scholarly journals From biobank and data silos into a data commons: convergence to support translational medicine

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Rebecca Asiimwe ◽  
Stephanie Lam ◽  
Samuel Leung ◽  
Shanzhao Wang ◽  
Rachel Wan ◽  
...  
Keyword(s):  
British Columbia ◽  
Cancer Research ◽  
Clinical Data ◽  
Translational Medicine ◽  
Gynecologic Cancer ◽  
Data Access ◽  
Collaborative Governance ◽  
Research Community ◽  
Data Intensive ◽  
Data Commons

Abstract Background To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and effective use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcomes unit, and a collection of data silos, into an integrated data commons to support data standardization and resource sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients. Results Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: (1) biospecimen collections, (2) molecular and genomics data, and (3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community. Conclusions Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing frameworks, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science.

scholarly journals From biobank and data silos into a data commons: convergence to support translational medicine

2021 ◽  
Author(s):  
Rebecca Asiimwe ◽  
Stephanie Lam ◽  
Samuel Leung ◽  
Shanzhao Wang ◽  
Rachel Wan ◽  
...  
Keyword(s):  
British Columbia ◽  
Cancer Research ◽  
Clinical Data ◽  
Translational Medicine ◽  
Gynecologic Cancer ◽  
Data Access ◽  
Collaborative Governance ◽  
Research Community ◽  
Data Intensive ◽  
Data Commons

Abstract Background To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcome unit, and a collection of data silos, into an integrated data commons to support data standardization, data, and resources sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients. Results Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: 1) biospecimen collections, 2) molecular and genomics data, and 3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community. Conclusions Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing framework, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science.

scholarly journals Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies

2020 ◽  
Author(s):  
Elena Pavlenko ◽  
Daniel Strech ◽  
Holger Langhof
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Clinical Data ◽  
Large Scale ◽  
Scientific Literature ◽  
Data Access ◽  
Data Warehouses ◽  
Data Intensive ◽  
Available Information ◽  
Access Policies

AbstractBackgroundThe promises of improved health care and health research through data-intensive applications rely on a growing amount of health data. At the core of large-scale data integration efforts, clinical data warehouses (CDW) are also responsible of data governance, managing data access and (re)use. As the complexity of the data flow increases, greater transparency and standardization of criteria and procedures is required in order to maintain objective oversight and control. This study assessed the spectrum of data access and use criteria and procedures in clinical data warehouses governance internationally.MethodsWe performed a systematic review of (a) the published scientific literature on CDW and (b) publicly available information on CDW data access, e.g., data access policies. A qualitative thematic analysis was applied to all included literature and policies.ResultsTwenty-three scientific publications and one policy document were included in the final analysis. The qualitative analysis led to a final set of three main thematic categories: (1) requirements, including recipient requirements, reuse requirements, and formal requirements; (2) structures and processes, including review bodies and review values; and (3) access, including access limitations.ConclusionsThe description of data access and use governance in the scientific literature is characterized by a high level of heterogeneity and ambiguity. In practice, this might limit the effective data sharing needed to fulfil the high expectations of data-intensive approaches in medical research and health care. The lack of publicly available information on access policies conflicts with ethical requirements linked to principles of transparency and accountability.CDW should publicly disclose by whom and under which conditions data can be accessed, and provide designated governance structures and policies to increase transparency on data access. The results of this review may contribute to the development of practice-oriented minimal standards for the governance of data access, which could also result in a stronger harmonization, efficiency, and effectiveness of CDW.

scholarly journals GYNOCARE Update: Modern Strategies to Improve Diagnosis and Treatment of Rare Gynecologic Tumors—Current Challenges and Future Directions

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 493
Author(s):  
Riccardo Di Fiore ◽  
Sherif Suleiman ◽  
Bridget Ellul ◽  
Sharon A. O’Toole ◽  
Charles Savona-Ventura ◽  
...  
Keyword(s):  
Cancer Research ◽  
Gynecological Cancer ◽  
Gynecologic Cancer ◽  
Delayed Diagnosis ◽  
Research Programme ◽  
Basic Research ◽  
Diagnosis And Treatment ◽  
Rare Cancer ◽  
Cost Action ◽  
Gynecologic Tumors

More than 50% of all gynecologic tumors can be classified as rare (defined as an incidence of ≤6 per 100,000 women) and usually have a poor prognosis owing to delayed diagnosis and treatment. In contrast to almost all other common solid tumors, the treatment of rare gynecologic tumors (RGT) is often based on expert opinion, retrospective studies, or extrapolation from other tumor sites with similar histology, leading to difficulty in developing guidelines for clinical practice. Currently, gynecologic cancer research, due to distinct scientific and technological challenges, is lagging behind. Moreover, the overall efforts for addressing these challenges are fragmented across different European countries and indeed, worldwide. The GYNOCARE, COST Action CA18117 (European Network for Gynecological Rare Cancer Research) programme aims to address these challenges through the creation of a unique network between key stakeholders covering distinct domains from concept to cure: basic research on RGT, biobanking, bridging with industry, and setting up the legal and regulatory requirements for international innovative clinical trials. On this basis, members of this COST Action, (Working Group 1, “Basic and Translational Research on Rare Gynecological Cancer”) have decided to focus their future efforts on the development of new approaches to improve the diagnosis and treatment of RGT. Here, we provide a brief overview of the current state-of-the-art and describe the goals of this COST Action and its future challenges with the aim to stimulate discussion and promote synergy across scientists engaged in the fight against this rare cancer worldwide.

Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience

2021 ◽  
pp. 561-569
Author(s):  
Steven A. Eschrich ◽  
Jamie K. Teer ◽  
Phillip Reisman ◽  
Erin Siegel ◽  
Chandan Challa ◽  
...  
Keyword(s):  
Cancer Research ◽  
Precision Medicine ◽  
Data Warehouse ◽  
Clinical Data ◽  
Molecular Data ◽  
Project Planning ◽  
Cancer Genome ◽  
Open Communication ◽  
Clinical Data Warehouse ◽  
Research Studies

PURPOSE The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research. METHODS Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing. RESULTS The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case–focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse. CONCLUSION The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

scholarly journals A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens

2015 ◽  
Vol 75 (24) ◽  
pp. 5194-5201 ◽  
Author(s):  
Rebecca S. Jacobson ◽  
Michael J. Becich ◽  
Roni J. Bollag ◽  
Girish Chavan ◽  
Julia Corrigan ◽  
...  
Keyword(s):  
Cancer Research ◽  
Clinical Data ◽  
Translational Cancer Research

Integrating Biomolecular and Clinical Data for Cancer Research: Concepts and Challenges

2012 ◽  
pp. 159-172
Author(s):  
Pornpimol Charoentong ◽  
Hubert Hackl ◽  
Bernhard Mlecnik ◽  
Gabriela Bindea ◽  
Jerome Galon ◽  
...  
Keyword(s):  
Cancer Research ◽  
Clinical Data

Information Processing in Research Paper Recommender System Classes

2019 ◽  
pp. 90-118
Author(s):  
Benard M. Maake ◽  
Sunday O. Ojo ◽  
Tranos Zuva
Keyword(s):  
Information Processing ◽  
Recommender Systems ◽  
Operational Research ◽  
Data Access ◽  
Research Paper ◽  
Research Community ◽  
The Internet ◽  
Research Papers ◽  
Art Research ◽  
Research Paper Recommender Systems

Research-related publications and articles have flooded the internet, and researchers are in the quest of getting better tools and technologies to improve the recommendation of relevant research papers. Ever since the introduction of research paper recommender systems, more than 400 research paper recommendation related articles have been so far published. These articles describe the numerous tools, methodologies, and technologies used in recommending research papers, further highlighting issues that need the attention of the research community. Few operational research paper recommender systems have been developed though. The main objective of this review paper is to summaries the state-of-the-art research paper recommender systems classification categories. Findings and concepts on data access and manipulations in the field of research paper recommendation will be highlighted, summarized, and disseminated. This chapter will be centered on reviewing articles in the field of research paper recommender systems published from the early 1990s until 2017.

scholarly journals Quantitative Imaging Informatics for Cancer Research

2020 ◽  
pp. 444-453 ◽  
Author(s):  
Andrey Fedorov ◽  
Reinhard Beichel ◽  
Jayashree Kalpathy-Cramer ◽  
David Clunie ◽  
Michael Onken ◽  
...  
Keyword(s):  
Cancer Research ◽  
Best Practices ◽  
Cancer Imaging ◽  
Quantitative Imaging ◽  
Imaging Informatics ◽  
Imaging Data ◽  
Communication Interface ◽  
3D Slicer ◽  
And Training ◽  
Data Commons

PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.

Sign in / Sign up

Export Citation Format

Share Document