My colleague Bruce Goldman has written elegantly here before about how large biological databases (a phenomenon commonly known as "big data") are a treasure trove of information - for those who know where to look for it. Bruce has referred to the computerized technique, which pairs existing drugs with human diseases (sometimes vastly different from the conditions for which the drugs were originally developed) as a molecular Match.com. And although the hook-ups are sometimes apples-and-oranges odd, they're showing lots of promise.
Last night, Stanford researchers Atul Butte, MD, PhD, and Julien Sage, PhD, published a study in Cancer Discovery (subscription required) describing how they've used this algorithm developed in the Butte lab to identify a possible new treatment for small cell lung cancer, which is particularly deadly. And because the drug, an antidepressant called imipramine, is already approved by the Food and Drug Administration for use in humans, they've been able to quickly and (relatively) inexpensively move into human trials.
As Butte, the director of the Center for Pediatric Bioinformatics at Lucile Packard Children’s Hospital at Stanford described in our release:
We are cutting down the decade or more and the $1 billion it can typically take to translate a laboratory finding into a successful drug treatment to about one to two years and spending about $100,000.
How exactly does it work? More from our release:
The pipeline works by scanning the hundreds of thousands of gene-expression profiles (gathered by multiple researchers and stored in large databases) across many different cell types and tissues — some normal and some diseased, some treated with medications and some not. Alone, these profiles may not mean much to any one investigator or group, but when viewed together, researchers can pick out previously unsuspected patterns and trends.
For example, if a particular molecular pathway is routinely activated (as indicated by an increase in the expression levels of the genes involved) in a cancer cell, and a drug is shown to block or suppress that same pathway (by decreasing the expression of genes in the pathway), it’s possible the drug could be used to treat that type of cancer — regardless of the disease for which it was originally approved.
Patients with small cell lung cancer account for only about 15 percent of all lung cancers, but their prognosis is particularly poor. The disease belongs to a particular class of cancers called neuroendocrine tumors that arise from cells that couple signals from our nervous system to the release of hormones within the body. Some pancreatic and aggressive gastrointestinal tumors also belong to this class. As Sage explains:
The five-year survival for small-cell lung cancer is only 5 percent. There has not been a single efficient therapy developed in the last 30 years. But when we began to test these drugs in human cancer cells grown in a dish and in a mouse model, they worked, and they worked, and they worked.
The drugs (one a tricylic antidepressant no longer often prescribed) identified by the algorithm activated a cellular self-destruct pathway that killed the cancer cells. Sage and Butte have since collaborated with Joel Neal, MD, PhD, to launch a Phase II clinical trial to test the treatment.
For more information about the ongoing clinical trial, which uses a molecule related to imipramine called desipramine, contact the Stanford Cancer Clinical Trials Office at (650) 498-7061 or e-mail ccto-office@stanford.edu.
Previously: Clinical trials: My next good chance, Mining medical discoveries from a mountain of ones and zeros, Cheap data! Stanford scientists' "opposites attract" algorithm plunders public databases, scores surprising drug-disease hook-ups, and Atul Butte discusses why big data is a big deal in biomedicine
Photo by Covert Oddity
