Comparison of Algorithms May Reveal New Cancer Treatment Targets
Tools that analyze the cancer genome provide new insight into the mechanisms that drive cancer development.
Scientists have conducted the first-ever comparative analysis of a newly emerging algorithm that hunts for cancer mutations using genetic information from cancer databases.
In a study published in Nature Methods, investigators described the strengths and weaknesses of more than 20 algorithms developed by independent research groups.
“Despite the increasing availability of high-resolution genome sequences, a common assumption is to consider a gene as a single unit,” said senior author Adam Godzik, PhD. “However, there are a number of events, such as single site DNA substitutions and splicing variants that can occur within a gene—–at the subgene level. Subgene algorithms provide a high-resolution view that can explain why different mutations in the same gene can lead to distinct phenotypes, depending on how they impact specific protein regions.
“A good example of how different subgene mutations influence cancer is the NOTCH1 gene. Mutations in certain regions of NOTCH1 cause it to act as a tumor suppressor in lung, skin, and head and neck cancers. But mutations in a different region can promote chronic lymphocytic leukemia and T cell acute lymphoblastic leukemia. So, it’s incorrect to assume that mutations in a gene will have the same consequences regardless of their location.”
For the study, investigators sought to compare subgene resolution algorithms with traditional methods that focus on genes treated as single units. They applied each subgene algorithm to the data from The Cancer Genome Atlas.
“Our goal was not to determine which algorithm works better than another, because that would depend on the question being asked,” said first author Eduard Porta-Pardo, PhD. “Instead, we want to inform potential users about how the different assumptions behind each subgene algorithm influence the results, and how the results differ from methods that work at the whole gene level.”
The results of the analysis showed that the algorithms could reproduce the list of known cancer genes already established by cancer researchers. This further validated the effectiveness of the subgene approach and the link between the genes and cancer, according to the study.
The investigators also found several new cancer genes that had been previously missed by whole-gene approaches.
“Finding new cancer driver genes is an important goal of cancer genome analysis,” Porta-Pardo. “This study should help researchers understand the advantages and drawbacks of subgene algorithms used to find new potential drug targets for cancer treatment.”