References of "Ertaylan, Gökhan 40080124"
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See detailStemness of the hybrid Epithelial/Mesenchymal State in Breast Cancer and Its Association with Poor Survival.
Grosse-Wilde, Anne; Fouquier d'Hérouël, Aymeric UL; McIntosh, Ellie et al

in PLoS ONE (2015), 10(5), 0126522

Breast cancer stem cells (CSCs) are thought to drive recurrence and metastasis. Their identity has been linked to the epithelial to mesenchymal transition (EMT) but remains highly controversial since ... [more ▼]

Breast cancer stem cells (CSCs) are thought to drive recurrence and metastasis. Their identity has been linked to the epithelial to mesenchymal transition (EMT) but remains highly controversial since-depending on the cell-line studied-either epithelial (E) or mesenchymal (M) markers, alone or together have been associated with stemness. Using distinct transcript expression signatures characterizing the three different E, M and hybrid E/M cell-types, our data support a novel model that links a mixed EM signature with stemness in 1) individual cells, 2) luminal and basal cell lines, 3) in vivo xenograft mouse models, and 4) in all breast cancer subtypes. In particular, we found that co-expression of E and M signatures was associated with poorest outcome in luminal and basal breast cancer patients as well as with enrichment for stem-like cells in both E and M breast cell-lines. This link between a mixed EM expression signature and stemness was explained by two findings: first, mixed cultures of E and M cells showed increased cooperation in mammosphere formation (indicative of stemness) compared to the more differentiated E and M cell-types. Second, single-cell qPCR analysis revealed that E and M genes could be co-expressed in the same cell. These hybrid E/M cells were generated by both E or M cells and had a combination of several stem-like traits since they displayed increased plasticity, self-renewal, mammosphere formation, and produced ALDH1+ progenies, while more differentiated M cells showed less plasticity and E cells showed less self-renewal. Thus, the hybrid E/M state reflecting stemness and its promotion by E-M cooperation offers a dual biological rationale for the robust association of the mixed EM signature with poor prognosis, independent of cellular origin. Together, our model explains previous paradoxical findings that breast CSCs appear to be M in luminal cell-lines but E in basal breast cancer cell-lines. Our results suggest that targeting E/M heterogeneity by eliminating hybrid E/M cells and cooperation between E and M cell-types could improve breast cancer patient survival independent of breast cancer-subtype. [less ▲]

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See detailCrowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression
Küffner, Robert; Zach, Neta; Norel, Raquel et al

in Nature Biotechnology (2015), 33(1), 51-57

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better ... [more ▼]

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development. [less ▲]

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See detailInference of surface membrane factors of HIV-1 infection through functional interaction networks.
Ertaylan, Gökhan UL; Jaeger, S.; van Dijk, D. et al

in PLoS ONE (2010), 5(10), 1-2

Background: HIV infection affects the populations of T helper cells, dendritic cells and macrophages. Moreover, it has a serious impact on the central nervous system. It is yet not clear whether this list ... [more ▼]

Background: HIV infection affects the populations of T helper cells, dendritic cells and macrophages. Moreover, it has a serious impact on the central nervous system. It is yet not clear whether this list is complete and why specifically those cell types are affected. To address this question, we have developed a method to identify cellular surface proteins that permit, mediate or enhance HIV infection in different cell/tissue types in HIV-infected individuals. Receptors associated with HIV infection share common functions and domains and are involved in similar cellular processes. These properties are exploited by bioinformatics techniques to predict novel cell surface proteins that potentially interact with HIV. METHODOLOGY/PRINCIPAL FINDINGS: We compiled a set of surface membrane proteins (SMP) that are known to interact with HIV. This set is extended by proteins that have direct interaction and share functional similarity. This resulted in a comprehensive network around the initial SMP set. Using network centrality analysis we predict novel surface membrane factors from the annotated network. We identify 21 surface membrane factors, among which three have confirmed functions in HIV infection, seven have been identified by at least two other studies, and eleven are novel predictions and thus excellent targets for experimental investigation. CONCLUSIONS: Determining to what extent HIV can interact with human SMPs is an important step towards understanding patient specific disease progression. Using various bioinformatics techniques, we generate a set of surface membrane factors that constitutes a well-founded starting point for experimental testing of cell/tissue susceptibility of different HIV strains as well as for cohort studies evaluating patient specific disease progression. [less ▲]

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See detailIdentifying potential survival strategies of HIV-1 through virus-host protein interaction networks
Ertaylan, Gökhan UL; van Dijk, D.; Boucher, C. A. et al

in BMC Systems Biology (2010), 15

Background: The National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human ... [more ▼]

Background: The National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics. RESULTS: Our analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems. CONCLUSIONS: HIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another. [less ▲]

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See detailHIV Decision Support: From Molecule to Man
Sloot, P. M. A.; Coveney, P. V.; Ertaylan, Gökhan UL et al

in Philosophical Transactions : Mathematical, Physical & Engineering Sciences (2009), 367(1898), 2691-2703

Human immunodeficiency virus (HIV) is recognized to be one of the most destructive pandemics in recorded history. Effective highly active antiretroviral therapy and the availability of genetic screening ... [more ▼]

Human immunodeficiency virus (HIV) is recognized to be one of the most destructive pandemics in recorded history. Effective highly active antiretroviral therapy and the availability of genetic screening of patient virus data have led to sustained viral suppression and higher life expectancy in patients who have been infected with HIV. The sheer complexity of the disease stems from the multiscale and highly dynamic nature of the system under study. The complete cascade from genome, proteome, metabolome and physiome to health forms a multidimensional system that crosses many orders of magnitude in temporal and spatial scales. Understanding, quantifying and handling this complexity is one of the biggest challenges of our time, which requires a highly multidisciplinary approach. In order to supply researchers with an interactive framework and to provide the medical professional with appropriate tools and information for making a balanced and reliable clinical decision, we have developed 'ViroLab', a collaborative decision-support system (http://www.virolab.org/). ViroLab contains computational models that cover various spatial and temporal scales from atomic-level interactions in nanoseconds up to sociological interactions on the epidemiological level, spanning years of disease progression. ViroLab allows for personalized drug ranking. It is on trial in six hospitals and various virology and epidemiology laboratories across Europe. [less ▲]

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See detailA complex automata model of HIV-1 co-receptor tropism: mutation rate prediction
Ertaylan, Gökhan UL; Sloot, P. M. A.

in Bubak, M.T.; Turala, M.; Wiatr, K. (Eds.) CGW'07 proceedings (2008)

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See detailA complex automata model of HIV-1 co-receptor tropism: Understanding mutation rate pressure
Ertaylan, Gökhan UL; Sloot, P. M. A.

in Reviews in Antiretroviral Therapy (2007)

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See detailVirolab: A Distributed Decision Support System for Viral Disease Treatment
Sloot, P. M. A.; Tirado-Ramos, A.; Ertaylan, Gökhan UL et al

in Bubak, M.T.; Turala, M.; Wiatr, K. (Eds.) CGW'07 proceedings (2007)

The HIV drug-resistance interpretation systems are used routinely throughout the world in a clinical setting. More knowledge is rapidly becoming available upon which clinical decisions could be made. This ... [more ▼]

The HIV drug-resistance interpretation systems are used routinely throughout the world in a clinical setting. More knowledge is rapidly becoming available upon which clinical decisions could be made. This knowledge, information, data and evidence from many sources are combined within a Decision Support System (DSS) to provide coherent judgements on drug-susceptibility. At the core of the DSS is a HIV drug-resistance interpretation system incorporating knowledge from the principal systems (Stanford HIVdb, Rega, ANRS, Virolab ) in use throughout the world. We describe an improved rule-based language which has adequate expressiveness and enjoys a fully-specified, formal semantics, allowing for automated reasoning over rule sets. Among the questions which can be addressed are: • Ambiguity: Is the rule set internally ambiguous? Does it allow more than one interpretation? • Completeness: Does the rule set have complete coverage? • Consistency: Are there rules in the set which make contradictory predictions? • Redundancy: Do some rules of a rule set subsume others? • Dissonance: How do rule sets differ in their predictions? • Predictive power: Can one rule set make more specific predictions than another or can it make predictions in cases where the other is silent? The formal language which we present has a well-defined semantics that will allow for making judgements of the above kinds using reasoning that is either completely automated or at least semi-automated. Furthermore recent findings have revealed the need to express multiplicative effects of certain mutations on drugs. The state of the art language for specifying HIV drug interpretation rules, ASI, in its present form, is limited to linear combinations of effects. In future work we will use Bayesian hierarchical modelling to make predictive distributions in the presence of uncertainty. The full chain of analysis will combine Bayesian hierarchical modelling with probabilistic decision analysis based on utility attribution and/or multi-objective optimisation of such quantities as cost,chance and duration of survival or quality-adjusted life years. [less ▲]

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