An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good

Objective: The Pediatric Cancer Data Commons (PCDC)-a project of Data for the Common Good-houses clinical pediatric oncology data and utilizes the open-source Gen3 platform. To meet the needs of end users, the PCDC development team expanded the out-of-box functionality and developed additional custom features that should be useful to any group developing similar data commons. Materials and methods: Modifications of the PCDC data portal software were implemented to facilitate desired functionality. Results: Newly developed functionality includes updates to authorization methods, expansion of filtering capabilities, and addition of data analysis functions. Discussion: We describe the process by which custom functionalities were developed. Features are open source and available to be implemented and adapted to suit needs of data portals that utilize the Gen3 platform. Conclusion: Data portals are indispensable tools for facilitating data sharing. Open-source infrastructure facilitates a modular and collaborative approach for meeting needs of end users and stakeholders.</p

Development of an open-source, flexible framework for complex inter-institutional disparate data sharing and collaboration

Clinical information, “-omic” datasets, and tissue samples are difficult to harmonize and manage for data mining. We have developed a platform for storing clinical research data while providing access to associated data from other information stores. Data on 34 metrics from 11,000 neuroblastoma patients were instantiated into a database. The Django web framework was used to create a model for rapid development of tools and views with a front-end interface for generating complex queries. Working with Nationwide Children’s Hospital, we can now consume their tissue inventory data through an API. The end-user sees the number of patients who both match their search and have tissue available. Since initial implementation, the current tasks revolve around developing a governance structure and the necessary data use agreements. Efforts now are to (1) update the data with 5000 more patients, and (2) link to genomic data stores, facilitating disparate data acquisition for research studies

Computer‐assisted Curie scoring for metaiodobenzylguanidine (MIBG) scans in patients with neuroblastoma

BackgroundRadiolabeled metaiodobenzylguanidine (MIBG) is sensitive and specific for detecting neuroblastoma. The extent of MIBG‐avid disease is assessed using Curie scores. Although Curie scoring is prognostic in patients with high‐risk neuroblastoma, there is no standardized method to assess the response of specific sites of disease over time. The goal of this study was to develop approaches for Curie scoring to facilitate the calculation of scores and comparison of specific sites on serial scans.ProcedureWe designed three semiautomated methods for determining Curie scores, each with increasing degrees of computer assistance. Method A was based on visual assessment and tallying of MIBG‐avid lesions. For method B, scores were tabulated from a schematic that associated anatomic regions to MIBG‐positive lesions. For method C, an anatomic mesh was used to mark MIBG‐positive lesions with automatic assignment and tallying of scores. Five imaging physicians experienced in MIBG interpretation scored 38 scans using each method, and the feasibility and utility of the methods were assessed using surveys.ResultsThere was good reliability between methods and observers. The user‐interface methods required 57 to 110 seconds longer than the visual method. Imaging physicians indicated that it was useful that methods B and C enabled tracking of lesions. Imaging physicians preferred method B to method C because of its efficiency.ConclusionsWe demonstrate the feasibility of semiautomated approaches for Curie score calculation. Although more time was needed for strategies B and C, the ability to track and document individual MIBG‐positive lesions over time is a strength of these methods.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146464/1/pbc27417.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146464/2/pbc27417_am.pd

The Global academic research organization network: Data sharing to cure diseases and enable learning health systems.

Introduction:Global data sharing is essential. This is the premise of the Academic Research Organization (ARO) Council, which was initiated in Japan in 2013 and has since been expanding throughout Asia and into Europe and the United States. The volume of data is growing exponentially, providing not only challenges but also the clear opportunity to understand and treat diseases in ways not previously considered. Harnessing the knowledge within the data in a successful way can provide researchers and clinicians with new ideas for therapies while avoiding repeats of failed experiments. This knowledge transfer from research into clinical care is at the heart of a learning health system. Methods:The ARO Council wishes to form a worldwide complementary system for the benefit of all patients and investigators, catalyzing more efficient and innovative medical research processes. Thus, they have organized Global ARO Network Workshops to bring interested parties together, focusing on the aspects necessary to make such a global effort successful. One such workshop was held in Austin, Texas, in November 2017. Representatives from Japan, Taiwan, Singapore, Europe, and the United States reported on their efforts to encourage data sharing and to use research to inform care through learning health systems. Results:This experience report summarizes presentations and discussions at the Global ARO Network Workshop held in November 2017 in Austin, TX, with representatives from Japan, Korea, Singapore, Taiwan, Europe, and the United States. Themes and recommendations to progress their efforts are explored. Standardization and harmonization are at the heart of these discussions to enable data sharing. In addition, the transformation of clinical research processes through disruptive innovation, while ensuring integrity and ethics, will be key to achieving the ARO Council goal to overcome diseases such that people not only live longer but also are healthier and happier as they age. Conclusions:The achievement of global learning health systems will require further exploration, consensus-building, funding aligned with incentives for data sharing, standardization, harmonization, and actions that support global interests for the benefit of patients

Mapping Pediatric Oncology Clinical Trial Collaborative Groups on the Global Stage

The global pediatric oncology clinical research landscape, particularly in Central and South America, Africa, and Asia, which bear the highest burden of global childhood cancer cases, is less characterized in the literature. Review of how existing pediatric cancer clinical trial groups internationally have been formed and how their research goals have been pursued is critical for building global collaborative research and data-sharing efforts, in line with the WHO Global Initiative for Childhood Cancer. METHODS: A narrative literature review of collaborative groups performing pediatric cancer clinical research in each continent was conducted. An inventory of research groups was assembled and reviewed by current pediatric cancer regional and continental leaders. Each group was narratively described with identification of common structural and research themes among consortia. RESULTS: There is wide variability in the structure, history, and goals of pediatric cancer clinical trial collaborative groups internationally. Several continental regions have longstanding endogenously-formed clinical trial groups that have developed and published numerous adapted treatment regimens to improve outcomes, whereas other regions have consortia focused on developing foundational database registry infrastructure supported by large multinational organizations or twinning relationships. CONCLUSION: There cannot be a one-size-fits-all approach to increasing collaboration between international pediatric cancer clinical trial groups, as this requires a nuanced understanding of local stakeholders and resources necessary to form partnerships. Needs assessments, performed either by local consortia or in conjunction with international partners, have generated productive clinical trial infrastructure. To achieve the goals of the Global Initiative for Childhood Cancer, global partnerships must be sufficiently granular to account for the distinct needs of each collaborating group and should incorporate grassroots approaches, robust twinning relationships, and implementation science

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

Bioinformatic Analysis and Post-Translational Modification Crosstalk Prediction of Lysine Acetylation

Recent proteomics studies suggest high abundance and a much wider role for lysine acetylation (K-Ac) in cellular functions. Nevertheless, cross influence between K-Ac and other post-translational modifications (PTMs) has not been carefully examined. Here, we used a variety of bioinformatics tools to analyze several available K-Ac datasets. Using gene ontology databases, we demonstrate that K-Ac sites are found in all cellular compartments. KEGG analysis indicates that the K-Ac sites are found on proteins responsible for a diverse and wide array of vital cellular functions. Domain structure prediction shows that K-Ac sites are found throughout a wide variety of protein domains, including those in heat shock proteins and those involved in cell cycle functions and DNA repair. Secondary structure prediction proves that K-Ac sites are preferentially found in ordered structures such as alpha helices and beta sheets. Finally, by mutating K-Ac sites in silico and predicting the effect on nearby phosphorylation sites, we demonstrate that the majority of lysine acetylation sites have the potential to impact protein phosphorylation, methylation, and ubiquitination status. Our work validates earlier smaller-scale studies on the acetylome and demonstrates the importance of PTM crosstalk for regulation of cellular function