One of the protein interfaces the researchers found was in this known cancer gene, EGFR. The red patches highlight mutations that destroy the protein-protein interface.
Image credit: Godzik/SBP
The study, led by Sanford Burnham Prebys Medical Discovery Institute (SBP) in La Jolla, CA, is published in the journal PLOS Computational Biology.
A growing field of cancer research is now involved in sifting through the wealth of data produced by large-scale tumor sequencing studies to separate the "passenger" mutations from the "driver" mutations in an effort to increase our understanding of cancer and improve treatments for the disease.
Within this field, there is an area that is not well understood and that is the role of gene mutations on so-called "protein-protein interaction" (PPI) interfaces as cancer drivers.
Proteins are important molecules that carry out various processes inside and between cells, mostly by linking up with other proteins. The way proteins interact - their PPI interfaces - are considered a vital function, and anything that causes them to malfunction - such as gene mutations - can lead to diseases like cancer.
For the new study, the team used cancer mutation and protein structure databases to identify mutations in patient tumors that alter normal PPI interfaces.
Lead author Eduard Porta-Pardo, a postdoctoral fellow at SBP says their study is the first to use three-dimensional protein features, such as PPIs, to identify driver genes across large cancer datasets. He summarizes the findings:
"We found 71 interfaces in proteins previously unrecognized as cancer drivers, representing potential new cancer predictive markers and/or drug targets. Our analysis also identified several driver interfaces in known cancer genes, such as TP53, HRAS, PI3KCA and EGFR, proving that our method can find relevant cancer driver genes and that alterations in protein interfaces are a common pathogenic mechanism of cancer."
Alterations of PPI interfaces enriched in cancer mutations
To crunch the data, the researchers used an extended version of e-Driver, an algorithm developed at SBP for identifying protein regions that drive cancer.
The authors say the study shows that "considering proteins as multifunctional factories instead of monolithic black boxes," it is possible to identify new cancer driver genes and propose molecular explanations for why patients with the same driver genes may have different outcomes or respond differently to treatment.
Senior author Dr. Adam Godzik, director of the Bioinformatics and Structural Biology Program at SBP, explains how they used e-Driver to process the huge dataset:
"The algorithm analyzes whether structural alterations of PPI interfaces are enriched in cancer mutations, and can therefore identify candidate driver genes."
He concludes with another key finding:
"Interestingly, we identified some potential cancer drivers that are involved in the immune system. With the growing appreciation of the importance of the immune system in cancer progression, the immunity genes we identified in this study provide new insight regarding which interactions may be most affected."
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