morgue. In addition, we also simulated a scenario wherein the body was left at room temperature for up to 24 hours before being found. A small fraction (< 2.5%) of genes showed fluctuations in expression over the 24 hr period and largely belong to immune and signal response and energy metabolism-related processes. Global expression analysis suggests that RNA expression is reproducible over 24 hours of autolysis with 95% genes showing < 1.2 fold change. Comparing the rapid autopsy to the failed heart identified 480 differentially expressed genes, including several types of collagens, lumican (LUM), natriuretic peptide A (NPPA) and connective tissue growth factor (CTGF), which allows for the clear separation between failing and non-failing heart based on gene expression profiles. CONCLUSIONS: Our results demonstrate that RNA from autopsy-derived tissue, even up to 24 hours of autolysis, can be used to identify biologically relevant expression pattern differences, thus serving as a practical source for gene expression experiments.

Pathway-based analysis using reduced gene subsets in genome-wide association studies.

BACKGROUND: Single Nucleotide Polymorphism (SNP) analysis only captures a small proportion of associated genetic variants in Genome-Wide Association Studies (GWAS) partly due to small marginal effects. Pathway level analysis incorporating prior biological information offers another way to analyze GWAS's of complex diseases, and promises to reveal the mechanisms leading to complex diseases. Biologically defined pathways are typically comprised of numerous genes. If only a subset of genes in the pathways is associated with disease then a joint analysis including all individual genes would result in a loss of power. To address this issue, we propose a pathway-based method that allows us to test for joint effects by using a pre-selected gene subset. In the proposed approach, each gene is considered as the basic unit, which reduces the number of genetic variants considered and hence reduces the degrees of freedom in the joint analysis. The proposed approach also can be used to investigate the joint effect of several genes in a candidate gene study. RESULTS: We applied this new method to a published GWAS of psoriasis and identified 6 biologically plausible pathways, after adjustment for multiple testing. The pathways identified in our analysis overlap with those reported in previous studies. Further, using simulations across a range of gene numbers and effect sizes, we demonstrate that the proposed approach enjoys higher power than several other approaches to detect associated pathways. CONCLUSIONS: The proposed method could increase the power to discover susceptibility pathways and to identify associated genes using GWAS. In our analysis of genome-wide psoriasis data, we have identified a number of relevant pathways for psoriasis.

Genetic susceptibility to schizophrenia: role of dopaminergic pathway gene polymorphisms.

AIM: We investigated 16 polymorphisms from three genes, dopamine receptor D2 (DRD2), catechol-O-methyl transferase (COMT) and brain derived neurotrophic factor (BDNF), which are involved in the dopaminergic pathways, and have been reported to be associated with susceptibility to schizophrenia and response to antipsychotic therapy. MATERIALS & METHODS: Single-locus association analyses of these polymorphisms were carried out in 254 patients with schizophrenia and 225 controls, all of southern Indian origin. Additionally, multifactor-dimensionality reduction analysis was performed in 422 samples (243 cases and 179 controls) to examine the gene-gene interactions and to identify combinations of multilocus genotypes associated with either high or low risk for the disease. RESULTS: Our results demonstrated initial significant associations of two SNPs for DRD2 (rs11608185, genotype: chi(2) = 6.29, p-value = 0.043; rs6275, genotype: chi(2) = 8.91, p-value = 0.011), and one SNP in the COMT gene (rs4680, genotype: chi(2) = 6.67, p-value = 0.035 and allele: chi(2) = 4.75, p-value = 0.029; odds ratio: 1.33, 95% confidence interval: 1.02-1.73), but not after correction for multiple comparisons indicating a weak association of individual markers of DRD2 and COMT with schizophrenia. Multifactor-dimensionality reduction analysis suggested a two locus model (rs6275/DRD2 and rs4680/COMT) as the best model for gene-gene interaction with 90% cross-validation consistency and 42.42% prediction error in predicting disease risk among schizophrenia patients. CONCLUSION: The present study thus emphasizes the need for multigene interaction studies in complex disorders such as schizophrenia and to understand response to drug treatment, which could lead to a targeted and more effective treatment.

Boolean network analysis of a neurotransmitter signaling pathway.

BACKGROUND: A Boolean network is a simple computational model that may provide insight into the overall behavior of genetic networks and is represented by variables with two possible states (on/off), of the individual nodes/genes of the network. In this study, a Boolean network model has been used to simulate a molecular pathway between two neurotransmitter receptor, dopamine and glutamate receptor, systems in order to understand the consequence of using logic gate rules between nodes, which have two possible states (active and inactive). RESULTS: The dynamical properties of this Boolean network model of the biochemical pathway shows that, the pathway is stable and that, deletion/knockout of certain biologically important nodes cause significant perturbation to this network. The analysis clearly shows that in addition to the expected components dopamine and dopamine receptor 2 (DRD2), Ca(2+) ions play a critical role in maintaining stability of the pathway. CONCLUSION: So this method may be useful for the identification of potential genetic targets, whose loss of function in biochemical pathways may be responsible for disease onset. The molecular pathway considered in this study has been implicated with a complex disorder like schizophrenia, which has a complex multifactorial etiology.

Pharmacogenomics: a path to predictive medicine for schizophrenia.

A significant variability is observed among patients in response to antipsychotics, and is caused by a variety of factors. This review summarizes the available knowledge of associations between pharmacogenetics and drug response in schizophrenia. The multifactorial etiology of schizophrenia makes it a complex interaction of symptoms. Adopting a pharmacogenomics approach represents a unique opportunity for the prediction of response to antipsychotic drugs by investigating genes implicated with specific symptoms and side effects. A network model of the interaction/crosstalk between the neurotransmitter signaling systems is presented to emphasize the importance of the genes associated with the molecular mechanisms of the disease and drug response. These genes may serve as potential susceptibility genes and drug targets for schizophrenia. The crucial point for the identification of a significant biologic marker(s) will include not only the experimental validation of the genes involved in the neurotransmitter signaling systems, but also the availability of large exactly comparable phenotyped patients samples. Coupling our knowledge of genetic polymorphisms with clinical response data promises a bright future for rapid advances in personalized medicine.

Association of DRD2 gene variant with schizophrenia.

Schizophrenia is a complex multifactorial disorder for which the pathobiology still remains elusive. Dysfunction of the dopamine D2 receptor signaling has been associated with the illness, but numerous studies provide confounding results. This study investigates the association of synonymous polymorphisms (His313 and Pro319) in the dopamine D2 receptor gene with schizophrenia using a case-control approach, with 101 cases and 145 controls. Our results demonstrated that genotype distribution for the His313 polymorphism was significantly different between schizophrenia patients and control subjects (p=0.0012), while the Pro319 polymorphism did not show any association with the disease. The results suggest that the synonymous SNP His313 in DRD2 may be associated with the illness. However, there is a need for further replication studies with larger sample sets.

beta2-Adrenergic receptor polymorphisms and asthma in the North Indian population.

INTRODUCTION: The beta(2)-adrenergic receptor (ADRB2) polymorphisms are known to be functionally relevant and disease modifying in subjects with asthma. However, the association of these polymorphisms with asthma remains to be established. Our objective is to investigate the association of the ADRB2 polymorphisms and haplotypes with asthma in North Indian subjects. METHODS: A subset of 101 unrelated cases and 55 unrelated unaffected individuals were used for a case-control disease-association test. RESULTS: Ten variable single nucleotide polymorphism (SNP) sites within a span of 2.193 kb were identified in the ADRB2 gene by the sequencing and genotyping of 351 bronchial asthma patients and healthy individuals. The distributions of genotype and allele frequencies for individual SNPs in the ADRB2 gene and ADRB2 haplotype frequencies were estimated in unrelated asthmatics and healthy individuals. No significant association was observed between ADRB2 genotypes and alleles with disease status after Bonferroni correction for multiple testing (reference p value = 0.0083). However, haplotype GGCTTTGCAA was found to be significantly associated with asthma (p = 0.021) in the studied population. CONCLUSION: Our results suggest that there is likely to be a functional significance of the ADRB2 gene with asthma.

Beta(2)-adrenergic receptor polymorphisms and response to salbutamol among Indian asthmatics*.

INTRODUCTION: The beta(2)-adrenergic receptor (beta(2)AR or ADRbeta(2)) is the target for beta(2)-agonist drugs used for bronchodilation in asthma and other respiratory diseases. The aim of this study was to identify common single nucleotide polymorphisms (SNPs) and haplotypes in asthmatics and healthy individuals from an Indian population, and determine the influence of beta(2)AR SNPs in responsiveness to beta(2)-agonist therapy in asthma patients. METHODS: Ten variable SNP sites within a span of 2.193 kb were identified in the beta(2)AR gene by sequencing and genotyping 374 bronchial asthma patients and healthy individuals from an Indian population. Spirometry tests were performed on 80 unrelated patients before and after administration of 200 microg of salbutamol. A post-bronchodilator forced expiratory volume in one second (FEV(1)) change of >or= 15.3% was considered a good response, and a change of<15.3% was defined as a poor response, to salbutamol. RESULTS: The pattern of linkage disequilibrium between the ten SNPs showed a single, linked SNP block consisting of sites -468, -367, -47, -20, and 79 having strong linkage disequilibrium, while the SNPs at sites -1023, -654, 46, 252, and 523 showed very low linkage with one another and with the linked region. The SNPs were found to be organized into 16 haplotypes in the studied population. We found that patients with a homozygous Arg-16 form at nucleotide position 46 are poor responders with probability of 0.81, and patients with a homozygous Gly-16 form are good responders with a probability of 0.73. The responder status to salbutamol treatment and the genotype at nucleotide position 46 in beta(2)AR gene of an asthmatic patient are significantly associated in the studied Indian population (chi2=9.98, df=2, p=0.0068). Most importantly, this association for responsiveness to salbutamol at nucleotide position 46 is independent of other SNPs in the beta(2)AR gene. CONCLUSION: This study suggests that the SNP at nucleotide position 46 has particular relevance to pharmacogenetics in the Indian population studied.