BigQ: a NoSQL based framework to handle genomic variants in i2b2.

BACKGROUND: Precision medicine requires the tight integration of clinical and molecular data. To this end, it is mandatory to define proper technological solutions able to manage the overwhelming amount of high throughput genomic data needed to test associations between genomic signatures and human phenotypes. The i2b2 Center (Informatics for Integrating Biology and the Bedside) has developed a widely internationally adopted framework to use existing clinical data for discovery research that can help the definition of precision medicine interventions when coupled with genetic data. i2b2 can be significantly advanced by designing efficient management solutions of Next Generation Sequencing data. RESULTS: We developed BigQ, an extension of the i2b2 framework, which integrates patient clinical phenotypes with genomic variant profiles generated by Next Generation Sequencing. A visual programming i2b2 plugin allows retrieving variants belonging to the patients in a cohort by applying filters on genomic variant annotations. We report an evaluation of the query performance of our system on more than 11 million variants, showing that the implemented solution scales linearly in terms of query time and disk space with the number of variants. CONCLUSIONS: In this paper we describe a new i2b2 web service composed of an efficient and scalable document-based database that manages annotations of genomic variants and of a visual programming plug-in designed to dynamically perform queries on clinical and genetic data. The system therefore allows managing the fast growing volume of genomic variants and can be used to integrate heterogeneous genomic annotations.

Transcriptome based identification of mouse cumulus cell markers that predict the developmental competence of their enclosed antral oocytes.

BACKGROUND: The cumulus cells (CCs) enveloping antral and ovulated oocytes have been regarded as putative source of non-invasive markers of the oocyte developmental competence. A number of studies have indeed observed a correlation between CCs gene expression, embryo quality, and final pregnancy outcome. Here, we isolated CCs from antral mouse oocytes of known developmental incompetence (NSN-CCs) or competence (SN-CCs) and compared their transcriptomes with the aim of identifying distinct marker transcripts. RESULTS: Global gene expression analysis highlighted that both types of CCs share similar transcriptomes, with the exception of 422 genes, 97.6% of which were down-regulated in NSN-CCs vs. SN-CCs. This transcriptional down-regulation in NSN-CCs was confirmed by qRT-PCR analysis of CC-related genes (Has2, Ptx3, Tnfaip6 and Ptgs2). Only ten of the 422 genes were up-regulated with Amh being the most up-regulated in NSN-CCs, with an average 4-fold higher expression when analysed by qRT-PCR. CONCLUSIONS: The developmental incompetence (NSN) or competence (SN) of antral oocytes can be predicted using transcript markers expressed by their surrounding CCs (i.e., Has2, Ptx3, Tnfaip6, Ptgs2 and Amh). Overall, the regulated nature of the group of genes brought out by whole transcriptome analysis constitutes the molecular signature of CCs associated either with developmentally incompetent or competent oocytes and may represent a valuable resource for developing new molecular tools for the assessment of oocyte quality and to further investigate the complex bi-directional interaction occurring between CCs and oocyte.

The Case Manager: An Agent Controlling the Activation of Knowledge Sources in a FHIR-Based Distributed Reasoning Environment.

BACKGROUND: Within the CAPABLE project the authors developed a multi-agent system that relies on a distributed architecture. The system provides cancer patients with coaching advice and supports their clinicians with suitable decisions based on clinical guidelines. OBJECTIVES: As in many multi-agent systems we needed to coordinate the activities of all agents involved. Moreover, since the agents share a common blackboard where all patients' data are stored, we also needed to implement a mechanism for the prompt notification of each agent upon addition of new information potentially triggering its activation. METHODS: The communication needs have been investigated and modeled using the HL7-FHIR (Health Level 7-Fast Healthcare Interoperability Resources) standard to ensure proper semantic interoperability among agents. Then a syntax rooted in the FHIR search framework has been defined for representing the conditions to be monitored on the system blackboard for activating each agent. RESULTS: The Case Manager (CM) has been implemented as a dedicated component playing the role of an orchestrator directing the behavior of all agents involved. Agents dynamically inform the CM about the conditions to be monitored on the blackboard, using the syntax we developed. The CM then notifies each agent whenever any condition of interest occurs. The functionalities of the CM and other actors have been validated using simulated scenarios mimicking the ones that will be faced during pilot studies and in production. CONCLUSION: The CM proved to be a key facilitator for properly achieving the required behavior of our multi-agent system. The proposed architecture may also be leveraged in many clinical contexts for integrating separate legacy services, turning them into a consistent telemedicine framework and enabling application reusability.

An eHealth App (CAPABLE) Providing Symptom Monitoring, Well-Being Interventions, and Educational Material for Patients With Melanoma Treated With Immune Checkpoint Inhibitors: Protocol for an Exploratory Intervention Trial.

BACKGROUND: Since treatment with immune checkpoint inhibitors (ICIs) is becoming standard therapy for patients with high-risk and advanced melanoma, an increasing number of patients experience treatment-related adverse events such as fatigue. Until now, studies have demonstrated the benefits of using eHealth tools to provide either symptom monitoring or interventions to reduce treatment-related symptoms such as fatigue. However, an eHealth tool that facilitates the combination of both symptom monitoring and symptom management in patients with melanoma treated with ICIs is still needed. OBJECTIVE: In this pilot study, we will explore the use of the CAPABLE (Cancer Patients Better Life Experience) app in providing symptom monitoring, education, and well-being interventions on health-related quality of life (HRQoL) outcomes such as fatigue and physical functioning, as well as patients' acceptance and usability of using CAPABLE. METHODS: This prospective, exploratory pilot study will examine changes in fatigue over time in 36 patients with stage III or IV melanoma during treatment with ICI using CAPABLE (a smartphone app and multisensory smartwatch). This cohort will be compared to a prospectively collected cohort of patients with melanoma treated with standard ICI therapy. CAPABLE will be used for a minimum of 3 and a maximum of 6 months. The primary endpoint in this study is the change in fatigue between baseline and 3 and 6 months after the start of treatment. Secondary end points include HRQoL outcomes, usability, and feasibility parameters. RESULTS: Study inclusion started in April 2023 and is currently ongoing. CONCLUSIONS: This pilot study will explore the effect, usability, and feasibility of CAPABLE in patients with melanoma during treatment with ICI. Adding the CAPABLE system to active treatment is hypothesized to decrease fatigue in patients with high-risk and advanced melanoma during treatment with ICIs compared to a control group receiving standard care. The Medical Ethics Committee NedMec (Amsterdam, The Netherlands) granted ethical approval for this study (reference number 22-981/NL81970.000.22). TRIAL REGISTRATION: ClinicalTrials.gov NCT05827289; https://clinicaltrials.gov/study/NCT05827289. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49252.

Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future.

Real-world evidence from multinational disease registries is becoming increasingly important not only for confirming the results of randomised controlled trials, but also for identifying phenotypes, monitoring disease progression, predicting response to new drugs and early detection of rare side-effects. With new open-access technologies, it has become feasible to harmonise patient data from different disease registries and use it for data analysis without compromising privacy rules. Here, we provide a blueprint for how a clinical research collaboration can successfully use real-world data from existing disease registries to perform federated analyses. We describe how the European severe asthma clinical research collaboration SHARP (Severe Heterogeneous Asthma Research collaboration, Patient-centred) fulfilled the harmonisation process from nonstandardised clinical registry data to the Observational Medical Outcomes Partnership Common Data Model and built a strong network of collaborators from multiple disciplines and countries. The blueprint covers organisational, financial, conceptual, technical, analytical and research aspects, and discusses both the challenges and the lessons learned. All in all, setting up a federated data network is a complex process that requires thorough preparation, but above all, it is a worthwhile investment for all clinical research collaborations, especially in view of the emerging applications of artificial intelligence and federated learning.

Information technology solutions to support translational research on inherited cardiomyopathies.

The INHERITANCE project, funded by the European Commission, is aimed at studying genetic or inherited Dilated cardiomyopathies (DCM) and at understanding the impact and management of the condition within families that suffer from heart conditions that are caused by DCMs. The project is supported by a number of advanced biomedical informatics tools, including data warehousing, automated literature search and decision support. The paper describes the design of these tools and the current status of implementation.

i2b2 to Optimize Patients Enrollment.

i2b2 data-warehouse could be a useful tool to support the enrollment phase of clinical studies. The aim of this work is to evaluate its performance on two clinical trials. We developed also an i2b2 extension to help in suggesting eligible patients for a study. The work showed good results in terms of ability to implement inclusion/exclusion criteria, but also in terms of identified patients actually enrolled and high number of patients suggested as potentially enrollable.

The Case Manager: Driving Medical Reasoning in a Distributed Environment for Home Patient Monitoring.

The CAPABLE project has been funded by the EU Horizon 2020 Programme over the years 2020-24 to support home care. A system is being designed and implemented supporting remote monitoring and virtual coaching for cancer patients. The system is based on a distributed modular architecture involving many components encapsulating various knowledge. The Case Manager has been designed as a separate component with the aim of coordinating the problem solving strategies. A first version of the Case Manager has been released and used by the components in a prototypical scenario shown at the first project review.

Text Mining approaches for automated literature knowledge extraction and representation.

Due to the overwhelming volume of published scientific papers, information tools for automated literature analysis are essential to support current biomedical research. We have developed a knowledge extraction tool to help researcher in discovering useful information which can support their reasoning process. The tool is composed of a search engine based on Text Mining and Natural Language Processing techniques, and an analysis module which process the search results in order to build annotation similarity networks. We tested our approach on the available knowledge about the genetic mechanism of cardiac diseases, where the target is to find both known and possible hypothetical relations between specific candidate genes and the trait of interest. We show that the system i) is able to effectively retrieve medical concepts and genes and ii) plays a relevant role assisting researchers in the formulation and evaluation of novel literature-based hypotheses.

An Extension of the i2b2 Data Warehouse to Support REDCap Dynamic Data Pull.

i2b2 and REDCap are two widely adopted solutions respectively to facilitate data re-use for research purpose and to manage non-for-profit research studies. REDCap provides the design specifications to build a web service used to import data from an external source with a procedure called DDP. In this work we have developed a web service that implements these specifications in order to import data from i2b2. Our approach has been tested with a real REDCap study.

Combining clinical and genomics queries using i2b2 - Three methods.

We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical "Big Data" challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine.

Beyond Cohort Selection: An Analytics-Enabled i2b2.

The i2b2 software is a widely adopted solution for secondary use of clinical data for clinical research, specifically designed for cohort identification. i2b2 is still lacking functionalities for data analysis. The aim of this work is to empower the i2b2 framework enabling clinical researchers to perform statistical analyses for accelerating the process of hypothesis testing. To this aim we have developed a flexible extension of i2b2 able to exploit different statistical engines. We have implemented some first applications for basic statistics and survival analyses, exploiting this extension and accessible through suitable user interfaces designed with a special consideration for usability.

A Unified Medical Language System (UMLS) based system for Literature-Based Discovery in medicine.

Literature-Based Discovery (LBD) is a technique that can be used in translational research to connect the very sparse and huge information available in scientific publications in order to extract new knowledge. This paper presents an LBD system based on the open discovery paradigm exploiting NLP techniques and UMLS medical concepts mapping, to provide a set of tools useful to discover unknown relationships. The system has been evaluated on the problem of discovering new candidate genes potentially related to dilated cardiomyopathies (DCM), and can be used in any medical context to connect different type of concepts. The validation of the system involves reproducing the discovery of genes currently associated to DCM. Validation showed that the system is able to discover many gene-disease associations by using the literature available before their first publication in a scientific article.