"Kinases are enzymes that integrate stimuli from different soluble, cellular and physical cues to generate specific cellular responses," explained Platt, who is also a Georgia Cancer Coalition Distinguished Cancer Scholar. "By using a systems biology approach to link cell differentiation cues and responses through integration of signals at the kinase level, we were able to mathematically predict relative amounts of cathepsin activity and distinguish which blood donors exhibited greater cathepsin activity compared to others."
Predictability for all cathepsins ranged from 90 to 95 percent for both macrophages and osteoclasts, despite a range in the level of each cathepsin among the blood samples tested.
"We were pleased with the results because our model achieved very high predictability from a simple blood draw and overcame the challenge of incorporating the complex, unknown cues from individual patients' unique genetic and biochemical backgrounds," said Platt.
According to Platt, the next step will be to assess the model's ability to predict cathepsin activity using blood samples from individuals with the diseases of interest: atherosclerosis, osteoporosis or cancer.
"Our ultimate goal is to create an assay that will inform a clinician whether an individual's case of cancer or other tissue-destructive disease will be very aggressive from the moment that individual is diagnosed, which will enable the clinician to develop and begin the best personalized treatment plan immediately," added Platt.
|Contact: John Toon|
Georgia Institute of Technology Research News