Genomic and molecular characterization of preterm birth.

Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology

Detection of Somatic Mutations by High-Resolution DNA Melting (HRM) Analysis in Multiple Cancers

Identification of somatic mutations in cancer is a major goal for understanding and monitoring the events related to cancer initiation and progression. High resolution melting (HRM) curve analysis represents a fast, post-PCR high-throughput method for scanning somatic sequence alterations in target genes. The aim of this study was to assess the sensitivity and specificity of HRM analysis for tumor mutation screening in a range of tumor samples, which included 216 frozen pediatric small rounded blue-cell tumors as well as 180 paraffin-embedded tumors from breast, endometrial and ovarian cancers (60 of each). HRM analysis was performed in exons of the following candidate genes known to harbor established commonly observed mutations: PIK3CA, ERBB2, KRAS, TP53, EGFR, BRAF, GATA3, and FGFR3. Bi-directional sequencing analysis was used to determine the accuracy of the HRM analysis. For the 39 mutations observed in frozen samples, the sensitivity and specificity of HRM analysis were 97% and 87%, respectively. There were 67 mutation/variants in the paraffin-embedded samples, and the sensitivity and specificity for the HRM analysis were 88% and 80%, respectively. Paraffin-embedded samples require higher quantity of purified DNA for high performance. In summary, HRM analysis is a promising moderate-throughput screening test for mutations among known candidate genomic regions. Although the overall accuracy appears to be better in frozen specimens, somatic alterations were detected in DNA extracted from paraffin-embedded samples

Adult cognitive outcomes in phenylketonuria:explaining causes of variability beyond average Phe levels

OBJECTIVE: The objective was to deepen the understanding of the causes of individual variability in phenylketonuria (PKU) by investigating which metabolic variables are most important for predicting cognitive outcomes (Phe average vs Phe variation) and by assessing the risk of cognitive impairment associated with adopting a more relaxed approach to the diet than is currently recommended. METHOD: We analysed associations between metabolic and cognitive measures in a mixed sample of English and Italian early-treated adults with PKU (N = 56). Metabolic measures were collected through childhood, adolescence and adulthood; cognitive measures were collected in adulthood. Metabolic measures included average Phe levels (average of median values for each year in a given period) and average Phe variations (average yearly standard deviations). Cognition was measured with IQ and a battery of cognitive tasks. RESULTS: Phe variation was as important, if not more important, than Phe average in predicting adult outcomes and contributed independently. Phe variation was particularly detrimental in childhood. Together, childhood Phe variation and adult Phe average predicted around 40% of the variation in cognitive scores. Poor cognitive scores (> 1 SD from controls) occurred almost exclusively in individuals with poor metabolic control and the risk of poor scores was about 30% higher in individuals with Phe values exceeding recommended thresholds. CONCLUSIONS: Our results provide support for current European guidelines (average Phe value = < 360 μmol/l in childhood; = < 600 μmo/l from 12 years onwards), but they suggest an additional recommendation to maintain stable levels (possibly Phe SD = < 180 μmol/l throughout life). PUBLIC SIGNIFICANCE STATEMENTS: We investigated the relationship between how well people with phenylketonuria control blood Phe throughout their life and their ability to carry out cognitive tasks in adulthood. We found that avoiding blood Phe peaks was as important if not more important that maintaining average low Phe levels. This was particularly essential in childhood. We also found that blood Phe levels above recommended European guidelines was associated with around 30% increase in the risk of poor cognitive outcomes

International network of cancer genome projects

The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumors from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of over 25,000 cancer genomes at the genomic, epigenomic, and transcriptomic levels will reveal the repertoire of oncogenic mutations, uncover traces of the mutagenic influences, define clinically-relevant subtypes for prognosis and therapeutic management, and enable the development of new cancer therapies

CloudForest: A Scalable and Efficient Random Forest Implementation for Biological Data

<p>Comparison between CloudForest and other RF implementations in terms of prediction performance (<b>a</b>) and training time (<b>b</b>). The RFs consisted of 500 trees and were trained using the same standard parameter settings for all implementations.</p

CloudForest: A Scalable and Efficient Random Forest Implementation for Biological Data

<p>Comparison between CloudForest and scikit-learn in terms of prediction performance (<b>a</b>) and training time (<b>b</b>) for a TCGA dataset with varying numbers of missing values (x-axis). For scikit-learn missing values are imputed before RF analysis, whereas CloudForest natively handles missing values without imputation. The time necessary for imputation for scikit-learn is not included in the training times depicted.</p

Germline Variation in Cancer-Susceptibility Genes in a Healthy, Ancestrally Diverse Cohort: Implications for Individual Genome Sequencing

<div><p>Technological advances coupled with decreasing costs are bringing whole genome and whole exome sequencing closer to routine clinical use. One of the hurdles to clinical implementation is the high number of variants of unknown significance. For cancer-susceptibility genes, the difficulty in interpreting the clinical relevance of the genomic variants is compounded by the fact that most of what is known about these variants comes from the study of highly selected populations, such as cancer patients or individuals with a family history of cancer. The genetic variation in known cancer-susceptibility genes in the general population has not been well characterized to date. To address this gap, we profiled the nonsynonymous genomic variation in 158 genes causally implicated in carcinogenesis using high-quality whole genome sequences from an ancestrally diverse cohort of 681 healthy individuals. We found that all individuals carry multiple variants that may impact cancer susceptibility, with an average of 68 variants per individual. Of the 2,688 allelic variants identified within the cohort, most are very rare, with 75% found in only 1 or 2 individuals in our population. Allele frequencies vary between ancestral groups, and there are 21 variants for which the minor allele in one population is the major allele in another. Detailed analysis of a selected subset of 5 clinically important cancer genes, <i>BRCA1</i>, <i>BRCA2</i>, <i>KRAS</i>, <i>TP53</i>, and <i>PTEN</i>, highlights differences between germline variants and reported somatic mutations. The dataset can serve a resource of genetic variation in cancer-susceptibility genes in 6 ancestry groups, an important foundation for the interpretation of cancer risk from personal genome sequences.</p></div