Waqas Amin

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

BackgroundRecent advances in genomics, proteomics, and the increasing demands for biomarker validation studies have catalyzed changes in the landscape of cancer research, fueling the development of tissue banks for translational research. A result of this transformation is the need for sufficient quantities of clinically annotated and well-characterized biospecimens to support the growing needs of the cancer research community. Clinical annotation allows samples to be better matched to the research question at hand and ensures that experimental results are better understood and can be verified. To facilitate and standardize such annotation in bio-repositories, we have combined three accepted and complementary sets of data standards: the College of American Pathologists (CAP) Cancer Checklists, the protocols recommended by the Association of Directors of Anatomic and Surgical Pathology (ADASP) for pathology data, and the North American Association of Central Cancer Registry (NAACCR) elements for epidemiology, therapy and follow-up data. Combining these approaches creates a set of International Standards Organization (ISO) – compliant Common Data Elements (CDEs) for the mesothelioma tissue banking initiative supported by the National Institute for Occupational Safety and Health (NIOSH) of the Center for Disease Control and Prevention (CDC).MethodsThe purpose of the project is to develop a core set of data elements for annotating mesothelioma specimens, following standards established by the CAP checklist, ADASP cancer protocols, and the NAACCR elements. We have associated these elements with modeling architecture to enhance both syntactic and semantic interoperability. The system has a Java-based multi-tiered architecture based on Unified Modeling Language (UML).ResultsCommon Data Elements were developed using controlled vocabulary, ontology and semantic modeling methodology. The CDEs for each case are of different types: demographic, epidemiologic data, clinical history, pathology data including block level annotation, and follow-up data including treatment, recurrence and vital status. The end result of such an effort would eventually provide an increased sample set to the researchers, and makes the system interoperable between institutions.ConclusionThe CAP, ADASP and the NAACCR elements represent widely established data elements that are utilized in many cancer centers. Herein, we have shown these representations can be combined and formalized to create a core set of annotations for banked mesothelioma specimens. Because these data elements are collected as part of the normal workflow of a medical center, data sets developed on the basis of these elements can be easily implemented and maintained.

Usefulness of a Synoptic Data Tool for Reporting of Head and Neck Neoplasms Based on the College of American Pathologists Cancer Checklists

The primary source of information that clinicians use when evaluating and managing patients with cancer is the surgical pathology report. Omission of critical information from the report is a recognized problem in pathology, especially considering the expanding amount of information, such as molecular diagnostics data, that is now routinely included in reports. To standardize surgical pathology reports, the College of American Pathologists (CAP) introduced the CAP checklists. In 2004, the American College of Surgeons Commission on Cancer mandated that 90% of pathology reports indicating a cancer diagnosis at participating centers contain all scientifically validated or regularly used data elements. The University of Pittsburgh Medical Center has implemented synoptic reporting based on the CAP checklists for all major tumor types. We report our experience with synoptic reporting on head and neck neoplasms, demonstrating, in particular, how this can be customized according to needs of each institution.

PaTH: towards a learning health system in the Mid-Atlantic region.

The PaTH (University of Pittsburgh/UPMC, Penn State College of Medicine, Temple University Hospital, and Johns Hopkins University) clinical data research network initiative is a collaborative effort among four academic health centers in the Mid-Atlantic region. PaTH will provide robust infrastructure to conduct research, explore clinical outcomes, link with biospecimens, and improve methods for sharing and analyzing data across our diverse populations. Our disease foci are idiopathic pulmonary fibrosis, atrial fibrillation, and obesity. The four network sites have extensive experience in using data from electronic health records and have devised robust methods for patient outreach and recruitment. The network will adopt best practices by using the open-source data-sharing tool, Informatics for Integrating Biology and the Bedside (i2b2), at each site to enhance data sharing using centrally defined common data elements, and will use the Shared Health Research Information Network (SHRINE) for distributed queries across the network.

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

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.

Adenomatoid Tumor of Testis

Adenomatoid tumors are responsible for 30% of all paratesticular masses. These are usually asymptomatic, slow growing masses. They are benign tumors comprising of cords and tubules of cuboidal to columnar cells with vacuolated cytoplasm and fibrous stroma. They are considered to be of mesothelial origin supported by histochemical studies and genetic analysis of Wilms tumor 1 gene expression. Excision biopsy is both diagnostic and therapeutic procedure. The main clinical consideration is accurate diagnosis preventing unnecessary orchiectomy. Diagnostic studies include serum tumor markers (negative alpha fetoprotein, beta HCG, LDH) ultrasonography (hypoechoic and homogenous appearance) and frozen section.

Biomedical Informatics for Anatomic Pathology

Biomedical informatics has made a deep impact in the overall workflow of the surgical pathology practice and has provided a variety of software tools that accelerate the overall turnaround time, cost effectiveness, and accuracy of information. Laboratory Information System functions as a single comprehensive piece of software managing laboratory workflow, reporting, and billing. Related technologies, such as digital imaging, whole slide imaging, voice recognition, telepathology, synoptic reporting, and use of the Internet, are also important in surgical pathology and will also be discussed in this chapter, because of their relevance to the development of biomedical informatics systems, and these important tools are evolving in parallel with these emerging technologies. These technologies are increasingly been used with the LIS workflow, particularly digital imaging tools. In this chapter, we will talk briefly about the software tools that have been adopted in surgical pathology practice over the last two decades.

Data Management, Databases, and Warehousing

This chapter provides a discussion on data management, databases, and data warehousing with particular reference to their utilization in cancer research. The section on data management describes the special requirements of data for research purposes. It discusses policies, ethics, and protocols involved in data collection, standardization, confidentiality, data entry and preparation, storage, quality assurance, and security. We have focused on the unique issues pertaining to data uniformity and consistency facilitating multi-institutional data sharing, data transfer, and collaboration. The section on Databases elaborates on the architecture and components of database systems. It also discusses various types of database systems with emphasis on the more commonly employed relational model of databases, database functions, and properties. In Data Warehousing the concept of data warehouses, along with warehouse architecture, technology, tools, and applications are discussed. A section on existing data resource systems has been detailed focusing on systems currently employed at the University of Pittsburgh to facilitate translational cancer research. There is a brief discussion on issues and approaches related to both databases and warehouses, which emphasizes their individual strengths and attributes.

Automated whole slide imaging

BACKGROUND Automated high-speed, high-resolution whole slide image (WSI) technology is being rapidly adopted in pathology owing to increased speed of computing, rapid networking, and high image quality and potential to reduce the overall turnaround time required for slide assessment. Method/objectives: This review presents the structure, functioning and performance of some of the WSI systems available in the market and highlights a few validation studies that have been performed to assess the overall utility of WSI systems. CONCLUSION The automated WSI is a robotic microscope that digitizes the entire slide field by field and uses software to merge or stitch individual fields into a composite image. Commercially available systems provide many improved functions that are useful for editing the digital images and improving the image quality.

Use of automated image analysis in evaluation of Mesothelioma Tissue Microarray (TMA) from National Mesothelioma Virtual Bank.

The National Mesothelioma Virtual Bank (NMVB) was established to provide annotated biospecimens to the mesothelioma research community. The resource provides tissue microarrays (TMA) to evaluate the biomarkers along with a variety of other resected mesothelioma specimens. In this manuscript, we describe the immunohistochemical evaluation of the mesothelioma TMA with three key antibodies that are used in making the diagnosis of mesothelioma, and compared the immunohistochemical assessment between manual scoring and image analysis. The TMA was assessed for the immunohistochemical expression of calretinin (N=39), cytokeratin (CK) 5/6 (N=33), and D2-40 (N=37). Immunohistochemistry was evaluated by semi-quantitative (manual) scoring using light microscope (MS) and by automated image analysis (AS). Calretinin staining was seen in both cytoplasmic and nuclear locations. CK5/6 stain was localized to the cytoplasm. D2-40 stain showed only membranous expression in our cases. • Based on the pathologist’s scores, calretinin was positive in 31 of the 39 cases (80%), CK 5/6 in 15 of the 33 cases (46%) and D2-40 in 18 of the 37 cases (49%). • The percent-positive agreement between manual scores and image analysis was 90% (35/39), 94% (31/33), and 95% (35/37) for calretinin, CK 5/6, and D2-40, respectively. There was a substantial agree-ment between manual and automated scores for calretinin (kappa = 0.614) and an almost perfect agreement for CK5/6 (kappa = 0.879) and D2-40 (kappa = 0.892). Our study confirms that the immunohistochemical staining pattern of mesotheliomas in the NMVB UPMC TMA is similar to other studies. Our findings also show that automated image analysis provides similar results to manual scoring by pathologists, and provides a reproducible, objective, and accurate platform for immunohistochemical assessment of biomarker expression.