title: A Model-Based Personalized Cancer Screening Strategy for Detecting Early-Stage Tumors Using Blood-Borne Biomarkers.
abstract: An effective cancer blood biomarker screening strategy must distinguish aggressive from nonaggressive tumors at an early, intervenable time. However, for blood-based strategies to be useful, the quantity of 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 a 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, whereas tumor viability and volume were monitored by bioluminescence imaging and ultrasound imaging. From these metrics, we developed a mathematical model of cancer biomarker kinetics 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 in which 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 were capable of enabling the detection and discrimination of simulated aggressive (2-month tumor doubling time) and nonaggressive (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 offers broad impact in their applicability to any solid cancer and associated biomarkers shed, thereby allowing a distinction between aggressive and nonaggressive tumors using blood biomarker sampling data alone.Cancer Res; 77(10); 2570-84. ©2017 AACR.
authors.affiliation: Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, California.
authors.forename: Sharon Seiko
authors.initials: SS
authors.lastname: Hori
authors.affiliation: Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, California.
authors.forename: Amelie M
authors.initials: AM
authors.lastname: Lutz
authors.affiliation: Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, California.
authors.forename: Ramasamy
authors.initials: R
authors.lastname: Paulmurugan
authors.affiliation: Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, California. sgambhir@stanford.edu.|Departments of Bioengineering and Materials Science & Engineering, Stanford University, Stanford, California.
authors.forename: Sanjiv Sam
authors.initials: SS
authors.lastname: Gambhir
mesh_terms: D000465:Algorithms; D000818:Animals; D014408:Biomarkers, Tumor; D045744:Cell Line, Tumor; D002470:Cell Survival; D003952:Diagnostic Imaging; D004195:Disease Models, Animal; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D015870:Gene Expression; D017930:Genes, Reporter; D064593:Heterografts; D006801:Humans; D051379:Mice; D008962:Models, Theoretical; D009367:Neoplasm Staging; D009369:Neoplasms / Q000097:blood* / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism; D057285:Precision Medicine / Q000379:methods; D047368:Tumor Burden
chemical_list: D014408:Biomarkers, Tumor