The biorepository portal toolkit: an honest brokered, modular service oriented software tool set for biospecimen-driven translational research
© The Author(s). 2016
Published: 18 August 2016
High throughput molecular sequencing and increased biospecimen variety have introduced significant informatics challenges for research biorepository infrastructures. We applied a modular system integration approach to develop an operational biorepository management system. This method enables aggregation of the clinical, specimen and genomic data collected for biorepository resources.
We introduce an electronic Honest Broker (eHB) and Biorepository Portal (BRP) open source project that, in tandem, allow for data integration while protecting patient privacy. This modular approach allows data and specimens to be associated with a biorepository subject at any time point asynchronously. This lowers the bar to develop new research projects based on scientific merit without institutional review for a proposal.
By facilitating the automated de-identification of specimen and associated clinical and genomic data we create a future proofed specimen set that can withstand new workflows and be connected to new associated information over time. Thus facilitating collaborative advanced genomic and tissue research.
As of Janurary of 2016 there are 23 unique protocols/patient cohorts being managed in the Biorepository Portal (BRP). There are over 4000 unique subject records in the electronic honest broker (eHB), over 30,000 specimens accessioned and 8 institutions participating in various biobanking activities using this tool kit. We specifically set out to build rich annotation of biospecimens with longitudinal clinical data; BRP/REDCap integration for multi-institutional repositories; EMR integration; further annotated specimens with genomic data specific to a domain; build application hooks for experiments at the specimen level integrated with analytic software; while protecting privacy per the Office of Civil Rights (OCR) and HIPAA.
Current research is yielding rapid advances in personalized, precision medicine through targeted therapies based on an individual’s genome, genomic biomarkers, and cell biology across adult and pediatric translational research [1, 2]. This type of research has become increasingly dependent on the collection of large cohorts of high quality human biospecimens that are paired with clinical annotations . While biospecimen-driven research is widely practiced, it is often limited in scope because it requires time-consuming manual processes such as retrospective annotation, cohort identification and institutional human subjects research oversight . Consequently, many academic medical centers are creating large institutional biospecimen resources that can be leveraged by numerous investigators . There is a trend towards these resources becoming indispensable in academic medical centers [3, 6].
Biorepository data is typically captured in longitudinal, asynchronous workflows that pose software design and data integration challenges . An optimal system must provide de-identified, granular and longitudinal data to researchers while also enabling data collection workflows that require patient identification . The required data often resides in separate systems such as a Laboratory Information Management System (LIMS), Research Data Capture tools, the Electronic Health Record (EHR), genomic data stores and high performance computing clusters [9, 10]. Integrative solutions are necessary at the point of collection and at information and specimen retrieval. The data must be curated to ensure it is persisted in an understandable representation for researchers in a specific medical domain [11, 12]. As biorepository resources include more clinical information and grow in scale, there are more opportunities for protected health information (PHI) to be injected into the process [13, 14]. Therefore, a central component to this toolkit is an informatics-based approach to honest brokering . We build on methods described in Dhir et al. and Boyd et al. that describe specific implementations of software to aid in the honest brokering between various types of clinical data collection and de-identified biorepositories [14, 16]. We take a slightly different approach by creating non-user facing software service similar to Boyd, et al. for the honest broker as one of many components of a toolkit of connected operational biorespository informatics resources. We remove the human component completely from the de-identification and re-identification of research records in connected research systems.
In this paper, we address the creation of a robust biorepository management platform that enables association of a physical biospecimens, clinical diagnoses, and patient, genomic and research. This platform utilizes a modular software architecture developed at the Children’s Hospital of Philadelphia (CHOP) in partnership with the Children’s Brain Tumor Tissue Consortium (CBTTC) . The platform was developed in the specific context of distributed biorepository and biobank studies in biological tissue and genomic research. In this manuscript, we describe the requirements, challenges in architectural design and implementation to create integrated data resources in biospecimen-driven translational research. We designed and utilized an open source, modular software toolkit that supports biorepository operations and de-identified secondary usage. We created an operational and scientific resource that protects subject privacy, allows for variable specimen and data management workflows and flexible resource queries. When new systems and workflows are introduced, the toolkit allows for flexible introduction of new data types, systems and operational workflows spanning specimen, clinical, imaging and genomic data. Our platform allows for extensible software and data resources for biospecimen-driven translational research.
Cancer is a main focus of current precision medicine initiatives. This is reflected in politics, the media, public funding and medical research community priorities . We are in an age of increased use of web technologies that allow us to reach new levels of productivity and connectivity in business, finance, government and entertainment . The time has come for us to use these same techniques to unravel the complexities of cancer . New breakthroughs are helping us use our own immune systems to target an increasing list of common cancers . Unfortunetely, time is not on the side of children suffering from rare brain tumors. Recent research and government population health programs identify over 120 types of pediatric brain cancers . To make matters more complex, the origins of brain tumors in kids is widely unknown [22–25]. Pediatric cancer patients are treated for cancers with adult-based therapies and there is a lack of investment from pharmaceutical companies in the specific diseases affecting children . It is essential to create biologic- and data-centric resources to find pathways and molecularly describe disease seen in research similar to Bastianos et al. and Parsons et al. where developments, respectively, uncover a molecular pathway in Craniopharyngiomas and a comprehensive molecular description of the common childhood brain tumor, Medulloblastoma [27, 28]. Though molecularly based research has become common with the availability of high throughput technologies, further progress is needed in infrastructure, specifically in cancer research, that allows for complete clinical annotation of specimen and genomic data from consented subjects (, p. 549).
Initial research of rare tumors at CHOP brought biorepository data sharing, management, and annotation issues to the forefront. A need for enhanced capabilities was particularly evident in two proposed studies targeted by our initial software system design. These tumor biorepository studies originally used a human honest broker to manage the de-identification and re-identification of records to exclude protected patient information (a/k/a patient health information) from the research LIMS. The process began with manual data intake by a data manager on hardcopy REDCap case report forms (CRFs) . The CRFs contained the patient identifiers: Medical Record Number (MRN), First Name, Last Name and Date of Birth. The CRF was physically delivered to a human honest broker that would create a new electronic REDCap record with a research identifier. The hardcopy CRF was then returned to the data manager with the research identifier attached and the patient identifiers removed. The data manager then abstracted the hand written CRFs to the associated REDCap project record. Each longitudinal data collection event required manual re-identification by the human honest broker. This process became unsustainable as biospecimen and clinical data collection increased and molecular experimentation associated with records began. It was also difficult to complete the CRF in a single patient encounter due to variations in encounter length and frequency. This experience clearly illustrated the need for a scalable solution that would abide by NCI Best Practices for Biospecimen Resources . The Biorepository Portal Toolkit (BRP Toolkit) project was subsequently developed to support biorepository development at institutional scale.
We took a modular and entity-driven integrated systems approach to facilitate variable specimen acquisition and data collection events. The primary entity is the patient enrolled as a research subject on the study. The subject entity is created in the electronic honest broker (eHB) and assoicated with a master patient index (MPI) and the subject’s instutional origin. Each external research record associated with the subject record, in this case the data management tool, REDCap, is not limited to a one-to-one relation of subject-to-REDCap record. The subject entity can be assoicated with many projects, forms and records in a 1-to-many entity relationaship . We, in tandem, built a research portal, dubbed the Biorepository Portal (BRP). The BRP can access subject records in the electronic Honest Broker (eHB) and subsequent external research records through token-based authorization from that client system. The BRP reproduces the REDCap electronic Case Report Forms (eCRFs) based on records stored in the eHB with a custom REDCap client utilizing the REDCap application programing interface (API), in real-time. This produces a complete form for that subject at time of access. It displays the subject information and identifiers at the top of the screen at all times during form data entry and while shifting from form to form. A research coordinator or data manager can enter any temporal and longitudinal research data based on their protocol subject list at anytime or in any order (i.e., asynchronously) while maintaining the continuous de-identification and re-identification of research data automatically.
The CHOP Biorepository Core Facility utilizes ThermoFisher Nautilus as its LIMS. As part of our method, we also built a client to this LIMS that allows for association of an arbitrary number of specimen records in the LIMS with the corresponding subject record. In this way specimens can also be collected longitudinally over time. Data and specimen coordinators have the ability to associate sets of specimens with a subject or event and annotate that specimen on the fly in one system. For downstream integration, we use the same eHB software service to perform our Extract Transform Load (ETL) processes that are tailored to each project. The result is a regularly updated non-human subject research database that allows for seamless queries spanning research and clinical data sets. We allow collaborators to access specific sets of data via the data exploration tool, Harvest , customized for each project. The phenotypic data associated with specimen records can be integrated with direct clinical data from the EHR with appropriate institutional permissions. The modular approach allows us to integrate genomic visualization tools at the specimen level where applicable. For cancer genomics specifically, we utilize the CBioPortal [33, 34], an open source tool to visualize mutation and gene expression data from The Cancer Genome Atlas (TCGA).
Web service oriented architecture
Electronic Honest Broker (eHB) software service
The concept of an honest broker has been implemented in other academic medical center environments to protect privacy when integrating research data ([14, 42], pp. 56–107). Central to our solution is the eHB, a web-based software service with end-to-end encryption that maintains an index of subjects linked to their associated research records. The initial studies/projects targeted with this solution began as a manual paper-based process of considerable complexity, and are now an “informatics tool” . The index in the eHB uniquely identifies each patient through a combination of organizational association (e.g., The Children’s Hospital of Philadelphia) and a unique organization provided identifier (e.g., Medical Record Number). The eHB associates each subject record with trusted external systems and known record identifiers in those systems. For annotated biorepository studies, the eHB maintains associations to de-identified records in REDCap and the LIMS. Following our SOA approach, the eHB makes data and system functionality available via a REST web service. This allows the addition of new data management application clients in a way that is system- and programming language-agnostic. To control access, the eHB uses token-based authorization, and encrypts its data both at rest and in transit, relying on client-side keys to decrypt the payload received from the API. For known applications, the eHB provides subject data with few restrictions. Client applications determine the context of what information is appropriate to display to a user, thereby enabling flexibility to meet different workflow and protocol requirements.
The Biorepository Portal (BRP)
The REDCap client in the BRP makes a request for the metadata and data from a subject record stored in the eHB and recreates the form requested utilizing the REDCap API. The REDCap form client shows the patient Medical Record Number (MRN), last name, first name and date of birth along with each customized research data capture form. After the form record is saved, the BRP utilizes the eHB software service to either create a new record or modify an existing record in the REDCap project. The REDCap project record identifier is hashed and randomly generated without use of derived patient information. eHB identifiers are generated utilizing the application client key, in combination with a salted hash value which is guaranteed to be unique . Creating a research identifier not derived from a direct patient identifier is required when using patient data for research . Research identifiers are created by the connected research system randomly and are not derived from any patient information.
Adding a layer that removes the subject entity from the REDCap projects associated with a study allows for REDCap to facilitate user access to projects, form building, data logging, and managing a study data dictionary . The ability to supplement an entire REDCap project(s) or form(s) as specimen annotations is accomplished by associating another REDCap project with a portal project and, in turn, a subject. Our approach includes the ability to have variable numbers of projects and nested project records per patient. There are many variations of studies that use a variable number of REDCap projects/forms and project records depending on the need of the domain. For example, a BRP protocol can capture demographics one time in one REDCap project while collecting many diagnosis-type forms with longitudinal events in another project that allows for multiple records per subject. The eHB mediates and stores the links between the subject entity and their project records. Conversely, we allow for the tools to maintain separate cohorts of identified subjects where the data are stored in the same REDCap project. This is particularly important for studies in which multiple institutions are participating in sending data and specimens to one data and specimen-coordinating center (DCC/SCC). The link to REDCap records depends on the domain and temporal requirements of a biorepository study.
A key requirement in our choice of a LIMS was that it implements an API that exposed the ability to create new specimen records, print labels and update specimen records with tags from external systems. The LIMS assigns a unique identifier to a specimen collection event, and this identifier is associated with the subject entity in the eHB by user input via the BRP. The BRP has a custom client that allows specimen coordinators with the proper credentials to associate pre-labeled specimen accession kits with the subject entity. Specimen collection kits with proper collection tubes and labels are created prior to subject enrollment in the CHOP Biorepository Core Facility.
The specimen coordinator then scans the barcoded label on the kit through a LIMS client in the BRP to associate the kit with the patient. Any downstream laboratory work, for example; receiving, processing, analysis and storage are performed directly in the LIMS. The laboratory technicians processing and receiving specimen kits do not see patient identifiers, only the LIMS assigned identifier. This facilitates the longitudinal capture of multiple specimen collection events associated with one subject.
Extract transform load
As Goble, et al., describe service oriented technology mechanisms “…[o]nce plumbed, the data have to be massaged and cleaned to make them fit together or conform to new schema” (, p. 689). We meet this requirement with ETL scripts written to integrate the disparate and de-identified data together for scientific use by researchers. This ETL process acts as another application with client access to the eHB. The first part of the ETL script uses application key-driven access to obtain a list of subject entities on specific protocols linked their respective research identifiers in data management applications. This linked list is used throughout the ETL process to join together and integrate data from disparate research systems and perform further de-identification where required by a study protocol. The ETL process produces data in a relational domain model suitable for researcher query. The ETL process is also where we integrate systems and data that are not part of the data entry in the BRP. If allowed by the protocol, the ETL process can query the eHB for patient records and pull clinical data from the health record and move it to the non-human subject biorepository database.
Researcher query tool and non-human subject research data resource
Results and discussions
Project list this table is a list of select high-volume projects utilizing the modular tool-kit architecture described in this manuscript that particularly integrate multiple research resources through the toolkit
Specimens accessioned (rounded)
Data integration points
The Children’s Brain Tumor Tissue Consortium
-Case Report Forms
University of Pennsylvania-CHOP Neurosurgery Tissue Collaborative
-Case Report Forms
IBD Center Biorepository Studies
-Case Report Forms
-Electronic Medical Record
Center for Childhood Cancer Research
-Case Report Forms
-Electronic Medical Record
PennCHOP Microbiome Center
-Case Report Forms
-Electronic Medical Record
-Genomic Analysis Pipelines
Operational and scientific complexity
Our methods allow for a significant amount of complexity in scientific data management. With a modern, web-service oriented architecture we abstract the subject entity from multiple related project data in supporting research systems, providing increased flexibility and adaptability over comparable monolithic systems. The tools facilitate the longitudinal collection of clinical phenotypic data over an arbitrary time period. We also promote asynchronous and variable collection of specimens.
Protection of subject privacy
The architecture of the eHB helps to protect patient privacy by limiting exposure of patient identifiers to research staff. Identifiers are only available to study operations staff responsible for associating data and specimens with subjects in the context of specific IRB protocols. The toolkit shares implementation of security and access controls with the connected downstream research system (e.g., access and logging features of REDCap). Similarly, authorization to connect LIMS specimen records to a patient record are configured with LIMS named users, and the toolkit relies on the security protocols exposed through the LIMS API. Researchers only access secondary data resources that result from downstream ETL and are never directly connected to the honest broker component of the tool kit. The ability to query the eHB allows the team to build domain specific data repositories and a web query tool limited risk of exposing patient information.
Evolving data types and workflows
Downstream data management systems and workflows can change project to project or as the science changes within a project. We allow all downstream applications do what they were designed to do without any impact on the underlying architecture of the toolkit. The toolkit is resilient to change, and the service-oriented architecture provides hooks to incorporate additional systems, reflecting the certainty of changing requirements, data and workflows.
Annotation of biospecimens with longitudinal clinical data was the initial impetus of creating this toolkit, but it has gone much further. The BRP and REDCap integration have enabled multiple multi-institutional biorepository studies, both international and national. As in Harris et al., we enable concurrent multi-institutional projects to be controlled by staff through a single common interface similar to other new research informatics initiatives . Exploration of HL7 for Oncology and honest brokering is ongoing, but we have not been able to implement these medical informatics type interchange languages for this purpose . Additionally, web services were not built to be HIPAA compliant , but we find that designing modular tools that have privacy built in by their specific usage to be sufficient in research. Further, we find the modern web architecture to allow for the integration of the favored web tools scientists are using for their research to be a novel approach.
The Children’s Brain Tumor Tissue Consortium
In Fig. 5, the data and specimen query tool of the Children’s Brain and Tumor Tissue Consortium, is some of the results of ambitious biorepository collaboration between six children’s hospitals. Informatics staff utilize this set of integrated tools to support the data and specimen-coordinating To date, the toolkit has been central to a number of grant-funded projects focused on next generation sequencing of biospecimens, and the identification of causative mutations in tumors and data integration heavy microbiologic research (See Table 1). A researcher can quickly determine study feasibility by reviewing available specimens and data in childhood brain cancer research and formulate new studies focused on finding pathways in rare cancer similar to Brastianos et al. and Parsons et al. [27, 28]. Current efforts are focused on integration of highly dimensional genomic data sets generated by such studies.
Integrating or retrieving data from the Electronic Medical Record (EMR) is a growing need for users of the toolkit. Projects originating at CHOP have been able to take advantage of the eHB to re-identify patients for the purpose of extracting clinical data from the CHOP EMR. We are able to, with appropriate IRB permission, obtain and de-identify clinical data directly from the EMR and incorporate these data as annotations on specimens and genomic data. This has proven useful in studies that require observational data such as medication at time of specimen collection, particularly in high frequency specimen collection in microbiologic research.
Genomic data integration
As of January of 2016 there are 23 unique protocols/patient cohorts being managed in the Biorepository Portal (BRP). There are over 4000 unique subject records in the electronic honest broker (eHB), over 30,000 specimens accessioned and 8 institutions participating in various biobanking activities using this tool kit. We specifically set out to build rich annotation of biospecimens with longitudinal clinical data; BRP/REDCap integration for multi-institutional repositories; EMR integration; further annotated specimens with genomic data specific to a domain; build application hooks for experiments at the specimen level integrated with analytic software; while protecting privacy per the Office of Civil Rights (OCR) and HIPAA. To meet this challenge, we created an open source modular software toolkit that automates many manual components of biorepository data workflows while also protecting patient privacy. Conversely the modular solutions allow for novel integration points for translational research spanning clinical and genomic data. We believe this work advances the state of the art within the biomedical domain by moving towards modern technologies and architectures to provide translational research resources.
Availability of software
Project Name: The Biorepository Portal Toolkit
Project home page: http://www.brptoolkit.com
Operating Systems: Linux
Other Requirements: Docker (optional but recommended)
License: We believe in open source software and open-source our work. The Biorepository Toolkit and all integration work with non-proprietary systems is licensed under BSD 2-clause License.
Restrictions to use by non-academics: None.
Ethics approval: No ethics approval was required for this work.
A demonstration of this software is available at http://www.brptoolkit.com. This website also contains documentation, webinars, descriptions and pointers to code repositories in context. Software discussed in this paper is available on the CHOP Department of Biomedical and Health Informatics github repository at https://github.com/chop-dbhi. A specific implementation of the toolkit is available through the Children’s Brain Tumor Tissue Consortium at http://www.cbttc.org.
The Children’s Hospital of Philadelphia Clinical and Translational Science Award (CTSA) through the Clinical and Translational Research Center (CTRC), U54 KL2RR024132, funded this project and publication. The project is also made possible by the generous philanthropic effort of the Children’s Brain Tumor Foundation (CBTF).
This article has been published as part of BMC Genomics Vol 17 Suppl 4 2016: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2015: genomics. The full contents of the supplement are available online at http://bmcgenomics.biomedcentral.com/articles/supplements/volume-17-supplement-4.
ASF was the main author of this manuscript and envisioned this research method along with AJM who is also a main contibutor to this manuscript, envisioned and developed large components of the software. TJR is the current lead developer for the project and a contributor to the manuscript. ACR is the scientific domain contributor, and JWP is a main contributor to design of the project and author of this manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Ethics, consent to participate and consent to publish
Not applicable for this research.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Brisson a R, Matsui D, Rieder MJ, Fraser DD. Translational research in pediatrics: tissue sampling and biobanking. Pediatrics. 2012;129(1):153–62.View ArticlePubMedGoogle Scholar
- Colman E, Golden J, Roberts M, Egan A, Weaver J, Pharm D, Rosebraugh C. The path to personalized medicine. N Engl J Med. 2010;363(4):2012–4.Google Scholar
- Hirtzlin I, Dubreuil C, Préaubert N, Duchier J, Jansen B, Simon J, Lobato De Faria P, Perez-Lezaun A, Visser B, Williams GD , Cambon-Thomsen A. An empirical survey on biobanking of human genetic material and data in six EU countries. Eur J Hum Genet. 2003;11(6):475–88.View ArticlePubMedGoogle Scholar
- Altekruse SF, Rosenfeld GE, Carrick DM, Pressman EJ, Schully SD, Mechanic LE, Cronin KA, Hernandez BY, Lynch CF, Cozen W, Khoury MJ, Penberthy LT. SEER cancer registry biospecimen research: yesterday and tomorrow. Cancer Epidemiol Biomarkers Prev. 2014;23(12):2681–7.Google Scholar
- Compton C. Getting to personalized cancer medicine: taking out the garbage. Cancer. 2007;110(8):1641–3.View ArticlePubMedGoogle Scholar
- Végvári A, Welinder C, Lindberg H, Fehniger TE, Marko-Varga G. Biobank resources for future patient care: developments, principles and concepts. J Clin Bioinf. 2011;1(1):24.View ArticleGoogle Scholar
- Vaught J, Rogers J, Carolin T, Compton C. Biobankonomics: developing a sustainable business model approach for the formation of a human tissue biobank. J Natl Cancer Inst Monogr. 2011;2011(42):24–31.View ArticlePubMedGoogle Scholar
- Kamm L, Bogdanov D, Laur S, Vilo J. A new way to protect privacy in large-scale genome-wide association studies. Bioinformatics. 2013;29(7):886–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang X, Liu L, Fackenthal J, Cummings S, Olopade OI, Hope K, Silverstein JC, Olopade OI. Translational integrity and continuity: personalized biomedical data integration. J Biomed Inform. 2009;42(1):100–12.View ArticlePubMedGoogle Scholar
- Wang X, Olopade O, Foster I. Personalized biomedical data integration. Pers Biomed Data Integr Biomed Eng Trends Electron Commun Software Mr Anthony Laskovski. 2011;1:100–12.Google Scholar
- Dhir R, Patel AA, Winters S, Bisceglia M, Swanson D, Aamodt R, et al. A multidisciplinary approach to honest broker services for tissue banks and clinical data: A pragmatic and practical model. Cancer. 2008;113:1705–15.Google Scholar
- The Health Insurance Portability and Accountability Act of 1996 (HIPAA) P.L. No. 104-191, 110 Stat. 1938 (1996).Google Scholar
- The Office for Civil Rights (OCR), B. Malin. Guidance regarding methods for de-identification of protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. Heal Inf Priv. 2012;1–32. http://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html.
- Dhir R, Patel A a, Winters S, Bisceglia M, Swanson D, Aamodt R, Becich MJ. A multidisciplinary approach to honest broker services for tissue banks and clinical data: a pragmatic and practical model. Cancer. 2008;113(7):1705–15.View ArticlePubMedPubMed CentralGoogle Scholar
- National Cancer Institute. NCI best practices for biospecimen resources. 2010.Google Scholar
- Boyd AD, Saxman PR, Hunscher DA, Smith KA, Morris TD, Kaston M, Bayoff F, Rogers B, Hayes P, Rajeev N, Kline-Rogers E, Eagle K, Clauw D, Greden JF, Green LA, Athey BD. The University of Michigan Honest Broker: a Web-based service for clinical and translational research and practice. J Am Med Inform Assoc. 2009;16(6):784–91. doi:10.1197/jamia.M2985.
- The Children’s Brain Tumor Tissue Consortium. [Online]. Available: www.cbttc.org. Accessed 25 June 2015.
- Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):2012–4.View ArticleGoogle Scholar
- Rainie L, Fox S, Duggan M. The Web at 25 in the U.S. Pew Res Cent. 2014;1:1–5.Google Scholar
- S. Mukherjee. The emperor of all maladies: a biography of cancer. New York, NY: Scribner. 2011.Google Scholar
- June CH, Maus MV, Plesa G, Johnson L a, Zhao Y, Levine BL, Grupp A, Porter DL. Engineered T cells for cancer therapy. Cancer Immunol Immunother. 2014;63(9):969–75.Google Scholar
- Ostrom QT, Gittleman H, Chen Y, Wolinsky Y, Barnholtz-Sloan J. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005–2009. Neuro Oncol. 2012;14 suppl 5:1–21.View ArticleGoogle Scholar
- American Brain Tumor Association. Brain tumor information. 2014. [Online]. Available: http://www.abta.org/brain-tumor-information/types-of-tumors/. Accessed 25 June 2015.Google Scholar
- Cancer in Children & Adolescents. 2014.Google Scholar
- Baldwin RT, Preston-Martin S. Epidemiology of brain tumors in childhood - a review. Toxicol Appl Pharmacol. 2004;199(2):118–31.View ArticlePubMedGoogle Scholar
- Boklan JJ. Little patients, losing patience: pediatric cancer drug development. Mol Cancer Ther. 2006;5(8):1905–8.View ArticlePubMedGoogle Scholar
- Brastianos PK, Taylor-Weiner A, Manley PE, Jones RT, Dias-Santagata D, Thorner AR, Lawrence MS, Rodriguez FJ, Bernardo LA, Schubert L, Sunkavalli A, Shillingford N, Calicchio ML, Lidov W, Taha H, Martinez-Lage M, Santi M, Storm PB, Lee JYK, Palmer JN, Adappa ND, Scott RM, Dunn IF, Laws ER, Stewart C, Ligon KL, Hoang MP , Van Hummelen P, Hahn WC, Louis DN, Resnick AC, Kieran MW, Getz G, Santagata S. Exome sequencing identifies BRAF mutations in papillary craniopharyngiomas. Nat Genet. 2014;46(2):161–5.Google Scholar
- Parsons DW, Li M, Zhang X, Jones S, Leary RJ, Lin JC-H, Boca SM, Carter H, Samayoa J, Bettegowda C, Gallia GL, Jallo Gl, Binder ZA, Nikolsky Y, Hartigan J, Smith DR, Gerhard DS, Fults DW, VandenBerg S, Berger MS, Marie SKN, Shinjo SMO, Clara C, Phillips PC, Minturn JE, Biegel JA, Judkins AR, Resnick AC, Storm PB, Curran T, He Y, Rasheed BA, Friedman HS, Keir ST, McLendon R, Northcott PA, Taylor MD, Burger PC, Riggins GJ, Karchin R, Parmigiani G, Bigner DD, Yan H, Papadopoulos N, Vogelstein B, Kinzler KW, Velculescu VE. The genetic landscape of the childhood cancer medulloblastoma. Science. 2011;331:435–9.Google Scholar
- Chin L, Hahn WC, Getz G, Meyerson M. Making sense of cancer genomic data. Genes & Development. 2011;25:534–555.Google Scholar
- Harris P a, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.View ArticlePubMedGoogle Scholar
- Chen PP-S. The entity-relationship model---toward a unified view of data. ACM Trans Database Syst. 1976;1(1):9–36.View ArticleGoogle Scholar
- Pennington JW, Ruth B, Italia MJ, Miller J, Wrazien S, Loutrel JG, Crenshaw EB, White PS. Harvest: an open platform for developing web-based biomedical data discovery and reporting applications. J Am Med Inform Assoc. 2013;21(2):379–83.View ArticlePubMedPubMed CentralGoogle Scholar
- Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C, Schultz N. The cBio Cancer Genomics Portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.View ArticlePubMedGoogle Scholar
- Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Poznań, Poland). 2015;19(1A):A68–77.Google Scholar
- Alkhatib H, Faraboschi P, Frachtenberg E, Kasahara H, Lange D, Laplante P, Merchant A, Burgess A. IEEE CS 2022 Report. 2014.Google Scholar
- Goble C, Stevens R. State of the nation in data integration for bioinformatics. J Biomed Inform. 2008;41(5):687–93.View ArticlePubMedGoogle Scholar
- Goble C, Stevens R, Hull D, Wolstencroft K, Lopez R. Data curation + process curation=data integration + science. Brief Bioinform. 2008;9(6):506.View ArticlePubMedGoogle Scholar
- Boyd AD, Hosner C, Hunscher D a, Athey BD, Clauw DJ, Green LA. An ‘Honest Broker’ mechanism to maintain privacy for patient care and academic medical research. Int J Med Inform. 2006;76(5–6):407–11.Google Scholar
- McConnell P, Dash RC, Chilukuri R, Pietrobon R, Johnson K, Annechiarico R, Cuticchia AJ. The cancer translational research informatics platform. BMC Med Inform Decis Mak. 2008;8:60.Google Scholar
- Kho AN, Cashy JP, Jackson KL, Pah AR, Goel S, Rn Boehnke J, Humphries JE, Kominers SD, Hota BN, Sims SA, Malin BA, French DD, Walunas TL, Meltzer DO, Kaleba EO, Jones RC, Galanter WL. Design and implementation of a privacy preserving electronic health record linkage tool in Chicago. J Am Med Inform Assoc. 2015;22(5):1072–1080. doi:10.1093/jamia/ocv038.
- Manion FJ, Robbins RJ, Weems W a, Crowley RS. Security and privacy requirements for a multi-institutional cancer research data grid: an interview-based study. BMC Med Inform Decis Mak. 2009;9:31.View ArticlePubMedPubMed CentralGoogle Scholar
- Hutton JJ. Pediatric biomedical informatics, vol. 2. Dordrecht: Springer Netherlands; 2012.Google Scholar
- Boyd AD, Hunscher DA, Kramer AJ, Hosner C, Saxman P, Athey BD, Greden JF, Clauw DC. The ‘Honest Broker’ method of integrating interdisciplinary research data. AMIA Annu Symp Proc. 2005;902.Google Scholar
- Blobel B, Nordberg R, Davis JM, Pharow P. Modelling privilege management and access control. Int J Med Inform. 2006;75:597–623.View ArticlePubMedGoogle Scholar
- Ecma International. ECMA-404: The JSON data interchange format. 1st ed. 2013.Google Scholar
- Morris R, Thompson K. Password security: a case history. Commun ACM. 1979;22(11):594–7.View ArticleGoogle Scholar
- Warner JL, Maddux SE, Hughes KS, Krauss JC, Yu PP, Shulman LN, Mayer DK, Hogarth M, Shafarman M, Fiscalini AS, Esserman L, Alschuler L, Koromia GA, Gonzaga Z, Ambinder EP. Development, implementation, and initial evaluation of a foundational open interoperability standard for oncology treatment planning and summarization. J Am Med Informatics Assoc. 2015;22(3):577–86.Google Scholar