In three papers being published in Science and Cancer Cell, scientists from MIT and Harvard make a case for the Connectivity Map, a computational tool that connects gene expression signatures via expressed proteins to chemical compounds that might influence these proteins.
The three papers demonstrate the map’s ability to accurately predict the molecular actions of novel therapeutic compounds and to suggest ways that existing drugs can be newly applied to treat diseases such as cancer.
“The Connectivity Map works much like a Google search to discover connections among drugs and diseases,” said senior author Todd Golub, the director of the Broad Institute’s Cancer program, an investigator at the Dana-Farber Cancer Institute, an associate professor at Harvard Medical School and an investigator at the Howard Hughes Medical Institute. “These connections are notoriously difficult to find in part because drugs and diseases are characterized in completely different scientific languages.”
To build the Connectivity Map, the scientists described the effects of different drugs and diseases using the common language of “genomic signatures” — the full complement of genes that are turned on and off by a particular drug or disease. The scientists compiled the genomic signatures of more than 160 drugs and other biologically active compounds, forming a database of biological “barcodes” that denote cells’ responses to the different drugs. Then, they developed a computer program that matches the barcodes based on the patterns shared among them. Together, these features enable the first-generation Connectivity Map to directly compare the biological effects of different drugs with each other, and also with those seen in diseases.
Like other scientific databases, the Connectivity Map can be queried by nearly any researcher with a computer, where the search “word” is the genomic signature of a particular human disease, drug or other biological response of interest, and the search results consist of a rank-ordered list of reference compounds that have matching signatures. These comparisons can yield new scientific insights, particularly when a connection exists between a poorly understood drug (or disease) and a drug whose effects have been extensively characterized — the case for many of the compounds currently referenced in the database. This potential is underscored by two cases where the Connectivity Map has already been used: one, to discover the mechanisms underlying a novel drug candidate for prostate cancer and another, to reveal that a drug currently used to treat one disease may be useful in another.
“This is a powerful discovery tool for the scientific community,” said Justin Lamb, the lead author of the Science paper and a senior scientist in the Broad Institute’s Cancer program. “By analyzing just a small fraction of available drugs, we have already confirmed several biological connections between drugs and human disease, and made entirely new ones, too.”
One of the surprising results to emerge from the use of the Connectivity Map involves gedunin, a plant derivative that, despite a long history of medicinal use, is not well understood molecularly. Described in Cancer Cell by first author Haley Hieronymus, a researcher at the Broad and the Dana-Farber Cancer Institute, and her colleauges, gedunin was first identified in a high-throughput chemical screen for molecules that disrupt hormone signals in prostate cancer cells. Then the researchers used the Connectivity Map to help uncover its molecular action, which as confirmed through additional work, disrupts a key quality control mechanism in the cell, mediated by the heat shock 90 protein (HSP90).