One of the central tenets of oncology is that cancers are most susceptible to treatment when diagnosed early, and the earlier the better. For example, 90 percent of women diagnosed with stage I ovarian cancer will survive five years or more, while fewer than 30 percent of those diagnosed with stage III disease will be alive in five years. This harsh reality has prompted a concerted effort to find molecules in blood – biomarkers – that would signal the presence of a tumor before it is detectable by imaging and long before it begins to spread throughout the body.
That search might be more difficult than initially thought given the predictions from a mathematical model developed by Sanjiv Gambhir of Stanford University and post-doctoral fellow Sharon Hori. In a paper published in the journal Science Translational Medicine, the two researchers present work suggesting that current clinical biomarkers for ovarian cancer are unlikely to be detectable until a tumor has been growing for at least a decade and contained almost 2 billion cells. Such a tumor would be 25 millimeters in diameter, about the size of an olive.
According to these calculations, it would take at least 10 years of growth to detect biomarkers shed at this rate using detection technology available today in clinical laboratories. They noted that to be detectable at an early enough time to be clinically useful, a biomarker would have to shed at levels 10,000 greater than CA125 or other known cancer biomarkers. By varying the parameters in the model, the investigators calculated that a 10-fold increase in biomarker shedding rate or a 10-fold decrease in assay detection limit could allow for the detection of a tumor only five millimeters in diameter that had been growing for nearly eight years. The researchers noted that while it is important that biomarker discovery efforts continue, there must be parallel efforts to improve the sensitivity of biomarker detection technologies.
“We have identified a group of nine biomarkers that signal recurrence of breast cancer,” Raftery said. “Our markers detect twice as many recurrences as the CA marker does at the same specificity. They also detect cancer recurrence earlier, about 11-12 months sooner than existing tests. They accomplish this with blood samples, rather than biopsies, with less discomfort to patients.”
To find these markers, Raftery’s team at Purdue University and Matrix-Bio, Inc., a company he founded, analyzed many hundreds of “metabolites” in the blood of breast cancer survivors.
Most clinical blood biomarkers lack the necessary sensitivity and specificity to reliably detect cancer at an early stage, when it is best treatable. It is not yet clear how early a clinical blood assay can be used to detect cancer or how biomarker-based strategies can be improved to enable earlier detection of smaller tumors. To address these issues, we developed a mathematical model describing dynamic plasma biomarker kinetics in relation to the growth of a tumor, beginning with a single cancer cell. To exemplify a realistic scenario in which biomarker is shed by both cancerous and noncancerous cells, we primed the model on ovarian tumor growth and CA125 shedding data, for which tumor growth parameters and shedding rates are readily available in published literature. We found that a tumor could grow unnoticed for more than 10.1 years and reach a volume of about π/6(25.36 mm)3, corresponding to a spherical diameter of about 25.36 mm, before becoming detectable by current clinical blood assays. Model parameters were perturbed over log orders of magnitude to quantify ideal shedding rates and identify other blood-based strategies required for early submillimeter tumor detectability. The detection times we estimated are consistent with recently published tumor progression time lines based on clinical genomic sequencing data for several cancers. Here, we rigorously showed that shedding rates of current clinical blood biomarkers are likely 104-fold too low to enable detection of a developing tumor within the first decade of tumor growth. The model presented here can be extended to virtually any solid cancer and associated biomarkers.