61 research outputs found
DNA-PK—A Candidate Driver of Hepatocarcinogenesis and Tissue Biomarker That Predicts Response to Treatment and Survival
No full textPublished OnlineFirst December 5, 2014Abstract not availableLiam Cornell, Joanne M. Munck, Clara Alsinet, Augusto Villanueva, Laura Ogle, Catherine E. Willoughby, Despina Televantou, Huw D. Thomas, Jennifer Jackson, Alastair D. Burt, David Newell, John Rose, Derek M. Manas, Geoffrey I. Shapiro, Nicola J. Curtin, and Helen L. Reeve
Eighth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
No full textA probabilistic author-centered model for Twitter discussions
No full textIn a recent work some of the authors have developed an argumentative approach for discovering relevant opinions in Twitter discussions with probabilistic valued relationships. Given a Twitter discussion, the system builds an argument graph where each node denotes a tweet and each edge denotes a criticism relationship between a pair of tweets of the discussion. Relationships between tweets are associated with a probability value, indicating the uncertainty that the relationships hold. In this work we introduce and investigate a natural extension of the representation model, referred as probabilistic author-centered model, in which tweets within a discussion are grouped by authors, in such a way that tweets of a same author describe his/her opinion in the discussion and are rep- resented with a single node in the graph, and criticism relationships denote controversies between opinions of Twitter users in the discussion. In this new model, the interactions between authors can give rise to circular criticism relationships, and the probability of one opinion criticizing another has to be evaluated from the probabilities of criticism among the tweets that compose both opinions.This work was partially funded by the Spanish MICINN Projects TIN2015-71799-C2-1-P and TIN2015-71799-C2-2-PPeer Reviewe
Anomaly Detection for Diagnosing Failures in a Centrifugal Compressor Train
No full textPredicting machine failures is of the utmost importance in industrial systems as it can turn expensive crashes and repair costs into affordable maintenance costs. To this end, this paper presents preliminary work for detecting failures in a centrifugal compressor train based on sensorial data. We show the detection capabilities of a two-step process consisting of: (1) a preprocessing step to reduce the dimensionality of the input data using Principal Component Analysis, and (2) an anomaly detection step using the Mahalanobis distance to detect anomalous observations on the sensors' data. The experiments using real-world data demonstrate the feasibility of our approach and the ability of the method to detect the failures eight days in advance
An efficient solver for weighted Max-SAT
No full textWe present a new branch and bound algorithm for weighted Max-SAT, called Lazy which incorporates original data structures and inference rules, as well as a lower bound of better quality. We provide experimental evidence that our solver is very competitive and outperforms some of the best performing Max-SAT and weighted Max-SAT solvers on a wide range of instances. © 2007 Springer Science+Business Media LLC.Acknowledgements Research partially supported by projects TIN2006-15662-C02-02 and TIN2004-07933-C03-03 funded by the Ministerio de Ciencia y Tecnología. The second author was supported by a grant Ramón y Cajal.Peer Reviewe
Wnt-pathway activation in two molecular classes of hepatocellular carcinoma and experimental modulation by sorafenib
No full textPurpose: Hepatocellular carcinoma (HCC) is a heterogeneous cancer with active Wnt signaling. Underlying biologic mechanisms remain unclear and no drug targeting this pathway has been approved to date.Weaimed to characterize Wnt-pathway aberrations inHCCpatients, and to investigate sorafenib as a potential Wnt modulator in experimental models of liver cancer. Experimental Design: The Wnt-pathway was assessed using mRNA (642 HCCs and 21 liver cancer cell lines) and miRNA expression data (89 HCCs), immunohistochemistry (108 HCCs), and CTNNB1-mutation data (91 HCCs). Effects of sorafenib on Wnt signaling were evaluated in four liver cancer cell lines with active Wnt signaling and a tumor xenograft model. Results: Evidence for Wnt activation was observed for 315 (49.1%) cases, and was further classified as CTNNB1 class (138 cases [21.5%]) or Wnt-TGFb class (177 cases [27.6%]). CTNNB1 class was characterized by upregulation of liver-specific Wnt-targets, nuclear β-catenin and glutamine-synthetase immunostaining, and enrichment of CTNNB1-mutation- signature, whereas Wnt-TGFβ class was characterized by dysregulation of classical Wnt-targets and the absence of nuclear β-catenin. Sorafenib decreased Wnt signaling and β-catenin protein in HepG2 (CTNNB1 class), SNU387 (Wnt-TGFβ class), SNU398 (CTNNB1-mutation), and Huh7 (lithium-chloride-pathway activation) cell lines. In addition, sorafenib attenuated expression of liver-related Wnt-targets GLUL, LGR5, and TBX3. The suppressive effect on CTNNB1 class-specific Wntpathway activation was validated in vivo using HepG2 xenografts in nude mice, accompanied by decreased tumor volume and increased survival of treated animals. Conclusions: Distinct dysregulation of Wnt-pathway constituents characterize two different Wnt-related molecular classes (CTNNB1 and Wnt-TGFβ), accounting for half of all HCC patients. Sorafenib modulates β-catenin/Wnt signaling in experimental models that harbor the CTNNB1 class signature
Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma
No full textBackground & Aims
In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (Barcelona–Clinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues.
Methods
We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence.
Results
Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified.
Conclusions
We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses
IGF activation in a molecular subclass of hepatocellular carcinoma and pre-clinical efficacy of IGF-1R blockage
No full textBackground & Aims: IGF signaling has a relevant role in a variety of human malignancies. We analyzed the underlying molecular mechanisms of IGF signaling activation in early hepatocellular carcinoma (HCC; BCLC class 0 or A) and assessed novel targeted therapies blocking this pathway.
Methods: An integrative molecular dissection of the axis was conducted in a cohort of 104 HCCs analyzing gene and miRNA expression, structural aberrations, and protein activation. The therapeutic potential of a selective IGF-1R inhibitor, the monoclonal antibody A12, was assessed in vitro and in a xenograft model of HCC.
Results: Activation of the IGF axis was observed in 21% of early HCCs. Several molecular aberrations were identified, such as overexpression of IGF2-resulting from reactivation of fetal promoters P3 and P4-, IGFBP3 downregulation and allelic losses of IGF2R (25% of cases). A gene signature defining IGF-1R activation was developed. Overall, activation of IGF signaling in HCC was significantly associated with mTOR signaling (p = 0.035) and was clearly enriched in the Proliferation subclass of the molecular classification of HCC (p = 0.001). We also found an inverse correlation between IGF activation and miR-100/miR-216 levels (FDR < 0.05). In vitro studies showed that A12-induced abrogation of IGF-1R activation and downstream signaling significantly decreased cell viability and proliferation. In vivo, A12 delayed tumor growth and prolonged survival, reducing proliferation rates and inducing apoptosis.
Conclusions: Integrative genomic analysis showed enrichment of activation of IGF signaling in the Proliferation subclass of HCC. Effective blockage of IGF signaling with A12 provides the rationale for testing this therapy in clinical trials. Published by Elsevier B.V. on behalf of the European Association for the Study of the Liver
The DeuteroNoise Dataset: An Initial Exploration of an Underwater Vessel Sound Dataset Within the Framework of a JPI-Oceans Project
No full textMicroRNA-based classification of hepatocellular carcinoma and oncogenic role of miR-517a
No full textBackground & Aims
Hepatocellular carcinoma (HCC) is a heterogeneous tumor that develops via activation of multiple pathways and molecular alterations. It has been a challenge to identify molecular classes of HCC and design treatment strategies for each specific subtype. MicroRNAs (miRNAs) are involved in HCC pathogenesis, and their expression profiles have been used to classify cancers. We analyzed miRNA expression in human HCC samples to identify molecular subclasses and oncogenic miRNAs.
Methods
We performed miRNA profiling of 89 HCC samples using a ligation-mediated amplification method. Subclasses were identified by unsupervised clustering analysis. We identified molecular features specific for each subclass using expression pattern (Affymetrix U133 2.0; Affymetrix, Santa Clara, CA), DNA change (Affymetrix STY Mapping Array), mutation (CTNNB1), and immunohistochemical (phosphor[p]–protein kinase B, p–insulin growth factor–IR, p-S6, p–epidermal growth factor receptor, β-catenin) analyses. The roles of selected miRNAs were investigated in cell lines and in an orthotopic model of HCC.
Results
We identified 3 main clusters of HCCs: the wingless-type MMTV integration site (32 of 89; 36%), interferon-related (29 of 89; 33%), and proliferation (28 of 89; 31%) subclasses. A subset of patients with tumors in the proliferation subclass (8 of 89; 9%) overexpressed a family of poorly characterized miRNAs from chr19q13.42. Expression of miR-517a and miR-520c (from ch19q13.42) increased proliferation, migration, and invasion of HCC cells in vitro. MiR-517a promoted tumorigenesis and metastatic dissemination in vivo.
Conclusions
We propose miRNA-based classification of 3 subclasses of HCC. Among the proliferation class, miR-517a is an oncogenic miRNA that promotes tumor progression. There is rationale for developing therapies that target miR-517a for patients with HCC
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