A simple inexpensive method for the production of tissue microarrays from needle biopsy specimens: examples with prostate cancer.

The use of tissue microarrays has become an efficient method for the high-throughput analysis of tissues with molecular markers, yet these studies have not been used to leverage the limited materials present in needle biopsies of human tissues. The use of these biopsy tissues is crucial to study diseases in patients who are treated by nonsurgical methods such as radiation, chemotherapy, or palliative care. The authors present a simple, inexpensive method for using needle biopsy specimens in tissue microarrays. Using this process with prostate cancer specimens, the authors demonstrate that over 150 slides can be produced from a single area of cancer in a needle biopsy and that the length of the core involved by cancer in the needle biopsy determines the number of available tissue microarray slides. The authors also note the optimal number of samples (three) needed from a single patient biopsy to guarantee sufficient material for analysis and perform an immunohistochemical correlation between needle biopsy and surgical resection tissue microarray samples for the quantitative marker Ki-67. This process can be extended to any type of needle biopsy specimen, increasing the number of studies and potential use of these tissues as a practical reality.

Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium.

BACKGROUND: Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal models to prioritize genes for validation. This approach allows one to develop a disease-based focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models. This method is made possible through the use of highly controlled spotted cDNA slide production and the use of comparative bioinformatics databases without the use of cross-species slide hybridizations. RESULTS: Using gene expression profiling we have demonstrated a similar whole transcriptome gene expression patterns in prostate cancer cells from human and rat prostate cancer cell lines both at baseline expression levels and after treatment with physiologic concentrations of the proposed chemopreventive agent Selenium. Using both the human PC3 and rat PAII prostate cancer cell lines have gone on to identify a subset of one hundred and fifty-four genes that demonstrate a similar level of differential expression to Selenium treatment in both species. Further analysis and data mining for two genes, the Insulin like Growth Factor Binding protein 3, and Retinoic X Receptor alpha, demonstrates an association with prostate cancer, functional pathway links, and protein-protein interactions that make these genes prime candidates for explaining the mechanism of Selenium's chemopreventive effect in prostate cancer. These genes are subsequently validated by western blots showing Selenium based induction and using tissue microarrays to demonstrate a significant association between downregulated protein expression and tumorigenesis, a process that is the reverse of what is seen in the presence of Selenium. CONCLUSIONS: Thus the outlined process demonstrates similar baseline and selenium induced gene expression profiles between rat and human prostate cancers, and provides a method for identifying testable functional pathways for the action of Selenium's chemopreventive properties in prostate cancer.

Simple, inexpensive method for automating tissue microarray production provides enhanced microarray reproducibility.

Tissue microarrays are a novel technology with the potential to impact cancer research by reducing the time, materials, and costs related to specimen-based marker validation. The process uses small cores of specimen tissue for molecular studies, maximizing the quantity of specimens that can be analyzed on a single slide and the results that can be obtained from a single antibody study. However, this process can be tedious and requires a significant time commitment for array production, particularly for the hand-produced tissue array blocks. In addition, this process has significant repetitive motions, risking repetitive stress injury for technical personnel. For these reasons, we have sought a simple, inexpensive system for automation of the existing microarray technologies. Using this system, slides containing as many as 400 specimens can be constructed in a simple and reproducible manner. Automation of the tissue microarray apparatus is accomplished by attaching two stepper motors to the micrometers of the apparatus that control array movement, and it has the advantages of standardizing the spacing between each specimen and eliminating repetitive motions by the user. A computer program is used to run the motors, allowing the user to input commands based on the desired moving distance. After assimilation of the motors, motor control boards, and corresponding program, the final product was tested and demonstrated to provide consistent, reproducible operation. Tissue microarrays were generated with specimen tissue diameters of 1.5 mm, 1.0 mm, and 0.6 mm with core densities upwards of 300 samples per slide.