Operational analysis of school-based delivery models to vaccinate children against influenza.

Large-scale immunisation programmes against seasonal influenza are characterised by logistical challenges related to the need for vaccinating large cohorts of people in a short amount of time. Careful operational planning of resources is essential for a successful implementation of such programmes. We focused on the process of child vaccination in schools and analysed the staffing and workflow aspects of a school-aged children vaccination programme in England. Our objectives were to document vaccination processes and analyse times and costs associated with different models deployed across England. We collected data through direct non-participatory observations. Statistical data analysis enabled us to identify potential factors influencing vaccine delivery time and informed the development of a tool to simulate vaccination sessions. Using this tool, we carried out scenario analyses and explored trade-offs between session times and costs in different settings. Our work ultimately supported the local implementation of school-based vaccination.

Exploring the role of mass immunisation in influenza pandemic preparedness: A modelling study for the UK context.

The nature and timing of the next influenza pandemic is unknown. This makes it difficult for policy makers to assess whether spending money now to prepare for mass immunisation in the event of a pandemic is worthwhile. We used simple epidemiological modelling and health economic analysis to identify the range of pandemic and policy scenarios under which plans to immunise the general UK population would have net benefit if a stockpiled vaccine or, alternatively, a responsively purchased vaccine were used. Each scenario we studied comprised a combination of pandemic, vaccine and immunisation programme characteristics in presence or absence of access to effective antivirals, with the chance of there being a pandemic each year fixed. Monetarised health benefits and cost savings from any influenza cases averted were set against the option, purchase, storage, distribution, administration, and disposal costs relevant for each scenario to give a discounted net present value over 10 years for planning to immunise, accounting for the possibility that there may be no pandemic over the period considered. To support understanding and exploration of model output, an interactive visualisation tool was devised and made available online. We evaluated over 29 million combinations of pandemic and policy characteristics. Preparedness plans incorporating mass immunisation show positive net present value for a wide range of scenarios, predominantly in the absence of effective antivirals. Plans based on the responsive purchase of vaccine have wider benefit than plans reliant on the purchase and maintenance of a stockpile if immunisation can start without extensive delays. This finding is not dependent on responsively purchased vaccine being more effective than stockpiled vaccine, but rather is driven by avoiding the costs of storing and replenishing a stockpile.

A method for evaluating the cost-benefit of different preparedness planning policies against pandemic influenza.

•Our work presents a unifying method to calculate the net-benefit of different preparedness policies against different pandemic influeunza strains. Unlike previous methods, which have focused on evaluating specific strategies against specific pandemics, our method allows assessment of mass immunisation strategies in presence and absence of antiviral drugs for a large range of pandemic influenza strain characteristics and programme features. Overall, the model described here combines two parts to evaluate different preparedness planning policies against pandemic influenza.•The first part is adaptation of an existing transmission model for seasonal influenza to include generalisation across large number of pandemic influenza scenarios.•The second part is development of a tailor-made health economic model devised in collaboration with colleagues at the UK Department of Health and Social Care.

A tool for routine monitoring and feedback of morbidities following paediatric cardiac surgery.

Short-term survival after paediatric cardiac surgery has improved significantly over the past 20 years and increasing attention is being given to measuring and reducing incidence of morbidities following surgery. How to best use routinely collected data to share morbidity information constitutes a challenge for clinical teams interested in analysing their outcomes for quality improvement. We aimed to develop a tool facilitating this process in the context of monitoring morbidities following paediatric cardiac surgery, as part of a prospective multi-centre research study in the United Kingdom.We developed a prototype software tool to analyse and present data about morbidities associated with cardiac surgery in children. We used an iterative process, involving engagement with potential users, tool design and implementation, and feedback collection. Graphical data displays were based on the use of icons and graphs designed in collaboration with clinicians.Our tool enables automatic creation of graphical summaries, displayed as a Microsoft PowerPoint presentation, from a spreadsheet containing patient-level data about specified cardiac surgery morbidities. Data summaries include numbers/percentages of cases with morbidities reported, co-occurrences of different morbidities, and time series of each complication over a time window.Our work was characterised by a very high level of interaction with potential users of the tool, enabling us to promptly account for feedback and suggestions from clinicians and data managers. The United Kingdom centres involved in the project received the tool positively, and several expressed their interest in using it as part of their routine practice.

Are we prepared for the next influenza pandemic? Lessons from modelling different preparedness policies against four pandemic scenarios.

In the event of a novel influenza strain that is markedly different to the current strains circulating in humans, the population have little/no immunity and infection spreads quickly causing a global pandemic. Over the past century, there have been four major influenza pandemics: the 1918 pandemic ("Spanish Flu"), the 1957-58 pandemic (the "Asian Flu"), the 1967-68 pandemic (the "Hong Kong Flu") and the 2009 pandemic (the "Swine flu"). To inform planning against future pandemics, this paper investigates how different is the net-present value of employing pre-purchase and responsive- purchased vaccine programmes in presence and absence of anti-viral drugs to scenarios that resemble these historic influenza pandemics. Using the existing literature and in discussions with policy decision makers in the UK, we first characterised the four past influenza pandemics by their transmissibility and infection-severity. For these combinations of parameters, we then projected the net-present value of employing pre-purchase vaccine (PPV) and responsive-purchase vaccine (RPV) programmes in presence and absence of anti-viral drugs. To differentiate between PPV and RPV policies, we changed the vaccine effectiveness value and the time to when the vaccine is first available. Our results are "heat-map" graphs displaying the benefits of different strategies in pandemic scenarios that resemble historic influenza pandemics. Our results suggest that immunisation with either PPV or RPV in presence of a stockpile of effective antiviral drugs, does not have positive net-present value for all of the pandemic scenarios considered. In contrast, in the absence of effective antivirals, both PPV and RPV policies have positive net-present value across all the pandemic scenarios. Moreover, in all considered circumstances, vaccination was most beneficial if started sufficiently early and covered sufficiently large number of people. When comparing the two vaccine programmes, the RPV policy allowed a longer timeframe and lower coverage to attain the same benefit as the PPV policy. Our findings suggest that responsive-purchase vaccination policy has a bigger window of positive net-present value when employed against each of the historic influenza pandemic strains but needs to be rapidly available to maximise benefit. This is important for future planning as it suggests that future preparedness policies may wish to consider utilising timely (i.e. responsive-purchased) vaccines against emerging influenza pandemics.

Informing decisions on the purchase of equipment used by health services in response to incidents involving hazardous materials.

Accidents involving release of chemical, biological, radiological or nuclear substances may prompt the need to decontaminate exposed casualties prior to further medical treatment. Health service workers who carry out decontamination procedures wear protective suits to avoid direct contact with contaminants. We developed an analytical framework based on queueing theory to inform UK Department of Health's decisions on the stock of protective suits that ambulance services and hospitals with emergency departments in England should hold. Our aim was to ensure that such allocation gave an accepted degree of resilience to locally identified hazards. Here we give an overview of our work and describe how we incorporated information in the public domain about local hazards with expert opinion about the patterns of demand for decontamination associated with different types of incident. We also give an account of how we worked with decision makers to inform national guidance on this topic.

The shortest path is not the one you know: application of biological network resources in precision oncology research.

Several decades of molecular biology research have delivered a wealth of detailed descriptions of molecular interactions in normal and tumour cells. This knowledge has been functionally organised and assembled into dedicated biological pathway resources that serve as an invaluable tool, not only for structuring the information about molecular interactions but also for making it available for biological, clinical and computational studies. With the advent of high-throughput molecular profiling of tumours, close to complete molecular catalogues of mutations, gene expression and epigenetic modifications are available and require adequate interpretation. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular profiles of tumours. Making sense out of these descriptions requires biological pathway resources for functional interpretation of the data. In this review, we describe the available biological pathway resources, their characteristics in terms of construction mode, focus, aims and paradigms of biological knowledge representation. We present a new resource that is focused on cancer-related signalling, the Atlas of Cancer Signalling Networks. We briefly discuss current approaches for data integration, visualisation and analysis, using biological networks, such as pathway scoring, guilt-by-association and network propagation. Finally, we illustrate with several examples the added value of data interpretation in the context of biological networks and demonstrate that it may help in analysis of high-throughput data like mutation, gene expression or small interfering RNA screening and can guide in patients stratification. Finally, we discuss perspectives for improving precision medicine using biological network resources and tools. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular patterns of tumours and enable to put precision oncology into general clinical practice.

Independent component analysis uncovers the landscape of the bladder tumor transcriptome and reveals insights into luminal and basal subtypes.

Extracting relevant information from large-scale data offers unprecedented opportunities in cancerology. We applied independent component analysis (ICA) to bladder cancer transcriptome data sets and interpreted the components using gene enrichment analysis and tumor-associated molecular, clinicopathological, and processing information. We identified components associated with biological processes of tumor cells or the tumor microenvironment, and other components revealed technical biases. Applying ICA to nine cancer types identified cancer-shared and bladder-cancer-specific components. We characterized the luminal and basal-like subtypes of muscle-invasive bladder cancers according to the components identified. The study of the urothelial differentiation component, specific to the luminal subtypes, showed that a molecular urothelial differentiation program was maintained even in those luminal tumors that had lost morphological differentiation. Study of the genomic alterations associated with this component coupled with functional studies revealed a protumorigenic role for PPARG in luminal tumors. Our results support the inclusion of ICA in the exploitation of multiscale data sets.

Computational modeling of the main signaling pathways involved in mast cell activation.

A global and rigorous understanding of the signaling pathways and cross-regulatory processes involved in mast cell activation requires the integration of published information with novel functional datasets into a comprehensive computational model. Based on an exhaustive curation of the existing literature and using the software CellDesigner, we have built and annotated a comprehensive molecular map for the FcεRI signaling network. This map can be used to visualize and interpret high-throughput expression data. Furthermore, leaning on this map and using the logical modeling software GINsim, we have derived a qualitative dynamical model, which recapitulates the most salient features of mast cell activation. The resulting logical model can be used to explore the dynamical properties of the system and its responses to different stimuli, in normal or mutant conditions.

[Biological network modelling and precision medicine in oncology]. / Modélisation de réseaux biologiques et médecine de précision en oncologie.

Precision medicine in oncology is becoming reality thanks to the next-generation sequencing of tumours and the development of targeted inhibitors enabling tailored therapies. Many clinical trials base their strategy on the identification of mutations to deliver the targeted inhibitor that counteract supposedly the effect of a mutated gene. Recent results have shown that this gene-centered strategy can be successful, but can also fall short in stopping progression. This is due to the many compensation mechanisms, cross-talks and feedback loops that enable the tumoral cell to escape treatment. Taking into account the regulatory network is necessary to establish which inhibitor or combination of inhibitors would achieve the best therapeutic results. Mathematical modelling of biological networks, together with high-quality pathway databases collecting our knowledge of the molecular circuitry of normal and tumoral cells, hold the hopes of an enhanced future for precision medicine in oncology.

Integrative modelling of the influence of MAPK network on cancer cell fate decision.

The Mitogen-Activated Protein Kinase (MAPK) network consists of tightly interconnected signalling pathways involved in diverse cellular processes, such as cell cycle, survival, apoptosis and differentiation. Although several studies reported the involvement of these signalling cascades in cancer deregulations, the precise mechanisms underlying their influence on the balance between cell proliferation and cell death (cell fate decision) in pathological circumstances remain elusive. Based on an extensive analysis of published data, we have built a comprehensive and generic reaction map for the MAPK signalling network, using CellDesigner software. In order to explore the MAPK responses to different stimuli and better understand their contributions to cell fate decision, we have considered the most crucial components and interactions and encoded them into a logical model, using the software GINsim. Our logical model analysis particularly focuses on urinary bladder cancer, where MAPK network deregulations have often been associated with specific phenotypes. To cope with the combinatorial explosion of the number of states, we have applied novel algorithms for model reduction and for the compression of state transition graphs, both implemented into the software GINsim. The results of systematic simulations for different signal combinations and network perturbations were found globally coherent with published data. In silico experiments further enabled us to delineate the roles of specific components, cross-talks and regulatory feedbacks in cell fate decision. Finally, tentative proliferative or anti-proliferative mechanisms can be connected with established bladder cancer deregulations, namely Epidermal Growth Factor Receptor (EGFR) over-expression and Fibroblast Growth Factor Receptor 3 (FGFR3) activating mutations.

Proteomic analysis of the SH2 domain-containing leukocyte protein of 76 kDa (SLP76) interactome in resting and activated primary mast cells [corrected].

We report the first proteomic analysis of the SLP76 interactome in resting and activated primary mouse mast cells. This was made possible by a novel genetic approach used for the first time here. It consists in generating knock-in mice that express signaling molecules bearing a C-terminal tag that has a high affinity for a streptavidin analog. Tagged molecules can be used as molecular baits to affinity-purify the molecular complex in which they are engaged, which can then be studied by mass spectrometry. We examined first SLP76 because, although this cytosolic adapter is critical for both T cell and mast cell activation, its role is well known in T cells but not in mast cells. Tagged SLP76 was expressed in physiological amounts and fully functional in mast cells. We unexpectedly found that SLP76 is exquisitely sensitive to mast cell granular proteases, that Zn(2+)-dependent metalloproteases are especially abundant in mast cells and that they were responsible for SLP76 degradation. Adding a Zn(2+) chelator fully protected SLP76 in mast cell lysates, thereby enabling an efficient affinity-purification of this adapter with its partners. Label-free quantitative mass spectrometry analysis of affinity-purified SLP76 interactomes uncovered both partners already described in T cells and novel partners seen in mast cells only. Noticeably, molecules inducibly recruited in both cell types primarily concur to activation signals, whereas molecules recruited in activated mast cells only are mostly associated with inhibition signals. The transmembrane adapter LAT2, and the serine/threonine kinase with an exchange factor activity Bcr were the most recruited molecules. Biochemical and functional validations established the unexpected finding that Bcr is recruited by SLP76 and positively regulates antigen-induced mast cell activation. Knock-in mice expressing tagged molecules with a normal tissue distribution and expression therefore provide potent novel tools to investigate signalosomes and to uncover novel signaling molecules in mast cells.

Comparison of immunological characteristics of peripheral, splenic and tonsilar naïve B cells by differential gene expression meta-analyses.

BACKGROUND: Naïve B cells isolated from peripheral blood, spleen and tonsil are commonly used in human B cell studies. However, little has been written about their possible variations in immunological properties. This study compared differential gene expression in human naive B subsets by meta-analysis using expression data available in Gene Expression Onimbus (GEO). METHODS: Gene expression files of the Affymetrix Human Genome U133A Array (Affymetrix) were downloaded to collect 21 total array data samples of peripheral naïve B cells (n=10), splenic naïve B cells (n=2), tonsilar naïve B cells (n=3), peripheral memory B cells (n=4) and splenic memory B cells (n=2). Prior to differential gene expression analyses, data were normalized in order to reduce non-biological variation among the datasets. RESULTS: Comparisons of peripheral naive B cells with their splenic and tonsilar counterparts showed remarkable differences in terms of gene expression (29 and 202 genes, respectively). However, only minor differences were detected between splenic and tonsilar naive B cells (10 genes), consistent with the clustering results classifying both of them as lymphoid naive B cells. Differential gene expression results also implied higher stimulating states of lymphoid naive B cells when compared with peripheral blood naive B cells. These included enhanced expressions of CD27, CR2, EGR1, GADD45B, ICAM1, ICOSLG, IGHA, IL6, MMP9, SAMSN1, SMAD7, TNFAIP3, but reduced HLA-DOB expression. CONCLUSIONS: Our findings suggest that results generated from peripheral naive B cells may not always be applicable to the biological activities of other lymphoid naïve B cells. Nonetheless, further biological study is warranted.