Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design.

Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer

Morbidity, Feeding Practices, and Immunization Status of Children 6 - 23 Months in Delhi

Optimal infant and young child feeding (IYCF) practices and immunization play a critical role in averting childhood illness and in achieving optimal growth and development among children. This was a cross-sectional study that assessed the morbidity and immunization status of children 6–23 months of age in three income groups in Delhi. The study also assessed feeding practices for children during illness. Results showed that the prevalence of diarrhea in the past 2 weeks preceding the survey was 8.6%, 18.3%, and 17.7% in urban slum, low-income group (LIG), and middle-income group (MIG), respectively. About 50% of children in LIG and MIG and about 45% in the urban slum had fever in the past 2 weeks preceding the survey. About 20% of mothers in urban slum and LIG reported that they were not washing their hands with soap before preparing food for their children. Although most of the children with diarrhea had received oral rehydration salts, they had not received zinc which is critical in the treatment of diarrhea. About 24% of mothers in the urban slum discontinued complementary feeding during illness. The immunization was complete for most of the children in all groups except for the 2nd dose of measles vaccine and booster dose of OPV and DPT vaccine whose coverage was found to be low in urban slums. There is an urgent need of counseling and support on IYCF during and after common childhood illnesses by the frontline health functionaries to reduce the high burden of undernutrition among children under 2 years

A review of selected nutrition & health surveys in India

Assessment of the status of health and nutrition of a population is imperative to design and implement sound public health policies and programmes. The various extensive national health and nutrition surveys provide national-level information on different domains of health. These provide vital information and statistics for the country, and the data generated are used to identify the prevalence and risk factors for the diseases and health challenges faced by a country. This review describes the various national health and nutrition surveys conducted in India and also compares the information generated by each of these surveys. These include the National Family Health Survey, District Level Household Survey, Annual Health Survey, National Nutrition Monitoring Bureau Survey, Rapid Survey on Children and Comprehensive National Nutrition Survey

NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab

PRIMA-1MET-induced neuroblastoma cell death is modulated by p53 and mycn through glutathione level

Abstract Background Neuroblastoma is the most common extracranial solid tumor in children. This cancer has a low frequency of TP53 mutations and its downstream pathway is usually intact. This study assessed the efficacy of the p53 activator, PRIMA-1MET, in inducing neuroblastoma cell death. Methods CellTiter 2.0 was used to study susceptibility and specificity of NB cell lines to PRIMA-1MET. Real-time PCR and western blot were used to assess the most common p53 transactivation targets. Induction of p53 and Noxa, and inhibition of Cas3/7, were used to assess impact on cell death after PRIMA-1MET treatment. Flow cytometry was used to analyze cell cycle phase and induction of apoptosis, reactive oxygen species, and the collapse of mitochondrial membrane potential. Results Neuroblastoma cell lines were at least four times more susceptible to PRIMA-1MET than were primary fibroblasts and keratinocyte cell lines. PRIMA-1MET induced cell death rapidly and in all cell cycle phases. Although PRIMA-1MET activated p53 transactivation activity, p53’s role is likely limited because its main targets remained unaffected, whereas pan-caspase inhibitor demonstrated no ability to prevent cell death. PRIMA-1MET induced oxidative stress and modulated the methionine/cysteine/glutathione axis. Variations of MYCN and p53 modulated intracellular levels of GSH and resulted in increased/decreased sensitivity of PRIMA-1MET. PRIMA-1MET inhibited thioredoxin reductase, but the effect of PRIMA-1MET was not altered by thioredoxin inhibition. Conclusions PRIMA-1MET could be a promising new agent to treat neuroblastoma because it demonstrated good anti-tumor action. Although p53 is involved in PRIMA-1MET-mediated cell death, our results suggest that direct interaction with p53 has a limited role in neuroblastoma but rather acts through modulation of GSH levels

Long noncoding RNAs are spatially correlated with transcription factors and regulate lung development

Long noncoding RNAs (lncRNAs) are thought to play important roles in regulating gene transcription, but few have well-defined expression patterns or known biological functions during mammalian development. Using a conservative pipeline to identify lncRNAs that have important biological functions, we identified 363 lncRNAs in the lung and foregut endoderm. Importantly, we show that these lncRNAs are spatially correlated with transcription factors across the genome. In-depth expression analyses of lncRNAs with genomic loci adjacent to the critical transcription factors Nkx2.1, Gata6, Foxa2 (forkhead box a2), and Foxf1 mimic the expression patterns of their protein-coding neighbor. Loss-of-function analysis demonstrates that two lncRNAs, LL18/NANCI (Nkx2.1-associated noncoding intergenic RNA) and LL34, play distinct roles in endoderm development by controlling expression of critical developmental transcription factors and pathways, including retinoic acid signaling. In particular, we show that LL18/NANCI acts upstream of Nkx2.1 and downstream from Wnt signaling to regulate lung endoderm gene expression. These studies reveal that lncRNAs play an important role in foregut and lung endoderm development by regulating multiple aspects of gene transcription, often through regulation of transcription factor expression

A transcriptome-based classifier to determine molecular subtypes in medulloblastoma.

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53-mutant, Sonic Hedgehog TP53-wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities

A comparison of survival analysis methods for cancer gene expression RNA-Sequencing data

Identifying genetic biomarkers of patient survival remains a major goal of large-scale cancer profiling studies. Using gene expression data to predict the outcome of a patient's tumor makes biomarker discovery a compelling tool for improving patient care. As genomic technologies expand, multiple data types may serve as informative biomarkers, and bioinformatic strategies have evolved around these different applications. For categorical variables such as a gene's mutation status, biomarker identification to predict survival time is straightforward. However, for continuous variables like gene expression, the available methods generate highly-variable results, and studies on best practices are lacking. We investigated the performance of eight methods that deal specifically with continuous data. K-means, Cox regression, concordance index, D-index, 25th–75th percentile split, median-split, distribution-based splitting, and KaplanScan were applied to four RNA-sequencing (RNA-seq) datasets from the Cancer Genome Atlas. The reliability of the eight methods was assessed by splitting each dataset into two groups and comparing the overlap of the results. Gene sets that had been identified from the literature for a specific tumor type served as positive controls to assess the accuracy of each biomarker using receiver operating characteristic (ROC) curves. Artificial RNA-Seq data were generated to test the robustness of these methods under fixed levels of gene expression noise. Our results show that methods based on dichotomizing tend to have consistently poor performance while C-index, D-index, and k-means perform well in most settings. Overall, the Cox regression method had the strongest performance based on tests of accuracy, reliability, and robustness