Where are we at regarding species translation? A review of the sbv IMPROVER challenge

Contact: [email protected]

Outcome of proximal esophageal cancer after definitive combined chemo-radiation: a Swiss multicenter retrospective study.

To report oncological outcomes and toxicity rates, of definitive platin-based chemoradiadiationtherapy (CRT) in the management of proximal esophageal cancer. We retrospectively reviewed the medical records of patients with cT1-4 cN0-3 cM0 cervical esophageal cancer (CEC) (defined as tumors located below the inferior border of the cricoid cartilage, down to 22 cm from the incisors) treated between 2004 and 2013 with platin-based definitive CRT in four Swiss institutions. Acute and chronic toxicities were retrospectively scored using the National Cancer Institute's Common Terminology Criteria for Adverse Events, version 4.0 (CTCAE-NCI v.4.0). Primary endpoint was loco-regional control (LRC). We also evaluated overall survival (OS) and disease-free survival (DFS) rates. The influence of patient- and treatment related features have been calculated using the Log-rank test and multivariate Cox proportional hazards model. We enrolled a total of 55 patients. Median time interval from diagnosis to CRT was 78 days (6-178 days). Median radiation dose was 56Gy (28-72Gy). Induction chemotherapy (ICHT) was delivered in 58% of patients. With a median follow up of 34 months (6-110months), actuarial 3-year LRC, DFS and OS were 52% (95% CI: 37-67%), 35% (95% CI: 22-50%) and 52% (95% CI: 37-67%), respectively. Acute toxicities (dysphagia, pain, skin-toxicity) ranged from grade 0 - 4 without significant dose-dependent differences. On univariable analyses, the only significant prognostic factor for LRC was the time interval > 78 days from diagnosis to CRT. On multivariable analysis, total radiation dose >56Gy (p <0.006) and ICHT (p < 0.004) were statistically significant positive predictive factors influencing DFS and OS. Definitive CRT is a reliable therapeutic option for proximal esophageal cancer, with acceptable treatment related toxicities. Higher doses and ICHT may improve OS and DFS and. These findings need to be confirmed in further prospective studies

Length-dependent disassembly maintains four different flagellar lengths in Giardia.

With eight flagella of four different lengths, the parasitic protist Giardia is an ideal model to evaluate flagellar assembly and length regulation. To determine how four different flagellar lengths are maintained, we used live-cell quantitative imaging and mathematical modeling of conserved components of intraflagellar transport (IFT)-mediated assembly and kinesin-13-mediated disassembly in different flagellar pairs. Each axoneme has a long cytoplasmic region extending from the basal body, and transitions to a canonical membrane-bound flagellum at the 'flagellar pore'. We determined that each flagellar pore is the site of IFT accumulation and injection, defining a diffusion barrier functionally analogous to the transition zone. IFT-mediated assembly is length-independent, as train size, speed, and injection frequencies are similar for all flagella. We demonstrate that kinesin-13 localization to the flagellar tips is inversely correlated to flagellar length. Therefore, we propose a model where a length-dependent disassembly mechanism controls multiple flagellar lengths within the same cell

A crowd-sourcing approach for the construction of species-specific cell signaling networks

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs. Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics onlin

Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge

Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and ‘translating' those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Generalized Simulated Annealing for Global Optimization: The GenSA Package

Many problems in statistics, finance, biology, pharmacology, physics, mathematics, economics, and chemistry involve determination of the global minimum of multidimensional functions. R packages for different stochastic methods such as genetic algorithms and differential evolution have been developed and successfully used in the R community. Based on Tsallis statistics, the R package GenSA was developed for generalized simulated annealing to process complicated non-linear objective functions with a large number of local minima. In this paper we provide a brief introduction to the R package and demonstrate its utility by solving a non-convex portfolio optimization problem in finance and the Thomson problem in physics. GenSA is useful and can serve as a complementary tool to, rather than a replacement for, other widely used R packages for optimization

Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge

Motivation: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. Results: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams. Availability: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Developing Network-Based Systems Toxicology by Combining Transcriptomics Data with Literature Mining and Multiscale Quantitative Modeling

We describe how the genome-wide transcriptional profiling can be used in network-based systems toxicology, an approach leveraging biological networks for assessing the health risks of exposure to chemical compounds. Driven by the technological advances changing the ways in which data are generated, systems toxicology has allowed traditional toxicity endpoints to be enhanced with far deeper levels of analysis. In combination, new experimental and computational methods have offered the potential for more effective, efficient, and reliable toxicological testing strategies. We illustrate these advances by the “network perturbation amplitude” methodology that quantifies the effects of exposure treatments on biological mechanisms represented by causal networks. We also describe recent developments in the assembly of high-quality causal biological networks using crowdsourcing and text-mining approaches. We further show how network-based approaches can be integrated into the multiscale modeling framework of response to toxicological exposure. Finally, we combine biological knowledge assembly and multiscale modeling to report on the promising developments of the “quantitative adverse outcome pathway” concept, which spans multiple levels of biological organization, from molecules to population, and has direct relevance in the context of the “Toxicity Testing in the 21st century” vision of the US National Research Council

Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models

BACKGROUND: High-throughput measurement technologies such as microarrays provide complex datasets reflecting mechanisms perturbed in an experiment, typically a treatment vs. control design. Analysis of these information rich data can be guided based on a priori knowledge, such as networks or set of related proteins or genes. Among those, cause-and-effect network models are becoming increasingly popular and more than eighty such models, describing processes involved in cell proliferation, cell fate, cell stress, and inflammation have already been published. A meaningful systems toxicology approach to study the response of a cell system, or organism, exposed to bio-active substances requires a quantitative measure of dose-response at network level, to go beyond the differential expression of single genes. RESULTS: We developed a method that quantifies network response in an interpretable manner. It fully exploits the (signed graph) structure of cause-and-effect networks models to integrate and mine transcriptomics measurements. The presented approach also enables the extraction of network-based signatures for predicting a phenotype of interest. The obtained signatures are coherent with the underlying network perturbation and can lead to more robust predictions across independent studies. The value of the various components of our mathematically coherent approach is substantiated using several in vivo and in vitro transcriptomics datasets. As a proof-of-principle, our methodology was applied to unravel mechanisms related to the efficacy of a specific anti-inflammatory drug in patients suffering from ulcerative colitis. A plausible mechanistic explanation of the unequal efficacy of the drug is provided. Moreover, by utilizing the underlying mechanisms, an accurate and robust network-based diagnosis was built to predict the response to the treatment. CONCLUSION: The presented framework efficiently integrates transcriptomics data and “cause and effect” network models to enable a mathematically coherent framework from quantitative impact assessment and data interpretation to patient stratification for diagnosis purposes

Animal Inhalation Models to Investigate Modulation of Inflammatory Bowel Diseases

Inflammatory bowel diseases (IBDs) comprise primarily two disease manifestations, ulcerative colitis (UC) and Crohn’s disease (CD), each with distinctive clinical and pathological features. Environmental and clinical factors strongly affect the development and clinical outcomes of IBDs. Among environmental factors, cigarette smoke (CS) is considered the most important risk factor for CD, while it attenuates the disease course of UC. Various animal models have been used to assess the impact of CS on intestinal pathophysiology. This chapter examines the suitability of animal inhalation/smoke exposure models for assessing the contrary effects of CS on UC and CD. It presents an updated literature review of IBD mouse models and a description of possible mechanisms relevant to relationships between IBD and smoking. In addition, it summarises various technical inhalation approaches, in the context of mouse disease models of IBD