A Path to Implement Precision Child Health Cardiovascular Medicine.

Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene-environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine

Metabolomics of the Tumor Microenvironment in Pediatric Acute Lymphoblastic Leukemia

Stefano Tiziani, Yunyi Kang, Ricky Harjanto, Joshua Axelrod, Giovanni Paternostro, Sanford-Burnham Medical Research Institute, La Jolla, California, United States of AmericaCarlo Piermarocchi, Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of AmericaWilliam Roberts, Rady Children’s Hospital, Department of Pediatrics, University of California San Diego, San Diego, California, United States of AmericaStefano Tiziani, Department of Nutritional Sciences, Dell Pediatric Research Institute, University of Texas at Austin, Austin, Texas, United States of AmericaThe tumor microenvironment is emerging as an important therapeutic target. Most studies, however, are focused on the protein components, and relatively little is known of how the microenvironmental metabolome might influence tumor survival. In this study, we examined the metabolic profiles of paired bone marrow (BM) and peripheral blood (PB) samples from 10 children with acute lymphoblastic leukemia (ALL). BM and PB samples from the same patient were collected at the time of diagnosis and after 29 days of induction therapy, at which point all patients were in remission. We employed two analytical platforms, high-resolution magnetic resonance spectroscopy and gas chromatography-mass spectrometry, to identify and quantify 102 metabolites in the BM and PB. Standard ALL therapy, which includes l-asparaginase, completely removed circulating asparagine, but not glutamine. Statistical analyses of metabolite correlations and network reconstructions showed that the untreated BM microenvironment was characterized by a significant network-level signature: a cluster of highly correlated lipids and metabolites involved in lipid metabolism (p less than 0.006). In contrast, the strongest correlations in the BM upon remission were observed among amino acid metabolites and derivatives (p less than 9.2×10-10). This study provides evidence that metabolic characterization of the cancer niche could generate new hypotheses for the development of cancer therapies.This work was funded by the National Science Foundation (Grant No. 0829891). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Nutritional SciencesDell Pediatric Research InstituteEmail: [email protected] (GP), Email: [email protected] (ST

The biological embedding of early-life socioeconomic status and family adversity in children's genome-wide DNA methylation.

AimTo examine variation in child DNA methylation to assess its potential as a pathway for effects of childhood social adversity on health across the life course.Materials & methodsIn a diverse, prospective community sample of 178 kindergarten children, associations between three types of social experience and DNA methylation within buccal epithelial cells later in childhood were examined.ResultsFamily income, parental education and family psychosocial adversity each associated with increased or decreased DNA methylation (488, 354 and 102 sites, respectively) within a unique set of genomic CpG sites. Gene ontology analyses pointed to genes serving immune and developmental regulation functions.ConclusionFindings provided support for DNA methylation as a biomarker linking early-life social experiences with later life health in humans

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics