Algorithm identifies networks of genetic changes across cancers
The algorithm, called Hotnet2, was used to analyze genetic data from 12 different types of cancer assembled as part of the pan-cancer project of The Cancer Genome Atlas (TCGA).
The research looked at somatic mutations—those that occur in cells during one's lifetime—and not genetic variants inherited from parents. The study identified 16 subnetworks of genes—several of which have not previously received much attention for their potential role in cancer—that are mutated with surprising frequency in the 3,281 samples in the dataset.
The researchers hope the new findings, published in Nature Genetics, will provide scientists with new leads in the search for somatic mutations that drive cancer. Additional data from the project, along with a downloadable version of the Hotnet2 software, is alsoavailable online.
"Ultimately, there will need to be laboratory experiments that confirm these findings," said Ben Raphael, associate professor of computer science, director of the Center for Computational Molecular Biology at Brown, and the paper's senior author. "But the hope is that the computational analysis will help prioritize the experiments toward those genes and mutations that are likely to be involved in cancer."
The research takes a different approach than many cancer genetics studies, which often look for mutations in single genes that occur frequently in cancer samples. Genes often do not work alone, but operate together to form networks and pathways that govern cell functions. In some cases, a mutation in any of the multiple genes in a pathway could cause a malfunction that leads to cancer. Because damaging mutations can be spread across multiple such networks of genes, it can be hard to detect them in statistical tests.
"When looking at single genes, you typically find a small number that you can confidently say are likely to be cancer genes," Raphael said. "But you also see many other genes that, statistically, you cannot say much about. You don't know if they're important or not."
The Hotnet2 algorithm analyzes genes at the network level, and that helps to identify mutations that occur rarely but are nonetheless important in cancer.
"For example, maybe there's a gene that's mutated in 80 percent of samples, but the other 20 percent have rare mutations in multiple other genes," Raphael said. "If we see that some of those rare mutations are in the same pathway as the more common one, it helps to build the case that those rare mutations are important."