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

By Artem Kaznatcheev, David Robert Grimes, Robert Vander Velde, Vincent Cannataro, Etienne Baratchart, Andrew Dhawan, Lin Liu, Daria Myroshnychenko, Jake P. Taylor-King, Nara Yoon, Eric Padron, Andriy Marusyk, David Basanta

Posted 30 Oct 2017
bioRxiv DOI: 10.1101/211151

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.

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