Speaker
Description
Tumor–immune interactions are central to cancer progression and treatment response, driving cell death through immune-mediated killing and resource-limited competition. In early-stage disease or following effective treatment, cancer populations are often small and difficult to observe directly. Disease monitoring therefore relies on biomarkers such as circulating tumor DNA (ctDNA) as noisy proxies for tumor size. Existing approaches lack robust frameworks to infer tumor burden from these signals near detection thresholds.
We present a coupled deterministic–stochastic framework linking tumor–immune dynamics to biomarker release. A two-prey, one-predator Lotka–Volterra model captures interactions between immune cells and competing tumor subpopulations under shared resource constraints. Biomarker production is modeled via stochastic differential equations driven by tumor cell death from immune-mediated apoptosis and competition-induced necrosis. We incorporate both square-root (CIR-type) noise, capturing count-limited fluctuations near detection, and multiplicative (geometric-type) noise, representing proportional variability at higher concentrations. We derive analytical expressions for biomarker trajectories and first-passage statistics, including mean detection times. Our results show how tumor heterogeneity, immune pressure, and stochastic variance structure jointly shape biomarker detectability.
