Integration of miRNA-regulatory networks in hepatic stellate cells identifies TIMP3 as a key factor in chronic liver disease.

BACKGROUND & AIMS: Activation of hepatic stellate cells (HSC) is a critical process involved in liver fibrosis. Several miRNAs are implicated in gene regulation during this process but their exact and respective contribution is still incompletely understood. Here we propose an integrative approach of miRNA-regulatory networks to predict new targets. METHODS: miRNA regulatory networks in activated HSCs were built using lists of validated miRNAs and the CyTargetLinker tool. The resulting graphs were filtered according to public transcriptomic data and the reduced graphs were analysed through GO annotation. A miRNA network regulating the expression of TIMP3 was further studied in human liver samples, isolated hepatic cells and mouse model of liver fibrosis. RESULTS: Within the up-regulated miRNAs, we identified a subnetwork of five miRNAs (miR-21-5p, miR-222-3p, miR-221-3p miR-181b-5p and miR-17-5p) that target TIMP3. We demonstrated that TIMP3 expression is inversely associated with inflammatory activity and IL1-ß expression in vivo. We further showed that IL1-ß inhibits TIMP3 expression in HSC-derived LX-2 cells. Using data from The Cancer Genome Atlas (TCGA), we showed that, in hepatocellular carcinoma (HCC), TIMP3 expression is associated with survival (P < .001), while miR-221 (P < .05), miR-222 (P < .01) and miR-181b (P < .01) are markers for a poor prognosis. CONCLUSIONS: Several miRNAs targeting TIMP3 are up-regulated in activated HSCs and down-regulation of TIMP3 expression is associated with inflammatory activity in liver fibrosis and poor prognosis in HCC. The regulatory network including specific miRNAs and TIMP3 is therefore central for the evolution of chronic liver disease.

Ontology for assessment studies of human-computer-interaction in surgery.

OBJECTIVE: New technologies improve modern medicine, but may result in unwanted consequences. Some occur due to inadequate human-computer-interactions (HCI). To assess these consequences, an investigation model was developed to facilitate the planning, implementation and documentation of studies for HCI in surgery. METHODS AND MATERIAL: The investigation model was formalized in Unified Modeling Language and implemented as an ontology. Four different top-level ontologies were compared: Object-Centered High-level Reference, Basic Formal Ontology, General Formal Ontology (GFO) and Descriptive Ontology for Linguistic and Cognitive Engineering, according to the three major requirements of the investigation model: the domain-specific view, the experimental scenario and the representation of fundamental relations. Furthermore, this article emphasizes the distinction of "information model" and "model of meaning" and shows the advantages of implementing the model in an ontology rather than in a database. RESULTS: The results of the comparison show that GFO fits the defined requirements adequately: the domain-specific view and the fundamental relations can be implemented directly, only the representation of the experimental scenario requires minor extensions. The other candidates require wide-ranging extensions, concerning at least one of the major implementation requirements. Therefore, the GFO was selected to realize an appropriate implementation of the developed investigation model. The ensuing development considered the concrete implementation of further model aspects and entities: sub-domains, space and time, processes, properties, relations and functions. CONCLUSIONS: The investigation model and its ontological implementation provide a modular guideline for study planning, implementation and documentation within the area of HCI research in surgery. This guideline helps to navigate through the whole study process in the form of a kind of standard or good clinical practice, based on the involved foundational frameworks. Furthermore, it allows to acquire the structured description of the applied assessment methods within a certain surgical domain and to consider this information for own study design or to perform a comparison of different studies. The investigation model and the corresponding ontology can be used further to create new knowledge bases of HCI assessment in surgery.

Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer.

BACKGROUND: Ensuring that all cancer patients have access to the appropriate treatment within an appropriate time is a strategic priority in many countries. There is in particular a need to describe and analyse cancer care trajectories and to produce waiting time indicators. We developed an algorithm for extracting temporally represented care trajectories from coded information collected routinely by the general cancer Registry in Poitou-Charentes region, France. The present work aimed to assess the performance of this algorithm on real-life patient data in the setting of non-metastatic breast cancer, using measures of similarity. METHODS: Care trajectories were modeled as ordered dated events aggregated into states, the granularity of which was defined from standard care guidelines. The algorithm generates each state from the aggregation over a period of tracer events characterised on the basis of diagnoses and medical procedures. The sequences are presented in simple form showing presence and order of the states, and in an extended form that integrates the duration of the states. The similarity of the sequences, which are represented in the form of chains of characters, was calculated using a generalised Levenshtein distance. RESULTS: The evaluation was performed on a sample of 159 female patients whose itineraries were also calculated manually from medical records using the same aggregation rules and dating system as the algorithm. Ninety-eight per cent of the trajectories were correctly reconstructed with respect to the ordering of states. When the duration of states was taken into account, 94% of the trajectories matched reality within three days. Dissimilarities between sequences were mainly due to the absence of certain pathology reports and to coding anomalies in hospitalisation data. CONCLUSIONS: These results show the ability of an integrated regional information system to formalise care trajectories and automatically produce indicators for time-lapse to care instatement, of interest in the planning of care in cancer. The next step will consist in evaluating this approach and extending it to more complex trajectories (metastasis, relapse) and to other cancer localisations.

OWL model of clinical trial eligibility criteria compatible with partially-known information.

BACKGROUND: Clinical trials are important for patients, for researchers and for companies. One of the major bottlenecks is patient recruitment. This task requires the matching of a large volume of information about the patient with numerous eligibility criteria, in a logically-complex combination. Moreover, some of the patient's information necessary to determine the status of the eligibility criteria may not be available at the time of pre-screening. RESULTS: We showed that the classic approach based on negation as failure over-estimates rejection when confronted with partially-known information about the eligibility criteria because it ignores the distinction between a trial for which patient eligibility should be rejected and trials for which patient eligibility cannot be asserted. We have also shown that 58.64% of the values were unknown in the 286 prostate cancer cases examined during the weekly urology multidisciplinary meetings at Rennes' university hospital between October 2008 and March 2009.We propose an OWL design pattern for modeling eligibility criteria based on the open world assumption to address the missing information problem. We validate our model on a fictitious clinical trial and evaluate it on two real clinical trials. Our approach successfully distinguished clinical trials for which the patient is eligible, clinical trials for which we know that the patient is not eligible and clinical trials for which the patient may be eligible provided that further pieces of information (which we can identify) can be obtained. CONCLUSIONS: OWL-based reasoning based on the open world assumption provides an adequate framework for distinguishing those patients who can confidently be rejected from those whose status cannot be determined. The expected benefits are a reduction of the workload of the physicians and a higher efficiency by allowing them to focus on the patients whose eligibility actually require expertise.

Grading glioma tumors using OWL-DL and NCI Thesaurus.

Brain tumors' treatment and prognosis depend to a large extent on their grades. Grading tumors follows a set of rules that refers to domain knowledge. Developing an automatic grading system requires explicit and formal representation of the domain. The NCI Thesaurus is the major ontological resource in the cancer domain. However, the description of brain tumors and grades in the NCI Thesaurus does not enable automatic grading. We have developed an ontology based on the NCI Thesaurus for automatic classification of glioma tumors based on a reference grading system. Two sets of tests have been done. The first one has been automatically generated and the second one consists of eleven pathology reports. The resulting ontology contains 243 classes, among which 234 correspond to NCI Thesaurus classes. Because all of the generated tests were correctly classified, we believe our system to be correct. Ten clinical reports are correctly graded and one is graded incompletely.