The Merkel Cell Carcinoma Patient Registry: From Promise to Prototype to Patient.

The Merkel Cell Carcinoma (MCC) Patient Registry is a national multi-institutional collaborative effort that will prospectively follow and record outcomes and events in MCC patients. MCC is the prototypical rare tumor, and this Registry will trail blaze new methodologies that will enable multiple investigators to examine real world outcome data in real time. Deliverables from the Registry include precise patient stratification into risk categories, identification of best practices, real-world data for drug development programs, revelations about optimal sequence and combinations therapies, uncovering low incidence toxicities, and the generation of novel testable hypotheses. Importantly, the Registry offers a way forward in the yet-unsolved dilemma of drug development for rare tumors, since the Registry's design will allow the creation of highly defined patient-level data that can be used as a robust comparator for single arm phase I and II clinical trials. The MCC Task Force comprises members from academic medical centers, the drug industry, the National Institutes of Health, and the US Food and Drug Administration. Project Data Sphere, LLC provides a secure, open-access data sharing platform and comprehensive support to optimize research performance and ensure rigorous and timely results. The Registry is currently in development and is based on a REDCap database integrated into the host institution's electronic medical record. We plan to have the first patient accessioned on Project Data Sphere's data platform in the second quarter of 2022. Members of the MCC Registry Task Force represent a joint effort of research and clinical investigators from academia, industry and regulatory science to develop the first publicly held MCC registry on Project Data Sphere's open-access data platform. Our hope is that this shared repository will allow investigators to identify new approaches, improve treatment outcomes, shorten the time from discovery to implementation and, ultimately, improve patient lives.

Immune-related adverse events associated with immune checkpoint inhibitors: a call to action for collecting and sharing clinical trial and real-world data.

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of cancer, improving outcomes in patients with advanced malignancies. The use of ICIs in clinical practice, and the number of ICI clinical trials, are rapidly increasing. The use of ICIs in combination with other forms of cancer therapy, such as chemotherapy, radiotherapy, or targeted therapy, is also expanding. However, immune-related adverse events (irAEs) can be serious in up to a third of patients. Critical questions remain surrounding the characteristics and outcomes of irAEs, and how they may affect the overall risk-benefit relationship for combination therapies. This article proposes a framework for irAE classification and reporting, and identifies limitations in the capture and sharing of data on irAEs from current clinical trial and real-world data. We outline key gaps and suggestions for clinicians, clinical investigators, drug sponsors, patients, and other stakeholders to make these critical data more available to researchers for pooled analysis, to advance contemporary understanding of irAEs, and ultimately improve the efficacy of ICIs.

Ion Channel ElectroPhysiology Ontology (ICEPO) - a case study of text mining assisted ontology development.

BACKGROUND: Computational modeling of biological cascades is of great interest to quantitative biologists. Biomedical text has been a rich source for quantitative information. Gathering quantitative parameters and values from biomedical text is one significant challenge in the early steps of computational modeling as it involves huge manual effort. While automatically extracting such quantitative information from bio-medical text may offer some relief, lack of ontological representation for a subdomain serves as impedance in normalizing textual extractions to a standard representation. This may render textual extractions less meaningful to the domain experts. METHODS: In this work, we propose a rule-based approach to automatically extract relations involving quantitative data from biomedical text describing ion channel electrophysiology. We further translated the quantitative assertions extracted through text mining to a formal representation that may help in constructing ontology for ion channel events using a rule based approach. We have developed Ion Channel ElectroPhysiology Ontology (ICEPO) by integrating the information represented in closely related ontologies such as, Cell Physiology Ontology (CPO), and Cardiac Electro Physiology Ontology (CPEO) and the knowledge provided by domain experts. RESULTS: The rule-based system achieved an overall F-measure of 68.93% in extracting the quantitative data assertions system on an independently annotated blind data set. We further made an initial attempt in formalizing the quantitative data assertions extracted from the biomedical text into a formal representation that offers potential to facilitate the integration of text mining into ontological workflow, a novel aspect of this study. CONCLUSIONS: This work is a case study where we created a platform that provides formal interaction between ontology development and text mining. We have achieved partial success in extracting quantitative assertions from the biomedical text and formalizing them in ontological framework. AVAILABILITY: The ICEPO ontology is available for download at http://openbionlp.org/mutd/supplementarydata/ICEPO/ICEPO.owl.

Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.