Speakers
Description
Circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) are increasingly used to monitor tumor burden, treatment response, and therapeutic resistance. A central challenge, however, is that liquid biopsy signals reflect multiple biological processes operating across scales, including stochastic biomarker shedding, clearance dynamics, and coupling to tumor–immune interactions. As a result, linking measured ctDNA and cfDNA trajectories to latent tumor states benefits from explicit mathematical structure.
This minisymposium brings together complementary modeling approaches that address these challenges using tools from stochastic processes, dynamical systems, and computational inference. The talks span hybrid ODE–SDE frameworks connecting tumor–immune dynamics to stochastic ctDNA release, mechanistic models of cfDNA fragmentation and clearance that shape observable signal distributions, and joint models of tumor kinetics and cell-free DNA dynamics developed to study and predict immunotherapy resistance. Disease-specific applications are also presented, including ctDNA modeling in HPV-associated solid tumors calibrated to longitudinal clinical data to track progression and therapeutic response.
Collectively, these contributions highlight how mathematically grounded, mechanistic models can improve quantitative interpretation of liquid biopsy data and help bridge biological complexity and clinical observables in translational oncology.
