Illustration by Kim Carney / Fred Hutch News Service
For the first time, researchers have uncovered a set of genes that predicts whether the flu vaccine will work in adults 35 years old or younger.
The prediction, undertaken by a nationwide team of researchers known as the Human Immunology Project Consortium, came to light through a Big Data-scale analysis of more than 32,000 genes from more than 500 people who had received the flu vaccine in different parts of the U.S. during several different flu seasons. The study, published August 25 in the journal Science Immunology, looked at adults 35 years old and under, as well as those 60 and older, who had received the vaccine.
That variety of data was important to make sure the researchers were capturing a signature that could apply to a larger population, said Dr. Raphael Gottardo, a computational biologist at Fred Hutchinson Cancer Research Center and a senior author on the study. But the result was a large and complex dataset that required new computational tools to separate the wheat from the chaff.
The analysis pinpointed a set of nine genes linked to the immune response to flu shots, a discovery that could lead to better flu vaccines for those who don’t currently benefit as much from the yearly shot, Gottardo said. He emphasized, however, that results like these don’t mean people shouldn’t bother getting vaccinated. In fact, the opposite is true, even in years that the flu vaccine is less effective.
“There is this thinking that the flu vaccine just doesn’t work, which is just not true,” Gottardo said. “It can be highly effective in preventing infection, but I think we have a way to potentially improve it further by looking at these baseline responses.”
Those baseline responses could one day let clinicians sort people into two groups: those with a gene signature that shows they will benefit the most from the standard flu vaccine and those without it, who may need a different type or amount of the flu vaccine to boost their protection.
Their findings could be the first step to a personalized flu vaccine, Gottardo said.
Photo by Robert Hood / Fred Hutch News Service
A Big Data problem that relies on collaboration
The yearly flu vaccine is the single best method to protect against the flu, which infects up to 35 million people in the U.S. alone every year, but the shot doesn’t protect everyone who receives it. The vaccine’s effectiveness varies from year to year, and researchers estimate it is only between 50 to 67 percent effective in adults under 65. That means that, depending on the year, a third to one half of adults who get the shot won’t mount as strong an immune response as the rest of the population, even though they may still have better protection against the flu virus than if they had never been vaccinated at all. For adults 65 and older, the standard flu vaccine is even less effective, although new, higher-dose vaccines can boost protection in this age group.
So clearly there’s room for improvement.
Although the researchers also looked for genes that would predict a vaccine response in older adults — with the hope of pointing to much-needed improvements in this population, which can be more vulnerable to dangerous effects from the virus — they couldn’t find any, Gottardo said. That might be because there are so few older adults who respond well to the standard vaccine in the first place.
To find the vaccine predictor in younger adults, the researchers looked at 32,034 genes in blood samples taken from several hundred adults — before the study volunteers were vaccinated. They then looked for genes whose activity increased specifically in those volunteers who also showed a better immune response to the vaccine. (Mounting a better immune response is tightly linked to being protected against the infection, Gottardo said.)
Sifting through that large gene set across several different groups of people yielded the set of just nine genes whose activity increased significantly only in the young adults who would later show a good response to the flu vaccine. Gottardo and his colleagues looked at that same set of genes in older adults, but the collection didn’t predict vaccine response in those 60 and older.
Finding that signature in the first place was only possible by combining the data across multiple different research teams from different institutions, Gottardo said.
That open and collaborative approach is going to be increasingly key to solving thorny problems in biomedical science, said Fred Hutch’s chief information officer Matthew Trunnell, who was not involved in the study. Trunnell is an expert on the intersections between Big Data and medical research. More and more, he said, certain questions in biomedicine require huge sets of data to answer them reliably.
“To be able to find the signal in that noise, you have to keep increasing the number of samples that you’re looking at. It’s a little bit like using a more powerful microscope,” Trunnell said. “One of the challenges we face as a field is that very few of us, either labs or centers, have a big enough set of whatever we want to look at on our own.”
So the solution is to team up. “This is a really important aspect of what is going to be needed for Big Data in biomedicine: data sharing,” he said.
Could the flu vaccine inform other diseases?
Before this set of genes could be turned into a predictive assay for whether the flu vaccine will work, these results would first need to be validated by other researchers, Gottardo said. He and his colleagues also want to understand the biology behind the link between these genes and the flu vaccine. Some of the genes they found are involved in producing antibodies, the immune molecules that protect against infection, but some are involved in other processes in the body whose tie to vaccination is less clear.
The researchers also saw that the nine genes’ activity stayed constant over the span of a few weeks, but they don’t yet know whether that signature will hold true over a lifetime or whether the genes’ activity could change year to year. This would mean the difference between a one-time test for vaccine response and needing to get a blood draw every year before receiving the flu vaccine. Of course, a research or industry group would need to research and develop such a test — and most importantly, researchers would also have to figure out a vaccination strategy that works better in those who don’t respond well to the current shots.
The flu vaccine study is the first that the large immune consortium has published together, and it took years to collect that data and do the analysis, Gottardo said. The next step will be to see if there are other diseases or vaccines where the research groups across the Human Immunology Project Consortium have enough data to perform a similar analysis. Gottardo, who is also part of the HIV Vaccine Trials Network headquartered at the Hutch, is interested in studying whether this set of genes — or another set the researchers could find using similar techniques — can predict response to experimental HIV vaccines.
He is particularly intrigued by the possibility that the research team could find a universal predictor for how well any given vaccine works, not just the flu vaccine, but stressed that there is no evidence yet that such a universal set of genes exists. A universal predictor of vaccine response could push forward vaccine development — and point to ways to develop personalized vaccines — for a wide variety of diseases all at once, he said.
“What would be potentially more interesting than studying another single vaccine is, can we do that across diseases?” Gottardo said. “Can we find these baseline predictors that are independent of the vaccine that predict vaccine response in general? If you can find that, it would be amazing.”
Rachel Tompa, a staff writer at Fred Hutchinson Cancer Research Center, joined Fred Hutch in 2009 as an editor working with infectious disease researchers and has since written about topics ranging from nanotechnology to global health. She has a Ph.D. in molecular biology from the University of California, San Francisco and a certificate in science writing from the University of California, Santa Cruz. Reach her at email@example.com or follow her on Twitter @Rachel_Tompa.
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