A model-based personalized cancer screening strategy for detecting early-stage tumors using blood-borne biomarkers

摘要

An effective cancer blood biomarker screening strategy must distinguish aggressive from non-aggressive tumors at an early, intervenable time. However, for blood-based strategies to be useful, the quality and quantiy of the biomarker shed into the blood and its relationship to tumor growth or progression must be validated. To study how blood biomarker levels correlate with early-stage viable tumor growth in an mouse model of human cancer, we monitored early tumor growth of engineered human ovarian cancer cells (A2780) implanted orthotopically into nude mice. Biomarker shedding was monitored by serial blood sampling, while tumor viability and volume was monitored by bioluminescence imaging and ultrasound imaging. From these metrics we developed a mathematical model of cancer biomarker kinetics in different compartments that accounts for biomarker shedding from tumor and healthy cells, biomarker entry into vasculature, biomarker elimination from plasma and subject-specific tumor growth. We validated the model in a separate set of mice where subject-specific tumor growth rates were accurately predicted. To illustrate clinical translation of this strategy, we allometrically scaled model parameters from mouse to human and used parameters for PSA shedding and prostate cancer. In this manner, we found that blood biomarker sampling data alone was capable of enabling the detection and discrimination of simulated aggressive (2-month tumor doubling time) and non-aggressive (18-month tumor doubling time) tumors as early as 7.2 months and 8.9 years before clinical imaging, respectively. Our model and screening strategy offer broad impact in their applicability to any solid cancer and the biomarkers they shed, thereby allowing a distinction between aggressive vs. non-aggressive tumors using blood biomarker sampling data alone.