"We wanted something a doctor could use in the clinical setting to ascertain a patient's condition," Storey said. "The way to do that was through a model that is incredibly simple and a straightforward depiction of natural processes.
"With the amount of data we started with, we struggled for years before we realized that this type of model was the way to go," Storey said. "It wasn't obvious, and our inclination was to produce a complicated model of all gene expression. It was similar to using simple tools to fix a car when the temptation is to rebuild the engine. But the simple tools worked very well."
That simple tool -- a practical clinical model fashioned from reams of expression data -- is a significant step toward using genomic analyses to directly treat patients, said Stephen Friend, president of Seattle-based Sage Bionetworks, as well as a biochemist and physician. Friend, whose company supports and assists with genomic research with clinical applications, had no role in the Princeton research, but he is familiar with it.
In the past five years, genomic profiles of patients have become commonplace in commercial and academic clinical trials, Friend said. When it comes to treatment, however, the application of genomics is not as widespread. One reason is that the advantage of monitoring a patient's gene expression -- namely that thousands of health markers can be identified through genetic activity -- also results in a problematically large dataset, as the Princeton researchers encountered.
"Their work shows a logical way to tame those tens of thousands of variables and put them in a practical, insightful guide that a clinician can use," Friend said.
"What's more important is that the Storey group applied genomics to an area that really needs it. They ordered patterns of expression in an elegant way that
|Contact: Morgan Kelly|