184 research outputs found
Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data
Motivation: Cancer is a complex disease, triggered by mutations in multiple genes and pathways. There is a growing interest in the application of systems biology approaches to analyze various types of cancer-related data to understand the overwhelming complexity of changes induced by the disease
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
Immunohistochemical analysis of changes in signaling pathway activation downstream of growth factor receptors in pancreatic duct cell carcinogenesis
<p>Abstract</p> <p>Background</p> <p>The pathogenesis of pancreatic ductal adenocarcinoma (PDAC) involves multi-stage development of molecular aberrations affecting signaling pathways that regulate cancer growth and progression. This study was performed to gain a better understanding of the abnormal signaling that occurs in PDAC compared with normal duct epithelia.</p> <p>Methods</p> <p>We performed immunohistochemistry on a tissue microarray of 26 PDAC, 13 normal appearing adjacent pancreatic ductal epithelia, and 12 normal non-PDAC ducts. We compared the levels of 18 signaling proteins including growth factor receptors, tumor suppressors and 13 of their putative downstream phosphorylated (p-) signal transducers in PDAC to those in normal ductal epithelia.</p> <p>Results</p> <p>The overall profiles of signaling protein expression levels, activation states and sub-cellular distribution in PDAC cells were distinguishable from non-neoplastic ductal epithelia. The ERK pathway activation was correlated with high levels of <sup>S2448</sup>p-mTOR (100%, p = 0.05), <sup>T389</sup>p-S6K (100%, p = 0.02 and <sup>S235/236</sup>p-S6 (86%, p = 0.005). Additionally, <sup>T389</sup>p-S6K correlated with <sup>S727</sup>p-STAT3 (86%, p = 0.005). Advanced tumors with lymph node metastasis were characterized by high levels of <sup>S276</sup>p-NFκB (100%, p = 0.05) and <sup>S9</sup>p-GSK3β (100%, p = 0.05). High levels of PKBβ/AKT2, EGFR, as well as nuclear <sup>T202/Y204</sup>p-ERK and <sup>T180/Y182</sup>p-p38 were observed in normal ducts adjacent to PDAC compared with non-cancerous pancreas.</p> <p>Conclusion</p> <p>Multiple signaling proteins are activated in pancreatic duct cell carcinogenesis including those associated with the ERK, PKB/AKT, mTOR and STAT3 pathways. The ERK pathway activation appears also increased in duct epithelia adjacent to carcinoma, suggesting tumor micro-environmental effects.</p
Linking Proteins to Signaling Pathways for Experiment Design and Evaluation
Biomedical experimental work often focuses on altering the functions of selected proteins. These changes can hit signaling pathways, and can therefore unexpectedly and non-specifically affect cellular processes. We propose PathwayLinker, an online tool that can provide a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization. We demonstrate through three case studies that the developed tool can point out unexpected signaling bias in normal laboratory experiments and identify likely novel signaling proteins among the interactors of known drug targets. In our first case study we show that knockdown of the Caenorhabditis elegans gene cdc-25.1 (meant to avoid progeny) may globally affect the signaling system and unexpectedly bias experiments. In the second case study we evaluate the loss-of-function phenotypes of a less known C. elegans gene to predict its function. In the third case study we analyze GJA1, an anti-cancer drug target protein in human, and predict for this protein novel signaling pathway memberships, which may be sources of side effects. Compared to similar services, a major advantage of PathwayLinker is that it drastically reduces the necessary amount of manual literature searches and can be used without a computational background. PathwayLinker is available at http://PathwayLinker.org. Detailed documentation and source code are available at the website
LSD1 inhibition attenuates androgen receptor V7 splice variant activation in castration resistant prostate cancer models
Background: Castrate resistant prostate cancer (CRPC) is often driven by constitutively active forms of the androgen receptor such as the V7 splice variant (AR-V7) and commonly becomes resistant to established hormonal therapy strategies such as enzalutamide as a result. The lysine demethylase LSD1 is a co-activator of the wild type androgen receptor and a potential therapeutic target in hormone sensitive prostate cancer. We evaluated whether LSD1 could also be therapeutically targeted in CRPC models driven by AR-V7. Methods: We utilised cell line models of castrate resistant prostate cancer through over expression of AR-V7 to test the impact of chemical LSD1 inhibition on AR activation. We validated findings through depletion of LSD1 expression and in prostate cancer cell lines that express AR-V7. Results: Chemical inhibition of LSD1 resulted in reduced activation of the androgen receptor through both the wild type and its AR-V7 splice variant forms. This was confirmed and validated in luciferase reporter assays, in LNCaP and 22Rv1 prostate cancer cell lines and in LSD1 depletion experiments. Conclusion: LSD1 contributes to activation of both the wild type and V7 splice variant forms of the androgen receptor and can be therapeutically targeted in models of CRPC. Further development of this approach is warranted
Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering
<p>Abstract</p> <p>Background</p> <p>Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments.</p> <p>Results</p> <p>We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification.</p> <p>Conclusion</p> <p>We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich-data poor' paradox in Systems Biology.</p
Confidence from uncertainty - A multi-target drug screening method from robust control theory
<p>Abstract</p> <p>Background</p> <p>Robustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty.</p> <p>Results</p> <p>We present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty.</p> <p>Conclusions</p> <p>The paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.</p
Non-clinical studies in the process of new drug development - Part II: Good laboratory practice, metabolism, pharmacokinetics, safety and dose translation to clinical studies
Androgen receptor footprint on the way to prostate cancer progression
The prostate gland is exquisitely sensitive to androgen receptor (AR) signaling. AR signaling is obligatory for prostate development and changes in AR levels, its ligands or shifts in AR mode of action are reflected in the physiology of the prostate. The AR is intimately linked to prostate cancer biology through the regulation of epithelial proliferation, suppression of apoptosis and the development of castration-resistant disease. Thus, AR is the primary therapeutic target in various prostate diseases such as BPH and cancer. Although some tumors lose AR expression, most retain the AR and have elevated levels and/or shifts in activity that are required for tumor progression and metastasis. New AR inhibitors currently in clinical trials with higher receptor affinity and specificity may improve prostate cancer patient outcome. Several events play an important role in initiation, primary tumor development and metastatic spread. Androgen receptor activity and promoter specificity change due to altered coregulator expression. Changes in epigenetic surveillance alter the AR cistrome. Both systemic and local inflammation increases with PCa progression affecting AR levels, activity, and requirement for ligand. Our current understanding of AR biology suggest that global androgen suppression may drive the development of castration-resistant disease and therefore the question remains: Does effective inhibition of AR activity mark the end of the road for PCa or only a sharp turn toward a different type of malignancy
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NBS monograph
From Introduction: "The purpose of this report is to present an analysis of time variations in: (1) the phase (relative to a stable phase reference) if a 9414 Mc/s signal transmitted over this path, referred to hereafter as single-path phase data; (2) the phase observed at one receiving antenna relative to that observed at an adjacent antenna, referred to as phase difference data; (3) atmospheric refractivity as recorded by a microwave refractometer located at the Haleakala terminal; (4) the wind velocity at both ends of the path, and (5) surface atmospheric refractivity measured at 5 stations located on or near the path.
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