Biostat Student Seminar

Shayna SteinDoctoral StudentMathematical modeling of tumor evolution and response to therapyAbstract: Recent advancements in using monotherapies and combinations of therapeutics have led to improvements in cancer patient outcomes. Despite these advancements, cure and toxicity rates remain poor for many cancers. Identifying optimal approaches to treatment is particularly important for combination therapies where the space of possible administration schedules is infinitely large. However, administration schedules for therapies are generally not optimized for either the specific combination of drugs or the effect of the treatment(s) on tumor evolution and selection; instead, they tend to be based on results from the clinical trials of the drugs tested as monotherapies. It is well known that drugs may interact synergistically, antagonistically, or additively, and furthermore, that the synergistic vs antagonistic effect can be both time and drug concentration dependent. These effects also heavily depend on drug pharmacokinetics (PK). In addition, it has been established that intratumor heterogeneity and resistance-conferring mutations affect the efficacy of different treatments. Therefore, it is likely the current method of scheduling combination therapies based on results from monotherapy trials is suboptimal, and that understanding the effects of various treatments on tumor evolution can improve treatment outcomes. I will talk about our use of mathematical modeling to optimize dose administration schedules to treat glioblastoma and estrogen receptor positive breast cancer, respectively. I will also talk about our use of high throughput sequencing data to investigate the response of HER2 positive breast cancer to different HER2 targeting therapies.