A Novel Cross-Disciplinary Multi-Institute Approach to Translational Cancer Research: Lessons Learned from Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC)

Abstract

Background:The Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC, http://www.pcabc.upmc.edu) is one of the first major project-based initiatives stemming from the Pennsylvania Cancer Alliance that was funded for four years by the Department of Health of the Commonwealth of Pennsylvania. The objective of this was to initiate a prototype biorepository and bioinformatics infrastructure with a robust data warehouse by developing a statewide data model (1) for bioinformatics and a repository of serum and tissue samples; (2) a data model for biomarker data storage; and (3) a public access website for disseminating research results and bioinformatics tools. The members of the Consortium cooperate closely, exploring the opportunity for sharing clinical, genomic and other bioinformatics data on patient samples in oncology, for the purpose of developing collaborative research programs across cancer research institutions in Pennsylvania. The Consortium’s intention was to establish a virtual repository of many clinical specimens residing in various centers across the state, in order to make them available for research. One of our primary goals was to facilitate the identification of cancer-specific biomarkers and encourage collaborative research efforts among the participating centers.Methods:The PCABC has developed unique partnerships so that every region of the state can effectively contribute and participate. It includes over 80 individuals from 14 organizations, and plans to expand to partners outside the State. This has created a network of researchers, clinicians, bioinformaticians, cancer registrars, program directors, and executives from academic and community health systems, as well as external corporate partners - all working together to accomplish a common mission.The various sub-committees have developed a common IRB protocol template, common data elements for standardizing data collections for three organ sites, intellectual property/tech transfer agreements, and material transfer agreements that have been approved by each of the member institutions. This was the foundational work that has led to the development of a centralized data warehouse that has met each of the institutions’ IRB/HIPAA standards.Results:Currently, this “virtual biorepository” has over 58,000 annotated samples from 11,467 cancer patients available for research purposes. The clinical annotation of tissue samples is either done manually over the internet or semi-automated batch modes through mapping of local data elements with PCABC common data elements. The database currently holds information on 7188 cases (associated with 9278 specimens and 46,666 annotated blocks and blood samples) of prostate cancer, 2736 cases (associated with 3796 specimens and 9336 annotated blocks and blood samples) of breast cancer and 1543 cases (including 1334 specimens and 2671 annotated blocks and blood samples) of melanoma. These numbers continue to grow, and plans to integrate new tumor sites are in progress. Furthermore, the group has also develope...

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

“…With leadership and supervision of the Coordinating Committee and the domain experts from the subcommittees, the CDE subcommittee was established in order to develop CDEs (demographics, epidemiology, pathology-specimen as well as block level annotation, genotype, follow-up and outcome) pertinent to pleural, pericardial, and peritoneal mesotheliomas (Figure 2 ). The CDE sub-committee utilized the experiences of the CBCTR [ 6 , 7 ], CPCTR [ 8 - 11 ], caBIG [ 12 ], PCABC [ 13 ], Cooperative Human Tissue Network (CHTN) [ 19 ] Early Diagnosis Research Network (EDRN) [ 20 ], Cancer Family Registries (CFR) [ 21 ], and Specialized Programs of Research Excellence (SPOREs) [ 22 ] when developing the CDEs. As stated earlier, the major standards used to formulate the CDEs include the NAACCR Data Standards for Cancer Registries [ 23 ], CAP Cancer Protocol and Checklist [ 24 ], the ADASP [ 25 ] and the American Joint Committee on Cancer (AJCC) Cancer Staging Manual [ 26 ].…”

“…Subsequently, well-characterized data elements were created and established by the National Cancer Institutes initiatives such as the Cooperative Breast Cancer Tissue Resource (CBCTR) [ 6 , 7 ], Cooperative Prostate Cancer Tissue Resource (CPCTR) [ 8 - 11 ] and the Cancer Biomedical Informatics Grid (caBIG) [ 12 ]. In a statewide effort, we have accomplished this standardization in the Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC) [ 13 ].…”

“…These systems are designed to facilitate semantic and syntactic interoperability in the development of data elements using controlled vocabulary and ontology in order to make the data understandable and shareable for end-users. During the initial phase of this project, the basic components and standards developed by CBCTR, CPCTR, PCABC, and cancer Data Standard Repository (caDSR) model of caBIG were utilized [ 6 - 13 , 16 ].…”

An Electronic Specimen Collection Protocol Schema (eSCPS)

“…The most important function of a tumor bank in translation research is its close integration in high-quality clinical trials, which involves the molecular profiling of samples with protocolized clinical information and strict anonymization criteria [29]. Many tissue banks, such as the EORTC [28], PCRC [30], CPCTR [31], CBCTR [32], CHTN [33], CFR [34], EDRN [35], and PCABC [36], exist and are primarily web-based and centrally coordinated. Many of these tissue bank protocols are introduced for the exchange of clinical information and related sample data between the institutions.…”

Biomedical Informatics for Cancer Research

“…In addition to the development and implementation of tissue banking databases, various Web-based query tools have been designed to help investigators query the annotated biospecimens (Table 3.1). The Organ Specific Database (OSD) model consists of the following integrated application layers that maintain data query/entry, data deidentification, and the user management module (Melamed et al 2004;Patel et al 2006Patel et al , 2007a (Fig. 3.3).…”