A Model for the Design and Construction of a Resource for the Validation of Prognostic Prostate Cancer Biomarkers: The Canary Prostate Cancer Tissue Microarray.

*Canary Foundation, Palo Alto Departments of §Pathology ∥Urology ##Epidemiology and Biostatistics, University of California ¶Helen Diller Family Comprehensive Cancer Center, San Francisco ∥∥Department of Urology, Stanford University, Stanford, CA †The Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada ‡Department of Pathology, Cleveland Clinic, Cleveland, OH Departments of #Pathology **Microbiology and Molecular Cell Biology ***Urology, Eastern Virginia Medical School, Norfolk, VI Departments of ††Pathology ¶¶Urology, University of Texas Health Science Center, San Antonio, TX Departments of ‡‡Urology §§Biostatistics, University of Washington †††Division of Human Biology ‡‡‡Program of Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center §§§Department of Pathology, University of Washington Medical Center, Seattle, WA.
Advances in anatomic pathology (Impact Factor: 3.23). 01/2013; 20(1):39-44. DOI: 10.1097/PAP.0b013e31827b665b
Source: PubMed


Tissue microarrays (TMAs) provide unique resources for rapid evaluation and validation of tissue biomarkers. The Canary Foundation Retrospective Prostate Tissue Microarray Resource used a rigorous statistical design, quota sampling, a variation of the case-cohort study, to select patients for inclusion in a multicenter, retrospective prostate cancer TMA cohort. The study is designed to definitively validate tissue biomarkers of prostate cancer recurrence after radical prostatectomy. Tissue samples from over 1000 participants treated for prostate cancer with radical prostatectomy between 1995 and 2004 were selected at 6 participating institutions in the United States and Canada. This design captured the heterogeneity of screening and clinical practices in the contemporary North American population. Standardized clinical data were collected in a centralized database. The project has been informative in several respects. The scale and complexity of assembling TMAs with over 200 cases at each of 6 sites involved unanticipated levels of effort and time. Our statistical design promises to provide a model for outcome-based studies where tissue localization methods are applied to high-density TMAs.

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