Members in the Cancer Biology & Evolution (CBE) Program are focused on understanding tumor development, progression and resistance from a basic and evolutionary perspective. Research within CBE is conducted at the interface of molecular cancer biology, translational research and mathematical modeling with a focus on priority cancers in Moffitt’s catchment area.
This innovative Program emerged from our recognition that cancers are complex, multi-scale, open dynamic systems. Given the interdisciplinary approaches required to combat such complexity, mathematicians, computer scientists, cancer biologists, and clinicians were recruited to CBE to challenge the long-held belief that "cancer is too complicated to model."
Notably, CBE has adopted the principles of evolution as a central driver that governs cancer biology and the response to therapies. While CBE supports traditional biomedical studies that have successfully defined new pathways and biomarkers that contribute to cancer development and progression, CBE uniquely integrates these data into theoretical frameworks organized around evolutionary first principles that are facilitated by sophisticated mathematical models.
CBE has flourished since its inception in 2012, where its multidisciplinary teams have driven major scientific advances at the interface of cancer biology, evolution, mathematical modeling, and clinical research and have generated several multi-PI grants, including a U54, T32, and eight U01s, as well as first-in-kind Adaptive Therapy clinical trials that have shown remarkable benefit in refractory and metastatic disease.
Together, CBE has 28 R01 equivalents, all with a cancer focus, totaling $10.3M annual direct costs and 591 publications over the last 5 years. The overarching goals of CBE are to define the complex multi-scale dynamics that govern the biology and therapeutic responses of cancer, and to deliver new agents and strategies for the prevention and treatment of refractory or relapsed malignancies. Fundamentally, CBE focuses on tumor dynamics, where Members seek to understand how and why the static “snapshot” measurements obtained in the lab and in the clinic vary both spatially and temporally, and how using the lens of evolution and in vivo and in silico modeling can inform cancer biology, prevention and treatment.