Association between preoperative frailty and myocardial injury after noncardiac surgery in geriatric patients: study protocol for a prospective, multicentre, real-world observational, cohort trial.

Introduction: Frailty has become a worldwide health burden that has a large influence on public health and clinical practice. The incidence of frailty is anticipated to increase as the ageing population increases. Myocardial injury after noncardiac surgery (MINS) is associated with short-term and long-term mortality.

A randomized phase 2 study of sapanisertib in combination with paclitaxel versus paclitaxel alone in women with advanced, recurrent, or persistent endometrial cancer.

Objective: This phase 2 study investigated sapanisertib (selective dual inhibitor of mTORC1/2) alone, or in combination with paclitaxel or TAK-117 (a selective small molecule inhibitor of PI3K), versus paclitaxel alone in advanced, recurrent, or persistent endometrial cancer.

Methods: Patients with histologic diagnosis of endometrial cancer (1-2 prior regimens) were randomized to 28-day cycles on four treatment arms: 1) weekly paclitaxel 80 mg/m (days 1, 8, and 15); 2) weekly paclitaxel 80 mg/m + oral sapanisertib 4 mg on days 2-4, 9-11, 16-18, and 23-25; 3) weekly sapanisertib 30 mg, or 4) sapanisertib 4 mg + TAK-117 200 mg on days 1-3, 8-10, 15-17, and 22-24.

Results: Of 241 patients randomized, 234 received treatment (paclitaxel, n = 87 [3 ongoing]; paclitaxel+sapanisertib, n = 86 [3 ongoing]; sapanisertib, n = 41; sapanisertib+TAK-117, n = 20).

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing.

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS.

Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers.

Prognostic relevance and performance characteristics of serum IGFBP-2 and PAPP-A in women with breast cancer: a long-term Danish cohort study.

Measurement of circulating insulin-like growth factors (IGFs), in particular IGF-binding protein (IGFBP)-2, at the time of diagnosis, is independently prognostic in many cancers, but its clinical performance against other routinely determined prognosticators has not been examined. We measured IGF-I, IGF-II, pro-IGF-II, IGF bioactivity, IGFBP-2, -3, and pregnancy-associated plasma protein A (PAPP-A), an IGFBP regulator, in baseline samples of 301 women with breast cancer treated on four protocols (Odense, Denmark: 1993-1998). We evaluated performance characteristics (expressed as area under the curve, AUC) using Cox regression models to derive hazard ratios (HR) with 95% confidence intervals (CIs) for 10-year recurrence-free survival (RFS) and overall survival (OS), and compared those against the clinically used Nottingham Prognostic Index (NPI).

Comparing genetic variants detected in the 1000 genomes project with SNPs determined by the International HapMap Consortium.

Single-nucleotide polymorphisms (SNPs) determined based on SNP arrays from the international HapMap consortium (HapMap) and the genetic variants detected in the 1000 genomes project (1KGP) can serve as two references for genomewide association studies (GWAS). We conducted comparative analyses to provide a means for assessing concerns regarding SNP array-based GWAS findings as well as for realistically bounding expectations for next generation sequencing (NGS)-based GWAS. We calculated and compared base composition, transitions to transversions ratio, minor allele frequency and heterozygous rate for SNPs from HapMap and 1KGP for the 622 common individuals.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

Background: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.

Results: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays.

Integrated microRNA, mRNA, and protein expression profiling reveals microRNA regulatory networks in rat kidney treated with a carcinogenic dose of aristolochic acid.

Background: Aristolochic Acid (AA), a natural component of Aristolochia plants that is found in a variety of herbal remedies and health supplements, is classified as a Group 1 carcinogen by the International Agency for Research on Cancer. Given that microRNAs (miRNAs) are involved in cancer initiation and progression and their role remains unknown in AA-induced carcinogenesis, we examined genome-wide AA-induced dysregulation of miRNAs as well as the regulation of miRNAs on their target gene expression in rat kidney.

Results: We treated rats with 10 mg/kg AA and vehicle control for 12 weeks and eight kidney samples (4 for the treatment and 4 for the control) were used for examining miRNA and mRNA expression by deep sequencing, and protein expression by proteomics.

An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era.

Background: Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment?

Results: We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets.

Whole genome sequencing of 35 individuals provides insights into the genetic architecture of Korean population.

Background: Due to a significant decline in the costs associated with next-generation sequencing, it has become possible to decipher the genetic architecture of a population by sequencing a large number of individuals to a deep coverage. The Korean Personal Genomes Project (KPGP) recently sequenced 35 Korean genomes at high coverage using the Illumina Hiseq platform and made the deep sequencing data publicly available, providing the scientific community opportunities to decipher the genetic architecture of the Korean population.

Methods: In this study, we used two single nucleotide variant (SNV) calling pipelines: mapping the raw reads obtained from whole genome sequencing of 35 Korean individuals in KPGP using BWA and SOAP2 followed by SNV calling using SAMtools and SOAPsnp, respectively.

Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias.

Toxicogenomics and cancer susceptibility: advances with next-generation sequencing.

The aim of this review is to comprehensively summarize the recent achievements in the field of toxicogenomics and cancer research regarding genetic-environmental interactions in carcinogenesis and detection of genetic aberrations in cancer genomes by next-generation sequencing technology. Cancer is primarily a genetic disease in which genetic factors and environmental stimuli interact to cause genetic and epigenetic aberrations in human cells. Mutations in the germline act as either high-penetrance alleles that strongly increase the risk of cancer development, or as low-penetrance alleles that mildly change an individual's susceptibility to cancer.

A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages.

The rat has been used extensively as a model for evaluating chemical toxicities and for understanding drug mechanisms. However, its transcriptome across multiple organs, or developmental stages, has not yet been reported. Here we show, as part of the SEQC consortium efforts, a comprehensive rat transcriptomic BodyMap created by performing RNA-Seq on 320 samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats.

Transcriptomic profiling of rat liver samples in a comprehensive study design by RNA-Seq.

RNA-Seq provides the capability to characterize the entire transcriptome in multiple levels including gene expression, allele specific expression, alternative splicing, fusion gene detection, and etc. The US FDA-led SEQC (i.e.

Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq.

Whole-transcriptome sequencing ('RNA-Seq') has been drastically changing the scale and scope of genomic research. In order to fully understand the power and limitations of this technology, the US Food and Drug Administration (FDA) launched the third phase of the MicroArray Quality Control (MAQC-III) project, also known as the SEquencing Quality Control (SEQC) project. Using two well-established human reference RNA samples from the first phase of the MAQC project, three sequencing platforms were tested across more than ten sites with built-in truths including spike-in of external RNA controls (ERCC), titration data and qPCR verification.

Comprehensive RNA-Seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats.

The rat is used extensively by the pharmaceutical, regulatory, and academic communities for safety assessment of drugs and chemicals and for studying human diseases; however, its transcriptome has not been well studied. As part of the SEQC (i.e.

EADB: an estrogenic activity database for assessing potential endocrine activity.

Endocrine-active chemicals can potentially have adverse effects on both humans and wildlife. They can interfere with the body's endocrine system through direct or indirect interactions with many protein targets. Estrogen receptors (ERs) are one of the major targets, and many endocrine disruptors are estrogenic and affect the normal estrogen signaling pathways.

Studies on abacavir-induced hypersensitivity reaction: a successful example of translation of pharmacogenetics to personalized medicine.

Abacavir is an effective nucleoside analog reverse transcriptase inhibitor used to treat human immunodeficiency virus (HIV) infected patients. Its main side effect is hypersensitivity reaction (HSR). The incidence of the HSR is associated with ethnicity among patients exposed to abacavir, and retrospective and prospective studies show a significantly increased risk of abacavir-induced HSR in human leukocyte antigen (HLA)-B*57:01-carrying patients.

Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine.

Realizing personalized medicine requires integrating diverse data types with bioinformatics. The most vital data are genomic information for individuals that are from advanced next-generation sequencing (NGS) technologies at present. The technologies continue to advance in terms of both decreasing cost and sequencing speed with concomitant increase in the amount and complexity of the data.

Technical reproducibility of genotyping SNP arrays used in genome-wide association studies.

During the last several years, high-density genotyping SNP arrays have facilitated genome-wide association studies (GWAS) that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. Moreover, discordance observed in results between independent GWAS indicates the potential for Type I and II errors.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

Background: Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity.

The liver toxicity biomarker study phase I: markers for the effects of tolcapone or entacapone.

The Liver Toxicity Biomarker Study is a systems toxicology approach to discover biomarkers that are indicative of a drug's potential to cause human idiosyncratic drug-induced liver injury. In phase I, the molecular effects in rat liver and blood plasma induced by tolcapone (a "toxic" drug) were compared with the molecular effects in the same tissues by dosing with entacapone (a "clean" drug, similar to tolcapone in chemical structure and primary pharmacological mechanism). Two durations of drug exposure, 3 and 28 days, were employed.

Constructing a robust protein-protein interaction network by integrating multiple public databases.

Background: Protein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus.