Vander Velde, R. | Search bioRxiv

bioRxiv2017

Dark selection for JAK/STAT-inhibitor resistance in chronic myelomonocytic leukemia

Acquired therapy resistance to cancer treatment is a common and serious clinical problem. The classic U-shape model for the emergence of resistance supposes that: (1) treatment changes the selective pressure on the treatment-naive tumour; (2) this shifting pressure creates a proliferative or survival difference between sensitive cancer cells and either an existing or de novo mutant; (3) the resistant cells then out-compete the sensitive cells and - if further interventions (like drug holidays or new drugs or dosage changes) are not pursued - take over the tumour: returning it to a state dangerous to the patient. The emergence of ruxolitinib resistance in chronic myelomonocytic leukemia (CMML) seems to challenge the classic model: we see the global properties of resistance, but not the drastic change in clonal architecture expected with the selection bottleneck. To study this, we explore three population-level models as alternatives to the classic model of resistance. These three effective models are designed in such a way that they are distinguishable based on limited experimental data on the time-progression of resistance in CMML. We also propose a candidate reductive implementation of the proximal cause of resistance to ground these effective theories. With these reductive implementations in mind, we also explore the impact of oxygen diffusion and spatial structure more generally on the dynamics of CMML in the bone marrow concluding that, even small fluctuations in oxygen availability can seriously impact the efficacy of ruxolitinib. Finally, we look at the ability of spatially distributed cytokine signaling feedback loops to produce a relapse in symptoms similar to what we observe in the clinic.

cancer biology↗

bioRxiv2017

Optimal Therapy Scheduling Based on a Pair of Collaterally Sensitive Drugs

Despite major strides in the treatment of cancer, the development of drug resistance remains a major hurdle. One strategy which has been proposed to address this is the sequential application of drug therapies where resistance to one drug induces sensitivity to another drug, a concept called collateral sensitivity. The optimal timing of drug switching in these situations, however, remains unknown.\n\nTo study this, we developed a dynamical model of sequential therapy on heterogeneous tumors comprised of resistant and sensitive cells. A pair of drugs (DrugA, DrugB) are utilized and are periodically switched during therapy. Assuming resistant cells to one drug are collaterally sensitive to the opposing drug, we classified cancer cells into two groups, AR and BR, each of which is a subpopulation of cells resistant to the indicated drug and concurrently sensitive to the other, and we subsequently explored the resulting population dynamics.\n\nSpecifically, based on a system of ordinary differential equations for AR and BR, we determined that the optimal treatment strategy consists of two stages: an initial stage in which a chosen effective drug is utilized until a specific time point, T, and a second stage in which drugs are switched repeatedly, during which each drug is used for a relative duration (i.e. f{Delta}t-long for DrugA and (1 - f) {Delta}t-long for DrugB with 0 [≤] f [≤] 1 and {Delta}t [≥] 0). We prove that the optimal duration of the initial stage, in which the first drug is administered, T, is shorter than the period in which it remains effective in decreasing the total population, contrary to current clinical intuition.\n\nWe further analyzed the relationship between population makeup, [Formula], and the effect of each drug. We determine a critical ratio, which we term [Formula], at which the two drugs are equally effective. As the first stage of the optimal strategy is applied, [Formula] changes monotonically to [Formula] and then, during the second stage, remains at [Formula] thereafter.\n\nBeyond our analytic results, we explored an individual based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology.

cancer biology↗