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A newly developed genetic “roadmap” promises to streamline the drug discovery process. Called the Connectivity Map, this public database matches drug compounds with diseased cells and the processes occurring within them.

“The reason it’s so difficult to find those disease and drug connections is that the languages in which they are conventionally described are very different,” says Justin Lamb, senior scientist at the Broad Institute in Cambridge, MA. “A physician would describe a disease in terms of its physical symptoms, whereas a chemist would describe drug actions in terms of binding that chemical to a particular protein.” The researchers want to bridge that gap using a common language: gene-expression signatures.

At any point in time, some genes in a cell are expressed, or “on”, while others are not. And a cell’s particular profile of activity is known as its gene-expression signature. When cells are exposed to a drug, that signature changes: some genes that were expressed are turned off and vice-versa. And different drugs leave different signatures. It is these signatures that the researchers used to build the Connectivity Map.

Lamb and his colleagues conducted a pilot study on a select number of compounds and cell types to create the first installment of the map, reported recently in the journal Science. They chose 164 molecules that were biologically active, including drugs approved by the FDA and compounds commonly used as tools in the lab. They tested the molecules on four types of cancer cells–breast cancer, prostate cancer, leukemia, and melanoma–looking at how the compounds affected gene expression in those cells.

The researchers did the analysis using DNA microarrays made by the company Affymetrix. These tiny glass chips are coated with thousands of short sequences of DNA that refer to parts of the human genome that often differ between individuals. For a given drug or cell type, the chips produce a unique pattern corresponding to the particular genes expressed. For example, the hormone estrogen might cause breast cancer cells to express certain genes, but have no effect in a prostate cancer cell, and that difference would be visible on the DNA chip.

The researchers then developed a computer program to compare the signatures to each other and rate the strength of the connections. The data from even this relatively small number of cell types and compounds, Lamb says, has yielded two new findings, described in papers in the journal Cancer Cell.

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