Comprehensive molecular characterization of gastric adenocarcinoma

Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies

iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets

Trans-Proteomic Pipeline supports and improves analysis of electron transfer dissociation data sets

Electron transfer dissociation (ETD) is an alternative fragmentation technique to CID that has recently become commercially available. ETD has several advantages over CID. It is less prone to fragmenting amino acid side chains, especially those that are modified, thus yielding fragment ion spectra with more uniform peak intensities. Further, precursor ions of longer peptides and higher charge states can be fragmented and identified. However, analysis of ETD spectra has a few important differences that require the optimization of the software packages used for the analysis of CID data or the development of specialized tools. We have adapted the Trans-Proteomic Pipeline to process ETD data. Specifically, we have added support for fragment ion spectra from high-charge precursors, compatibility with charge-state estimation algorithms, provisions for the use of the Lys-C protease, capabilities for ETD spectrum library building, and updates to the data formats to differentiate CID and ETD spectra. We show the results of processing data sets from several different types of ETD instruments and demonstrate that application of the ETD-enhanced Trans-Proteomic Pipeline can increase the number of spectrum identifications at a fixed false discovery rate by as much as 100\% over native output from a single sequence search engine

iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol Cell Proteomics 2011;10

SUMMARY The combination of tandem mass spectrometry (MS/MS) and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of MS/MS spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline (TPP). Applied in tandem with PeptideProphet, it provides more accurate representation of the multi-level nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the TPP increases the number of correctly identified peptides at a constant false discovery rate (FDR) as compared to both PeptideProphet and another state-of-theart tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and FDR estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the TPP, it supports all commonly used MS instruments, search engines and computer platforms. The performance of iProphet is demonstrated on two publicly available datasets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic datasets, and from a set of Streptococcus pyogenes experiments more representative of organismspecific composite datasets.

Adaptive Behavior in Young Children with Neurofibromatosis Type 1

Neurofibromatosis-1 is the most common single gene disorder affecting 1 in 3000. In children, it is associated not only with physical features but also with attention and learning problems. Research has identified a downward shift in intellectual functioning as well, but to date, there are no published studies about the everyday adaptive behavior of children with NF1. In this study, parental reports of adaptive behavior of 61 children with NF1 ages 3 through 8 were compared to an unaffected contrast group (n=55) that comprised siblings and community members. Significant group differences in adaptive skills were evident and were largely related to group differences in intellectual functioning. In a subsample of children with average-range intellectual functioning, group differences in parent-reported motor skills were apparent even after controlling statistically for group differences in intellectual functioning. The implications of the findings for the care of children with NF1 are discussed

MaRiMba: A Software Application for Spectral Library-Based MRM Transition List Assembly

Multiple reaction monitoring mass spectrometry (MRM-MS) is a targeted analysis method that has been increasingly viewed as an avenue to explore proteomes with unprecedented sensitivity and throughput. We have developed a software tool, called MaRiMba, to automate the creation of explicitly defined MRM transition lists required to program triple quadrupole mass spectrometers in such analyses. MaRiMba creates MRM transition lists from downloaded or custom-built spectral libraries, restricts output to specified proteins or peptides, and filters based on precursor peptide and product. ion properties. MaRiMba can also create MRM lists containing corresponding transitions for isotopically heavy peptides, for which the precursor and product ions are adjusted according to user specifications. This open-source application is operated through a graphical user interface incorporated into the Trans-Proteomic Pipeline, and it outputs the final MRM list to a text file for upload to MS instruments. To illustrate the use of MaRiMba, we used the tool to design and execute an MRM-MS experiment in which we targeted the proteins of a well-defined and previously published standard mixture

Social skills and autism spectrum disorder symptoms in children with neurofibromatosis type 1: evidence for clinical trial outcomes.

AIM: We examined key features of two outcome measures for social dysfunction and autism spectrum disorder traits, the Social Responsiveness Scale, Second Edition (SRS-2) and the Social Skills Improvement System - Rating Scales (SSIS-RS), in children with neurofibromatosis type 1 (NF1). The aim of the study was to provide objective evidence as to which behavioural endpoint should be used in clinical trials. METHOD: Cross-sectional behavioural and demographic data were pooled from four paediatric NF1 tertiary referral centres in Australia and the United States (N=122; 65 males, 57 females; mean age [SD] 9y 2mo [3y], range 3-15y). RESULTS: Distributions of SRS-2 and SSIS-RS scores were unimodal and both yielded deficits, with a higher proportion of severely impaired scores on the SRS-2 (16.4%) compared to the SSIS-RS (8.2%). Pearson's product-moment correlations revealed that both questionnaires were highly related to each other (r=-0.72, p<0.001) and to measures of adaptive social functioning (both p<0.001). Both questionnaires were significantly related to attention-deficit/hyperactivity disorder symptoms, but only very weakly associated with intelligence. INTERPRETATION: The SRS-2 and SSIS-RS capture social dysfunction associated with NF1, suggesting both may be suitable choices for assessing social outcomes in this population in a clinical trial. However, careful thought needs to be given to the nature of the intervention when selecting either as a primary endpoint. WHAT THIS PAPER ADDS: The Social Responsiveness Scale, Second Edition yielded a large deficit relative to population norms. The Social Skills Improvement System - Rating Scales yielded a moderate deficit relative to population norms. Both scales were highly correlated, suggesting that they are measuring a unitary construct

A cross-platform toolkit for mass spectrometry and proteomics

To the Editor: Mass spectrometry–based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2 and characterize protein complexes3. Despite successes, the interpretation of vast proteomics data