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Deferasirox (DFX) is used for the management of iron overload (IOL) in many haematological malignancies including myelofibrosis (MF). The ‘RUX-IOL’ study retrospectively collected 69 MF patients treated with ruxolitinib (RUX) and DFX for IOL to assess: safety, efficacy in term of iron chelation response (ICR) and erythroid response (ER), and impact on overall survival of the combination therapy. The RUX–DFX therapy was administered for a median time of 12.4 months (interquartile range 3.1–71.2). During treatment, 36 (52.2%) and 34 (49.3%) patients required RUX and DFX dose reductions, while eight (11.6%) and nine (13.1%) patients discontinued due to RUX- or DFX-related adverse events; no unexpected toxicity was reported. ICR and ER were achieved by 33 (47.8%) and 32 patients (46.4%) respectively. Thirteen (18.9%) patients became transfusion-independent. Median time to ICR and ER was 6.2 and 2 months respectively. Patients achieving an ER were more likely to obtain an ICR also (p = 0.04). In multivariable analysis, the absence of leukocytosis at baseline (p = 0.02) and achievement of an ICR at any time (p = 0.02) predicted improved survival. In many MF patients, the RUX–DFX combination provided ICR and ER responses that correlated with improved outcome in the absence of unexpected toxicities. This strategy deserves further clinical investigation.

Background and Objectives: While tyrosine kinase inhibitors (TKIs) have transformed CP-CML management, limited data exist on their use in clinical practice. Methods: SIMPLICITY (NCT01244750) is an observational study in CP-CML patients, exploring first-line (1L) TKI use and management patterns in the US and Europe. Over half of the patients recruited in Europe are from Italy (n=266). This is an analysis of the Italian cohort and a comparison with the rest of the European SIMPLICITY population. Baseline demographic, factors influencing the choice of first-line TKI, response monitoring patterns and predictors of monitoring, and treatment interruptions, discontinuations and switching by index TKIs are presented for the Italian cohort in the first year of treatment and compared with that for the overall European SIMPLICITY cohort. Results: Italian patients received 1L imatinib (IM; retrospective [(n=31]; prospective [n=106]), dasatinib (DAS; n=56) or nilotinib (NIL; n=73). Documented cytogenetic response monitoring by 12 months was lower than expected, but almost all patients had documented molecular response monitoring. Fewer patients discontinued first-line TKI by 12 months in Italy compared with the rest of the European SIMPLICITY population (p=0.003). Of those with ≥12 months follow-up since the start of 1L TKI, only 7.1% (n=19) of Italian patients switched to a second-line TKI, a third less than in the rest of the European SIMPLICITY population. Of interest, intolerance as opposed to resistance, was the main reason for switching. Conclusions: This analysis provides valuable insights into management and treatment patterns in Italian patients with CML within routine clinical practice.

Background and Objectives: While tyrosine kinase inhibitors (TKIs) have transformed CP-CML management, limited data exist on their use in clinical practice. Methods: SIMPLICITY (NCT01244750) is an observational study in CP-CML patients, exploring first-line (1L) TKI use and management patterns in the US and Europe. Over half of the patients recruited in Europe are from Italy (n=266). This is an analysis of the Italian cohort and a comparison with the rest of the European SIMPLICITY population. Baseline demographic, factors influencing the choice of first-line TKI, response monitoring patterns and predictors of monitoring, and treatment interruptions, discontinuations and switching by index TKIs are presented for the Italian cohort in the first year of treatment and compared with that for the overall European SIMPLICITY cohort. Results: Italian patients received 1L imatinib (IM; retrospective [(n=31]; prospective [n=106]), dasatinib (DAS; n=56) or nilotinib (NIL; n=73). Documented cytogenetic response monitoring by 12 months was lower than expected, but almost all patients had documented molecular response monitoring. Fewer patients discontinued first-line TKI by 12 months in Italy compared with the rest of the European SIMPLICITY population (p=0.003). Of those with ≥12 months follow-up since the start of 1L TKI, only 7.1% (n=19) of Italian patients switched to a second-line TKI, a third less than in the rest of the European SIMPLICITY population. Of interest, intolerance as opposed to resistance, was the main reason for switching. Conclusions: This analysis provides valuable insights into management and treatment patterns in Italian patients with CML within routine clinical practice.

An increasing number of research and industrial initiatives have focused on publishing Linked Open Data, but little attention has been provided to help consumers to better understand existing data sets. In this paper we discuss how an ontology-driven data abstraction model supports the extraction and the representation of summaries of linked data sets. The proposed summarization model is the backbone of the ABSTAT framework, that aims at helping users understanding big and complex linked data sets. The proposed model produces a summary that is correct and complete with respect to the assertions of the data set and whose size scales well with respect to the ontology and data size. Our framework is evaluated by showing that it is capable of unveiling information that is not explicitly represented in underspecified ontologies and that is valuable to users, e.g., helping them in the formulation of SPARQL queries.

A Knowledge Graph (KG) is a semantically organized, machine readable collection of types, entities, and relations holding between them. A KG helps in mitigating semantic heterogeneity in scenarios that require the integration of data from independent sources into a so called dataspace, realized through the establishment of mappings between the sources and the KG. Applications built on top of a dataspace provide advanced data access features to end-users based on the representation provided by the KG, obtained through the enrichment of the KG with domain specific facets. A facet is a specialized type of relation that models a salient characteristic of entities of particular domains (e.g., the vintage of wines) from an end-user perspective. In order to enrich a KG with a salient and meaningful representation of data, domain experts in charge of maintaining the dataspace must be in possess of extensive knowledge about disparate domains (e.g., from wines to football players). From an end-user perspective, the difficulties in the definition of domain specific facets for dataspaces significantly reduce the user-experience of data access features and thus the ability to fulfill the information needs of end-users. Remarkably, this problem has not been adequately studied in the literature, which mostly focuses on the enrichment of the KG with a generalist, coverage oriented, and not domain specific representation of data occurring in the dataspace. Motivated by this challenge, this dissertation introduces automatic techniques to support domain experts in the enrichment of a KG with facets that provide a domain specific representation of data. Since facets are a specialized type of relations, the techniques proposed in this dissertation aim at extracting salient domain specific relations. The fundamental components of a dataspace, namely the KG and the mappings between sources and KG elements, are leveraged to elicitate such domain specific representation from specialized dat

An increasing number of research and industrial initiatives have focused on publishing Linked Open Data, but little attention has been provided to help consumers to better understand existing data sets. In this paper we discuss how an ontology-driven data abstraction model supports the extraction and the representation of summaries of linked data sets. The proposed summarization model is the backbone of the ABSTAT framework, that aims at helping users understanding big and complex linked data sets. Our framework is evaluated by showing that it is capable of unveiling information that is not explicitly represented in underspecified ontologies and that is valuable to users, e.g., helping them in the formulation of SPARQL queries.

While much work has focused on continuously publishing Linked Open Data, little work considers how to help consumers to better understand existing datasets. ABSTAT framework aims at providing a better understanding of big and complex datasets by extracting summaries of linked data sets based on an ontology-driven data abstraction model. Our ABSTAT framework takes as input a data set and an ontology and returns an ontology-driven data summary as output. The summary is exported into RDF and then made accessible through a SPARQL endpoint and a web interface to support the navigation.