Big Data Screen Uncovers New Cancer Driver Genes

October 22, 2015
Big Data Screen Uncovers New Cancer Driver Genes
This is a structure showing EGFR—a cancer driver—in its active dimer conformation. Red indicates mutations that destroy the protein-protein interface. [Eduard Porta Pardo]

Using publically available information from genomic and proteomic databases, a team of scientists lead by researchers at Sanford Burnham Prebys Medical Discovery Institute (SBP) have created a new and more comprehensive catalog of driver mutations for cancer. Driver mutations are genes that handle the progression of cancerous growths. The researchers used cancer mutation and protein structure databases to identify mutations in patient tumors that alter normal protein-protein interaction (PPI) interfaces—identifying more than 100 novel cancer driver genes that may help explain how tumors that are driven by the same gene often lead to vastly different clinical outcomes.  

"This is the first time that three-dimensional protein features, such as PPIs, have been used to identify driver genes across large cancer datasets," explained lead author Eduard Porta-Pardo, Ph.D., postdoctoral fellow at SBP. "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."

The findings from this study were published online recently in PLOS Computational Biology through an article entitled “A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces.”

The last several years have seen a massive rise in the collection of “omic” data as well as a push by institutions such as the NIH to encourage data sharing. These efforts have led to an era of extraordinary ability to systematically analyze large-scale genomic, clinical, and molecular data to better explain and predict patient outcomes—all the while hoping to find new drug targets to prevent, treat, and potentially cure cancer.

"For this study we used an extended version of e-Driver, our proprietary computational method of identifying protein regions that drive cancer. We integrated tumor data from almost 6,000 patients in The Cancer Genome Atlas (TCGA) with more than 18,000 three-dimensional protein structures from the Protein Data Bank (PDB)," remarked senior author Adam Godzik, Ph.D., director of the bioinformatics and structural biology program at SBP. "The algorithm analyzes whether structural alterations of PPI interfaces are enriched in cancer mutations, and can, therefore, identify candidate driver genes."

The researchers acknowledged that one of the aims of the current study was to change the mindset and begun to view proteins as multifunctional factories instead of an imposing unknown void, thereby making it possible to identify novel cancer driver genes and propose molecular hypotheses to explain the diverse heterogeneity in tumor populations.   

"Genes are not monolithic black boxes. They have different regions that code for distinct protein domains that are usually responsible for different functions. It's possible that a given protein only acts as a cancer driver when a specific region of the protein is mutated," Dr. Godzik noted. "Our method helps identify novel cancer driver genes and propose molecular hypotheses to explain how tumors apparently driven by the same gene have different behaviors, including patient outcomes."

"Interestingly, we identified some potential cancer drivers that are involved in the immune system,” Dr. Godzik added. “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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