scholarly journals 4565 Sex-Specific Differences in the Genomic Landscape of Pediatric and Adult Glioblastoma

2020 ◽  
Vol 4 (s1) ◽  
pp. 112-113
Author(s):  
Nikolay A. Ivanov ◽  
Nadia Dahmane ◽  
Jeffrey P. Greenfield ◽  
Christopher E. Mason
Keyword(s):  
Pediatric Population ◽  
Cpg Islands ◽  
Multiple Linear Regression Model ◽  
High Grade Glioma ◽  
Genomic Landscape ◽  
Surrogate Variables ◽  
Rnaseq Data ◽  
Study Population ◽  
Differential Gene ◽  
Sex Disparity

OBJECTIVES/GOALS: It has been previously shown that pediatric high-grade glioma (pHGG) survival is different between sexes. We set out to find out whether there are sex-specific differences in the genomic landscapes of pHGG that may underlie this sex disparity. METHODS/STUDY POPULATION: We downloaded Illumina 450k DNAm data from ArrayExpress and GeneExpressionOmnibus. The minfi package was used to process raw DNAm data. Sex chromosomes and CpGs that are common SNPs were removed. Surrogate variables (SVs) were estimated via the sva Bioconductor package. Differentially methylated CpGs were identified by fitting a multiple linear regression model for the DNAm level at each CpG, with independent variables being sex (a binary variable) and the estimated SVs. RNAseq data was downloaded from Cavatica, and differential gene expression analysis was carried out via the DESeq2 package. RESULTS/ANTICIPATED RESULTS: In the pediatric glioblastoma (GBM) DNAm data [58 female & 91 male IDH wt samples; ages 0.1–21 yrs;], we found 7,371 differentially methylated cytosines (DMCs) at FDR≤0.05. Of the DMCs, 289 had DNAm differences between male and female samples ≥10%. The majority of probes (68%) were in CpG islands, shelves, or shores. We also found 4 differentially methylated regions (DMRs) between sexes (FWER≤0.1). In the adult GBM DNAm samples [32 F & 32 M IDH wt samples; ages 22–75 yrs], we found only 117 DMCs at FDR≤0.05, and no DMRs. In the RNAseq dataset [68 F & 54 M pHGG samples, ages 0.08–30.6 yrs], we found 383 differentially expressed genes (at FDR≤0.05), and 16 of them (4%) overlapped a DMC. DISCUSSION/SIGNIFICANCE OF IMPACT: Our findings demonstrate that pHGG exhibits sex-specific methylome differences. Interestingly, this difference is greater in the pediatric population as compared to adults. The pHGG transcriptome also differs by sex, which may be related to differential DNAm in a minority of cases.

scholarly journals DPAC: a tool for Differential Poly(A) Site usage from poly(A)–targeted RNAseq data

2019 ◽  
Author(s):  
Andrew Routh
Keyword(s):  
Gene Expression ◽  
Alternative Polyadenylation ◽  
Data Mapping ◽  
Library Synthesis ◽  
Sequencing Data ◽  
Synthesis Strategy ◽  
Rnaseq Data ◽  
Differential Gene ◽  
A Site

AbstractPoly(A)-tail targeted RNAseq approaches, such as 3’READS, PAS-seq and Poly(A)-ClickSeq, are becoming popular alternatives to random-primed RNAseq for simplified gene expression analyses as well as to measure changes in poly(A) site usage. We and others have recently demonstrated that these approaches perform similarly to other RNAseq strategies, while saving on the volume of sequencing data required and providing a simpler library synthesis strategy. Here, we present DPAC; a streamlined pipeline for the preprocessing of poly(A)-tail targeted RNAseq data, mapping of poly(A)-sites and poly(A) clustering, and determination of differential poly(A) site usage using DESeq2. Changes in poly(A) site usage is simultaneously used to report differential gene expression, differential terminal exon usage and alternative polyadenylation (APA).

scholarly journals 4381 Examination of FDA Pediatric Regulations: Inclusion of Pediatric Populations in Clinical Trials, 2016 - 2018

2020 ◽  
Vol 4 (s1) ◽  
pp. 131-131
Author(s):  
Annie Ly ◽  
Apurva Uniyal ◽  
Terry Church
Keyword(s):  
Clinical Trials ◽  
Pharmaceutical Industry ◽  
Drug Administration ◽  
Pediatric Population ◽  
Eligibility Criteria ◽  
Data Presentation ◽  
Study Population ◽  
Pediatric Populations ◽  
Approved Drugs ◽  
The U.S

OBJECTIVES/GOALS: To assess whether FDA regulations aimed at the pediatric population following the Best Pharmaceuticals for Children Act (BPCA) of 2002 are effective, this study examines the inclusion of the pediatric population in recent clinical trials for drugs used by both adult and pediatric groups. METHODS/STUDY POPULATION: From the U.S. Food and Drug Administration (FDA) a list of drugs approved between 2016 and 2018 was compiled. A search of clinicaltrials.gov provided corresponding clinical trials for the approved drugs. Study information such as eligibility criteria and demographics was gathered from each trial. From studies that included both adult and pediatric populations, the percentage of pediatric and adult subjects was calculated, resulting in values expressing exclusively pediatric subjects or the pediatric subjects as part of a category that included both populations (i.e. 18 years old). RESULTS/ANTICIPATED RESULTS: Between 2016 and 2018, 26 drugs were approved under the BPCA. From an assessment of 220 total studies, a lack of standardization is evident in terms of which ages constitute a particular pediatric sub-population even though guidelines for these sub-populations already exist under the BPCA. This lack of standardization resulted in the separate examination of each drug for pediatric inclusion. For the majority of the trials evaluated, 1% of the pediatric population was represented in trials that were open to both adult and pediatric populations. DISCUSSION/SIGNIFICANCE OF IMPACT: There is a need for more effective regulations and incentives for the pharmaceutical industry to standardize data presentation and better incorporate the pediatric population in clinical trials, especially for drugs targeted for this group.

Abstract IA14: The unique genomic landscape of pediatric high-grade glioma

2015 ◽  
Author(s):  
Jon D. Larson ◽  
Andre B. Silveira ◽  
Lawryn H. Kasper ◽  
Helen R. Russell ◽  
Chunxu Qu ◽  
...  
Keyword(s):  
High Grade Glioma ◽  
High Grade ◽  
Genomic Landscape ◽  
Pediatric High Grade Glioma

P1443Accuracy of three algorithms in predicting accessory pathway localization in a pediatric population with ventricular pre-excitation

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
P Ferrari ◽  
G Malanchini ◽  
C Leidi ◽  
S Gulletta ◽  
P De Filippo
Keyword(s):  
Pediatric Population ◽  
Accessory Pathway ◽  
True Positive ◽  
Retrospective Clinical Study ◽  
Study Population ◽  
Excitation Pattern ◽  
The One ◽  
Total Accuracy ◽  
Test Probability ◽  
Mapping Systems

Abstract BACKGROUND Prediction of accessory pathway location is extremely important before scheduling ablation, even more in paediatric patients. Given the absence in the literature of conclusive and recent studies that established the best algorithm to predict location in these patients, especially after the introduction of 3D mapping systems, we designed a study to compare three previously published algorithms. AIM The aim of this study is to assess and compare the accuracy in predicting accessory pathway location of the algorithm by Arruda, Boersma and Chiang. METHODS This is a multicenter, observational, retrospective clinical study based on the analysis of the resting 12-leads ECGs of children (aged 0-18 years) with ventricular pre-excitation pattern. The study lasts from January 2013 to June 2019. We analyzed the accuracy (defined as percentage of true positive values) of predicted location by each algorithm, which could include one or more than one of 13 regions around mitral and tricuspid annuli. RESULTS Study population was composed by 120 patients, mean age was 12.7 +/- 3.6 years (height 155.6 +/-19.3 cm and weight 48.3 +/- 17.1 kg). The algorithm by Boersma has the highest percentage of predicted pathway locations that are found to be concordant with the site of successful AP ablation (see table). When we corrected for pre-test probability, the algorithm by Arruda makes the possibility of one specific location 8 times higher, the one by Boersma 6.4 times higher and the one by Chiang 6.9 times higher than by chance.  CONCLUSIONS The algorithm by Boersma showed the highest accuracy in accessory pathway location, on the other hand the one by Arruda seems to have the highest corrected accuracy in accessory pathway among children. table 1 Arruda Boersma Chiang Number of locations 13 9 13 Total accuracy 0,617 0,717 0,533 Corrected accuracy per locations 8,0158 6,4494 6,929 Abstract Figure. picture 1

scholarly journals 2356

2017 ◽  
Vol 1 (S1) ◽  
pp. 16-16
Author(s):  
Adam C. Gower ◽  
Avrum Spira ◽  
Marc E. Lenburg
Keyword(s):  
Gene Expression ◽  
Drug Repositioning ◽  
Gene Expression Omnibus ◽  
Disease States ◽  
Data Profiling ◽  
Pairwise Correlations ◽  
Target Disease ◽  
Drug Treatments ◽  
Study Population ◽  
Differential Gene

OBJECTIVES/SPECIFIC AIMS: Microarray technology has produced large volumes of gene expression data profiling differences in gene expression in a vast array of conditions, much of which is publicly available. Methods to query these data for similarities in patterns of gene regulation are limited to comparisons between preannotated groups. In response, we developed openSESAME to find experiments where a set of genes is similarly coregulated without regard to experimental design. An important application of openSESAME is drug repositioning: if a pattern associated with disease is reversed by a given drug, the drug might target disease-related processes. METHODS/STUDY POPULATION: Experiments from the Gene Expression Omnibus (GEO) were normalized, signature-association (SA) scores computed for each sample, experiments assigned enrichment scores, and ANOVAs used to assign significance to experimental variables automatically extracted from GEO. SA scores were also generated for hundreds of publicly available signatures, and pairwise correlations used to create a relevance network. RESULTS/ANTICIPATED RESULTS: Using signatures of estrogen and p63, we recovered relevant experimental variables, and with the network approach, we recovered previously reported associations between disease states and/or drug treatments. DISCUSSION/SIGNIFICANCE OF IMPACT: openSESAME has the potential to illuminate “dark data” and discover novel relationships between drugs and diseases on the basis of common patterns of differential gene expression.

scholarly journals Gender and sex disparity in cancer trials

ESMO Open ◽  
2020 ◽  
Vol 5 (Suppl 4) ◽  
pp. e000773 ◽  
Author(s):  
Eudocia Lee ◽  
Patrick Wen
Keyword(s):  
Clinical Trials ◽  
Elderly Women ◽  
Phase Iii ◽  
Cancer Clinical Trials ◽  
Phase Iii Clinical Trials ◽  
The Usa ◽  
Study Population ◽  
Cancer Trials ◽  
Early Phase Clinical Trials ◽  
Sex Disparity

The study population within phase III clinical trials leading to approval of new cancer agents should ideally more closely mirror the population who will ultimately receive these agents. Although the number of females participating in clinical trials has increased over the past several decades, females are still under-represented in preclinical studies, in early phase clinical trials and even in some later phase cancer clinical trials. In the USA, this is particularly true for women from minority populations and elderly women. In this review, we review gender and sex disparities in cancer trials, the reasons for these disparities, the barriers to clinical trial enrolment and ways to improve diversity in cancer clinical trials.

scholarly journals Active surveillance of adverse events following immunization (AEFI): a prospective 3-year vaccine safety study

2019 ◽  
Vol 7 ◽  
pp. 251513551988900 ◽  
Author(s):  
Juny Sebastian ◽  
Parthasarathi Gurumurthy ◽  
Mandyam Dhati Ravi ◽  
Madhan Ramesh
Keyword(s):  
Adverse Events ◽  
Pediatric Population ◽  
Local Population ◽  
Vaccine Safety ◽  
World Health ◽  
Vaccination Programs ◽  
National Immunization ◽  
Study Population ◽  
Health Organization ◽  
Study Participants

Background: Vaccines used in national immunization programs are considered safe and effective but immunization safety has become as important as the efficacy of vaccination programs. The objective of the study was to detect adverse events following immunization (AEFIs) to all vaccines administered to a pediatric population in India. Methods: The prospective active vaccine safety surveillance study enrolled eligible children in the age group 0–5 years receiving vaccination from the immunization center at JSS Hospital, Mysuru. Study participants were monitored at the site for 30 min following vaccination and a telephonic survey was made after 8 days to identify all AEFIs. Causality assessment of the AEFIs were done using a new algorithm developed by the safety and vigilance department of the World Health Organization. Results: The incidence of reported AEFIs was 13.7%. The most frequently reported AEFI was fever ( n = 3095, 93.2%) with an incidence of 109.7 per 1000 doses of vaccine administered, followed by persistent crying ( n = 69, 2.4 per 1000 doses of vaccine) and diarrhea ( n = 57, 2.0 per 1000 doses of vaccine). The majorly implicated vaccine for AEFIs was Pentavac® followed by BCG. Consistent causal association to immunization was observed in 93.4% of cases. Conclusions: A high incidence rate of AEFI was observed in our study population when compared with previous published studies. AEFI surveillance studies help to detect changes in the frequency of adverse events, which may be an alert to consider vaccine quality or identify a specific risk among the local population.

Profile of Prescription Medication in a Pediatric Population

1988 ◽  
Vol 22 (12) ◽  
pp. 999-1002 ◽  
Author(s):  
Joseph O. Olubadewo ◽  
Ann Ikponmwamba
Keyword(s):  
Pediatric Population ◽  
Age Groups ◽  
Prescription Medication ◽  
Respiratory Disorder ◽  
Drug Usage ◽  
American Hospital ◽  
Adolescent Group ◽  
Younger Age ◽  
Age Dependent ◽  
Study Population

This retrospective study describes the prescription medication profile in an outpatient pediatric population (n = 510) retrieved from a hospital pharmacy computer file. The survey covers a three-month period. The study population included 281 male and 229 female patients divided according to age into three groups: infant (age 0–12 months); children (age 1–12 years); and adolescents (age 13–18 years). Medications prescribed were classified according to their pharmacotherapeutic properties as described in the American Hospital Formulary Service Drug Information 87. The findings pointed out that three pharmacotherapeutic categories (the antiinfective/chemotherapeutic, central nervous system (CNS), and respiratory agents) constituted 78.1 percent of the 1402 prescribed medications. The most frequently prescribed agents in each of these categories were, respectively, amoxicillin, aminophylline, and acetaminophen. These agents represent recent advances in drug usage because they became most frequently used only within the past ten years. The age-dependent medication profile indicated that there was a higher prescription rate of antiinfectives and respiratory disorder agents in the younger age groups; in the adolescent group CNS agents were more often prescribed.

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