Brown University researchers used an algorithm that analyzes genetic data to identify networks of genes that, when hit by a mutation, could play a role in the development of multiple types of cancer.
The HotNet2 algorithm was used to analyze genetic data from 12 different types of cancer assembled as part of the pan-cancer project at the Cancer Genome Atlas. The study identified 16 subnetworks of genes that are mutated with surprising frequency in the 3,281 samples in the dataset. "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," says Brown professor Ben Raphael.
The HotNet2 algorithm analyzes genes at the network level, which helps to identify mutations that occur rarely but are nonetheless important in cancer. The algorithm projects mutation data from patients onto a map of known gene interactions and looks for connected networks that are mutated more often than would be expected by chance. HotNet2 identified 16 different networks that appear to be important across cancer types, and several of those were networks associated with genes and pathways that are known cancer drivers, validating the algorithm. "The next step is translating all of this information from cancer sequencing into clinically actionable decisions," Raphael says.
From Brown University
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