Kevin Bleakley

Control-FREEC

Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks. Availability: C++ source code is available at: http://bioinfo.curie.fr/projects/freec/ Contact: freec@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Supervised prediction of drug–target interactions using bipartite local models

Motivation: In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs for known diseases. This problem is currently being attacked from many different points of view, a strong indication of its current importance. Precisely, being able to predict new drug–target interactions with both high precision and accuracy is the holy grail, a fundamental requirement for in silico methods to be useful in a biological setting. This, however, remains extremely challenging due to, amongst other things, the rarity of known drug–target interactions. Results: We propose a novel supervised inference method to predict unknown drug–target interactions, represented as a bipartite graph. We use this method, known as bipartite local models to first predict target proteins of a given drug, then to predict drugs targeting a given protein. This gives two independent predictions for each putative drug–target interaction, which we show can be combined to give a definitive prediction for each interaction. We demonstrate the excellent performance of the proposed method in the prediction of four classes of drug–target interaction networks involving enzymes, ion channels, G protein-coupled receptors (GPCRs) and nuclear receptors in human. This enables us to suggest a number of new potential drug–target interactions. Availability: An implementation of the proposed algorithm is available upon request from the authors. Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/bipartitelocal/. Contact: kevbleakley@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization

Summary: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs. Availability: Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/. Contact: freec@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Supervised reconstruction of biological networks with local models

MOTIVATION Inference and reconstruction of biological networks from heterogeneous data is currently an active research subject with several important applications in systems biology. The problem has been attacked from many different points of view with varying degrees of success. In particular, predicting new edges with a reasonable false discovery rate is highly demanded for practical applications, but remains extremely challenging due to the sparsity of the networks of interest. RESULTS While most previous approaches based on the partial knowledge of the network to be inferred build global models to predict new edges over the network, we introduce here a novel method which predicts whether there is an edge from a newly added vertex to each of the vertices of a known network using local models. This involves learning individually a certain subnetwork associated with each vertex of the known network, then using the discovered classification rule associated with only that vertex to predict the edge to the new vertex. Excellent experimental results are shown in the case of metabolic and protein-protein interaction network reconstruction from a variety of genomic data. AVAILABILITY An implementation of the proposed algorithm is available upon request from the authors.

Search for a Gene Expression Signature of Breast Cancer Local Recurrence in Young Women

Purpose: A gene expression signature, predictive for local recurrence after breast-conserving treatment, has previously been identified from a series of 165 young patients with breast cancer. We evaluated this signature on both another platform and an independent series, compared its performance with other published gene-sets, and investigated the gene expression profile of a larger data set. Experimental Design: Gene expression tumor profiles were obtained on 148 of the initial 165 Dutch patients and on an independent validation series of 195 French patients. Both unsupervised and supervised classifications were used to study the gene expression profile of the 343 breast cancers and to identify subgroups that differ for their risk of local recurrence. Results: The previous local recurrence signature was validated across platforms. However, when applied to the French patients, the signature did not reproduce its reported performance and did not better classify the patients than other published gene sets. Hierarchical clustering of all 343 breast cancers did not show any grouping reflecting local recurrence status. Genes related to proliferation were found differentially expressed between patients with or without local recurrence only in triple-negative tumors. Supervised classification revealed no significant gene set predictive for local recurrence or able to outperform classification based on clinical variables. Conclusions: Although the previously identified local recurrence signature was robust on another platform, we were neither able to validate it on an independent data set, nor able to define a strong gene expression classifier for local recurrence using a larger data set. We conclude that there are no significant differences in gene expression pattern in tumors from patients with and without local recurrence after breast-conserving treatment. Clin Cancer Res; 18(6); 1704–15. ©2012 AACR.

Nonparametric sequential prediction of time series

Time series prediction covers a vast field of everyday statistical applications in medical, environmental and economic domains. In this paper, we develop nonparametric prediction strategies based on the combination of a set of ‘experts’ and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalised cumulative prediction error.

Increased adaptive immune responses and proper feedback regulation protect against clinical dengue

Increased activation of adaptive immunity and proper feedback mechanisms can eliminate dengue viral infection without clinical symptoms. Distinguishing dengue presentation Although dengue hemorrhagic fever can be life-threatening, not all dengue virus infections even present with symptoms. To determine what may be driving the differences in clinical and asymptomatic infections, Simon-Lorière et al. examined the serum and immune gene transcripts of Cambodian children infected with dengue virus serotype 1. They saw that relative to those with clinical infections, the small subset of asymptomatic children had increased signs of antigen presentation, T cell activation, and T cell apoptosis; plasmablast differentiation and anti-dengue antibodies seemed relatively lower. These results provide clues for pathways that may drive pathologic responses in severe dengue virus infections. Clinical symptoms of dengue virus (DENV) infection, the most prevalent arthropod-borne viral disease, range from classical mild dengue fever to severe, life-threatening dengue shock syndrome. However, most DENV infections cause few or no symptoms. Asymptomatic DENV-infected patients provide a unique opportunity to decipher the host immune responses leading to virus elimination without negative impact on an individual’s health. We used an integrated approach of transcriptional profiling and immunological analysis to compare a Cambodian population of strictly asymptomatic viremic individuals with clinical dengue patients. Whereas inflammatory pathways and innate immune response pathways were similar between asymptomatic individuals and clinical dengue patients, expression of proteins related to antigen presentation and subsequent T cell and B cell activation pathways was differentially regulated, independent of viral load and previous DENV infection history. Feedback mechanisms controlled the immune response in asymptomatic viremic individuals, as demonstrated by increased activation of T cell apoptosis–related pathways and FcγRIIB (Fcγ receptor IIB) signaling associated with decreased anti-DENV–specific antibody concentrations. Together, our data illustrate that symptom-free DENV infection in children is associated with increased activation of the adaptive immune compartment and proper control mechanisms, leading to elimination of viral infection without excessive immune activation, with implications for novel vaccine development strategies.

Automatic data binning for improved visual diagnosis of pharmacometric models

Visual Predictive Checks (VPC) are graphical tools to help decide whether a given model could have plausibly generated a given set of real data. Typically, time-course data is binned into time intervals, then statistics are calculated on the real data and data simulated from the model, and represented graphically for each interval. Poor selection of bins can easily lead to incorrect model diagnosis. We propose an automatic binning strategy that improves reliability of model diagnosis using VPC. It is implemented in version 4 of the Monolix software.

Joint modeling of longitudinal and repeated time-to-event data using nonlinear mixed-effects models and the SAEM algorithm

We propose a nonlinear mixed-effects framework to jointly model longitudinal and repeated time-to-event data. A parametric nonlinear mixed-effects model is used for the longitudinal observations and a parametric mixed-effects hazard model for repeated event times. We show the importance for parameter estimation of properly calculating the conditional density of the observations (given the individual parameters) in the presence of interval and/or right censoring. Parameters are estimated by maximizing the exact joint likelihood with the stochastic approximation expectation–maximization algorithm. This workflow for joint models is now implemented in the Monolix software, and illustrated here on five simulated and two real datasets.

A Blood RNA Signature Detecting Severe Disease in Young Dengue Patients at Hospital Arrival

An 18-gene RNA signature detects severe cases among young Cambodian secondary-infected dengue patients and yields insights into the underlying pathogenesis. We present evidence that the detection is robust for peripheral blood mononuclear cells and whole blood and different experimental techniques.