Research Abstracts Online
January 2009 - March 2010
University of Minnesota Twin Cities
Department of Laboratory Medicine and Pathology
PI: Donald P. Connelly
The Detection and Measurement of Bone Marrow Biopsy Diagnostic Discordance
Genomic Analysis of Oscillating Genes
Cancer Center Research
These researchers use the CGL for various projects. One project is a study of bone marrow diagnostic testing by cytogenetics, flow cytometry, and hematopathology laboratories. It is expected that diagnoses determined by these laboratories on aliquots of the same bone marrow would be from the same World Health Organization diagnostic category even though they use different testing methods. The first phase of the project found statistically significant diagnostic discordance rates of these laboratories on the bone marrow specimens. The second phase of research categorizes discordant cases from Phase I by five methods. The first method determines the relationship between the discordant diagnoses, while the second method looks at issues contributing to diagnostic discordance by the laboratory or other means. The third means of categorization analyzes where issues contributing to discordance occur in the testing process, while the fourth methodology looks at where in the testing process issues occur and the etiologies that contribute to the discordances seen. Lastly, aspects of reporting are analyzed to determine if amended reports and typographical errors contribute to discordances. These categorizations all provide information about the information process and where issues occur contributing to diagnostic discordance so that the appropriate decision support tool can be designed and implemented.
A second project involves taking time-series microarray expression data and analyzing it to extract the gene sets from the genomic data that are oscillating in their expression. The gene sets will be used in the analysis of SNPs and CNVs in the human genome.
These researchers also provide computational and analytic support to the Masonic Cancer Center using numerous Supercomputing Institute resources.
Andrea Pitkus, Graduate Student
HoJung Yoon, Research Associate