Predicting Vaccine Efficacy

The researchers used correlation cluster analysis of expressed genes to confirm that subjects could be sorted clearly into two categories: "high" or "low" responders to the vaccine, based on the strength of T cell response. Courtesy of Bali Pulendran. Reprinted from Querec TD et al., Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans, Nature Immunology (2009) 10(1):116-125 with permission from Macmillan, publishers.Researchers developing a new vaccine currently have no direct way of predicting its efficacy short of exposing patients to the disease. A new study that combines gene expression data with advanced computational analysis provides the first evidence that the vaccine-induced immune response can be predicted.

 

“We develop vaccines but can never say how effective they will be,” says Bali Pulendran, PhD, a researcher at the Emory Vaccine Center in Atlanta who led the study, published in Nature Immunology in November 2008. “Only after exposure do we really know.”

 

To gauge a vaccine’s effectiveness, scientists evaluate indicators of the so-called “adaptive” immune response, which develops over time. The titer—a measurement of the concentration—of long-term antibodies in the blood is one indicator. The number of killer T cells is another. But a more complete profile of the early or “innate” immune system reaction could help researchers screen vaccine candidates or help identify individuals whose adaptive immune systems don’t respond.

 

To develop such a profile of the immune response, Pulendran and his colleagues monitored patients given the yellow fever vaccine—a vaccine that has been given to more than 600 million people and is considered one of the most powerful ever developed, proving effective 80 to 90 percent of the time. In two sets of volunteers (15 in the first group and 10 in the second) Pulendran’s group sought to correlate patients’ innate (early) immune response to the vaccine with the later T cell response. Several cytokines and 65 genes responded to the vaccine in significant ways, but there was no apparent link between this innate signature and the subsequent T cell reaction.

 

To zero in on what was evidently a subtle connection, the researchers looked more broadly at the gene expression signatures for the first set of patients. They found 839 genes whose expression correlated with the T cell response. Using these data and a supervised learning algorithm developed by Eva Lee, PhD, at the Georgia Institute of Technology, they pulled out eight different genetic signatures from data from the first group that strongly predicted the T cell response in the second group of volunteers. The researchers also used that algorithm to generate signatures predicting the titer of long-term antibodies.

 

In the case of both T cells and antibodies, the researchers were particularly interested in a small number of genes that featured in all the predictive signatures. These genes form a core set that doctors could potentially monitor to predict how effective a vaccine will be in a patient. Pulendran also hopes that by working to replicate the innate reaction to yellow fever, scientists may be able to make potent vaccines against other pathogens.

 

“If the approach could be extended to development of vaccines against different sorts of pathogens, it would be a real advance,” says Larry Stern, PhD, an immunologist at the University of Massachusetts. “The key here is whether the same signature would be induced by other pathogens,” he says, noting that even if the method works only for related pathogens, such as dengue fever and West Nile virus, that would still be a very valuable contribution.
 



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