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Andre Kajdacsy-Balla

Publications and source records attributed to Andre Kajdacsy-Balla.

8 recordsLinked to original sources
PubMed2006

An informatics model for tissue banks--lessons learned from the Cooperative Prostate Cancer Tissue Resource.

BACKGROUND: Advances in molecular biology and growing requirements from biomarker validation studies have generated a need for tissue banks to provide quality-controlled tissue samples with standardized clinical annotation. The NCI Cooperative Prostate Cancer Tissue Resource (CPCTR) is a distributed tissue bank that comprises four academic centers and provides thousands of clinically annotated prostate cancer specimens to researchers. Here we describe the CPCTR information management system architecture, common data element (CDE) development, query interfaces, data curation, and quality control. METHODS: Data managers review the medical records to collect and continuously update information for the 145 clinical, pathological and inventorial CDEs that the Resource maintains for each case. An Access-based data entry tool provides de-identification and a standard communication mechanism between each group and a central CPCTR database. Standardized automated quality control audits have been implemented. Centrally, an Oracle database has web interfaces allowing multiple user-types, including the general public, to mine de-identified information from all of the sites with three levels of specificity and granularity as well as to request tissues through a formal letter of intent. RESULTS: Since July 2003, CPCTR has offered over 6,000 cases (38,000 blocks) of highly characterized prostate cancer biospecimens, including several tissue microarrays (TMA). The Resource developed a website with interfaces for the general public as well as researchers and internal members. These user groups have utilized the web-tools for public query of summary data on the cases that were available, to prepare requests, and to receive tissues. As of December 2005, the Resource received over 130 tissue requests, of which 45 have been reviewed, approved and filled. Additionally, the Resource implemented the TMA Data Exchange Specification in its TMA program and created a computer program for calculating PSA recurrence. CONCLUSION: Building a biorepository infrastructure that meets today's research needs involves time and input of many individuals from diverse disciplines. The CPCTR can provide large volumes of carefully annotated prostate tissue for research initiatives such as Specialized Programs of Research Excellence (SPOREs) and for biomarker validation studies and its experience can help development of collaborative, large scale, virtual tissue banks in other organ systems.

Databases as Topic↗
PubMed2005

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.

Biomarkers, Tumor↗
PubMed2004

The tissue microarray data exchange specification: implementation by the Cooperative Prostate Cancer Tissue Resource.

BACKGROUND: Tissue Microarrays (TMAs) have emerged as a powerful tool for examining the distribution of marker molecules in hundreds of different tissues displayed on a single slide. TMAs have been used successfully to validate candidate molecules discovered in gene array experiments. Like gene expression studies, TMA experiments are data intensive, requiring substantial information to interpret, replicate or validate. Recently, an open access Tissue Microarray Data Exchange Specification has been released that allows TMA data to be organized in a self-describing XML document annotated with well-defined common data elements. While this specification provides sufficient information for the reproduction of the experiment by outside research groups, its initial description did not contain instructions or examples of actual implementations, and no implementation studies have been published. The purpose of this paper is to demonstrate how the TMA Data Exchange Specification is implemented in a prostate cancer TMA. RESULTS: The Cooperative Prostate Cancer Tissue Resource (CPCTR) is funded by the National Cancer Institute to provide researchers with samples of prostate cancer annotated with demographic and clinical data. The CPCTR now offers prostate cancer TMAs and has implemented a TMA database conforming to the new open access Tissue Microarray Data Exchange Specification. The bulk of the TMA database consists of clinical and demographic data elements for 299 patient samples. These data elements were extracted from an Excel database using a transformative Perl script. The Perl script and the TMA database are open access documents distributed with this manuscript. CONCLUSIONS: TMA databases conforming to the Tissue Microarray Data Exchange Specification can be merged with other TMA files, expanded through the addition of data elements, or linked to data contained in external biological databases. This article describes an open access implementation of the TMA Data Exchange Specification and provides detailed guidance to researchers who wish to use the Specification.

Confidentiality↗
PubMed2004

Borderline ovarian tumors: diverse contemporary viewpoints on terminology and diagnostic criteria with illustrative images.

The National Cancer Institute sponsored a Borderline Ovarian Tumor Workshop held in August 2003 in Bethesda, MD. This report was developed from discussions at the Workshop. The participants acknowledged several areas of disagreement on basic terminology issues and agreed that a glossary with example images would help clarify many commonly misunderstood issues. This report defines terminology used in the pathological description of borderline tumors and their variants, and illustrates examples of each of the most common entities. It also addresses controversial aspects of the definitions and issues involving specimen handling and reporting. For those issues where there is disagreement, the terminology and diagnostic approaches reflecting the differing views are presented.

Cystadenocarcinoma↗
PubMed2004

Immunohistochemical expression patterns of germinal center and activation B-cell markers correlate with prognosis in diffuse large B-cell lymphoma.

Recent studies with cDNA microarrays showed that diffuse large B-cell lymphoma (DLBCL) cases with gene expression profiles similar to germinal center (GC) B cells had much better prognosis than DLBCL cases with gene expression profiles resembling activated B cells. The goal of the current study is to evaluate if using a panel of GC B-cell (CD10 and Bcl-6) and activation (MUM1/IRF4 and CD138) markers by immunohistochemistry defines prognosis in patients with de novo DLBCL. Immunohistochemical stains for the above markers were performed on paraffin-embedded tissues from 42 de novo DLBCL patients. Median follow-up in all patients was 41 months (range, 1-103 months) and in surviving patients was 65 months (range, 14-103 months). These cases could be classified into three expression patterns: GC B-cell pattern (pattern A) expressing CD10 and/or Bcl-6 but not activation markers; activated GC B-cell pattern (pattern B) expressing at least one of GC B-cell markers and one of activation markers; and activated non-GC B-cell pattern (pattern C) expressing MUM1/IRF4 and/or CD138 but not GC B-cell markers. Patients with pattern A had much better overall survival than those with the other two patterns (Kaplan-Meier survival analysis, P < 0.008, log rank test). Using multivariate Cox proportional hazards regression analysis, the international prognostic index scores and the expression pattern of these markers were independent prognostic indicators. Our results suggest that expression patterns of this panel of GC B-cell and activation markers by immunohistochemistry correlate with the prognosis of patients with DLBCL. Immunohistochemical analysis on paraffin-embedded tissues is more readily available than gene expression profiling by cDNA microarray and may provide similar prognostic information.

Adult↗
PubMed2002

Requirement of cyclooxygenase-2 expression and prostaglandins for human prostate cancer cell invasion.

The PC-3 Low Invasive cells and the PC-3 High Invasive cells were used to investigate the correlation of the COX-2 expression and its arachidonic acid metabolites, prostaglandins, with their invasiveness through Matrigel using a Boyden chamber assay. The COX-2 expression in PC-3 High Invasive cells was approximately 3-fold higher than in PC-3 Low Invasive cells while the COX-1 expression was similar in both cell sublines. When incubated with arachidonic acid, PGE2 was the major prostaglandin produced by these cells. PC-3 High Invasive cells produced PGE2 approximately 2.5-fold higher than PC-3 Low Invasive cells. PGD2 was the second most abundant prostaglandin produced by these cells. Both indomethacin (a nonspecific COX inhibitor) and NS-398 (a specific COX-2 inhibitor) inhibited the production of prostaglandins and the cell invasion. PGE2 alone did not induce the cell invasion of PC-3 Low Invasive cells. However, PGE2 reversed the inhibition of cell invasion by NS-398 and enhanced the cell invasion of the PC-3 High Invasive cells. In contrast, PGD2 slightly inhibited the cell invasion. These results suggest that in the PC-3 Low Invasive cells, COX-2-derived PGE2 may not be sufficient to induce cell invasion while in the PC-3 High Invasive cells, PGE2 may be sufficient to act as an enhancer for the cell invasion. Further, PGD2 may represent a weak inhibitor and counteracts the effect of PGE2 in the cell invasion.

6-Ketoprostaglandin F1 alpha↗