Relational database structure to manage high-density tissue microarray data and images for pathology studies focusing on clinical outcome - The Prostate Specialized Program of Research Excellence Model
S. Manley et al., Relational database structure to manage high-density tissue microarray data and images for pathology studies focusing on clinical outcome - The Prostate Specialized Program of Research Excellence Model, AM J PATH, 159(3), 2001, pp. 837-843
Citations number
8
Categorie Soggetti
Research/Laboratory Medicine & Medical Tecnology","Medical Research Diagnosis & Treatment
With the completion of the Human Genome Project and high-throughput screeni
ng methods using cDNA array and tissue microarray (TMA) technology, there i
s a pressing need to manage the voluminous data sets generated from these t
ypes of investigations. Herein is described a database model to handle 1) c
linical and pathology data, 2) TMA location information, and 3) web-based h
istology results. The model is useful for managing clinical, pathology, and
molecular data on > 1300 prostate cancer patients dating back to 1995 from
the University of Michigan Specialized Program of Research Excellence for
prostate cancer. The key components in this multidatabase model are 1) the
TMA database, 2) the TMA-image database (TMA-I DB), and 3) the prostate pat
hology and clinical information databases. All databases were created in Mi
crosoft Access (Microsoft, Redmond, WA). Desired patient, tissue, block, di
agnosis, array location, and respective clinical and pathology information
is obtained by linking the unique identifier fields among database tables.
The TMA database is comprised of interrelated data from 336 prostate cancer
patients transferred into 19 TMA blocks with 5451 TMA biopsy cores. Tissue
samples include 1695 normal prostate, 3171 prostate cancer, 464 prostatic
intraepithelial neoplasia, and 121 atrophy. All 19 TMA blocks have been ana
lyzed over the Internet for several immunohistochemical biomarkers; includi
ng E-cadherin, prostate-specific antigen, p27(Kip1), and Ki-67 labeling ind
ex. This system facilitates the statistical analysis of high-density TMA da
ta with clinical and pathology information in an efficient and cost-effecti
ve manner. Because the review is performed over the Internet, this system i
s ideal for collaborative multi-institutional studies.