QCB Colloquium | Dr. Jill Gallaher

Dr. Jill Gallaher
Research Scientist, Moffit Cancer Center
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Systemic dynamics of multiple metastases during adaptive therapy

Tuesday, October 15
2 PM
RRI 101

Abstract: Although metastatic disease is thought to be responsible for about 90% of cancer deaths, there has been relatively little improvement in the understanding and treatment of cancer at this advanced stage. Increasingly, data point toward intra- and inter-tumor heterogeneity as a major driver of treatment failure in metastatic cancer. We have recently shown that disseminated disease may be better managed using evolutionary-designed maintenance therapies as opposed to maximum tolerated dose, treat-to-kill strategies. Adaptive therapy is one such evolutionary treatment strategy that exploits sensitive and resistant cell competition; a lower dose is given to a shrinking tumor and a higher dose to a growing tumor. From clinical and pre-clinical data, we are learning how the total tumor burden (for example, PSA in prostate cancer) can be used to control disease using this strategy, but details on how multiple distinct heterogeneous metastatic lesions contribute to systemic measures of burden are not fully understood or well documented. We use an off-lattice agent-based computational model to simulate different treatment schedules of an anti-proliferative drug applied systemically to multiple individual micro-metastases. We assume that there is a tradeoff between fast proliferation and drug resistance, and use the total tumor burden from all metastases to make treatment decisions for adaptive therapy. We simulate how intra- and inter- tumor heterogeneity and seeding dynamics affect the best treatment strategy between a maximum continuous dose or an adaptive therapy schedule. When adaptive therapy is optimal, we investigate how tumor composition and number of metastases change the treatment cycling times and indicate future treatment failure. We examine how using different biomarkers that only measure a subset of the tumor phenotypes versus the total tumor burden affect the dose schedule and overall disease control. With these trends in mind, we aim to identify which which patients are best suited for an adaptive therapy strategy, and for those that qualify, identify metrics to assess ongoing treatment response.