Cancer Biology and Evolution

Cancer Biology & Evolution (CBE) is a first-in-kind CCSG Program that emerged from systematic in-house collaborations of mathematicians, evolutionary biologists, and basic and clinical cancer researchers. Although these research teams investigate cancer via traditional means, they include mathematicians and theorists who integrate multi-scalar data through quantitative models founded on evolutionary first principles.

The overall goals of CBE are to investigate and define the complex dynamics that govern the biology and therapeutic responses of cancer, and to deliver new agents and strategies to prevent and treat refractory or relapsed malignancies.

The specific aims of the program are:

Aim 1: Empirically and mathematically define the dynamics operational in cancer and therapy.
The CBE has developed a research paradigm where multidisciplinary teams test hypotheses empirically and then frame them mathematically. This integrative approach analyzes data from CBE experimentalists studying cancer biology and therapy. However, in addition, mathematical models are developed prior to performing studies, to design optimal methods for systems that are dominated by non-linear dynamics. Accordingly, the CBE Program: (i) promotes the development and application of experimental platforms to quantify cancer’s molecular, cellular, and tissue properties; and (ii) mathematically defines data that can inform new strategies for controlling cancer development, progression, maintenance, metastasis, and treatment.

Aim 2: Exploit evolution by natural selection as the first principle operational in cancer.
Cancer biologists often view Darwinian dynamics only in a genetic context; i.e., mutations and selection. CBE rather views evolution by natural selection as the first principle of cancer development and progression, and in the resistance/response to therapy, where there are mutual spatial and temporal interactions of environmental selection forces with cancer cell phenotypes. The Program integrates diverse, multi-scalar data into a unifying Darwinian theoretical framework to test new strategies for controlling cancer progression and treatment.

Aim 3: Translate evolutionary dynamics into personalized, modeled therapies.
Despite effective initial responses to therapy, patients with disseminated cancers succumb to their disease, due to the remarkable capacity of cancer populations to evolve phenotypic resistance. Based on Darwinian principles and parameterized by clinical data, CBE develops patient-specific computational models to predict response and the evolution of resistance. Two clinical trials founded on these models are open and more are planned. These models will be used to predict and deliver optimal treatment strategies to maximize survival.

Program Leaders:
Elsa Flores, PhD

Mark G. Alexandrow, PhD
Philipp Altrock, PhD
Alexander RA Anderson, PhD
Yoga Balagurunathan, PhD
Robert Gatenby, MD
David Basanta Gutierrez, PhD
Jennifer Binning, PhD
Joel Brown, PhD
Srikumar P. Chellappan, PhD
Jiandong Chen, PhD
John L. Cleveland, PhD
W. Douglas Cress, PhD
Gina DeNicola, PhD
Heiko Enderling, PhD
Robert J. Gillies, PhD
Florian Karreth, PhD
Youngchul Kim, PhD
Conor Lynch, PhD
Karen Mann, PhD
Michael Mann, PhD
Andriy Marusyk, PhD
Qianxing Mo, PhD
Kasia A. Rejniak, PhD
Gary W. Reuther, PhD
Ariosto S. Silva, PhD
Jianguo Tao, MD, PhD
Jamie Teer, PhD
Mingxiang Teng, PhD