Computational Method That can Determine Gene Changes in Breast Cancer Cells Developed

So researchers often pool microarray data from several cell states in hopes of gaining enough samples to draw solid conclusions about networks, Wu said. That approach wouldn't work in a study that sought to differentiate between the various cell states. But Xing's research group had developed a computational method called Treegl that can detect multiple networks by examining the relationships between the cell types. The method pools microarray data to build statistical power in similar samples in which the gene regulatory networks appear similar while also taking the differences into account. In this way, the researchers were able to identify different signaling networks with just three microarrays for each of five cell states — normal, malignant and three types of reverted cells. Though the reverted cells looked physically normal in culture, Wu said their signaling pathways differed not only from the malignant cells, but also the normal cells. In fact, each had distinctly different signaling pathways depending on what drug had been used to treat them, as each compensated for the effects of the drugs in different ways. In the case of cells that had been treated with MMP inhibitors, the researchers could see how the rewired signaling pathways had created compensatory signaling which would cause them to resist the drug — an effect that would explain why cancer patients receiving MMP inhibitors in clinical trials show either poor survival or no survival benefit, Wu said. Wu also said by using this approach for studying cancer cells, it should be possible to eliminate drugs that appear promising but are ultimately flawed earlier in the development process. It also might result in clinical trials that require fewer patients and less time to complete, she added. Differences in gene regulatory networks in patients also might be used as the basis for personalized medicine.



Source: Eurekalert