A study funded by the National Cancer Institute, part of the National Institutes of Health, shows for the first time that microarray data generated in different laboratories can produce highly comparable results. For this comparison study, appearing in the Jan. 15, 2005, Clinical Cancer Research*, four separate laboratories analyzed gene expression (whether genes are turned on or off) for the same set of human tumor tissues. Overall, the expression profiles of portions of individual samples were highly comparable, and the experimental correlation between separate labs was only slightly lower than correlation of duplicated experiments within the same labs.
“This study is a key first step in moving gene expression data from small-scale bench science into large-scale clinical evaluation,” said James Jacobson, Ph.D., chief of NCI’s Diagnostic Biomarkers and Technology Branch.
Gene expression microarrays have been used in numerous applications, including identifying novel genes associated with certain cancers, classifying tumors, and predicting patient outcome. So far, though, microarray studies have been performed by individual institutions. Evaluation of the potential clinical use of microarrays may require larger studies carried out in multiple locations and would necessitate that microarray data produced in different laboratories be combined for analysis. Even if all procedures and equipment were the same, small differences between labs, such as in handling the tissue samples, extracting the RNA, or scanning the microarrays, could result in different profiles.
To test the feasibility of multi-lab microarray studies, the NCI’s Director’s Challenge program set up this preliminary study to compare results between labs. Twelve different tumor tissues, five cancer cell lines, and five purified RNA samples were prepared, blinded, and randomized for analysis in four laboratories: University of Michigan Medical School, Ann Arbor, Mich.; Dana Farber Cancer Institute/Whitehead Institute, Boston, Mass.; Memorial Sloan-Kettering Cancer Center, New York, N.Y.; and H. Lee Moffitt Cancer Center, Tampa, Fla. All five cell lines and RNA samples were derived from lung carcinomas, while the tissue samples were derived from multiple normal and cancerous tissues. The labs followed a common protocol for all steps of sample preparation and microarray analysis and the resulting gene expression profiles were analyzed and compared.
Sample correlation within each lab was extremely high; microarray data from the RNA samples had the highest correlation values, followed by the cell lines, and then finally the tissue samples. This was not surprising, as RNA required the fewest steps of preparation, while tissues required the most. The between-lab correlation values decreased slightly for all samples, but in all cases expression profiles of similar samples could be accurately grouped together.
The researchers also examined variations of individual gene measurements to help determine causes of variability, and they found that laboratory practices comprised the smallest source of variation in these studies. Measurement errors common to all labs were the next most common contributor, and biological differences in different samples were the largest source of variation.
“This study indicates that microarrays have a high degree of reproducibility, as long as standardized protocols are carefully followed,” said Jacobson. These promising results will allow this same research group to proceed with a larger gene expression analysis of 600 stage I lung adenocarcinomas, with the hopes of confirming a previous association between gene expression profiles and patient outcome.
This project is also an example of NCI interest in developing public/private partnerships. Affymetrix, of Santa Clara, Calif., contributed part of the arrays for this comparison study and provided technical assistance to the four sites carrying out the study. Ardais Corporation, Lexington, Mass., provided the RNA samples used for analysis.
* Kevin K. Dobbin, David G. Beer, Matthew Meyerson, Timothy J. Yeatman, William L. Gerald, James W. Jacobson, Barbara Conley, Kenneth H. Buetow, Mervi Heiskanen, Richard M. Simon, John D. Minna, Luc Girard, David E. Misek, Jeremy M.G. Taylor, Samir Hanash, Katsuhiko Naoki, D. Neil Hayes, Christine Ladd-Acosta, Stecen A. Enkemann, Agnes Viale, and Thomas J. Giordano. Interlaboratory Comparability Study of Cancer Gene Expression Analysis Using Oligonucleotide Microarrays. Clinical Cancer Research, Vol. 11; Jan 15, 2005.
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