There are a number of proteins that, when activated, allow scientists to distinguish between cancer and normal cells with almost 97% accuracy, say researchers from Boston University (BU) School of Medicine and the Boston University Biomedical Engineering Department studying lung cancer.
In addition, the BU researchers have developed a new computational strategy to analyze this data and specifically identify key biological pathways that are active in cancer and inactive in normal cells. Scientists hope this information will ultimately lead to the development of drugs specifically aimed to inhibit these proteins. The study, titled “A Predictive Phosphorylation Signature of Lung Cancer,” appears in the November 25 issue of PLoS ONE.
There are many features that distinguish cancer cells from normal cells, according to the BU researchers. Cancer cells look different histologically, they proliferate and divide at different rates, and they are less communicative with neighboring cells. In addition, they resist undergoing cell death, which normal cells do when their genomes become unstable.
Much of the cellular machinery involved with these biological processes is controlled by a command control and communication system called signal transduction. Signal transduction is in large part controlled by phosphorylation, which causes a protein to become either active or repressed depending on its function.
"Therefore, identifying the phosphorylation status of proteins in cancer cells versus normal cells provides us with a unique ability to understand and perhaps intervene with the command and control center of cancer cells," says co-senior author Simon Kasif, Ph.D., who is the co-director of the Center of Advanced Genomic Technology and a professor in the department of biomedical engineering at BU. "Drugs are most effective on cancers when they attack the proteins that are activated," he adds.
The team first documented the largest available set of tyrosine phosphorylation sites that are, individually, differentially phosphorylated in lung cancer. This provided an immediate set of drug targets. Next, they developed a novel computational methodology to identify pathways whose phosphorylation activity is strongly correlated with the lung-cancer phenotype. Lastly, they demonstrated the feasibility of classifying lung cancers based on multi-variate phosphorylation signatures.
The researchers found highly predictive and biologically transparent phosphorylation signatures of lung cancer, which they say provides evidence for the existence of a robust set of phosphorylation mechanisms (captured by the signatures) present in the majority of lung cancers. While cancers are highly heterogeous in their make-up, the BU researchers believe that a drug that would target this collection of activated proteins would be effective treatment for most lung cancers.
"This is the first statistically validated phosphopeptide signature to diagnose any disease, much less cancer or lung cancer," explained senior co-author Martin Steffen, M.D., Ph.D., an assistant professor of pathology and laboratory medicine at BU School of Medicine and director of its proteomics core facility.