Toward a rational design of combination therapy in cancer.

By merging computational systems modeling and experimental approaches, we have uncovered treatments reprogramming pro-angiogenic monocytes present in breast tumor into immunologically potent cells capable of mediating an anti-tumor immune response. The unraveled pathways and ligands which underlie monocyte pro-angiogenic activity have a strong predictive value for breast cancer patient relapse - free survival

Social networks help to infer causality in the tumor microenvironment.

BACKGROUND: Networks have become a popular way to conceptualize a system of interacting elements, such as electronic circuits, social communication, metabolism or gene regulation. Network inference, analysis, and modeling techniques have been developed in different areas of science and technology, such as computer science, mathematics, physics, and biology, with an active interdisciplinary exchange of concepts and approaches. However, some concepts seem to belong to a specific field without a clear transferability to other domains. At the same time, it is increasingly recognized that within some biological systems-such as the tumor microenvironment-where different types of resident and infiltrating cells interact to carry out their functions, the complexity of the system demands a theoretical framework, such as statistical inference, graph analysis and dynamical models, in order to asses and study the information derived from high-throughput experimental technologies. RESULTS: In this article we propose to adopt and adapt the concepts of influence and investment from the world of social network analysis to biological problems, and in particular to apply this approach to infer causality in the tumor microenvironment. We showed that constructing a bidirectional network of influence between cell and cell communication molecules allowed us to determine the direction of inferred regulations at the expression level and correctly recapitulate cause-effect relationships described in literature. CONCLUSIONS: This work constitutes an example of a transfer of knowledge and concepts from the world of social network analysis to biomedical research, in particular to infer network causality in biological networks. This causality elucidation is essential to model the homeostatic response of biological systems to internal and external factors, such as environmental conditions, pathogens or treatments

Differentially phased leaf growth and movements in Arabidopsis depend on coordinated circadian and light regulation.

In contrast to vastly studied hypocotyl growth, little is known about diel regulation of leaf growth and its coordination with movements such as changes in leaf elevation angle (hyponasty). We developed a 3D live-leaf growth analysis system enabling simultaneous monitoring of growth and movements. Leaf growth is maximal several hours after dawn, requires light, and is regulated by daylength, suggesting coupling between growth and metabolism. We identify both blade and petiole positioning as important components of leaf movements in Arabidopsis thaliana and reveal a temporal delay between growth and movements. In hypocotyls, the combination of circadian expression of PHYTOCHROME INTERACTING FACTOR4 (PIF4) and PIF5 and their light-regulated protein stability drives rhythmic hypocotyl elongation with peak growth at dawn. We find that PIF4 and PIF5 are not essential to sustain rhythmic leaf growth but influence their amplitude. Furthermore, EARLY FLOWERING3, a member of the evening complex (EC), is required to maintain the correct phase between growth and movement. Our study shows that the mechanisms underlying rhythmic hypocotyl and leaf growth differ. Moreover, we reveal the temporal relationship between leaf elongation and movements and demonstrate the importance of the EC for the coordination of these phenotypic traits

Zipf's Law in Gene Expression

Using data from gene expression databases on various organisms and tissues, including yeast, nematodes, human normal and cancer tissues, and embryonic stem cells, we found that the abundances of expressed genes exhibit a power-law distribution with an exponent close to -1, i.e., they obey Zipf's law. Furthermore, by simulations of a simple model with an intra-cellular reaction network, we found that Zipf's law of chemical abundance is a universal feature of cells where such a network optimizes the efficiency and faithfulness of self-reproduction. These findings provide novel insights into the nature of the organization of reaction dynamics in living cells.Comment: revtex, 11 pages, 3 figures, submitted to Phys. Rev. Let

Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis.

Survival analyses based on the Kaplan-Meier estimate have been pervasively used to support or validate the relevance of biological mechanisms in cancer research. Recently, with the appearance of gene expression high-throughput technologies, this kind of analysis has been applied to tumor transcriptomics data. In a 'bottom-up' approach, gene-expression profiles that are associated with a deregulated pathway hypothetically involved in cancer progression are first identified and then subsequently correlated with a survival effect, which statistically supports or requires the rejection of such a hypothesis. In this work, we propose a 'top-down' approach, in which the clinical outcome (survival) is the starting point that guides the identification of deregulated biological mechanisms in cancer by a non-hypothesis-driven iterative survival analysis. We show that the application of our novel method to a population of ~2,000 breast cancer patients of the METABRIC consortium allows the identification of several well-known cancer mechanisms, such as ERBB4, HNF3A and TGFB pathways, and the investigation of their paradoxical dual effect. In addition, several novel biological mechanisms are proposed as potentially involved in cancer progression. The proposed exploratory methodology can be considered both alternative and complementary to classical 'bottom-up' approaches for validation of biological hypotheses. We propose that our method may be used to better characterize cancer, and may therefore impact the future design of therapies that are truly molecularly tailored to individual patients. The method, named SURCOMED, was implemented as a web-based tool, which is publicly available at http://surcomed.vital-it.ch. R scripts are also available at http://surcomed.sourceforge.net)

Introducing a Regulatory Policy Framework of Bait Fishing in European Coastal Lagoons: The Case of Ria de Aveiro in Portugal

The harvesting of bait through digging in coastal mudflats is practiced for recreational and commercial purposes in European coastal systems including the Ria de Aveiro coastal lagoon on the northwest Atlantic coast of Portugal. The scale of harvesting in the Ria de Aveiro has recently increased due to the current economic climate in Portugal, with targeting of the polychaete, Diopatra neapolitana species or “casulo” as it is widely known in the Aveiro region. The national authorities have attempted to control casulo digging by issuing a regulation (Ordinance) in 2014 on the maximum daily catch limit to be caught by each individual. The daily catch limit is intended to represent the Maximum Sustainable Yield (MSY) for casulo beyond which overfishing will occur. The monitoring of the regulatory measures is expected to be conducted through on-site inspections in the digging areas. However, weak law enforcement was noticed, while there is also controversy over the daily catch limit (quota) stipulated by the Ordinance. To this end, the current study attempted to assess digging activities through remote monitoring and random inspections for a better policy enforcement of the national regulation. In addition, different harvesting scenarios were employed through a simplified bioeconomic model to attribute the current and future harvesting trends of bait digging in Aveiro coastal lagoon. The study findings indicate that remote monitoring coupled with some onsite interviews could be a more effective approach for the implementation of the current bait digging policy. Further, the results point to a distinctive discrepancy between the daily catch amount (MSY) introduced by the national legislation and the study findings which should be further scrutinized. The diggers seem to have reached the sustainable harvest identified by the present research. The current economic hardship in Portugal and the low profitability in similar employment sectors will possibly attract more diggers and increase harvesting in the near future. An increased harvest would likely trigger overfishing of D. neapolitana with unknown consequences for the population of the species as well as the aquatic ecosystem. The socio-economic and environmental effects are yet to be further clarified with more detailed data and advanced modeling techniques to ensure the sustainability of the activity

Analysis of stop-gain and frameshift variants in human innate immunity genes.

Loss-of-function variants in innate immunity genes are associated with Mendelian disorders in the form of primary immunodeficiencies. Recent resequencing projects report that stop-gains and frameshifts are collectively prevalent in humans and could be responsible for some of the inter-individual variability in innate immune response. Current computational approaches evaluating loss-of-function in genes carrying these variants rely on gene-level characteristics such as evolutionary conservation and functional redundancy across the genome. However, innate immunity genes represent a particular case because they are more likely to be under positive selection and duplicated. To create a ranking of severity that would be applicable to innate immunity genes we evaluated 17,764 stop-gain and 13,915 frameshift variants from the NHLBI Exome Sequencing Project and 1,000 Genomes Project. Sequence-based features such as loss of functional domains, isoform-specific truncation and nonsense-mediated decay were found to correlate with variant allele frequency and validated with gene expression data. We integrated these features in a Bayesian classification scheme and benchmarked its use in predicting pathogenic variants against Online Mendelian Inheritance in Man (OMIM) disease stop-gains and frameshifts. The classification scheme was applied in the assessment of 335 stop-gains and 236 frameshifts affecting 227 interferon-stimulated genes. The sequence-based score ranks variants in innate immunity genes according to their potential to cause disease, and complements existing gene-based pathogenicity scores. Specifically, the sequence-based score improves measurement of functional gene impairment, discriminates across different variants in a given gene and appears particularly useful for analysis of less conserved genes

TIE-2 expressing monocytes in human cancers.

Tumor-associated macrophages (TAM) are well known as a key player in the tumor microenvironment, which support cancer progression. More recently, a lineage of monocytes characterized by the expression of the TIE-2/Tek angiopoietin receptor identified a subset of circulating and tumor-associated monocytes endowed with proangiogenic activity. TIE-2 expressing monocytes (TEM) were found both in humans and mice. Here, we review the phenotypes and functions of TEM reported so far in human cancer and their potential use as markers of cancer progression and metastasis. Finally, we discuss the therapeutic approaches currently used or proposed to target TEM

Distance, dissimilarity index, and network community structure

We address the question of finding the community structure of a complex network. In an earlier effort [H. Zhou, {\em Phys. Rev. E} (2003)], the concept of network random walking is introduced and a distance measure defined. Here we calculate, based on this distance measure, the dissimilarity index between nearest-neighboring vertices of a network and design an algorithm to partition these vertices into communities that are hierarchically organized. Each community is characterized by an upper and a lower dissimilarity threshold. The algorithm is applied to several artificial and real-world networks, and excellent results are obtained. In the case of artificially generated random modular networks, this method outperforms the algorithm based on the concept of edge betweenness centrality. For yeast's protein-protein interaction network, we are able to identify many clusters that have well defined biological functions.Comment: 10 pages, 7 figures, REVTeX4 forma

Multiple imputations applied to the DREAM3 phosphoproteomics challenge: a winning strategy.

DREAM is an initiative that allows researchers to assess how well their methods or approaches can describe and predict networks of interacting molecules [1]. Each year, recently acquired datasets are released to predictors ahead of publication. Researchers typically have about three months to predict the masked data or network of interactions, using any predictive method. Predictions are assessed prior to an annual conference where the best predictions are unveiled and discussed. Here we present the strategy we used to make a winning prediction for the DREAM3 phosphoproteomics challenge. We used Amelia II, a multiple imputation software method developed by Gary King, James Honaker and Matthew Blackwell[2] in the context of social sciences to predict the 476 out of 4624 measurements that had been masked for the challenge. To chose the best possible multiple imputation parameters to apply for the challenge, we evaluated how transforming the data and varying the imputation parameters affected the ability to predict additionally masked data. We discuss the accuracy of our findings and show that multiple imputations applied to this dataset is a powerful method to accurately estimate the missing data. We postulate that multiple imputations methods might become an integral part of experimental design as a mean to achieve cost savings in experimental design or to increase the quantity of samples that could be handled for a given cost