Evolution of Drug-Resistance in Cancer: Computational Modeling of Combination Treatment and the Dynamics of Cellular Subpopulations

September 19, 2018 -
2:00pm to 3:00pm
Lisa Tucker-Kellogg, Center for Computational Biology and Program in Cancer and Stem Cell Biology - Duke-NUS Medical School

The search for effective combination therapies for cancer has focused heavily on synergistic combinations because they exhibit enhanced therapeutic efficacy at lower doses. Although synergism is intuitively attractive, therapeutic success often depends on whether drug resistance develops. The impact of synergistic combinations (vs. antagonistic or additive combinations) on the process of drug-resistance evolution has not been investigated. In this study, we use a simplified computational model of cancer cell numbers in a population of drug-sensitive, singly-resistant, and fully-resistant cells to simulate the dynamics of resistance evolution in the presence of two-drug combinations. When we compared combination therapies administered at the same combination of effective doses, simulations showed synergistic combinations most effective at delaying onset of resistance. Paradoxically, when the therapies were compared using dose combinations with equal initial efficacy, antagonistic combinations were most successful at suppressing expansion of resistant subclones. These findings suggest that, although synergistic combinations could suppress resistance through early decimation of cell numbers (making them "pro-efficacy" strategies), they are inherently fragile toward the development of single resistance. In contrast, antagonistic combinations suppressed the clonal expansion of singly-resistant cells, making them "anti-resistance" strategies. The distinction between synergism and antagonism was intrinsically connected to the distinction between offensive and defensive strategies, where offensive strategies inflicted early casualties and defensive strategies established protection against anticipated future threats. Our findings question the exclusive focus on synergistic combinations and motivate further consideration of non-synergistic combinations for cancer therapy.

Lisa Tucker-Kellogg, PhD, is an Assistant Professor at the Duke-NUS Medical School in Singapore, with joint appointments in Cancer and Stem Cell Biology (CSCB) and the Centre for Computational Biology.  Her lab uses computational and experimental methods to study how cells respond to stress during proliferation and regeneration. One focus is cancer cell proliferation and clonal dynamics during drug treatment and the evolution of drug-resistance, studied using computational modeling and cell culture experiments.  Another focus is muscle wound healing in response to pressure ulcers, studied using mouse models and computational analysis of confetti-colored lineage tracing. Tucker-Kellogg received her training as an undergraduate in math and computer science at Yale, earned her doctoral degree at MIT working under joint supervisors in Computer Science and Biology, and conducted postdoctoral work under a Lee Kuan Yew Fellowship at NUS and the Mechanobiology Institute in Singapore.

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