Applying their equations to CA125, Gambhir and Hori showed that before the currently best available test for CA125 could reliably detect an ovarian tumor, the tumor would need to reach a size of about 1.7 billion cells, or the volume of a cube with about a 2-cm edge. That would take about 10.1 to 12.6 years of development, at typical tumor-growth rates, from the first, single cancer cell.
The model further calculated that a biomarker otherwise equivalent to CA125 but shed only by ovarian tumor cells would allow reliable detection within 7.7 years, when a tumor's size would be that of a tiny cube about one-sixth of an inch high.
In the last decade, many potential new biomarkers for different cancers have been identified. There's no shortage of promising candidates six for lung cancer alone, for example. But validating a biomarker in large clinical trials is a long, expensive process. So it is imperative to determine as efficiently as possible which, among many potential tumor biomarkers, is the best prospective candidate.
"This model could take some of the guesswork out of it," Gambhir said. "It can be applied to all kinds of solid cancers and prospective biomarkers as long as we have enough data on, for instance, how much of it a tumor cell secretes per hour, how long the biomarker can circulate before it's degraded and how quickly tumor cells divide.
"We can tweak one or another variable for instance, whether a biomarker is also made in healthy tissues or just the tumor, or assume we could manage to boost the sensitivity of our blood tests by 10-fold or 100-fold and see how much it advances our ability to detec
|Contact: Bruce Goldman|
Stanford University Medical Center