Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology.Comment: 20 pages, 5 figures. In Press in Sci Rep 201

Oncogenesis- kaleidoscopic and multi-level reality

Oncogenesis is an extremely complex phenomenon. The mechanisms by which cancer is induced is only partially known. Consequently, therapeutic targets may be uncertain and results are often unsatisfactory. The purpose of this paper is to develop a trans-level and multiple transdisciplinary perspective describing the kaleidoscopic reality of oncogenesis. This manner of understanding oncogenesis as a complex process characterized by a non-linear dynamic, far from equilibrium and with unpredictable evolution, transcends the classical perspective and requires a paradigm shift. This approach is also facilitated by recent studies that focus on group phenomena, with emerging behaviors in a continuous phase transition. Biological systems, and obviously the human organism, express this type of behavior with critical self-organizing valences in the context of a genome - mesotope (environment) - phenotype interaction. For example, nature has transposed in the ecosystem, among other things, the performance pattern of its mineral history represented by the dynamic energy-matter-information unit (the principle of invariance). And multi-cell biological systems in the phylogenetic tree crown have multiple directed aerobic metabolisms in accordance with specific functions. Cancers, in turn, have a hybrid (anaerobic and aerobic) and unidirectional metabolism whose only and ultimate reason is the survival of the malignant cell. Understanding the transdisciplinary reality of oncogenesis offers novel development paths for new therapeutic strategies compared to current ones which have relatively limited efficiency

Network Entropy measures applied to different systemic perturbations of cell basal state

NOTE: includes supplementary materialNOTE: includes supplementary materialNOTE: includes supplementary materialWe characterize different cell states, related to cancer and ageing phenotypes, by a measure of entropy of network ensembles, integrating gene expression values and protein interaction networks. The entropy measure estimates the parameter space available to the network ensemble, that can be interpreted as the level of plasticity of the system for high entropy values (the ability to change its internal parameters, e.g. in response to environmental stimuli), or as a fine tuning of the parameters (that restricts the range of possible parameter values) in the opposite case. This approach can be applied at different scales, from whole cell to single biological functions, by defining appropriate subnetworks based on a priori biological knowledge, thus allowing a deeper understanding of the cell processes involved. In our analysis we used specific network features (degree sequence, subnetwork structure and distance between gene profiles) to obtain informations at different biological scales, providing a novel point of view for the integration of experimental transcriptomic data and a priori biological knowledge, but the entropy measure can also highlight other aspects of the biological systems studied depending on the constraints introduced in the model (e.g. community structures)

Animal lectins as cell adhesion molecules

Protein-carbohydrate interaction is exploited in cell adhesion mechanisms besides the recognition of peptide motifs. The sugar code thus significantly contributes to the intriguing specificity of cellular selection of binding partners. Focusing on two classes of lectins (selectins and galectins), it is evident that their functionality for mediation of adhesive contacts is becoming increasingly appreciated, as is the integration of this type of interaction with other recognition modes to yield the noted specificity. The initial contact formation between leukocytes and activated endothelium makes use of selectins to guide lymphocyte trafficking. In addition to the three selectins which bind a distinct array of ligands, galectin-1 and galectin-3 and possibly other members of this family are involved in cell-cell or cell-matrix interactions. This review summarizes structural and functional aspects of these two classes of endogenous lectins relevant for cell adhesion

Prognostic gene network modules in breast cancer hold promise

A substantial proportion of lymph node-negative patients who receive adjuvant chemotherapy do not derive any benefit from this aggressive and potentially toxic treatment. However, standard histopathological indices cannot reliably detect patients at low risk of relapse or distant metastasis. In the past few years several prognostic gene expression signatures have been developed and shown to potentially outperform histopathological factors in identifying low-risk patients in specific breast cancer subgroups with predictive values of around 90%, and therefore hold promise for clinical application. We envisage that further improvements and insights may come from integrative expression pathway analyses that dissect prognostic signatures into modules related to cancer hallmarks

Measurement and Modeling of Signaling at the Single-Cell Level

It has long been recognized that a deeper understanding of cell function, with respect to execution of phenotypic behaviors and their regulation by the extracellular environment, is likely to be achieved by analyzing the underlying molecular processes for individual cells selected from across a population, rather than averages of many cells comprising that population. In recent years, experimental and computational methods for undertaking these analyses have advanced rapidly. In this review, we provide a perspective on both measurement and modeling facets of biochemistry at a single-cell level. Our central focus is on receptor-mediated signaling networks that regulate cell phenotypic functions.David H. Koch Institute for Integrative Cancer Research at MIT (Ludwig Fellowship)National Institutes of Health (U.S.) (grant R01-EB010246)National Institutes of Health (U.S.) (grant P50-GM68762)United States. Army Research Office (Institute for Collaborative Biotechnologies, Grant W911NF-09-0001