Published February 5, 2021
Researchers in the Department of Microbiology and Immunology have developed a new, statistically more powerful method that can more effectively detect key functional pathways in cancer using genomics data generated by next-generation sequencing technology.
Published in Nature Computational Science on Jan. 14, the new method, called FDRnet, has the potential to give biologists more precise data with which to zero in on therapeutic targets.
“Using the new method, we can find biological pathways in which genes are significantly mutated or disrupted,” explains Yijun Sun, PhD, associate professor of microbiology and immunology and the corresponding author.
“It addresses some key challenges in molecular pathway analysis in cancer studies. Once the tumor biologists obtain this information, they can use it to verify our findings and from there develop new cancer treatments,” he adds.
Cancer is a notoriously complex disease, in part because it may be caused by mutations among hundreds or even thousands of genes. In addition, most cancers exhibit an extraordinary amount of variation among genetic mutations, even between patients with the same types of cancers.
Consequently, cancer researchers have chosen to study interactions among groups of genes in certain biological pathways that are disrupted.
When genes in certain pathways are frequently mutated or disrupted, that pathway may play a critical role in the initiation or development of cancer. But unraveling the molecular mechanisms underlying those disruptions is extremely complex.
“By overcoming the limitations of existing approaches, FDRnet can facilitate the detection of key functional pathways in cancer and other genetic diseases,” Sun says.
When Sun and his co-authors tested FDRnet on simulation data and on breast cancer and B-cell lymphoma data, they found that FDRnet was able to detect which subnetworks or pathways are significantly perturbed in these cancers, potentially leading tumor biologists to identify new therapeutic targets.
A companion piece also published Jan. 14 in Nature Computational Science featured comments from fellow researchers on the FDRnet method, who said “this study nicely revamps a topic that has been extensively studied and infuses new lifeblood into a field that can be considered a cornerstone of systems and computational biology.”
Co-authors with Sun are Le Yang, PhD, and Runpu Chen, PhD, both postdoctoral associates in the Department of Microbiology and Immunology, and Steven Goodison, PhD, of the Department of Health Sciences Research at the Mayo Clinic in Jacksonville, Fla.
The research was funded by the National Institutes of Health.