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Extracting signal from noise to combat organ rejection

Extracting signal from noise to combat organ rejection

Last year, 28,000 patients in the United States received organ transplants. Because the body’s immune system is programmed to recognize a transplanted organ as foreign and attack it, the organ is often rejected.

Although immunosuppressant drugs such as cyclosporine act to prevent organ rejection, and better knowledge of molecular mechanisms of rejection has improved donor-recipient matching, organ rejection is still a frequent problem. For instance, 15 to 20 percent of adults who received kidney transplants between 2005 and 2009 experienced acute organ rejection within five years, according to statistics from the Organ Procurement and Transplantation Network and the Scientific Registry of Transplant Recipients.

To improve both prediction and treatment methods, Purvesh Khatri, PhD, harnessed the power of multiple data sets to find the common links among thousands of organ rejection cases. Khatri is an acting assistant professor of medicine at the Stanford Center for Biomedical Informatics Research; among his collaborators were co-senior authors Minnie Sarwal, MD, PhD, of the California Pacific Medical Center, and Atul Butte, MD, PhD, associate professor of pediatrics at Stanford and director of the Center for Pediatric Bioinformatics at Lucile Packard Children’s Hospital. In a paper published today in the Journal of Experimental Medicine, Khatri and his colleagues found that the same 11 genes were highly expressed in four types of rejected organs – heart, lung, liver, and kidney.

By examining this 11-gene signature in patient biopsies taken six months after transplants, the researchers could predict the risk of organ rejection after two years. This finding could have implications for more targeted treatment of patients at risk for organ rejection.

In addition, Khatri and his team searched the medical literature for existing drugs that might target any of the 11 genes involved in rejection. They came up with two drugs, atorvastatin and dasatinib, which are currently used for the treatment of high cholesterol and cancer, respectively. To find out whether these drugs could effectively prevent organ rejection, they teamed up with colleagues at the Stanford Cardiovascular Institute to test the drugs on a mouse heart-transplant model. They found that both atorvastatin and dasatinib increased survival rates for mice after heart transplants.

In a testament to the power of publically available electronic medical records, the team obtained data on more than 2,500 kidney transplant patients from University Hospital in Leuven, Belgium. Sixty percent of these patients were receiving statin treatment for heart-related conditions, and these patients had a 30 percent lower risk of organ rejection. This suggests that statins may be effective as a routine anti-rejection treatment. “You’ve been giving this drug to these patients for other reasons and their grafts are surviving longer,” Khatri, lead author of the study, told me. “So you might as well start making it part of the standard protocol.”

Because atorvastatin and dasatinib are already approved for other uses by the Food and Drug Administration, the time between laboratory studies and approval for patients can be shortened. The innovative approach taken in this study – to use large data sets and computational methods to extrapolate common genes involved in medical conditions, and then to predict new treatments – is likely to become more common. As Khatri, a computer scientist, explained, “Despite all the heterogeneity in the data, we can find the signal that can be used for predicting drugs, and then we can use electronic medical records to see if the drug would actually work before we do the experiment.”

Molly Sharlach is a writing intern in the medical school’s Office of Communication & Public Affairs. She is a student in the Science Communication Program at the University of California-Santa Cruz.

Previously: Shushing T cells promotes acceptance of stem cell therapies, say Stanford researchers and Biomarker can predict graft-versus-host disease in men after transplants from women donors

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