Mathematical modeling of biomarker dynamics in cancer patients

Principal Investigator

Name
Frederick Adler

Degrees
Ph.D.

Institution
University of Utah

Position Title
Professor

Email
adler@math.utah.edu

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