Speaker
Description
In hematologic cancers, the initial response to therapy can be observed via temporal changes in tumor burden or approximate measures of tumor burden during treatment. Once the cancer cell population is small, further disease evolution is dominated by underlying stochastic disease kinetics. Many treatments evoke an initial response and tumor decline, but hidden (or difficult to detect) cancer cell populations can persist for extended times. Probabilistic outcomes arise through rare evolutionary events such as mutations that endow fitness advantage or by neutral evolution and small-population-size fluctuations. In hematologic malignancies, the persisting disease is often called minimal residual disease (MRD). MRD results from an evolutionary process determined by cancer-intrinsic factors such as clonal evolution and cancer-extrinsic influences such as cancer therapy and cancer-immune system interactions. The observable kinetics of disease burden during treatment are stochastic and nonlinear. At the same time, properties of the dynamics at the beginning of treatment can be expected to predict some properties at later stages, e.g., at the time of relapse. We investigate the influence of stochasticity of MRD dynamics in leukemias and lymphomas to elucidate the laws of disease evolution during complex treatment schedules applied in the clinic.
