Michor Research Group |
Publications  |
The research of our lab focuses on the evolutionary dynamics of cancer. Cancer emerges due to an evolutionary process in somatic tissue. The fundamental laws of evolution can best be formulated as exact mathematical equations. Therefore, the process of cancer initiation and progression is amenable to mathematical investigation. We are interested in important questions in cancer research and use the tools of theoretical evolutionary biology to approach them.
Traditional chemotherapy tends to kill all dividing cells, leading to considerable toxicity and limited success. Targeted therapy, however, specifically inhibits cancer cells. The first small molecule targeted to a specific protein that is genetically altered in cancer was imatinib (STI571, Gleevec). Imatinib inhibits the BCR-ABL oncoprotein, which causes chronic myeloid leukemia (CML). Despite the success of imatinib as targeted cancer therapeutic, several critical questions remain. Can imatinib eradicate leukemic stem cells? What are the dynamics of relapse due to resistance mutations?
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.
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.
Tumor metastasis accounts for the majority of deaths in cancer patients. The metastatic behavior of cancer cells is promoted by mutations in many genes, including activation of oncogenes such as RAS and MYC, or inactivation of metastasis suppressor genes like NM23 or MKK4. Our lab is interested in developing a mathematical framework to analyze the dynamics of mutations enabling cells to metastasize. We study whether metastatic potential is the property of all (or the majority of) cells in the main tumor or only the property of a small subset. We consider situations in which the tumor population is of constant size, and in which tumor cells increase exponentially in number. We apply our models to clinical data of pancreatic cancer and investigate the dynamics of pancreatic metastases.
