More biology articles in the 'Microarray' category

For everyone doing or reading a paper about microarray-based experiments, reproductibility, especially inter-lab, is the #1 concern. Can I trust these results? If I redo the same experiment in one month, will I be able to compare both? The NIH recently demonstrated that microarrays experiments performed in different labs "have a high degree of reproducibility, as long as standardized protocols are carefully followed". Good news for the scientific community (and the microarrays producing companies)! 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. Source : NIH

Anonymous (unregistered) writes:

When the decision is made to treat a patient with chemotherapy, most patients are treated with a combination of drugs. The "whole cell profiling" method differs from existing DNA and RNA tests in that it assesses the activity of a drug upon combined effect of all cellular processes, using several metabolic and morphologic endpoints. Other tests, such as those which identify DNA or RNA sequences or gene expression of individual proteins often examine only one component of a much larger, interactive process.

Researchers have put their efforts into molecular profiling as a way of predicting patient response to anti-cancer drug therapies. However, no gene-based test can discriminate differing levels of anti-tumor activity occurring among different therapy drugs. Nor can available gene-based tests identify situations in which it is advantageous to combine the new "targeted" drugs with other types of cancer drugs. So far, only cellular profiling has demonstrated this critical ability.

Not only is this an important predictive test, it is also a unique tool that can help to identify newer and better drugs, evaluate promising drug combinations, and serve as a "gold standard" correlative model with which to develop new DNA, RNA, and protein-based tests that better predict for drug activity.

Using gene expression microarrays to predict for responsiveness to drug therapy, is not the answer without cell culture analysis. The way to identify informative gene expression patterns is to have a gold standard, and cell culture assays are by far the most powerful, efficient, useful gold standard to have.

It's not either/or. It is a combination.

06/29/2006 12:40 am

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January 19, 2005 06:55 PMMicroarray



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