Normal Cells and Cancer Cells Differentiated by Degree of Protein Phosphorylation

Cancer researchers have developed a method to differentiate cancer and normal cells at the molecular level based on different degrees of protein phosphorylation.

At the molecular level, cancers are heterogeneous diseases, arising from genetic factors, environmental carcinogens, and random, somatic mutation. Phosphorylation of proteins is a key regulator of protein activity, and in particular, modification of tyrosine residues modulates critical signaling and control processes. In cancers, aberrant phosphorylation status of key residues (its presence or absence) has been observed and documented in many studies.

Investigators at Boston University (MA, USA) collaborated with specialists at Cell Signaling Technology (Danvers, MA, USA) in order to apply the "Phosphoscan" methodology to establish a comparative profile of the phosphorylation properties of proteins in normal and cancer cells.

They reported in the November 25, 2009, issue of the journal PLoS ONE that a large set of sites were differentially phosphorylated in tumors, many of which can be used as direct targets for new drugs. Furthermore they employed a novel computational approach to perform a protein variant of gene set enrichment analysis that showed that certain pathways were differentially activated, based on their global phosphorylation status. A relatively small number of phosphorylated peptides observed in that data could discriminate between normal tissue and tumors with a high degree of sensitivity and specificity.

"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," said contributing author Dr. Simon Kasif, professor of biomedical engineering at Boston University. "Drugs are most effective on cancers when they attack the proteins that are activated."

Related Links:
Boston University
Cell Signaling Technology