LIMS and Clinical Data Management

One tool to find them all: a case of data integration and querying in a distributed LIMS platform

In the last years, Laboratory Information Management Systems (LIMS) have been growing from mere inventory systems into increasingly comprehensive software platforms, spanning functionalities as diverse as data search, annotation and analysis. Our institution started in 2011 a LIMS project named the Laboratory Assistant Suite with the purpose of assisting researchers throughout all of their laboratory activities, providing graphical tools to support decision-making tasks and building complex analyses on integrated data. The modular architecture of the system exploits multiple databases with different technologies. To provide an efficient and easy tool for retrieving information of interest, we developed the Multi-Dimensional Data Manager (MDDM). By means of intuitive interfaces, scientists can execute complex queries without any knowledge of query languages or database structures, and easily integrate heterogeneous data stored in multiple databases. Together with the other software modules making up the platform, the MDDM has helped improve the overall quality of the data, substantially reduced the time spent with manual data entry and retrieval and ultimately broadened the spectrum of interconnections among the data, offering novel perspectives to the biomedical analysts.

Semalytics: a semantic analytics platform for the exploration of distributed and heterogeneous cancer data in translational research

Each cancer is a complex system with unique molecular features determining its dynamics, such as its prognosis and response to therapies. Understanding the role of these biological traits is fundamental in order to personalize cancer clinical care according to the characteristics of each patient’s disease. To achieve this, translational researchers propagate patients’ samples through in vivo and in vitro cultures to test different therapies on the same tumor and to compare their outcomes with the molecular profile of the disease. This in turn generates information that can be subsequently translated into the development of predictive biomarkers for clinical use. These large-scale experiments generate huge collections of hierarchical data (i.e. experimental trees) with relative annotations that are extremely difficult to analyze. To address such issues in data analyses, we came up with the Semalytics data framework, the core of an analytical platform that processes experimental information through Semantic Web technologies. Semalytics allows (i) the efficient exploration of experimental trees with irregular structures together with their annotations. Moreover, (ii) the platform links its data to a wider open knowledge base (i.e. Wikidata) to add an extended knowledge layer without the need to manage and curate those data locally. Altogether, Semalytics provides augmented perspectives on experimental data, allowing the generation of new hypotheses, which were not anticipated by the user a priori.

In this work, we present the data core we created for Semalytics, focusing on its semantic nucleus and on how it exploits semantic reasoning and data integration to tackle issues of this kind of analyses. Finally, we describe a proof-of-concept study based on the examination of several dozen cases of metastatic colorectal cancer in order to illustrate how Semalytics can help researchers generate hypotheses about the role of genes alterations in causing resistance or sensitivity of cancer cells to specific drugs.

The High-Throughput Analyses Era: Are We Ready for the Data Struggle?

Recent and rapid technological advances in molecular sciences have dramatically increased the ability to carry out high-throughput studies characterized by big data production. This, in turn, led to the consequent negative effect of highlighting the presence of a gap between data yield and their analysis. Indeed, big data management is becoming an increasingly important aspect of many fields of molecular research including the study of human diseases. Now, the challenge is to identify, within the huge amount of data obtained, that which is of clinical relevance. In this context, issues related to data interpretation, sharing and storage need to be assessed and standardized. Once this is achieved, the integration of data from different -omic approaches will improve the diagnosis, monitoring and therapy of diseases by allowing the identification of novel, potentially actionably biomarkers in view of personalized medicine.

The ins and outs of molecular pathology reporting

The raid evolution in molecular pathology resulting in an increasing complexity requires careful reporting. The need for standardisation is clearer than ever. While synoptic reporting was first used for reporting hereditary genetic diseases, it is becoming more frequent in pathology, especially molecular pathology reports too. The narrative approach is no longer feasible with the growing amount of essential data present on the report, although narrative components are still necessary for interpretation in molecular pathology. On the way towards standardisation of reports, guidelines can be a helpful tool. There are several guidelines that focus on reporting in the field of hereditary diseases, but it is not always feasible to extrapolate these to the reporting of somatic variants in molecular pathology. The rise of multi-gene testing causes challenges for the laboratories. In order to provide a continuous optimisation of the laboratory testing process, including reporting, external quality assessment is essential and has already proven to improve the quality of reports. In general, a clear and concise report for molecular pathology can be created by including elements deemed important by different guidelines, adapting the report to the process flows of the laboratory and integrating the report with the laboratory information management system and the patient record.