A decade of experience in the development and implementation of tissue banking informatics tools for intra and inter-institutional translational research

Our plain-English summary of A decade of experience in the development and implementation of tissue banking informatics tools for intra and inter-institutional translational research, by W. Amin, H. Singh, A.K. Pople, et al (Journal of Pathology Informatics, 2010).

The study

• Authors: W. Amin, H. Singh, A.K. Pople, S. Winters, R. Dhir, A.V. Parwani, M.J. Becich
• Journal: Journal of Pathology Informatics
• Published: 2010;1
• Cited by: 11 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Context: Tissue banking informatics deals with standardized annotation, collection and storage of biospecimens that can further be shared by researchers. Over the last decade, the Department of Biomedical Informatics (DBMI) at the University of Pittsburgh has developed various tissue banking informatics tools to expedite translational medicine research. In this review, we describe the technical approach and capabilities of these models.

Design: Clinical annotation of biospecimens requires data retrieval from various clinical information systems and the de-identification of the data by an honest broker. Based upon these requirements, DBMI, with its collaborators, has developed both Oracle-based organ-specific data marts and a more generic, model-driven architecture for biorepositories. The organ-specific models are developed utilizing Oracle 9.2.0.1 server tools and software applications and the model-driven architecture is implemented in a J2EE framework.

Result: The organ-specific biorepositories implemented by DBMI include the Cooperative Prostate Cancer Tissue Resource (http://www.cpctr.info/), Pennsylvania Cancer Alliance Bioinformatics Consortium (http://pcabc.upmc.edu/main.cfm), EDRN Colorectal and Pancreatic Neoplasm Database (http://edrn.nci.nih.gov/) and Specialized Programs of Research Excellence (SPORE) Head and Neck Neoplasm Database (http://spores.nci.nih.gov/current/hn/index.htm). The model-based architecture is represented by the National Mesothelioma Virtual Bank (http://mesotissue.org/). These biorepositories provide thousands of well annotated biospecimens for the researchers that are searchable through query interfaces available via the Internet.

Conclusion: These systems, developed and supported by our institute, serve to form a common platform for cancer research to accelerate progress in clinical and translational research. In addition, they provide a tangible infrastructure and resource for exposing research resources and biospecimen services in collaboration with the clinical anatomic pathology laboratory information system (APLIS) and the cancer registry information systems.

In plain English

This article is a review in which the authors described tissue banking software built over about a decade by the Department of Biomedical Informatics at the University of Pittsburgh. These systems manage records about tissue and blood samples from cancer patients, which are stored at tissue banks, linked to de-identified clinical information that researchers can search, with access varying by repository. The article did not study asbestos exposure or measure disease risk. The authors described two designs. One was a set of Oracle-based databases for specific cancers, such as prostate cancer. The other was a model-driven design used for the National Mesothelioma Virtual Bank, which was funded by CDC/NIOSH. The article's table listed 844 cases in the mesothelioma bank, with 306 paraffin blocks, 255 frozen blocks and 670 blood, serum or plasma samples. Staff called honest brokers removed patient identities before data reached researchers. The authors concluded that the two models had greatly facilitated sharing of samples and clinical data. They noted that matching records across sources can produce errors needing manual review, and that free-text records can hamper searching.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Review article describing the technical approach and capabilities of tissue banking informatics systems developed at one institution
• Who was studied: Virtual biorepositories (tissue banks) developed at the University of Pittsburgh over the last decade. Organ-specific databases: Cooperative Prostate Cancer Tissue Resource (CPCTR), Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC), EDRN Colorectal and Pancreatic Neoplasm database, and SPORE Head and Neck Neoplasm Database. Model-driven system: the National Mesothelioma Virtual Bank (NMVB).
• Main results:
  • The databases store and manage information about the specimens (enrollment data, inventory, quality assurance data and availability), which is presented as de-identified data
  • The physical biospecimens themselves are stored at various tissue banks
  • Data access differs by repository: Table 2 listed the EDRN and SPORE repositories as open to University of Pittsburgh EDRN or SPORE researchers only, without a public statistical query interface
  • The National Mesothelioma Virtual Bank was built using a model-driven approach adapted from the caTISSUE Clinical Annotation Engine (CAE)
  • Table 1 listed for the NMVB: 844 total cases, with 306 paraffin blocks, 255 frozen blocks and 670 blood/serum/plasma samples (column assignment read from the flattened table)
  • The NMVB system incorporated common data elements based on CAP protocol and NAACCR standards, a web-based data entry application, and data query tools
  • The NMVB query interface was password protected and let users query de-identified annotated mesothelioma specimens and view clinical data on each case
  • Table 2 listed CDC/NIOSH as the funding organization for the NMVB
  • Honest brokers de-identified the data, and only the de-identified data set was made available to researchers
  • The organ-specific databases used a three-tiered Oracle-based data warehouse architecture
• The authors' conclusion: The authors concluded that the two models deployed at their institute had greatly facilitated the sharing of biospecimens and clinical phenotyping data for translational research, and that future work would focus on interoperability across institutions
• Limitations the authors state:
  • Data collection errors can occur when combining multiple data sources for the same patient or when unique patient identifiers are missing, and these require manual review
  • Unstructured systems that use free text instead of structured fields can hamper the process and complicate searching
  • Human involvement by honest brokers is still needed in matching patient records across clinical systems

Topics

Medical terms in this study, explained in our Lexicon: Neoplasm, Mesothelioma, Pathology, Cancer Registry.

Related studies

• The development and deployment of Common Data Elements for tissue banks for translational research in cancer - an emerging standard based approach for the Mesothelioma Virtual Tissue Bank — S.K. Mohanty, A.T. Mistry, et al. (2008)
• National Mesothelioma Virtual Bank: a standard based biospecimen and clinical data resource to enhance translational research — W. Amin, A.V. Parwani, et al. (2008)
• Multi-institutional experience of diffuse intra-abdominal multicystic peritoneal mesothelioma — T.C. Chua, T.D. Yan, et al. (2011)
• National Mesothelioma Virtual Bank: A Platform for Collaborative Research and Mesothelioma Biobanking Resource to Support Translational Research — W. Amin, A.V. Parwani, et al. (2013)
• Mapping three decades of air pollution-lung cancer research: trends, hotspots, and networks (1990-2025) — H. Fan, L. Wang, et al. (2025)
• Diagnostic biomarker of asbestos-related mesothelioma: example of translational research — H. Okio, S. Kazu, et al. (2007)

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