Mathematical modeling of biomarker dynamics in cancer patients
Principal Investigator
Name
Frederick Adler
Degrees
Ph.D.
Institution
University of Utah
Position Title
Professor
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1099
Initial CDAS Request Approval
Nov 14, 2022
Title
Mathematical modeling of biomarker dynamics in cancer patients
Summary
Treatment for many cancers is determined by biomarkers that provide a measure of response to the previous line of therapy. For ovarian cancer, serum levels of cancer antigen 125 (CA-125) provide that biomarker. Prior work fitting mathematical models to detailed time series of CA-125 levels for high grade serous ovarian cancer (HGSOC) patients from the Australian Ovarian Cancer Study shows that data can be captured by simple models that include two cancer traits: resistance (the rate of decline of CA-125 during treatment), and aggressiveness (the rate of CA-125 increase between lines of treatment). The estimates of these two parameters predict survivorship although they have limited power to predict biomarker dynamics. Mathematical models can be used during the course of treatment to gain additional information about cancer progression.
Aims
Simple mathematical models describe the evolution of resistance and aggressiveness as revealed by the dynamics of CA-125 levels in ovarian cancer patients in a single large study. To advance and generalize this work, we will
1) test models in additional and more recent patient cohorts,
2) investigate the role of cross-resistance to different therapies,
3) Develop optimization methods to refine therapy choice and timing for individual patients.
Collaborators
Kanyarat Jitmana, University of Utah