A new mathematical model developed by Stanford University School of Medicine scientists finds that ovarian cancer tumors can grow for 10 years or longer before currently available blood tests will detect them.
A new mathematical model developed by Stanford University School of Medicine scientists indicates that tumors can grow for 10 years or longer before currently available blood tests will detect them. The analysis, which was restricted to ovarian cancer tumors but is broadly applicable across all solid tumor types, was published online November 16 in Science Translational Medicine.
“The study’s results can be viewed as both bad and good news,” said Sanjiv “Sam” Gambhir, M.D., Ph.D., professor and chair of radiology and the study’s senior author. Sharon Hori, Ph.D., a postdoctoral scholar in Dr. Gambhir’s laboratory, is the lead study author.
The mathematical model developed by Dr. Sam Gambhir’s lab shows that it would be possible to detect tumors years before they grow big enough to metastasize if researchers can develop the right biomarkers.
The bad news, as explained by Dr. Gambhir, is that by time a tumor reaches a detectable size using today’s available blood tests, it is likely to have metastasized to other areas of the body, making it much more deadly than if it had been caught earlier. “The good news is that we have, potentially, 10 or even 20 years to find the tumor before it reaches this size, if only we can improve our blood-based methods of detecting tumors,” said Dr. Gambhir. “We think our mathematical model will help guide attempts to do that.”
The study advances previous research about the limits of current detection methods. For instance, it is strikingly consistent with a finding reported two years ago by Stanford biochemistry professor Patrick Brown, M.D., Ph.D., that current ovarian cancer tests could not detect tumors early enough to make a significant dent in the mortality rate. There is a push to develop more-sensitive diagnostic tests and find better biomarkers, and Dr. Gambhir’s new model could be an essential tool in this effort. For the first time, the new model connects the size of a tumor with blood biomarker levels being shed by that tumor.
To create their model, Drs. Gambhir and Hori used mathematical models originally developed to predict the concentration of drugs injected into the blood. The investigators linked these to additional models of tumor cell growth.
Tumors do not secrete drugs, but they can shed telltale molecules into surrounding tissue, from which those substances, known as “biomarkers,” diffuse into the blood. Some biomarkers may be made predominantly by tumor cells. These substances can be measured in the blood as proxies for a tumor.
Some biomarkers are in wide use today. One is the well-known PSA (prostate specific antigen) for prostate cancer. Another example of a biomarker is CA-125 (cancer antigen 125) for ovarian cancer. But these and other currently used blood tests for cancer biomarkers were not specifically developed for early detection, and are generally more effective for relatively noninvasive monitoring of the progress of a late-stage tumor or tumor response to treatment. That is, rising blood levels of the substance may indicate that the tumor is growing, while declining levels may indicate possible tumor shrinkage.
Both CA-125 and PSA are also produced, albeit in smaller amounts, by healthy tissue, complicating efforts to detect cancer at an early stage when the tumor’s output of the biomarker is relatively low.
The new mathematical model employs separate equations, each governing the movement of a biomarker from one compartment into the next. Into these equations, one can plug known values — such as how fast a particular type of tumor grows, how much of the biomarker a tumor cell of this type sheds per hour, and the minimum levels of the biomarker that must be present in the blood for a currently available assay to detect it.
As a test case, Drs. Gambhir and Hori chose CA-125, a well-studied biomarker which is shed into the blood by ovarian cancer tumors. Ovarian cancer is a notorious example of a condition for which early detection would make a significant difference in survival outcomes.
CA-125 is a protein made almost exclusively by ovarian tumor cells. The well-known pharmacokinetics, metabolic fates (typical amounts secreted by an ovarian cell), typical ovarian tumor growth rates, and other properties of CA-125 make the biomarker an excellent candidate for “road testing” with Gambhir and Hori’s model. CA-125 is by no means the ideal biomarker, said Dr. Gambhir, while noting that it can still be used to better understand the ideal properties of biomarkers for early ovarian cancer detection.
Applying their equations to CA-125, Drs. Gambhir and Hori determined that an ovarian cancer tumor would need to reach a size of approximately 1.7 billion cells, or the volume of a cube with a 2-centimeter edge, before the currently available CA-125 blood test could reliably detect it. At typical tumor-growth rates, it would take a single cancer cell approximately 10.1 to 12.6 years of development to become a tumor containing 1.7 billion cells.
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, while the 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 forms of 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 [mathematical] model could take some of the guesswork out of it,” Gambhir said. He also stated:
“It [the mathematical model] 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 detect the tumor earlier on.”
There are new detection technologies capable of detecting biomarkers at concentrations as low as a few hundred molecules per milliliter (1-cubic centimeter) of blood. In 2009, Dr. Gambhir and his colleagues reported on one such developing technology: “magneto-nanosensors” that can detect biomarkers with a 100-fold greater sensitivity than current methods.
Better biomarker detection alone might allow ovarian cancer tumor detection at the 9-year point, said Gambhir.
A second priority is to come up with new and better biomarkers. “It’s really important for us to find biomarkers that are made exclusively by tumor cells,” Dr. Gambhir said.
Under the right conditions (a highly sensitive assay measuring levels of a biomarker that is shed only by cancer cells), Gambhir stated, the model predicts that a tiny tumor with a volume equivalent to a cube less than one-fifteenth of an inch (or 1.7 millimeters) on a side could be detected.
Dr. Gambhir is also the Virginia and D.K. Ludwig Professor in Cancer Research and director of the Molecular Imaging Program at Stanford, the director of the Canary Center at Stanford for Cancer Early Detection, and a member of the Stanford Cancer Institute.
The study was funded by the Canary Foundation and the National Cancer Institute.
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