Breast cancer is the second major cause of cancer death in US women, with an estimated 44,190 lives lost (290 men and 43,900 women) in the US in 1997. While ovarian cancer accounts for fewer deaths than breast cancer, it still represents 4 per cent of all female cancers. For some of the cases of both types of cancer, there is also a clear genetic link.
Researchers led by Benjamin Cravatt, a Scripps Research professor and a member of its Skaggs Institute for Chemical Biology, began working on KIAA1363 - an enzyme that has been implicated as a key regulator of a lipid-signalling network that contributes to cancer.
"Using a combination of enzyme activity and metabolite profiling, we determined that this protein, whose function was previously unknown," said Cravatt.
"The heightened expression of KIAA1363 in several cancers indicates that it may be a critical factor in tumorgenesis. In addition, network components, including KIAA1363 itself, might be considered potential diagnostic markers for ovarian cancer."
According to Cravatt, this experimental method of integrated molecular profiling used in the study should advance the functional study of metabolic enzymes in any biological system.
To date, understanding the roles of uncharacterised enzymes in cell physiology and pathology has remained problematic. Typically, the activities of enzymes have been studied in vitro using purified protein preparations.
An advantage of metabolite profiling in natural biological systems is that it avoids time-consuming steps that accompany in vitro enzyme analysis while generating data more related to their naturally occurring activities.
"Our hypothesis was that the determination of catalytic activities for enzymes like KIAA1363 could be done directly in living systems through the integrated application of profiling technologies that survey both the enzymatic proteome and its primary biochemical output, the metabolome," Cravatt said.
The team utilised a large-scale study of the structure and function of proteins and the systematic study of cellular processes, specifically their small-molecule metabolite profiles to begin to understand the metabolic and signalling networks of cancer.
According to the study, one of the advantages of the functional proteomic technology employed (activity-based protein profiling) is that it can be used to identify inhibitors for uncharacterised enzymes like KIAA1363.
In addition, because inhibitors are screened against many enzymes in parallel, both potency and selectivity factors are assigned simultaneously.
The development of a selective inhibitor of KIAA1363 was possible due to the availability of an activity-based proteomics probe for this enzyme. Such probes are now available for many enzyme classes that participate in cell metabolism, so Cravatt suggests "a large swath of the enzyme proteome" could be addressed using the study's experimental strategy.
"The success of our study opens the door to assembling the full range of enzymes into both metabolic and signalling networks contributing to complex pathologies like cancer," Cravatt said. "This could lead to the discovery of new markers for diagnosis and targets for treatment."