Franziska Michor, PhD

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The ability to measure disease burden by quantitative PCR, as well as a detailed understanding of the mode of action of imatinib, allow for the development of a mathematical approach to answer those questions (Michor et al., Nature 435, 1267-1270, 2005). Imatinib leads to a biphasic exponential decline in the leukemic cell burden during the first 12 months of therapy. The molecular response to imatinib suggests that the leukemia can be described by a mathematical model containing four different subpopulations: leukemic stem cells, progenitors, differentiated, and terminally differentiated cells. Terminally differentiated leukemic cells live on average one day, leukemic differentiated cells 20 days, and leukemic progenitors 125 days during treatment. Leukemic stem cells, however, do not seem to be depleted by imatinib therapy. In patients who discontinue imatinib after up to three years of successful therapy, the leukemic cell count rises within weeks to levels at or beyond pre-treatment baseline. This observation suggests that the cell population that drives the disease, the leukemic stem cells, does not decrease in abundance during therapy. This conclusion is supported by experimental findings that CML stem cells are insensitive to imatinib.

An Interview with Franziska Michor "It is an exciting time to be working in the growing field of evolutionary cancer biology" more

Acquired drug resistance is a major limitation for successful treatment of cancer. Drug resistance can result from two general causes: (i) host factors such as poor absorption and rapid metabolism reduce the maximum achievable serum levels of the drug, and (ii) specific genetic or epigenetic alterations enable resistant cancer cell clones to outgrow and escape from otherwise effective treatment. Depending on therapy, the type of cancer and its stage, one or several (epi)genetic changes are necessary to confer drug resistance. Our lab is interested in the dynamics of resistance emerging in diverse scenarios of anti-cancer therapy. We are deriving stochastic models to calculate the risk of resistance emerging before diagnosis of the cancer as well as once treatment has been initiated. We consider therapy with one drug, and with multiple agents that require cancer cells to evolve a complex series of mutations. We study the dynamics of resistance arising in different subpopulations of cancer cells, and investigate the emergence of resistance in response to diverse concentrations of the drug.