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Genetic database to fight disease

September 28, 2006 By Helen Pearson This article courtesy of Nature News.

Pilot project serves up new uses for old drugs.

A vast database showing how human genes react to drugs and diseases could be used in a scheme to find new therapies. A pilot project has now proved that such a project could work and has already revealed potential drugs to fight cancer and other diseases.

To build the pilot database, Todd Golub of the Broad Institute mdash a collaboration involving the Massachusetts Institute of Technology and Harvard University in Cambridge, Massachusetts and his colleagues threw 164 different drugs or other chemical compounds onto human cancer cells, particularly breast cancers. They used DNA microarrays small chips that expose in one swoop the activity of every known human gene in a particular tissue to record which genes were boosted or repressed by each drug.

The team entered the results of these microarrays into a database. They could then search it for patterns in gene expression caused by other diseases or drugs (or the opposites of these patterns), in a similar way to forensic fingerprint matching.

When they searched their database for matches to two previously published microarray results from the brains of Alzheimer's patients, for example, they found that one of the drugs they had tested has roughly the opposite effect on human genes. This implies that the drug might reverse the abnormal activity of genes involved in Alzheimer's and so tackle the disease. In fact, similar compounds are already being tested for this purpose.

Match point

In another project, Golub worked with Scott Armstrong at Children's Hospital Boston and his team to try to help patients with acute lymphoblastic leukaemia, who tend to fare badly because they are resistant to drugs called glucocorticoids. They obtained an expression pattern of genes for leukaemia cells that are not resistant to these drugs and plugged this into the database.

The search spat out a drug called rapamycin that produces a similar expression pattern when put on cells, suggesting that the drug might be able to convert resistant leukaemia cells into susceptible ones. When the researchers tested this on drug-resistant cells in the laboratory "that's exactly what happened", Golub says. The researchers are now working towards clinical trials of this drug against this type of leukaemia.

In yet another project, Golub's group showed that you can use the database to find how a drug is working by comparing the expression pattern it produces to that of other drugs. The pilot database is already available to researchers, and the results are published in Science1 and Cancer Cell2,3.


Golub says that the database should now be expanded to contain microarray data for every previously approved drug. Researchers could search it to find new uses for old drugs or to reveal previously unknown side effects.

The database could also contain microarray data showing how the gene activity of a human cell changes when each one of its genes is removed in turn. This could be matched against fingerprints of gene activity produced by diseases, in order to reveal the genes behind the illness.

"You can't argue against that idea," says Atul Butte of Stanford University, California, who works on ways to analyse gene-expression experiments.

Butte points out that there is already a mass of data from microarray experiments sitting in public databases such as the Gene Expression Omnibus, which contains over 99,000 microarray results. But it has been difficult to compare patterns in this database because each experiment that has contributed to it was carried out in a slightly different way. Butte says that improvements in bioinformatics could make this possible in the near future and perhaps save the trouble of building Golub's database from scratch.

Golub says that he is discussing his idea of an expanded database with the US National Institutes of Health, but that it would probably not be possible until the price of microarrays, which can cost hundreds of dollars per chip, has dropped.

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  1. Lamb J., et al. Science, 313. 1929 - 1835 (2006).
  2. Wei G., et al. Cancer Cell, 10. DOI 10.1016/j.ccr.2006.09.005 (2006).
  3. Hieronymus H., et al. Cancer Cell, 10. DOI 10.1016/j.ccr.2006.09.006 (2006).


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