Research and Publications

Drug resistance in cancer chemotherapy

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The primary factor limiting the success of chemotherapy in cancer treatment is the phenomenon of drug resistance. Both molecular and microenvironmental factors have been implicated in the development of drug resistance. Molecular mechanisms include the upregulation of drug efflux transporters on the cell membrane, modification of drug targets, enhanced DNA damage repair mechanisms, dysregulation of apoptotic pathways, and the presence of cancer stem cells. Irregular tumor vasculature, increased regions of acidity, immune cell infiltration and activation, and the tumor stroma are all examples of microenvironmental factors that may inhibit response to chemotherapy. Experimental, clinical, and mathematical research continues to shed light on the multitude of factors that contribute to cancer drug resistance.

 

Besides mechanisms, a more fundamental question relates to the method by which these resistance-inducing traits arise in a cellular population. Classically, these mechanisms are understood as conferred to the cell by random genetic mutations, from which clonal expansion occurs via Darwian evolution. However, the recent experimental discovery of epigenetics and phenotype plasticity complicates this hypothesis. It is now believed that chemotherapy can produce drug-resistant clones in a dose-dependent manner. That is, the application of chemotherapy has contradictory effects: the agent eliminates a certain subpopulation while simultaneously promoting transitions to a less-sensitive phenotype. It is in this phenomenon, and the resulting control structures, that we are interested in understanding.

 

In a series of works, we have established a mathematical framework to distinguish between drug-selected and drug-induced resistance. Specifically, we have introduced mathematical models and studied basic dynamical properties related to treatment outcome and control as a function of induction.  We are also currently collaborating with experimentalists to fit models to in vitro data, with the aim of understanding dose-dependencies on model parameters; we then apply optimal control theory to discover novel dosing strategies.